diff --git a/Theme-1/laundryPaper/DEMAND-BA-MTUS-W6-Laundry-Change-Over-Time-v1.0-adult.R b/Theme-1/laundryPaper/DEMAND-BA-MTUS-W6-Laundry-Change-Over-Time-v1.0-adult.R
new file mode 100644
index 0000000000000000000000000000000000000000..912824471824add27fec48f7fab137ee1061ae0a
--- /dev/null
+++ b/Theme-1/laundryPaper/DEMAND-BA-MTUS-W6-Laundry-Change-Over-Time-v1.0-adult.R
@@ -0,0 +1,206 @@
+############################################
+# Time Use data analysis for 'Laundry' paper
+# Use MTUS World 6 time-use data (UK subset) to examine:
+# - distributions of laundry in 1975 & 2005
+# - changing laundry practices
+
+# Data source: www.timeuse.org/mtus
+# data already in long format (but episodes)
+
+# This work was funded by RCUK through the End User Energy Demand Centres Programme via the
+# "DEMAND: Dynamics of Energy, Mobility and Demand" Centre (www.demand.ac.uk, gow.epsrc.ac.uk/NGBOViewGrant.aspx?GrantRef=EP/K011723/1)
+
+# Copyright (C) 2014 University of Southampton
+# Author: Ben Anderson (b.anderson@soton.ac.uk, @dataknut, https://github.com/dataknut)
+
+# This program is free software; you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation; either version 2 of the License
+# (http://choosealicense.com/licenses/gpl-2.0/), or
+# (at your option) any later version.
+
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+
+# clear out all old objects etc to avoid confusion
+rm(list = ls())
+
+# add libraries
+library("lattice", "Hmisc")
+
+# set up some useful vars
+ifile_d <- c("~/Documents/Work/Data/Social Science Datatsets/MTUS/World 6/processed/MTUS-adult-episode-UK-only-wf.dta")
+ifile_s <- c("~/Documents/Work/Data/Social Science Datatsets/MTUS/World 6/processed/MTUS-adult-aggregate-UK-only-wf.dta")
+
+rpath <- c("~/Documents/Work/Projects/RCUK-DEMAND/Theme 1/results/MTUS")
+# paste!
+
+# Loading the data
+# load as stata file
+library(foreign)
+# diary
+MTUSW6UK_d <- read.dta(ifile_d)
+# survey
+MTUSW6UK_s <- read.dta(ifile_s)
+
+# create a reduced survey frame with the few variables we need so the merge
+# does not break memory
+MTUSW6UK_s_redvars <- c("diarypid", "empstat", "urban")
+MTUSW6UK_s_red <- MTUSW6UK_s[MTUSW6UK_s_redvars]
+
+# merge
+MTUSW6UK_m <- merge(MTUSW6UK_d,MTUSW6UK_s_red,by="diarypid")
+
+# Take a quick look at the data
+head(MTUSW6UK_m)
+
+# Check what's in it?
+names(MTUSW6UK_m)
+
+# check the distribution of episodes by time of day and year of survey
+# NB: stata has already set the date to 1960! We need to re-format to half hours!
+table("Half hour"= MTUSW6UK_m$s_halfhour)
+
+# 21 = "laundry, ironing, clothing repair"
+# We've imported from stata so we'll have to use the value label not the value
+# Could have imported as csv and then applied labels... might be easier
+
+# check incidence of laundry as primary & secondary act
+table("Laundry as primary"= MTUSW6UK_m$main == "laundry, ironing, clothing repair")
+table("Laundry as secondary"= MTUSW6UK_m$sec == "laundry, ironing, clothing repair")
+
+# create 2 new variables (columns) which are 'laundry'
+MTUSW6UK_m$laundry_p <- 0
+MTUSW6UK_m$laundry_p[MTUSW6UK_m$main == "laundry, ironing, clothing repair"] <- 1
+
+MTUSW6UK_m$laundry_s <- 0
+MTUSW6UK_m$laundry_s[MTUSW6UK_m$sec == "laundry, ironing, clothing repair"] <- 1
+
+MTUSW6UK_m$laundry_all <- 0
+MTUSW6UK_m$laundry_all[MTUSW6UK_m$laundry_p == 1 | MTUSW6UK_m$laundry_s == 1] <- 1
+
+table(MTUSW6UK_m$laundry_all)
+
+# check location of laundry
+# lact = -1 (unknown), 1 = home, 2 = elsewhere
+table("Laundry as primary"= MTUSW6UK_m$laundry_p == 1, MTUSW6UK_m$eloc)
+table("Laundry as secondary"= MTUSW6UK_m$laundry_s == 1, MTUSW6UK_m$eloc)
+
+# create a frame to hold the various results
+# NB the value of the column (x) is meaningless
+laundry_fr <- aggregate(MTUSW6UK_m$year, by=list(MTUSW6UK_m$s_halfhour), FUN=mean)
+names(laundry_fr) <- c("s_halfhour","junk")
+# drop junk
+laundry_fr <- laundry_fr["s_halfhour"]
+
+# there must be a simple way to do this as a loop switching p for s and all
+# primary
+laundry_p_tod <- aggregate(MTUSW6UK_m$laundry_p, by=list(MTUSW6UK_m$s_halfhour), FUN=sum)
+names(laundry_p_tod) <- c("s_halfhour","freq")
+# each half hour as a proportion of laundry episodes
+laundry_fr$p_laundry_pr <- (laundry_p_tod$freq/sum(laundry_p_tod$freq))
+
+# secondary
+laundry_s_tod <- aggregate(MTUSW6UK_m$laundry_s, by=list(MTUSW6UK_m$s_halfhour), FUN=sum)
+names(laundry_s_tod) <- c("s_halfhour","freq")
+# each half hour as a proportion of laundry episodes
+laundry_fr$s_laundry_pr <- (laundry_s_tod$freq/sum(laundry_s_tod$freq))
+
+# all
+laundry_all_tod <- aggregate(MTUSW6UK_m$laundry_all, by=list(MTUSW6UK_m$s_halfhour), FUN=sum)
+names(laundry_all_tod) <- c("s_halfhour","freq")
+# each half hour as a proportion of laundry episodes
+laundry_fr$all_laundry_pr <- (laundry_all_tod$freq/sum(laundry_all_tod$freq))
+
+# plot with primary & secondary for all years
+# direct graph to file
+png(paste(rpath,"/laundry-time-of-day-all-years.png", sep=""))
+plot(x = laundry_fr$s_halfhour, y = laundry_fr$p_laundry_pr,
+ xlab = "Half Hour",
+ ylab = "% of laundry of that type",
+ type = "l",
+ col = "red")
+points(x = laundry_fr$s_halfhour, y = laundry_fr$s_laundry_pr, type = "l")
+# cex = scaling factor
+legend('topright',c("Primary act","Secondary act"), lty=1, col=c('red', 'black'), bty='n', cex=1)
+title("% of laundry done at different times of day (all years)", cex=0.75)
+dev.off()
+
+# laundry for each year - how to loop over?
+# make subsets to speed things up
+
+MTUSW6UK_m1974 <- subset(MTUSW6UK_m,survey==1974)
+
+laundry_tod_1974p <- aggregate(MTUSW6UK_m1974$laundry_p, by=list(MTUSW6UK_m1974$s_halfhour), FUN=sum)
+names(laundry_tod_1974p) <- c("s_halfhour","freq")
+laundry_fr$laundry_p_1974_pr <- (laundry_tod_1974p$freq/sum(laundry_tod_1974p$freq))
+
+laundry_tod_1974s <- aggregate(MTUSW6UK_m1974$laundry_s, by=list(MTUSW6UK_m1974$s_halfhour), FUN=sum)
+names(laundry_tod_1974s) <- c("s_halfhour","freq")
+laundry_fr$laundry_s_1974_pr <- (laundry_tod_1974s$freq/sum(laundry_tod_1974s$freq))
+
+laundry_tod_1974all <- aggregate(MTUSW6UK_m1974$laundry_s, by=list(MTUSW6UK_m1974$s_halfhour), FUN=sum)
+names(laundry_tod_1974all) <- c("s_halfhour","freq")
+laundry_fr$laundry_all_1974_pr <- (laundry_tod_1974all$freq/sum(laundry_tod_1974all$freq))
+
+
+MTUSW6UK_m2005 <- subset(MTUSW6UK_m,survey==2005)
+
+laundry_tod_2005p <- aggregate(MTUSW6UK_m2005$laundry_p, by=list(MTUSW6UK_m2005$s_halfhour), FUN=sum)
+names(laundry_tod_2005p) <- c("s_halfhour","freq")
+laundry_fr$laundry_p_2005_pr <- (laundry_tod_2005p$freq/sum(laundry_tod_2005p$freq))
+
+laundry_tod_2005s <- aggregate(MTUSW6UK_m2005$laundry_s, by=list(MTUSW6UK_m2005$s_halfhour), FUN=sum)
+names(laundry_tod_2005s) <- c("s_halfhour","freq")
+laundry_fr$laundry_s_2005_pr <- (laundry_tod_2005s$freq/sum(laundry_tod_2005s$freq))
+
+laundry_tod_2005all <- aggregate(MTUSW6UK_m2005$laundry_all, by=list(MTUSW6UK_m2005$s_halfhour), FUN=sum)
+names(laundry_tod_2005all) <- c("s_halfhour","freq")
+laundry_fr$laundry_all_2005_pr <- (laundry_tod_2005all$freq/sum(laundry_tod_2005all$freq))
+
+# now compare laundry for 1974 & 2005
+# must be a simple way to loop over these
+# direct graph to file
+# primary episodes
+png(paste(rpath,"/laundry-time-of-day-1974-2005-primary.png", sep=""))
+plot(x = laundry_fr$s_halfhour, y = laundry_fr$laundry_p_1974_pr,
+ xlab = "Half Hour",
+ ylab = "Proportion of laundry of that type",
+ pch = 1,
+ col = "red")
+points(x = laundry_fr$s_halfhour, y = laundry_fr$laundry_p_2005_pr, col = "blue", pch=2)
+# cex = scaling factor
+legend('topright',c("Primary act 1974","Primary act 2005"),
+ col=c('red', 'blue'), pch=c(1,2), cex=1)
+title("% of laundry done at different times of day (1974-2005)", cex=0.75)
+dev.off()
+
+# secondary episodes
+png(paste(rpath,"/laundry-time-of-day-1974-2005-secondary.png", sep=""))
+plot(x = laundry_fr$s_halfhour, y = laundry_fr$laundry_s_1974_pr,
+ xlab = "Half Hour",
+ ylab = "Proportion of laundry of that type",
+ pch = 1,
+ col = "red")
+points(x = laundry_fr$s_halfhour, y = laundry_fr$laundry_s_2005_pr, col = "blue", pch = 2)
+# cex = scaling factor
+legend('topright',c("Secondary act 1974","Secondary act 2005"),
+ col=c('red','blue'), pch=c(1,2), cex=1)
+title("% of laundry done at different times of day (1974-2005)", cex=0.75)
+dev.off()
+
+# all laundry episodes
+png(paste(rpath,"/laundry-time-of-day-1974-2005-all.png", sep=""))
+plot(x = laundry_fr$s_halfhour, y = laundry_fr$laundry_all_1974_pr,
+ xlab = "Half Hour",
+ ylab = "Proportion of laundry of that type",
+ pch = 1,
+ col = "red")
+points(x = laundry_fr$s_halfhour, y = laundry_fr$laundry_all_2005_pr, col = "blue", pch = 2)
+# cex = scaling factor
+legend('topright',c("All laundry 1974","All laundry 2005"),
+ col=c('red','blue'), pch=c(1,2), cex=1)
+title("% of laundry done at different times of day (1974-2005)", cex=0.75)
+dev.off()
diff --git a/Theme-1/laundryPaper/DEMAND-BA-MTUS-W6-Laundry-Change-Over-Time-v2.0-adult-analysis.R b/Theme-1/laundryPaper/DEMAND-BA-MTUS-W6-Laundry-Change-Over-Time-v2.0-adult-analysis.R
new file mode 100644
index 0000000000000000000000000000000000000000..fb3964e14d21c59b3f56924ca9768155d1645c43
--- /dev/null
+++ b/Theme-1/laundryPaper/DEMAND-BA-MTUS-W6-Laundry-Change-Over-Time-v2.0-adult-analysis.R
@@ -0,0 +1,1307 @@
+# Begin header ###########################################
+# Data analysis for 'Laundry' paper:
+
+# Use MTUS World 6 time-use data (UK subset) to examine:
+# - distributions of laundry in 1985 & 2005
+# - changing laundry practice
+# Data source: www.timeuse.org/mtus
+# data already in long format (but episodes) processed using
+# DEMAND-BA-MTUS-W6-Laundry-Change-Over-Time-v2.0-adult-data-processing.R
+
+# Uses FES/EFS/LCFS to examine:
+# - historical ownership of washing machines/tumble dryers
+# Data source: http://discover.ukdataservice.ac.uk/series/?sn=200016
+
+# Uses SPRG water practices survey:
+# - reported laundry practices
+# Data source: http://www.sprg.ac.uk/projects-fellowships/patterns-of-water
+
+# This work was funded by RCUK through the End User Energy Demand Centres Programme via the
+# "DEMAND: Dynamics of Energy, Mobility and Demand" Centre (www.demand.ac.uk, gow.epsrc.ac.uk/NGBOViewGrant.aspx?GrantRef=EP/K011723/1)
+
+# Copyright (C) 2014 University of Southampton
+# Author: Ben Anderson (b.anderson@soton.ac.uk, @dataknut, https://github.com/dataknut)
+
+# This program is free software; you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation; either version 2 of the License
+# (http://choosealicense.com/licenses/gpl-2.0/), or
+# (at your option) any later version.
+
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+#
+# End header ###########################################
+
+# MTUS codes of interest:
+# 1983/4/7: Main/Sec21 Laundry, ironing, clothing repair
+# <- 0701 Wash clothes, hang out / bring in washing
+# 0702 Iron clothes
+# 0801 Repair, upkeep of clothes
+# so may over-estimate laundry
+
+# 2005: Main/Sec21 Laundry, ironing, clothing repair <- Pact=7 (washing clothes)
+
+# Housekeeping ----
+# clear out all old objects etc to avoid confusion
+rm(list = ls())
+
+
+# set up some useful data paths
+tudpath <- "~/Documents/Work/Data/MTUS/World 6/processed/" # presume processed data is already in here
+efs1985path <- "~/Documents/Work/Data/Family Expenditure Survey/1985/stata8"
+sprgpath <- "~/Documents/Work/Projects/ESRC-SPRG/WP4-Micro_water/data/sprg_survey/data/safe/v6"
+
+rpath <- "~/Documents/Work/Papers and Conferences/The Time and Timing of Demand - Laundry/results"
+
+# define laundry for time use data
+laundry <- "laundry, ironing, clothing repair"
+
+# Generic functions ----
+
+# load packages
+loadPackages <- function() {
+ # add packages
+ library(foreign) # as loading stata files
+ library(lattice) # fancy graphs
+ library(ggplot2) # fancy graphs II
+ library(data.table) # why use anything else?
+ library(survey) # weighted survey analysis
+ library(car) # regression diagnostics
+ library(gmodels) # nice crosstabs
+ library(broom) # turns stats objects into dataframes - useful for table output
+ library(fasttime) # VERY fast time string conversion to POSIXct
+ # but only IF the input string is in a fixed format - see http://rforge.net/doc/packages/fasttime/fastPOSIXct.html
+}
+
+# Feedback function - cos I can't be bothered to keep writing it out
+feedBack <- function(string) {
+ print(paste0("Feedback: ", string))
+}
+
+###############################################
+# Create a function called #
+# logisticPseudoR2s(). To use it #
+# type logisticPseudoR2s(myLogisticModel)
+# From: Field, A. P., Miles, J., and Field, Z. C. (2012).
+# Discovering statistics using R: and sex and drugs and rock ’n’ roll. London: Sage #
+###############################################
+
+logisticPseudoR2s <- function(LogModel) {
+ dev <- LogModel$deviance
+ nullDev <- LogModel$null.deviance
+ modelN <- length(LogModel$fitted.values)
+ R.hl <- 1 - dev / nullDev
+ R.cs <- 1- exp ( -(nullDev - dev) / modelN)
+ R.n <- R.cs / ( 1 - ( exp (-(nullDev / modelN))))
+ cat("*")
+ cat("Pseudo R^2 for logistic regression\n")
+ cat("-> Hosmer and Lemeshow R^2 ", round(R.hl, 3), "\n")
+ cat("-> Cox and Snell R^2 ", round(R.cs, 3), "\n")
+ cat("-> Nagelkerke R^2 ", round(R.n, 3), "\n")
+}
+
+# function to run a logit model & print out diagnostics
+doLogit <- function(form, DT) {
+ print("# <-- New model --> #")
+ print(paste0("Running logit model: ", form))
+ result <- glm(formula = form,
+ family = binomial(logit),
+ data = DT
+ )
+ ci <- confint(result)
+ tidyResult <- tidy(result) # tidy results
+ tidyResult <- cbind(tidyResult, ci) # stack columns side by side
+ print("")
+ print("# Model results")
+ print (
+ tidyResult
+ )
+ print("# Model summary")
+ print(
+ glance(result)
+ )
+ logisticPseudoR2s(result) # print rsq
+ print("")
+ print("# Diagnostics: independence of errors")
+ print(
+ durbinWatsonTest(result)
+ )
+ # value around 2 = OK, 0 or 4 not OK
+ # if p < 0.05 then a problem as implies autocorrelation
+
+ print("# Diagnostics: collinearity (vif)")
+ print (
+ vif(result)
+ )
+ # if any values > 10 -> problem
+
+ print("# Diagnostics: collinearity (tolerance)")
+ print(
+ 1/vif(result)
+ )
+ # if any values < 0.2 -> possible problem
+ # if any values < 0.1 -> definitely a problem
+}
+
+# function to run an lm model & print out diagnostics
+doLm <- function(form, DT) {
+ print("# <-- New model --> #")
+ print(paste0("Running linear model: ", form))
+ result <- lm(formula = form,
+ data = DT
+ )
+ ci <- confint(result)
+ tidyResult <- tidy(result)
+ tidyResult <- cbind(tidyResult, ci)
+ print("# Model results")
+ print (
+ tidyResult
+ )
+ print(
+ glance(result)
+ )
+ print("# Diagnostics: independence of errors")
+ print(
+ durbinWatsonTest(result)
+ )
+ # value around 2 = OK, 0 or 4 not OK
+ # if p < 0.05 then a problem as implies autocorrelation
+
+ print("# Diagnostics: collinearity (vif)")
+ print (
+ vif(result)
+ )
+ # if any values > 10 -> problem
+
+ print("# Diagnostics: collinearity (tolerance)")
+ print(
+ 1/vif(result)
+ )
+ # if any values < 0.2 -> possible problem
+ # if any values < 0.1 -> definitely a problem
+}
+
+# FES data ----
+# Needed for estimates of washing machine adoption in 1985
+# Load as STATA file
+efs1985file <- paste0(efs1985path, "/hchars.dta")
+feedBack(paste0("Loading ", efs1985file))
+efs1985_DT <- as.data.table(read.dta(efs1985file))
+
+# a108 byte %8.0g no of washing machines in h/h
+wmachines <- table(efs1985_DT$a108)
+prop.table(wmachines)
+
+# TU data ----
+# Functions for loading pre-processed data
+
+loadCoreMtusSurvey <- function() {
+ dfile <- "gMTUSW6UKsurveyCore_DT.csv.gz"
+ dir <- getwd() # get current
+ setwd(tudpath) # easier than building file paths
+ cmd <- paste0("gunzip -c ", dfile)
+ print(paste0("Loading via ", cmd))
+
+ # read in the gzipped file using gunzip to 'pipe' the file to fread
+ # this is a lot faster than non-piped gunzip then fread and you get feedback from fread
+ # Don't forget to globalise!
+ gMTUSW6UKsurveyCore_DT <<- fread(cmd,
+ stringsAsFactors = FALSE)
+ setwd(dir) # set back to where we started
+ feedBack("Done loading TU survey data")
+} # works
+
+loadMtusEpisodes <- function() {
+ dfile <- "gMTUSW6UKdiaryEps_DT.csv.gz"
+ dir <- getwd() # get current
+ setwd(tudpath) # easier than building file paths
+ cmd <- paste0("gunzip -c ", dfile)
+ print(paste0("Loading via ", cmd))
+
+ # read in the gzipped file using gunzip to 'pipe' the file to fread
+ # this is a lot faster than non-piped gunzip then fread and you get feedback from fread
+ # Don't forget to globalise!
+ gMTUSW6UKdiaryEps_DT <<- fread(cmd,
+ stringsAsFactors = FALSE)
+ setwd(dir) # set back to where we started
+ feedBack("Done loading TU episodes data")
+} # not tested - file does not exist (yet)
+
+loadMtusSampled <- function() {
+ # two files:
+ # - all sampled
+ # - laundry half hour
+ dfile <- "gMTUSW6UKdiarySampled_DT.csv.gz"
+ dir <- getwd() # get current
+ setwd(tudpath) # easier than building file paths
+ cmd <- paste0("gunzip -c ", dfile)
+ print(paste0("Loading via ", cmd))
+
+ # read in the gzipped file using gunzip to 'pipe' the file to fread
+ # this is a lot faster than non-piped gunzip then fread and you get feedback from fread
+ # Don't forget to globalise!
+ gMTUSW6UKdiarySampled_DT <<- fread(cmd,
+ stringsAsFactors = FALSE)
+ feedBack("Done loading TU sampled data")
+
+ dfile <- "gMTUSW6UK_halfhours_laundry_DT.csv.gz"
+
+ cmd <- paste0("gunzip -c ", dfile)
+ print(paste0("Loading via ", cmd))
+
+ # read in the gzipped file using gunzip to 'pipe' the file to fread
+ # this is a lot faster than non-piped gunzip then fread and you get feedback from fread
+ # Don't forget to globalise!
+ gMTUSW6UK_halfhours_laundry_DT <<- fread(cmd,
+ stringsAsFactors = FALSE)
+ setwd(dir) # set back to where we started
+ feedBack("Done loading TU sampled laundry data aggregated to half hour level")
+
+} # works
+
+# Functions for analysing loaded data
+analyseMtusEpisodes <- function() {
+ # Check on number of primary & secondary acts ----
+ # episode data
+ testEpisodes_p <- with(gMTUSW6UKdiaryEps_DT, xtabs(~ main + ba_survey))
+ testEpisodes_s <- with(gMTUSW6UKdiaryEps_DT, xtabs(~ sec + ba_survey))
+ feedBack(paste0("Writing results to ", rpath))
+ pefile <- paste0(rpath, "/testEpisodes_p.csv")
+ write.csv(testEpisodes_p, file = pefile)
+ sefile <- paste0(rpath, "/testEpisodes_s.csv")
+ write.csv(testEpisodes_s, file = sefile)
+
+ # sampled data
+ testSampled_p <- with(gMTUSW6UKdiarySampled_DT, xtabs(~ pact + ba_survey))
+ testSampled_s <- with(gMTUSW6UKdiarySampled_DT, xtabs(~ sact + ba_survey))
+ psfile <- paste0(rpath, "/testSampled_p.csv")
+ write.csv(testSampled_p, file = psfile)
+ ssfile <- paste0(rpath, "/testSampled_s.csv")
+ write.csv(testSampled_s, file = ssfile)
+
+ # Laundry = code 21
+ laundry <- "laundry, ironing, clothing repair"
+ # n episodes in total per year
+ with(gMTUSW6UKdiaryEps_DT,
+ table(ba_survey)
+ )
+ # set a laundry code
+ gMTUSW6UKdiaryEps_DT$laundry_p <- ifelse(gMTUSW6UKdiaryEps_DT$main == laundry,
+ 1, # laundry as main act
+ 0)
+ gMTUSW6UKdiaryEps_DT$laundry_s <- ifelse(gMTUSW6UKdiaryEps_DT$sec == laundry,
+ 1, # laundry as main act
+ 0)
+ gMTUSW6UKdiaryEps_DT$laundry_all <- ifelse(gMTUSW6UKdiaryEps_DT$main == laundry | gMTUSW6UKdiaryEps_DT$sec == laundry,
+ 1, # laundry as either act
+ 0)
+ # totals
+ with(gMTUSW6UKdiaryEps_DT,
+ table(laundry_p, ba_survey))
+ with(gMTUSW6UKdiaryEps_DT,
+ table(laundry_s, ba_survey))
+ with(gMTUSW6UKdiaryEps_DT,
+ table(laundry_all, ba_survey))
+
+ feedBack("# n episodes by duration - to show how recording period varies things")
+ print(
+ with(gMTUSW6UKdiaryEps_DT,
+ xtabs(~ time + ba_survey))
+ )
+ feedBack("# n episodes of laundry as a primary act by duration (to show how recording period varies things)")
+ print(
+ with(gMTUSW6UKdiaryEps_DT,
+ xtabs(laundry_p ~ time + ba_survey))
+ )
+
+ eps1985 <- length(gMTUSW6UKdiaryEps_DT$diarypid[gMTUSW6UKdiaryEps_DT$ba_survey == 1985]) # n episodes in 1985
+ epslaundry1985 <- length(gMTUSW6UKdiaryEps_DT$diarypid[gMTUSW6UKdiaryEps_DT$ba_survey == 1985 & gMTUSW6UKdiaryEps_DT$laundry_all == 1])
+
+ print(paste0("% episodes that are laundry_all in 1985 = ", (epslaundry1985/eps1985)*100))
+
+ eps2005 <- length(gMTUSW6UKdiaryEps_DT$diarypid[gMTUSW6UKdiaryEps_DT$ba_survey == 2005]) # n episodes in 2005
+ epslaundry2005 <- length(gMTUSW6UKdiaryEps_DT$diarypid[gMTUSW6UKdiaryEps_DT$ba_survey == 2005 & gMTUSW6UKdiaryEps_DT$laundry_all == 1])
+
+ print(paste0("% episodes that are laundry_all in 2005 = ", (epslaundry2005/eps2005)*100))
+
+ # work with split files - easier?
+ eps_1985DT <- gMTUSW6UKdiaryEps_DT[gMTUSW6UKdiaryEps_DT$ba_survey == 1985]
+ eps_2005DT <- gMTUSW6UKdiaryEps_DT[gMTUSW6UKdiaryEps_DT$ba_survey == 2005]
+
+ # when do most episodes start in 1985?
+ alleps_byhh1985 <- eps_1985DT[main == laundry,
+ .(
+ N_episodes1985 = length(st_halfhour), #n episodes starting in a given half hour
+ Pr_episodes1985 = length(st_halfhour)/eps1985 # proportion of all episodes in that year starting...
+ ),
+ by = .(
+ Start = st_halfhour
+ )
+ ]
+ setkey(alleps_byhh1985,Start)
+ feedBack("Plotting 1985 all episodes start")
+ plot(alleps_byhh1985$Pr_episodes)
+
+ # and laundry episodes?
+ laundryeps_byhh1985 <- eps_1985DT[main == laundry,
+ .(
+ N_episodes1985 = length(st_halfhour), #n episodes starting in a given half hour
+ Pr_episodes1985 = length(st_halfhour)/eps1985 # proportion of all episodes in that year starting...
+ ),
+ by = .(
+ Start = st_halfhour
+ )
+ ]
+ setkey(laundryeps_byhh1985,Start)
+ feedBack("Plotting 1985 laundry episodes start")
+ plot(laundryeps_byhh1985$Pr_episodes)
+
+ # when do most episodes start in 2005?
+ alleps_byhh2005 <- eps_2005DT[,
+ .(
+ N_episodes2005 = length(st_halfhour), #n episodes starting in a given half hour
+ Pr_episodes2005 = length(st_halfhour)/eps2005 # proportion of all episodes in that year starting...
+ ),
+ by = .(
+ Start = st_halfhour
+ )
+ ]
+ setkey(alleps_byhh2005,Start)
+ feedBack("Plotting 2005 all episodes start")
+ plot(alleps_byhh2005$Pr_episodes)
+
+ # and laundry episodes?
+ laundryeps_byhh2005 <- eps_2005DT[main == laundry,
+ .(
+ N_episodes2005 = length(st_halfhour), #n episodes starting in a given half hour
+ Pr_episodes2005 = length(st_halfhour)/eps2005 # proportion of all episodes in that year starting...
+ ),
+ by = .(
+ Start = st_halfhour
+ )
+ ]
+ setkey(laundryeps_byhh2005,Start)
+ feedBack("Plotting 2005 laundry episodes start")
+ plot(laundryeps_byhh2005$Pr_episodes)
+
+ # join tables for ease of comparison
+ # ideally these need to be weighted & need 95% CIs for proportions
+ laundryeps_byhh <- laundryeps_byhh2005[laundryeps_byhh1985]
+ laundryeps_byhh$diff <- laundryeps_byhh$Pr_episodes2005 - laundryeps_byhh$Pr_episodes1985
+ plot(laundryeps_byhh$diff)
+
+ # use ggplot to make a nice plot of 1985, 2005 & difference?
+
+ outf <- paste0(rpath,"/laundryeps_byhh.csv")
+ feedBack(paste0("Saving episodes results into: ", rpath))
+ write.csv(laundryeps_byhh,
+ file = outf,
+ na = ""
+ )
+
+ feedBack("Done analysing episode file")
+} # works
+
+analyseMtus10minSampled <- function() {
+
+ feedBack("Join survey to sampled data")
+ MTUSW6UKjoinedSampled_DT <- gMTUSW6UKdiarySampled_DT[gMTUSW6UKsurveyCore_DT]
+
+ with(MTUSW6UKjoinedSampled_DT[],
+ table(badcase,ba_survey)
+ )
+
+ # Keep only good cases for 1985 & 2005
+ MTUSW6UKjoinedSampled_DT <- MTUSW6UKjoinedSampled_DT[badcase == "good case"]
+ MTUSW6UKjoinedSampled_DT <- MTUSW6UKjoinedSampled_DT[ba_survey %in% c("1985","2005")]
+
+ # check
+ with(MTUSW6UKjoinedSampled_DT,
+ table(badcase,ba_survey)
+ )
+ # looks like we only had the good cases in the sampled file anyway
+
+ # totals
+ feedBack("Number of 10 min obs of laundry as primary act")
+ print(
+ with(MTUSW6UKjoinedSampled_DT,
+ table(laundry_p, ba_survey))
+ )
+
+} # works
+
+analyseMtusHalfhourSampled <- function() {
+ feedBack("Join survey to derived half hour data")
+ setkey(gMTUSW6UK_halfhours_laundry_DT, diarypid)
+ setkey(gMTUSW6UKsurveyCore_DT, diarypid)
+ gMTUSW6UKjoinedHalfhour_laundry_DT <<- gMTUSW6UK_halfhours_laundry_DT[gMTUSW6UKsurveyCore_DT]
+
+ head(gMTUSW6UKjoinedHalfhour_laundry_DT) # check
+ tail(gMTUSW6UKjoinedHalfhour_laundry_DT) # check
+
+ # Lots of NA in ba_survey = other surveys. Drop them
+ table(gMTUSW6UKjoinedHalfhour_laundry_DT$ba_survey, exclude = FALSE)
+ gMTUSW6UKjoinedHalfhour_laundry_DT <- gMTUSW6UKjoinedHalfhour_laundry_DT[
+ ba_survey == 1985 | ba_survey == 2005]
+ # check
+ table(gMTUSW6UKjoinedHalfhour_laundry_DT$ba_survey)
+
+ feedBack("How many half hours have N_obs = 3 (should be all!)")
+ # gives a count of half hours
+ print(
+ xtabs(~ gMTUSW6UKjoinedHalfhour_laundry_DT$ba_survey +
+ gMTUSW6UKjoinedHalfhour_laundry_DT$N_obs)
+ )
+ # number of half hours by number of obs in the half hour
+
+ feedBack("Number of half hours by frequency of observation ( 0 - 3)")
+ print(
+ xtabs(~ gMTUSW6UKjoinedHalfhour_laundry_DT$ba_survey +
+ gMTUSW6UKjoinedHalfhour_laundry_DT$N_laundry_p)
+ )
+ # now do the comparison by half hour to see if it changes the shape much
+ feedBack("Distribution of halfhours with given number (1-3) obs of laundry as primary act?")
+ print(
+ ftable(gMTUSW6UKjoinedHalfhour_laundry_DT$st_halfhour,
+ gMTUSW6UKjoinedHalfhour_laundry_DT$ba_survey,
+ gMTUSW6UKjoinedHalfhour_laundry_DT$N_laundry_p,
+ col.vars= c(2, 3) # arrange with half hours as rows and frequencies per survey year as cols
+ )
+ )
+ feedBack("Same but for any laundry indicator (should be sum of above!)")
+ print(
+ ftable(gMTUSW6UKjoinedHalfhour_laundry_DT$st_halfhour,
+ gMTUSW6UKjoinedHalfhour_laundry_DT$ba_survey,
+ gMTUSW6UKjoinedHalfhour_laundry_DT$N_laundry_p,
+ col.vars= c(2, 3) # arrange with half hours as rows and frequencies per survey year as cols
+ )
+ )
+
+ # the above analysis confirms that the 'any laundry' indicator masks the difference between short duration laundry acts and longer ones
+ # Longer duration acts are more common and seem to have a different distribution - perhaps this is different components of the practice?
+ # But NB - 1985 = 2 * 15 min slots per half hour, 2005 = 3 * 10 minutes (as per sampling method)
+
+ # So from here on we use anylaundry_p as we are not sure about reporting of secondary acts and we only care about the timing of laundry
+ # not the duration of acts at particular times (although it might be interesting for a methodological study)
+
+ # use weighted analysis from here on
+
+ feedBack("Done analysing sampled file")
+} # works
+
+analyseMtusHalfhourSampled_Svy <- function() {
+ feedBack("Join survey to derived half hour data")
+ setkey(gMTUSW6UK_halfhours_laundry_DT, diarypid)
+ setkey(gMTUSW6UKsurveyCore_DT, diarypid)
+ gMTUSW6UKjoinedHalfhour_laundry_DT <<- gMTUSW6UK_halfhours_laundry_DT[gMTUSW6UKsurveyCore_DT]
+
+ head(gMTUSW6UKjoinedHalfhour_laundry_DT) # check
+ tail(gMTUSW6UKjoinedHalfhour_laundry_DT) # check
+
+ # Lots of NA in ba_survey = other surveys. Drop them
+ table(gMTUSW6UKjoinedHalfhour_laundry_DT$ba_survey, exclude = FALSE)
+ gMTUSW6UKjoinedHalfhour_laundry_DT <- gMTUSW6UKjoinedHalfhour_laundry_DT[
+ ba_survey == 1985 | ba_survey == 2005]
+ # check
+ table(gMTUSW6UKjoinedHalfhour_laundry_DT$ba_survey)
+
+ print("Recode to 'at a home' vs 'not at a home'")
+ gMTUSW6UKjoinedHalfhour_laundry_DT$Any_laundry_home <- ifelse(
+ gMTUSW6UKjoinedHalfhour_laundry_DT$Any_laundry_ph == 1 |
+ gMTUSW6UKjoinedHalfhour_laundry_DT$Any_laundry_photh == 1, 1, 0
+ )
+ gMTUSW6UKjoinedHalfhour_laundry_DT$Any_laundry_nothome <- ifelse(
+ gMTUSW6UKjoinedHalfhour_laundry_DT$Any_laundry_psh == 1 |
+ gMTUSW6UKjoinedHalfhour_laundry_DT$Any_laundry_poth == 1, 1, 0
+ )
+ # check
+ with(gMTUSW6UKjoinedHalfhour_laundry_DT,
+ table(
+ Any_laundry_home, Any_laundry_nothome
+ )
+ )
+
+ print("create types of laundry here so become part of survey design object")
+ # Specific types of laundry (for Table 3)
+ # weekday early morning laundry (06:00 – 09:00)
+ # weekday morning laundry (09:00-12:00)
+ # weekday evening laundry (18:00 – 21:00)
+ # Sunday morning laundry (09:00 – 11:00)
+
+ print("Weekday early morning 06:00-09:00")
+ gMTUSW6UKjoinedHalfhour_laundry_DT$weekday_earlyam <- ifelse(
+ gMTUSW6UKjoinedHalfhour_laundry_DT$r_dow != "Saturday" &
+ gMTUSW6UKjoinedHalfhour_laundry_DT$r_dow != "Sunday" &
+ gMTUSW6UKjoinedHalfhour_laundry_DT$r_hour >= 6 &
+ gMTUSW6UKjoinedHalfhour_laundry_DT$r_hour <= 8 &
+ gMTUSW6UKjoinedHalfhour_laundry_DT$Any_laundry_home == 1, 1, 0
+ )
+ # check
+ with(gMTUSW6UKjoinedHalfhour_laundry_DT[weekday_earlyam == 1],
+ table(
+ r_hour, r_dow
+ )
+ )
+
+ print("Weekday morning 09:00-12:00")
+ gMTUSW6UKjoinedHalfhour_laundry_DT$weekday_am <- ifelse(
+ gMTUSW6UKjoinedHalfhour_laundry_DT$r_dow != "Saturday" &
+ gMTUSW6UKjoinedHalfhour_laundry_DT$r_dow != "Sunday" &
+ gMTUSW6UKjoinedHalfhour_laundry_DT$r_hour >= 9 &
+ gMTUSW6UKjoinedHalfhour_laundry_DT$r_hour <= 11 &
+ gMTUSW6UKjoinedHalfhour_laundry_DT$Any_laundry_home == 1, 1, 0
+ )
+ # check
+ with(gMTUSW6UKjoinedHalfhour_laundry_DT[weekday_am == 1],
+ table(
+ r_hour, r_dow
+ )
+ )
+
+ print("Weekday evening peak 18:00-21:00")
+ gMTUSW6UKjoinedHalfhour_laundry_DT$weekday_pm <- ifelse(
+ gMTUSW6UKjoinedHalfhour_laundry_DT$r_dow != "Saturday" &
+ gMTUSW6UKjoinedHalfhour_laundry_DT$r_dow != "Sunday" &
+ gMTUSW6UKjoinedHalfhour_laundry_DT$r_hour >= 18 &
+ gMTUSW6UKjoinedHalfhour_laundry_DT$r_hour <= 20 &
+ gMTUSW6UKjoinedHalfhour_laundry_DT$Any_laundry_home == 1, 1, 0
+ )
+ # check
+ with(gMTUSW6UKjoinedHalfhour_laundry_DT[weekday_pm == 1],
+ table(
+ r_hour, r_dow
+ )
+ )
+
+ print ("Sunday morning 09:00-12:00")
+ gMTUSW6UKjoinedHalfhour_laundry_DT$sunday_am <- ifelse(
+ gMTUSW6UKjoinedHalfhour_laundry_DT$r_dow == "Sunday" &
+ gMTUSW6UKjoinedHalfhour_laundry_DT$r_hour >= 9 &
+ gMTUSW6UKjoinedHalfhour_laundry_DT$r_hour <= 11 &
+ gMTUSW6UKjoinedHalfhour_laundry_DT$Any_laundry_home == 1, 1, 0
+ )
+ # check
+ with(gMTUSW6UKjoinedHalfhour_laundry_DT[sunday_am == 1],
+ table(
+ r_hour, r_dow
+ )
+ )
+
+ print("Other")
+ gMTUSW6UKjoinedHalfhour_laundry_DT$other <- ifelse(
+ gMTUSW6UKjoinedHalfhour_laundry_DT$sunday_am != 1 &
+ gMTUSW6UKjoinedHalfhour_laundry_DT$weekday_earlyam != 1 &
+ gMTUSW6UKjoinedHalfhour_laundry_DT$weekday_am != 1 &
+ gMTUSW6UKjoinedHalfhour_laundry_DT$weekday_pm != 1 &
+ gMTUSW6UKjoinedHalfhour_laundry_DT$Any_laundry_home == 1, 1, 0
+ )
+ # check
+ with(gMTUSW6UKjoinedHalfhour_laundry_DT[other == 1],
+ table(
+ r_hour, r_dow
+ )
+ )
+
+ print("Set survey data")
+ # tell survey that the diarypids are the ids (they repeat)
+ svygMTUSW6UK_halfhours_laundry <<- svydesign(ids = ~diarypid,
+ weight = ~propwt,
+ data = gMTUSW6UKjoinedHalfhour_laundry_DT) # does not produce a data table
+
+ # how many weighted half hours do we have?
+ # any laundry
+ all_hhs <- svytable(~ba_survey + sex + N_laundry_p, # the row * columns we want
+ svygMTUSW6UK_halfhours_laundry # the data in survey form
+ )
+ print(
+ ftable(all_hhs) # nice table
+ )
+ # how many weighted half hours do we have?
+ # any laundry
+ all_hhs <- svytable(~ba_survey + Any_laundry_p, # the row * columns we want
+ svygMTUSW6UK_halfhours_laundry # the data in survey form
+ )
+ print(
+ ftable(all_hhs) # nice table
+ )
+
+ # any laundry at home (Table 1)
+ all_hhs <- svytable(~ba_survey + Any_laundry_ph, # the row * columns we want
+ svygMTUSW6UK_halfhours_laundry # the data in survey form
+ )
+ print(
+ ftable(all_hhs) # nice table
+ )
+
+ # any laundry at others' home (Table 1)
+ all_hhs <- svytable(~ba_survey + Any_laundry_photh, # the row * columns we want
+ svygMTUSW6UK_halfhours_laundry # the data in survey form
+ )
+ print(
+ ftable(all_hhs) # nice table
+ )
+ # any laundry at shops/services (Table 1)
+ all_hhs <- svytable(~ba_survey + Any_laundry_psh, # the row * columns we want
+ svygMTUSW6UK_halfhours_laundry # the data in survey form
+ )
+ print(
+ ftable(all_hhs) # nice table
+ )
+ # any laundry at other locations (Table 1)
+ all_hhs <- svytable(~ba_survey + Any_laundry_poth, # the row * columns we want
+ svygMTUSW6UK_halfhours_laundry # the data in survey form
+ )
+ print(
+ ftable(all_hhs) # nice table
+ )
+
+
+
+ # any laundry at a home (Table 1)
+ # NB: some missing as no location known
+ all_hhs <- svytable(~ba_survey + Any_laundry_home, # the row * columns we want
+ svygMTUSW6UK_halfhours_laundry # the data in survey form
+ )
+ print(
+ ftable(all_hhs) # nice table
+ )
+ # any laundry not at a home (Table 1)
+ # NB: some missing as no location known
+ all_hhs <- svytable(~ba_survey + Any_laundry_nothome, # the row * columns we want
+ svygMTUSW6UK_halfhours_laundry # the data in survey form
+ )
+ print(
+ ftable(all_hhs) # nice table
+ )
+
+
+ # any laundry by gender (Table 1) - frequencies
+ all_hhs_anylf <- svytable(~ba_survey + sex + Any_laundry_p, # the row * columns we want
+ svygMTUSW6UK_halfhours_laundry # the data in survey form
+ )
+ print(
+ ftable(all_hhs_anylf) # nice table
+ )
+ # any laundry by gender (Table 1) - % of halfhours using svyby
+ all_hhs_anylp <- svyby(~Any_laundry_p, # the data to summarise
+ ~ba_survey + sex, # the row * columns we want
+ svygMTUSW6UK_halfhours_laundry, # the data in survey form
+ svymean # the function to use to summarise
+ )
+ print(all_hhs_anylp)
+
+ # all laundry by gender (Table 1) - % of laundry halfhours
+ anyl_genderp85 <- svymean(~sex, # the row * columns we want
+ svygMTUSW6UK_halfhours_laundry[
+ svygMTUSW6UK_halfhours_laundry$variables$Any_laundry_home == 1
+ & svygMTUSW6UK_halfhours_laundry$variables$ba_survey == 1985
+ ], # the data in survey form
+ keep.var = TRUE
+ )
+ print(
+ ftable(anyl_genderp85) # nice table
+ )
+
+ anyl_genderp05 <- svymean(~sex, # the row * columns we want
+ svygMTUSW6UK_halfhours_laundry[
+ svygMTUSW6UK_halfhours_laundry$variables$Any_laundry_home == 1
+ & svygMTUSW6UK_halfhours_laundry$variables$ba_survey == 2005
+ ], # the data in survey form
+ keep.var = TRUE
+ )
+ print(
+ ftable(anyl_genderp05) # nice table
+ )
+ # laundry not at home (recoded for Table 1)
+ all_hhs_anylh <- svytable(~ba_survey + sex + Any_laundry_home, # the row * columns we want
+ svygMTUSW6UK_halfhours_laundry # the data in survey form
+ )
+ print(
+ ftable(all_hhs_anylh) # nice table
+ )
+
+ all_hhs_anylnh <- svytable(~ba_survey + sex + Any_laundry_nothome, # the row * columns we want
+ svygMTUSW6UK_halfhours_laundry # the data in survey form
+ )
+ print(
+ ftable(all_hhs_anylnh) # nice table
+ )
+
+
+ ## By day of the week (Fig 1) ##
+
+ # This gives the frequencies (pattern) within all halfhours
+ svyby(~Any_laundry_home, ~r_dow + ba_survey,
+ svygMTUSW6UK_halfhours_laundry,
+ svymean
+ )
+
+ # This gives the frequencies (pattern) within laundry
+ svytable(~r_dow + ba_survey,
+ svygMTUSW6UK_halfhours_laundry[svygMTUSW6UK_halfhours_laundry$variables$Any_laundry_home == 1]
+ )
+ # this gives the proportion per day
+ dow_1985 <- svymean(~r_dow,
+ svygMTUSW6UK_halfhours_laundry[svygMTUSW6UK_halfhours_laundry$variables$Any_laundry_home == 1 &
+ svygMTUSW6UK_halfhours_laundry$variables$ba_survey == 1985]
+ )
+ feedBack("Any laundry by day of the week: 1985")
+ print(dow_1985)
+ confint( # check
+ dow_1985
+ )
+
+ # this gives the proportion per day
+ dow_2005 <- svymean(~r_dow,
+ svygMTUSW6UK_halfhours_laundry[svygMTUSW6UK_halfhours_laundry$variables$Any_laundry_home == 1 &
+ svygMTUSW6UK_halfhours_laundry$variables$ba_survey == 2005]
+ )
+ feedBack("Any laundry by day of the week: 2005")
+ print(dow_2005)
+ confint( # check
+ dow_2005
+ )
+
+ ## By gender & day of the week (Fig 2) ##
+ dowg_1985 <- svyby(~r_dow, ~sex,
+ svygMTUSW6UK_halfhours_laundry[svygMTUSW6UK_halfhours_laundry$variables$Any_laundry_home == 1 &
+ svygMTUSW6UK_halfhours_laundry$variables$ba_survey == 1985 ],
+ svymean
+ )
+ feedBack("Any laundry by day of the week/gender: 1985")
+ print(dowg_1985)
+ confint( # check
+ dowg_1985
+ )
+
+ dowg_2005 <- svyby(~r_dow, ~sex,
+ svygMTUSW6UK_halfhours_laundry[svygMTUSW6UK_halfhours_laundry$variables$Any_laundry_home == 1 &
+ svygMTUSW6UK_halfhours_laundry$variables$ba_survey == 2005 ],
+ svymean
+ )
+ feedBack("Any laundry by day of the week/gender: 2005")
+ print(dowg_2005)
+ confint( # check
+ dowg_2005
+ )
+
+ ## By women's labour market status (Fig 3) ##
+ # all half hours
+ pwork_1985allhh <- svyby(~ba_survey, ~empstat,
+ svygMTUSW6UK_halfhours_laundry,
+ svytotal
+ )
+ ftable(pwork_1985allhh)
+ dowpwork_1985allhh <- svyby(~Any_laundry_home, ~empstat + r_dow,
+ svygMTUSW6UK_halfhours_laundry[
+ svygMTUSW6UK_halfhours_laundry$variables$ba_survey == 1985 &
+ svygMTUSW6UK_halfhours_laundry$variables$sex == "Woman"],
+ svymean
+ )
+ dowpwork_1985allhh
+ # laundry half hours
+ dowfwork_1985 <- svyby(~r_dow, ~empstat,
+ svygMTUSW6UK_halfhours_laundry[svygMTUSW6UK_halfhours_laundry$variables$Any_laundry_home == 1 &
+ svygMTUSW6UK_halfhours_laundry$variables$ba_survey == 1985 &
+ svygMTUSW6UK_halfhours_laundry$variables$sex == "Woman"],
+ svymean
+ )
+ feedBack("Any laundry by day of the week/women/labour market status: 1985")
+ print(dowfwork_1985)
+
+ ## By women's labour market status (Fig 4) ##
+ dowfwork_2005 <- svyby(~r_dow, ~empstat,
+ svygMTUSW6UK_halfhours_laundry[svygMTUSW6UK_halfhours_laundry$variables$Any_laundry_home == 1 &
+ svygMTUSW6UK_halfhours_laundry$variables$ba_survey == 2005 &
+ svygMTUSW6UK_halfhours_laundry$variables$sex == "Woman"],
+ svymean,
+ keep.var = TRUE
+ )
+ feedBack("Any laundry by day of the week/women/labour market status: 1985")
+ print(dowfwork_2005)
+ # ftable(dowfwork_2005) #if desired
+
+
+ ## By time of day (Fig 5) ##
+ # get the mean % of half hours with any laundry as a primary act (and SE) over 24 hours
+ pcAnylp_hh <- svyby(~Any_laundry_home, # the variable to do the stats on
+ ~st_halfhour + ba_survey, # the row * columns we want (produces long form)
+ svygMTUSW6UK_halfhours_laundry, # the data
+ svymean, # the stats function(s)
+ keep.var = TRUE)
+ #ftable(pcAnyl_hh) # flat table fails
+ print(
+ pcAnylp_hh # table
+ )
+ # by day (all half hours)
+ pcAnylp_hhByDay85 <- svyby(~Any_laundry_home,
+ ~st_halfhour + r_dow,
+ svygMTUSW6UK_halfhours_laundry[
+ svygMTUSW6UK_halfhours_laundry$variables$ba_survey == 1985],
+ svymean)
+ print(pcAnylp_hhByDay85)
+ pcAnylp_hhByDay05 <- svyby(~Any_laundry_home,
+ ~st_halfhour + r_dow,
+ svygMTUSW6UK_halfhours_laundry[
+ svygMTUSW6UK_halfhours_laundry$variables$ba_survey == 2005],
+ svymean)
+ print(pcAnylp_hhByDay05)
+
+ ## By time of day but within laundry (Fig 6) ##
+ # get the mean % of half hours with any laundry as a primary act (and SE) over 24 hours
+ svygMTUSW6UK_halfhours_laundry$variables$ba_survey <- factor(svygMTUSW6UK_halfhours_laundry$variables$ba_survey)
+ pcAnyl_hh85 <- svymean(~st_halfhour,
+ svygMTUSW6UK_halfhours_laundry[
+ svygMTUSW6UK_halfhours_laundry$variables$Any_laundry_home == 1 &
+ svygMTUSW6UK_halfhours_laundry$variables$ba_survey == 1985] # the data
+
+ )
+ print(pcAnyl_hh85)
+ pcAnyl_hh05 <- svymean(~st_halfhour,
+ svygMTUSW6UK_halfhours_laundry[
+ svygMTUSW6UK_halfhours_laundry$variables$Any_laundry_home == 1 &
+ svygMTUSW6UK_halfhours_laundry$variables$ba_survey == 2005] # the data
+
+ )
+ print(pcAnyl_hh05)
+
+ # within laundry by day, (Fig 7)
+ pcOnlyl_hhByDay85 <- svymean(~interaction(st_halfhour,r_dow),
+ design = svygMTUSW6UK_halfhours_laundry[svygMTUSW6UK_halfhours_laundry$variables$ba_survey == 1985 &
+ svygMTUSW6UK_halfhours_laundry$variables$Any_laundry_home == 1])
+ print(pcOnlyl_hhByDay85)
+
+ pcOnlyl_hhByDay05 <- svymean(~interaction(st_halfhour,r_dow),
+ design = svygMTUSW6UK_halfhours_laundry[svygMTUSW6UK_halfhours_laundry$variables$ba_survey == 2005 &
+ svygMTUSW6UK_halfhours_laundry$variables$Any_laundry_home == 1])
+ print(pcOnlyl_hhByDay05)
+
+ # you cannot be serious:
+ ftable(pcOnlyl_hhByDay85, rownames = list(r_dow = c("S", "M", "T", "W", "T", "F", "S"),
+ st_halfhour = c("00:00", "00:30", "01:00")))
+
+ # weekday/weekend flag
+ svygMTUSW6UK_halfhours_laundry$variables$wd <- ifelse(
+ svygMTUSW6UK_halfhours_laundry$variables$r_dow != "Sunday" &
+ svygMTUSW6UK_halfhours_laundry$variables$r_dow != "Saturday" ,
+ 1, # yes = weekday
+ 0 # no = weekend
+ )
+
+ # Specific types of laundry (for Table 3)
+ # weekday early morning laundry (06:00 – 09:00)
+ # weekday morning laundry (09:00-12:00)
+ # weekday evening laundry (17:00 – 21:00)
+ # Sunday morning laundry (08:00 – 11:00)
+
+ # Weekday early morning 06:00-09:00
+ weekday_earlyam_pLhh <- svyby(~weekday_earlyam,
+ ~ba_survey,
+ svygMTUSW6UK_halfhours_laundry[svygMTUSW6UK_halfhours_laundry$variables$Any_laundry_home == 1],
+ svymean,
+ keep.var = TRUE
+ )
+ feedBack("Weekday early morning laundry (06:00 – 09:00) out of all laundry")
+ print(weekday_earlyam_pLhh)
+
+ # Weekday morning 09:00-12:00
+ weekday_am_pLhh <- svyby(~weekday_am,
+ ~ba_survey,
+ svygMTUSW6UK_halfhours_laundry[svygMTUSW6UK_halfhours_laundry$variables$Any_laundry_home == 1],
+ svymean,
+ keep.var = TRUE
+ )
+ feedBack("Weekday morning laundry out of all laundry")
+ print(weekday_am_pLhh)
+
+ # Weekday evening peak 17:00-21:00
+ weekday_pm_pLhh <- svyby(~weekday_pm,
+ ~ba_survey,
+ svygMTUSW6UK_halfhours_laundry[svygMTUSW6UK_halfhours_laundry$variables$Any_laundry_home == 1],
+ svymean,
+ keep.var = TRUE
+ )
+ feedBack("Weekday evening peak laundry out of all laundry")
+ print(weekday_pm_pLhh)
+
+ # Sunday morning 09:00-12:00
+ sunday_am_pLhh <- svyby(~sunday_am,
+ ~ba_survey,
+ svygMTUSW6UK_halfhours_laundry[svygMTUSW6UK_halfhours_laundry$variables$Any_laundry_home == 1],
+ svymean,
+ keep.var = TRUE
+ )
+ feedBack("Sunday morning laundry out of all laundry")
+ print(sunday_am_pLhh)
+ confint(
+ sunday_am_pLhh
+ )
+
+ # Other
+ other_pLhh <- svyby(~other,
+ ~ba_survey,
+ svygMTUSW6UK_halfhours_laundry[svygMTUSW6UK_halfhours_laundry$variables$Any_laundry_home == 1],
+ svymean,
+ keep.var = TRUE
+ )
+ feedBack("Other laundry out of all laundry")
+ print(other_pLhh)
+
+ # analysis by employment status in 2005
+ print("% within empstat who do sunday_am")
+ print(
+ svyby(~sunday_am, ~empstat,
+ svygMTUSW6UK_halfhours_laundry[svygMTUSW6UK_halfhours_laundry$variables$Any_laundry_home == 1 &
+ svygMTUSW6UK_halfhours_laundry$variables$ba_survey == 2005],
+ svymean,
+ keep.var = TRUE
+ )
+ )
+ print("% within empstat who do weekday early am")
+ print(
+ svyby(~weekday_earlyam, ~empstat,
+ svygMTUSW6UK_halfhours_laundry[svygMTUSW6UK_halfhours_laundry$variables$Any_laundry_home == 1 &
+ svygMTUSW6UK_halfhours_laundry$variables$ba_survey == 2005],
+ svymean,
+ keep.var = TRUE
+ )
+ )
+ print("% within empstat who do weekday am")
+ print(
+ svyby(~weekday_am, ~empstat,
+ svygMTUSW6UK_halfhours_laundry[svygMTUSW6UK_halfhours_laundry$variables$Any_laundry_home == 1 &
+ svygMTUSW6UK_halfhours_laundry$variables$ba_survey == 2005],
+ svymean,
+ keep.var = TRUE
+ )
+ )
+ print("% within empstat who do weekday pm")
+ print(
+ svyby(~weekday_pm, ~empstat, #
+ svygMTUSW6UK_halfhours_laundry[svygMTUSW6UK_halfhours_laundry$variables$Any_laundry_home == 1 &
+ svygMTUSW6UK_halfhours_laundry$variables$ba_survey == 2005],
+ svymean,
+ keep.var = TRUE
+ )
+ )
+ print("% within empstat who do other")
+ print(
+ svyby(~other, ~empstat, #
+ svygMTUSW6UK_halfhours_laundry[svygMTUSW6UK_halfhours_laundry$variables$Any_laundry_home == 1 &
+ svygMTUSW6UK_halfhours_laundry$variables$ba_survey == 2005],
+ svymean,
+ keep.var = TRUE
+ )
+ )
+
+ print("summarise the laundry types by diarypid for 1985 to see how many different kinds of laundry")
+ laundrySummary1985 <- gMTUSW6UKjoinedHalfhour_laundry_DT[ba_survey == 1985,
+ .(
+ N_Any_laundry_home = sum(Any_laundry_home),
+ N_sunday_morning = sum(sunday_am),
+ N_weekday_earlyam = sum(weekday_earlyam),
+ N_weekday_am = sum(weekday_am),
+ N_weekday_pm = sum(weekday_pm),
+ N_other = sum(other)
+ ),
+ by = .(
+ diarypid,
+ empstat,
+ sex,
+ r_dow,
+ r_month,
+ nchild,
+ age,
+ hhtype,
+ hhldsize,
+ income,
+ urban,
+ propwt
+ )
+
+ ]
+
+ print("Summarise the laundry types by diarypid for 2005 to see how many different kinds of laundry")
+ # globalize for future use
+ laundrySummary2005 <- gMTUSW6UKjoinedHalfhour_laundry_DT[ba_survey == 2005,
+ .(
+ N_Any_laundry_home = sum(Any_laundry_home),
+ N_sunday_morning = sum(sunday_am),
+ N_weekday_earlyam = sum(weekday_earlyam),
+ N_weekday_am = sum(weekday_am),
+ N_weekday_pm = sum(weekday_pm),
+ N_other = sum(other)
+ ),
+ by = .(
+ diarypid,
+ empstat,
+ sex,
+ r_dow,
+ r_month,
+ nchild,
+ age,
+ hhtype,
+ hhldsize,
+ income,
+ urban,
+ propwt
+ )
+
+ ]
+
+ print("Check laundry by sex & hh type - any change in proportions done by men in couples?")
+ print(
+ table(laundrySummary1985$hhtype[laundrySummary1985$N_Any_laundry_home > 0],
+ laundrySummary1985$sex[laundrySummary1985$N_Any_laundry_home > 0])
+ )
+ print(
+ table(laundrySummary2005$hhtype[laundrySummary2005$N_Any_laundry_home > 0],
+ laundrySummary2005$sex[laundrySummary2005$N_Any_laundry_home > 0])
+ )
+ # convert 1985 to dummies
+ laundrySummary1985$Sunday_amd <- ifelse(
+ laundrySummary1985$N_sunday_morning > 0,
+ 1, # true
+ 0 # false
+ )
+ laundrySummary1985$WeekdayEarly_amd <- ifelse(
+ laundrySummary1985$N_weekday_earlyam > 0,
+ 1, # true
+ 0 # false
+ )
+ laundrySummary1985$Weekday_amd <- ifelse(
+ laundrySummary1985$N_weekday_am > 0,
+ 1, # true
+ 0 # false
+ )
+ laundrySummary1985$Weekday_pmd <- ifelse(
+ laundrySummary1985$N_weekday_pm > 0,
+ 1, # true
+ 0 # false
+ )
+ laundrySummary1985$Otherd <- ifelse(
+ laundrySummary1985$N_other > 0,
+ 1, # true
+ 0 # false
+ )
+ laundrySummary1985$Any_laundry_homed <- ifelse(
+ laundrySummary1985$N_Any_laundry_home > 0,
+ 1, # true
+ 0 # false
+ )
+
+ # convert 2005 to dummies for logitstic models
+ laundrySummary2005$Sunday_amd <- ifelse(
+ laundrySummary2005$N_sunday_morning > 0,
+ 1, # true
+ 0 # false
+ )
+ laundrySummary2005$WeekdayEarly_amd <- ifelse(
+ laundrySummary2005$N_weekday_earlyam > 0,
+ 1, # true
+ 0 # false
+ )
+ laundrySummary2005$Weekday_amd <- ifelse(
+ laundrySummary2005$N_weekday_am > 0,
+ 1, # true
+ 0 # false
+ )
+ laundrySummary2005$Weekday_pmd <- ifelse(
+ laundrySummary2005$N_weekday_pm > 0,
+ 1, # true
+ 0 # false
+ )
+ laundrySummary2005$Otherd <- ifelse(
+ laundrySummary2005$N_other > 0,
+ 1, # true
+ 0 # false
+ )
+ laundrySummary2005$Any_laundry_homed <- ifelse(
+ laundrySummary2005$N_Any_laundry_home > 0,
+ 1, # true
+ 0 # false
+ )
+
+ # recode age
+ laundrySummary2005$ba_age <- cut(laundrySummary2005$age,
+ breaks = c(9,24,34,44,54,64,74,84,104),
+ as.factor.result = TRUE
+ )
+ laundrySummary1985$ba_age <- cut(laundrySummary1985$age,
+ breaks = c(9,24,34,44,54,64,74,84,104),
+ as.factor.result = TRUE
+ )
+ # check
+ tapply(laundrySummary2005$age, laundrySummary2005$ba_age, mean)
+ # recode in work var to make label easier and in a different order
+ laundrySummary2005$empstat_r <- factor(laundrySummary2005$empstat,
+ levels = c("could not be created", "missing", "not applicable/not asked",
+ "unkown job hours", "not in paid work", "part-time", "full-time" ),
+ labels = c("could not be created", "missing", "not applicable/not asked",
+ "unkown-hours", "not_in_work","part-time","full-time")
+ )
+ laundrySummary1985$empstat_r <- factor(laundrySummary1985$empstat,
+ levels = c("could not be created", "missing", "not applicable/not asked",
+ "unkown job hours", "not in paid work", "part-time", "full-time" ),
+ labels = c("could not be created", "missing", "not applicable/not asked",
+ "unkown-hours", "not_in_work","part-time","full-time")
+ )
+ # check
+ table(laundrySummary2005$empstat_r, laundrySummary2005$empstat)
+
+ # recode children var
+ laundrySummary1985$ba_nchild[laundrySummary1985$nchild == 0] <- "0_children"
+ laundrySummary1985$ba_nchild[laundrySummary1985$nchild == 1] <- "1_child"
+ laundrySummary1985$ba_nchild[laundrySummary1985$nchild == 2] <- "2_children"
+ laundrySummary1985$ba_nchild[laundrySummary1985$nchild >= 3] <- "3+_children"
+
+ laundrySummary2005$ba_nchild[laundrySummary2005$nchild == 0] <- "0_children"
+ laundrySummary2005$ba_nchild[laundrySummary2005$nchild == 1] <- "1_child"
+ laundrySummary2005$ba_nchild[laundrySummary2005$nchild == 2] <- "2_children"
+ laundrySummary2005$ba_nchild[laundrySummary2005$nchild >= 3] <- "3+_children"
+
+ # check
+ table(laundrySummary2005$ba_nchild, laundrySummary2005$nchild)
+
+ # create a 'season' variable
+ laundrySummary1985$ba_season[laundrySummary1985$r_month == 11 |
+ laundrySummary1985$r_month == 0 |
+ laundrySummary1985$r_month == 1 ] <- "Winter"
+ laundrySummary1985$ba_season[laundrySummary1985$r_month == 2 |
+ laundrySummary1985$r_month == 3 |
+ laundrySummary1985$r_month == 4 ] <- "Spring"
+ laundrySummary1985$ba_season[laundrySummary1985$r_month == 5 |
+ laundrySummary1985$r_month == 6 |
+ laundrySummary1985$r_month == 7 ] <- "Summer"
+ laundrySummary1985$ba_season[laundrySummary1985$r_month == 8 |
+ laundrySummary1985$r_month == 9 |
+ laundrySummary1985$r_month == 10 ] <- "Autumn"
+ # check
+ table(laundrySummary1985$ba_season, laundrySummary1985$r_month)
+
+ # create a 'season' variable
+ laundrySummary2005$ba_season[laundrySummary2005$r_month == 11 |
+ laundrySummary2005$r_month == 0 |
+ laundrySummary2005$r_month == 1 ] <- "Winter"
+ laundrySummary2005$ba_season[laundrySummary2005$r_month == 2 |
+ laundrySummary2005$r_month == 3 |
+ laundrySummary2005$r_month == 4 ] <- "Spring"
+ laundrySummary2005$ba_season[laundrySummary2005$r_month == 5 |
+ laundrySummary2005$r_month == 6 |
+ laundrySummary2005$r_month == 7 ] <- "Summer"
+ laundrySummary2005$ba_season[laundrySummary2005$r_month == 8 |
+ laundrySummary2005$r_month == 9 |
+ laundrySummary2005$r_month == 10 ] <- "Autumn"
+ # check
+ table(laundrySummary2005$ba_season, laundrySummary2005$r_month)
+
+ # how many people exhibit combinations of laundry?
+ laundrySummary1985$laundry_sum <- laundrySummary1985$Sunday_amd +
+ laundrySummary1985$WeekdayEarly_amd + laundrySummary1985$Weekday_amd + laundrySummary1985$Weekday_pmd
+
+ # NB - only 1 day diary in 2005 so cannot have Sunday + weekday laundry
+ laundrySummary2005$laundry_sum <- laundrySummary2005$Sunday_amd +
+ laundrySummary2005$WeekdayEarly_amd + laundrySummary2005$Weekday_amd + laundrySummary2005$Weekday_pmd
+
+ # combinations by employment type
+ table(laundrySummary1985$laundry_sum,laundrySummary1985$empstat_r)
+ table(laundrySummary2005$laundry_sum,laundrySummary2005$empstat_r)
+
+ # Get number of people reporting any laundry
+ table(laundrySummary2005$laundry_sum,laundrySummary2005$N_Any_laundry_home)
+
+ ## logit models predicting performing a given laundry type ##
+
+ doLogit(Sunday_amd ~empstat + ba_age + ba_nchild, laundrySummary2005[laundrySummary2005$Any_laundry_homed == 1])
+ # test with season
+ doLogit(Sunday_amd ~empstat + ba_age + ba_nchild + ba_season, laundrySummary2005[laundrySummary2005$Any_laundry_homed == 1])
+
+ doLogit(WeekdayEarly_amd ~empstat + ba_age + ba_nchild, laundrySummary2005[laundrySummary2005$Any_laundry_homed == 1])
+
+ doLogit(Weekday_amd ~empstat + ba_age + ba_nchild, laundrySummary2005[laundrySummary2005$Any_laundry_homed == 1])
+
+ doLogit(Weekday_pmd ~empstat + ba_age + ba_nchild, laundrySummary2005[laundrySummary2005$Any_laundry_homed== 1])
+
+ # number of different habits
+ doLm(laundry_sum ~empstat_r + ba_age + ba_nchild, laundrySummary2005[laundrySummary2005$Any_laundry_homed == 1])
+
+ # survey it &
+ # globalize for future use
+ glaundrySummary1985 <<- laundrySummary1985
+ glaundrySummary2005 <<- laundrySummary2005
+ # survey it &
+ # globalize for future use
+ gSvylaundrySummary1985 <<- svydesign(ids = ~diarypid,
+ weight = ~propwt,
+ data = glaundrySummary1985) # does not produce a data table
+ gSvylaundrySummary2005 <<- svydesign(ids = ~diarypid,
+ weight = ~propwt,
+ data = glaundrySummary2005) # does not produce a data table
+
+ # check changes in % of women < 60 in paid work
+ print("% in work in 1985")
+ print(
+ svyby(~empstat, ~sex,
+ gSvylaundrySummary1985[gSvylaundrySummary1985$variables$age > 19 & gSvylaundrySummary1985$variables$age < 60],
+ svymean,
+ keep.var = TRUE
+ )
+ )
+ print("% in work in 2005")
+ print(
+ svyby(~empstat, ~sex,
+ gSvylaundrySummary2005[gSvylaundrySummary2005$variables$age > 19 & gSvylaundrySummary2005$variables$age < 60],
+ svymean,
+ keep.var = TRUE
+ )
+ )
+
+ print("% people who report laundry in 1985")
+ print(
+ svymean(~Any_laundry_homed, gSvylaundrySummary1985)
+ )
+ print("% people who report laundry in 2005")
+ print(
+ svymean(~Any_laundry_homed, gSvylaundrySummary2005)
+ )
+
+ print("Number of people reporting different number of laundry habits")
+ print(
+ svytable(~laundry_sum, gSvylaundrySummary2005)
+ )
+
+ print("1985: N of respondents who reported laundry of given type")
+ print(
+ svytotal(~Sunday_amd + WeekdayEarly_amd + Weekday_amd + Weekday_pmd + Otherd + Any_laundry_homed,
+ gSvylaundrySummary1985
+ )
+ )
+ print("2005: N of respondents who reported laundry of given type")
+ print(
+ svytotal(~Sunday_amd + WeekdayEarly_amd + Weekday_amd + Weekday_pmd + Otherd + Any_laundry_homed,
+ gSvylaundrySummary2005
+ )
+ )
+
+
+ feedBack("Done analysing sampled file using survey methods")
+
+} # works
+
+# Run to here to load functions
+
+# Controller ----
+loadPackages()
+loadCoreMtusSurvey()
+#loadMtusEpisodes() # only run if needed
+loadMtusSampled()
+
+#analyseMtusEpisodes() # only run if needed
+#analyseMtus10minSampled() # only run if needed
+#analyseMtusHalfhourSampled() # only run if needed
+analyseMtusHalfhourSampled_Svy() # only run if needed
+
+print("Finished")
diff --git a/Theme-1/laundryPaper/DEMAND-BA-MTUS-W6-Laundry-Change-Over-Time-v2.0-adult-analysis.Rmd b/Theme-1/laundryPaper/DEMAND-BA-MTUS-W6-Laundry-Change-Over-Time-v2.0-adult-analysis.Rmd
new file mode 100644
index 0000000000000000000000000000000000000000..7b2ab423f8a496250e097d36d51e15c8f6bf7620
--- /dev/null
+++ b/Theme-1/laundryPaper/DEMAND-BA-MTUS-W6-Laundry-Change-Over-Time-v2.0-adult-analysis.Rmd
@@ -0,0 +1,1350 @@
+---
+title: "Laundry, Energy & Time (ER&SS submission)"
+author: Ben Anderson (b.anderson@soton.ac.uk/@dataknut) [Energy & Climate Change,
+ Faculty of Engineering & Environment, University of Southampton]
+date: 'Last run at: `r Sys.time()`'
+output:
+ html_document:
+ fig_caption: yes
+ keep_md: yes
+ number_sections: yes
+ theme: journal
+ toc: yes
+---
+
+```{r knitrSetUp, include=FALSE}
+# set default echo to FALSE (code not in output)
+knitr::opts_chunk$set(echo = FALSE)
+knitr::opts_chunk$set(warning = FALSE)
+knitr::opts_chunk$set(message = FALSE)
+knitr::opts_chunk$set(fig_caption = TRUE)
+knitr::opts_chunk$set(fig_height = 4)
+knitr::opts_chunk$set(fig_height = 8)
+knitr::opts_chunk$set(tidy = TRUE)
+```
+
+
+# Data analysis for 'Laundry' paper:
+
+Use MTUS World 6 time-use data (UK subset) to examine:
+ * distributions of laundry in 1985 & 2005
+ * changing laundry practice
+ * data source: www.timeuse.org/mtus
+ * data already in long format (but episodes) processed using DEMAND-BA-MTUS-W6-Laundry-Change-Over-Time-v2.0-adult-data-processing.R
+
+Uses FES/EFS/LCFS to examine:
+ * historical ownership of washing machines/tumble dryers
+ * Data source: http://discover.ukdataservice.ac.uk/series/?sn=200016
+
+Uses SPRG water practices survey:
+ * reported laundry practices
+ * data source: http://www.sprg.ac.uk/projects-fellowships/patterns-of-water
+
+This work was funded by RCUK through the End User Energy Demand Centres Programme via the
+"DEMAND: Dynamics of Energy, Mobility and Demand" Centre (www.demand.ac.uk, gow.epsrc.ac.uk/NGBOViewGrant.aspx?GrantRef=EP/K011723/1)
+
+> This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License (http://choosealicense.com/licenses/gpl-2.0/), or (at your option) any later version.
+
+> This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
+
+
+# MTUS codes of interest:
+
+1983/4/7: Main/Sec21 Laundry, ironing, clothing repair
+ * 0701 Wash clothes, hang out / bring in washing
+ * 0702 Iron clothes
+ * 0801 Repair, upkeep of clothes
+so may over-estimate laundry
+
+2005: Main/Sec21 Laundry, ironing, clothing repair
+ * Pact=7 (washing clothes)
+
+```{r houseKeeping}
+# clear out all old objects etc to avoid confusion
+rm(list = ls())
+
+
+# set up some useful data paths
+tudpath <- "~/Data/MTUS/World_6/processed/" # presume processed data is already in here
+efs1985path <- "~/Data/Family Expenditure Survey/1985/stata8/"
+efs2005path <- "~/Data/Expenditure and Food Survey/2004-2005/stata/"
+
+sprgpath <- "~/Dropbox/Work/Projects/ESRC-SPRG/WP4-Micro_water/data/sprg_survey/data/safe/v6/"
+
+rpath <- "~/Dropbox/Work/DraftPapers/The Time and Timing of Demand - Laundry/results/"
+
+# define laundry for time use data
+laundry <- "laundry, ironing, clothing repair"
+
+
+
+# load packages ----
+
+library(foreign) # as loading stata files
+library(lattice) # fancy graphs
+library(ggplot2) # fancy graphs II
+library(data.table) # why use anything else?
+library(survey) # weighted survey analysis
+library(car) # regression diagnostics
+library(knitr) # for kable
+library(gmodels) # nice crosstabs
+library(broom) # turns stats objects into dataframes - useful for table output
+library(fasttime) # VERY fast time string conversion to POSIXct
+# but only IF the input string is in a fixed format - see http://rforge.net/doc/packages/fasttime/fastPOSIXct.html
+library(dplyr) # data manipulation
+library(dtplyr) # data table & dplyr code
+
+# Generic functions ----
+
+# Feedback function - cos I can't be bothered to keep writing it out
+feedBack <- function(string) {
+ print(paste0("Feedback: ", string))
+}
+
+###############################################
+# Create a function called #
+# logisticPseudoR2s(). To use it #
+# type logisticPseudoR2s(myLogisticModel)
+# From: Field, A. P., Miles, J., and Field, Z. C. (2012).
+# Discovering statistics using R: and sex and drugs and rock ’n’ roll. London: Sage #
+###############################################
+
+logisticPseudoR2s <- function(LogModel) {
+ dev <- LogModel$deviance
+ nullDev <- LogModel$null.deviance
+ modelN <- length(LogModel$fitted.values)
+ R.hl <- 1 - dev / nullDev
+ R.cs <- 1- exp ( -(nullDev - dev) / modelN)
+ R.n <- R.cs / ( 1 - ( exp (-(nullDev / modelN))))
+ cat("*")
+ cat("Pseudo R^2 for logistic regression\n")
+ cat("-> Hosmer and Lemeshow R^2 ", round(R.hl, 3), "\n")
+ cat("-> Cox and Snell R^2 ", round(R.cs, 3), "\n")
+ cat("-> Nagelkerke R^2 ", round(R.n, 3), "\n")
+}
+
+# function to run a logit model & print out diagnostics
+doLogit <- function(form, DT) {
+ print("# <-- New model --> #")
+ print(paste0("Running logit model: ", form))
+ result <- glm(formula = form,
+ family = binomial(logit),
+ data = DT
+ )
+ ci <- confint(result)
+ tidyResult <- tidy(result) # tidy results
+ tidyResult <- cbind(tidyResult, ci) # stack columns side by side
+ print("")
+ print("# Model results")
+ print (
+ tidyResult
+ )
+ print("# Model summary")
+ print(
+ glance(result)
+ )
+ logisticPseudoR2s(result) # print rsq
+ print("")
+ print("# Diagnostics: independence of errors")
+ print(
+ durbinWatsonTest(result)
+ )
+ # value around 2 = OK, 0 or 4 not OK
+ # if p < 0.05 then a problem as implies autocorrelation
+
+ print("# Diagnostics: collinearity (vif)")
+ print (
+ vif(result)
+ )
+ # if any values > 10 -> problem
+
+ print("# Diagnostics: collinearity (tolerance)")
+ print(
+ 1/vif(result)
+ )
+ # if any values < 0.2 -> possible problem
+ # if any values < 0.1 -> definitely a problem
+}
+
+# function to run an lm model & print out diagnostics
+doLm <- function(form, DT) {
+ print("# <-- New model --> #")
+ print(paste0("Running linear model: ", form))
+ result <- lm(formula = form,
+ data = DT
+ )
+ ci <- confint(result)
+ tidyResult <- tidy(result)
+ tidyResult <- cbind(tidyResult, ci)
+ print("# Model results")
+ print (
+ tidyResult
+ )
+ print(
+ glance(result)
+ )
+ print("# Diagnostics: independence of errors")
+ print(
+ durbinWatsonTest(result)
+ )
+ # value around 2 = OK, 0 or 4 not OK
+ # if p < 0.05 then a problem as implies autocorrelation
+
+ print("# Diagnostics: collinearity (vif)")
+ print (
+ vif(result)
+ )
+ # if any values > 10 -> problem
+
+ print("# Diagnostics: collinearity (tolerance)")
+ print(
+ 1/vif(result)
+ )
+ # if any values < 0.2 -> possible problem
+ # if any values < 0.1 -> definitely a problem
+}
+```
+
+```{r loadEFSSurvey}
+# FES data ----
+
+# Needed for estimates of washing machine adoption in 1985
+# Load as STATA file
+efs1985file <- paste0(efs1985path, "/hchars.dta")
+feedBack(paste0("Loading ", efs1985file))
+efs1985_DT <- as.data.table(read.dta(efs1985file))
+
+
+
+efs2005file <- paste0(efs2005path, "dvhh.dta")
+feedBack(paste0("Loading ", efs2005file))
+efs2005_DT <- as.data.table(read.dta(efs2005file))
+
+```
+
+```{r loadCoreMtusSurvey}
+ dfile <- paste0(tudpath,"MTUSW6UKsurveyCore_DT.csv.gz")
+ cmd <- paste0("gunzip -c ", dfile)
+ print(paste0("Loading via ", cmd))
+
+ # read in the gzipped file using gunzip to 'pipe' the file to fread
+ # this is a lot faster than non-piped gunzip then fread and you get feedback from fread
+ # Don't forget to globalise!
+ MTUSW6UKsurveyCore_DT <- fread(cmd,
+ stringsAsFactors = FALSE)
+ feedBack("Done loading TU survey data")
+# works
+```
+
+```{r loadMtusEpisodes}
+ dfile <- paste0(tudpath,"MTUSW6UKdiaryEps_DT.csv.gz")
+ cmd <- paste0("gunzip -c ", dfile)
+
+ print(paste0("Loading via ", cmd))
+
+ # read in the gzipped file using gunzip to 'pipe' the file to fread
+ # this is a lot faster than non-piped gunzip then fread and you get feedback from fread
+ # Don't forget to globalise!
+ MTUSW6UKdiaryEps_DT <- fread(cmd,
+ stringsAsFactors = FALSE)
+
+ feedBack("Done loading TU episodes data")
+# not tested - file does not exist (yet)
+```
+
+```{r loadMtusSampled}
+ # two files:
+ # - all sampled
+ # - laundry half hour
+ dfile <- paste0(tudpath,"MTUSW6UKdiarySampled_DT.csv.gz")
+ cmd <- paste0("gunzip -c ", dfile)
+ print(paste0("Loading via ", cmd))
+
+ # read in the gzipped file using gunzip to 'pipe' the file to fread
+ # this is a lot faster than non-piped gunzip then fread and you get feedback from fread
+
+ MTUSW6UKdiarySampled_DT <- fread(cmd,
+ stringsAsFactors = FALSE)
+ feedBack("Done loading TU sampled data")
+
+ dfile <- paste0(tudpath,"MTUSW6UK_halfhours_laundry_DT.csv.gz")
+
+ cmd <- paste0("gunzip -c ", dfile)
+ print(paste0("Loading via ", cmd))
+
+ # read in the gzipped file using gunzip to 'pipe' the file to fread
+ # this is a lot faster than non-piped gunzip then fread and you get feedback from fread
+ # Don't forget to globalise!
+ MTUSW6UK_halfhours_laundry_DT <- fread(cmd,
+ stringsAsFactors = FALSE)
+
+ feedBack("Done loading TU sampled laundry data aggregated to half hour level")
+
+# works
+```
+
+# Analysing loaded data
+
+## EFS/FES data
+```{r analyseEfsSurvey}
+
+# 1985
+# a108 byte %8.0g no of washing machines in h/h
+wmachines <- table(efs1985_DT$a108)
+prop.table(wmachines)
+
+
+# Needed for estimates of washing machine & tumble drier adoption in 2005
+# Load as STATA file
+efs1985file <- paste0(efs1985path, "/hchars.dta")
+feedBack(paste0("Loading ", efs1985file))
+efs1985_DT <- as.data.table(read.dta(efs1985file))
+
+# a108 byte %8.0g no of washing machines in h/h
+wmachines <- table(efs1985_DT$a108)
+prop.table(wmachines)
+
+# 2005
+
+wmachines <- table(efs2005_DT$a108)
+prop.table(wmachines)
+
+# a054 byte %8.0g number of workers in household
+# a062 byte %8.0g a062 composition of household
+# a167 byte %8.0g a167 tumble dryer in household
+tdryers <- table(efs2005_DT$a167)
+prop.table(tdryers)
+
+
+```
+
+## MTUS episodes
+```{r analyseMtusEpisodes}
+ # Check on number of primary & secondary acts ----
+ # episode data
+ testEpisodes_p <- with(MTUSW6UKdiaryEps_DT, xtabs(~ main + ba_survey))
+ testEpisodes_s <- with(MTUSW6UKdiaryEps_DT, xtabs(~ sec + ba_survey))
+ feedBack(paste0("Writing results to ", rpath))
+ pefile <- paste0(rpath, "/testEpisodes_primary.csv")
+ write.csv(testEpisodes_p, file = pefile)
+ sefile <- paste0(rpath, "/testEpisodes_ssecondary.csv")
+ write.csv(testEpisodes_s, file = sefile)
+
+ # sampled data
+ testSampled_p <- with(MTUSW6UKdiarySampled_DT, xtabs(~ pact + ba_survey))
+ testSampled_s <- with(MTUSW6UKdiarySampled_DT, xtabs(~ sact + ba_survey))
+ psfile <- paste0(rpath, "/testSampled_primary.csv")
+ write.csv(testSampled_p, file = psfile)
+ ssfile <- paste0(rpath, "/testSampled_secondary.csv")
+ write.csv(testSampled_s, file = ssfile)
+
+ # Laundry = code 21
+ laundry <- "laundry, ironing, clothing repair"
+ #
+ kable(caption="n episodes in total per year",
+ with(MTUSW6UKdiaryEps_DT,
+ as.data.table(table(ba_survey))
+ )
+ )
+ # set a laundry code
+ MTUSW6UKdiaryEps_DT$laundry_p <- ifelse(MTUSW6UKdiaryEps_DT$main == laundry,
+ 1, # laundry as main act
+ 0)
+ MTUSW6UKdiaryEps_DT$laundry_s <- ifelse(MTUSW6UKdiaryEps_DT$sec == laundry,
+ 1, # laundry as main act
+ 0)
+ MTUSW6UKdiaryEps_DT$laundry_all <- ifelse(MTUSW6UKdiaryEps_DT$main == laundry | MTUSW6UKdiaryEps_DT$sec == laundry,
+ 1, # laundry as either act
+ 0)
+ # totals
+ with(MTUSW6UKdiaryEps_DT,
+ table(laundry_p, ba_survey))
+ with(MTUSW6UKdiaryEps_DT,
+ table(laundry_s, ba_survey))
+ with(MTUSW6UKdiaryEps_DT,
+ table(laundry_all, ba_survey))
+
+ feedBack("# n episodes by duration - to show how recording period varies things")
+ print(
+ with(MTUSW6UKdiaryEps_DT,
+ xtabs(~ time + ba_survey))
+ )
+ feedBack("# n episodes of laundry as a primary act by duration (to show how recording period varies things)")
+ print(
+ with(MTUSW6UKdiaryEps_DT,
+ xtabs(laundry_p ~ time + ba_survey))
+ )
+
+ eps1985 <- nrow(subset(MTUSW6UKdiaryEps_DT, ba_survey == 1985)) # n episodes in 1985
+ epslaundry1985 <- nrow(subset(MTUSW6UKdiaryEps_DT, ba_survey == 1985 & laundry_all == 1))
+
+ print(paste0("% episodes that are laundry_all in 1985 = ", (epslaundry1985/eps1985)*100))
+
+ eps2005 <- nrow(subset(MTUSW6UKdiaryEps_DT, ba_survey == 2005)) # n episodes in 2005
+ epslaundry2005 <- nrow(subset(MTUSW6UKdiaryEps_DT, ba_survey == 2005 & laundry_all == 1))
+
+ print(paste0("% episodes that are laundry_all in 2005 = ", (epslaundry2005/eps2005)*100))
+
+ # work with split files - easier?
+ eps_1985DT <- MTUSW6UKdiaryEps_DT[MTUSW6UKdiaryEps_DT$ba_survey == 1985]
+ eps_2005DT <- MTUSW6UKdiaryEps_DT[MTUSW6UKdiaryEps_DT$ba_survey == 2005]
+
+ # when do most episodes start in 1985?
+ alleps_byhh1985 <- eps_1985DT[,
+ .(
+ N_episodes1985 = length(st_halfhour), #n episodes starting in a given half hour
+ Pr_episodes1985 = length(st_halfhour)/eps1985 # proportion of all episodes in that year starting...
+ ),
+ by = .(
+ Start = st_halfhour
+ )
+ ]
+ setkey(alleps_byhh1985,Start)
+ feedBack("Plotting 1985 all episodes start")
+ plot(alleps_byhh1985$Pr_episodes)
+
+ # and laundry episodes?
+ laundryeps_byhh1985 <- eps_1985DT[main == laundry,
+ .(
+ N_episodes1985 = length(st_halfhour), #n episodes starting in a given half hour
+ Pr_episodes1985 = length(st_halfhour)/eps1985 # proportion of all episodes in that year starting...
+ ),
+ by = .(
+ Start = st_halfhour
+ )
+ ]
+ setkey(laundryeps_byhh1985,Start)
+ feedBack("Plotting 1985 laundry episodes start")
+ plot(laundryeps_byhh1985$Pr_episodes)
+
+ # when do most episodes start in 2005?
+ alleps_byhh2005 <- eps_2005DT[,
+ .(
+ N_episodes2005 = length(st_halfhour), #n episodes starting in a given half hour
+ Pr_episodes2005 = length(st_halfhour)/eps2005 # proportion of all episodes in that year starting...
+ ),
+ by = .(
+ Start = st_halfhour
+ )
+ ]
+ setkey(alleps_byhh2005,Start)
+ feedBack("Plotting 2005 all episodes start")
+ plot(alleps_byhh2005$Pr_episodes)
+
+ # and laundry episodes?
+ laundryeps_byhh2005 <- eps_2005DT[main == laundry,
+ .(
+ N_episodes2005 = length(st_halfhour), #n episodes starting in a given half hour
+ Pr_episodes2005 = length(st_halfhour)/eps2005 # proportion of all episodes in that year starting...
+ ),
+ by = .(
+ Start = st_halfhour
+ )
+ ]
+ setkey(laundryeps_byhh2005,Start)
+ feedBack("Plotting 2005 laundry episodes start")
+ plot(laundryeps_byhh2005$Pr_episodes)
+
+ # join tables for ease of comparison
+ # ideally these need to be weighted & need 95% CIs for proportions
+ laundryeps_byhh <- laundryeps_byhh2005[laundryeps_byhh1985]
+ laundryeps_byhh$diff <- laundryeps_byhh$Pr_episodes2005 - laundryeps_byhh$Pr_episodes1985
+ plot(laundryeps_byhh$diff)
+
+ # use ggplot to make a nice plot of 1985, 2005 & difference?
+
+ outf <- paste0(rpath,"/laundryeps_byhh.csv")
+ feedBack(paste0("Saving episodes results into: ", rpath))
+ write.csv(laundryeps_byhh,
+ file = outf,
+ na = ""
+ )
+
+ # test what comes before/after episode (main act)
+ eps_1985DT[,
+ ep_before := shift(main, type = "lag")
+ ]
+ eps_1985DT[,
+ ep_after := shift(main, type = "lead")
+ ]
+
+ bef_1985DT <- as.data.table(100*(round(prop.table(table(eps_1985DT$ep_before[eps_1985DT$laundry_p == 1])),4))) # % of before laundry
+ bef_1985DT <- bef_1985DT[order(-N)]
+ kable(caption = "Main acts before laundry in 1985",
+ head(bef_1985DT)
+ )
+ aft_1985DT <- as.data.table(100*(round(prop.table(table(eps_1985DT$ep_after[eps_1985DT$laundry_p == 1])),4))) # % of after laundry
+ aft_1985DT <- bef_1985DT[order(-N)]
+ kable(caption = "Main acts after laundry in 1985",
+ head(aft_1985DT)
+ )
+
+ eps_2005DT[,
+ ep_before := shift(main, type = "lag")
+ ]
+ eps_2005DT[,
+ ep_after := shift(main, type = "lead")
+ ]
+ eps_2005DT[,
+ ep_2after := shift(main, n = 2, type = "lead")
+ ]
+ eps_2005DT[,
+ ep_3after := shift(main, n = 3, type = "lead")
+ ]
+
+ bef_2005DT <- as.data.table(100*(round(prop.table(table(eps_2005DT$ep_before[eps_2005DT$laundry_p == 1])),4))) # % of before laundry
+ bef_2005DT <- bef_2005DT[order(-N)]
+ kable(caption = "Main acts after laundry in 2005",
+ head(bef_2005DT)
+ )
+ aft_2005DT <- as.data.table(100*(round(prop.table(table(eps_2005DT$ep_after[eps_2005DT$laundry_p == 1])),4))) # % of after laundry
+ aft_2005DT <- aft_2005DT[order(-N)]
+ kable(caption = "Main acts after laundry in 2005",
+ head(aft_2005DT)
+ )
+ # need an hour variable
+ eps_2005DT[,
+ r_hour := as.POSIXlt(r_epStartTime)$hour
+ ]
+
+ # now for early morning weekday laundry in 2005
+ aft_2005DT <- as.data.table(100*(round(prop.table(table(eps_2005DT$ep_after[eps_2005DT$laundry_p == 1 &
+ eps_2005DT$r_hour >= 6 &
+ eps_2005DT$r_hour < 9 &
+ eps_2005DT$r_dow != "Saturday" &
+ eps_2005DT$r_dow != "Sunday"]
+ )),4))) # % of after laundry
+ aft_2005DT <- aft_2005DT[order(-N)]
+ kable(caption = "Main acts after early weekday morning laundry in 2005",
+ head(aft_2005DT)
+ )
+
+ # now for early weekday morning laundry in 2005 - 2 after
+ aft_2005DT <- as.data.table(100*(round(prop.table(table(eps_2005DT$ep_2after[eps_2005DT$laundry_p == 1 &
+ eps_2005DT$r_hour >= 6 &
+ eps_2005DT$r_hour < 9&
+ eps_2005DT$r_dow != "Saturday" &
+ eps_2005DT$r_dow != "Sunday"]
+ )),4))) # % of after laundry
+ aft_2005DT <- aft_2005DT[order(-N)]
+ kable(caption = "Main acts 2 after early weekday morning laundry in 2005",
+ head(aft_2005DT)
+ )
+
+ # now for early weekday morning laundry in 2005 - 3 after
+ aft_2005DT <- as.data.table(100*(round(prop.table(table(eps_2005DT$ep_3after[eps_2005DT$laundry_p == 1 &
+ eps_2005DT$r_hour >= 6 &
+ eps_2005DT$r_hour < 9&
+ eps_2005DT$r_dow != "Saturday" &
+ eps_2005DT$r_dow != "Sunday"]
+ )),4))) # % of after laundry
+ aft_2005DT <- aft_2005DT[order(-N)]
+ kable(caption = "Main acts 3 after early weekday morning laundry in 2005",
+ head(aft_2005DT)
+ )
+
+
+ # now evening peak laundry in 2005
+ aft_2005DT <- as.data.table(100*(round(prop.table(table(eps_2005DT$ep_before[eps_2005DT$laundry_p == 1 &
+ eps_2005DT$r_hour >= 17 &
+ eps_2005DT$r_hour < 20 &
+ eps_2005DT$r_dow != "Saturday" &
+ eps_2005DT$r_dow != "Sunday"]
+ )),4))) # % of after laundry
+ aft_2005DT <- aft_2005DT[order(-N)]
+ kable(caption = "Main acts before evening peak weekday laundry in 2005",
+ head(aft_2005DT)
+ )
+ # now evening peak laundry in 2005
+ aft_2005DT <- as.data.table(100*(round(prop.table(table(eps_2005DT$ep_after[eps_2005DT$laundry_p == 1 &
+ eps_2005DT$r_hour >= 17 &
+ eps_2005DT$r_hour < 20 &
+ eps_2005DT$r_dow != "Saturday" &
+ eps_2005DT$r_dow != "Sunday"]
+ )),4))) # % of after laundry
+ aft_2005DT <- aft_2005DT[order(-N)]
+ kable(caption = "Main acts before evening peak weekday laundry in 2005",
+ head(aft_2005DT)
+ )
+ feedBack("Done analysing episode file")
+```
+
+
+## Switch to survey analysis of half hour sampled data
+```{r analyseMtusHalfhourSampled_Svy}
+
+feedBack("Join survey to derived half hour data")
+ # uses new pid created using dplyr:group
+setkey(MTUSW6UK_halfhours_laundry_DT, ba_diarypid)
+ setkey(MTUSW6UKsurveyCore_DT, ba_diarypid)
+
+ # keep '1985' & 2005 only
+ MTUSW6UKsurveyCore_DT <- subset(MTUSW6UKsurveyCore_DT,
+ ba_survey == 1985 | ba_survey == 2005
+ )
+
+ MTUSW6UK_halfhours_laundry_DT <- subset(MTUSW6UK_halfhours_laundry_DT,
+ ba_survey == 1985 | ba_survey == 2005
+ )
+
+ MTUSW6UKjoinedHalfhour_laundry_DT <- MTUSW6UK_halfhours_laundry_DT[MTUSW6UKsurveyCore_DT]
+
+ head(MTUSW6UKjoinedHalfhour_laundry_DT) # check
+ tail(MTUSW6UKjoinedHalfhour_laundry_DT) # check
+
+ # check
+ table(MTUSW6UKjoinedHalfhour_laundry_DT$ba_survey, useNA = "always")
+
+ # some NAs, remove them
+ MTUSW6UKjoinedHalfhour_laundry_DT <- subset(MTUSW6UKjoinedHalfhour_laundry_DT, !is.na(ba_survey))
+
+ # check
+ table(MTUSW6UKjoinedHalfhour_laundry_DT$ba_survey, useNA = "always")
+
+ print("Recode to 'at a home' vs 'not at a home'")
+ MTUSW6UKjoinedHalfhour_laundry_DT$Any_laundry_home <- ifelse(
+ MTUSW6UKjoinedHalfhour_laundry_DT$Any_laundry_ph == 1 |
+ MTUSW6UKjoinedHalfhour_laundry_DT$Any_laundry_photh == 1, 1, 0
+ )
+ MTUSW6UKjoinedHalfhour_laundry_DT$Any_laundry_nothome <- ifelse(
+ MTUSW6UKjoinedHalfhour_laundry_DT$Any_laundry_psh == 1 |
+ MTUSW6UKjoinedHalfhour_laundry_DT$Any_laundry_poth == 1, 1, 0
+ )
+ # check
+ with(MTUSW6UKjoinedHalfhour_laundry_DT,
+ table(
+ Any_laundry_home, Any_laundry_nothome
+ )
+ )
+
+ print("create types of laundry here so become part of survey design object")
+ # Specific types of laundry (for Table 3)
+ # weekday early morning laundry (06:00 – 09:00)
+ # weekday morning laundry (09:00-12:00)
+ # weekday evening laundry (18:00 – 21:00)
+ # Sunday morning laundry (09:00 – 11:00)
+
+ print("Weekday early morning 06:00-09:00")
+ MTUSW6UKjoinedHalfhour_laundry_DT$weekday_earlyam <- ifelse(
+ MTUSW6UKjoinedHalfhour_laundry_DT$r_dow != "Saturday" &
+ MTUSW6UKjoinedHalfhour_laundry_DT$r_dow != "Sunday" &
+ MTUSW6UKjoinedHalfhour_laundry_DT$r_hour >= 6 &
+ MTUSW6UKjoinedHalfhour_laundry_DT$r_hour <= 8 &
+ MTUSW6UKjoinedHalfhour_laundry_DT$Any_laundry_home == 1, 1, 0
+ )
+ # check
+ with(MTUSW6UKjoinedHalfhour_laundry_DT[weekday_earlyam == 1],
+ table(
+ r_hour, r_dow
+ )
+ )
+
+ print("Weekday morning 09:00-12:00")
+ MTUSW6UKjoinedHalfhour_laundry_DT$weekday_am <- ifelse(
+ MTUSW6UKjoinedHalfhour_laundry_DT$r_dow != "Saturday" &
+ MTUSW6UKjoinedHalfhour_laundry_DT$r_dow != "Sunday" &
+ MTUSW6UKjoinedHalfhour_laundry_DT$r_hour >= 9 &
+ MTUSW6UKjoinedHalfhour_laundry_DT$r_hour <= 11 &
+ MTUSW6UKjoinedHalfhour_laundry_DT$Any_laundry_home == 1, 1, 0
+ )
+ # check
+ with(MTUSW6UKjoinedHalfhour_laundry_DT[weekday_am == 1],
+ table(
+ r_hour, r_dow
+ )
+ )
+
+ print("Weekday evening peak 18:00-21:00")
+ MTUSW6UKjoinedHalfhour_laundry_DT$weekday_pm <- ifelse(
+ MTUSW6UKjoinedHalfhour_laundry_DT$r_dow != "Saturday" &
+ MTUSW6UKjoinedHalfhour_laundry_DT$r_dow != "Sunday" &
+ MTUSW6UKjoinedHalfhour_laundry_DT$r_hour >= 18 &
+ MTUSW6UKjoinedHalfhour_laundry_DT$r_hour <= 20 &
+ MTUSW6UKjoinedHalfhour_laundry_DT$Any_laundry_home == 1, 1, 0
+ )
+ # check
+ with(MTUSW6UKjoinedHalfhour_laundry_DT[weekday_pm == 1],
+ table(
+ r_hour, r_dow
+ )
+ )
+
+ print ("Sunday morning 09:00-12:00")
+ MTUSW6UKjoinedHalfhour_laundry_DT$sunday_am <- ifelse(
+ MTUSW6UKjoinedHalfhour_laundry_DT$r_dow == "Sunday" &
+ MTUSW6UKjoinedHalfhour_laundry_DT$r_hour >= 9 &
+ MTUSW6UKjoinedHalfhour_laundry_DT$r_hour <= 11 &
+ MTUSW6UKjoinedHalfhour_laundry_DT$Any_laundry_home == 1, 1, 0
+ )
+ # check
+ with(MTUSW6UKjoinedHalfhour_laundry_DT[sunday_am == 1],
+ table(
+ r_hour, r_dow
+ )
+ )
+
+ print("Other")
+ MTUSW6UKjoinedHalfhour_laundry_DT$other <- ifelse(
+ MTUSW6UKjoinedHalfhour_laundry_DT$sunday_am != 1 &
+ MTUSW6UKjoinedHalfhour_laundry_DT$weekday_earlyam != 1 &
+ MTUSW6UKjoinedHalfhour_laundry_DT$weekday_am != 1 &
+ MTUSW6UKjoinedHalfhour_laundry_DT$weekday_pm != 1 &
+ MTUSW6UKjoinedHalfhour_laundry_DT$Any_laundry_home == 1, 1, 0
+ )
+ # check
+ with(MTUSW6UKjoinedHalfhour_laundry_DT[other == 1],
+ table(
+ r_hour, r_dow
+ )
+ )
+
+ print("Set survey data")
+ # tell survey that the diarypids are the ids (they repeat)
+ svyMTUSW6UkHalfhoursLaundry <- svydesign(ids = ~diarypid,
+ weight = ~propwt,
+ data = MTUSW6UKjoinedHalfhour_laundry_DT) # does not produce a data table
+
+ # how many weighted half hours do we have?
+ # any laundry
+ all_hhs <- svytable(~ba_survey + sex + N_laundry_p, # the row * columns we want
+ svyMTUSW6UkHalfhoursLaundry # the data in survey form
+ )
+ print(
+ ftable(all_hhs) # nice table
+ )
+ # how many weighted half hours do we have?
+ # any laundry
+ all_hhs <- svytable(~ba_survey + Any_laundry_p, # the row * columns we want
+ svyMTUSW6UkHalfhoursLaundry # the data in survey form
+ )
+ print(
+ ftable(all_hhs) # nice table
+ )
+
+ # any laundry at home (Table 1)
+ all_hhs <- svytable(~ba_survey + Any_laundry_ph, # the row * columns we want
+ svyMTUSW6UkHalfhoursLaundry # the data in survey form
+ )
+ print(
+ ftable(all_hhs) # nice table
+ )
+
+ # any laundry at others' home (Table 1)
+ all_hhs <- svytable(~ba_survey + Any_laundry_photh, # the row * columns we want
+ svyMTUSW6UkHalfhoursLaundry # the data in survey form
+ )
+ print(
+ ftable(all_hhs) # nice table
+ )
+ # any laundry at shops/services (Table 1)
+ all_hhs <- svytable(~ba_survey + Any_laundry_psh, # the row * columns we want
+ svyMTUSW6UkHalfhoursLaundry # the data in survey form
+ )
+ print(
+ ftable(all_hhs) # nice table
+ )
+ # any laundry at other locations (Table 1)
+ all_hhs <- svytable(~ba_survey + Any_laundry_poth, # the row * columns we want
+ svyMTUSW6UkHalfhoursLaundry # the data in survey form
+ )
+ print(
+ ftable(all_hhs) # nice table
+ )
+
+
+
+ # any laundry at a home (Table 1)
+ # NB: some missing as no location known
+ all_hhs <- svytable(~ba_survey + Any_laundry_home, # the row * columns we want
+ svyMTUSW6UkHalfhoursLaundry # the data in survey form
+ )
+ print(
+ ftable(all_hhs) # nice table
+ )
+ # any laundry not at a home (Table 1)
+ # NB: some missing as no location known
+ all_hhs <- svytable(~ba_survey + Any_laundry_nothome, # the row * columns we want
+ svyMTUSW6UkHalfhoursLaundry # the data in survey form
+ )
+ print(
+ ftable(all_hhs) # nice table
+ )
+
+
+ # any laundry by gender (Table 1) - frequencies
+ all_hhs_anylf <- svytable(~ba_survey + sex + Any_laundry_p, # the row * columns we want
+ svyMTUSW6UkHalfhoursLaundry # the data in survey form
+ )
+ print(
+ ftable(all_hhs_anylf) # nice table
+ )
+ # any laundry by gender (Table 1) - % of halfhours using svyby
+ all_hhs_anylp <- svyby(~Any_laundry_p, # the data to summarise
+ ~ba_survey + sex, # the row * columns we want
+ svyMTUSW6UkHalfhoursLaundry, # the data in survey form
+ svymean # the function to use to summarise
+ )
+ print(all_hhs_anylp)
+
+ # all laundry by gender (Table 1) - % of laundry halfhours
+ anyl_genderp85 <- svymean(~sex, # the row * columns we want
+ svyMTUSW6UkHalfhoursLaundry[
+ svyMTUSW6UkHalfhoursLaundry$variables$Any_laundry_home == 1
+ & svyMTUSW6UkHalfhoursLaundry$variables$ba_survey == 1985
+ ], # the data in survey form
+ keep.var = TRUE
+ )
+ print(
+ ftable(anyl_genderp85) # nice table
+ )
+
+ anyl_genderp05 <- svymean(~sex, # the row * columns we want
+ svyMTUSW6UkHalfhoursLaundry[
+ svyMTUSW6UkHalfhoursLaundry$variables$Any_laundry_home == 1
+ & svyMTUSW6UkHalfhoursLaundry$variables$ba_survey == 2005
+ ], # the data in survey form
+ keep.var = TRUE
+ )
+ print(
+ ftable(anyl_genderp05) # nice table
+ )
+ # laundry not at home (recoded for Table 1)
+ all_hhs_anylh <- svytable(~ba_survey + sex + Any_laundry_home, # the row * columns we want
+ svyMTUSW6UkHalfhoursLaundry # the data in survey form
+ )
+ print(
+ ftable(all_hhs_anylh) # nice table
+ )
+
+ all_hhs_anylnh <- svytable(~ba_survey + sex + Any_laundry_nothome, # the row * columns we want
+ svyMTUSW6UkHalfhoursLaundry # the data in survey form
+ )
+ print(
+ ftable(all_hhs_anylnh) # nice table
+ )
+
+
+ ## By day of the week (Fig 1) ##
+
+ # This gives the frequencies (pattern) within all halfhours
+ svyby(~Any_laundry_home, ~r_dow + ba_survey,
+ svyMTUSW6UkHalfhoursLaundry,
+ svymean
+ )
+
+ # This gives the frequencies (pattern) within laundry
+ svytable(~r_dow + ba_survey,
+ svyMTUSW6UkHalfhoursLaundry[svyMTUSW6UkHalfhoursLaundry$variables$Any_laundry_home == 1]
+ )
+ # this gives the proportion per day
+ dow_1985 <- svymean(~r_dow,
+ svyMTUSW6UkHalfhoursLaundry[svyMTUSW6UkHalfhoursLaundry$variables$Any_laundry_home == 1 &
+ svyMTUSW6UkHalfhoursLaundry$variables$ba_survey == 1985]
+ )
+ feedBack("Any laundry by day of the week: 1985")
+ print(dow_1985)
+ confint( # check
+ dow_1985
+ )
+
+ # this gives the proportion per day
+ dow_2005 <- svymean(~r_dow,
+ svyMTUSW6UkHalfhoursLaundry[svyMTUSW6UkHalfhoursLaundry$variables$Any_laundry_home == 1 &
+ svyMTUSW6UkHalfhoursLaundry$variables$ba_survey == 2005]
+ )
+ feedBack("Any laundry by day of the week: 2005")
+ print(dow_2005)
+ confint( # check
+ dow_2005
+ )
+
+ ## By gender & day of the week (Fig 2) ##
+ dowg_1985 <- svyby(~r_dow, ~sex,
+ svyMTUSW6UkHalfhoursLaundry[svyMTUSW6UkHalfhoursLaundry$variables$Any_laundry_home == 1 &
+ svyMTUSW6UkHalfhoursLaundry$variables$ba_survey == 1985 ],
+ svymean
+ )
+ feedBack("Any laundry by day of the week/gender: 1985")
+ print(dowg_1985)
+ confint( # check
+ dowg_1985
+ )
+
+ dowg_2005 <- svyby(~r_dow, ~sex,
+ svyMTUSW6UkHalfhoursLaundry[svyMTUSW6UkHalfhoursLaundry$variables$Any_laundry_home == 1 &
+ svyMTUSW6UkHalfhoursLaundry$variables$ba_survey == 2005 ],
+ svymean
+ )
+ feedBack("Any laundry by day of the week/gender: 2005")
+ print(dowg_2005)
+ confint( # check
+ dowg_2005
+ )
+
+ ## By women's labour market status (Fig 3) ##
+ # all half hours
+ pwork_1985allhh <- svyby(~ba_survey, ~empstat,
+ svyMTUSW6UkHalfhoursLaundry,
+ svytotal
+ )
+ kable(pwork_1985allhh)
+ dowpwork_1985allhh <- svyby(~Any_laundry_home, ~empstat + r_dow,
+ svyMTUSW6UkHalfhoursLaundry[
+ svyMTUSW6UkHalfhoursLaundry$variables$ba_survey == 1985 &
+ svyMTUSW6UkHalfhoursLaundry$variables$sex == "Woman"],
+ svymean
+ )
+ kable(dowpwork_1985allhh)
+ # laundry half hours
+ dowfwork_1985 <- svyby(~r_dow, ~empstat,
+ svyMTUSW6UkHalfhoursLaundry[svyMTUSW6UkHalfhoursLaundry$variables$Any_laundry_home == 1 &
+ svyMTUSW6UkHalfhoursLaundry$variables$ba_survey == 1985 &
+ svyMTUSW6UkHalfhoursLaundry$variables$sex == "Woman"],
+ svymean
+ )
+ feedBack("Any laundry by day of the week/women/labour market status: 1985")
+ kable(dowfwork_1985)
+
+ ## By women's labour market status (Fig 4) ##
+ dowfwork_2005 <- svyby(~r_dow, ~empstat,
+ svyMTUSW6UkHalfhoursLaundry[svyMTUSW6UkHalfhoursLaundry$variables$Any_laundry_home == 1 &
+ svyMTUSW6UkHalfhoursLaundry$variables$ba_survey == 2005 &
+ svyMTUSW6UkHalfhoursLaundry$variables$sex == "Woman"],
+ svymean,
+ keep.var = TRUE
+ )
+ feedBack("Any laundry by day of the week/women/labour market status: 1985")
+ kable(dowfwork_2005)
+ # ftable(dowfwork_2005) #if desired
+
+
+ ## By time of day (Fig 5) ##
+ # get the mean % of half hours with any laundry as a primary act (and SE) over 24 hours
+ pcAnylp_hh <- svyby(~Any_laundry_home, # the variable to do the stats on
+ ~st_halfhour + ba_survey, # the row * columns we want (produces long form)
+ svyMTUSW6UkHalfhoursLaundry, # the data
+ svymean, # the stats function(s)
+ keep.var = TRUE)
+ #ftable(pcAnyl_hh) # flat table fails
+ kable(
+ pcAnylp_hh # table
+ )
+ # by day (all half hours)
+ pcAnylp_hhByDay85 <- svyby(~Any_laundry_home,
+ ~st_halfhour + r_dow,
+ svyMTUSW6UkHalfhoursLaundry[
+ svyMTUSW6UkHalfhoursLaundry$variables$ba_survey == 1985],
+ svymean)
+ kable(pcAnylp_hhByDay85)
+ pcAnylp_hhByDay05 <- svyby(~Any_laundry_home,
+ ~st_halfhour + r_dow,
+ svyMTUSW6UkHalfhoursLaundry[
+ svyMTUSW6UkHalfhoursLaundry$variables$ba_survey == 2005],
+ svymean)
+ kable(pcAnylp_hhByDay05)
+
+ ## By time of day but within laundry (Fig 6) ##
+ # get the mean % of half hours with any laundry as a primary act (and SE) over 24 hours
+ svyMTUSW6UkHalfhoursLaundry$variables$ba_survey <- factor(svyMTUSW6UkHalfhoursLaundry$variables$ba_survey)
+ pcAnyl_hh85 <- svymean(~st_halfhour,
+ svyMTUSW6UkHalfhoursLaundry[
+ svyMTUSW6UkHalfhoursLaundry$variables$Any_laundry_home == 1 &
+ svyMTUSW6UkHalfhoursLaundry$variables$ba_survey == 1985] # the data
+
+ )
+ kable(as.data.table(pcAnyl_hh85))
+ pcAnyl_hh05 <- svymean(~st_halfhour,
+ svyMTUSW6UkHalfhoursLaundry[
+ svyMTUSW6UkHalfhoursLaundry$variables$Any_laundry_home == 1 &
+ svyMTUSW6UkHalfhoursLaundry$variables$ba_survey == 2005] # the data
+
+ )
+ kable(as.data.table(pcAnyl_hh05))
+
+ # within laundry by day, (Fig 7)
+ pcOnlyl_hhByDay85 <- svymean(~interaction(st_halfhour,r_dow),
+ design = svyMTUSW6UkHalfhoursLaundry[svyMTUSW6UkHalfhoursLaundry$variables$ba_survey == 1985 &
+ svyMTUSW6UkHalfhoursLaundry$variables$Any_laundry_home == 1])
+ kable(as.data.table(pcOnlyl_hhByDay85))
+
+ pcOnlyl_hhByDay05 <- svymean(~interaction(st_halfhour,r_dow),
+ design = svyMTUSW6UkHalfhoursLaundry[svyMTUSW6UkHalfhoursLaundry$variables$ba_survey == 2005 &
+ svyMTUSW6UkHalfhoursLaundry$variables$Any_laundry_home == 1])
+ kable(as.data.table(pcOnlyl_hhByDay05))
+
+ # you cannot be serious:
+ ftable(pcOnlyl_hhByDay85, rownames = list(r_dow = c("S", "M", "T", "W", "T", "F", "S"),
+ st_halfhour = c("00:00", "00:30", "01:00")))
+
+ # weekday/weekend flag
+ svyMTUSW6UkHalfhoursLaundry$variables$wd <- ifelse(
+ svyMTUSW6UkHalfhoursLaundry$variables$r_dow != "Sunday" &
+ svyMTUSW6UkHalfhoursLaundry$variables$r_dow != "Saturday" ,
+ 1, # yes = weekday
+ 0 # no = weekend
+ )
+
+ # Specific types of laundry (for Table 3)
+ # weekday early morning laundry (06:00 – 09:00)
+ # weekday morning laundry (09:00-12:00)
+ # weekday evening laundry (17:00 – 21:00)
+ # Sunday morning laundry (08:00 – 11:00)
+
+ # Weekday early morning 06:00-09:00
+ weekday_earlyam_pLhh <- svyby(~weekday_earlyam,
+ ~ba_survey,
+ svyMTUSW6UkHalfhoursLaundry[svyMTUSW6UkHalfhoursLaundry$variables$Any_laundry_home == 1],
+ svymean,
+ keep.var = TRUE
+ )
+ feedBack("Weekday early morning laundry (06:00 – 09:00) out of all laundry")
+ kable(caption = "Weekday early morning laundry (06:00 – 09:00) out of all laundry",weekday_earlyam_pLhh)
+
+ # Weekday morning 09:00-12:00
+ weekday_am_pLhh <- svyby(~weekday_am,
+ ~ba_survey,
+ svyMTUSW6UkHalfhoursLaundry[svyMTUSW6UkHalfhoursLaundry$variables$Any_laundry_home == 1],
+ svymean,
+ keep.var = TRUE
+ )
+ feedBack("Weekday morning laundry out of all laundry")
+ kable(caption = "Weekday morning laundry out of all laundry", weekday_am_pLhh)
+
+ # Weekday evening peak 17:00-21:00
+ weekday_pm_pLhh <- svyby(~weekday_pm,
+ ~ba_survey,
+ svyMTUSW6UkHalfhoursLaundry[svyMTUSW6UkHalfhoursLaundry$variables$Any_laundry_home == 1],
+ svymean,
+ keep.var = TRUE
+ )
+ feedBack("Weekday evening peak laundry out of all laundry")
+ kable(caption ="Weekday evening peak laundry out of all laundry", weekday_pm_pLhh)
+
+ # Sunday morning 09:00-12:00
+ sunday_am_pLhh <- svyby(~sunday_am,
+ ~ba_survey,
+ svyMTUSW6UkHalfhoursLaundry[svyMTUSW6UkHalfhoursLaundry$variables$Any_laundry_home == 1],
+ svymean,
+ keep.var = TRUE
+ )
+ feedBack("Sunday morning laundry out of all laundry")
+ kable(caption = "Sunday morning laundry out of all laundry", sunday_am_pLhh)
+ confint(
+ sunday_am_pLhh
+ )
+
+ # Other
+ other_pLhh <- svyby(~other,
+ ~ba_survey,
+ svyMTUSW6UkHalfhoursLaundry[svyMTUSW6UkHalfhoursLaundry$variables$Any_laundry_home == 1],
+ svymean,
+ keep.var = TRUE
+ )
+ feedBack("Other laundry out of all laundry")
+ kable(caption = "Other laundry out of all laundry", other_pLhh)
+
+ # analysis by employment status in 2005
+ print("% within empstat who do sunday_am")
+ print(
+ svyby(~sunday_am, ~empstat,
+ svyMTUSW6UkHalfhoursLaundry[svyMTUSW6UkHalfhoursLaundry$variables$Any_laundry_home == 1 &
+ svyMTUSW6UkHalfhoursLaundry$variables$ba_survey == 2005],
+ svymean,
+ keep.var = TRUE
+ )
+ )
+ print("% within empstat who do weekday early am")
+ print(
+ svyby(~weekday_earlyam, ~empstat,
+ svyMTUSW6UkHalfhoursLaundry[svyMTUSW6UkHalfhoursLaundry$variables$Any_laundry_home == 1 &
+ svyMTUSW6UkHalfhoursLaundry$variables$ba_survey == 2005],
+ svymean,
+ keep.var = TRUE
+ )
+ )
+ print("% within empstat who do weekday am")
+ print(
+ svyby(~weekday_am, ~empstat,
+ svyMTUSW6UkHalfhoursLaundry[svyMTUSW6UkHalfhoursLaundry$variables$Any_laundry_home == 1 &
+ svyMTUSW6UkHalfhoursLaundry$variables$ba_survey == 2005],
+ svymean,
+ keep.var = TRUE
+ )
+ )
+ print("% within empstat who do weekday pm")
+ print(
+ svyby(~weekday_pm, ~empstat, #
+ svyMTUSW6UkHalfhoursLaundry[svyMTUSW6UkHalfhoursLaundry$variables$Any_laundry_home == 1 &
+ svyMTUSW6UkHalfhoursLaundry$variables$ba_survey == 2005],
+ svymean,
+ keep.var = TRUE
+ )
+ )
+ print("% within empstat who do other")
+ print(
+ svyby(~other, ~empstat, #
+ svyMTUSW6UkHalfhoursLaundry[svyMTUSW6UkHalfhoursLaundry$variables$Any_laundry_home == 1 &
+ svyMTUSW6UkHalfhoursLaundry$variables$ba_survey == 2005],
+ svymean,
+ keep.var = TRUE
+ )
+ )
+
+ print("summarise the laundry types by diarypid for 1985 to see how many different kinds of laundry")
+ byList <- c("diarypid", "empstat", "sex", "r_dow","r_month","ba_nchild","age", "ba_age_r","hhtype","ba_npeople","income","urban","propwt")
+ laundrySummary1985 <- MTUSW6UKjoinedHalfhour_laundry_DT[ba_survey == 1985,
+ .(
+ N_Any_laundry_home = sum(Any_laundry_home),
+ N_sunday_morning = sum(sunday_am),
+ N_weekday_earlyam = sum(weekday_earlyam),
+ N_weekday_am = sum(weekday_am),
+ N_weekday_pm = sum(weekday_pm),
+ N_other = sum(other)
+ ),
+ by = byList # saves typos, easy to add new ones
+ ]
+
+ print("Summarise the laundry types by diarypid for 2005 to see how many different kinds of laundry")
+ # globalize for future use
+ laundrySummary2005 <- MTUSW6UKjoinedHalfhour_laundry_DT[ba_survey == 2005,
+ .(
+ N_Any_laundry_home = sum(Any_laundry_home),
+ N_sunday_morning = sum(sunday_am),
+ N_weekday_earlyam = sum(weekday_earlyam),
+ N_weekday_am = sum(weekday_am),
+ N_weekday_pm = sum(weekday_pm),
+ N_other = sum(other)
+ ),
+ by = byList
+
+ ]
+
+ print("Check laundry by sex & hh type - any change in proportions done by men in couples?")
+ kable(caption = "Check laundry by sex & hh type - any change in proportions done by men in couples?",
+ table(laundrySummary1985$hhtype[laundrySummary1985$N_Any_laundry_home > 0],
+ laundrySummary1985$sex[laundrySummary1985$N_Any_laundry_home > 0])
+ )
+ kable(
+ table(laundrySummary2005$hhtype[laundrySummary2005$N_Any_laundry_home > 0],
+ laundrySummary2005$sex[laundrySummary2005$N_Any_laundry_home > 0])
+ )
+
+ # convert 1985 (7 day diary) to dummies so can compare with 2005 (1 day diary)
+ laundrySummary1985$Sunday_amd <- ifelse(
+ laundrySummary1985$N_sunday_morning > 0,
+ 1, # true
+ 0 # false
+ )
+ laundrySummary1985$WeekdayEarly_amd <- ifelse(
+ laundrySummary1985$N_weekday_earlyam > 0,
+ 1, # true
+ 0 # false
+ )
+ laundrySummary1985$Weekday_amd <- ifelse(
+ laundrySummary1985$N_weekday_am > 0,
+ 1, # true
+ 0 # false
+ )
+ laundrySummary1985$Weekday_pmd <- ifelse(
+ laundrySummary1985$N_weekday_pm > 0,
+ 1, # true
+ 0 # false
+ )
+ laundrySummary1985$Otherd <- ifelse(
+ laundrySummary1985$N_other > 0,
+ 1, # true
+ 0 # false
+ )
+ laundrySummary1985$Any_laundry_homed <- ifelse(
+ laundrySummary1985$N_Any_laundry_home > 0,
+ 1, # true
+ 0 # false
+ )
+
+ # convert 2005 to dummies for logitstic models
+ laundrySummary2005$Sunday_amd <- ifelse(
+ laundrySummary2005$N_sunday_morning > 0,
+ 1, # true
+ 0 # false
+ )
+ laundrySummary2005$WeekdayEarly_amd <- ifelse(
+ laundrySummary2005$N_weekday_earlyam > 0,
+ 1, # true
+ 0 # false
+ )
+ laundrySummary2005$Weekday_amd <- ifelse(
+ laundrySummary2005$N_weekday_am > 0,
+ 1, # true
+ 0 # false
+ )
+ laundrySummary2005$Weekday_pmd <- ifelse(
+ laundrySummary2005$N_weekday_pm > 0,
+ 1, # true
+ 0 # false
+ )
+ laundrySummary2005$Otherd <- ifelse(
+ laundrySummary2005$N_other > 0,
+ 1, # true
+ 0 # false
+ )
+ laundrySummary2005$Any_laundry_homed <- ifelse(
+ laundrySummary2005$N_Any_laundry_home > 0,
+ 1, # true
+ 0 # false
+ )
+
+ # check
+ table(laundrySummary2005$empstat, useNA = "always")
+ # recode in work var to make label easier and in a different order
+ laundrySummary2005$empstat_r <- factor(laundrySummary2005$empstat,
+ labels = c("full-time", "notInPaidWork", "part-time", "unknownHours" )
+ )
+ laundrySummary1985$empstat_r <- factor(laundrySummary1985$empstat,
+ labels = c("full-time", "notInPaidWork", "part-time", "unkown" )
+ )
+ # check
+ table(laundrySummary2005$empstat_r, laundrySummary2005$empstat, useNA = "always")
+
+ # create a 'season' variable
+ laundrySummary1985$ba_season[laundrySummary1985$r_month == 11 |
+ laundrySummary1985$r_month == 0 |
+ laundrySummary1985$r_month == 1 ] <- "Winter"
+ laundrySummary1985$ba_season[laundrySummary1985$r_month == 2 |
+ laundrySummary1985$r_month == 3 |
+ laundrySummary1985$r_month == 4 ] <- "Spring"
+ laundrySummary1985$ba_season[laundrySummary1985$r_month == 5 |
+ laundrySummary1985$r_month == 6 |
+ laundrySummary1985$r_month == 7 ] <- "Summer"
+ laundrySummary1985$ba_season[laundrySummary1985$r_month == 8 |
+ laundrySummary1985$r_month == 9 |
+ laundrySummary1985$r_month == 10 ] <- "Autumn"
+ # check
+ table(laundrySummary1985$ba_season, laundrySummary1985$r_month) # small n for Summer
+
+ # create a 'season' variable
+ laundrySummary2005$ba_season[laundrySummary2005$r_month == 11 |
+ laundrySummary2005$r_month == 0 |
+ laundrySummary2005$r_month == 1 ] <- "Winter"
+ laundrySummary2005$ba_season[laundrySummary2005$r_month == 2 |
+ laundrySummary2005$r_month == 3 |
+ laundrySummary2005$r_month == 4 ] <- "Spring"
+ laundrySummary2005$ba_season[laundrySummary2005$r_month == 5 |
+ laundrySummary2005$r_month == 6 |
+ laundrySummary2005$r_month == 7 ] <- "Summer"
+ laundrySummary2005$ba_season[laundrySummary2005$r_month == 8 |
+ laundrySummary2005$r_month == 9 |
+ laundrySummary2005$r_month == 10 ] <- "Autumn"
+ # check season
+ kable(caption = "Check season flag", table(laundrySummary2005$ba_season, laundrySummary2005$r_month))
+
+ # how many people exhibit combinations of laundry?
+ laundrySummary1985$laundry_sum <- laundrySummary1985$Sunday_amd +
+ laundrySummary1985$WeekdayEarly_amd + laundrySummary1985$Weekday_amd + laundrySummary1985$Weekday_pmd
+
+ # NB - only 1 day diary in 2005 so cannot have Sunday + weekday laundry
+ laundrySummary2005$laundry_sum <- laundrySummary2005$Sunday_amd +
+ laundrySummary2005$WeekdayEarly_amd + laundrySummary2005$Weekday_amd + laundrySummary2005$Weekday_pmd
+
+ # combinations by employment type
+ kable(caption = "combinations by employment type 1985", table(laundrySummary1985$laundry_sum,laundrySummary1985$empstat))
+ kable(caption = "combinations by employment type 2005", table(laundrySummary2005$laundry_sum,laundrySummary2005$empstat))
+
+ # Get number of people reporting any laundry by employment status (to give denominator)
+ kable(caption = "Get number of people reporting any laundry by employment status (to give denominator 1985)",
+ table(laundrySummary1985$Any_laundry_homed,laundrySummary1985$empstat, useNA = "always")
+ )
+ kable(caption = "Get number of people reporting any laundry by employment status (to give denominator 2005)",
+ table(laundrySummary2005$Any_laundry_homed,laundrySummary2005$empstat, useNA = "always")
+ )
+
+ # combinations by age
+ kable(caption = "combinations by age - 1985",
+ table(laundrySummary1985$laundry_sum,laundrySummary1985$ba_age_r))
+ kable(caption = "combinations by age - 2005",
+ table(laundrySummary2005$laundry_sum,laundrySummary2005$ba_age_r))
+
+ # Get number of people reporting any laundry by age (to give denominator)
+ kable(table(laundrySummary1985$Any_laundry_homed,laundrySummary1985$ba_age_r, useNA = "always"))
+ kable(table(laundrySummary2005$Any_laundry_homed,laundrySummary2005$ba_age_r, useNA = "always"))
+
+ ## logit models predicting performing a given laundry type ##
+
+ doLogit(Sunday_amd ~empstat + ba_age_r + ba_nchild, laundrySummary2005[laundrySummary2005$Any_laundry_homed == 1])
+ # test with season
+ doLogit(Sunday_amd ~empstat + ba_age_r + ba_nchild + ba_season, laundrySummary2005[laundrySummary2005$Any_laundry_homed == 1])
+
+ doLogit(WeekdayEarly_amd ~empstat + ba_age_r + ba_nchild, laundrySummary2005[laundrySummary2005$Any_laundry_homed == 1])
+
+ doLogit(Weekday_amd ~empstat + ba_age_r + ba_nchild, laundrySummary2005[laundrySummary2005$Any_laundry_homed == 1])
+
+ doLogit(Weekday_pmd ~empstat + ba_age_r + ba_nchild, laundrySummary2005[laundrySummary2005$Any_laundry_homed== 1])
+
+ # number of different habits
+ doLm(laundry_sum ~empstat + ba_age_r + ba_nchild, laundrySummary2005[laundrySummary2005$Any_laundry_homed == 1])
+
+ # survey it &
+ # globalize for future use
+ svyLaundrySummary1985 <- svydesign(ids = ~diarypid,
+ weight = ~propwt,
+ data = laundrySummary1985) # does not produce a data table
+ svyLaundrySummary2005 <- svydesign(ids = ~diarypid,
+ weight = ~propwt,
+ data = laundrySummary2005) # does not produce a data table
+
+ # check changes in % of women < 60 in paid work
+ print("% in work in 1985")
+ print(
+ svyby(~empstat, ~sex,
+ subset(svyLaundrySummary1985, svyLaundrySummary1985$variables$age > 19 & svyLaundrySummary1985$variables$age < 60),
+ svymean,
+ keep.var = TRUE
+ )
+ )
+ print("% in work in 2005")
+ all_hhs_anylp <- svyby(~Any_laundry_p, # the data to summarise
+ ~ba_survey + sex, # the row * columns we want
+ svyMTUSW6UkHalfhoursLaundry, # the data in survey form
+ svymean # the function to use to summarise
+ )
+ print(
+ svyby(~empstat, # the data to summarise
+ ~sex,
+ svyLaundrySummary2005[svyLaundrySummary2005$variables$age > 19 & svyLaundrySummary2005$variables$age < 60],
+ svymean,
+ keep.var = TRUE
+ )
+ )
+
+ print("% people who report laundry in 1985")
+ print(
+ svymean(~Any_laundry_homed, svyLaundrySummary1985)
+ )
+ print("% people who report laundry in 2005")
+ print(
+ svymean(~Any_laundry_homed, svyLaundrySummary2005)
+ )
+
+ print("Number of people reporting different number of laundry habits")
+ print(
+ svytable(~laundry_sum, svyLaundrySummary2005)
+ )
+
+ print("1985: N of respondents who reported laundry of given type")
+ print(
+ svytotal(~Sunday_amd + WeekdayEarly_amd + Weekday_amd + Weekday_pmd + Otherd + Any_laundry_homed,
+ svyLaundrySummary1985
+ )
+ )
+ print("2005: N of respondents who reported laundry of given type")
+ print(
+ svytotal(~Sunday_amd + WeekdayEarly_amd + Weekday_amd + Weekday_pmd + Otherd + Any_laundry_homed,
+ svyLaundrySummary2005
+ )
+ )
+
+
+ feedBack("Done analysing sampled file using survey methods")
+ # works
+```
+
+Finished
diff --git a/Theme-1/laundryPaper/DEMAND-BA-MTUS-W6-Laundry-Change-Over-Time-v2.0-adult-analysis.html b/Theme-1/laundryPaper/DEMAND-BA-MTUS-W6-Laundry-Change-Over-Time-v2.0-adult-analysis.html
new file mode 100644
index 0000000000000000000000000000000000000000..58a9f7992784042219f711ccfb1e50526d6ec4a3
--- /dev/null
+++ b/Theme-1/laundryPaper/DEMAND-BA-MTUS-W6-Laundry-Change-Over-Time-v2.0-adult-analysis.html
@@ -0,0 +1,10817 @@
+<!DOCTYPE html>
+
+<html xmlns="http://www.w3.org/1999/xhtml">
+
+<head>
+
+<meta charset="utf-8">
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
+<meta name="generator" content="pandoc" />
+
+
+<meta name="author" content="Ben Anderson (b.anderson@soton.ac.uk/@dataknut) [Energy & Climate Change, Faculty of Engineering & Environment, University of Southampton]" />
+
+
+<title>Laundry, Energy & Time (ER&SS submission)</title>
+
+<script src="data:application/x-javascript;base64,/*! jQuery v1.11.3 | (c) 2005, 2015 jQuery Foundation, Inc. | jquery.org/license */
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rfP3rfP0iAAAAAAEAUP%2FwBF4F%2FAAtAAASJjQ%2BAjc2MzIXFhcHLgQnJiIGBwYREBcWMyATNjUhNSERIzUGBwYiLgF1JRw4XTuAwI18kUKRAQMTGjUhTOikLVdQaeIBGSwJ%2Fq8B4nhxfji4v34BsdrTtqyRN3RVY%2FkkBxlIQU8dRGxetP7%2B%2FsyYyAFERll8%2FTXAnCQQXJgAAQCHAAAEXwXrAAsAADMRMxEhETMRIxEhEYeUArCUlP1QBev9bwKR%2BhUC3v0iAAAAAQCHAAABGwXrAAMAADMRMxGHlAXr%2BhUAAQAZ%2F%2FAC%2BQXrABQAAD8BFhceATMyNjURMxEQBwYiLgEnJhlnGEAbXDN6aZTZRaiEShwo9DVAOxkpp5oEPvvC%2FrpbHDVDKz0AAQCHAAAEWwXrAAsAADMRMxEBMwkBIwEDEYeVAmmf%2Fk4B6ab%2BX%2FgF6%2FzIAzj9x%2FxOAzb%2Buf4RAAABAIcAAAO7BesABQAAMxEzESEVh5QCoAXr%2BqOOAAAAAAEAgAAABSoF6wAPAAAzESEBMwEhESMRIwEjASMRgAEMAUQKAUQBDJQK%2FoJy%2FoIKBev7dQSL%2BhUFXvqiBV76ogABAIAAAARjBesACQAAMxEzAREzESMBEYDsAmOUf%2F0wBev7fgSC%2BhUFWPqoAAIAUP%2FwBJ8F%2FAATACYAABImND4DMh4BFxYVEAcGIyIuARIGFB4CMj4BNzY1ECcuASIOAXoqJlZ%2FwOe5eypP43vAb7yBKRovX6PFjVYcMnkrjLSPXQGl2ebazJdbXZlmwPD%2BSdV0WZUDN7vu4bZpT35XmsYBSbVBUUh5AAACAIcAAAQsBesADAAUAAAzESEyFxYUDgIjIRkBITI2ECYjIYcCEetvOixbomv%2BgwEYosPEof7oBeu6YsORfUr9TAMwjgEkjQACAFD%2B8QSfBfwAIQA0AAASJjQ%2BAzIeARcWFRAHDgEHFBcWMyEVIyIuAicmNS4BAgYUHgIyPgE3NjUQJy4BIg4BkUEmVn%2FA57l7Kk9rNbJzIBtCAQiB1mExGAoOeMQMGi9fo8WNVhwyeSuMtI9dAW3%2B%2BdrMl1tdmWbA8P7112uVF1wZFXwbHSMcK2ERkgN%2Bu%2B7htmlPfleaxgFJtUFRSHkAAgCHAAAELAXrAA8AGgAAMxEhMhcWFRQHBgcBIwEhGQEhMjc2NTQnJiMhhwH%2B73dBQEaeAR6i%2Fuz%2BqwF4aUpS3kJO%2FvEF67plb4ZveiL9NAK0%2FUwDMEJJm846EQABACj%2F8APcBfwARQAAEzcyHgMXFiA2NCYnJi8BJicmNTQ3PgEyHgIfARYXBy4FJyYjIgYVFBceAx8BFhcWFRQHBgcGIi4CJyYnKIkBBxMgOydWARqKV20gNF%2F3VTtwNaS1i1VEDx0NA4ICDwsbHS8bQlN7mkQlM0UgL1ToVzs%2BTZ07nZhnUxkuEwFwMCI2QkIbPZrhgS0NEiFSgFl3jXo4RjFIXiVKIwYyBTUiPCkxDySReIM1HRoZDQ4aR4xfh2JriCwQKkRSKUs3AAABABkAAAQNBesABwAAEzUhFSERIxEZA%2FT%2BUJQFb3x8%2BpEFbwABAIf%2F8AQ9BesAFQAAExEzERQXFjMgNzY1ETMRFAcGISAnJoeUQlOxAQgxDJdZeP71%2FsFuLQHHBCT73Ihddu43NgQk%2B9ykgbL%2BagAAAQAZAAAENwXrAAYAABMzCQEzASMZmgF1AXWa%2Fj6aBev7GATo%2BhUAAAEAGQAABjUF6wAPAAATMwEzATMBMwEzASMBIwEjGZoBAwoBEqoBEgoBA5r%2BoZj%2B7gr%2B7pgF6%2FtSBK77UgSu%2BhUErvtSAAAAAAEAGQAABCIF6wALAAAzCQEzCQEzCQEjCQEZAbX%2BXZoBZwFcmv5UAamc%2Fpn%2BmQMFAub9hgJ6%2FQP9EgJ8%2FYQAAAABABkAAARJBesACAAAEzMJATMBESMRGZoBfgF%2Bmv4ylAXr%2FTcCyfyp%2FWwClAAAAAABAEQAAAQyBesACQAAMzUBITUhFQEhFUQDOfz9A7j85gMUawUAgHv7Ho4AAAEAZP7jAhkGZgAHAAATESEVIREhFWQBtf7fASH%2B4weDXPk1XAAAAAABAFH%2FwQOlBisAAwAAEzcBB1FsAuhtBfgz%2BckzAAAAAAEAZP7jAhkGZgAHAAAXIREhNSERIWQBIf7fAbX%2BS8EGy1z4fQAAAAABAFoEbAIWBYsABQAAEzcXBycHWt7eQpydBK3e3kCcnQAAAAABAFwAAAP8AHAAAwAAMzUhFVwDoHBwAAABALMERwIxBa8AAwAAEzcBB7NXASdCBVhX%2FtlBAAAAAAIASP%2FzA0wEKAAhAC4AABI%2BAjc2NzQnJiMiBwYHJzYhMhcWFREUFhcjJjUGIyInJjcGFBYyNjc2NxEGBwZIJE1iSYXAPTZnflsnD19wAQ3xQxsWCYUejaq8SSWdGWh%2FYShJJL4jsAFJa1NAGi8mnjUvYCcfP9e5S2v92x1kEw%2BZtYRDui6UTyYeNysBPTELOQAAAAACAF7%2F9QOXBesAFwAkAAA3FAcjNjURMxE%2BATMyEhUUBwYHBiImJyYnFhcWMjY3NjUQISIH%2FBmFGoQToFy02DdAdkKKZiM7HmVNJnZrHz%2F%2B1oNqppoMN1sFWf2qKWz%2By%2FeTg5Y8ISggNqOLGQ0%2FOHGxAbyXAAEAS%2F%2F1A00EKgAgAAATECEyFxYXFhcHLgMjIBEUFjMyNzY%2FARcGBw4BIyICSwGpaUhHFygPagInLFM0%2Ftubil8%2FNxMJaCNQJHxG2s8CCAIiMjIoRC0wFE4yKf5Xzt5JPzEWMmFPIzoBFwAAAAIAS%2F%2F1A4QF6wAVACIAACUGIyImJyY1ND4BMhYXETMRFBYXIyYBFBcWMzI3NjcRJiMgAuZmtluWLmBfuNGgE4QRCYUZ%2Fek%2FQo9fPTE6aoP%2B1qaxW0udxpP8nWwpAlb6px5hEwwB8rFxdzYrUAINlwACAEv%2F9ANMBCoAGwAkAAA2JjQ%2BAzc2Mh4BFxYRIRAXFjMyNxcGBwYiJgMhNCcmIyIHBn4zHC5CRilHgFpnJVb9hodDUppsWyc%2FeOqkCwHwRTprrzsc3bTGo3JZNhEeF0c6iP73%2FvVhML8%2FSDpuVgImlWJSokwAAAABAA0AAAItBfQAEwAAARUjIh0BMxUjESMRIzUzNTQ3NjICLVmW7%2B%2BEra2NQpsF629%2B33D8UQOvcN%2BjORoAAAAAAwAq%2FkMDzQQsADoATgBWAAASJjQ%2BAzcuATQ2Nz4BNy4BND4BMhYXNjc2NxUjIhUeARUUBiMiJwYHBhUUFx4CFxYXHgEVECEiLwEUFxYyPgM3NjQuAycGBwYSFBYyNjQmIntRHj0tTghSVBsbLDoDPFttt6xpPEwwPjQCqzMZz6VXWwgcPCogXycucJphe%2F4hqYQQalufNFI0PhIqUHt4bQtrIFJAjPF9e%2Br%2Bn29sPDAdJwURVkc6GSckAjOlxa5iKik%2BCQ0BeB9eWS2h3yYHFCoqLhENDgUBASUXimD%2B4j3XYiMfAQcNGhIqeEceDQUDKBMwA4Hqm57imQAAAQCBAAADZQXrABMAADMRMxE2NzYzMhYVESMRNCYjIgcRgYSUXzE1c5SEaz6UnwXr%2FYeNHQ6MjPzuAxJLXa388wAAAAACAHgAAAEOBToAAwAHAAATNTMVAxEzEXiWjYQEpJaW%2B1wEH%2FvhAAAAAv%2FB%2Fq8BBQU6AAMAFAAAEzUzFQEyNjURMxEUBw4DBwYrAW%2BW%2FrxhVoQqEBoxIB8qISQEpJaW%2BntEgAQ8%2B%2BGrQxkhFwsDBAAAAQCCAAADdgXrAAsAADMRMxEBMwkBIwEHEYKEAZSe%2FuwBUpL%2B5sQF6%2FwqAgr%2Bmf1IAkbz%2Fq0AAAABAIH%2F%2FgEFBesAAwAAFxEzEYGEAgXt%2BhMAAAAAAQB4AAAFuwQqACEAADMRMxU2NzYyFhc2NzYzMhYVESMRNCYjIgcRIxE0JiMiBxF4hIppMZSJFpFoMTdzlIRrPpOfhGs%2BlJ8EH66KIA9gYZEgEIyM%2FO4DEktdrfzzAxJLXa388wAAAAABAHgAAANcBCoAEQAAMxEzFT4BMhYVESMRNCYjIgcReIRYntaUhGs%2BlJ8EH61XYYyM%2FO4DEktdrfzzAAACAEv%2F9AOBBCoACQAVAAAaASASEAIjIicmNxAXFjI%2BATc2NRAgS9kBhtfZwcVtaoSlMoJlPBMh%2FdIDCAEi%2Ftz%2BGP7Wmpfr%2FsNVGjRSOmWGAasAAAACAIH%2BZgOsBCoAEwAkAAATETMVNjc2MzIXFhUQBwYjIiYnGQEeATI%2BATc2NTQnJiMiBwYHgYQYN2hx0m4%2Fh2KjXKwTLoR3UU8cPnQ%2BXn5eGB%2F%2BZgW5pC0tVeaEnf7so3dvKP3aAphCVxc9MWzN%2BmY3ahsqAAIAS%2F5mA3YEKgAVACQAABImND4BNzYzMhcWFzczESMRDgEiLgESBhQeARcWMzI3ESYnJiJnHB9ALGKSj3UYDBFzhBOsrYdaXzUiOCdGVZxrPCxMvQEznZ6ThjNwfxoWpPpHAiYob0BtAqqy36BiHzWZAg1SITwAAAEAeAAAAmYEKgARAAAzMBEzETY3NjMyFxUOAQcGBxF4hC8ZYpwMGERaK0tWBB%2F%2B5GgnmAJwCSEkPrn9jQAAAAABAFD%2F9QMeBCoAMgAAPwEeATMyNzY0JicuAycmNDYzMhcWFwcuAS8BJi8BJiIGFBYXHgUXFhUUBiAmUGM1gl5iPTMsKEORU1ggSbuRq3YWD1sHKgYaEgwiIJhuJCQ6mDFTLzwQJ8r%2B9rXIQFJRNSyATBgmIx0yH0fpmXsXFkQHLQYVEAUMDFdoPxckLhAhHjEbRFCFqmUAAAEAJP%2FzAkwFoAAYAAATNTMRMxEzFSMRFBYzMjY1FQYiLgEnJjURJLKE8vI%2BRx1QQn0uQhUyA69wAYH%2Bf3D9Y2VKCgFjGAYgHUaWAp0AAAAAAQCB%2F%2FUDZQQfABMAABMRMxEUFjMyNxEzESM1BgcGIyImgYRrPpSfhISOZTE1c5QBDQMS%2FO5LXa0DDfvhrosfD4wAAAABABkAAANsBB8ABwAAEzMBMwEzASMZigEWEwEWiv68ywQf%2FHMDjfvhAAAAAAEAGQAABQsEHwAMAAATMxsBMxsBMwEjCwEjGXvo0Hji3of%2B03bgyYcEH%2FzlAxv84AMg%2B%2BEDKfzXAAAAAAEAGQAAA00EHwALAAAzCQEzEwEzCQEjCQEZAUr%2BwJT1AQCU%2FrQBWZT%2B9f7%2FAiAB%2F%2F56AYb%2BB%2F3aAan%2BVwAAAAABABn%2BdwNwBB8AFAAAEiInNxYyPgI3NjcBMwkBMwEGBwbVfjwKJHhOMikMFQn%2BhY4BKwEYhv6QOTk%2F%2FncIeREfMTseODUEIvylA1v7oas9QgAAAAABABkAAALyBB8ACQAAMzUBITUhFQEhFRkCMP4FAqT9yAI4VQNacEr8o3gAAAEAUP7gAlIGZgAnAAATNTI1NCcmNTQ2MxUiBgcGFRQWFAYHHgEUBhQeAjMVIiY1NDc2NTRQwQ8ttsdCax8%2FPFRgYFQ8IT1rQse2LQ8CZnr1JCVvZa%2FFaTInUFEVzdOJIiKJ0808VE0yacWvZW8lJPUAAAAAAQBk%2F3wA%2BAYbAAMAABcRMxFklIQGn%2FlhAAAAAAEAUP7gAlIGZgAnAAAXMjY3NjU0JjQ2Ny4BNDY0LgIjNTIWFRQHBhUUMxUiFRQXFhUUBiNQQmsfPzxUYGBUPCE9a0LHti0PwcEPLbbHtzInUFEVzdOJIiKJ0808VE0yacWvZW8lJPV69SQlb2WvxQABAFoEWAK7BSMAFwAAAQ4BIi4BKwEOAQcnNjc2MhYXFjsBPgE3ArsZYW5TSSIKIDIMUxktNGVFFjkzCiAyDAT0SFQuLQMuIyhIJy0cES4DLiMAAAACAFz%2FWADyBP4AAwAHAAAXEzMTAzUzFVwXaBeWlqgEevuGBRCWlgAAAgBu%2FxQDcAUBABwAIgAABSQQJTUzFR4BFxYfAQcmJyYnETY3Fw4DBxUjAxQWFxEGAcL%2BrAFUhD5nHTwSB2oQUyEpf0NoCD0%2FajyE0G1j0AU3A78z3doIOSNINxcwYDkXCPy2IagyGmRDQAnkAv2r1SADPjUAAAAAAQAqAAAD6AX1ACgAACUGKQE1PgE9ASM1MzUQNzY3NjMyFxUnIg4CHQEhFSEVFAYHITI3NjcD6D3%2Buf4tDROHh09LgEk%2BmQNnZYBJHAFF%2FrsRDwFH6UMHA4WFiiLxXn6HbwFEh34nFgpwAjmFvJRvh35h9BxCCAkAAgBLAbECuQQgABcAHwAAEzcmNDcnNxc2Mhc3FwcWFAcXBycGIicHEhQWMjY0JiJLZC0sY0FiQac%2FY0FkLCxkQWJCo0NiWliFWFiDAfJkQKRCY0FiMDFjQWRDn0NkQWIwMGIBeYJgYYBgAAAAAQAZAAAESQXrABYAABMzCQEzATMVIxUzFSMRIxEjNTM1IzUzGZoBfgF%2Bmv4y8PDw8JTl5eXlBev9NwLJ%2FKlcrlz%2B0gEuXK5cAAAAAAIAZP98APgGGwADAAcAABcRMxEDETMRZJSUlIQDCvz2A44DEfzvAAACAFD%2F9QMeBVAAOQBHAAA%2FAR4BMzI3NjQmJy4DJyY0NyY1NDYzMhcWFwcuAS8BJi8BJiIGFBYXHgUXFhQHFhUUBiAmEwYUFhceARc2NCYnLgFQYzWCXmI9MywoQ5FTWCBJLS27kat2Fg9bByoGGhIMIiCYbiQkOpgxUy88ECcoKMr%2B9rVTCiQkOvg%2BBCwoQ9DIQFJRNSyATBgmIx0yH0e%2BQz9OgZl7FxZEBy0GFRAFDAxXaD8XJC4QIR4xG0SfQz9VhaplAvwZQj8XJEslGENMGCYyAAIAXASzAjEFSQADAAcAABM1MxUzNTMVXJaplgSzlpaWlgAAAAMASwESA%2FcEvgAHAA8AMAAAEhAAIAAQACACEBYgNhAmIBImNDY3NjMyFxYXByYnJiIOAQcGFBYXFjMyNxcGBwYjIksBFAGEART%2B7P580%2B4BTu3t%2FrIFJxgYNWZgOQ0KQhESIksyHAgMDA4cTmMTQgIeOV9FAiYBhAEU%2Fuz%2BfP7sAn3%2Bsu3tAU7t%2FaJ%2BfmQqXG0ZIw1CEB0ZJx8tfkofP28IHDFgAAAAAAIAZAQZAbYF7AAXACIAAAEGIiY1NDY3NCYiBwYHJzYzMh0BFBcjJicyNjc1BwYPAQYUAWc8izx8hyVYGicOLDBymA1CDXwkRRMpGw0iTgRmTVEuUEwbNS8PFB0eW5zpLhoHLC8XfgoGBAsZjAAAAAACAGQEHgKPBdoABQALAAATNxcHFwc%2FARcHFwdk3kCcnUEu3kCcnUEE%2FN5CnJ1B3t5CnJ1BAAAAAAEAXAFuA%2FwDJgAFAAATNSERIxFcA6BwArZw%2FkgBSAAAAQBcArYD%2FAMSAAMAABM1IRVcA6ACtlxcAAAAAAQASwEaA%2FkEtgANABUAIgAqAAATNAAzMhceARQOASIuARIQFiA2ECYgExEzMhYUBgcXIycjFREzMjU0JisBSwEUw8WIP0t%2F2f7Zf0TuAUvt7P6yBtRBSC0xXUxab3pWPjlZAui%2FAQ%2BGPqve1Xp81QEg%2Frro5wFJ5v2BAexZYVMK1crKAQlZLB8AAAAAAQBaBM8CaQUrAAMAABM1IRVaAg8Ez1xcAAAAAAIASwPbAkkF2QAHAA8AABI0NjIWFAYiAhQWMjY0JiJLldSVldQ1XYJdXYIEcNSVldSVAUCCXV2CXQAA%2F%2F8ASwD7A0IE8RAnANQAAP5ZEAcADgAAAJEAAAABAGQEHwGaBeoAHgAAEzQ3Nj8BNjc2NTQjIgYVIzQ2MhYVFAcGBwYHBhUzFWQ8Hx0wMwgRTicwQlmAUCgZGhwqTukEH1I8HhUjJA8eG0UxHTpKRjU2KRgUFh43IDoAAAABAGQEEAGdBfAAHgAAABYUBxYUBiInNx4BMjY0JisBNTMyNTQmIgYHJz4CATtUOUdZoEAoCkJRMiopS0tEKUU7DSYSGUEF8Ep6JCqJRVEkDy4nTTA3UBsqJxEoFRccAAABAHoERwH4Ba8AAwAAEwEXAXoBJ1f%2BxASIASdX%2Fu8AAAEAgf5lA2UEHwATAAATETMRFBYzMjcRMxEjNQYHBiInEYGEaz6Un4SEjmUxezn%2BZQW6%2FO5LXa0DDfvhrosfDxr%2BVgAAAwBEAAAD1AXrABEAFwAbAAASJjQ%2BATc2MyERIxEjESMRIiYSBhQWFxETMxEjbiobPy1lqAH8lNqUa6CXhoV1lNraA3uRhWtqKVz6FQK0%2FUwCtEoCVInwjBQCLf3KAj8AAQBcAxIA8gOoAAMAABM1MxVclgMSlpYAAQAW%2FscBBwAAAAsAABMnNTI2NTQnMxYVFDokWTtOYkn%2BxwFeIA9LYGZgcwABAGQEHwE%2BBewACgAAEzczETMVIzUzEQdkYjZCzklHBckj%2Fm06OgFTFQAAAAIAZAQhAcEF6wAHAA8AABImNDYyFhQGJxQyNTQmIgbBXVymW1y70S9yMAQhgM97fM9%2F5aamU1NSAAAAAAIAZAQeAo8F2gAFAAsAABM3JzcXBz8BJzcXB2SdnEDe3sudnEDe3gRfnZxC3t5BnZxC3t4AAAAABAA5%2F9cEQAYPAAoADQAYABwAACU1ATMRMxUjFSM1JzMRATczETMVITUzEQcTATMBAhcBSG5zc1%2F29vzLo0pu%2Frp6d0MC6Gz9GbhOAfL%2BEFC%2Fv1ABewMxOv1VUFACVCL6YgY4%2BcgAAAADADn%2F2gRUBhIACgAsADAAABM3MxEzFSE1MxEHATQ3Nj8BNjc2NTQjIgYVIzQ2MhYVFA8BBg8BBg8BBhUhFQUBMwE5o0pu%2Frp6dwIONT5%2BUi4LGopPSV%2BdvIdwYAMbSBgLGhwBhPwwAuhs%2FRkFtDr9VVBQAlQi%2Bn5yR1FdPCMULiuJXzhfhHxZbVdJAhY8FQ4gIh1QGQY4%2BcgAAAAEADb%2F2gSLBhIACgANADAANAAAJTUBMxEzFSMVIzUnMxElNx4BMjY0JisBNTMyNjQmIgYHJz4BMhYVFAcGBxcWFAYjIhMBMwECYgFIbnNzX%2Fb2%2FH04EHWGW007f384OEt4ZRY0JIKiiRMcLx5Zjl6vVwLobP0ZuE4B8v4QUL%2B%2FUAF77zAYS01%2BV1FPbEY8GjkqQ3RTLyk6IBQ823D86wY4%2BcgAAAIAXQAAAwQF2QAXABsAABM3Nj0BMxUUDwEGFBYzMjY3Fw4BIyImEAE1MxXaZn2Ee2Z7fEhncydaMrZzlrYBXZYCoHiOXpWVdZN6lemRQU45XWzaAS4DO5aWAAD%2F%2FwAZAAAEUQevECcAQwASAgASBgAkAAD%2F%2FwAZAAAEUQevECcAdQGtAgASBgAkAAD%2F%2FwAZAAAEUQeVECcAQQD9AgoSBgAkAAD%2F%2FwAZAAAEUQc%2BECcAYQCqAhsSBgAkAAD%2F%2FwAZAAAEUQcMECcAaQDuAcMSBgAkAAAAAwAZAAAEUQfSAAcAGgAdAAAAFBYyNjQmIgI0NjIWFAcGBzMBIwMhAyMBJicDIQMBllyDXV2DvJXTlkshKAEBsJqC%2FgOFmgGwJyImAbjdBxSCXV2CXf741JWV1EshEvoVAd7%2BIgXrEiH8PAMTAAAAAAIAGQAABfwF6wAPABMAADMBIRUhESEVIREhFSERIQMTIREjGQGwBA39XwHj%2Fh0Cx%2Fyl%2FpeFpwFHbAXrfP3rfP2nhQHe%2FiICWgMTAAABAFD%2BwQRSBfwAOwAAASc1MjY1NCcmJy4FND4DMh4CFxYfARUHLgInJiMgERAXFjMyNzY1Fw4FBwYHFhUUAhckWTtIQTJIbFc6JhEjUnq5wIdcTBYuCwaYEDExHkdj%2FnpSXdewYkSSBB0ULS1LKlVYQ%2F7BAV4gD0hbBhAYU3WHm5zD18uWWzFOXzBjMxgBKXJpQxc1%2FXf%2B6rDFimF4FwtWNl0%2BSxYsCGFccwD%2F%2FwCHAAAD4gevECcAQ%2F%2F%2BAgASBgAoAAD%2F%2FwCHAAAD4gevECcAdQGaAgASBgAoAAD%2F%2FwCHAAAD4geVECcAQQDqAgoSBgAoAAD%2F%2FwCHAAAD4gcMECcAaQDbAcMSBgAoAAD%2F%2F%2F9hAAABGwevECcAQ%2F6uAgASBgAsAAD%2F%2FwCHAAACQQevECcAdQBJAgASBgAsAAD%2F%2F%2F%2FzAAABrweVECcAQf%2BZAgoSBgAsAAD%2F%2F%2F%2FmAAABuwcMECcAaf%2BKAcMSBgAsAAAAAgAJAAAEhAXrABAAHwAAEzUzESEyHgEXFhEQBwYpARETMyA3NhEQJyYrAREhFSEJgAFpm%2ByQLU7aof7p%2FpeU1QEAgnzccbHVAUT%2BvAKkiAK%2FVo1msv75%2FnbJlgKk%2FdispQEcAbKMSP29iAD%2F%2FwCAAAAEYwc%2BECcAYQDnAhsSBgAxAAD%2F%2FwBQ%2F%2FAEnwevECcAQwBeAgASBgAyAAD%2F%2FwBQ%2F%2FAEnwevECcAdQH5AgASBgAyAAD%2F%2FwBQ%2F%2FAEnweVECcAQQFJAgoSBgAyAAD%2F%2FwBQ%2F%2FAEnwc%2BECcAYQD2AhsSBgAyAAD%2F%2FwBQ%2F%2FAEnwcMECcAaQE6AcMSBgAyAAAAAQBLAW8DNQRaAAsAABMJATcJARcJAQcJAUsBGP7oXQEYARdd%2FukBGF3%2B6P7oAcwBGAEZXf7nARhd%2Fuj%2B6F0BGP7oAAADAFD%2F2gSnBg0AGQAhACsAABICED4DMzIXNxcHFhIQDgMjIicHJz8BASYjIAMGEBMWMzI%2BATc2ECehUSZWf8B2roRKcmFLTiZWfbtyr4VMc2FKAjhhj%2F7%2FZDipZY9ZjlkdNVEBMQElAQ7azJdbcIFCp2%2F%2B1f7y2MiUWG6EQqh%2BA9hm%2Fuqd%2Fh%2F%2B4GBPflaZAcewAAD%2F%2FwCH%2F%2FAEPQevECcAQwA%2BAgASBgA4AAD%2F%2FwCH%2F%2FAEPQevECcAdQHYAgASBgA4AAD%2F%2FwCH%2F%2FAEPQeVECcAQQEoAgoSBgA4AAD%2F%2FwCH%2F%2FAEPQcMECcAaQEaAcMSBgA4AAD%2F%2FwAZAAAESQevECcAdQGpAgASBgA8AAAAAgCHAAAELAXrAA4AFgAAMxEzESEyFxYUDgIjIRkBITI2ECYjIYeUAX3rbzosW6Jr%2FoMBGKLDxKH%2B6AXr%2FrG6YsORfUr%2BmwHhjgEkjQAAAAABAIf%2F9QRNBfUARQAAMxEQNzY3NiAWFAYHDgQHBhQWFx4FFxYVFAYgJic3HgEzMjc2NCYnLgMnJjU0NzY3Njc2NTQmIg4BBwYVEYclL0poATe7bFARPRgoDwoPJCU5mDFTLzwQJ8r%2B%2FpxCYzReX2I9MywpQpFTWCBJRD93XRQKf55hQBIfA28BMmWBLkCq35QfBxYKEw8MFVQ%2FFyQuECEeMRtEUIWqTmpAUTc1LIBMGCYjHTIfR2h%2FPTkgGkIfLVNdJ0tCd%2BP8kQAAAP%2F%2FAEj%2F8wNMBd0QJgBDuy4SBgBEAAAAAP%2F%2FAEj%2F8wNOBd0QJwB1AVYALhIGAEQAAP%2F%2FAEj%2F8wNMBcMQJwBBAKYAOBIGAEQAAP%2F%2FAEj%2F8wNMBWwQJgBhVEkSBgBEAAAAAP%2F%2FAEj%2F8wNMBToQJwBpAJj%2F8RIGAEQAAP%2F%2FAEj%2F8wNMBoQQJwBxAIIAqxIGAEQAAAADAEj%2F9AWmBCoALgBAAEkAABI%2BAjc2NzQnJiMiBwYHJzYhMhc2MzIXFhEhEBcWMjY3NjcXBgcGICYnBiMiJyY3BhQWMj4BNzY3JjU0NjUGBwYlITQnJiMiBwZIJE1iSYXAPTZnflsnD19wAQ3dS2jHuGBW%2FYaHQ4hhID4SWiVAd%2F7%2BujSjxbxJJZ0ZaHpYQyMwJBoDviOwAhcB8EU6a687HAFLa1NAGi8mnjUvYCcfP9ednZiI%2Fvf%2B9WEwLSJDLT9KOmyBcvOEQ7oulE8hLB4sKkVvEkkTMQs5npViUqJMAAEAS%2F7GA00EKgAtAAABJzUyNjU0JyYnJjUQITIXFhcWFwcuAyMgERQWMzI3Nj8BFwYHBgcGBxYVFAGhJFk7R7peZwGpaUhHFygPagInLFM0%2Ftubil8%2FNxMJaCNQJD4mKUX%2BxgFeIA9IWg19jPwCIjIyKEQtMBROMin%2BV87eST8xFjJhTyMdEgdiXnP%2F%2FwBL%2F%2FUDTAXdECYAQ7guEgYASAAAAAD%2F%2FwBL%2F%2FUDTAXdECcAdQFTAC4SBgBIAAD%2F%2FwBL%2F%2FUDTAXDECcAQQCjADgSBgBIAAD%2F%2FwBL%2F%2FUDTAU6ECcAaQCU%2F%2FESBgBIAAD%2F%2F%2F9TAAABBQXdECcAQ%2F6gAC4SBgDBAAD%2F%2FwCBAAACMwXdECYAdTsuEgYAwQAAAAD%2F%2F%2F%2FlAAABoQXDECYAQYs4EgYAwQAAAAD%2F%2F%2F%2FYAAABrQU6ECcAaf98%2F%2FESBgDBAAAAAgBL%2F%2FUDgQXrABsAJwAAARYXNxcHEhEQBwYjIgIQEjMyFyYvAQcnNy4BJwMQFxYyPgE3NjUQIAIUMkuiLpu7u16BwtrZw5NVTwwRyy7HOW0CuqUygmU8EyH90gXrH1xeUFr%2B0%2F4Y%2FreMRQEtAeYBIkbOFiJ0UHJNZAL8Jf7EVRo0UjlkhwGr%2F%2F8AeAAAA1wFbBAmAGFgSRIGAFEAAAAA%2F%2F8AS%2F%2F0A4EF3RAmAEPELhIGAFIAAAAA%2F%2F8AS%2F%2F0A4EF3RAnAHUBXwAuEgYAUgAA%2F%2F8AS%2F%2F0A4EFwxAnAEEArwA4EgYAUgAA%2F%2F8AS%2F%2F0A4EFbBAmAGFcSRIGAFIAAAAA%2F%2F8AS%2F%2F0A4EFOhAnAGkAoP%2FxEgYAUgAAAAMASwE%2FAzwEiQADAAcACwAAEzUhFQE1MxUDNTMVSwLx%2Fj2WlpYCooSE%2Fp2WlgK0lpYAAAADAEv%2F3AOBBE4AEQAYACEAADcmEBIzMhc3FwcWEAIjIicHJxMUFwEmIyATFjI%2BATc2ECexZtnDa1Ywcjxz2cF3XjJzYikBdzpO%2Fuh9QZtlPBMhMpSUAeABIjBUQmqU%2FhD%2B1j9XQgHymGkCiSL83jQ0UjplATxqAP%2F%2FAIH%2F9QNlBd0QJgBD0C4SBgBYAAAAAP%2F%2FAIH%2F9QNlBd0QJwB1AWsALhIGAFgAAP%2F%2FAIH%2F9QNlBcMQJwBBALsAOBIGAFgAAP%2F%2FAIH%2F9QNlBToQJwBpAKz%2F8RIGAFgAAP%2F%2FABn%2BdwNwBd0QJwB1ATwALhIGAFwAAAACAIH%2BZgOsBesAEwAkAAATETMRNjc2MzIXFhUQBwYjIiYnGQEeATI%2BATc2NTQnJiMiBwYHgYQYN2hx0m4%2Fh2KjXKwTLoR3UU8cPnQ%2BXn5eGB%2F%2BZgeF%2FZAtLVXmhJ3%2B7KN3byj92gKYQlcXPTFszfpmN2obKgAAAP%2F%2FABn%2BdwNwBT8QJgBpfvYSBgBcAAAAAAABAIEAAAEFBB8AAwAAMxEzEYGEBB%2F74QACAFAAAAZyBesAFQAjAAASJjQ%2BAzMhFSERIRUhESEVISIuATcWFx4BNhYzESIHBgIQeiomVn%2FAdgPL%2FV8B4%2F4dAsf8D2%2B8gWkrVy6WTDwLmz2yqQGq1uPVx5RYfP3rfP2nhVaSUUw1GxoEAgTqCh7%2B1f25AAMAS%2F%2F0Bf4EKgAiAC4ANwAAGgEzMhYXNjc2Mh4BFxYRIRAXFjMyNxcGBwYjIicOASImJyY3EBcWMj4BNzY1ECABITQnJiMiBwZL2cN1sTJOqSteWmclVv2Gh0NSmmxbJz94g%2BxyNK%2FXnTNqhKUygmU8EyH90gK4AfBFOmuvOxwDCAEicGalJwoXRzqI%2Fvf%2B9WEwvz9IOm7YZ3JSSJfr%2FsNVGjRSOmWGAav%2Bt5ViUqJMAAAA%2F%2F8AWgRsAhYFixIGAEEAAAAC%2FwIAgQEAAn8ABwAPAAACNDYyFhQGIgIUFjI2NCYi%2FpXTlpbTNVyDXV2DARbUlZXUlQFAgl1dgl0AAP%2F%2F%2FtAAdwExAUIQBwBh%2Fnb8HwAAAAEAAAILAgACewADAAARNSEVAgACC3BwAAEAAAILBAACewADAAARIRUhBAD8AAJ7cAAAAAABAAACCwgAAnsAAwAAESEVIQgA%2BAACe3AAAAD%2F%2FwBGBBYA8gWmEgYACgAAAAIARgQWAfsFpgAIABEAABM1MxUjFyMuATc1MxUjFyMuAUaWQVcsA2LulkFXLANiBRCWlvoDvjmWlvoDvgAAAP%2F%2FAEYEFgH7BaYSBgAFAAD%2F%2FwBG%2F%2F8B%2BwGPEAcAzAAA%2B%2BkAAAABAFwCAQGfA0QABwAAEiY0NjIWFAa4XF2FYWECAWKBYGJ%2BY%2F%2F%2FAEAAvQO6BRMSBgAfAAD%2F%2FwBAAL0DugUTEgYAIQAAAAEARv%2FaA5oGEgADAAAXATMBRgLobP0ZJgY4%2BcgAAAAAAgBkBB8BvAXrAAoADQAAEzUTMxEzFSMVIzUnMzVky0tCQkKNjQSGNwEu%2FtM4Z2c42AAAAAEAKP%2FwBJsF%2FAA1AAATNTMmNDcjNTM2Nz4BMh4CFxYfARUHJyYnJiMgAyEVIQYUFyEVIRIhMhM2NxcGBwYjICcmJyh0AwJzfCF4PsDGh11LFi8LBpYIIFdIdv6zMQF%2B%2FnsBAwGD%2FoQ3AUXlWhAIkSVqhdT%2B6o1QGwI8Zi44NGb5p1djMU5eL2A2GAErJqJYSv4iZhpSLmb%2BMAEIMSoXqICg%2BIzIAAAAAAEASwKiA0IDJgADAAATNSEVSwL3AqKEhAAAAP%2F%2FAEb%2FygOaBjQSBgASAAkABwBc%2F%2FwFCgIUAAoADQAdACgALAA7AEYAACU1EzMRMxUjFSM1JzMRACY0Njc2MzIXFhUUBwYiJhMiEDMyNzY0JicmEzUzFSQmNDY3NjMyFxYUDgEiJhMiEDMyNzY0JicmAhTmPlFRNLu7%2FWoSERMqYT0nQlUiX0d3e3tKFhMJCxh3NAHJERESKWJ%2BIQgbTm5Hd3t7ShYTCQsYiSoBYP6hK4mJKwEh%2FqRYZFwpWixJnbc5Fi4Bv%2F4%2BQTiTSiNJ%2Fhc0NHlXZlspWrAueW9SLgG%2F%2Fj5BOJNKI0kADgBJAAADhwK1AAMABwALAA8AEwAXABsAHwAjACcAKwAvADMANwAANxEzEQMRMxkBNSEVATUhFQE1IRUZATMRAxEzERMRMxEDETMZATUhFQE1IRUBNSEVGQEzEQMRMxFJHR0dATX%2BywE1%2FssBNR0dHWEdHR0BNP7MATT%2BzAE0HR0dHwEq%2FtYBTAEq%2Ftb%2BlR0dAUwdHQFMHR39hwEq%2FtYBTAEq%2Ftb%2BtAEq%2FtYBTAEq%2Ftb%2BlR0dAUwdHQFMHR39hwEq%2FtYBTAEq%2FtYAAAAAAgANAAADoAX0ABsAJAAAATYyFxUjIh0BMxUjESMRIxEjESM1MzU0NzYzMgEzNTQ3JiMiFQKVUrAJWZbv74TvhK2tj0ZIfP7r7xVELpIFxy0JcH3fcPxRA6%2F8UQOvcN%2BnNRr%2BK982ORB%2FAAAAAQAN%2F%2F4CwwX0ABsAAAEnIgcGBwYdASERIxEjESMRIzUzNTQ3NjMXFSMCLUdYHx0GDgGAhPyEra2NQlLolgV7ARUTECMj3%2FvfA7H8UQOvcN%2BjORoJlgAAAAIADf%2F%2BAtIF9AARABcAABM1MzU0NzYzMh8BESMRIREjEQEiHQEhEQ2tokpBelEghP7whAE7twEQA69w36E7GgYD%2BhMDsfxRA68BzX7fAV0AAgAN%2F%2F4ENgX0ACAAKQAAATYzMhYXFSM1IyIdASERIxEjESMRIxEjESM1MzU0ITIWATM1NDcmIyIVApVXYiTDAZZZlgGAhPyE74StrQE0S1j%2Bre8VRC6SBcctCAGWJ37f%2B98DsfxRA6%2F8UQOvcN%2F2Kf5U3zY5EH8AAwAN%2F%2F4EJAX1AB0AJgAsAAABNjIXESMRIxEjESMRIxEjNTM1NDc2MhYfAR4DATM1NDcmIyIVBTMRIyIVApVNxX2E74TvhK2toEllHgwXGh8GDP6q7xVELpIBc%2B9hjgXHLgr6EwOx%2FFEDr%2FxRA69w36I6GgICBQUTAwn%2BWN82ORB%2F3wFdfgAAAAEAAADdAGoADgBhAAgAAgABAAIAFgAAAQAAAAAIAAUAAAAUABQAFAAUACYANABmAMoBCAFkAW4BmgHEAeIB%2BgIOAhYCIgIyAm4ChALCAwADHANWA2ADdAPOBBwELgRIBFwEcASEBLQFKAVEBYAFxgXyBgoGIAZmBn4GigauBsoG2gb6BxAHTgdyB8IH8AhWCGgIjgiiCMYI5gj%2BCRQJKAk4CUwJXglqCXoJxAn%2BCjQKbAqoCsgLRgtoC3wLoAu8C8oL%2FgwcDEYMgAy8DNwNKA1QDXINiA2mDcYN8A4GDkAOTg6GDrAOxA8ADzwPcg%2BYD6wQFhAoEHwQtBDQEOAQ7hE0EUIRYBFuEZ4RzhHeEgASMBI8ElISaBKGEqIS1hMkE3YTpBOwE7wTyBPUE%2BAUGBQ%2BFJQUoBSsFLgUxBTQFNwU6BT0FSoVNhVCFU4VWhVmFXIVlBXeFeoV9hYCFg4WGhZCFqgWtBbAFswW2BbkFvAXYBemF7IXvhfKF9YX4hfuF%2FoYBhhKGFYYYhhuGHoYhhiSGKwY6Bj0GQAZDBkYGSQZYBlsGXgZshoMGhQaMho8GkgaVhpkGmwajBqUGp4asBq4GsAa0BrqGz4bTBtUG8AcJhxcHIgcsBzsHTAAAQAAAABmZsg6KOdfDzz1AB8IAAAAAADLC9avAAAAAMsL1q%2F96fzmC%2B0JzgAAAAgAAgAAAAAAAALsAEQAAAAACAAAAAIAAAABTgBcAkEARgUwAJMEsQCdBDMAQwV%2FAG4BTgBGAiIAXAIiAFwCsAA%2FA40ASwFOAEYCAAAAAU4AXAPrAEYEbwBQA3cAagScAFoEkwBGBPkAgAQYAFcEiwBpA9YAVAS0AGYEiwBpAU4AXAFOAEYD%2BgBAA6UASwP6AEADXwBdBkkAUARqABkEbACHBJYAUAS%2BAIcEJgCHBAAAhwTeAFAE5gCHAaIAhwOAABkEdACHA9QAhwWxAIAE4wCABO8AUARwAIcE7wBQBEUAhwQJACgEJgAZBL0AhwRQABkGTgAZBDsAGQRiABkEdgBEAl8AZAPrAFECXwBkAnAAWgRYAFwCqwCzA6AASAPiAF4DngBLA9gASwOdAEsCOgANA%2B0AKgPTAIEBoQB4AWr%2FwQOPAIIBoQCBBikAeAPKAHgDzABLA%2FcAgQPkAEsCfwB4A24AUAJeACQD0wCBA4UAGQUkABkDZgAZA4kAGQMLABkDBABQAVwAZAMEAFADFQBaAU4AXAPKAG4EAQAqAwQASwRiABkBXABkA24AUAKNAFwEQgBLAhAAZALzAGQEWABcBFgAXAREAEsCwwBaApQASwONAEsB%2FgBkAgEAZAKrAHoD0wCBBFsARAFOAFwBXgAWAaIAZAIlAGQC8wBkBHIAOQSfADkEvQA2A18AXQRqABkEagAZBGoAGQRqABkEagAZBGoAGQZAABkElgBQBCYAhwQmAIcEJgCHBCYAhwGi%2F2EBogCHAaL%2F8wGi%2F%2BYEyAAJBOMAgATvAFAE7wBQBO8AUATvAFAE7wBQA4AASwT3AFAEvQCHBL0AhwS9AIcEvQCHBGIAGQTUAIcEegCHA6AASAOgAEgDoABIA6AASAOgAEgDoABIBfcASAOeAEsDnQBLA50ASwOdAEsDnQBLAaH%2FUwGhAIEBof%2FlAaH%2F2APVAEsDygB4A8wASwPMAEsDzABLA8wASwPMAEsDhwBLA8wASwPTAIED0wCBA9MAgQPTAIEDiQAZA%2FcAgQOJABkBoQCBBrYAUAZPAEsCcABaAdv%2FAgHb%2FtACAAAABAAAAAgAAAABTgBGAkEARgJBAEYCQQBGAfsAXAP6AEAD%2BgBAA%2BsARgIgAGQE3wAoA40ASwPrAEYFZgBcA9EASQOyAA0DVgANA24ADQTSAA0EwAANAAEAAAnO%2FeYAAAw3%2Fen%2B2gvtAAEAAAAAAAAAAAAAAAAAAADdAAEDGAH0AAUAAAUzBZkAAAEeBTMFmQAAA9cAZgISCAYCAAUDAAAAAAAAoAAAYwAAAAIAAAAAAAAAAFBmRWQAQAAN%2BwYJzv3mAAAJzgIaoAAAnwAAAAAAAAAAAAIAAAADAAAAFAADAAEAAAAUAAQAuAAAACoAIAAEAAoADQB%2BAP8BMQFTAsYC2gLcIBQgGSAeICIgOiBEIHQgrCISIhXv%2FfAA%2F%2F8AAAANACAAoQExAVICxgLaAtwgEyAZIBwgIiA5IEQgdCCsIhIiFe%2F98AD%2F%2F%2F%2F1%2F%2BP%2Fwf%2BQ%2F3D9%2Fv3r%2FergteCx4K%2FgrOCW4I3gXuAn3sLewBDZENcAAQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAALAALLAAE0uwKlBYsEp2WbAAIz8YsAYrWD1ZS7AqUFh9WSDUsAETLhgtsAEsINqwDCstsAIsS1JYRSNZIS2wAyxpGCCwQFBYIbBAWS2wBCywBitYISMheljdG81ZG0tSWFj9G%2B1ZGyMhsAUrWLBGdllY3RvNWVlZGC2wBSwNXFotsAYssSIBiFBYsCCIXFwbsABZLbAHLLEkAYhQWLBAiFxcG7AAWS2wCCwSESA5Ly2wCSwgfbAGK1jEG81ZILADJUkjILAEJkqwAFBYimWKYSCwAFBYOBshIVkbiophILAAUlg4GyEhWVkYLbAKLLAGK1ghEBsQIVktsAssINKwDCstsAwsIC%2BwBytcWCAgRyNGYWogWCBkYjgbISFZGyFZLbANLBIRICA5LyCKIEeKRmEjiiCKI0qwAFBYI7AAUliwQDgbIVkbI7AAUFiwQGU4GyFZWS2wDiywBitYPdYYISEbINaKS1JYIIojSSCwAFVYOBshIVkbISFZWS2wDywjINYgL7AHK1xYIyBYS1MbIbABWViKsAQmSSOKIyCKSYojYTgbISEhIVkbISEhISFZLbAQLCDasBIrLbARLCDSsBIrLbASL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E2PwE1BgcGASY1BwYjIicmNDY3NiU0LgMnJiIOAQcGByc%2BAjc2Mh4BFxYVERQWFwEcOlYrFSUQFB0IEhLgRT0BgB4TVqDCSCU%2FR4EBMQkLEBgQJ19TMRQaC4wsQD4jVK5nXR5AFQkBEDs1CgoTCRAXBxAR9zQ8Nv6QEksWXIlEsos1YD1HIBwPEgQKHSQYIBhWWz0sDR8TMSlZq%2F3YHGYTAAAAAgA%2F%2F%2B4DvgXrABIAKwAAASIHER4CFxYyPgE3NjQuAScmAQYrATY1ETMRPgEyHgMXFhQOAQcGIyICCnVjARoeFzKFVDIPGhopHzL%2B3ggI4hzWEnKcgltFKA0VHkMxbLCSA3xn%2FggBIx4RJyU8Lk%2FdjVEYKPzTT0BPBVz97hs9IzpXXjxk0JOJNHEAAAABADv%2F7gNqBDEAGwAAExA3NiAXFhcHJicmIyIRFBYzMjc2NxcGBwYjIDuJbAGFbiQOtQg7KDvpenVSMywUpBdLaaT%2BQQIIATqFaqQ1LlBSMCD%2BlbO7PDQ7SFlQbwAAAAACADv%2F7gO6BesADgAkAAABJiMiBwYVFBcWMzI3NjcXFBcjJi8BBiMiJyY1EDc2MzIWFxEzAsdjdXo1L0I4bnRMDQHWHOIDAwpMkvxyQLxknE1xEtYDFWdpXLjLTkNnEgGORkkFCUFh6YKjAX14QD0bAhIAAAIAO%2F%2FuA20EMQAJACYAAAEhNCcmIyIGBwYCJjQ%2BATc2MzIXFgMhFBcWMj4BNzY3FwYHBiIuAQEbAXIbNFw3UxUoxxk0Ujpjhn5esAj9qWs3eVAwFBsKiEpUXd%2BdYwKMTjlpMSdJ%2FjaR3dB7JkFLjv5%2FykYkHiYZIxlYgTc%2BOmAAAQACAAACSwX6ABQAAAEyFxUjBgcGHQEzFSMRIxEjNTM1NAH7SgVEURks2trWmJgF%2Bg%2BzARIhMbS1%2FJYDarXi%2BQAAAAADABT%2BPAPhBDQAOQBFAFwAAAAGIicGBwYVFBceBBcWFxYVECEiJyY1NDcuATU0Ny4BND4BMzIWFzY3PgEzFSMmBwYeARcWFAYABhQWMzI3NjQmJyYABhQeAxcWMj4DNzY0LgMnBgLdnaAzCRgvlxwOOiBHIKwwZP4UzYSNwDxSmjRYc8R5OHcZVmIOQA4EYSEKARYGETn%2BVnFnZYEnChQULf6oEhIaLSYdKFspRCw0DiREaGhdDGkBZDwUCRAgIy8SBAEBAQgHJytbfv7eREiVhEkIWT2AQxu3zKxcKxY%2BBAEBtAoRBQo%2BETiFkAGuaMVxjCNHRB9F%2FB8qLyEVEAcCAwEECRAMHFI2GQ4IBSoAAAEAdwAAA4YF6wASAAABESMRNCYiBgcRIxEzETYzMhcWA4bWV3h2HdbWgox%2BU1kDF%2FzpAt9IVUcj%2FO4F6%2F3EgkJHAAAAAgBbAAABTwVuAAMABwAAMxEzEQM1MxVq1uXzBB%2F74QR78%2FMAAv%2FD%2FqYBZQVuAA8AEwAABzI2NREzERQHDgMHBiMTNTMVPGRY1jcVI0AsKTVRpvKlQXoECfvaqkQaIRcMAwQF1fPzAAABAHwAAAOZBesACwAAMxEzEQEhCQEjAwcRfNYBMAEE%2Fs4BRPLmbgXr%2FKsBif56%2FWcB3Iv%2BrwAAAAEAfP%2F9AVMF6wADAAAXETMRfNYDBe76EgAAAAABAG4AAAXbBDEAHwAAATYzMhYXNjMyFxYVESMRNCYiBgcRIxE0JiIGBxEjETMBRIKfXpAilZN%2FXGPWaHt2HdZXeYob1tYDr4JKS5VCR5H86QLfR1ZHI%2FzuAt9IVUkh%2FO4EHwAAAQBuAAADfAQxABIAAAERIxE0JiIGBxEjETMVPgEzMhYDfNZXeHYd1tY6f1WCqAMX%2FOkC30hVRyP87gQfcDpIjQAAAAACADv%2F7QOcBDEADgAeAAABBhQeARcWMzIRNCcmIyIDNBIzMhcWERAFBiIuAScmARcGDB8YNmLZQDRlufjnyoZix%2F74SbuUYiE9Aok1emJiJFABbdZTRP6U%2BgEnRIv%2Brf5ZYBs2YEaCAAIAdv5mA9kEMQAOACAAAAEWMzI3NjU0JyYjIgcGBzU2MzIXFhUQBwYjIiYnESMRMwFNY3V6NS9COG5zTQ0BTJL8ckC8ZJxMchLW1gEKZ2lcuMtOQ2cSAc5h6YKj%2FoN4QD0b%2FiAFuQAAAgA7%2FmYDngQxAA8AIQAAATARJicmIyIHBhUUFxYzMhM3MxEjEQ4BIyInJhE0NzYzMgLHAQ1MdG44Qi81enVjEMbWEnFNnGS8QHL8kgEKAfgBEmdDTsu4XGkDLU%2F6RwHgGz1AeAF9o4LpAAAAAQBuAAAChgQxABAAADMRMxU2PwE2MzIXFQYHBgcRbtYEDhhKqhIStlAkGAQfkQUVJGUDswNiLEX9WwAAAQBD%2F%2B4DOAQxADAAAD8BFhcWMzI3NjU0JyYvASYnJjU0NzYzMhcWFwcmJyYiBhUUFxYfARYXFhQOASImJyZDlCMeR2xXJSIxLCteo0hJW2CzoXQyDXxGRiR1Uzs0NVrUMxVjoqVrMmHUWjIeRiQhQ0wmIQwbLk5Pgm9LUG4vIGJVFAs8OkMlIRAZPI86n4RIFBkxAAEAEf%2FrAmIFoAAbAAATNTMRMxEzFSMRFBYzMjY1FQ4BIi4DJyY1ERKd1t3dPUgdOzY7VSk7Ky4PIQNqtQGB%2Fn%2B1%2FcpVPwgBlxkOAgwVKBxAZgJyAAEAdv%2FuA4UEHwASAAATETMRFBYyNjcRMxEjNQYjIicmd9ZXd3cd1taCjH5TWQEIAxf9IUhVRyMDEvvhcIJCRwABAAUAAAOBBB8ABgAAEzMbATMBIwbi2d3i%2Frz4BB%2F9GALo%2B%2BEAAAAAAQAGAAAFIgQfAAwAACEjATMbATMbATMBIwMB%2Ftr%2B4t64nLict9%2F%2B4NqUBB%2F9VAKs%2FVYCqvvhAowAAAAAAQACAAADfwQfAAsAABMzGwEzCQEjCwEjAQLmw8zl%2Fr8BY%2Bbc0%2BcBRgQf%2FskBN%2F4W%2FcsBXv6iAhgAAAAAAQAC%2FmkDgwQfABcAABMzGwEzAQYHDgEHBicmMTcWMj4CNzY3Au7exu7%2BoDUjIzkhac0JCiV5SS0gCA8BBB%2F9hwJ5%2B8GkNDQ8EjsfAccQIDI9Hz0zAAAAAAEAAgAAAxcEHwAJAAAzNQEhNSEVASEVAgHa%2FlwCzP4qAeiJAuG1eP0bwgAAAQB7%2FsAC1AaGAC4AABMwNT4BNTQnLgE0NzYhFSIHDgEUFxYVFAYHHgEUBwYUFxYzFSInLgI0NzY1NCZ7cmAnDhkPQAGFl0cfHxQ6gGRkgDoUEDzQzXE1RB0zG2ACRbwBeXsyQBdbfTf4q0siSjwvi1B2shMTssaLLzwlkqtDIF5usFMsMnt5AAEAQP9zATAGGwADAAAXETMRQe%2BNBqj5WAAAAAABAEL%2BwAKaBoYALAAAAQ4BFRQXFhQGBwYhNTI3NjQnJjQ2Ny4BNTQ3NjQmJyYjNSAXFhQGBwYVFBYXAppxYRszLzVt%2Fv3PPRAUOoBkZIA6FB8fRpgBbk4YGA8nYXECRQF5ezQrUMOEMmirkiU8LozGshMTsnZQjC48SiJLq9xCjlsXPjR7eQEAAQAvBFgC8AViABMAAAEGIyImIgYHJz4BMhYXFjMyNzY3Au9HmFSDTygRgheFbk4YQDAmERYNBSDIWiopPFtsHREuFx8iAAAAAgAw%2F1cBJwT9AAMABwAABSMTMxMVIzUBJvMlqCT0qQRMAVrz8wAAAAIAY%2F8PA5AFBgAFAC0AAAAGEBYXEQA%2BAjc2NzUzFR4BFxYfAQc0JyYnET4BPwEXBwYHBgcVIzUmJyYnJgFhKCdD%2FsAKGDAiSYPWPmMaOAsFrC4RGCk9CQqeCC1zMzzWhElIFhUDTar%2B0aQZArP%2B0WZ6ai9jEN%2FaDUYqVzwbKEM3FQz9ORJVIiE5HI1RJQzk6RBnZHBuAAAAAQAYAAAEMgX7ADIAAAEUByEyNzY3FxQOBAcGIyE1PgE9ASM1MzUQNzY3NjMyFhcVJyIHDgIHBh0BIRUhAYIeATqeUBMLhwYKHyZFKWOJ%2FegQJIaGLzNacLIiawFmoTkiFQoCBQEW%2FuoB6OwdTREZlgEOEiogKA0ghCfcS3jcfAEObHQvPAwEtQM%2BJkc1JkBfbtwAAAIALQGPAuAEQgAHACMAAAAUFjI2NCYiAyY1NDcnNxc2MzIXNxcHFhUUBxcHJwYjIicHJwEAUHJQUHK9KSlmXWVEVlNBY19iKy1iXWNCUlRDZl8DInJQUHJQ%2FuJCU1JCZl1lLSljX2JDVVdEYl1jKCtmXwAAAQAEAAAEXwXrABYAABMzCQEzATMVIxUzFSMRIxEjNTM1IzUzBP4BLwEv%2Fv5B9fDw8Obc3NzhBev9ywI1%2FMWVdZX%2B7wERlXWVAAAAAAIAQP9zATAGGwADAAcAABMRMxEDETMRQe%2Fv7wMaAwH8%2F%2FxZAvn9BwACAET%2F7gM5BVkAOgBOAAABFhcWFA4BIiYnJic3FhcWMzI3NjU0JyYvASYnJjU0NyY1NDc2MzIXFhcHJicmIgYVFBcWHwEWFxYVFAEGFRQXFh8BFhcmJy4ELwEmAxUIBhVjoqRsMmFMlCMeR2xXJSIxLCteo0hJJydbYLOhdDINfEZGI3ZTOzQ1WtQzFf3oAjs0NVoqJAEFBxIlHDQQQjcBtBAROp%2BESBQZMYhaMh5GJCFDTCYhDBsuTk%2BCTDhEYG9LUG4vIGJVFAs8OkMlIRAZPI86TUYBNwoLQyUhEBkMDyISFh8cEBMFEg8AAAIAEASzAn8FpgADAAcAAAE1MxUhNTMVAYzy%2FZLyBLPz8%2FPzAAMAOwD8BBAE0AAcACwAOAAAAQYjIicmNTQ3NjMyFxYXByYnJiIHBhUUMzI3NjcEND4CMh4CFA4CIi4BEhQeATI%2BATQuASIGAvUymEgzYzY5b3I1EQZZDC4VPRkvcEIhBwH9pE6Etce1hE1NhLXHtYQbZ7LRsmdnstGyAn%2B9Kk%2BrdVRaciYeFUgfDh44d85TEg4IxraDTk6Dtsa2g05OgwGE0rJnZ7LSsmdnAAACAGQEFAHHBe8ABwAmAAATMjc1DgEVFBcGIiYnJjU0Njc0JyYiDgEHBgcnNjc2MhYdARQXIyb2Jz9QS5sxaTcNGnCIHhAuJBYIDAQ9GyYrjFANXQ0EYzdsEi8rNx4xGBQlK1VUGj4KBQ0QCg4KJjkWGUVc8CcaCAACAG4D0gNuBiYABQALAAATARcHFwcTARcHFwduAShmvL5qSgEoZry%2BagT8ASpsvsBqASoBKmy%2BwGoAAAABAE8BUwQgA1EABQAAEzUhESMRTwPQtAKbtv4CAUgAAAEAZQKaBAYDLwADAAATNSEVZQOgApqVlQAAAAAEADsA%2FAQQBNAABwAVACUAMQAAATI1NCYrARUDETMyFxYUBgcXIycjFSQ0PgIyHgIUDgIiLgESFB4BMj4BNC4BIgYCOEYxNjVn2XEiCC0sWnRdPf5ZToS1x7WETU2Etce1hBtnstGyZ2ey0bIDE0EgF3j%2B2QH%2BaBg%2BYBDQy8uXxraDTk6Dtsa2g05OgwGE0rJnZ7LSsmdnAAEAYwSzAsQFSAADAAATNSEVYwJhBLOVlQAAAAACAHADugKvBfgABwAPAAAAFBYyNjQmIgI0NjIWFAYiAQZQclBQcuap7aio7QUSclBQclD%2FAO6oqO6oAP%2F%2FACIAvQNsBTAQJwDUAAP%2BRBAHAA4AAwCmAAAAAQBeBB8BkQXyABgAABM0Nz4CNCYiBhUjNDYyFhQGBw4CBzMVXyQpgBwmSyJJVYJOExMcVEoC2wQfTTI3Uyo9JjQgQFFMSy8UHz1HEEYAAAABAGUEFgGaBe0AHwAAEzYzMhYUBgceARQGIyInNxYzMjU0JisBNTMyNjQmIgdxMWo0SicSGy1LQ2w6MDMvWi0iQEAfISVVLQWoRUpOQQgJRFxNUSc7SR0yPiw2JTAAAAABAG8ERwIrBe8AAwAACQEnAQIq%2FpxXAUcFdP7TVgFSAAEAdv5mA4UEHwASAAABIxEzERQWMjY3ETMRIzUGIyInAU3W1ld3dx3W1oKMLCj%2BZgW5%2FSFIVUcjAxL74XCCCAADADQAAAQBBesAAwANACEAAAEzESMFBhQeARcWFxEGBzQ%2BAzc2MyERIxEjESMRIicmApOKiv6SAgYTDiNCfvwKGylCKluJAi7kiuTMbz8DLQH1uBk4OEYcQQIB1wziMk5YT0oaO%2FoVAmP9nQJj0XcAAQAyAugBJwPbAAMAABM1MxUz9ALo8%2FMAAQAG%2FqUBIwAAAAsAADMWFRQHBic1MjU0J8NgcEJqfThgd1MiFAWYLlBFAAABAHQEHwFQBesACgAAEzczETMVIzUzEQd1YzVD1Uo7Bcgj%2FnlFRQFCEQAAAAIAXwQYAdgF9AAHAA8AABIyNTQmIgYVBCA1NDYyFhW9vilrKgEb%2FohjsGUEZ59NUlJN7u5tgYJsAAAAAAIAbgPSA24GJgAFAAsAAAkBJzcnNwMBJzcnNwNu%2FthmvL5qSv7YZry%2BagT8%2FtZsvsBq%2Ftb%2B1my%2BwGoAAAQAOv%2FRBE4GEgACAA0AGAAcAAABMxEBNQEzETMVIxUjNQE3MxEzFSE1MxEHEwEzAQKpuv7FATt3c3N3%2FNejV23%2BonlgFwLsnv0UARMBIP58YQHj%2FiBkr68FAzn9gHNzAhAc%2BnIGQfm%2FAAAAAAMAOv%2FRBFoGEgAKAA4AKwAAEzczETMVITUzEQcTATMBJTQ%2BATc%2BAjQmIgYHBhUjNDYyFhUUBw4CByEVOqNXbf6ieWAXAuye%2FRQBUiEnHEKqLz9bMw0Yd43VfoIUXnsEAWgFsjn9gHNzAhAc%2BnIGQfm%2FIllLPxxEb0VkPhsXKS9phXtRdGYQRXUacwAABAA9%2F9EEnAYSAAIADQARADQAAAEzEQE1ATMRMxUjFSM1BQEzCQE3FjMyNjQmKwE1MzI2NCYjIgcnPgEyFhQGBx4BFAYHBiMiAvi6%2FsUBO3dzc3f9EALsnv0U%2Ft5PSGs6R0s2aWkyNz4yV0pHIoere0EdK0seHD5xtAETASD%2BfGEB4%2F4gZK%2Bv3gZB%2Bb8DtUBhPWlUZkhZPE5BMUB6f2wNDnBoSx9DAAAAAgBKAAADIgX2AAMAIQAAARUjNQEGBwYiLgE0PgM3Nj0BMxUUBwYPAQ4BFBYzMjcCg%2FMBkkdYUdepZyU8SEkeQ9JCHSNGVC1fQnhFBfbz8%2Fr3jDMuYq%2BleGFeUyZWP6SXZ2QsLFZkcXl8kQD%2F%2FwACAAAEaAgpECcAxwBFAjsSBgAkAAD%2F%2FwACAAAEaAgpECcAyAF5AjoSBgAkAAD%2F%2FwACAAAEaAfYECcAxAD%2FAhMSBgAkAAD%2F%2FwACAAAEaAeLECcAxgI0BgoSBgAkAAD%2F%2FwACAAAEaAd0ECcAaQDvAc4SBgAkAAAAAwACAAAEaAfxABEAGQAcAAASJjYyFhQHBgcBIwMhAyMBJicSFBYyNjQmIgMhA%2FkBqe2oVBMVAa7nff5zffgBpjIpQVByUFByLgEbjQZc7Kmp7FUTD%2FobAbf%2BSQXJFSkBBHJQUHJQ%2ByUB6QAAAgACAAAGOQXrAAMAEwAAAREjAxchAyMBIRUhESEVIREhFSECrznB%2Bv7Nff0BsARg%2FYwBtf5LApr8dwKAAqL9Xsn%2BSQXryf5dyv4i1wAAAAABAD%2F%2BlgR4BgcANwAAEyY0PgI3NjMyFxYXFQcuAicmIg4BBwYVEBcWMj4DNzY1FwYHBgcWFRQHBic1MjU0JyYnJlAQHjtfPYLB9Y5FGNAQLCwaPp1wSBgs4DBsVTotGwgP3y1wd6tZcEFrfTHkkVsCOl7AurGVOHf1d34BOGtjQxYzR3NNjrL%2BKFwUHC0%2BPyE6Mh%2FAjZQXXnJUIhQGmC5KQhbijQAAAP%2F%2FAHsAAAQFCCkQJwDHAD0COxIGACgAAP%2F%2FAHsAAAQFCCkQJwDIAXECOhIGACgAAP%2F%2FAHsAAAQFB9gQJwDEAPYCExIGACgAAP%2F%2FAHsAAAQFB3QQJwBpAOcBzhIGACgAAP%2F%2F%2F5MAAAFSCCkQJwDH%2FuoCOxIGACwAAP%2F%2FAGMAAAJJCCkQJwDIAB4COhIGACwAAP%2F%2F%2F%2BgAAAHaB9gQJwDE%2F6QCExIGACwAAP%2F%2F%2F6UAAAITB3QQJwBp%2F5QBzhIGACwAAAACAAkAAASPBesAEQAhAAAlMzI%2BATc2NC4BJyYrAREhFSkBNTMRISATFhAOAQcGKQERAV%2BQZ6BbHTIXPC9pxpABI%2F7d%2FqpnAWUCJXAlLGdOqv7R%2FpvJTHJNgPGblzV2%2Fh%2B1tQKq%2FkeP%2FsrWwESTAoz%2F%2FwBzAAAEhQeLECcAxgJ7BgoSBgAxAAD%2F%2FwA%2F%2F%2BYEuwgpECcAxwCZAjsSBgAyAAD%2F%2FwA%2F%2F%2BYEuwgpECcAyAHNAjoSBgAyAAD%2F%2FwA%2F%2F%2BYEuwfYECcAxAFSAhMSBgAyAAD%2F%2FwA%2F%2F%2BYEuweLECcAxgKIBgoSBgAyAAD%2F%2FwA%2F%2F%2BYEuwd0ECcAaQFDAc4SBgAyAAAAAf%2F6AR0DigSsAAsAAAM3CQEXCQEHCQEnAQaYATABMJj%2B0AEumP7S%2FtCYATAEFJj%2B0AEwl%2F7Q%2FtCYATD%2B0JcBMAAAAAADADn%2FsgS0BjQADAAsADgAAAAWFxYXASYjIgcGBwYCJjQ%2BAzc2MzIXNxcHFhcWFA4DIicmJwcnNycmCQEWMzI2NzY0JicmASgMDgUGAeFGaIpOTRka0h0SKj5fO3%2B2pHNJuW1bHwwfUXvI5Fc2LU%2B3bgkxAxP%2BJkdviZEWFQcJBwKve0QaFwM%2BQWdkhYP%2BfLGjmJ2MfS1hUn9rvZXZV8XP0pphIhQfiW2%2BDkwC6PzNPcKAgbVmPC3%2F%2FwB7%2F%2BYEYAgpECcAxwB9AjsSBgA4AAD%2F%2FwB7%2F%2BYEYAgpECcAyAGxAjoSBgA4AAD%2F%2FwB7%2F%2BYEYAfYECcAxAE3AhMSBgA4AAD%2F%2FwB7%2F%2BYEYAd0ECcAaQEnAc4SBgA4AAD%2F%2FwACAAAEYQgpECcAyAF1AjoSBgA8AAAAAgB9AAAETAXrAAkAGAAAATMgNzY0JyYrAQMRMxEhIBcWFRQHBiMhEQFtigEASRsvWdyK8PABGgEIe0GUcMD%2B5gIHiTOVOGn8BwXr%2FtfLbHzrg2P%2BwgAAAAEAff%2FuBHAF%2BwBGAAAAPgE1NCYiDgUHBhURIxE0PgM3NjIeARQGBwYHBhQeBB8BFhcWFA4BIiYnJic3FhcWMzI3NjU0JyYvASYnJgGHaeVRZzotIRcPCQID5BsfLkUsad6gYl5eYQ8eEBYrIkATONQzFWOipGszYE2UJB5GbFclIjEsK16jSEkDhH99TTQ4DRErI0w3OE9%2F%2FLwDcvV7ZTpCESdRi7eaLC4UJ0AnHBwRFgUQO5A6n4RIFBkxiFoyHkYkIUNMJiENGi5OTwAAAP%2F%2FAEb%2F6wNzBlMQJgDH%2F2USBgBEAAAAAP%2F%2FAEb%2F6wOLBh0QJwB1AWAALhIGAEQAAP%2F%2FAEb%2F6wNzBd4QJwBBALoAOBIGAEQAAP%2F%2FAEb%2F6wNzBasQJgBhWEkSBgBEAAAAAP%2F%2FAEb%2F6wNzBZcQJwBpAJr%2F8RIGAEQAAP%2F%2FAEb%2F6wNzBqMQJwBxAHIAqxIGAEQAAAADADn%2F6wWmBDEACABGAFUAAAEhNCcmIyIHBgEyFzYzMhcWAyEUFxYyPgE3NjcXBgcGIyAnMAYPAQYPAQYjIicmNDY3NiU0LgMnJiIOAQcGByc%2BAjc2AxQzMj4BPwE2PwE1BgcGA1MBchs0XHszGf6PzVJpssVnYQb9pms3eVAwFBoLiEdXXHv%2B%2BXAQBxUNDyNajMJJJD1HgAE0CQsQGBAnX1MxFBsKjCw%2FPyNUaHotPiAUHQgSEuBFPQKOTjhodDoBYGdqpZr%2B5ctFJB4mGiAbWH86PcQaCRsSDh5LiUS1jTVeOkcgHA8SBAodJBggGFZbPSwNH%2FzygB8REBcHEBH3NDw2AAEAO%2F6eA2oEMQAmAAATEDc2IBcWFwcmJyYjIhEUFjMyNzY3FwYHBgcWBxQHBic1MjU0JyQ7iWwBhW4kDrUIOyg76Xp1UjMsFKQXS1N3WgFvQmt%2BMf50AggBOoVqpDUuUFIwIP6Vs7s8NDtIWVBYEl5yVCIUBpguSkEeAP%2F%2FADv%2F7gNtBlMQJgDH9GUSBgBIAAAAAP%2F%2FADv%2F7gNtBlMQJwDIASgAZBIGAEgAAP%2F%2FADv%2F7gNtBgIQJwDEAK4APRIGAEgAAP%2F%2FADv%2F7gNtBZ4QJwBpAJ7%2F%2BBIGAEgAAP%2F%2F%2F4UAAAFBBlMQJwDH%2FtwAZRIGAMEAAP%2F%2FAGAAAAI6BlMQJgDIEGQSBgDBAAAAAP%2F%2F%2F9kAAAHLBgIQJgDElT0SBgDBAAAAAP%2F%2F%2F5YAAAIFBZ4QJgBphvgSBgDBAAAAAAACADv%2F7gOcBesADgAwAAABBhQeARcWMzIRNCcmIyIDNBIzMhYXLgEnByc3LgEnMxYXNxcHEhMWFA4BBwYjICcmARYFDSAYNWDaQDRmvPTnyUk6GQQyDLNGtihzEM8kQaFGjacNAh1CMmm2%2FvdoPwKEM3tmYCJLAWzRVkb%2BlPoBJxAJGIIUZndqNWwJE0lcd1L%2B7f5xO6ybgy1g24MAAAD%2F%2FwBuAAADfAW1ECcAxgH0BDQSBgBRAAD%2F%2FwA7%2F%2B0DnAZTECYAx%2FxlEgYAUgAAAAD%2F%2FwA7%2F%2B0DnAZTECcAyAEwAGQSBgBSAAD%2F%2FwA7%2F%2B0DnAYCECcAxAC2AD0SBgBSAAD%2F%2FwA7%2F%2B0DnAW1ECcAxgHrBDQSBgBSAAD%2F%2FwA7%2F%2B0DnAWeECcAaQCm%2F%2FgSBgBSAAAAAwAfAPoDaQTMAAMABwALAAATNSEVATUzFQM1MxUgA0n94%2FTy9AJ519f%2BgfT0At709AAAAAMAO%2F%2FAA5wEaAAIABEAJAAAASYiDgEHBhQXCQEWMj4BNzY0ASc3JhASMzIXNxcHFhUQBQYiJwIwHlhNMQ8cCgGe%2FtsnZE8vDxn%2BQLpDU%2BfKV0owukJm%2FvhJx1ADcwkjPS9UzDoBIP4EFSxHMVbV%2FU9rdIgB4wEnHlVrcpLq%2FllgGyIA%2F%2F8Adv%2FuA4UGUxAmAMcOZRIGAFgAAAAA%2F%2F8Adv%2FuA4UGUxAnAMgBQgBkEgYAWAAA%2F%2F8Adv%2FuA4UGAhAnAMQAxwA9EgYAWAAA%2F%2F8Adv%2FuA4UFnhAnAGkAuP%2F4EgYAWAAA%2F%2F8AAv5pA4MGUxAnAMgBBgBkEgYAXAAAAAIAdv5nA8sF6wAOACQAACUWMzI3NjU0JyYiBgcGBwMRMxE2NzYyHgEXFhUQBwYjIi4BJxEBTThnmD4zjjRkMhQfHdbWDSdMrotgI0KGZ7Q2aToE50RyW7D6SBoPDxci%2B0IHhP3aFx04PmdEhKH%2B35x4JioN%2FhwA%2F%2F8AAv5pA4MFnhAmAGl8%2BBIGAFwAAAAAAAEAYAAAATcEHwADAAAzETMRYdYEH%2FvhAAIAPgAABo8F6wAKACIAACURIyIHBhEQFxYzFyInJicmND4DNzYzIRUhESEVIREhFQMGgKdZWOsxPAL4nYcjChIqPl87frcD4f2MAbX%2BSwKa1wRLlZb%2B%2Bv5KUxHXvaH9SpaVmIZ3KlzJ%2Fl3K%2FiLXAAADADv%2F7QX4BDEACAApADgAAAEhNCcmIyIHBgU0EiAXNjMyFxYDIRQXFjI%2BATc2NxcGBwYjIicGIyAnJhMGFB4BFxYzMhE0JyYjIgOlAXIbNFx7Mxn8lucBlXVs1sVnYQb9pms3eVAwFBoLiEdWXXvgcW3Y%2FvNnPdwGDB8YNmLZQDRluQKOTjhodDq%2B%2BgEnmZmlmv7ly0UkHiYaIBtYfzo9jI3cggE%2BNXpiYiRQAW3WU0QAAP%2F%2FAEQEbgI2BcUSBgBB%2Fx8AAv7EAGABAwKeAAcADwAAAhQWMjY0JiICNDYyFhQGIqZQclBQcuap7aio7QG4clBQclD%2FAO6oqO6oAAD%2F%2F%2F6CAHcBQwGBEAcAYf5T%2FB8AAP%2F%2FAKkERgJlBe4QBgBDDwD%2F%2FwBvBEcCKwXvEgYAdQAAAAEAAAHoBAACnQADAAARIRUhBAD8AAKdtQAAAAABAAAB6AgAAp0AAwAAESEVIQgA%2BAACnbUAAAD%2F%2FwAsA%2BgBJgXVEgYACgAAAAIARgQWAfsFpgAIABEAAAE1MxUjFyMuASU1MxUjFyMuAQFPlkFXLANi%2FtyWQVcsA2IFEJaW%2BgO%2BOZaW%2BgO%2BAP%2F%2FABYD6AJIBdUSBgAFAAD%2F%2FwAY%2F9ECSgG%2BEAcAzQAC%2B%2BkAAAABAFwCAQGfA0QABwAAEiY0NjIWFAa4XF2FYWECAWKBYGJ%2BY%2F%2F%2FADAAWAPQBXgSBgAfAAD%2F%2FwAwAFgD0AV4EgYAIQAAAAEARv%2FRA9AGEgADAAAXATMBRgLsnv0ULwZB%2Bb8AAAAAAQAS%2F%2BYEvAYHAD8AABM1MzY3PgE3NjMyFxYXFQcuAicmIg4BBwYHIRUhBhUUFyEVIRIXFjI%2BAzc2NRcGBwYjICcmJyM1MyY1NDcThQ4ZHl89gsHah2Mc0BAsLBs%2BnW9IGA8KAXj%2BdgECAYn%2Bhi2iMGxVOi0bCA%2FfLXCO1%2F74olAkiXQDAgM8lU5LWZU4d8GNnAE4a2NDFjNHc00xNZUiIywplf74QhQcLT4%2FIToyH8CNsPt9r5UrKyIiAAABAB8CeQNpA1AAAwAAEzUhFSADSQJ519cAAAD%2F%2FwAS%2F58DzgZZEgYAEgAJAAYAXP%2F7BQoCFAAkADAAPwBDAE8AXwAAJTcWFxY3Mjc2JzQnJiIGBwYHIxEhFSMVPgEyHgIUDgEjIicmASIQMzY3NjQnJicmAiY0Njc2MzIXFhQOASImBTUzFQMiEDM2NzY0JyYnJgImNDY3NjMyFxYVFAcGIiYCNDcMHBogJBseASUTKB4JFAJHARvHAis0Oy0bK08ySjMZAih7e0oWEwQEDBjkERESKWJ%2FIAgbTm5H%2Fd008nt7ShYTBAQMGOQSERMqYT0nQlUiX0dnKCAXFwEcID1BGw4MCBINASJHcAUJGS1CX1gxNhsBnf4%2BAUA4kyUlI0n%2BkFdmWylasC55b1IuKjQ0Aen%2BPgFAOJMlJSNJ%2FpBYZFwpWixKnLg4Fi4AAA4ASQAAA4cCtQADAAcACwAPABMAFwAbAB8AIwAnACsALwAzADcAAAERMxEDETMRATUhFQE1IRUBNSEVAREzEQMRMxEDETMRAxEzEQE1IRUBNSEVATUhFQERMxEDETMRA2odHR3%2BrwE0%2FswBNP7MATT%2Brx0dHZsdHR3%2BrgE1%2FssBNf7LATX%2Brh0dHQFrASr%2B1v60ASr%2B1gJ5HR3%2BtB0d%2FrQdHQFrASr%2B1v60ASr%2B1gFMASr%2B1v60ASr%2B1gJ5HR3%2BtB0d%2FrQdHQFrASr%2B1v60ASr%2B1gAAAv%2F5AAADwwX6AAcAIQAAASYGHQEzNTQlIh0BMxUjESMRIxEjESM1MzU0ITIXNjIXFQIYO3asAWyW2trWrNaYmAF1VDlV1AcFOBA2P7TiGxxltLX8lgNq%2FJYDarXi%2BScnD7MAAAH%2F%2BQAAAzkF%2BgAZAAATNCEyFxUjNSYiDgIdASERIxEjESMRIzUzkQF1db7zCR5NOzABxNbu1piYBQH5D%2FNAAQcRLSG0%2B%2BEDavyWA2q1AAAAAv%2F5%2F%2F0DKwX6AAQAFAAAASYdATMlNCE2HwERIxEjESMRIzUzAlXu7v48AXVVnzHW7taYmAU4BWq04vkCDQT6EgNt%2FJYDarUAAv%2F5AAAEuwX6AAYAJwAAASIdATM1NCcyFzYgFxUjNSYiDgIdASERIxEjESMRIxEjESM1MzU0AhixrA1VN14BDb7zCR5NOzABxNbu1qzWmJgFOGW04hveJycP80ABBxEtIbT74QNq%2FJYDavyWA2q14vkAAAAD%2F%2Fn%2F%2FQStBfoABQAgACgAAAEiHQEzEQU0ITIXNj8BNh4CMxEjESMRIxEjESMRIzUzASYGHQEzNTQDrMPu%2FLoBdWYnLkVgOK8dQgHW7tas1piYAYc7dqwFOGW0ARk3%2BScXBAYDBwEE%2BhIDbfyWA2r8lgNqtQEZEDY%2FtOIbAAAAAAEAAADdAGAADgBpAAcAAgAAAAEAAQAAAEAAAAAEAAMAAAAUABQAFAAUACgANgBmAMQBAgFiAWwBmgHIAeYB%2FgIQAhwCKAI4AngCjgLK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qtCF6UlrE5GXRmbu%2Bvtm3BFSxI6ND6UxIE7GsGMgWqghXxSnaRJuGFveTcK5ZVSPJyjUxe1dKgI6kNF7EZhIZs8y8FVqwEfbM0Xk2ltORVDKZZM40SD3qQoQe0orJEKwPfZwm3YPqwixhUMOndis6MhbmfvLBKjC8sKKIZKbJk8L11oNkCQzCgvjhyyEiQSuJcgCQSG4Mocfgc0Hkwcjal1UNgP0CBPikYqBIk9tONv4kLtBswH07vUCjEaHiFGlLf8MgXKzSgjp2HolRRccAOh0ILHz9qlGgIFkwAnzHJRjWFhlA7ROwINyB5HFj59PRZHFor6voq7l23EPNRwdWhgawqbivLSjRA4htEYUFkjESu67icTg5S0aW1sOkCiIysfJ9UnIWevOOLGpepcBxy1wEhd2WI3AZg7sr9WBmHWyasxMcvY%2FiOmsLtHSWNUWEGk9hScMPShasUA1AcHOtRZlqMeQ0OzYS9vQvYUjOLrzP07BUAFikcJNMi7gIxEw4pL1G54TcmmmoAQ5s7TGWErJZ2Io4yQ0ljRYhL8H5e62oDtLF8aDpnIvZ5R3GWJyAugdiiJW9hQAVTsnCBHhwu7rkBlBX6r3b7ejEY0k5GGeyKv66v%2B6dg7mcJTrWHbtMywbedYqCQ0FPwoytmSWsL8WTtChZCKKzEF7vP6De4x2BJkkniMgSdWhbeBSLtJZR9CTHetK1xb34AYIJ37OegYIoPVbXgJ%2FqDQK%2BbfCtxQRVKQu77WzOoM6SGL7MaZwCGJVk46aImai9fmam%2BWpHG%2B0BtQPWUgZ7RIAlPq6lkECUhZQ2gqWkMYKcYMYaIc4gYCDFHYa2d1nzp3%2BJ1eCBay8IYZ0wQRKGAqvCuZ%2FUgbQPyllosq%2BXtfKIZOzmeJqRazpmmoP%2F76YfkjzV2NlXTDSBYB04SVlNQsFTbGPk1t%2FI4Jktu0XSgifO2ozFOiwd%2F0SssJDn0dn4xqk4GDTTKX73%2FwQyBLdqgJ%2BWx6AQaba3BA9CKEzjtQYIfAsiYamapq80LAamYjinlKXUkxdpIDk0puXUEYzSalfRibAeDAKpNiqQ0FTwoxuGYzRnisyTotdVTclis1LHRQCy%2FqqL8oUaQzWRxilq5Mi0IJGtMY02cGLD69vGjkj3p6pGePKI8bkBv5evq8SjjyU04vJR2cQXQwSJyoinDsUJHCQ50jrFTT7yRdbdYQMB3MYCb6uBzJ9ewhXYPAIZSXfeEQBZZ3GPN3Nbhh%2FwkvAJLXnQMdi5NYYZ5GHE400GS5rXkOZSQsdZgIbzRnF9ueLnsfQ47wHAsirITnTlkCcuWWIUhJSbpM3wWhXNHvt2xUsKKMpdBSbJnBMcihkoDqAd1Zml%2FR4yrzow1Q2A5G%2Bkzo%2FRhRxQS2lCSDRV8LlYLBOOoo1bF4jwJAwKMK1tWLHlu9i0j4Ig8qVm6wE1DxXwAwQwsaBWUg2pOOol2dHxyt6npwJEdLDDVYyRc2D0HbcbLUJQj8gPevQBUBOUHXPrsAPBERICpnYESeu2OHotpXQxRGlCCtLdIsu23MhZVEoJg8Qumj%2FUMMc34IBqTKLDTp76WzL%2FdMjCxK7MjhiGjeYAC%2Fkj%2FjY%2FRde7hpSM1xChrog6yZ7OWTuD56xBJnGFE%2BpT2ElSyCnJcwVzCjkqeNLfMEJqKW0G7OFIp0G%2B9mh50I9o8k1tpCY0xYqFNIALgIfc2me4n1bmJnRZ89oepgLPT0NTMLNZsvSCZAc3TXaNB07vail36%2FdBySis4m9%2FDR8izaLJW6bWCkVgm5T%2Bius3ZXq4xI%2BGnbveLbdRwF2mNtsrE0JjYc1AXknCOrLSu7Te%2Fr4dPYMCl5qtiHNTn%2BTPbh1jCBHH%2BdMJNhwNgs3nT%2BOhQoQ0vYif56BMG6WowAcHR3DjQolxLzyVekHj00PBAaW7IIAF1EF%2BuRIWyXjQMAs2chdpaKPNaB%2BkSezYt0%2BCA04sOg5vx8Fr7Ofa9sUv87h7SLAUFSzbetCCZ9pmyLt6l6%2FTzoA1%2FZBG9bIUVHLAbi%2FkdBFgYGyGwRQGBpkqCEg2ah9UD6EedEcEL3j4y0BQQCiExEnocA3SZboh%2Bepgd3YsOkHskZwPuQ5OoyA0fTA5AXrHcUOQF%2BzkJHIA7PwCDk1gGVmGUZSSoPhNf%2BTklauz98QofOlCIQ%2FtCD4dosHYPqtPCXB3agggQQIqQJsSkB%2Bqn0rkQ1toJjON%2FOtCIB9RYv3PqRA4C4U68ZMlZn6BdgEvi2ziU%2BTQ6NIw3ej%2BAtDwMGEZk7e2IjxUWKdAxyaw9OCwSmeADTPPleyk6UhGDNXQb%2B%2BW6Uk4q6F7%2Frg6WVTo82IoCxSIsFDrav4EPHphD3u4hR53WKVvYZUwNCCeM4PMBWzK%2BEfIthZOkuAwPo5C5jgoZgn6dUdvx5rIDmd58cXXdKNfw3l%2BwM2UjgrDJeQHhbD7HW2QDoZMCujgIUkk5Fg8VCsdyjOtnGRx8wgKRPZN5dR0zPUyfGZFVihbFRniXZFOZGKPnEQzU3AnD1KfR6weHW2XS6KbPJxUkOTZsAB9vTVp3Le1F8q5l%2BDMcLiIq78jxAImD2pGFw0VHfRatScGlK6SMu8leTmhUSMy8Uhdd6xBiH3Gdman4tjQGLboJfqz6fL2WKHTmrfsKZRYX6BTDjDldKMosaSTLdQS7oDisJNqAUhw1PfTlnacCO8vl8706Km1FROgLDmudzxg%2BEWTiArtHgLsRrAXYWdB0NmToNCJdKm0KWycZQqb%2BMw76Qy29iQ5up%2FX7oyw8QZ75kP5F6iJAJz6KCmqxz8fEa%2FxnsMYcIO%2FvEkGRuMckhr4rIeLrKaXnmIzlNLxbFspOphkcnJdnz%2FChp%2FVlpj2P7jJQmQRwGnltkTV5dbF9fE3%2FfxoSqTROgq9wFUlbuYzYcasE0ouzBo%2BdDCDzxKAfhbAZYxQiHrLzV2iVexnDX%2FQnT1fsT%2Fxuhu1ui5qIytgbGmRoQkeQooO8eJNNZsf0iALur8QxZFH0nCMnjerYQqG1pIfjyVZWxhVRznmmfLG00BcBWJE6hzQWRyFknuJnXuk8A5FRDCulwrWASSNoBtR%2BCtGdkPwYN2o7DOw%2FVGlCZPusRBFXODQdUM5zeHDIVuAJBLqbO%2Ff9Qua%2BpDqEPk230Sob9lEZ8BHiCorjVghuI0lI4JDgHGRDD%2FprQ84B1pVGkIpVUAHCG%2Biz3Bn3qm2AVrYcYWhock4jso5%2BJ7HfHVj4WMIQdGctq3psBCVVzupQOEioBGA2Bk%2BUILT7%2BVoX5mdxxA5fS42gISQVi%2FHTzrgMxu0fY6hE1ocUwwbsbWcezrY2n6S8%2F6cxXkOH4prpmPuFoikTzY7T85C4T2XYlbxLglSv2uLCgFv8Quk%2FwdesUdWPeHYIH0R729JIisN9Apdd4eB10aqwXrPt%2BSu9mA8k8n1sjMwnfsfF2j3jMUzXepSHmZ%2FBfqXvzgUNQQWOXO8YEuFBh4QTYCkOAPxywpYu1VxiDyJmKVcmJPGWk%2Fgc3Pov02StyYDahwmzw3E1gYC9wkupyWfDqDSUMpCTH5e5N8B%2F%2FlHiMuIkTNw4USHrJU67bjXGqNav6PBuQSoqTxc8avHoGmvqNtXzIaoyMIQIiiUHIM64cXieouplhNYln7qgc4wBVAYR104kO%2BCvKqsg4yIUlFNThVUAKZxZt1XA34h3TCUUiXVkZ0w8Hh2R0Z5L0b4LZvPd%2Fp1gi%2F07h8qfwHrByuSxglc9cI4QIg2oqvC%2Fqm0i7tjPLTgDhoWTAKDO2ONW5oe%2B%2FeKB9vZB8K6C25yCZ9RFVMnb6NRdRjyVK57CHHSkJBfnM2%2Fj4ODUwRkqrtBBCrDsDpt8jhZdXoy%2F1BCqw3sSGhgGGy0a5Jw6BP%2FTExoCmNFYjZl248A0osgPyGEmRA%2BfAsqPVaNAfytu0vuQJ7rk3J4kTDTR2AlCHJ5cls26opZM4w3jMULh2YXKpcqGBtuleAlOZnaZGbD6DHzMd6i2oFeJ8z9XYmalg1Szd%2FocZDc1C7Y6vcALJz2lYnTXiWEr2wawtoR4g3jvWUU2Ngjd1cewtFzEvM1NiHZPeLlIXFbBPawxNgMwwAlyNSuGF3zizVeOoC9bag1qRAQKQE%2FEZBWC2J8mnXAN2aTBboZ7HewnObE8CwROudZHmUM5oZ%2FUgd%2FJZQK8lvAm43uDRAbyW8gZ%2BZGq0EVerVGUKUSm%2FIdn8AQHdR4m7bue88WBwft9mSCeMOt1ncBwziOmJYI2ZR7ewNMPiCugmSsE4EyQ%2BQATJG6qORMGd4snEzc6B4shPIo4G1T7PgSm8PY5eUkPdF8JZ0VBtadbHXoJgnEhZQaODPj2gpODKJY5Yp4DOsLBFxWbvXN755KWylJm%2BoOd4zEL9Hpubuy2gyyfxh8oEfFutnYWdfB8PdESLWYvSqbElP9qo3u6KTmkhoacDauMNNjj0oy40DFV7Ql0aZj77xfGl7TJNHnIwgqOkenruYYNo6h724%2BzUQ7%2BvkCpZB%2BpGA562hYQiDxHVWOq0oDQl%2FQsoiY%2BcuI7iWq%2FZIBtHcXJ7kks%2Bh2fCNUPA82BzjnqktNts%2BRLdk1VSu%2BtqEn7QZCCsvEqk6FkfiOYkrsw092J8jsfIuEKypNjLxrKA9kiA19mxBD2suxQKCzwXGws7kEJvlhUiV9tArLIdZW0IORcxEzdzKmjtFhsjKy%2F44XYXdI5noQoRcvjZ1RMPACRqYg2V1%2BOwOepcOknRLLFdYgTkT5UApt%2FJhLM3jeFYprZV%2BZow2g8fP%2BU68hkKFWJj2yBbKqsrp25xkZX1DAjUw52IMYWaOhab8Kp05VrdNftqwRrymWF4OQSjbdfzmRZirK8FMJELEgER2PHjEAN9pGfLhCUiTJFbd5LBkOBMaxLr%2FA1SY9dXFz4RjzoU9ExfJCmx%2FI9FKEGT3n2cmzl2X42L3Jh%2BAbQq6sA%2BSs1kitoa4TAYgKHaoybHUDJ51oETdeI%2F9ThSmjWGkyLi5QAGWhL0BG1UsTyRGRJOldKBrYJeB8ljLJHfATWTEQBXBDnQexOHTB%2BUn44zExFE4vLytcu5NwpWrUxO%2F0ZICUGM7hGABXym0V6ZvDST0E370St9MIWQOTWngeoQHUTdCJUP04spMBMS8LSker9cReVQkULFDIZDFPrhTzBl6sed9wcZQTbL%2BBDqMyaN3RJPh%2Fanbx%2BIv%2BqgQdAa3M9Z5JmvYlh4qop%2BHo1F1W5gbOE9YKLgAnWytXElU4G8GtW47lhgFE6gaSs%2Bgs37sFvi0PPVvA5dnCBgILTwoKd%2F%2BDoL9F6inlM7H4rOTzD79KJgKlZO%2FZgt22UsKhrAaXU5ZcLrAglTVKJEmNJvORGN1vqrcfSMizfpsgbIe9zno%2BgBoKVXgIL%2FVI8dB1O5o%2FR3Suez%2FgD7M781ShjKpIIORM%2FnxG%2BjjhhgPwsn2IoXsPGPqYHXA63zJ07M2GPEykQwJBYLK808qYxuIew4frk52nhCsnCYmXiR6CuapvE1IwRB4%2FQftDbEn%2BAucIr1oxrLabRj9q4ae0%2BfXkHnteAJwXRbVkR0mctVSwEbqhJiMSZUp9DNbEDMmjX22m3ABpkrPQQTP3S1sib5pD2VRKRd%2BeNAjLYyT0hGrdjWJZy24OYXRoWQAIhGBZRxuBFMjjZQhpgrWo8SiFYbojcHO8V5DyscJpLTHyx9Fimassyo5U6WNtquUMYgccaHY5amgR3PQzq3ToNM5ABnoB9kuxsebqmYZm0R9qxJbFXCQ1UPyFIbxoUraTJFDpCk0Wk9GaYJKz%2F6oHwEP0Q14lMtlddQsOAU9zlYdMVHiT7RQP3XCmWYDcHCGbVRHGnHuwzScA0BaSBOGkz3lM8CArjrBsyEoV6Ys4qgDK3ykQQPZ3hCRGNXQTNNXbEb6tDiTDLKOyMzRhCFT%2BmAUmiYbV3YQVqFVp9dorv%2BTsLeCykS2b5yyu8AV7IS9cxcL8z4Kfwp%2BxJyYLv1OsxQCZwTB4a8BZ%2F5EdxTBJthApqyfd9u3ifr%2FWILTqq5VqgwMT9SOxbSGWLQJUUWCVi4k9tho9nEsbUh7U6NUsLmkYFXOhZ0kmamaJLRNJzSj%2Fqn4Mso6zb6iLLBXoaZ6AqeWCjHQm2lztnejYYM2eubnpBdKVLORZhudH3JF1waBJKA9%2BW8EhMj3Kzf0L4vi4k6RoHh3Z5YgmSZmk6ns4fjScjAoL8GoOECgqgYEBYUGFVO4FUv4%2FYtowhEmTs0vrvlD%2FCrisnoBNDAcUi%2FteY7OctFlmARQzjOItrrlKuPO6E2Ox93L4O%2F4DcgV%2FdZ7qR3VBwVQxP1GCieA4RIpweYJ5FoYrHxqRBdJjnqbsikA2Ictbb8vE1GYIo9dacK0REgDX4smy6GAkxlH1yCGGsk%2BtgiDhNKuKu3yNrMdxafmKTF632F8Vx4BNK57GvlFisrkjN9WDAtjsWA0ENT2e2nETUb%2Fn7qwhvGnrHuf5bX6Vh%2Fn3xffU3PeHdR%2BFA92i6ufT3AlyAREoNDh6chiMWTvjKjHDeRhOa9YkOQRq1vQXEMppAQVwHCuIcV2g5rBn6GmZZpTR7vnSD6ZmhdSl176gqKTXu5E%2BYbfL0adwNtHP7dT7t7b46DVZIkzaRJOM%2BS6KcrzYVg%2BT3wSRFRQashjfU18NutrKa%2F7PXbtuJvpIjbgPeqd%2BpjmRw6YKpnANFSQcpzTZgpSNJ6J7uiagAbir%2F8tNXJ%2FOsOnRh6iuIexxrmkIneAgz8QoLmiaJ8sLQrELVK2yn3wOHp57BAZJhDZjTBzyoRAuuZ4eoxHruY1pSb7qq79cIeAdOwin4GdgMeIMHeG%2BFZWYaiUQQyC5b50zKjYw97dFjAeY2I4Bnl105Iku1y0lMA1ZHolLx19uZnRdILcXKlZGQx%2FGdEqSsMRU1BIrFqRcV1qQOOHyxOLXEGcbRtAEsuAC2V4K3p5mFJ22IDWaEkk9ttf5Izb2LkD1MnrSwztXmmD%2FQi%2FEmVEFBfiKGmftsPwVaIoZanlKndMZsIBOskFYpDOq3QUs9aSbAAtL5Dbokus2G4%2FasthNMK5UQKCOhU97oaOYNGsTah%2BjfCKsZnTRn5TbhFX8ghg8CBYt%2FBjeYYYUrtUZ5jVij%2Fop7V5SsbA4mYTOwZ46hqdpbB6Qvq3AS2HHNkC15pTDIcDNGsMPXaBidXYPHc6PJAkRh29Vx8KcgX46LoUQBhRM%2B3SW6Opll%2FwgxxsPgKJKzr5QCmwkUxNbeg6Wj34SUnEzOemSuvS2OetRCO8Tyy%2BQbSKVJcqkia%2BGvDefFwMOmgnD7h81TUtMn%2BmRpyJJ349HhAnoWFTejhpYTL9G8N2nVg1qkXBeoS9Nw2fB27t7trm7d%2FQK7Cr4uoCeOQ7%2F8JfKT77KiDzLImESHw%2F0wf73QeHu74hxv7uihi4fTX%2BXEwAyQG3264dwv17aJ5N335Vt9sdrAXhPOAv8JFvzqyYXwfx8WYJaef1gMl98JRFyl5Mv5Uo%2FoVH5ww5OzLFsiTPDns7fS6EURSSWd%2F92BxMYQ8sBaH%2Bj%2BwthQPdVgDGpTfi%2BJQIWMD8xKqULliRH01rTeyF8x8q%2FGBEEEBrAJMPf25UQwi0b8tmqRXY7kIvNkzrkvRWLnxoGYEJsz8u4oOyMp8cHyaybb1HdMCaLApUE%2B%2F7xLIZGP6H9xuSEXp1zLIdjk5nBaMuV%2FyTDRRP8Y2ww5RO6d2D94o%2B6ucWIqUAvgHIHXhZsmDhjVLczmZ3ca0Cb3PpKwt2UtHVQ0BgFJsqqTsnzZPlKahRUkEu4qmkJt%2Bkqdae76ViWe3STan69yaF9%2BfESD2lcQshLHWVu4ovItXxO69bqC5p1nZLvI8NdQB9s9UNaJGlQ5mG947ipdDA0eTIw%2FA1zEdjWquIsQXXGIVEH0thC5M%2BW9pZe7IhAVnPJkYCCXN5a32HjN6nsvokEqRS44tGIs7s2LVTvcrHAF%2BRVmI8L4HUYk4x%2B67AxSMJKqCg8zrGOgvK9kNMdDrNiUtSWuHFpC8%2Fp5qIQrEo%2FH%2B1l%2F0cAwQ2nKmpWxKcMIuHY44Y6DlkpO48tRuUGBWT0FyHwSKO72Ud%2BtJUfdaZ4CWNijzZtlRa8%2BCkmO%2FEwHYfPZFU%2FhzjFWH7vnzHRMo%2BaF9u8qHSAiEkA2HjoNQPEwHsDKOt6hOoK3Ce%2F%2B%2F9boMWDa44I6FrQhdgS7OnNaSzwxWKZMcyHi6LN4WC6sSj0qm2PSOGBTvDs%2FGWJS6SwEN%2FULwpb4LQo9fYjUfSXRwZkynUazlSpvX9e%2BG2zor8l%2BYaMxSEomDdLHGcD6YVQPegTaA74H8%2BV4WvJkFUrjMLGLlvSZQWvi8%2FQA7yzQ8GPno%2F%2F5SJHRP%2FOqKObPCo81s%2F%2B6WgLqykYpGAgQZhVDEBPXWgU%2FWzFZjKUhSFInufPRiMAUULC6T11yL45ZrRoB4DzOyJShKXaAJIBS9wzLYIoCEcJKQW8GVCx4fihqJ6mshBUXSw3wWVj3grrHQlGNGhIDNNzsxQ3M%2BGWn6ASobIWC%2BLbYOC6UpahVO13Zs2zOzZC8z7FmA05JhUGyBsF4tsG0drcggIFzgg%2Fkpf3%2BCnAXKiMgIE8Jk%2FMhpkc8DUJEUzDSnWlQFme3d0sHZDrg7LavtsEX3cHwjCYA17pMTfx8Ajw9hHscN67hyo%2BRJQ4458RmPywXykkVcW688oVUrQhahpPRvTWPnuI0B%2BSkQu7dCyvLRyFYlC1LG1gRCIvn3rwQeINzZQC2KXq31FaR9UmVV2QeGVqBHjmE%2BVMd3b1fhCynD0pQNhCG6%2FWCDbKPyE7NRQzL3BzQAJ0g09aUzcQA6mUp9iZFK6Sbp%2FYbHjo%2B%2B7%2FWj8S4YNa%2BZdqAw1hDrKWFXv9%2BzaXpf8ZTDSbiqsxnwN%2FCzK5tPkOr4tRh2kY3Bn9JtalbIOI4b3F7F1vPQMfoDcdxMS8CW9m%2FNCW%2FHILTUVWQIPiD0j1A6bo8vsv6P1hCESl2abrSJWDrq5sSzUpwoxaCU9FtJyYH4QFMxDBpkkBR6kn0LMPO%2B5EJ7Z6bCiRoPedRZ%2FP0SSdii7ZnPAtVwwHUidcdyspwncz5uq6vvm4IEDbJVLUFCn%2FLvIHfooUBTkFO130FC7CmmcrKdgDJcid9mvVzsDSibOoXtIf9k6ABle3PmIxejodc4aob0QKS432srrCMndbfD454q52V01G4q913mC5HOsTzWF4h2No1av1VbcUgWAqyoZl%2B11PoFYnNv2HwAODeNRkHj%2B8SF1fcvVBu6MrehHAZK1Gm69ICcTKizykHgGFx7QdowTVAsYEF2tVc0Z6wLryz2FI1sc5By2znJAAmINndoJiB4sfPdPrTC8RnkW7KRCwxC6YvXg5ahMlQuMpoCSXjOlBy0Kij%2BbsCYPbGp8BdCBiLmLSAkEQRaieWo1SYvZIKJGj9Ur%2FeWHjiB7SOVdqMAVmpBvfRiebsFjger7DC%2B8kRFGtNrTrnnGD2GAJb8rQCWkUPYHhwXsjNBSkE6lGWUj5QNhK0DMNM2l%2BkXRZ0KLZaGsFSIdQz%2FHXDxf3%2FTE30%2BDgBKWGWdxElyLccJfEpjsnszECNoDGZpdwdRgCixeg9L4EPhH%2BRptvRMVRaahu4cySjS3P5wxAUCPkmn%2BrhyASpmiTaiDeggaIxYBmtLZDDhiWIJaBgzfCsAGUF1Q1SFZYyXDt9skCaxJsxK2Ms65dmdp5WAZyxik%2FzbrTQk5KmgxCg%2Ff45L0jywebOWUYFJQAJia7XzCV0x89rpp%2Ff3AVWhSPyTanqmik2SkD8A3Ml4NhIGLAjBXtPShwKYfi2eXtrDuKLk4QlSyTw1ftXgwqA2jUuopDl%2B5tfUWZNwBpEPXghzbBggYCw%2Fdhy0ntds2yeHCDKkF%2FYxQjNIL%2FF%2F37jLPHCKBO9ibwYCmuxImIo0ijV2Wbg3kSN2psoe8IsABv3RNFaF9uMyCtCYtqcD%2BqNOhwMlfARQUdJ2tUX%2BMNJqOwIciWalZsmEjt07tfa8ma4cji9sqz%2BQ9hWfmMoKEbIHPOQORbhQRHIsrTYlnVTNvcq1imqmmPDdVDkJgRcTgB8Sb6epCQVmFZe%2BjGDiNJQLWnfx%2BdrTKYjm0G8yH0ZAGMWzEJhUEQ4Maimgf%2Fbkvo8PLVBsZl152y5S8%2BHRDfZIMCbYZ1WDp4yrdchOJw8k6R%2B%2F2pHmydK4NIK2PHdFPHtoLmHxRDwLFb7eB%2BM4zNZcB9NrAgjVyzLM7xyYSY13ykWfIEEd2n5%2FiYp3ZdrCf7fL%2Ben%2BsIJu2W7E30MrAgZBD1rAAbZHPgeAMtKCg3NpSpYQUDWJu9bT3V7tOKv%2BNRiJc8JAKqqgCA%2FPNRBR7ChpiEulyQApMK1AyqcWnpSOmYh6yLiWkGJ2mklCSPIqN7UypWj3dGi5MvsHQ87MrB4VFgypJaFriaHivwcHIpmyi5LhNqtem4q0n8awM19Qk8BOS0EsqGscuuydYsIGsbT5GHnERUiMpKJl4ON7qjB4fEqlGN%2FhCky89232UQCiaeWpDYCJINXjT6xl4Gc7DxRCtgV0i1ma4RgWLsNtnEBRQFqZggCLiuyEydmFd7WlogpkCw5G1x4ft2psm3KAREwVwr1Gzl6RT7FDAqpVal34ewVm3VH4qn5mjGj%2BbYL1NgfLNeXDwtmYSpwzbruDKpTjOdgiIHDVQSb5%2FzBgSMbHLkxWWgghIh9QTFSDILixVwg0Eg1puooBiHAt7DzwJ7m8i8%2Fi%2BjHvKf0QDnnHVkVTIqMvIQImOrzCJwhSR7qYB5gSwL6aWL9hERHCZc4G2%2BJrpgHNB8eCCmcIWIQ6rSdyPCyftXkDlErUkHafHRlkOIjxGbAktz75bnh50dU7YHk%2BMz7wwstg6RFZb%2BTZuSOx1qqP5C66c0mptQmzIC2dlpte7vZrauAMm%2F7RfBYkGtXWGiaWTtwvAQiq2oD4YixPLXE2khB2FRaNRDTk%2B9sZ6K74Ia9VntCpN4BhJGJMT4Z5c5FhSepRCRWmBXqx%2BwhVZC4me4saDs2iNqXMuCl6iAZflH8fscC1sTsy4PHeC%2BXYuqMBMUun5YezKbRKmEPwuK%2BCLzijPEQgfhahQswBBLfg%2FGBgBiI4QwAqzJkkyYAWtjzSg2ILgMAgqxYfwERRo3zruBL9WOryUArSD8sQOcD7fvIODJxKFS615KFPsb68USBEPPj1orNzFY2xoTtNBVTyzBhPbhFH0PI5AtlJBl2aSgNPYzxYLw7XTDBDinmVoENwiGzmngrMo8OmnRP0Z0i0Zrln9DDFcnmOoBZjABaQIbPOJYZGqX%2BRCMlDDbElcjaROLDoualmUIQ88Kekk3iM4OQrADcxi3rJguS4MOIBIgKgXrjd1WkbCdqxJk%2F4efRIFsavZA7KvvJQqp3Iid5Z0NFc5aiMRzGN3vrpBzaMy4JYde3wr96PjN90AYOIbyp6T4zj8LoE66OGcX1Ef4Z3KoWLAUF4BTg7ug%2FAbkG5UNQXAMkQezujSHeir2uTThgd3gpyzDrbnEdDRH2W7U6PeRvBX1ZFMP5RM%2BZu6UUZZD8hDPHldVWntTCNk7To8IeOW9yn2wx0gmurwqC60AOde4r3ETi5pVMSDK8wxhoGAoEX9NLWHIR33VbrbMveii2jAJlrxwytTHbWNu8Y4N8vCCyZjAX%2FpcsfwXbLze2%2BD%2Bu33OGBoJyAAL3jn3RuEcdp5If8O%2Ba4NKWvxOTyDltG0IWoHhwVGe7dKkCWFT%2B%2Btm%2BhaBCikRUUMrMhYKZJKYoVuv%2FbsJzO8DwfVIInQq3g3BYypiz8baogH3r3GwqCwFtZnz4xMjAVOYnyOi5HWbFA8n0qz1OjSpHWFzpQOpvkNETZBGpxN8ybhtqV%2FDMUxd9uFZmBfKXMCn%2FSqkWJyKPnT6lq%2B4zBZni6fYRByJn6OK%2BOgPBGRAJluwGSk4wxjOOzyce%2FPKODwRlsgrVkdcsEiYrqYdXo0Er2GXi2GQZd0tNJT6c9pK1EEJG1zgDJBoTVuCXGAU8BKTvCO%2FcEQ1Wjk3Zzuy90JX4m3O5IlxVFhYkSUwuQB2up7jhvkm%2BbddRQu5F9s0XftGEJ9JSuSk%2BZachCbdU45fEqbugzTIUokwoAKvpUQF%2FCvLbWW5BNQFqFkJg2f30E%2F48StNe5QwBg8zz3YAJ82FZoXBxXSv4QDooDo79NixyglO9AembuBcx5Re3CwOKTHebOPhkmFC7wNaWtoBhFuV4AkEuJ0J%2B1pT0tLkvFVZaNzfhs%2FKd3%2BA9YsImlO4XK4vpCo%2FelHQi%2F9gkFg07xxnuXLt21unCIpDV%2BbbRxb7FC6nWYTsMFF8%2B1LUg4JFjVt3vqbuhHmDKbgQ4e%2BRGizRiO8ky05LQGMdL2IKLSNar0kNG7lHJMaXr5mLdG3nykgj6vB%2FKVijd1ARWkFEf3yiUw1v%2FWaQivVUpIDdSNrrKbjO5NPnxz6qTTGgYg03HgPhDrCFyYZTi3XQw3HXCva39mpLNFtz8AiEhxAJHpWX13gCTAwgm9YTvMeiqetdNQv6IU0hH0G%2BZManTqDLPjyrOse7WiiwOJCG%2BJ0pZYULhN8NILulmYYvmVcV2MjAfA39sGKqGdjpiPo86fecg65UPyXDIAOyOkCx5NQsLeD4gGVjTVDwOHWkbbBW0GeNjDkcSOn2Nq4cEssP54t9D749A7M1AIOBl0Fi0sSO5v3P7LCBrM6ZwFY6kp2FX6AcbGUdybnfChHPyu6WlRZ2Fwv9YM0RMI7kISRgR8HpQSJJOyTfXj%2F6gQKuihPtiUtlCQVPohUgzfezTg8o1b3n9pNZeco1QucaoXe40Fa5JYhqdTspFmxGtW9h5ezLFZs3j%2FN46f%2BS2rjYNC2JySXrnSAFhvAkz9a5L3pza8eYKHNoPrvBRESpxYPJdKVUxBE39nJ1chrAFpy4MMkf0qKgYALctGg1DQI1kIymyeS2AJNT4X240d3IFQb%2F0jQbaHJ2YRK8A%2Bls6WMhWmpCXYG5jqapGs5%2FeOJErxi2%2F2KWVHiPellTgh%2FfNl%2F2KYPKb7DUcAg%2BmCOPQFCiU9Mq%2FWLcU1xxC8aLePFZZlE%2BPCLzf7ey46INWRw2kcXySR9FDgByXzfxiNKwDFbUSMMhALPFSedyjEVM5442GZ4hTrsAEvZxIieSHGSgkwFh%2FnFNdrrFD4tBH4Il7fW6ur4J8Xaz7RW9jgtuPEXQsYk7gcMs2neu3zJwTyUerHKSh1iTBkj2YJh1SSOZL5pLuQbFFAvyO4k1Hxg2h99MTC6cTUkbONQIAnEfGsGkNFWRbuRyyaEZInM5pij73EA9rPIUfU4XoqQpHT9THZkW%2BoKFLvpyvTBMM69tN1Ydwv1LIEhHsC%2BueVG%2Bw%2BkyCPsvV3erRikcscHjZCkccx6VrBkBRusTDDd8847GA7p2Ucy0y0HdSRN6YIBciYa4vuXcAZbQAuSEmzw%2BH%2FAuOx%2BaH%2BtBL88H57D0MsqyiZxhOEQkF%2F8DR1d2hSPMj%2FsNOa5rxcUnBgH8ictv2J%2Bcb4BA4v3MCShdZ2vtK30vAwkobnEWh7rsSyhmos3WC93Gn9C4nnAd%2FPjMMtQfyDNZsOPd6XcAsnBE%2FmRHtHEyJMzJfZFLE9OvQa0i9kUmToJ0ZxknTgdl%2FXPV8xoh0K7wNHHsnBdvFH3sv52lU7UFteseLG%2FVanIvcwycVA7%2BBE1Ulyb20BvwUWZcMTKhaCcmY3ROpvonVMV4N7yBXTL7IDtHzQ4CCcqF66LjF3xUqgErKzolLyCG6Kb7irP%2FMVTCCwGRxfrPGpMMGvPLgJ881PHMNMIO09T5ig7AzZTX%2F5PLlwnJLDAPfuHynSGhV4tPqR3gJ4kg4c06c%2FF1AcjGytKm2Yb5jwMotF7vro4YDLWlnMIpmPg36NgAZsGA0W1spfLSue4xxat0Gdwd0lqDBOgIaMANykwwDKejt5YaNtJYIkrSgu0KjIg0pznY0SCd1qlC6R19g97UrWDoYJGlrvCE05J%2F5wkjpkre727p5PTRX5FGrSBIfJqhJE%2FIS876PaHFkx9pGTH3oaY3jJRvLX9Iy3Edoar7cFvJqyUlOhAEiOSAyYgVEGkzHdug%2BoRHIEOXAExMiTSKU9A6nmRC8mp8iYhwWdP2U%2F5EkFAdPrZw03YA3gSyNUtMZeh7dDCu8pF5x0VORCTgKp07ehy7NZqKTpIC4UJJ89lnboyAfy5OyXzXtuDRbtAFjZRSyGFTpFrXwkpjSLIQIG3N0Vj4BtzK3wdlkBJrO18MNsgseR4BysJilI0wI6ZahLhBFA0XBmV8d4LUzEcNVb0xbLjLTETYN8OEVqNxkt10W614dd1FlFFVTIgB7%2FBQQp1sWlNolpIu4ekxUTBV7NmxOFKEBmmN%2BnA7pvF78%2FRII5ZHA09OAiE%2F66MF6HQ%2BqVEJCHxwymukkNvzqHEh52dULPbVasfQMgTDyBZzx4007YiKdBuUauQOt27Gmy8ISclPmEUCIcuLbkb1mzQSqIa3iE0PJh7UMYQbkpe%2BhXjTJKdldyt2mVPwywoODGJtBV1lJTgMsuSQBlDMwhEKIfrvsxGQjHPCEfNfMAY2oxvyKcKPUbQySkKG6tj9AQyEW3Q5rpaDJ5Sns9ScLKeizPRbvWYAw4bXkrZdmB7CQopCH8NAmqbuciZChHN8lVGaDbCnmddnqO1PQ4ieMYfcSiBE5zzMz%2BJV%2F4eyzrzTEShvqSGzgWimkNxLvUj86iAwcZuIkqdB0VaIB7wncLRmzHkiUQpPBIXbDDLHBlq7vp9xwuC9AiNkIptAYlG7Biyuk8ILdynuUM1cHWJgeB%2BK3wBP%2FineogxkvBNNQ4AkW0hvpBOQGFfeptF2YTR75MexYDUy7Q%2F9uocGsx41O4IZhViw%2F2FvAEuGO5g2kyXBUijAggWM08bRhXg5ijgMwDJy40QeY%2FcQpUDZiIzmvskQpO5G1zyGZA8WByjIQU4jRoFJt56behxtHUUE%2Fom7Rj2psYXGmq3llVOCgGYKNMo4pzwntITtapDqjvQtqpjaJwjHmDzSVGLxMt12gEXAdLi%2FcaHSM3FPRGRf7dB7YC%2BcD2ho6oL2zGDCkjlf%2FDFoQVl8GS%2F56wur3rdV6ggtzZW60MRB3g%2BU1W8o8cvqIpMkctiGVMzXUFI7FacFLrgtdz4mTEr4aRAaQ2AFQaNeG7GX0yOJgMRYFziXdJf24kg%2FgBQIZMG%2FYcPEllRTVNoDYR6oSJ8wQNLuihfw81UpiKPm714bZX1KYjcXJdfclCUOOpvTxr9AAJevTY4HK%2FG7F3mUc3GOAKqh60zM0v34v%2BELyhJZqhkaMA8UMMOU90f8RKEJFj7EqepBVwsRiLbwMo1J2zrE2UYJnsgIAscDmjPjnzI8a719Wxp757wqmSJBjXowhc46QN4RwKIxqEE6E5218OeK7RfcpGjWG1jD7qND%2B%2FGTk6M56Ig4yMsU6LUW1EWE%2BfIYycVV1thldSlbP6ltdC01y3KUfkobkt2q01YYMmxpKRvh1Z48uNKzP%2FIoRIZ%2FF6buOymSnW8gICitpJjKWBscSb9JJKaWkvEkqinAJ2kowKoqkqZftRqfRQlLtKoqvTRDi2vg%2FRrPD%2Fd3a09J8JhGZlEkOM6znTsoMCsuvTmywxTCDhw5dd0GJOHCMPbsj3QLkTE3MInsZsimDQ3HkvthT7U9VA4s6G07sID0FW4SHJmRGwCl%2BMu4xf0ezqeXD2PtPDnwMPo86sbwDV%2B9PWcgFcARUVYm3hrFQrHcgMElFGbSM2A1zUYA3baWfheJp2AINmTJLuoyYD%2FOwA4a6V0ChBN97E8YtDBerUECv0u0TlxR5yhJCXvJxgyM73Bb6pyq0jTFJDZ4p1Am1SA6sh8nADd1hAcGBMfq4d%2FUfwnmBqe0Jun1n1LzrgKuZMAnxA3NtCN7Klf4BH%2B14B7ibBmgt0TGUafVzI4uKlpF7v8NmgNjg90D6QE3tbx8AjSAC%2BOA1YJvclyPKgT27QpIEgVYpbPYGBsnyCNrGz9XUsCHkW1QAHgL2STZk12QGqmvAB0NFteERkvBIH7INDsNW9KKaAYyDMdBEMzJiWaJHZALqDxQDWRntumSDPcplyFiI1oDpT8wbwe01AHhW6%2BvAUUBoGhY3CT2tgwehdPqU%2F4Q7ZLYvhRl%2FogOvR9O2%2BwkkPKW5vCTjD2fHRYXONCoIl4Jh1bZY0ZE1O94mMGn%2FdFSWBWzQ%2FVYk%2BGezi46RgiDv3EshoTmMSlioUK6MQEN8qeyK6FRninyX8ZPeUWjjbMJChn0n%2FyJvrq5bh5UcCAcBYSafTFg7p0jDgrXo2QWLb3WpSOET%2FHh4oSadBTvyDo10IufLzxiMLAnbZ1vcUmj3w7BQuIXjEZXifwukVxrGa9j%2BDXfpi12m1RbzYLg9J2wFergEwOxFyD0%2FJstNK06ZN2XdZSGWxcJODpQHOq4iKqjqkJUmPu1VczL5xTGUfCgLEYyNBCCbMBFT%2FcUP6pE%2FmujnHsSDeWxMbhrNilS5MyYR0nJyzanWXBeVcEQrRIhQeJA6Xt4f2eQESNeLwmC10WJVHqwx8SSyrtAAjpGjidcj1E2FYN0LObUcFQhafUKTiGmHWRHGsFCB%2BHEXgrzJEB5bp0QiF8ZHh11nFX8AboTD0PS4O1LqF8XBks2MpjsQnwKHF6HgaKCVLJtcr0XjqFMRGfKv8tmmykhLRzu%2BvqQ02%2BKpJBjaLt9ye1Ab%2BBbEBhy4EVdIJDrL2naV0o4wU8YZ2Lq04FG1mWCKC%2BUwkXOoAjneU%2FxHplMQo2cXUlrVNqJYczgYlaOEczVCs%2FOCgkyvLmTmdaBJc1iBLuKwmr6qtRnhowngsDxhzKFAi02tf8bmET8BO27ovJKF1plJwm3b0JpMh38%2BxsrXXg7U74QUM8ZCIMOpXujHntKdaRtsgyEZl5MClMVMMMZkZLNxH9%2Bb8fH6%2Bb8Lev30A9TuEVj9CqAdmwAAHBPbfOBFEATAPZ2CS0OH1Pj%2F0Q7PFUcC8hDrxESWdfgFRm%2B7vvWbkEppHB4T%2F1ApWnlTIqQwjcPl0VgS1yHSmD0OdsCVST8CQVwuiew1Y%2Bg3QGFjNMzwRB2DSsAk26cmA8lp2wIU4p93AUBiUHFGOxOajAqD7Gm6NezNDjYzwLOaSXRBYcWipTSONHjUDXCY4mMI8XoVCR%2FRrs%2FJLKXgEx%2BqkmeDlFOD1%2FyTQNDClRuiUyKYCllfMiQiyFkmuTz2vLsBNyRW%2Bxz%2B5FElFxWB28VjYIGZ0Yd%2B5wIjkcoMaggxswbT0pCmckRAErbRlIlcOGdBo4djTNO8FAgQ%2BlT6vPS60BwTRSUAM3ddkEAZiwtEyArrkiDRnS7LJ%2B2hwbzd2YDQagSgACpsovmjil5wfPuXq3GuH0CyE7FK3M4FgRaFoIkaodORrPx1%2BJpI9psyNYIFuJogZa0%2F1AhOWdlHQxdAgbwacsHqPZo8u%2FngAH2GmaTdhYnBfSDbBfh8CHq6Bx5bttP2%2BRdM%2BMAaYaZ0Y%2FADkbNCZuAyAVQa2OcXOeICmDn9Q%2FeFkDeFQg5MgHEDXq%2FtVjj%2Bjtd26nhaaolWxs1ixSUgOBwrDhRIGOLyOVk2%2FBc0UxvseQCO2pQ2i%2BKrfhu%2FWeBovNb5dJxQtJRUDv2mCwYVpNl2efQM9xQHnK0JwLYt%2FU0Wf%2BphiA4uw8G91slC832pmOTCAoZXohg1fewCZqLBhkOUBofBWpMPsqg7XEXgPfAlDo2U5WXjtFdS87PIqClCK5nW6adCeXPkUiTGx0emOIDQqw1yFYGHEVx20xKjJVYe0O8iLmnQr3FA9nSIQilUKtJ4ZAdcTm7%2BExseJauyqo30hs%2B1qSW211A1SFAOUgDlCGq7eTIcMAeyZkV1SQJ4j%2Fe1Smbq4HcjqgFbLAGLyKxlMDMgZavK5NAYH19Olz3la%2FQCTiVelFnU6O%2FGCvykqS%2FwZJDhKN9gBtSOp%2F1SP5VRgJcoVj%2Bkmf2wBgv4gjrgARBWiURYx8xENV3bEVUAAWWD3dYDKAIWk5opaCFCMR5ZjJExiCAw7gYiSZ2rkyTce4eNMY3lfGn%2B8p6%2BvBckGlKEXnA6Eota69OxDO9oOsJoy28BXOR0UoXNRaJD5ceKdlWMJlOFzDdZNpc05tkMGQtqeNF2lttZqNco1VtwXgRstLSQ6tSPChgqtGV5h2DcDReIQadaNRR6AsAYKL5gSFsCJMgfsaZ7DpKh8mg8Wz8V7H%2BgDnLuMxaWEIUPevIbClgap4dqmVWSrPgVYCzAoZHIa5z2Ocx1D%2FGvDOEqMOKLrMefWIbSWHZ6jbgA8qVBhYNHpx0P%2BjAgN5TB3haSifDcApp6yymEi6Ij%2FGsEpDYUgcHATJUYDUAmC1SCkJ4cuZXSAP2DEpQsGUjQmKJfJOvlC2x%2FpChkOyLW7KEoMYc5FDC4v2FGqSoRWiLsbPCiyg1U5yiHZVm1XLkHMMZL11%2Fyxyw0UnGig3MFdZklN5FI%2FqiT65T%2BjOXOdO7XbgWurOAZR6Cv9uu1cm5LjkXX4xi6mWn5r5NjBS0gTliHhMZI2WNqSiSphEtiCAwnafS11JhseDGHYQ5%2BbqWiAYiAv6Jsf79%2FVUs4cIl%2Bn6%2BWOjcgB%2F2l5TreoAV2717JzZbQIR0W1cl%2FdEqCy5kJ3ZSIHuU0vBoHooEpiHeQWVkkkOqRX27eD1FWw4BfO9CJDdKoSogQi3hAAwsPRFrN5RbX7bqLdBJ9JYMohWrgJKHSjVl1sy2xAG0E3sNyO0oCbSGOxCNBRRXTXenYKuwAoDLfnDcQaCwehUOIDiHAu5m5hMpKeKM4sIo3vxACakIxKoH2YWF2QM84e6F5C5hJU4g8uxuFOlAYnqtwxmHyNEawLW%2FPhoawJDrGAP0JYWHgAVUByo%2FbGdiv2T2EMg8gsS14%2FrAdzlOYazFE7w4OzxeKiWdm3nSOnQRRKXSlVo8HEAbBfyJMKqoq%2BSCcTSx5NDtbFwNlh8VhjGGDu7JG5%2FTAGAvniQSSUog0pNzTim8Owc6QTuSKSTXlQqwV3eiEnklS3LeSXYPXGK2VgeZBqNcHG6tZHvA3vTINhV0ELuQdp3t1y9%2BogD8Kk%2FW7QoRN1UWPqM4%2BxdygkFDPLoTaumKReKiLWoPHOfY54m3qPx4c%2B4pgY3MRKKbljG8w4wvz8pxk3AqKsy4GMAkAtmRjRMsCxbb4Q2Ds0Ia9ci8cMT6DmsJG00XaHCIS%2Bo3F8YVVeikw13w%2BOEDaCYYhC0ZE54kA4jpjruBr5STWeqQG6M74HHL6TZ3lXrd99ZX%2B%2B7LhNatQaZosuxEf5yRA15S9gPeHskBIq3Gcw81AGb9%2FO53DYi%2F5CsQ51EmEh8Rkg4vOciClpy4d04eYsfr6fyQkBmtD%2BP8sNh6e%2BXYHJXT%2FlkXxT4KXU5F2sGxYyzfniMMQkb9OjDN2C8tRRgTyL7GwozH14PrEUZc6oz05Emne3Ts5EG7WolDmU8OB1LDG3VrpQxp%2BpT0KYV5dGtknU64JhabdqcVQbGZiAxQAnvN1u70y1AnmvOSPgLI6uB4AuDGhmAu3ATkJSw7OtS%2F2ToPjqkaq62%2F7WFG8advGlRRqxB9diP07JrXowKR9tpRa%2BjGJ91zxNTT1h8I2PcSfoUPtd7NejVoH03EUcqSBuFZPkMZhegHyo2ZAITovmm3zAIdGFWxoNNORiMRShgwdYwFzkPw5PA4a5MIIQpmq%2Bnsp3YMuXt%2FGkXxLx%2FP6%2BZJS0lFyz4MunC3eWSGE8xlCQrKvhKUPXr0hjpAN9ZK4PfEDrPMfMbGNWcHDzjA7ngMxTPnT7GMHar%2BgMQQ3NwHCv4zH4BIMYvzsdiERi6gebRmerTsVwZJTRsL8dkZgxgRxmpbgRcud%2BYlCIRpPwHShlUSwuipZnx9QCsEWziVazdDeKSYU5CF7UVPAhLer3CgJOQXl%2Fzh575R5rsrmRnKAzq4POFdgbYBuEviM4%2BLVC15ssLNFghbTtHWerS1hDt5s4qkLUha%2FqpZXhWh1C6lTQAqCNQnaDjS7UGFBC6wTu8yFnKJnExCnAs3Ok9yj5KpfZESQ4lTy5pTGTnkAUpxI%2ByjEldJfSo4y0QhG4i4IwkRFGcjWY8%2BEzgYYJUK7BXQksLxAww%2FYYWBMhJILB9e8ePEJ4OP7z%2B4%2FwOQDl64iOYDp26DaONPxpKtBxq%2FaTzRGarm3VkPYTLJKx6Z%2FMw2YbBGseJhPMwhhNswrIkyvV2BYzrvZbxLpKwcWJhYmFtVZ%2BlPEq91FzVp1HlQY1bZVLqeNR9SAUn6n0E28k%2FUuGkNpP1DBI5ch%2FEehZfjUQ9aE41NhETExoPT2gGQz0IhWJbEOvTQ4wgcXCHHFBhewYUiFHuhRSAUVmEHeCRQHQkXGFwkAgyzREJCVN7TRnTon36Zw3tPhx4EALwNdwDv%2BJ41YSP4B2CQqz0EFgARZ4ESgBHQgROwAVn9GTI%2BHYexTUevLUeta4%2FDqKrbMVS%2BYqb8hUwYCrlgKtmAq1YCrFgKrd4qpXiqZcKn1oqdWipjYKpWwVPVYqW6xUpVipKqFR3QKjagVEtAqHpxUMTitsnFaJOKx2cVhswq35RVpyiq9lFVNIKnOQVMkgqtYxVNxiqQjFS7GKlSIVIsQqPIhUWwioigFQ%2B%2BKkN8VHr49HDw9Ebo9EDo9DTo9Crg9BDg9%2FWx7gWx7YWwlobYrOGxWPNisAaAHEyALpkAVDIAeWAArsABVXACYuAD5cAF6wAKFQAQqgAbVAAsoAAlQAUaYAfkwAvogBWQACOgAD9AAHSAAKT4GUdMiOvFngBTwCn2AZ7Dv6B6k%2F90B8%2ByRnkV144AIBoAMTQATGgAjNAA4YABgwABZgB%2FmQCwyAVlwCguASlwCEuAQFwB4uAMlwBYuAJlQAUVAAhUD2KgdpUDaJgaRMDFJgX5MC1JgWJEAokQCWRAHxEAWkQBMRADpEAMkQAYROAEecC484DRpwBDTnwNOdw05tjTmiNOYwtswhYFwLA7BYG4LA2BYGOLAwRYFuLAsxYFQJAohIEyJAMwkAwiQC0JAJgkAeiQBkJAFokAPCQA0JABwcD4Dgc4cDdDgaYcDIDgYgUC6CgWgUClCgUYUAVBQBOFAEYMALgwAgDA9QYAdIn8AZzeBB2L5EcWrenUT1KXienEsuJJ7x5U8XlTjc1NVzUyXFTGb1LlpUtWlTDIjqwE4LsagowoCi2gJLKAkpoBgJQNpAIhNqaEoneI6kiiqQ6Go%2Fn6j0cS%2Ba2gEU8gIHJ%2BBwfgZX4GL%2BBd%2FgW34FZ%2BBS%2FgUH4FN6BTegTvoEv6BJegRnYEF2A79gOvYDl2BdEjCkqkGtwXp0LNToIskOTXzh%2FF062yJ7AAAAEDAWAAABWhJ%2BKPEIJgBFxMVP7w2QJBGHASQnOBKXKFIdUK4igKA9IEaYJg%29%3Bsrc%3Aurl%28data%3Aapplication%2Fvnd%2Ems%2Dfontobject%3Bbase64%2Cn04AAEFNAAACAAIABAAAAAAABQAAAAAAAAABAJABAAAEAExQAAAAAAAAAAIAAAAAAAAAAAEAAAAAAAAAJxJ%2FLAAAAAAAAAAAAAAAAAAAAAAAACgARwBMAFkAUABIAEkAQwBPAE4AUwAgAEgAYQBsAGYAbABpAG4AZwBzAAAADgBSAGUAZwB1AGwAYQByAAAAeABWAGUAcgBzAGkAbwBuACAAMQAuADAAMAA5ADsAUABTACAAMAAwADEALgAwADAAOQA7AGgAbwB0AGMAbwBuAHYAIAAxAC4AMAAuADcAMAA7AG0AYQBrAGUAbwB0AGYALgBsAGkAYgAyAC4ANQAuADUAOAAzADIAOQAAADgARwBMAFkAUABIAEkAQwBPAE4AUwAgAEgAYQBsAGYAbABpAG4AZwBzACAAUgBlAGcAdQBsAGEAcgAAAAAAQlNHUAAAAAAAAAAAAAAAAAAAAAADAKncAE0TAE0ZAEbuFM3pjM%2FSEdmjKHUbyow8ATBE40IvWA3vTu8LiABDQ%2BpexwUMcm1SMnNryctQSiI1K5ZnbOlXKmnVV5YvRe6RnNMFNCOs1KNVpn6yZhCJkRtVRNzEufeIq7HgSrcx4S8h%2Fv4vnrrKc6oCNxmSk2uKlZQHBii6iKFoH0746ThvkO1kJHlxjrkxs%2BLWORaDQBEtiYJIR5IB9Bi1UyL4Rmr0BNigNkMzlKQmnofBHviqVzUxwdMb3NdCn69hy%2BpRYVKGVS%2F1tnsqv4LL7wCCPZZAZPT4aCShHjHJVNuXbmMrY5LeQaGnvAkXlVrJgKRAUdFjrWEah9XebPeQMj7KS7DIBAFt8ycgC5PLGUOHSE3ErGZCiViNLL5ZARfywnCoZaKQCu6NuFX42AEeKtKUGnr%2FCm2Cy8tpFhBPMW5Fxi4Qm4TkDWh4IWFDClhU2hRWosUWqcKLlgyXB%2BlSHaWaHiWlBAR8SeSgSPCQxdVQgzUixWKSTrIQEbU94viDctkvX%2BVSjJuUmV8L4CXShI11esnp0pjWNZIyxKHS4wVQ2ime1P4RnhvGw0aDN1OLAXGERsB7buFpFGGBAre4QEQR0HOIO5oYH305G%2BKspT%2FFupEGGafCCwxSe6ZUa%2B073rXHnNdVXE6eWvibUS27XtRzkH838mYLMBmYysZTM0EM3A1fbpCBYFccN1B%2FEnCYu%2FTgCGmr7bMh8GfYL%2BBfcLvB0gRagC09w9elfldaIy%2FhNCBLRgBgtCC7jAF63wLSMAfbfAlEggYU0bUA7ACCJmTDpEmJtI78w4%2FBO7dN7JR7J7ZvbYaUbaILSQsRBiF3HGk5fEg6p9unwLvn98r%2BvnsV%2B372uf1xBLq4qU%2F45fTuqaAP%2BpssmCCCTF0mhEow8ZXZOS8D7Q85JsxZ%2BAzok7B7O%2Ff6J8AzYBySZQB%2FQHYUSA%2BEeQhEWiS6AIQzgcsDiER4MjgMBAWDV4AgQ3g1eBgIdweCQmCjJEMkJ%2BPKRWyFHHmg1Wi%2F6xzUgA0LREoKJChwnQa9B%2B5RQZRB3IlBlkAnxyQNaANwHMowzlYSMCBgnbpzvqpl0iTJNCQidDI9ZrSYNIRBhHtUa5YHMHxyGEik9hDE0AKj72AbTCaxtHPUaKZdAZSnQTyjGqGLsmBStCejApUhg4uBMU6mATujEl%2BKdDPbI6Ag4vLr%2BhjY6lbjBeoLKnZl0UZgRX8gTySOeynZVz1wOq7e1hFGYIq%2BMhrGxDLak0PrwYzSXtcuyhXEhwOYofiW%2BEcI%2Fjw8P6IY6ed%2BetAbuqKp5QIapT77LnAe505lMuqL79a0ut4rWexzFttsOsLDy7zvtQzcq3U1qabe7tB0wHWVXji%2BzDbo8x8HyIRUbXnwUcklFv51fvTymiV%2BMXLSmGH9d9%2BaXpD5X6lao41anWGig7IwIdnoBY2ht%2FpO9mClLo4NdXHAsefqWUKlXJkbqPOFhMoR4aiA1BXqhRNbB2Xwi%2B7u%2FjpAoOpKJ0UX24EsrzMfHXViakCNcKjBxuQX8BO0ZqjJ3xXzf%2B61t2VXOSgJ8xu65QKgtN6FibPmPYsXbJRHHqbgATcSZxBqGiDiU4NNNsYBsKD0MIP%2FOfKnlk%2FLkaid%2FO2NbKeuQrwOB2Gq3YHyr6ALgzym5wIBnsdC1ZkoBFZSQXChZvlesPqvK2c5oHHT3Q65jYpNxnQcGF0EHbvYqoFw60WNlXIHQF2HQB7zD6lWjZ9rVqUKBXUT6hrkZOle0RFYII0V5ZYGl1JAP0Ud1fZZMvSomBzJ710j4Me8mjQDwEre5Uv2wQfk1ifDwb5ksuJQQ3xt423lbuQjvoIQByQrNDh1JxGFkOdlJvu%2FgFtuW0wR4cgd%2BZKesSV7QkNE2kw6AV4hoIuC02LGmTomyf8PiO6CZzOTLTPQ%2BHW06H%2Btx%2BbQ8LmDYg1pTFrp2oJXgkZTyeRJZM0C8aE2LpFrNVDuhARsN543%2FFV6klQ6Tv1OoZGXLv0igKrl%2FCmJxRmX7JJbJ998VSIPQRyDBICzl4JJlYHbdql30NvYcOuZ7a10uWRrgoieOdgIm4rlq6vNOQBuqESLbXG5lzdJGHw2m0sDYmODXbYGTfSTGRKpssTO95fothJCjUGQgEL4yKoGAF%2F0SrpUDNn8CBgBcSDQByAeNkCXp4S4Ro2Xh4OeaGRgR66PVOsU8bc6TR5%2FxTcn4IVMLOkXSWiXxkZQCbvKfmoAvQaKjO3EDKwkwqHChCDEM5loQRPd5ACBki1TjF772oaQhQbQ5C0lcWXPFOzrfsDGUXGrpxasbG4iab6eByaQkQfm0VFlP0ZsDkvvqCL6QXMUwCjdMx1ZOyKhTJ7a1GWAdOUcJ8RSejxNVyGs31OKMyRyBVoZFjqIkmKlLQ5eHMeEL4MkUf23cQ%2F1SgRCJ1dk4UdBT7OoyuNgLs0oCd8RnrEIb6QdMxT2QjD4zMrJkfgx5aDMcA4orsTtKCqWb%2FVeyceqa5OGSmB28YwH4rFbkQaLoUN8OQQYnD3w2eXpI4ScQfbCUZiJ4yMOIKLyyTc7BQ4uXUw6Ee6%2FxM%2B4Y67ngNBknxIPwuppgIhFcwJyr6EIj%2BLzNj%2FmfR2vhhRlx0BILZoAYruF0caWQ7YxO66UmeguDREAFHYuC7HJviRgVO6ruJH59h%2FC%2FPkgSle8xNzZJULLWq9JMDTE2fjGE146a1Us6PZDGYle6ldWRqn%2FpdpgHKNGrGIdkRK%2BKPETT9nKT6kLyDI8xd9A1FgWmXWRAIHwZ37WyZHOVyCadJEmMVz0MadMjDrPho%2BEIochkVC2xgGiwwsQ6DMv2P7UXqT4x7CdcYGId2BJQQa85EQKmCmwcRejQ9Bm4oATENFPkxPXILHpMPUyWTI5rjNOsIlmEeMbcOCEqInpXACYQ9DDxmFo9vcmsDblcMtg4tqBerNngkIKaFJmrQAPnq1dEzsMXcwjcHdfdCibcAxxA%2Bq%2Fj9m3LM%2FO7WJka4tSidVCjsvo2lQ%2F2ewyoYyXwAYyr2PlRoR5MpgVmSUIrM3PQxXPbgjBOaDQFIyFMJvx3Pc5RSYj12ySVF9fwFPQu2e2KWVoL9q3Ayv3IzpGHUdvdPdrNUdicjsTQ2ISy7QU3DrEytIjvbzJnAkmANXjAFERA0MUoPF3%2F5KFmW14bBNOhwircYgMqoDpUMcDtCmBE82QM2YtdjVLB4kBuKho%2FbcwQdeboqfQartuU3CsCf%2BcXkgYAqp%2F0Ee3RorAZt0AvvOCSI4JICIlGlsV0bsSid%2FNIEALAAzb6HAgyWHBps6xAOwkJIGcB82CxRQq4sJf3FzA70A%2BTRqcqjEMETCoez3mkPcpnoALs0ugJY8kQwrC%2BJE5ik3w9rzrvDRjAQnqgEVvdGrNwlanR0SOKWzxOJOvLJhcd8Cl4AshACUkv9czdMkJCVQSQhp6kp7StAlpVRpK0t0SW6LHeBJnE2QchB5Ccu8kxRghZXGIgZIiSj7gEKMJDClcnX6hgoqJMwiQDigIXg3ioFLCgDgjPtYHYpsF5EiA4kcnN18MZtOrY866dEQAb0FB34OGKHGZQjwW%2FWDHA60cYFaI%2FPjpzquUqdaYGcIq%2BmLez3WLFFCtNBN2QJcrlcoELgiPku5R5dSlJFaCEqEZle1AQzAKC%2B1SotMcBNyQUFuRHRF6OlimSBgjZeTBCwLyc6A%2BP%2FoFRchXTz5ADknYJHxzrJ5pGuIKRQISU6WyKTBBjD8WozmVYWIsto1AS5rxzKlvJu4E%2FvwOiKxRtCWsDM%2BeTHUrmwrCK5BIfMzGkD%2B0Fk5LzBs0jMYXktNDblB06LMNJ09U8pzSLmo14MS0OMjcdrZ31pyQqxJJpRImlSvfYAK8inkYU52QY2FPEVsjoWewpwhRp5yAuNpkqhdb7ku9Seefl2D0B8SMTFD90xi4CSOwwZy9IKkpMtI3FmFUg3%2FkFutpQGNc3pCR7gvC4sgwbupDu3DyEN%2BW6YGLNM21jpB49irxy9BSlHrVDlnihGKHwPrbVFtc%2Bh1rVQKZduxIyojccZIIcOCmhEnC7UkY68WXKQgLi2JCDQkQWJRQuk60hZp0D3rtCTINSeY9Ej2kIKYfGxwOs4j9qMM7fYZiipzgcf7TamnehqdhsiMiCawXnz4xAbyCkLAx5EGbo3Ax1u3dUIKnTxIaxwQTHehPl3V491H0%2BbC5zgpGz7Io%2BmjdhKlPJ01EeMpM7UsRJMi1nGjmJg35i6bQBAAxjO%2FENJubU2mg3ONySEoWklCwdABETcs7ck3jgiuU9pcKKpbgn%2B3YlzV1FzIkB6pmEDOSSyDfPPlQskznctFji0kpgZjW5RZe6x9kYT4KJcXg0bNiCyif%2BpZACCyRMmYsfiKmN9tSO65F0R2OO6ytlEhY5Sj6uRKfFxw0ijJaAx%2Fk3QgnAFSq27%2F2i4GEBA%2BUvTJKK%2F9eISNvG46Em5RZfjTYLdeD8kdXHyrwId%2FDQZUaMCY4gGbke2C8vfjgV%2FY9kkRQOJIn%2FxM9INZSpiBnqX0Q9GlQPpPKAyO5y%2BW5NMPSRdBCUlmuxl40ZfMCnf2Cp044uI9WLFtCi4YVxKjuRCOBWIb4XbIsGdbo4qtMQnNOQz4XDSui7W%2FN6l54qOynCqD3DpWQ%2BmpD7C40D8BZEWGJX3tlAaZBMj1yjvDYKwCJBa201u6nBKE5UE%2B7QSEhCwrXfbRZylAaAkplhBWX50dumrElePyNMRYUrC99UmcSSNgImhFhDI4BXjMtiqkgizUGCrZ8iwFxU6fQ8GEHCFdLewwxYWxgScAYMdMLmcZR6b7rZl95eQVDGVoUKcRMM1ixXQtXNkBETZkVVPg8LoSrdetHzkuM7DjZRHP02tCxA1fmkXKF3VzfN1pc1cv%2F8lbTIkkYpqKM9VOhp65ktYk%2BQ46myFWBapDfyWUCnsnI00QTBQmuFjMZTcd0V2NQ768Fhpby04k2IzNR1wKabuGJqYWwSly6ocMFGTeeI%2BejsWDYgEvr66QgqdcIbFYDNgsm0x9UHY6SCd5%2B7tpsLpKdvhahIDyYmEJQCqMqtCF6UlrE5GXRmbu%2Bvtm3BFSxI6ND6UxIE7GsGMgWqghXxSnaRJuGFveTcK5ZVSPJyjUxe1dKgI6kNF7EZhIZs8y8FVqwEfbM0Xk2ltORVDKZZM40SD3qQoQe0orJEKwPfZwm3YPqwixhUMOndis6MhbmfvLBKjC8sKKIZKbJk8L11oNkCQzCgvjhyyEiQSuJcgCQSG4Mocfgc0Hkwcjal1UNgP0CBPikYqBIk9tONv4kLtBswH07vUCjEaHiFGlLf8MgXKzSgjp2HolRRccAOh0ILHz9qlGgIFkwAnzHJRjWFhlA7ROwINyB5HFj59PRZHFor6voq7l23EPNRwdWhgawqbivLSjRA4htEYUFkjESu67icTg5S0aW1sOkCiIysfJ9UnIWevOOLGpepcBxy1wEhd2WI3AZg7sr9WBmHWyasxMcvY%2FiOmsLtHSWNUWEGk9hScMPShasUA1AcHOtRZlqMeQ0OzYS9vQvYUjOLrzP07BUAFikcJNMi7gIxEw4pL1G54TcmmmoAQ5s7TGWErJZ2Io4yQ0ljRYhL8H5e62oDtLF8aDpnIvZ5R3GWJyAugdiiJW9hQAVTsnCBHhwu7rkBlBX6r3b7ejEY0k5GGeyKv66v%2B6dg7mcJTrWHbtMywbedYqCQ0FPwoytmSWsL8WTtChZCKKzEF7vP6De4x2BJkkniMgSdWhbeBSLtJZR9CTHetK1xb34AYIJ37OegYIoPVbXgJ%2FqDQK%2BbfCtxQRVKQu77WzOoM6SGL7MaZwCGJVk46aImai9fmam%2BWpHG%2B0BtQPWUgZ7RIAlPq6lkECUhZQ2gqWkMYKcYMYaIc4gYCDFHYa2d1nzp3%2BJ1eCBay8IYZ0wQRKGAqvCuZ%2FUgbQPyllosq%2BXtfKIZOzmeJqRazpmmoP%2F76YfkjzV2NlXTDSBYB04SVlNQsFTbGPk1t%2FI4Jktu0XSgifO2ozFOiwd%2F0SssJDn0dn4xqk4GDTTKX73%2FwQyBLdqgJ%2BWx6AQaba3BA9CKEzjtQYIfAsiYamapq80LAamYjinlKXUkxdpIDk0puXUEYzSalfRibAeDAKpNiqQ0FTwoxuGYzRnisyTotdVTclis1LHRQCy%2FqqL8oUaQzWRxilq5Mi0IJGtMY02cGLD69vGjkj3p6pGePKI8bkBv5evq8SjjyU04vJR2cQXQwSJyoinDsUJHCQ50jrFTT7yRdbdYQMB3MYCb6uBzJ9ewhXYPAIZSXfeEQBZZ3GPN3Nbhh%2FwkvAJLXnQMdi5NYYZ5GHE400GS5rXkOZSQsdZgIbzRnF9ueLnsfQ47wHAsirITnTlkCcuWWIUhJSbpM3wWhXNHvt2xUsKKMpdBSbJnBMcihkoDqAd1Zml%2FR4yrzow1Q2A5G%2Bkzo%2FRhRxQS2lCSDRV8LlYLBOOoo1bF4jwJAwKMK1tWLHlu9i0j4Ig8qVm6wE1DxXwAwQwsaBWUg2pOOol2dHxyt6npwJEdLDDVYyRc2D0HbcbLUJQj8gPevQBUBOUHXPrsAPBERICpnYESeu2OHotpXQxRGlCCtLdIsu23MhZVEoJg8Qumj%2FUMMc34IBqTKLDTp76WzL%2FdMjCxK7MjhiGjeYAC%2Fkj%2FjY%2FRde7hpSM1xChrog6yZ7OWTuD56xBJnGFE%2BpT2ElSyCnJcwVzCjkqeNLfMEJqKW0G7OFIp0G%2B9mh50I9o8k1tpCY0xYqFNIALgIfc2me4n1bmJnRZ89oepgLPT0NTMLNZsvSCZAc3TXaNB07vail36%2FdBySis4m9%2FDR8izaLJW6bWCkVgm5T%2Bius3ZXq4xI%2BGnbveLbdRwF2mNtsrE0JjYc1AXknCOrLSu7Te%2Fr4dPYMCl5qtiHNTn%2BTPbh1jCBHH%2BdMJNhwNgs3nT%2BOhQoQ0vYif56BMG6WowAcHR3DjQolxLzyVekHj00PBAaW7IIAF1EF%2BuRIWyXjQMAs2chdpaKPNaB%2BkSezYt0%2BCA04sOg5vx8Fr7Ofa9sUv87h7SLAUFSzbetCCZ9pmyLt6l6%2FTzoA1%2FZBG9bIUVHLAbi%2FkdBFgYGyGwRQGBpkqCEg2ah9UD6EedEcEL3j4y0BQQCiExEnocA3SZboh%2Bepgd3YsOkHskZwPuQ5OoyA0fTA5AXrHcUOQF%2BzkJHIA7PwCDk1gGVmGUZSSoPhNf%2BTklauz98QofOlCIQ%2FtCD4dosHYPqtPCXB3agggQQIqQJsSkB%2Bqn0rkQ1toJjON%2FOtCIB9RYv3PqRA4C4U68ZMlZn6BdgEvi2ziU%2BTQ6NIw3ej%2BAtDwMGEZk7e2IjxUWKdAxyaw9OCwSmeADTPPleyk6UhGDNXQb%2B%2BW6Uk4q6F7%2Frg6WVTo82IoCxSIsFDrav4EPHphD3u4hR53WKVvYZUwNCCeM4PMBWzK%2BEfIthZOkuAwPo5C5jgoZgn6dUdvx5rIDmd58cXXdKNfw3l%2BwM2UjgrDJeQHhbD7HW2QDoZMCujgIUkk5Fg8VCsdyjOtnGRx8wgKRPZN5dR0zPUyfGZFVihbFRniXZFOZGKPnEQzU3AnD1KfR6weHW2XS6KbPJxUkOTZsAB9vTVp3Le1F8q5l%2BDMcLiIq78jxAImD2pGFw0VHfRatScGlK6SMu8leTmhUSMy8Uhdd6xBiH3Gdman4tjQGLboJfqz6fL2WKHTmrfsKZRYX6BTDjDldKMosaSTLdQS7oDisJNqAUhw1PfTlnacCO8vl8706Km1FROgLDmudzxg%2BEWTiArtHgLsRrAXYWdB0NmToNCJdKm0KWycZQqb%2BMw76Qy29iQ5up%2FX7oyw8QZ75kP5F6iJAJz6KCmqxz8fEa%2FxnsMYcIO%2FvEkGRuMckhr4rIeLrKaXnmIzlNLxbFspOphkcnJdnz%2FChp%2FVlpj2P7jJQmQRwGnltkTV5dbF9fE3%2FfxoSqTROgq9wFUlbuYzYcasE0ouzBo%2BdDCDzxKAfhbAZYxQiHrLzV2iVexnDX%2FQnT1fsT%2Fxuhu1ui5qIytgbGmRoQkeQooO8eJNNZsf0iALur8QxZFH0nCMnjerYQqG1pIfjyVZWxhVRznmmfLG00BcBWJE6hzQWRyFknuJnXuk8A5FRDCulwrWASSNoBtR%2BCtGdkPwYN2o7DOw%2FVGlCZPusRBFXODQdUM5zeHDIVuAJBLqbO%2Ff9Qua%2BpDqEPk230Sob9lEZ8BHiCorjVghuI0lI4JDgHGRDD%2FprQ84B1pVGkIpVUAHCG%2Biz3Bn3qm2AVrYcYWhock4jso5%2BJ7HfHVj4WMIQdGctq3psBCVVzupQOEioBGA2Bk%2BUILT7%2BVoX5mdxxA5fS42gISQVi%2FHTzrgMxu0fY6hE1ocUwwbsbWcezrY2n6S8%2F6cxXkOH4prpmPuFoikTzY7T85C4T2XYlbxLglSv2uLCgFv8Quk%2FwdesUdWPeHYIH0R729JIisN9Apdd4eB10aqwXrPt%2BSu9mA8k8n1sjMwnfsfF2j3jMUzXepSHmZ%2FBfqXvzgUNQQWOXO8YEuFBh4QTYCkOAPxywpYu1VxiDyJmKVcmJPGWk%2Fgc3Pov02StyYDahwmzw3E1gYC9wkupyWfDqDSUMpCTH5e5N8B%2F%2FlHiMuIkTNw4USHrJU67bjXGqNav6PBuQSoqTxc8avHoGmvqNtXzIaoyMIQIiiUHIM64cXieouplhNYln7qgc4wBVAYR104kO%2BCvKqsg4yIUlFNThVUAKZxZt1XA34h3TCUUiXVkZ0w8Hh2R0Z5L0b4LZvPd%2Fp1gi%2F07h8qfwHrByuSxglc9cI4QIg2oqvC%2Fqm0i7tjPLTgDhoWTAKDO2ONW5oe%2B%2FeKB9vZB8K6C25yCZ9RFVMnb6NRdRjyVK57CHHSkJBfnM2%2Fj4ODUwRkqrtBBCrDsDpt8jhZdXoy%2F1BCqw3sSGhgGGy0a5Jw6BP%2FTExoCmNFYjZl248A0osgPyGEmRA%2BfAsqPVaNAfytu0vuQJ7rk3J4kTDTR2AlCHJ5cls26opZM4w3jMULh2YXKpcqGBtuleAlOZnaZGbD6DHzMd6i2oFeJ8z9XYmalg1Szd%2FocZDc1C7Y6vcALJz2lYnTXiWEr2wawtoR4g3jvWUU2Ngjd1cewtFzEvM1NiHZPeLlIXFbBPawxNgMwwAlyNSuGF3zizVeOoC9bag1qRAQKQE%2FEZBWC2J8mnXAN2aTBboZ7HewnObE8CwROudZHmUM5oZ%2FUgd%2FJZQK8lvAm43uDRAbyW8gZ%2BZGq0EVerVGUKUSm%2FIdn8AQHdR4m7bue88WBwft9mSCeMOt1ncBwziOmJYI2ZR7ewNMPiCugmSsE4EyQ%2BQATJG6qORMGd4snEzc6B4shPIo4G1T7PgSm8PY5eUkPdF8JZ0VBtadbHXoJgnEhZQaODPj2gpODKJY5Yp4DOsLBFxWbvXN755KWylJm%2BoOd4zEL9Hpubuy2gyyfxh8oEfFutnYWdfB8PdESLWYvSqbElP9qo3u6KTmkhoacDauMNNjj0oy40DFV7Ql0aZj77xfGl7TJNHnIwgqOkenruYYNo6h724%2BzUQ7%2BvkCpZB%2BpGA562hYQiDxHVWOq0oDQl%2FQsoiY%2BcuI7iWq%2FZIBtHcXJ7kks%2Bh2fCNUPA82BzjnqktNts%2BRLdk1VSu%2BtqEn7QZCCsvEqk6FkfiOYkrsw092J8jsfIuEKypNjLxrKA9kiA19mxBD2suxQKCzwXGws7kEJvlhUiV9tArLIdZW0IORcxEzdzKmjtFhsjKy%2F44XYXdI5noQoRcvjZ1RMPACRqYg2V1%2BOwOepcOknRLLFdYgTkT5UApt%2FJhLM3jeFYprZV%2BZow2g8fP%2BU68hkKFWJj2yBbKqsrp25xkZX1DAjUw52IMYWaOhab8Kp05VrdNftqwRrymWF4OQSjbdfzmRZirK8FMJELEgER2PHjEAN9pGfLhCUiTJFbd5LBkOBMaxLr%2FA1SY9dXFz4RjzoU9ExfJCmx%2FI9FKEGT3n2cmzl2X42L3Jh%2BAbQq6sA%2BSs1kitoa4TAYgKHaoybHUDJ51oETdeI%2F9ThSmjWGkyLi5QAGWhL0BG1UsTyRGRJOldKBrYJeB8ljLJHfATWTEQBXBDnQexOHTB%2BUn44zExFE4vLytcu5NwpWrUxO%2F0ZICUGM7hGABXym0V6ZvDST0E370St9MIWQOTWngeoQHUTdCJUP04spMBMS8LSker9cReVQkULFDIZDFPrhTzBl6sed9wcZQTbL%2BBDqMyaN3RJPh%2Fanbx%2BIv%2BqgQdAa3M9Z5JmvYlh4qop%2BHo1F1W5gbOE9YKLgAnWytXElU4G8GtW47lhgFE6gaSs%2Bgs37sFvi0PPVvA5dnCBgILTwoKd%2F%2BDoL9F6inlM7H4rOTzD79KJgKlZO%2FZgt22UsKhrAaXU5ZcLrAglTVKJEmNJvORGN1vqrcfSMizfpsgbIe9zno%2BgBoKVXgIL%2FVI8dB1O5o%2FR3Suez%2FgD7M781ShjKpIIORM%2FnxG%2BjjhhgPwsn2IoXsPGPqYHXA63zJ07M2GPEykQwJBYLK808qYxuIew4frk52nhCsnCYmXiR6CuapvE1IwRB4%2FQftDbEn%2BAucIr1oxrLabRj9q4ae0%2BfXkHnteAJwXRbVkR0mctVSwEbqhJiMSZUp9DNbEDMmjX22m3ABpkrPQQTP3S1sib5pD2VRKRd%2BeNAjLYyT0hGrdjWJZy24OYXRoWQAIhGBZRxuBFMjjZQhpgrWo8SiFYbojcHO8V5DyscJpLTHyx9Fimassyo5U6WNtquUMYgccaHY5amgR3PQzq3ToNM5ABnoB9kuxsebqmYZm0R9qxJbFXCQ1UPyFIbxoUraTJFDpCk0Wk9GaYJKz%2F6oHwEP0Q14lMtlddQsOAU9zlYdMVHiT7RQP3XCmWYDcHCGbVRHGnHuwzScA0BaSBOGkz3lM8CArjrBsyEoV6Ys4qgDK3ykQQPZ3hCRGNXQTNNXbEb6tDiTDLKOyMzRhCFT%2BmAUmiYbV3YQVqFVp9dorv%2BTsLeCykS2b5yyu8AV7IS9cxcL8z4Kfwp%2BxJyYLv1OsxQCZwTB4a8BZ%2F5EdxTBJthApqyfd9u3ifr%2FWILTqq5VqgwMT9SOxbSGWLQJUUWCVi4k9tho9nEsbUh7U6NUsLmkYFXOhZ0kmamaJLRNJzSj%2Fqn4Mso6zb6iLLBXoaZ6AqeWCjHQm2lztnejYYM2eubnpBdKVLORZhudH3JF1waBJKA9%2BW8EhMj3Kzf0L4vi4k6RoHh3Z5YgmSZmk6ns4fjScjAoL8GoOECgqgYEBYUGFVO4FUv4%2FYtowhEmTs0vrvlD%2FCrisnoBNDAcUi%2FteY7OctFlmARQzjOItrrlKuPO6E2Ox93L4O%2F4DcgV%2FdZ7qR3VBwVQxP1GCieA4RIpweYJ5FoYrHxqRBdJjnqbsikA2Ictbb8vE1GYIo9dacK0REgDX4smy6GAkxlH1yCGGsk%2BtgiDhNKuKu3yNrMdxafmKTF632F8Vx4BNK57GvlFisrkjN9WDAtjsWA0ENT2e2nETUb%2Fn7qwhvGnrHuf5bX6Vh%2Fn3xffU3PeHdR%2BFA92i6ufT3AlyAREoNDh6chiMWTvjKjHDeRhOa9YkOQRq1vQXEMppAQVwHCuIcV2g5rBn6GmZZpTR7vnSD6ZmhdSl176gqKTXu5E%2BYbfL0adwNtHP7dT7t7b46DVZIkzaRJOM%2BS6KcrzYVg%2BT3wSRFRQashjfU18NutrKa%2F7PXbtuJvpIjbgPeqd%2BpjmRw6YKpnANFSQcpzTZgpSNJ6J7uiagAbir%2F8tNXJ%2FOsOnRh6iuIexxrmkIneAgz8QoLmiaJ8sLQrELVK2yn3wOHp57BAZJhDZjTBzyoRAuuZ4eoxHruY1pSb7qq79cIeAdOwin4GdgMeIMHeG%2BFZWYaiUQQyC5b50zKjYw97dFjAeY2I4Bnl105Iku1y0lMA1ZHolLx19uZnRdILcXKlZGQx%2FGdEqSsMRU1BIrFqRcV1qQOOHyxOLXEGcbRtAEsuAC2V4K3p5mFJ22IDWaEkk9ttf5Izb2LkD1MnrSwztXmmD%2FQi%2FEmVEFBfiKGmftsPwVaIoZanlKndMZsIBOskFYpDOq3QUs9aSbAAtL5Dbokus2G4%2FasthNMK5UQKCOhU97oaOYNGsTah%2BjfCKsZnTRn5TbhFX8ghg8CBYt%2FBjeYYYUrtUZ5jVij%2Fop7V5SsbA4mYTOwZ46hqdpbB6Qvq3AS2HHNkC15pTDIcDNGsMPXaBidXYPHc6PJAkRh29Vx8KcgX46LoUQBhRM%2B3SW6Opll%2FwgxxsPgKJKzr5QCmwkUxNbeg6Wj34SUnEzOemSuvS2OetRCO8Tyy%2BQbSKVJcqkia%2BGvDefFwMOmgnD7h81TUtMn%2BmRpyJJ349HhAnoWFTejhpYTL9G8N2nVg1qkXBeoS9Nw2fB27t7trm7d%2FQK7Cr4uoCeOQ7%2F8JfKT77KiDzLImESHw%2F0wf73QeHu74hxv7uihi4fTX%2BXEwAyQG3264dwv17aJ5N335Vt9sdrAXhPOAv8JFvzqyYXwfx8WYJaef1gMl98JRFyl5Mv5Uo%2FoVH5ww5OzLFsiTPDns7fS6EURSSWd%2F92BxMYQ8sBaH%2Bj%2BwthQPdVgDGpTfi%2BJQIWMD8xKqULliRH01rTeyF8x8q%2FGBEEEBrAJMPf25UQwi0b8tmqRXY7kIvNkzrkvRWLnxoGYEJsz8u4oOyMp8cHyaybb1HdMCaLApUE%2B%2F7xLIZGP6H9xuSEXp1zLIdjk5nBaMuV%2FyTDRRP8Y2ww5RO6d2D94o%2B6ucWIqUAvgHIHXhZsmDhjVLczmZ3ca0Cb3PpKwt2UtHVQ0BgFJsqqTsnzZPlKahRUkEu4qmkJt%2Bkqdae76ViWe3STan69yaF9%2BfESD2lcQshLHWVu4ovItXxO69bqC5p1nZLvI8NdQB9s9UNaJGlQ5mG947ipdDA0eTIw%2FA1zEdjWquIsQXXGIVEH0thC5M%2BW9pZe7IhAVnPJkYCCXN5a32HjN6nsvokEqRS44tGIs7s2LVTvcrHAF%2BRVmI8L4HUYk4x%2B67AxSMJKqCg8zrGOgvK9kNMdDrNiUtSWuHFpC8%2Fp5qIQrEo%2FH%2B1l%2F0cAwQ2nKmpWxKcMIuHY44Y6DlkpO48tRuUGBWT0FyHwSKO72Ud%2BtJUfdaZ4CWNijzZtlRa8%2BCkmO%2FEwHYfPZFU%2FhzjFWH7vnzHRMo%2BaF9u8qHSAiEkA2HjoNQPEwHsDKOt6hOoK3Ce%2F%2B%2F9boMWDa44I6FrQhdgS7OnNaSzwxWKZMcyHi6LN4WC6sSj0qm2PSOGBTvDs%2FGWJS6SwEN%2FULwpb4LQo9fYjUfSXRwZkynUazlSpvX9e%2BG2zor8l%2BYaMxSEomDdLHGcD6YVQPegTaA74H8%2BV4WvJkFUrjMLGLlvSZQWvi8%2FQA7yzQ8GPno%2F%2F5SJHRP%2FOqKObPCo81s%2F%2B6WgLqykYpGAgQZhVDEBPXWgU%2FWzFZjKUhSFInufPRiMAUULC6T11yL45ZrRoB4DzOyJShKXaAJIBS9wzLYIoCEcJKQW8GVCx4fihqJ6mshBUXSw3wWVj3grrHQlGNGhIDNNzsxQ3M%2BGWn6ASobIWC%2BLbYOC6UpahVO13Zs2zOzZC8z7FmA05JhUGyBsF4tsG0drcggIFzgg%2Fkpf3%2BCnAXKiMgIE8Jk%2FMhpkc8DUJEUzDSnWlQFme3d0sHZDrg7LavtsEX3cHwjCYA17pMTfx8Ajw9hHscN67hyo%2BRJQ4458RmPywXykkVcW688oVUrQhahpPRvTWPnuI0B%2BSkQu7dCyvLRyFYlC1LG1gRCIvn3rwQeINzZQC2KXq31FaR9UmVV2QeGVqBHjmE%2BVMd3b1fhCynD0pQNhCG6%2FWCDbKPyE7NRQzL3BzQAJ0g09aUzcQA6mUp9iZFK6Sbp%2FYbHjo%2B%2B7%2FWj8S4YNa%2BZdqAw1hDrKWFXv9%2BzaXpf8ZTDSbiqsxnwN%2FCzK5tPkOr4tRh2kY3Bn9JtalbIOI4b3F7F1vPQMfoDcdxMS8CW9m%2FNCW%2FHILTUVWQIPiD0j1A6bo8vsv6P1hCESl2abrSJWDrq5sSzUpwoxaCU9FtJyYH4QFMxDBpkkBR6kn0LMPO%2B5EJ7Z6bCiRoPedRZ%2FP0SSdii7ZnPAtVwwHUidcdyspwncz5uq6vvm4IEDbJVLUFCn%2FLvIHfooUBTkFO130FC7CmmcrKdgDJcid9mvVzsDSibOoXtIf9k6ABle3PmIxejodc4aob0QKS432srrCMndbfD454q52V01G4q913mC5HOsTzWF4h2No1av1VbcUgWAqyoZl%2B11PoFYnNv2HwAODeNRkHj%2B8SF1fcvVBu6MrehHAZK1Gm69ICcTKizykHgGFx7QdowTVAsYEF2tVc0Z6wLryz2FI1sc5By2znJAAmINndoJiB4sfPdPrTC8RnkW7KRCwxC6YvXg5ahMlQuMpoCSXjOlBy0Kij%2BbsCYPbGp8BdCBiLmLSAkEQRaieWo1SYvZIKJGj9Ur%2FeWHjiB7SOVdqMAVmpBvfRiebsFjger7DC%2B8kRFGtNrTrnnGD2GAJb8rQCWkUPYHhwXsjNBSkE6lGWUj5QNhK0DMNM2l%2BkXRZ0KLZaGsFSIdQz%2FHXDxf3%2FTE30%2BDgBKWGWdxElyLccJfEpjsnszECNoDGZpdwdRgCixeg9L4EPhH%2BRptvRMVRaahu4cySjS3P5wxAUCPkmn%2BrhyASpmiTaiDeggaIxYBmtLZDDhiWIJaBgzfCsAGUF1Q1SFZYyXDt9skCaxJsxK2Ms65dmdp5WAZyxik%2FzbrTQk5KmgxCg%2Ff45L0jywebOWUYFJQAJia7XzCV0x89rpp%2Ff3AVWhSPyTanqmik2SkD8A3Ml4NhIGLAjBXtPShwKYfi2eXtrDuKLk4QlSyTw1ftXgwqA2jUuopDl%2B5tfUWZNwBpEPXghzbBggYCw%2Fdhy0ntds2yeHCDKkF%2FYxQjNIL%2FF%2F37jLPHCKBO9ibwYCmuxImIo0ijV2Wbg3kSN2psoe8IsABv3RNFaF9uMyCtCYtqcD%2BqNOhwMlfARQUdJ2tUX%2BMNJqOwIciWalZsmEjt07tfa8ma4cji9sqz%2BQ9hWfmMoKEbIHPOQORbhQRHIsrTYlnVTNvcq1imqmmPDdVDkJgRcTgB8Sb6epCQVmFZe%2BjGDiNJQLWnfx%2BdrTKYjm0G8yH0ZAGMWzEJhUEQ4Maimgf%2Fbkvo8PLVBsZl152y5S8%2BHRDfZIMCbYZ1WDp4yrdchOJw8k6R%2B%2F2pHmydK4NIK2PHdFPHtoLmHxRDwLFb7eB%2BM4zNZcB9NrAgjVyzLM7xyYSY13ykWfIEEd2n5%2FiYp3ZdrCf7fL%2Ben%2BsIJu2W7E30MrAgZBD1rAAbZHPgeAMtKCg3NpSpYQUDWJu9bT3V7tOKv%2BNRiJc8JAKqqgCA%2FPNRBR7ChpiEulyQApMK1AyqcWnpSOmYh6yLiWkGJ2mklCSPIqN7UypWj3dGi5MvsHQ87MrB4VFgypJaFriaHivwcHIpmyi5LhNqtem4q0n8awM19Qk8BOS0EsqGscuuydYsIGsbT5GHnERUiMpKJl4ON7qjB4fEqlGN%2FhCky89232UQCiaeWpDYCJINXjT6xl4Gc7DxRCtgV0i1ma4RgWLsNtnEBRQFqZggCLiuyEydmFd7WlogpkCw5G1x4ft2psm3KAREwVwr1Gzl6RT7FDAqpVal34ewVm3VH4qn5mjGj%2BbYL1NgfLNeXDwtmYSpwzbruDKpTjOdgiIHDVQSb5%2FzBgSMbHLkxWWgghIh9QTFSDILixVwg0Eg1puooBiHAt7DzwJ7m8i8%2Fi%2BjHvKf0QDnnHVkVTIqMvIQImOrzCJwhSR7qYB5gSwL6aWL9hERHCZc4G2%2BJrpgHNB8eCCmcIWIQ6rSdyPCyftXkDlErUkHafHRlkOIjxGbAktz75bnh50dU7YHk%2BMz7wwstg6RFZb%2BTZuSOx1qqP5C66c0mptQmzIC2dlpte7vZrauAMm%2F7RfBYkGtXWGiaWTtwvAQiq2oD4YixPLXE2khB2FRaNRDTk%2B9sZ6K74Ia9VntCpN4BhJGJMT4Z5c5FhSepRCRWmBXqx%2BwhVZC4me4saDs2iNqXMuCl6iAZflH8fscC1sTsy4PHeC%2BXYuqMBMUun5YezKbRKmEPwuK%2BCLzijPEQgfhahQswBBLfg%2FGBgBiI4QwAqzJkkyYAWtjzSg2ILgMAgqxYfwERRo3zruBL9WOryUArSD8sQOcD7fvIODJxKFS615KFPsb68USBEPPj1orNzFY2xoTtNBVTyzBhPbhFH0PI5AtlJBl2aSgNPYzxYLw7XTDBDinmVoENwiGzmngrMo8OmnRP0Z0i0Zrln9DDFcnmOoBZjABaQIbPOJYZGqX%2BRCMlDDbElcjaROLDoualmUIQ88Kekk3iM4OQrADcxi3rJguS4MOIBIgKgXrjd1WkbCdqxJk%2F4efRIFsavZA7KvvJQqp3Iid5Z0NFc5aiMRzGN3vrpBzaMy4JYde3wr96PjN90AYOIbyp6T4zj8LoE66OGcX1Ef4Z3KoWLAUF4BTg7ug%2FAbkG5UNQXAMkQezujSHeir2uTThgd3gpyzDrbnEdDRH2W7U6PeRvBX1ZFMP5RM%2BZu6UUZZD8hDPHldVWntTCNk7To8IeOW9yn2wx0gmurwqC60AOde4r3ETi5pVMSDK8wxhoGAoEX9NLWHIR33VbrbMveii2jAJlrxwytTHbWNu8Y4N8vCCyZjAX%2FpcsfwXbLze2%2BD%2Bu33OGBoJyAAL3jn3RuEcdp5If8O%2Ba4NKWvxOTyDltG0IWoHhwVGe7dKkCWFT%2B%2Btm%2BhaBCikRUUMrMhYKZJKYoVuv%2FbsJzO8DwfVIInQq3g3BYypiz8baogH3r3GwqCwFtZnz4xMjAVOYnyOi5HWbFA8n0qz1OjSpHWFzpQOpvkNETZBGpxN8ybhtqV%2FDMUxd9uFZmBfKXMCn%2FSqkWJyKPnT6lq%2B4zBZni6fYRByJn6OK%2BOgPBGRAJluwGSk4wxjOOzyce%2FPKODwRlsgrVkdcsEiYrqYdXo0Er2GXi2GQZd0tNJT6c9pK1EEJG1zgDJBoTVuCXGAU8BKTvCO%2FcEQ1Wjk3Zzuy90JX4m3O5IlxVFhYkSUwuQB2up7jhvkm%2BbddRQu5F9s0XftGEJ9JSuSk%2BZachCbdU45fEqbugzTIUokwoAKvpUQF%2FCvLbWW5BNQFqFkJg2f30E%2F48StNe5QwBg8zz3YAJ82FZoXBxXSv4QDooDo79NixyglO9AembuBcx5Re3CwOKTHebOPhkmFC7wNaWtoBhFuV4AkEuJ0J%2B1pT0tLkvFVZaNzfhs%2FKd3%2BA9YsImlO4XK4vpCo%2FelHQi%2F9gkFg07xxnuXLt21unCIpDV%2BbbRxb7FC6nWYTsMFF8%2B1LUg4JFjVt3vqbuhHmDKbgQ4e%2BRGizRiO8ky05LQGMdL2IKLSNar0kNG7lHJMaXr5mLdG3nykgj6vB%2FKVijd1ARWkFEf3yiUw1v%2FWaQivVUpIDdSNrrKbjO5NPnxz6qTTGgYg03HgPhDrCFyYZTi3XQw3HXCva39mpLNFtz8AiEhxAJHpWX13gCTAwgm9YTvMeiqetdNQv6IU0hH0G%2BZManTqDLPjyrOse7WiiwOJCG%2BJ0pZYULhN8NILulmYYvmVcV2MjAfA39sGKqGdjpiPo86fecg65UPyXDIAOyOkCx5NQsLeD4gGVjTVDwOHWkbbBW0GeNjDkcSOn2Nq4cEssP54t9D749A7M1AIOBl0Fi0sSO5v3P7LCBrM6ZwFY6kp2FX6AcbGUdybnfChHPyu6WlRZ2Fwv9YM0RMI7kISRgR8HpQSJJOyTfXj%2F6gQKuihPtiUtlCQVPohUgzfezTg8o1b3n9pNZeco1QucaoXe40Fa5JYhqdTspFmxGtW9h5ezLFZs3j%2FN46f%2BS2rjYNC2JySXrnSAFhvAkz9a5L3pza8eYKHNoPrvBRESpxYPJdKVUxBE39nJ1chrAFpy4MMkf0qKgYALctGg1DQI1kIymyeS2AJNT4X240d3IFQb%2F0jQbaHJ2YRK8A%2Bls6WMhWmpCXYG5jqapGs5%2FeOJErxi2%2F2KWVHiPellTgh%2FfNl%2F2KYPKb7DUcAg%2BmCOPQFCiU9Mq%2FWLcU1xxC8aLePFZZlE%2BPCLzf7ey46INWRw2kcXySR9FDgByXzfxiNKwDFbUSMMhALPFSedyjEVM5442GZ4hTrsAEvZxIieSHGSgkwFh%2FnFNdrrFD4tBH4Il7fW6ur4J8Xaz7RW9jgtuPEXQsYk7gcMs2neu3zJwTyUerHKSh1iTBkj2YJh1SSOZL5pLuQbFFAvyO4k1Hxg2h99MTC6cTUkbONQIAnEfGsGkNFWRbuRyyaEZInM5pij73EA9rPIUfU4XoqQpHT9THZkW%2BoKFLvpyvTBMM69tN1Ydwv1LIEhHsC%2BueVG%2Bw%2BkyCPsvV3erRikcscHjZCkccx6VrBkBRusTDDd8847GA7p2Ucy0y0HdSRN6YIBciYa4vuXcAZbQAuSEmzw%2BH%2FAuOx%2BaH%2BtBL88H57D0MsqyiZxhOEQkF%2F8DR1d2hSPMj%2FsNOa5rxcUnBgH8ictv2J%2Bcb4BA4v3MCShdZ2vtK30vAwkobnEWh7rsSyhmos3WC93Gn9C4nnAd%2FPjMMtQfyDNZsOPd6XcAsnBE%2FmRHtHEyJMzJfZFLE9OvQa0i9kUmToJ0ZxknTgdl%2FXPV8xoh0K7wNHHsnBdvFH3sv52lU7UFteseLG%2FVanIvcwycVA7%2BBE1Ulyb20BvwUWZcMTKhaCcmY3ROpvonVMV4N7yBXTL7IDtHzQ4CCcqF66LjF3xUqgErKzolLyCG6Kb7irP%2FMVTCCwGRxfrPGpMMGvPLgJ881PHMNMIO09T5ig7AzZTX%2F5PLlwnJLDAPfuHynSGhV4tPqR3gJ4kg4c06c%2FF1AcjGytKm2Yb5jwMotF7vro4YDLWlnMIpmPg36NgAZsGA0W1spfLSue4xxat0Gdwd0lqDBOgIaMANykwwDKejt5YaNtJYIkrSgu0KjIg0pznY0SCd1qlC6R19g97UrWDoYJGlrvCE05J%2F5wkjpkre727p5PTRX5FGrSBIfJqhJE%2FIS876PaHFkx9pGTH3oaY3jJRvLX9Iy3Edoar7cFvJqyUlOhAEiOSAyYgVEGkzHdug%2BoRHIEOXAExMiTSKU9A6nmRC8mp8iYhwWdP2U%2F5EkFAdPrZw03YA3gSyNUtMZeh7dDCu8pF5x0VORCTgKp07ehy7NZqKTpIC4UJJ89lnboyAfy5OyXzXtuDRbtAFjZRSyGFTpFrXwkpjSLIQIG3N0Vj4BtzK3wdlkBJrO18MNsgseR4BysJilI0wI6ZahLhBFA0XBmV8d4LUzEcNVb0xbLjLTETYN8OEVqNxkt10W614dd1FlFFVTIgB7%2FBQQp1sWlNolpIu4ekxUTBV7NmxOFKEBmmN%2BnA7pvF78%2FRII5ZHA09OAiE%2F66MF6HQ%2BqVEJCHxwymukkNvzqHEh52dULPbVasfQMgTDyBZzx4007YiKdBuUauQOt27Gmy8ISclPmEUCIcuLbkb1mzQSqIa3iE0PJh7UMYQbkpe%2BhXjTJKdldyt2mVPwywoODGJtBV1lJTgMsuSQBlDMwhEKIfrvsxGQjHPCEfNfMAY2oxvyKcKPUbQySkKG6tj9AQyEW3Q5rpaDJ5Sns9ScLKeizPRbvWYAw4bXkrZdmB7CQopCH8NAmqbuciZChHN8lVGaDbCnmddnqO1PQ4ieMYfcSiBE5zzMz%2BJV%2F4eyzrzTEShvqSGzgWimkNxLvUj86iAwcZuIkqdB0VaIB7wncLRmzHkiUQpPBIXbDDLHBlq7vp9xwuC9AiNkIptAYlG7Biyuk8ILdynuUM1cHWJgeB%2BK3wBP%2FineogxkvBNNQ4AkW0hvpBOQGFfeptF2YTR75MexYDUy7Q%2F9uocGsx41O4IZhViw%2F2FvAEuGO5g2kyXBUijAggWM08bRhXg5ijgMwDJy40QeY%2FcQpUDZiIzmvskQpO5G1zyGZA8WByjIQU4jRoFJt56behxtHUUE%2Fom7Rj2psYXGmq3llVOCgGYKNMo4pzwntITtapDqjvQtqpjaJwjHmDzSVGLxMt12gEXAdLi%2FcaHSM3FPRGRf7dB7YC%2BcD2ho6oL2zGDCkjlf%2FDFoQVl8GS%2F56wur3rdV6ggtzZW60MRB3g%2BU1W8o8cvqIpMkctiGVMzXUFI7FacFLrgtdz4mTEr4aRAaQ2AFQaNeG7GX0yOJgMRYFziXdJf24kg%2FgBQIZMG%2FYcPEllRTVNoDYR6oSJ8wQNLuihfw81UpiKPm714bZX1KYjcXJdfclCUOOpvTxr9AAJevTY4HK%2FG7F3mUc3GOAKqh60zM0v34v%2BELyhJZqhkaMA8UMMOU90f8RKEJFj7EqepBVwsRiLbwMo1J2zrE2UYJnsgIAscDmjPjnzI8a719Wxp757wqmSJBjXowhc46QN4RwKIxqEE6E5218OeK7RfcpGjWG1jD7qND%2B%2FGTk6M56Ig4yMsU6LUW1EWE%2BfIYycVV1thldSlbP6ltdC01y3KUfkobkt2q01YYMmxpKRvh1Z48uNKzP%2FIoRIZ%2FF6buOymSnW8gICitpJjKWBscSb9JJKaWkvEkqinAJ2kowKoqkqZftRqfRQlLtKoqvTRDi2vg%2FRrPD%2Fd3a09J8JhGZlEkOM6znTsoMCsuvTmywxTCDhw5dd0GJOHCMPbsj3QLkTE3MInsZsimDQ3HkvthT7U9VA4s6G07sID0FW4SHJmRGwCl%2BMu4xf0ezqeXD2PtPDnwMPo86sbwDV%2B9PWcgFcARUVYm3hrFQrHcgMElFGbSM2A1zUYA3baWfheJp2AINmTJLuoyYD%2FOwA4a6V0ChBN97E8YtDBerUECv0u0TlxR5yhJCXvJxgyM73Bb6pyq0jTFJDZ4p1Am1SA6sh8nADd1hAcGBMfq4d%2FUfwnmBqe0Jun1n1LzrgKuZMAnxA3NtCN7Klf4BH%2B14B7ibBmgt0TGUafVzI4uKlpF7v8NmgNjg90D6QE3tbx8AjSAC%2BOA1YJvclyPKgT27QpIEgVYpbPYGBsnyCNrGz9XUsCHkW1QAHgL2STZk12QGqmvAB0NFteERkvBIH7INDsNW9KKaAYyDMdBEMzJiWaJHZALqDxQDWRntumSDPcplyFiI1oDpT8wbwe01AHhW6%2BvAUUBoGhY3CT2tgwehdPqU%2F4Q7ZLYvhRl%2FogOvR9O2%2BwkkPKW5vCTjD2fHRYXONCoIl4Jh1bZY0ZE1O94mMGn%2FdFSWBWzQ%2FVYk%2BGezi46RgiDv3EshoTmMSlioUK6MQEN8qeyK6FRninyX8ZPeUWjjbMJChn0n%2FyJvrq5bh5UcCAcBYSafTFg7p0jDgrXo2QWLb3WpSOET%2FHh4oSadBTvyDo10IufLzxiMLAnbZ1vcUmj3w7BQuIXjEZXifwukVxrGa9j%2BDXfpi12m1RbzYLg9J2wFergEwOxFyD0%2FJstNK06ZN2XdZSGWxcJODpQHOq4iKqjqkJUmPu1VczL5xTGUfCgLEYyNBCCbMBFT%2FcUP6pE%2FmujnHsSDeWxMbhrNilS5MyYR0nJyzanWXBeVcEQrRIhQeJA6Xt4f2eQESNeLwmC10WJVHqwx8SSyrtAAjpGjidcj1E2FYN0LObUcFQhafUKTiGmHWRHGsFCB%2BHEXgrzJEB5bp0QiF8ZHh11nFX8AboTD0PS4O1LqF8XBks2MpjsQnwKHF6HgaKCVLJtcr0XjqFMRGfKv8tmmykhLRzu%2BvqQ02%2BKpJBjaLt9ye1Ab%2BBbEBhy4EVdIJDrL2naV0o4wU8YZ2Lq04FG1mWCKC%2BUwkXOoAjneU%2FxHplMQo2cXUlrVNqJYczgYlaOEczVCs%2FOCgkyvLmTmdaBJc1iBLuKwmr6qtRnhowngsDxhzKFAi02tf8bmET8BO27ovJKF1plJwm3b0JpMh38%2BxsrXXg7U74QUM8ZCIMOpXujHntKdaRtsgyEZl5MClMVMMMZkZLNxH9%2Bb8fH6%2Bb8Lev30A9TuEVj9CqAdmwAAHBPbfOBFEATAPZ2CS0OH1Pj%2F0Q7PFUcC8hDrxESWdfgFRm%2B7vvWbkEppHB4T%2F1ApWnlTIqQwjcPl0VgS1yHSmD0OdsCVST8CQVwuiew1Y%2Bg3QGFjNMzwRB2DSsAk26cmA8lp2wIU4p93AUBiUHFGOxOajAqD7Gm6NezNDjYzwLOaSXRBYcWipTSONHjUDXCY4mMI8XoVCR%2FRrs%2FJLKXgEx%2BqkmeDlFOD1%2FyTQNDClRuiUyKYCllfMiQiyFkmuTz2vLsBNyRW%2Bxz%2B5FElFxWB28VjYIGZ0Yd%2B5wIjkcoMaggxswbT0pCmckRAErbRlIlcOGdBo4djTNO8FAgQ%2BlT6vPS60BwTRSUAM3ddkEAZiwtEyArrkiDRnS7LJ%2B2hwbzd2YDQagSgACpsovmjil5wfPuXq3GuH0CyE7FK3M4FgRaFoIkaodORrPx1%2BJpI9psyNYIFuJogZa0%2F1AhOWdlHQxdAgbwacsHqPZo8u%2FngAH2GmaTdhYnBfSDbBfh8CHq6Bx5bttP2%2BRdM%2BMAaYaZ0Y%2FADkbNCZuAyAVQa2OcXOeICmDn9Q%2FeFkDeFQg5MgHEDXq%2FtVjj%2Bjtd26nhaaolWxs1ixSUgOBwrDhRIGOLyOVk2%2FBc0UxvseQCO2pQ2i%2BKrfhu%2FWeBovNb5dJxQtJRUDv2mCwYVpNl2efQM9xQHnK0JwLYt%2FU0Wf%2BphiA4uw8G91slC832pmOTCAoZXohg1fewCZqLBhkOUBofBWpMPsqg7XEXgPfAlDo2U5WXjtFdS87PIqClCK5nW6adCeXPkUiTGx0emOIDQqw1yFYGHEVx20xKjJVYe0O8iLmnQr3FA9nSIQilUKtJ4ZAdcTm7%2BExseJauyqo30hs%2B1qSW211A1SFAOUgDlCGq7eTIcMAeyZkV1SQJ4j%2Fe1Smbq4HcjqgFbLAGLyKxlMDMgZavK5NAYH19Olz3la%2FQCTiVelFnU6O%2FGCvykqS%2FwZJDhKN9gBtSOp%2F1SP5VRgJcoVj%2Bkmf2wBgv4gjrgARBWiURYx8xENV3bEVUAAWWD3dYDKAIWk5opaCFCMR5ZjJExiCAw7gYiSZ2rkyTce4eNMY3lfGn%2B8p6%2BvBckGlKEXnA6Eota69OxDO9oOsJoy28BXOR0UoXNRaJD5ceKdlWMJlOFzDdZNpc05tkMGQtqeNF2lttZqNco1VtwXgRstLSQ6tSPChgqtGV5h2DcDReIQadaNRR6AsAYKL5gSFsCJMgfsaZ7DpKh8mg8Wz8V7H%2BgDnLuMxaWEIUPevIbClgap4dqmVWSrPgVYCzAoZHIa5z2Ocx1D%2FGvDOEqMOKLrMefWIbSWHZ6jbgA8qVBhYNHpx0P%2BjAgN5TB3haSifDcApp6yymEi6Ij%2FGsEpDYUgcHATJUYDUAmC1SCkJ4cuZXSAP2DEpQsGUjQmKJfJOvlC2x%2FpChkOyLW7KEoMYc5FDC4v2FGqSoRWiLsbPCiyg1U5yiHZVm1XLkHMMZL11%2Fyxyw0UnGig3MFdZklN5FI%2FqiT65T%2BjOXOdO7XbgWurOAZR6Cv9uu1cm5LjkXX4xi6mWn5r5NjBS0gTliHhMZI2WNqSiSphEtiCAwnafS11JhseDGHYQ5%2BbqWiAYiAv6Jsf79%2FVUs4cIl%2Bn6%2BWOjcgB%2F2l5TreoAV2717JzZbQIR0W1cl%2FdEqCy5kJ3ZSIHuU0vBoHooEpiHeQWVkkkOqRX27eD1FWw4BfO9CJDdKoSogQi3hAAwsPRFrN5RbX7bqLdBJ9JYMohWrgJKHSjVl1sy2xAG0E3sNyO0oCbSGOxCNBRRXTXenYKuwAoDLfnDcQaCwehUOIDiHAu5m5hMpKeKM4sIo3vxACakIxKoH2YWF2QM84e6F5C5hJU4g8uxuFOlAYnqtwxmHyNEawLW%2FPhoawJDrGAP0JYWHgAVUByo%2FbGdiv2T2EMg8gsS14%2FrAdzlOYazFE7w4OzxeKiWdm3nSOnQRRKXSlVo8HEAbBfyJMKqoq%2BSCcTSx5NDtbFwNlh8VhjGGDu7JG5%2FTAGAvniQSSUog0pNzTim8Owc6QTuSKSTXlQqwV3eiEnklS3LeSXYPXGK2VgeZBqNcHG6tZHvA3vTINhV0ELuQdp3t1y9%2BogD8Kk%2FW7QoRN1UWPqM4%2BxdygkFDPLoTaumKReKiLWoPHOfY54m3qPx4c%2B4pgY3MRKKbljG8w4wvz8pxk3AqKsy4GMAkAtmRjRMsCxbb4Q2Ds0Ia9ci8cMT6DmsJG00XaHCIS%2Bo3F8YVVeikw13w%2BOEDaCYYhC0ZE54kA4jpjruBr5STWeqQG6M74HHL6TZ3lXrd99ZX%2B%2B7LhNatQaZosuxEf5yRA15S9gPeHskBIq3Gcw81AGb9%2FO53DYi%2F5CsQ51EmEh8Rkg4vOciClpy4d04eYsfr6fyQkBmtD%2BP8sNh6e%2BXYHJXT%2FlkXxT4KXU5F2sGxYyzfniMMQkb9OjDN2C8tRRgTyL7GwozH14PrEUZc6oz05Emne3Ts5EG7WolDmU8OB1LDG3VrpQxp%2BpT0KYV5dGtknU64JhabdqcVQbGZiAxQAnvN1u70y1AnmvOSPgLI6uB4AuDGhmAu3ATkJSw7OtS%2F2ToPjqkaq62%2F7WFG8advGlRRqxB9diP07JrXowKR9tpRa%2BjGJ91zxNTT1h8I2PcSfoUPtd7NejVoH03EUcqSBuFZPkMZhegHyo2ZAITovmm3zAIdGFWxoNNORiMRShgwdYwFzkPw5PA4a5MIIQpmq%2Bnsp3YMuXt%2FGkXxLx%2FP6%2BZJS0lFyz4MunC3eWSGE8xlCQrKvhKUPXr0hjpAN9ZK4PfEDrPMfMbGNWcHDzjA7ngMxTPnT7GMHar%2BgMQQ3NwHCv4zH4BIMYvzsdiERi6gebRmerTsVwZJTRsL8dkZgxgRxmpbgRcud%2BYlCIRpPwHShlUSwuipZnx9QCsEWziVazdDeKSYU5CF7UVPAhLer3CgJOQXl%2Fzh575R5rsrmRnKAzq4POFdgbYBuEviM4%2BLVC15ssLNFghbTtHWerS1hDt5s4qkLUha%2FqpZXhWh1C6lTQAqCNQnaDjS7UGFBC6wTu8yFnKJnExCnAs3Ok9yj5KpfZESQ4lTy5pTGTnkAUpxI%2ByjEldJfSo4y0QhG4i4IwkRFGcjWY8%2BEzgYYJUK7BXQksLxAww%2FYYWBMhJILB9e8ePEJ4OP7z%2B4%2FwOQDl64iOYDp26DaONPxpKtBxq%2FaTzRGarm3VkPYTLJKx6Z%2FMw2YbBGseJhPMwhhNswrIkyvV2BYzrvZbxLpKwcWJhYmFtVZ%2BlPEq91FzVp1HlQY1bZVLqeNR9SAUn6n0E28k%2FUuGkNpP1DBI5ch%2FEehZfjUQ9aE41NhETExoPT2gGQz0IhWJbEOvTQ4wgcXCHHFBhewYUiFHuhRSAUVmEHeCRQHQkXGFwkAgyzREJCVN7TRnTon36Zw3tPhx4EALwNdwDv%2BJ41YSP4B2CQqz0EFgARZ4ESgBHQgROwAVn9GTI%2BHYexTUevLUeta4%2FDqKrbMVS%2BYqb8hUwYCrlgKtmAq1YCrFgKrd4qpXiqZcKn1oqdWipjYKpWwVPVYqW6xUpVipKqFR3QKjagVEtAqHpxUMTitsnFaJOKx2cVhswq35RVpyiq9lFVNIKnOQVMkgqtYxVNxiqQjFS7GKlSIVIsQqPIhUWwioigFQ%2B%2BKkN8VHr49HDw9Ebo9EDo9DTo9Crg9BDg9%2FWx7gWx7YWwlobYrOGxWPNisAaAHEyALpkAVDIAeWAArsABVXACYuAD5cAF6wAKFQAQqgAbVAAsoAAlQAUaYAfkwAvogBWQACOgAD9AAHSAAKT4GUdMiOvFngBTwCn2AZ7Dv6B6k%2F90B8%2ByRnkV144AIBoAMTQATGgAjNAA4YABgwABZgB%2FmQCwyAVlwCguASlwCEuAQFwB4uAMlwBYuAJlQAUVAAhUD2KgdpUDaJgaRMDFJgX5MC1JgWJEAokQCWRAHxEAWkQBMRADpEAMkQAYROAEecC484DRpwBDTnwNOdw05tjTmiNOYwtswhYFwLA7BYG4LA2BYGOLAwRYFuLAsxYFQJAohIEyJAMwkAwiQC0JAJgkAeiQBkJAFokAPCQA0JABwcD4Dgc4cDdDgaYcDIDgYgUC6CgWgUClCgUYUAVBQBOFAEYMALgwAgDA9QYAdIn8AZzeBB2L5EcWrenUT1KXienEsuJJ7x5U8XlTjc1NVzUyXFTGb1LlpUtWlTDIjqwE4LsagowoCi2gJLKAkpoBgJQNpAIhNqaEoneI6kiiqQ6Go%2Fn6j0cS%2Ba2gEU8gIHJ%2BBwfgZX4GL%2BBd%2FgW34FZ%2BBS%2FgUH4FN6BTegTvoEv6BJegRnYEF2A79gOvYDl2BdEjCkqkGtwXp0LNToIskOTXzh%2FF062yJ7AAAAEDAWAAABWhJ%2BKPEIJgBFxMVP7w2QJBGHASQnOBKXKFIdUK4igKA9IEaYJg%29%20format%28%27embedded%2Dopentype%27%29%2Curl%28data%3Aapplication%2Fx%2Dfont%2Dwoff%3Bbase64%2Cd09GRgABAAAAAFuAAA8AAAAAsVwAAQAAAAAAAAAAAAAAAAAAAAAAAAAAAABGRlRNAAABWAAAABwAAAAcbSqX3EdERUYAAAF0AAAAHwAAACABRAAET1MvMgAAAZQAAABFAAAAYGe5a4ljbWFwAAAB3AAAAsAAAAZy2q3jgWN2dCAAAAScAAAABAAAAAQAKAL4Z2FzcAAABKAAAAAIAAAACP%2F%2FAANnbHlmAAAEqAAATRcAAJSkfV3Cb2hlYWQAAFHAAAAANAAAADYFTS%2FYaGhlYQAAUfQAAAAcAAAAJApEBBFobXR4AABSEAAAAU8AAAN00scgYGxvY2EAAFNgAAACJwAAAjBv%2B5XObWF4cAAAVYgAAAAgAAAAIAFqANhuYW1lAABVqAAAAZ4AAAOisyygm3Bvc3QAAFdIAAAELQAACtG6o%2BU1d2ViZgAAW3gAAAAGAAAABsMYVFAAAAABAAAAAMw9os8AAAAA0HaBdQAAAADQdnOXeNpjYGRgYOADYgkGEGBiYGRgZBQDkixgHgMABUgASgB42mNgZulmnMDAysDCzMN0gYGBIQpCMy5hMGLaAeQDpRCACYkd6h3ux%2BDAoPD%2FP%2FOB%2FwJAdSIM1UBhRiQlCgyMADGWCwwAAAB42u2UP2hTQRzHf5ekaVPExv6JjW3fvTQ0sa3QLA5xylBLgyBx0gzSWEUaXbIoBBQyCQGHLqXUqYNdtIIgIg5FHJxEtwqtpbnfaV1E1KFaSvX5vVwGEbW6OPngk8%2FvvXfv7pt3v4SImojIDw6BViKxRgIVBaZwVdSv%2BxvXA%2BIuzqcog2cOkkvDNE8Lbqs74k64i%2B5Sf3u8Z2AnIRLbyVCyTflVSEXVoEqrrMqrgiqqsqqqWQ5xlAc5zWOc5TwXucxVnuE5HdQhHdFRHdNJndZZndeFLc%2FzsKJLQ%2FWV6BcrCdWkwspVKZVROaw0qUqqoqZZcJhdTnGGxznHBS5xhad5VhNWCuturBTXKZ3RObuS98pb9c57k6ql9rp2v1as5deb1r6s9q1GV2IrHSt73T631424YXzjgPwqt%2BRn%2BVG%2BlRvyirwsS%2FKCPCfPytPypDwhj8mjctRZd9acF86y89x55jxxHjkPnXstXfbt%2FpNjj%2FnwXW%2BcHa6%2FSYvZ7yEwbDYazDcIgoUGzY3h2HtqgUcs1AFPWKgTXrRQF7xkoQhRf7uF9hPFeyzUTTSwY6EoUUJY6AC8bSGMS4Ys1Au3WaiPSGGsMtkdGH2rzJgYHAaYjxIwQqtB1CnYkEZ9BM6ALOpROAfyqI%2FDBQudgidBETXuqRIooz4DV0AV9UV4GsyivkTEyMMmw1UYGdhkuAYjA5sMGMvIwCbDDRgZeAz1TXgcmDy3YeRhk%2BcOjCxsMjyAkYFNhscwMrDJ8BQ2886gXoaRhedQvyTSkDZ7uA6HLLQBI5vGntAbGHugTc53cMxC7%2BE4SKL%2BACOzNpk3YWTWJid%2BiRo5NXIKM3fBItAPW55FdJLY3FeHBDr90606JCIU9Jk%2BMs3%2FY%2F8L8jUq3y79bJ%2F0%2F%2BROoP4v9v%2F4%2Fmj%2Bi7HBXUd0%2FelU6IHfHt8Aj9EPGAAoAvgAAAAB%2F%2F8AAnjaxb0JfBvVtTA%2BdxaN1hltI1m2ZVuSJVneLVlSHCdy9oTEWchqtrBEJRAgCYEsQNhC2EsbWmpI2dqkQBoSYgKlpaQthVL0yusrpW77aEubfq%2Fly%2BujvJampSTW5Dvnzmi1E%2Bjr%2F%2F3%2BXmbu3Llz77nnbuece865DMu0MAy5jGtiOEZkOp8lTNeUwyLP%2FDH%2BrEH41ZTDHAtB5lkOowWMPiwayNiUwwTjE46AI5xwhFrINPXYn%2F7ENY0dbWHfZAiTZbL8ID%2FInAd5xz2NpIH4STpDGonHIJNE3OP1KG4ISaSNeBuITAyRLgIxoiEUhFAnmUpEiXSRSGqAQEw0kuyFUIb0k2gnGSApyBFi0il2SI5YLGb5MdFjXCey4mNHzQ7WwLGEdZiPPgYR64we8THZHAt%2BwnT84D%2Fx8YTpGPgheKH4CMEDVF9xBOIeP3EbQgGH29BGgpGkIxCMTCW9qUTA0Zsir%2BQUP1mt%2BP2KusevwIO6Bx%2FIaj8%2FOD5O0VNrZW2EsqZBWbO1skRiEKE0DdlKKaSVO5VAuRpqk8VQJAqY7ydxaK44YJvrO2EWjOoDBoFYzQbDNkON%2BUbiKoRkywMWWf1j4bEY2iIY1AeMgvmEz%2FkVo9v4FSc%2FaMZMrFbjl4zWLL0%2BY5FlyzNlEVYDudJohg8gPUP7kcB%2Fmn%2BG6cd%2B5PV4Q72dXCgocWJADBgUuDTwiXiGSyZo14HOEQ2lE6k0XDIEusexDzZOMXwt1Dutz%2BtqmxTvlskNWXXUQIbhaurum9GrePqm9Yaeabjkiqf%2BbUvzDOvb2Y1E%2BEX2DnemcTP%2FzLcuu7xjQXdAtjR0Lo5n4%2FHs%2FGtntMlysHt%2B29NXbH6se%2F%2FWbFcyu%2Br28H0MwzI30DYeYTLMXIA2EG8QlHpAsyS0EfEToR0a3utIxFPJ3kiIHCCrZ66b0e2xEmL1dM9YN%2FMwS5p01N5jMX%2FBLKt%2F1R83l0LyC29M6%2BiYxo%2FUNg%2FEF7c2WyyW5tYl8WnhWg2%2FhyySbD5UhnDyS7OcU0dnrFw%2BDfGdI7v4QfYIIzOMq9hFtY55gmvC7jZ2FK7sEdrn6IXBuucYhjsGdQ8z0yEbWkkczjjsE5hNAIZrPx2zOLZDmKNXcXtg7EMqidAEEWg%2BSJCBBNwxvxJfc%2FbZa%2BKKf%2BxoKZybnq5vaqpPTye7CiF%2BZFjxZ8%2F7Qij0hfOG%2FcowPA1rT1l4ymWnrKmxxqfErTVrpgwPlz1kC%2BOy8NMDz6c%2BIO38K%2Fx0xkPnLW8Kx6qGAoQdL%2BTD9V9rb%2B%2Fctn%2F%2Ftrxz8dUrZrD%2Fzk%2FferF0cNt1BzctmX2FZPXt%2FjnFCQNz4Ah%2FiKllGiCMs1w5Lkg0kiEwj6VTXCDKsX9rMpnvIj9pcDecXAIXMnqn2dTUbN6w0XQ9ue6FV%2FnnXCH7S3lPWGltVcLsH75ub3ab7A8M28caNrIeOr3o5Q0yFsYL80xaa0EY%2FUEczV7icUMY5pnelAkmUAXmHYjvFWFGxuqlSaow3OM%2B%2FiYY7%2Fl%2FhVELF4EjRqNR%2FbvRbOY%2BDUGzGR%2FOh3EqmE%2FugIQQguGt%2FeMYz%2F%2BL0cimjeZfQDI3phXMbMQsqH%2BCjwVz%2Fhf4idHovgVmB8gLvjbicDcC%2FNypP536E%2F9N%2FpuMibExdohBmNwyiaZdJGoigos7GpF222xrfnZhML%2F7Z%2BylaqP63Hr%2Bm7bdUkQ6%2F2cXqdfmvwixY%2Bs2ksXFeXcE%2BiX0Z%2BIow76DBNgjJ7TOdUK18iPsPflfQD%2BDPsZG2Aj9VmKMMJ4fYRrhIaxhTDR0Elh2vA6h%2FAE6xUb29mj3sjmL72petXjejPy%2Boel60M99tFduCI59N3221xe7apOvxs6aHs7vab1IqY2tv7q2xsHeHGml%2FcV06u%2F8S%2FxTjJ%2BJYc0bWEX0ukW6YmIbGkJRMdjJ9mYIH5QIdJF4hvRGyK7cC7ctImQRcUET99fGXOoft35GYLMQu%2Bg2smnkgZUrH8AL%2F9Si217IssJ916nv14ZrJrvdxLkQvrvtBcjgPC0NXOicO8Qf4mcxPqh3hgUw3DDfdvLJXngg7N3dN2zbPJSaed3OfZnMU7dvmznp3C3bruO%2BNmue0LFsy7S%2B6265%2BfCKFYdvvuW6vmlblnUI8xCXp37CrOZv4B9gauDBlYp7adcUXB5DNCwYImlXOJJKkAdvExXxVvKEYnCo%2B3eIskP9qrrfIYs71CccBjfXRC52udTHHdaP1A1ui%2FVvH1otbrLrpNXBsGX5B89QghDyimlvNB2KfkxZ5C9%2Fem3%2Bd1%2Bd%2F%2FIfFp2%2B2Oxn%2Fs%2B9n%2F79p39S3s8idN6g0yZObwJOgKUpNB3GyU0Ls0PbRzIRq4lcarLKOJBkLRzJQD4j2090XrbA7DW8K3jNF5hlGS5e4V2D17zgss4T20egOJte5iD0bReM9yjTxnQxCRj3c5kFzGJmGbNKmwGw39IJDJcXJZGMkaAB4jyJAKw0jt5IAuIE%2BA%2BU3cVAZZrq9zhDyBrU8oosuxcGNTzCKJfla7JjNVmuSb%2F%2BtuzN2H%2BX4vlB%2BPpdfMXXmuVsNiub1T34SFbjYw5itEvVi0K0Nt9pNJUMI7SLGRhf2xipfCYf8z5OdlGKayOucFeVPeS%2Fdbo3lBrbSMmwUiQN5%2Fed7g0Ds1s17IuZC5kNzM3MZ6EWCa0DtekdJfAxz%2BR%2FOX28sND7yRMTBcf%2B%2Bs8mQCQWHya4qBv%2FufeMoWyslPA9DtMxUknxkH%2FyfTnm2CMYzs%2BCq3r7PxY%2FMXomrvTEsRpfEGHa%2BWN8E1AHjElb7d06ddA7oK%2F%2B5Mdsv9EtPms0jv0Z5kf1FqPxWdFtfFr0kHfgDX0Y%2B5PRSG7RUj0tQr7rmfX8DH4G5W28kKeJLtmQsQkuwMP1pk16EV4sl7vrMJATfyUWo%2FGwEco4rh4XFQgaiUX9qxZHrMQqKnz%2Fc2d8b9TysYrAuXpP%2FRf%2FGr8b1qwwc5a%2BeuLa6S6sneNXToG2XrEJi4R5SGs8Sq2S3d97bsfCRaTdaLwKClRHt37mkudvXbjwVrLhuYeGhh56bvfQkHpk2CwvwClqgWwuBfndC3c8dwmstj81KkagcUgbfPY8Zje0W%2F82VPWJHmSq6pP8hPWpotc%2FEexDOK3qU%2BwngPhOCiO9MJRm8TJefjelrzoKnG2Bn%2B1NCUmPE4gHFmBN9jrTigRIpsACrc9Gstg58ULkp9467%2BGf%2FeFnD5%2F31lNrt2967dhrm7bzI%2BVT5m%2BfzKhvf2MzpICEm79Bopkn07lt1762adNr127LwVqQLdJ5%2BlpQDcvHPQtVY5knhYrK6q8%2FJsiP6EuhGZdFdaNszjvpqvc%2BPI0CdjN0AXsFOC3ZfALDJwr4q2Xq%2BGF%2BGNbsxUg5NLLIEXi8otcDQcUts0D8eQ1iVDRAMBTsYiNdRIxE09EIBJO9A2xqgERTaW86BUFn0OD2xFO97FAgFhF6OoQ7prYt4XwSeUgQHiJyDbeke9IdQntciLQ1FlJMaYcUNvZBg%2BFB1ubjlnRNvl3o6IEU2w7fdNPhm%2Fhh%2BFLysUu6%2B%2BDLHkOkrSHYEjH0tEPe7WdD3uyDgvAgK%2Fm4szFFR7ch0toUgBTdWHr7EpaWru6%2B6dmbbnqWEbV2EtxAsXiZAPTtGPSbHsotI2leoM8TePEqgSQprs7AGFf8kuOkPdZPXGb55POAW1d%2FjLST9v5YflasP6v%2FCO7%2BGNAPC2BMZWmsOjp2NNbfHwMCJD%2BLPVL%2BD%2FOYlWEEI%2F9jpPddOFkB5d1GSuKZYggmCCd7JUxD7EXAzxyirYnNDLdDZoFdx14kivkvGc3579Jm36reTTvDgBnaO6vzyQ6chQmlsMoIkIQ2%2BbBDWBud1Va4pcCn8CPqxlh%2FfgtG8IPaPH8C5wk6%2FnZDv69jurV5QhtwE0x2iqOsj9Mx8B9%2F0EaUdiPfOYYDCi%2Fq9jhWRuupMDEU0%2BCtX0sDFxv07T%2FK5niBPqN9%2BtQjgEc31NGCXFeMcCEuQBIc%2FBK4CO78u7EPYvl3yaEfK3vcb6qP1R2tI7vUjVDDUdKubsSrNjYKY1qBEa2P50SJoaXiksIoLiCwnxS6EBuBde87botNfdEWwYvF%2FR0%2Fu5yCqhGeEOR2ynSeyXjt6ka7neyye8kryBSWE52y%2BRBgogrXPZ8E1yIHoHIFUM%2BAbJhE7lbMtt8ApL%2BxmZW7PwbjAO0fAVoXQOuiSP%2FksIVdFZ0aulsamKUzwPZ%2FNYDMJRBPCxsBqLzqHyneXF6Ej9HlIFo7%2Bpg%2BjUb3unRmGpstGkm6etOuDBGA5wCMefp1gTHcdZlvPBXlOslvYTp1cd8UjYLVd%2FJ5awNrIOKLnIt9MD9qdrKrWCvA6ALm3QV9VrsPm60Q7%2BRHJHP%2B2hqfugo%2FMvI2H%2Fmqr4b9tFnKSRY1Y5Ek80Nm%2FWIhr1ikKnxGz9TWXrokf9xwujfvcOTtNTWnxd0F37Y2W79tteBqZ4G5qLCuomw%2BnSr28QESCRVLTyYKILGJOPfcnaIFOsewhRdvv%2BrWa%2FWih0vlbX6Zb75T5C0qNKVFvH1QL%2FvazSWgC2s6oWXXIuUxQelKiJbowuJDQViatLmLijg9CQBMg8WiPgiw3LEeYRmm5f%2BXdnvkDnxLLjMLxtvX74C3OlwPQqx4xwIdpPx38LrlDphiyWUWHWKAzzxurS%2FxTo%2BP5wGFak62ap1PVFFN4v%2Fy%2BxuR39WnIO7lsWfwgVsK17wxrs9K8ltIKuhkw7f%2F6dhK6gQokFKhWX3urrjk%2FrnI0pgfpGMeuQIUaEM7%2BGF5q2iMkCaMQwxxOzcvU0eXbsnS9XknXvP7Gtw5dwPXlFu2ecvSHEZgNDsU6x%2FGdXBYXyOQjzZReSedeEPY6nEv9gJR4oBQJtFO6Kd0fwC6BO4LNHDeBujB6dSNcUQC9zIv2LnAzGk99bUDrdFY%2B9yGFQtEo0GQPNv6vS2drj4%2B1jHbv3aJSMUWP%2BQTZrmbNTjU8wyG%2FiXNNpskybLcJ3CiTF5Ir%2BJYzmJwE0mSVhlxbtbmvweB3ulB6Til5UuUZydpgiFVeobhU0WaBqpJ198d%2B%2FXeNRTZ9%2F1OPfG7%2B2hwzd5W3D%2BhmyjsRcUg%2F%2BCavb%2B%2BVh2ls3L7zT%2FetOnHNxeerv313vzLVqPai4nJv%2BK1FC6040%2F4udw7sAb3laSg0XCkAAs0npBO6VJabS4Elk%2FU%2BD4gTXW%2Bj0wnrMlqNamq4tMIYB87tE10i0FR3LZNhJsb7%2FR561btmes8YBCRkhYNByRtKd55mqTas9FYhJnbRGHuOh3M4QTdgQSqmgRxuzGdSvZGcbMxNQGk5C3ebLjoXIOFM4l%2BWKHmLTJwRv9E8GWJ6dYvf%2FFmEyEGr%2Bgyrr1p5zrgkz0Cw2j94Hv8Jdx7dIVegBSNtgsqGsRQEYiIBoXwD0LNvQ5d7s5Z00QzwNhqZA0b%2BtMG1tQq5nd84uq8R0zPvX35G8uRaze4jcOHzz0w1%2BQ2BIRvf6J6Kgatnrbiem%2BCFvAxfkrndzD9MFPP1GWTUHclpASUkCNAQkpCCcCgDSUDAhDZ%2BCuEkgn8J7i9nMA7pA4lISappxILKfAeSAbIcSDuN2bJcfZILqeO5rLs0MnngSHYRdrHjmaz7JEsEPw51ZqDJDmUIOZIe34WaQeegNsJn1qz8AIpT3yCjyEih%2FxELkuJ0lEMYTLVCiWpo5oYMleMH6USyYJcD%2BuOe%2BkWKpn1Qns34iyYDjkSLvgnZXcgVQNeqINXr48m3iS7cjm8tedyY0f1QvTnHHdsrKby%2F%2BSSbPY8%2FNH6vpl%2FEsq3Ae4ZU1HC44KFiI9o7CEgab%2FRqHbj7s5KAg06s39ZP%2FzxI%2FmVuF%2FTbTSy%2B3Fb8If9%2Fcv7%2Bwt91yy8RfP1QXtW5RzQn7qIiZyuFM5QfJ5E9uVnqT85TanFx0lkP3ukBAMprvsRyi%2FC8NAJL1xbIIirSvnSj4O5netb4JxmNANHPssHAcHMHsFRgEug816gDBeMbdfiuRcghqYcm0%2BXxx%2F5IAEtN3fqFF3LzAXqwoT0PN0OVTNqxo8sxMkd5Ig6k79Zk7VxxX6gMLOZFQgvpW2RrMW1D0BDihaXQ9wVRoBxPLfpknmkeMtoB%2FqM9cRc9IqmMD2XUmdZ7GSRKPUZvChf8BoykriM2MnKYbOHX8R7cLdNCxSFFVQqoYswnlWtlFS2mNkhswVpZiQW1J%2FUKFfipHGlUkM6UKBhMz1istELIHJLMSctu3ugzfaVSOjKvUgc%2FTHK4Sdg2Wscz69leKIkkrwuuWiOe9yGYKQXRumkC3qbRcMwrvhjNXgdZk3RxAUEhuSPvn3nnd%2B%2BU%2F3vlVOmrJzCD8JLxV1OHRjrZifbcFDOuRNTGqdgQm1tSNJ2OcQ04YiEXuxtII1ECSQRoQGYioEsgCfchB4ghAtw7FfJre4WZ9hkVi9MtjuWqtdNDlpMrfEG9fOT6q21okg%2Be4As38MfGquNt7oUws6Ysarj1%2FefE%2Byst86YUVNvDdts3Pv5c8m%2FaP0C%2Bf8%2FQb%2BIMnGq09BgwN01oIOAnAdagI8mBSrqk1gxTDUBOtk2ousEtBH2z4Ir2d3f6k8PXXVlt2qN9RODxRuoJT%2Fv27wm09jRYVc%2Fe%2B%2Biyx2tyzJb%2Fn3J0htXP87eSsQaf2Ly0s6Zmxela88REy1cf4273mI3iXNJ7KxrZibOm9xm6rl4fqy%2Ft27smU8tOfdW2ucBzg2UfmOIVyLIl3kpYlwphDISTXJXsctmiDtN7fNV6zelgxwnWxsVr83Aj%2FS5ki1jL%2Fa0GC6%2B2L6Um%2BaoddlNFuj%2BbJ8mH%2FiaLh8I0%2FU51NspIEfq0dohwyFXKgm4NggwQ4rRhCOUFtxxo8XnitT4cnGfT93IS8FaT85XE3H5LMY4zIEPL1hw443wz%2B1UmhTJyJGxZzw%2BwsKkKZgUiVtKOKMEb2AKHTv61FNc01PQFwKnvsZ%2F9pPA4RKTASWahmh%2B8MxwzHxKy74IRn5LGRjsPUUwTu64UYNY38caqd7HKucZ%2FtHnODtENw%2F2UfHRMaq1UUPDJQ0OKkWCeet5fYOhII1VRz8%2B%2FElg5j4Gxur3J8o2PJ4rg%2B2d08T%2FfwEzSVbyZ9XPro95T477lRKqUSRXQnauHNsISAl27oWi6Fv9z48JMv8r%2FaMMj8onCP%2FDuDZOuN%2BGPPr%2F%2Bp7bx%2B7JlbYdppcNhzKU%2F1Px5aiaGDn%2Fs1iGMaBcleKUo%2Fv9rcxkZj7DBEKOfrayytXNLYiUdBY%2BpleQXdnscKlQcpzuWluxsieeyuXIK6SdxozitWyGOV3vOHHjguyCQ6fpIYy2JwvrQEF%2FQa9Pdf%2FQqOSqCiE%2FEE1%2FXIVKTc2tzWbHnimrEd%2BVyz311Ml3P0GVTj7PD5aDnsvCvH36alEaPMePcMegXs7x8igTu4B9v7G9vTHvhCu%2FkzIdx%2BBxC0ay9zRSvoS0F2lIxI%2BX7klU63I40gLQ3w5ep5na%2BSFnba3z5D64zv%2BQtM4n4ffG3tq4aNHGRfxgrXPMim%2B5487abL7xhdseIRn1KDl%2B7aINixdv0OD%2BJSPwKf5%2BxoP6aiTeQIDVlIhMcL1H5R9PYXvprs3fv2bO7MOplCmweuiq2JRZ1zz%2B9a%2Fv2PH1Hfz9236w%2BZrPXvWfAxlj4NLLHpq3c%2FPQ3uvmvbrjG7fe%2Bo2y%2FcLdtE6VUlXi0ASb1VLUBVSUWSU4HdvAraTyS8xzM8NxvxFkXV6pUVRiJwcgC5zEeht4rwcp7ki0k41G0qlQhG1Vzlq8alEmnFi58caB5Q9vn988MLhqyVlHvLEWjtQFeupdiocF%2FtkkOGPW2ibWaBTkeZ%2FdvPWazXfOnnvL6jkRXpi85sFzZt%2B55ZptW3bl1cCCHZPD06MhySha7UFzjcjbp8fOecFCirzAG%2FyVjBX6OFIaadSjQq1nNhyIe8tVbaaSdHlXIWKacMeuZA1uxS95zILhyrxAdsXTL6m7kNQlx2P9uZf2qhufePFFbpI6%2FOU0WcP99RrCsrwseVot5mtytpf6Y0gm9sdeyKnPQ7onyK4nXlR%2Frg7H95M1upzu89DH6pgUcikoiihJ6NJKmRxV1x%2BMJiOA3YwhDRQrWU0u%2F0rvq0VYXnyCwsLeTJYBq3dAtJDavuzyoVpzZ99Z0%2Ba0uoiFH%2FxcqgDR7rUFeOrUn6Cywb8ZeNMbhLV5ugP9l0zv9UN5b5mFkjzxUcpPJCn3V402pRxtJd2GrnLdhtVk9ZSZh9W91fCSH5B7ofxPiWL%2Bj3D%2FuwhBRdyAyozeZwvQzs79soi%2BBKSnafLviZCcfrpBpLyimfLfTyJtbyruIQKD01tUwJyKEo%2FybaxkSNFUMdMkhQoJyRBQFhnUkDQSXhTM%2B3NmY0EDM7ffLIjqWEGt8lCO6mLia3PukFnghosJD5p5SIho%2FVDkzQfLE%2BIrYoJXkD19pdP7OwG%2FvoIUtagiWiZ4PAFTHHlTVhRZ7dYmPar%2BNJ%2B8JhmR6DFK5DV1foHoLNO%2FpHrvZfmWZ15RQlwvoVDKhCWNK3CCch9lfFBuAqUgpFSShmNaPj%2Bi5%2B%2BWZfKeViJfW5HnUakVL4UCNVkA4%2BETfIqx4B5xSaP2L1yn0zn2ltPn4%2BOqZGmwwEVCaCSqG53ldtL1oLGAhdMLd09MpCCF6tD6ZnAZBY9hDaYsP0jzZ0j5ZjKsF4i1UmLuhbJMCnYJPt5VwFNvmZawXjEvLJqIH8STonZjq7BZ8gKgR20C9MDFqJAX1H64QW2NEup6qgzLP8cvppL%2FNNTOBTCJABOHeWoXzLhw4Wuy7gaBtjKr9kgKq8ZlRYBS32Lpxc8vIhpNDTfyNXWybMJbn2RyQ5EmWc2QF9wmSZ0KYCE%2BcPuYO6b15Uotj2Kd4MItLS7gtFbkTdrFND6pvEZqv5Yv7jXAus7Pg7avo7KDot50NX3CPkP%2BKps8J9%2F3mGQIteY%2FLGPC%2BL7872SPR2br5fy8MtKBMHedGuM28%2FMZmPJMrGgi3Gb1S%2BSi1%2FL%2FzrZwO9XH1ce%2Fz7ZQ1WSoY%2F%2BpMb5FT4ua0Wm%2BJf%2F298nFmChEQ%2BTi71est4mq9VYI6RsymoRJKYidElT2FGnDTZvqtfhGAFTbeqEw68GqtfmbVa%2F1IFO1%2FjdWr%2F8BDRRtQh9XNjubEm4aWVpVonpTGR7PVGc%2BKJNoBIWF7kYi4gUV3r1U6723i6TxUl3n3%2FtM27aZfKb7THiHW9VzFSwHJ05VfK6Ar7kaB0XgPPE0BSkSFKsBUpaLihEWoA9wBt8qirh2VSOkZwXEwyrxZ5jyt2rJmSo9gX7cg6jsEUGJU9z9xJPOEM3uQQxKgkh35DNATnVyrmJ3mbCNyIB%2Fyox4wH1bg2DwN7q9kov4pFqny8oSm3RQbGgJ1QQTs6ZMLilOVYJ9v6Wha3HcJ9jddsXp9YhGUXLXt%2FqMDnvLpPNTXfNa60z5%2FyjXQOMq%2BlNmwh5egpYrdfZQZV9rI47xlRkuyTjpzsmCBSWNkAXVoK8sgYWqQJWbo1RLo6QH0YW6pxqfCnRgkd%2BRiFjUQUQ7poIaYoakgXxwFd9BuuI38H1xBxXSFb%2FpBDIKQFn7YB3dB36l7sG1FLaKiBdp1KxLvfswap%2F30lnVESgNnvjbUoT6w9N%2BXoio0qcYOIM%2Bheg940YimsucQVvli9NEcft2UZwGQwLuilj1fFr1i3NP94X%2BPE7Hpvtj6lBJfJ4R6NvWiaL6MgzWHxiN66DExa%2BdAdAbMYX6HVF8A%2B7rjEZIXAVbDe7PVI9rmN69JOLV1DOSvRPxWNPZBZf%2FNf%2BNy65BhYxxxV%2B77XJ2wfQ389%2FIQPgajXbwMsuAz%2F0IaQcXJavKbRqR2IqyZruXjVC2%2Bhdee%2F5vdnYOedpmVtR3NGXldxSzDSIiBVpkGb9by89UpEPKrSLZmyFDzMab%2FwXl2CNe7s%2FqCtTvWgG5kpBmCBlSzDS%2Fr8N4uwBwohRW63JTS1y32f0TQsPfXVGEHQrV8%2FNCfiOUVirYcBbIeA2%2BiF68rQIo3B%2FS628vYESr79ehzS7Q9LEL9UXmik9XVHb1yBO3Ngvt5935%2Bk1efkV51mzzrM0LL3%2F20avnwMeKuWyOUZg2TasSqZ%2BKcZQiOn1Iu2Vh497ALUVZiCKt%2Fgh6IvTIj1ZLRjWAkpHKOKovNwp00eqPROiAbiNEKieXwMLcXhVJ1%2FuzmLP4tfxaHR59cBdJVG1kTAgl9ze9QKUEQ946Hkb%2BokJ5JRDyf54Axur1D%2BWS49cLr0tTPEu7UmXrxcSr3XNvumv4yXzInXKH4F7Tc7p17Zt%2Bt%2FqW2%2B93k063X7VW6lALxTY7i1nBXMxcxmzQbabxz%2BtJo%2BwijYaIGMNS8AoSMgAPt84DdHOoMPfjXhF%2BkuH1tZvuFQrRCN07xGcXRX9MYxYchDe5BcHj%2BZ4i%2B42WyPc8Xofi7bbZJN5nJLJ5qr6IqRtzqNlM17SpFsnkEyTWoABEjz4JXOQvzWYuwdnV5LNGOwTM5v9r4RpQ8ZXsYodks3o31JBlzbYtNotisnm22MxiwGFXam5oN1n0TA%2FhRvshvTSDwHff4nNzRo9Dum6PaJbMXzDz%2Bx%2BFkj4L4bFNBb1asqsgH7Dyh4DvbkPtf5yMDKzEwyoaESMSNS9P9gJVA3%2FRTlwoMwZvxECFWxIPNw9gi01nOHjP32esZTtmXHnxvZd8ZtakqQ7ekajbXetpNa6ocTVxJtY%2BuSe69OLz77zh5bDR3xjZMzUz6fxrz1nqrZGcHQHfPVefN%2BfiK86LeXj%2BSc5lPKy%2Bk%2FvCUI%2FDaLFYCWHr6nbXuILTIsb5imNKY%2FrCm28fSMxPhkN1XbNMNZGuqwOBhtTSxWuTk6bw0ZaG86b1hKddePOKuBvmiguYBn4T%2FyOqOyGRBt7bKUI1GjioBC8aUKwF7Q319UgcmtFGIzCJGBqwQij0ynDsfdFGc3TS3BlNfJ25xmzniMkpXXTPvCaD3ZaZvyzjmZdudBostmhb0ORZNN2sJBeed1HXkrUsywueQH%2BL0eCPxmsa5ZpgRJSDZ11yDv%2Bjmbd86vxZfc1WcZJ3UkMq1BOOOVtvu%2F%2BpB%2Ben186d3GTwWAw2jheaJs09%2F%2BLNfZft37DALyrNj1wABMuUKbODyTVnT%2FKYbJ3Tpq8IrNh92dkxOj5P%2FYpZx4%2FycyiVcDYdn4JbEoKdQi9054iBKsygLW46FRGxAb0NPNCm8BSNCPjoKcj6EAus4SuP3rB%2BcV99%2FeTF6294dA8%2BTK6v74MHVpYNRt%2FI30e8QGTOOdfGWzzxcy%2B87a7bLjw37rHw1nPzp0KyyRSeZO%2BQQhInt3dYgvycjrPOv%2BT8s1rptaP84VeywdWX2T4ysr0%2F7TLIs6%2Bx9zib56ye1dM9e%2FXsZmePY3NDs9zlnNVt4%2BWgHJbbz3Livg4P9WWgviOMm4kCRT6I8vw0NbUUEnFvOuFKoxQW1gTsvFirsF5pb7qTUCx4i7VmtToveaDxvK9uOaedVvPRpVOnNz0Q6bry7uiSdQ8t7Vy4JQKVS%2BXPplV2ts4bvCwZu%2BKzgITtxepaPRzWdpv74muvv6RO0SorX6cu%2FdqKn%2FXWnrtp%2FZragz13DUCl5myiFW2Ycvb0PtsXnU%2Btx8pvLFbUspLX68mdegwmOif%2FNPDONajTGoUh6tU56HBJCTBASVvNUB5VIiKpc9kd7kludodSFz7xQbiOmMk5dOYk56gzL6uaf7N8a6MQOHm0ae6snZpFDfuT3%2FjdYzjzwkXXIVHoXNuCfQslQZqBZjTsoHMqrkE4jaYdgkGz2ATOgB3cPkSukD01DnV3ttb1wx%2B6arPqbkcNAHoFPzKUUQ%2BqL0k97pjbZv1I%2FegC9zTFbrrlFpNdmea%2BgIgfWW3wqkcis8ky5FAcRd1If5nNZrl2FFpungc8wpoCl1BpQV%2FScS%2BzjlASyUTVv%2FAJ46gkJI4bHX4lTnloctxPZE1ckS3%2BjG2fKIjkQFyzuo8jvYQG1OrGvJPSTu%2FnSp9PHNTl4z5hK%2F8gtXVKF6gEKiglgcKiRlCESsQCV5QIlKWKpr34lt%2FwkSx%2FJCmP5%2FcBKQfl%2F5gd%2BrOS%2F%2Bp91%2F%2BYCg5CXK2W4M9fu%2B%2F6xxX%2BvnelVuldIDCG0VQTpU9Dw4pRfei%2B6zWx0MLie0gPbyrkmRU7OwT16JGeyXLHqOLqAfVN1GPlBzWtFNzj0TRTCjogtP1NjIvu5habN5Aoa1k66wGpqriVetJgiGdwDZtKhnN0y4n9sXYnsqGmZfDSR15%2B5NLBlhoDaedEm7sxmpqRija6ZEEg2EAnTiAC8IrmFbGz1q08P9PSkjl%2F5bqzYqT9hMmptEXDgTqP3Wiye%2BsD4Wir4jCeoHbbp5hRfpB7BakUIppIlPCD30dR1GtslDz8OsqbXmejFC%2Fv8wu5X2myq7SJ8Avzv9DFUJySf5uNvq4%2BTi7W9D%2FOZrLChdwxmPNiBRqVjnpK%2FaGxRCDspVYKAW9AN1JANoo8wP4BJUlGqdgw6m1qPQ2QW3%2BOfU5%2FieLS%2FNuKpDU3uf8bcAXyBal5jMR2NEAbPAZt0K3hvxHBEDlUxfIGcD%2BN2gNSNx36nfqlAYow0puatNpRz0e4W2oahKzQHsjf2c16ad%2F3t2KTtPobnX6D8C8pd0MDP%2BKx7wnXqGGlLQcvikMErm6TmfsuxJXbSAxqNjOogJLQBLiKEHAE%2BJGTS3JoEhTrz8%2FCB%2B5YlupJ58aOat8Kv4JvregxwcU5Cp8GFAFm1FyOfto6GS2m1NGTS6CPNKkbsTdCBlnN9onMho55BX8IJZtEQ35lk%2BhtwN5A0V3RCPoD%2FyXAcv6pAtbZczRUA64JmcUf4q7Q89ZHLeJVZ5D1Ps%2Ft%2B0iCT3AHVtZC7JDCXfR7OSb%2FXja5H3zQbZL1B%2BULX1BMTEk3AseSpmnKEK4T9ekMIidUCRQFfcbj7z8gNLvzF7mbhQN8h6ZbRset%2BnQWdS%2FZX3k7WpS8P9sfo0iGS64wV516pOhjI6TZ2dApgI5%2BLhxywYoWxKUrykKJsIoDsR4mSrCTg0egMPnLW%2F3Q5Nn8BZEuzqEI7HK3n0%2BzFmuO3TtWQ5WJoG9YqCD6Gc32SxnbnVPfsxvrFXK2dILl7bLthDp6glhcsfp4bYvbSmj%2FmQ94uBTw0E73x2jbNRCvC6VL6GCFDwU7eWQDcC5FY5s0slieRDwtAbRsbLXbaXAuu14e2OJw1dc6jQ3ZdY8v7rv2%2FBWZLqvFWVvvcmwZkK9f5jS4muO9yR5res4kfkRxhV03L1RfPOiPtYi8pd7jNEsOpyTwxpaY%2FyCZu%2FAmd5Or9uS3DYaeqVOhH7gZN%2F8I%2Fwi1fEuLXvyNivibjuKvN%2B1Nc01HF%2F3h%2Bef%2FsOhox8MPd5SFucPjorQwXT%2BytA8EmA5mamHNFDVhBI5pjZbQpugBNkO8MvRub8KVDKST1Wag7D3xlin1ZF7LFP%2F79nbvCXFOY%2BPUjrT7%2FotsPXXZ4exdPzuhZuL5LUXVAn7k7PbhG89uz3b41X01gbjP1xwlu5rrvvf9%2Bpbs6E%2FVu7Nk642%2FPYRaAiUBdrmO6CDTBLPQFA1ur0uXoBR1INDMkypKpoTqnSMx5GiEdTEaSHLs0Alvu%2F19%2F5QW9Rv1U1ridT22i%2B53pzumbs%2BXFFXYC%2B%2BCGsTj5JUT%2FGCgRt3n78i2n71FHG4%2Fu6X%2B%2B9%2Braya7os3ZbDmgWfXun44e%2Bu2NZKuGZ0HiF8M4TlMPR%2BEU6rPKRJ8wOU2RFUFLex3egEsz3YqEAq0cqhAAW19dBZIlVzR61tuIdTnpXH7l%2BuXrbjPUyep%2B8cl6aXKWhPHpDcXl9KiTWDNr4mBQc8Tq%2BNzK%2FOKSbsfl79o9G20R%2BbrBXYvUg0rLHhtrc4TN81TTOWSZ0gL1ZVlOYH2ery%2F7XVUjFMbzYpg7UswcqJPQwBd0LKLabJ8IaCr2otcjSkIrGwootKECaUd4XH1%2BSdazRrfddkBU98t1htvWrbjqSqjaCguxrffM%2F5zDCpBALUycmajhd%2BR6ww4SWafuZ5eU%2BtPid4lgd3gt%2Bb%2FY9rQoZNmiXYPXyRHbRs8zX%2Ff4WIFjWZJtUdSD55AP3xtXH%2BZipC0EqdBGDA4CoYEU6gRLGPU11QhkLTBiEYPiqOeQgwTCl9aok1Qr5pFf71qEeNxjy%2F8F0GoqYPv75Yh9j3x4DuJ%2BuEzHRpAq2lMqb%2BqfTdiq6kGtzfOWsv0c7lSeMXDHBDe1MT%2BLUgx0Pg%2Fp87u2UicdIvqQi8DkxhcUwUXCedMpb4NQjwY3npTmgsURJavLwCRyEcN2HfWsDVGfv%2Fu9ZUWUx%2BPYFueUKwaNvbtu%2BXps3eVWbN1GcgVrdMnWJ7WmJz9SD66EBidag0NF1Ukep0t5A7sFCWdhzvYwHv6L%2FBehXuHqfaBwBEU7hfVLcXvS4VQv%2BT%2FvaSIl7cbeMc7ekv9i8S3e1L5xxpvMGcu1EYPbKyCiijjGXcDKckm43PqU2qNWlXusZMiqF82cuVzolUHN9NNR0HZPxFPV9V0wLtvq%2Bk4DqOwVWDlzuQLVdqFiP08cRX7aRlBVfR8cb55bWe5LExnlcsDp1vAP8Q9BucPMk1Ulh4GnN0SAdxcNHv3q9ohx1Ati4S%2FtkWjIDe3hQdkUGrGRaFBiUdiTSkI41UkMuuQHP%2BEaSQYlPQTFWJF03BNPpTu5KFAdkWgDukzsZKMG0Q1TAQQglScOaP%2FdsZ8%2BfP75D%2F9Uu5Gs3FY%2F2SxPld0DHOciXI9gqjcEidXjE%2B3BLosy0OcX3T7O5g65ROGyzQ2BZs7WbZVnO5ydLe32hMwTQ4wnnKXW6XW5LAa7oaXOIHoUl0FgLQLH2by8wSTWeAx2Y5PDazK3BqZbeJZwXGPaYhX87ZNszoDdaRxotXO1nNlpdvAPFWHDm8PqEE0sZxDEqGzxisFNnuCWetPcGrObN0p23tTZwMuRVodSV8%2BLTrOV3eRvzjQZiSjaLYS1WEJe0kNsJlZu9LFun7%2B%2BwW4gRDRbaxw2nrOGm%2BxOj9cmtbp9ZqeTM1m8UXfQQCSTVSQox6pvtjot%2FFpHvIUjJovFEoYvHYV9C5Y%2FxN9OfcalvII37UEhTbTg%2FAQIaPb4Vz6j5u8%2FaViycMod%2FfkDcpu8QZbZoeBi%2FvbzP3XPsZvOubMtaPHkD9jt6%2BU2O7vqU%2F9C9SMvgrXpQNG%2FE0oJxun%2BCiElUa0IKQSUwERxOntKSV7ekcuh9VBZBBo3VUcB58ofKBHCwLyf9qFosz9Ibf8dGqwaBMjRig4SGOZ2UkWI7UiO9OfUPdxOYFApUZyfpY7mgEc5rtNGGk2H1lPhAk1Hp%2FVAMqQEHEUfEYkkUQq1JMdzsX7kklRrTrUi1wMcDjmu1YYfATj7Y%2BpGpPEBXuoQIj8rR9mgCl4C9yqmF7xnVWxGVniNqtpVmXBvQ6iwni5YQ8a1jYrXtc2J13HvgkvqWxuva1sbr%2BP2S5ceKGyBwDv2DbrToe1u6BkAJV7xnVLUaq0sJB8pFqcUIPi3yuwxi4JuLr%2BP30f3OkPQ72aO0xYo3%2FEsmO3QO5qEF8S0qQH0UsKXv0brnl9%2B8M7jF174%2BDsfvPOl1au%2FRL5%2F9DsbNnwHL2pHR1NTRxMZhJtHktOOxLxErPF6YlLvpC9YP73x%2B4ofw%2B3xVdrHcDE0dQQCmCRgvt9b35xINDf1CDcRSfJ%2BpYl%2BSf8YcurfmXP5F%2Fkj6J82jNsrkWiEuhVlgFfyNkB3S5MUzLhoNiwSCYcxQ7Ui4J0Xh7fmqRbaPa1tzujxkBRlsEHy0%2FOM4pYLPb7g9O6BQJN6l9zQ0OGyCaZz0vMTbHOzXfQ7a2tsterTcqxeInODoemdktw%2B1SbVhKwtW9ffe8VKadK0OVuC3bWzyKm5LeddsWTeorWyY9IMtUFutdu5g%2BRn533qkocdvLs2HmhU75br%2FMmWtD8zA3OP2t1ea636jEzqYxJZGAwFiDEd61oTsrRuW3%2F3pYNi3bS%2BRd%2BGjOfVpAPNd6y64Gsz1GaZleWIPoYL%2Fv9mTeQBENVEguiF1aC4YeXxFETw6QyPfn0m9g8IrMFAvKM1EI11DARnbqibHk%2FIojy5rSdgCyZi06y8sS024PeuO4MfwQ5Y9yKRZCqyYaF30vzeHlmUprR21tR0t0yz8KZY66zWuGvxVQB%2F36kP%2BK38t2Hu6NQ9SFJfw0AdpqPEK2qTMpf2VCqJwqPoJezTL824b8akoL%2Bx03nhh%2BoNo5e77psxg9Q5LzebIKD%2BfsY34f2MtB9fk9v5b8PT6tYrgv4kRPwd0q9z3gdJSJ0653KjCYPwCaR5aUY63eW48O%2Fkdo33yxX9wCiMv2QTrk8eGSI6Ag6moG9t2P%2FF7GRNlDjl0gw7pJ5aOXXqyqn8SENnXBmbSwUYLyqJjv3UmY1nKr4t80no0faXsaIEiF%2FBRaIBnItSce4OUif7W6Vm9T9H1X9Vj71BEm%2BRdmIJQST%2FZfVdudUvh9S%2FqqNvqT98g9SQ3lHibZY0mRVHooyDN%2FFHmTgzjdozKw28NwQ0hwN6BCoPKaEk3YtKwNhwRLXuk076CGoZNXDQcRwZvreTZY9EZi%2Bd0s4%2Bztv8iei04JQl6ZbDD2eHV7X4uHuFVfPrOmcs6m6Kr7hssr%2B1VZFcEZ%2FPdJkn1hOs8SXS%2FNFFgqt94PIZzZ3tdaL6Q5vo6piSzdy737pwsX1VyxUrF15iJ4uNkq%2Brbyg1Z%2BO8VsNC1UmcvORPRfxtPrfRwL2p%2FoA1eZp6Z%2FaGffoewaXcA%2FxBlKlQLfhQL%2FoPgBGP3qsA7IQS8qDVNswHKRSheDUvA3Q7MZoRcJMxlEygujn1QdyzfPfq3dEp%2FbXh5e5YXW2Ngfvza0ZF6UgFL%2FE0fTq4LBlvTE2qb%2FKuuzYSXVnjTfM1osvqMHVbm9950quIZlbqaL6YP7jk3kUtA0GnX2nvq53f3WoSsvEdDRnULgo2fN7lNZJgI8%2FVWi33c3bBZnGY05%2Bdm%2B3qc7fNmj4YGKLj2nfqFP%2Bg7jdDlxEV5XsJQZP6hYrS1l0VQr4c69Xueixp90gnZPmE5OF22j%2BSYEWHlZ0K%2FHgsh%2FZtsbh6h2DNRlvv6jJh9XaJaHCZDiUDKNTMkvb8vsqCyf3ZNdSmO0fa0Y4baJTtpbKzuVzeeSI7fCKr2Z0WypapnXJ4gnoWy3PoUIlIQ1TXdqhQJIXp9Wx5fYdpeWh2TY5D%2BYVyKd0jw3iumwi%2FBC3cEy4o83QlZnW79MrCgCjbhWXBlRZVVZZv4rIKpXC01HFlHdHLoeWVl6UVc%2FJ5uGm6CViW5mulYMk%2BHqNYr0AyUPivLg2oMs2MPqtuhHyRyiwvNJej1Br%2BfcLyoAyu8D9B7bgmzUqfFobF5nKnK4%2Bt8MPJkI%2FxHUNWk117jugWF%2BxazTAALQn6%2BUE9lhoI5ApGA%2FiuJOsrlNP28SVVuBVajXmircLel46w2bJS1Q0Ft0KDuikDFL%2F3pYrid1Q4FvofwRIo4R9h2ftSwc6jHAMqLcCql8YPHtlzGoByNXYN6v8hXnRaOhUvx0sVLCexwupGDR4NOYC7PePa5keIPACnuAdD7dEadRuTIiS6Lb7uskb381My5yjzF8lGCjBRqdwrWJCagfB3yCy7XT1i92hbcZ5Ci1FJkgYMDf6n%2BjspIsHFjJrTOdzSMuOa9DbDcj%2FnH9N9bIoGVgzHPWIQuFuYtaMRaq8eCKI0gEF6lPOZjBz3EEvaaxwSUT9U%2F8JbJZPJJLBLolH1La%2FRbF9AbC8JJjv%2FmMnssKjLRBJyqj9QXxNko0Ux%2FX79epfiXkm6fmKwF%2Fen1HLc6LxloXWKvGa5rVCVL83VuiPcDEX%2FK5pTXOxHfx6HHB0t2FI0qI2rCZFTrvPWU67zVuS%2FkTsLnc7IKhFg30e4FOkqNSfH5PtkmUy6Cpiv%2F36k2sbqCeCFNa%2BURpoY0sZoYmCgCr3qgZz6s8I0gP1bYiR%2BD79H56NOz0EVWCTy2%2FfffvSCCx59W7uRV9995eqrX8GLesOXNm360iZ%2BT%2FEl3uZqL%2BFyzSZ8XxpTiI%2FG0nkT4zznFZ0t4ipMz5v4q9ssqbdKUZt6u82knPCrt6PZwsnn0XySVnyPR1ZXAn72yx48bWJsu7apnI3Hy8bygUK5Js32qcytapqgmn95uexccj205vGgJ%2BeuOeG2SORmKZr%2FqKzcx9SFctMJdwMUFZDJITs7dnOp1EKZCxg304Cevyfya%2BvlKqv6aXK1qIj3imL%2BL6hL%2ByvUlFfE0VKZ7E8gBY3M%2F8VoJCFgizH1W6VyC76nH6b7jiibYVxUmVIEspry%2FLgZIlCeP11Z4zs%2FAwvVwtGFEut5S1JY4lfyT0N%2FevOLo%2BrUEgjcqc9IkGpQbv3iW7Co5b%2BKgjvpzYdH85PLcc4X21ouwEGl%2FS4qnUAvoSlXUUhR1eKr2VWFTB%2BGMl6FsiQsVD1R3urlAAIoSn7JQkmiVVCHSpCwDH%2FqPepXQ0Db77CJOAImohB%2BRPWr31ev5g%2FkE%2BzTa4lbvZo8xdWPffQu9yJTPCNB66s%2BzXoJt%2F0L6hSoCuBIoK8fnBGG87OoRckJpLqyWe4YbpGi50g0%2B3I3UD85Oa0fzubfoXxPLbW3FDWzigmyJeM0tQkax7PqTy80%2BUxfUHPlBZIRVNQ%2Bv0xRm8REKPoLmNr0%2BUo48v9GFbXPKylqQ2IKm00QddgyWGMROCTxdLB9nCY8P7j2DjlsV%2F%2Bmfr0C0r%2FNkeXbbpPlOTBBwT0mVz1zx9S%2FwJecBF9Wgv3p032iP2v4VSgfgW2G%2BHUEdEXU6iq4CtpLJfIN9XQG8dwa1VoO8XC2SrPDDyCOQptXgbcPvlAgBfxBoGwftQKeKFrNTASPt3pGGqDt%2FQRasn2kri%2BH6L80MJRsmVYJrAKyDItpJUy3%2F15WYIJqcJ9Q5N%2FLFJ4c3dc1URpWl9hW6mu50MUIelg4ucTPf15zs5DFo1c0VSp1tKB9jkwIyuM45kb%2BIP8gHed%2B6jO3v0KbIknzLy636E8KPTdCuUpB0wLo9JKnAO6pv0vS31EtBha%2FfJemkgLVVnd8KCk4qBTpQ5m7FbifBKrPJcq0pZAFVG%2FXbOFz%2BTcq2MLrcmV28Nmi%2FOHskh82bau0k8eWCaPijQPWQ5lUvslwVCfHkXBMIehqUgtDNLeauH1huvZTbYmw%2BluPjyWoNGEuxRLR7LK5fSyXFUyK7PURQv2v8D3XOt2NJ6liBbmPGOsakw1kbeOs%2B31Wm5qpH%2BiJWSzqdPr2O7zc2TmtnrzCig6bBd%2FvgQmzOlz0STWIlmZEQfupogOZFHUZ7EkUnMn0RrpIMqAgHRJAOjIJ3yGw1I%2FMAp9q9S3Q%2FclADNm1wEeO%2Bxbwg5OIYHZLY3ehG5lJk2xhco%2B6JWybpEVz2wrR6hZyD0QXZbeDVB%2BonmlimpkWprdAs4WEZDSQppsDlcdCBJJESIYFuAtUnC4GIF2C3Uu2Kv7L1bdz6FxtqxpG4TqQOqOUNAJ2HLvPWA2GgDy4O4vaDrtyl6P%2B1fAll%2BSyFcQ28GHqh7fvvf37udylf0fNwhzgz87Y%2Bcf5x9GnF6ygHu18sAbipWeF0YPBgp2GaKeQduxxdEr3SgbH1kvH7tvqSLhedomOvZyts2dw8acu3dY%2Ff%2BucuMtCuP%2Fe4zC4XnH3OLZ8ZuxTWxy8dJfU5dhDeKPSlJy5pn%2F%2B7u3XrJhmr9C5CuleGflGQocKnlAUaRKp0BAHV0ZwUt9VCqk6zYOgRIuMfePJzdmBdpPJ7%2F6B23%2Bf%2Bsp9NMDZevovvfYHG5dGPISQq1DojqNckchVrCcCYz%2FQ0hI0m3NKDRfkgsrnamo%2Bp0CAq1FyvC3a3Nak%2Fs5VX282x9Ufy3E39VAx6o7LpCvO2wK%2Bch9jNqpJCutcIOooKnYWtDK8gTRVYygRQfwgzKM5%2BjP2jOZdx3r32Py7rQUPOzAnoRs95NvRAR0qLGU11Taqu1bUYSzMcWjMEir067JQQHfIrLBHsrgv00%2FWavd8HRLMEEYFSW3HCSNQehnrHztKqHcDyo4VfZ6gPKCR%2BgufwA8GegxUEo4A%2Bgd0BASHiH6jYMLIsUdQJTs%2FC641KN4oCHWolCMLlMfIdtWKScjx7SM5LD9HnfmhrGI0S139UWfUnxgOXdJFW%2BAMcGjKr6eHAttHF5sUoeArYKDcxMSYcKA%2FxUDhPiEOEAPafSIUFArN0r24ynI91EPARDXvIDYyvqZaWeroBOUABQA%2FE%2BDXC7PWafDLQY2oiwpUEyj4RQtVlUp1GrM7In2p2A7VuiOW6otMiGOo5Mrp05ejVuTy6dNX%2Fk%2F7mybZQ0nUmfrbx3U4KueDnlHm5wdh8FFeKnoaKKh%2FTK18StOPhwG9Xo5mqXAxvw%2F79YQwwDR%2BnAKQQ4izVXioB84qcppWB7IqjU45z4CE17OvF1Dw%2BoTFqxtz8dxwtogBnF9MjIl%2Fin%2BK8s3hM9laIn0TiCbTAXL0T798bPXqx36p3chrv0O%2BGC9Xaj48Ecv8U8UEeBvUEsDlTepiU5OvlpeNGvpnKF0RvUooWhIjnx6GeBapXCQYTw9DNg6%2FOC3gZjp76oNTj9Kz6Jqobxb9NDqc08vcKReOpcsQV2K8InXFaXW3aI6Ofr1k48rp7CX7rx%2Bv1UKPsfvzQU0Kc83i2VdILmd2%2FyX55zT9luN2%2BCu4nKfwPcK%2FCvDVU%2BpHh8%2BLaldIf1fA5h3ndT6Fln9%2FW%2F9Ce1vndfvJtnPVO2xhm3qbafHVCN1X363UXHq9xuVD8OSD29Z8pZ5cZrern9cAdGW%2Fuib%2Fud%2BVK0L9a42r6C90kL8KzxwLQw9NkIQJL0ASU8M%2BVG0KsUdgdvpgP%2F6NqqP0%2FgHZFUfGEijZLHpiIgvV5%2FBltrj8Qd7XQd5p4P%2B7tJo30NMO6VGBwahSPMYiaaBYoLY6uEnciyhhh1Z%2FvvacG%2Frjpsvnpzs0B1Id6fmX8119l88XnOxe%2FuGrzzHcdu7UtY3%2B2vmXN5zUyj3ZcPl8p1sZSs6%2FnGXtwrV7Ka0XZdz83fwjjINpZWYw85lL8BRK4nGyIir2RiOsEyipuEcIakpGjWgBjLiHWOgj0Yi34gW1kKPxHt2Na5q%2Blwg1RdRSpFDNzosb44YJXnAfoEOpZW%2F%2F6u1lhYA6leevezbI26zNHO811M2dc5HFxpk4i1jPC0s21%2FBWW5DnPQbn2X1WK43%2FaM2n18DfSoybbNHijFpamzXI31eRibGUOxSu%2FlT96YZlq1Yt20DaSBuG6knw2eusHs5EPBfNmVvHKdaQzcDfz9ZsXmLDWGXy2U5OsYSsIn8CS12jQIyD12KKqZrLPy7mSPdICmd6WGHG8NDZkkHuE4h9TU8FpmUO%2FVjC%2FEinToFyoNDz2p9XD6g78WgQdPG7Z3R0T%2FZ5dTM9lsL8Ktek7szl2L%2BgQwGgwkZHc2g5Su7NvVqwGy2Ua4KSXUwt1X4PaM5paaEu6jQ5zVFyNabxvUksVt2T%2F4VeamYPlLtffdQsk%2B2sUTY%2FzDXl%2F05W53%2FBz9UK3p7LjapZ2ZxOm%2BUlZXrL3HHGqO8%2BwVroDaCTTnTxitMxmiAAYQzVJQH%2Bnj3oIHnPaN6Zq6sNSLjBl8tKgVr2mj%2F9CWi9dnKca8rBQBsd5R1tzVlgrl5pbnPw6kZclCr2CHxMnHohLz%2B3KRQokzALyeIKFU1TNCiayJdoHvDYe7K6mZLm8S3uJ9dojuaJ62%2FqN%2FtjQxnSnhnKPw%2BLNrLi8ZKyJ3x1YhiI1aNAtP6NzCGzYv3DmaGh%2FLvQZnt0evgIhTFV0kE%2FPYxAnOHhCQUZdCWY5JWJwMzlAGl1mpNbDU7yyGnhRMILsYhH3VRAijrPcBU8%2FCj1Y9NY6cnGVW0CjTLaz7E3epvaT%2FLtTV72Rs%2B0WVVmd0dz%2FMGTI5F0OsIviaqDlbbO5X6xT3PeXbXHRtf%2Fz%2Bfdka%2BeKPr8KF7IF4vBsT9MFPuPJMBTBMq9hQxXelQ%2Bbewnf18ap4Ib%2BmSMrtDU5zqlD8QANa5MBGh%2FOwOvSDfcV2d66mfEWsbGWmIz6nsyZDWQSmqmxDneYyvjHPmRXHZxeueyRGLZzvRioKnGto9nIPkibAJA16adcOZRQr1iAP3bUyBR7T4RgAWTKxhkCYFwshq%2B7iV9r0whk50cmRcTg4fy5x4OmmNkHndIA2%2BYuMbmE9dwGYB4KFTsvnDE6Ah47r%2FfE3AYI%2BoXADpkdlENcZ8OZEEf8FFGZNxMs6ZLpG3SUFLL7Q2kcFU%2FA%2FJsw%2BvWDa%2F7emewLaoeibaF1B9qUNnuqWK3%2BUfXYVL1v%2FomD15xxeDkPnXTOKSVcCbDGtOu0YQNpGAP7U1HU58UrqGu8xIbHtkQ3LVhb7Dx46ET3Ffcm1q0YcOizNmf3bC3VjWfAcpSv3MyTlgJ23FHQgmgvk%2Bgk8pL0mcCDOn08MDAQlf%2B%2FSlTZ1z12fnqntOhbOTL9%2FZdevbAPN%2Byby1f%2FuUtC%2Fixm8ZBo59LTXEW060hGrTDplNprWd58fwB%2Fb%2FE27BdS%2Fs7U%2BrGVCeQ46nzaw9QccnmZerGZZs3Yw9aVHt%2BKh6HN4ti6lxIhT%2FwahnZtWwzlY9QHQ2c79C%2BdxzvVDKy8GqKWQERO9YAKbpsDUTLdWV5dE8PVPjvj9pqw7ah%2FPFVtkit7aj6G5xY9mfJrCz1j1e0BcnPol4UjtrCdbahIVtd2HaURujnFJR8CuOuUUfhrGhgKKgjCYNSvCc1WKlEp8wHUaAYynFNyzZn%2B2MnYv36dbMDBTonl%2FT%2Fma5IKAyEGz%2B4eRnVtaX6tss2o34u8mWorFtuFgm4A6qK%2Fyp%2FgLEBVat5WnPDdKA574ubuFJ%2FIUfZ%2FY2Nt6mN%2BZNNTSTaeI56gKwkXerTe9DDHUw8%2FH35FY3nNN7GGuBKWhrV9ep%2B0k1WjNWVaHkW1yA%2BQHWNu8rtBw2a5YXuE40rs7%2FGA%2Bj09V3hA98yRnFPOGr8ltGlsFdD%2F7tRce3LH6Trcneuiy7K7J3khKu%2B3qUaXPWaX7T6%2FKfj9BX2eZq2XAcZT79u1ClJzUtHUqfqSMWBcZS43Ena0cUGLgpkKxB1QM%2B0Fxz10wgg6r5rltnFpH05pepUq3Y2HfYqeKRntmUFNz%2BXmcOs1H31U6cC6RTVLfCg7RNBF1UF2%2FwBgu0fFQtPEU1sSg3VcNsR7dWq3af87tUFn1l3ltXpaJxpNvtcZkH2WmMst3JqRpxUH%2BWC0E1qOGtP66s1MYv%2BVLu8%2FXFXvV%2FZbunYYBeVN64ls0ur6NzpV9xzlmQwB5qC4Tq70WC0tk8dWJXeHvkD0h9zJOM0vD86%2F1NJMaIAolctvlByferCsqOKDKceOfUu1PsmoFCamV5mCrMUOCi6V6FJosMF22AcrKJgQDVhfYh6tepp%2FlYgvnCEAbJQ1L0rOpajEmRcasMiPfxhgGoVo4rwreQpV6fUJHH2e8fa1s2c13Apl1b89a58ozdoap2sjgLN9uISl7P1DrulyeIkt0zr6JjWocoPOZsaXPb6jtqBblsgsaRre2xHi4nELm0MhG1%2Bx1SXwLpFi53b%2BaHRYo%2FIrbZtuWAKu5cSEXfybnnmUCaXGTpQr0xK2O2WWY76f%2BnAjNVf7nCZHU5XqIkTnpt6VtvsFlPXg1031g%2FVRdpkkyVpD7jnmax88QwDvg%2F66NnMRdRXTcGTmQc3cuINwN5IQqi0yzb%2BYFVHuVqI5s4ADfg5oE4ybDLd28mFSFmYvRoomsWXEdLU2Wl3GJy93ZNb%2Fd5gqmNaqJZSO1l6PVRy0nZIj%2F45EetjLguh1rLqR%2BSK0hO6NrsqcNX8zoUdjQYDJ7tb4os6%2Bi%2BY0qpY2AWlnLRDWdGFTfGY1gV0zNAtJ7pdo24se0D88AwLY%2FgZmE9iuP4V5v7CSR%2FRThaHLh%2BUeBkXwU6BC7lGOevK65udTv%2BtS%2FPfW7qj3ljTcj3b9OkbV85t8xsMj7Ddj7DGpthZKwKPvso%2Fc%2F1K9aLE12fMWLV1y1D9ua8lyJdWXr%2FbG%2BnoCFutf%2FmLILe39ITUV4igr3876fpX5g2zeB52sWnIL4fXHlgeUzOx5QfIvJQyrKQE9wHUqVq%2BPEaOrz0wVvNbJZVSfsuMzxN4l9PkedFzw9V5Dj%2BnzpgoT4ZxCxJfC5RWLc74YVHxKlExCYt0JAOMatREhHBSCAtSfod6x6Ls8HCWECLwXZ9nd5Dz1T24JUdWs6fU3%2B%2BfcnT49Qe%2BkBs%2BwdsMZgPXMp3U5S958snPP%2FEE7bvkOPCuTUDTUQ%2FUzirLhML9yPahoe1D5Fj5jWsaoveyP00PehdUAHk%2FseDVWsvDWXXXsyn%2F4wfpXc2V3%2FQxli3jl%2F5hj%2F83avSCfpTNxOEKLmTjxOEKuxgNlsQn0xgct724mhynupNW1Ph6o3RYS3%2F%2B2TJrzLlkFz%2Bip3qCHKf6eqW02QJLjBYuuj4sobhCWqa%2FYHGEHpcnumuWSOhxeaL7sOakNR6vvmo%2BYcfFA8UFXEPZf9UjyudIOyNwx%2Fi90DdsujS%2FFX2UAwvWSVK4NxaMhAGw3oowp%2Fuc8CTi7D2rBgZWwb%2F60faR7SPsEbjkXy4G0XaqhXPwe2cePjxjxuHD6ssQuR1fq6PF0E%2Bo2t1nePTn8TUmxz%2FA3crMoCc7egESuoTHYc7mYdg6etORoOhR7BBGD%2BqJopELrl4S6cJNRtEAsLP%2FOdvnJq0Wo0GolY2Et9VFB2Kf%2B4bZvVyxfOMz3WdFfSIryj6DwWghre7aQbdiDrkTL3A3vNDuDpk93HqXwam%2BbWmUJZfNn5ozKV5Pmmq8PF%2FjVY%2B2Tlk2M2RzSXKjmbQ4RZcQavEYrN%2F9rlXwtIQqzxQNMzPPfHYLvuPoO9TbT8bpGw5CQPGd%2BSyX%2FCyf0Vxjd2R9NmsunnXYa8xGHzn%2BsSfM5J0y0DZEXWWxkXjcR75KBLNLHi7XvX2G8VOrf4Ykg0AMdBESIpo7MgAfyakA6rkqpI6UjNs0px7cMV%2BD5BF49Tez1VGnYmq0WIijp985m4Sn2gJR9b07riPPFo97OYbUZbxJCpot7H%2FlpZBicglCPN7WOfJkcHqc3ElWqvvz%2F1E6bIQrG%2Btz6WkM1SM9FBTR7FSs8KyBBytSmNEoquJNFN5EQyTiCrnKDx1h58yxCepPHU5nxGoxEQeeOZi2m80DxNxncVhr6BmEfUarxejw%2BWSiHhWk19bSY7aKR5MsteblJpfTLtjimBouXsm3d3djjYM%2BwEW0El9dM%2FueVRWIsXwe43R7SgbVZqrnqoJ1X%2FkuF7pcgf8duv4q6vayV5U9zMV91GxO59UUjW8rHV6u799WzKMT7umRCXbYUKM%2BfoaCcwgaoqZUtmodV3p%2BX7akb4dnU9B9La38RPFUG2SCC90tVA4XwEFhyOpZZrUCsgWYHsczLFBBVGNtstoN1bw0Z%2BO4fYIbvZVt4EUcJEKOhHeincWqONw%2Bq6w5Go%2BWGOSR7LhKV%2BKBqbBPpfUvOf9QqkpDyVhBeyyZQGMsdA5FBUqvFMtUyGq9vjnsAJU4UcrxldP1CCaofyDkSAifoP5QwWx%2BSyUGxp75BzGAvtG7uQ38LehlyEQMeh0TeE6Bm7tYdXqdkt0uOb3kfYlNwmOdDyacOq%2FqlFo1v%2BPTmTi3E%2FglC9W11b34A22zmLzvb231Q0L2Bgg60OTW4YdstO%2BYOJnO38TtpH7zy9ymokWyA79qlVSn38HtpFlImFnhu3b4boNWXklOXV0Iwo7lQ1hrZyPFcwtjwFP7iEKSHSSJw509kh8kj6pr%2BH1jR7km9vcvqN9657vffefkv%2BfKxge1X%2B7RdjYUPIESN7gTvRkB%2FRMYtEkaVkdHApmdBPpnKmz0n1xSWFOyVIuLrinZwpoCRe6kyiVZoHX088F%2BUX4%2BWKS4iBTP0IWxGtZgOdMaV4KTayqHQF%2FVihBwTbgDXTCmKoOBJeNhwJMzEVjtjIFLuU38fPR7hqNG1JS7g%2FqRCuy3vmQ3W9Vu8qbVbP%2BSzazGR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QAAQBkAFgErwREABkAAAE%2BAh4CFRQOAwcuBDU0PgIeAQKJMHt4dVg2Q3mEqD4%2Bp4V4Qzhadnh5A7VESAUtU3ZAOXmAf7JVVbJ%2FgHk5QHZTLQVIAAAAAf%2FTAF4EewSUABgAAAETNjIXEyEyFgcFExYGJyUFBiY3EyUmNjMBl4MHFQeBAaUVBhH%2BqoIHDxH%2Bqf6qEQ8Hgv6lEQYUAyABYRMT%2Fp8RDPn%2BbxQLDPb3DAsUAZD7DBEAAv%2FTAF4EewSUABgAIgAAARM2MhcTITIWBwUTFgYnJQUGJjcTJSY2MwUjFwc3Fyc3IycBl4MHFQeBAaUVBhH%2BqoIHDxH%2Bqf6qEQ8Hgv6lEQYUAfPwxUrBw0rA6k4DIAFhExP%2BnxEM%2Bf5vFAsM9vcMCxQBkPsMEWSO4ouM5YzTAAABAAAAAASwBLAAJgAAATIWHQEUBiMVFBYXBR4BHQEUBiMhIiY9ATQ2NyU%2BAT0BIiY9ATQ2Alh8sD4mDAkBZgkMDwr7ggoPDAkBZgkMJj6wBLCwfPouaEsKFwbmBRcKXQoPDwpdChcF5gYXCktoLvp8sAAAAA0AAAAABLAETAAPABMAIwAnACsALwAzADcARwBLAE8AUwBXAAATITIWFREUBiMhIiY1ETQ2FxUzNSkBIgYVERQWMyEyNjURNCYzFTM1BRUzNSEVMzUFFTM1IRUzNQchIgYVERQWMyEyNjURNCYFFTM1IRUzNQUVMzUhFTM1GQR%2BCg8PCvuCCg8PVWQCo%2F3aCg8PCgImCg8Pc2T8GGQDIGT8GGQDIGTh%2FdoKDw8KAiYKDw%2F872QDIGT8GGQDIGQETA8K%2B%2BYKDw8KBBoKD2RkZA8K%2FqIKDw8KAV4KD2RkyGRkZGTIZGRkZGQPCv6iCg8PCgFeCg9kZGRkZMhkZGRkAAAEAAAAAARMBEwADwAfAC8APwAAEyEyFhURFAYjISImNRE0NikBMhYVERQGIyEiJjURNDYBITIWFREUBiMhIiY1ETQ2KQEyFhURFAYjISImNRE0NjIBkBUdHRX%2BcBUdHQJtAZAVHR0V%2FnAVHR39vQGQFR0dFf5wFR0dAm0BkBUdHRX%2BcBUdHQRMHRX%2BcBUdHRUBkBUdHRX%2BcBUdHRUBkBUd%2FagdFf5wFR0dFQGQFR0dFf5wFR0dFQGQFR0AAAkAAAAABEwETAAPAB8ALwA%2FAE8AXwBvAH8AjwAAEzMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYhMzIWHQEUBisBIiY9ATQ2ATMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYhMzIWHQEUBisBIiY9ATQ2ATMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYhMzIWHQEUBisBIiY9ATQ2MsgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR389cgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR389cgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR0ETB0VyBUdHRXIFR0dFcgVHR0VyBUdHRXIFR0dFcgVHf5wHRXIFR0dFcgVHR0VyBUdHRXIFR0dFcgVHR0VyBUd%2FnAdFcgVHR0VyBUdHRXIFR0dFcgVHR0VyBUdHRXIFR0ABgAAAAAEsARMAA8AHwAvAD8ATwBfAAATMzIWHQEUBisBIiY9ATQ2KQEyFh0BFAYjISImPQE0NgEzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2ATMyFh0BFAYrASImPQE0NikBMhYdARQGIyEiJj0BNDYyyBUdHRXIFR0dAaUCvBUdHRX9RBUdHf6FyBUdHRXIFR0dAaUCvBUdHRX9RBUdHf6FyBUdHRXIFR0dAaUCvBUdHRX9RBUdHQRMHRXIFR0dFcgVHR0VyBUdHRXIFR3%2BcB0VyBUdHRXIFR0dFcgVHR0VyBUd%2FnAdFcgVHR0VyBUdHRXIFR0dFcgVHQAAAAABACYALAToBCAAFwAACQE2Mh8BFhQHAQYiJwEmND8BNjIfARYyAdECOwgUB7EICPzxBxUH%2FoAICLEHFAirBxYB3QI7CAixBxQI%2FPAICAGACBQHsQgIqwcAAQBuAG4EQgRCACMAAAEXFhQHCQEWFA8BBiInCQEGIi8BJjQ3CQEmND8BNjIXCQE2MgOIsggI%2FvUBCwgIsggVB%2F70%2FvQHFQiyCAgBC%2F71CAiyCBUHAQwBDAcVBDuzCBUH%2FvT%2B9AcVCLIICAEL%2FvUICLIIFQcBDAEMBxUIsggI%2FvUBDAcAAwAX%2F%2BsExQSZABkAJQBJAAAAMh4CFRQHARYUDwEGIicBBiMiLgI0PgEEIg4BFB4BMj4BNCYFMzIWHQEzMhYdARQGKwEVFAYrASImPQEjIiY9ATQ2OwE1NDYBmcSzgk1OASwICG0HFQj%2B1HeOYrSBTU2BAW%2BzmFhYmLOZWFj%2BvJYKD0sKDw8KSw8KlgoPSwoPDwpLDwSZTYKzYo15%2FtUIFQhsCAgBK01NgbTEs4JNWJmzmFhYmLOZIw8KSw8KlgoPSwoPDwpLDwqWCg9LCg8AAAMAF%2F%2FrBMUEmQAZACUANQAAADIeAhUUBwEWFA8BBiInAQYjIi4CND4BBCIOARQeATI%2BATQmBSEyFh0BFAYjISImPQE0NgGZxLOCTU4BLAgIbQcVCP7Ud45itIFNTYEBb7OYWFiYs5lYWP5YAV4KDw8K%2FqIKDw8EmU2Cs2KNef7VCBUIbAgIAStNTYG0xLOCTViZs5hYWJizmYcPCpYKDw8KlgoPAAAAAAIAFwAXBJkEsAAPAC0AAAEzMhYVERQGKwEiJjURNDYFNRYSFRQOAiIuAjU0EjcVDgEVFB4BMj4BNTQmAiZkFR0dFWQVHR0BD6fSW5vW6tabW9KnZ3xyxejFcnwEsB0V%2FnAVHR0VAZAVHeGmPv7ZuHXWm1tbm9Z1uAEnPqY3yHh0xXJyxXR4yAAEAGQAAASwBLAADwAfAC8APwAAATMyFhURFAYrASImNRE0NgEzMhYVERQGKwEiJjURNDYBMzIWFREUBisBIiY1ETQ2BTMyFh0BFAYrASImPQE0NgQBlgoPDwqWCg8P%2Ft6WCg8PCpYKDw%2F%2B3pYKDw8KlgoPD%2F7elgoPDwqWCg8PBLAPCvuCCg8PCgR%2BCg%2F%2BcA8K%2FRIKDw8KAu4KD%2F7UDwr%2BPgoPDwoBwgoPyA8K%2BgoPDwr6Cg8AAAAAAgAaABsElgSWAEcATwAAATIfAhYfATcWFwcXFh8CFhUUDwIGDwEXBgcnBwYPAgYjIi8CJi8BByYnNycmLwImNTQ%2FAjY%2FASc2Nxc3Nj8CNhIiBhQWMjY0AlghKSYFMS0Fhj0rUAMZDgGYBQWYAQ8YA1AwOIYFLDIFJisfISkmBTEtBYY8LFADGQ0ClwYGlwINGQNQLzqFBS0xBSYreLJ%2BfrJ%2BBJYFmAEOGQJQMDmGBSwxBiYrHiIoJgYxLAWGPSxRAxkOApcFBZcCDhkDUTA5hgUtMAYmKiAhKCYGMC0Fhj0sUAIZDgGYBf6ZfrF%2BfrEABwBkAAAEsAUUABMAFwAhACUAKQAtADEAAAEhMhYdASEyFh0BITU0NjMhNTQ2FxUhNQERFAYjISImNREXETMRMxEzETMRMxEzETMRAfQBLCk7ARMKD%2Fu0DwoBEzspASwBLDsp%2FUQpO2RkZGRkZGRkBRQ7KWQPCktLCg9kKTtkZGT%2B1PzgKTs7KQMgZP1EArz9RAK8%2FUQCvP1EArwAAQAMAAAFCATRAB8AABMBNjIXARYGKwERFAYrASImNREhERQGKwEiJjURIyImEgJsCBUHAmAIBQqvDwr6Cg%2F%2B1A8K%2BgoPrwoFAmoCYAcH%2FaAICv3BCg8PCgF3%2FokKDw8KAj8KAAIAZAAAA%2BgEsAARABcAAAERFBYzIREUBiMhIiY1ETQ2MwEjIiY9AQJYOykBLB0V%2FOAVHR0VA1L6FR0EsP5wKTv9dhUdHRUETBUd%2FnAdFfoAAwAXABcEmQSZAA8AGwAwAAAAMh4CFA4CIi4CND4BBCIOARQeATI%2BATQmBTMyFhURMzIWHQEUBisBIiY1ETQ2AePq1ptbW5vW6tabW1ubAb%2FoxXJyxejFcnL%2BfDIKD68KDw8K%2BgoPDwSZW5vW6tabW1ub1urWmztyxejFcnLF6MUNDwr%2B7Q8KMgoPDwoBXgoPAAAAAAL%2FnAAABRQEsAALAA8AACkBAyMDIQEzAzMDMwEDMwMFFP3mKfIp%2FeYBr9EVohTQ%2Fp4b4BsBkP5wBLD%2B1AEs%2FnD%2B1AEsAAAAAAIAZAAABLAEsAAVAC8AAAEzMhYVETMyFgcBBiInASY2OwERNDYBMzIWFREUBiMhIiY1ETQ2OwEyFh0BITU0NgImyBUdvxQLDf65DSYN%2FrkNCxS%2FHQJUMgoPDwr75goPDwoyCg8DhA8EsB0V%2Fj4XEP5wEBABkBAXAcIVHfzgDwr%2BogoPDwoBXgoPDwqvrwoPAAMAFwAXBJkEmQAPABsAMQAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgUzMhYVETMyFgcDBiInAyY2OwERNDYB4%2BrWm1tbm9bq1ptbW5sBv%2BjFcnLF6MVycv58lgoPiRUKDd8NJg3fDQoViQ8EmVub1urWm1tbm9bq1ps7csXoxXJyxejFDQ8K%2Fu0XEP7tEBABExAXARMKDwAAAAMAFwAXBJkEmQAPABsAMQAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JiUTFgYrAREUBisBIiY1ESMiJjcTNjIB4%2BrWm1tbm9bq1ptbW5sBv%2BjFcnLF6MVycv7n3w0KFYkPCpYKD4kVCg3fDSYEmVub1urWm1tbm9bq1ps7csXoxXJyxejFAf7tEBf%2B7QoPDwoBExcQARMQAAAAAAIAAAAABLAEsAAZADkAABMhMhYXExYVERQGBwYjISImJyY1EzQ3Ez4BBSEiBgcDBhY7ATIWHwEeATsBMjY%2FAT4BOwEyNicDLgHhAu4KEwO6BwgFDBn7tAweAgYBB7kDEwKX%2FdQKEgJXAgwKlgoTAiYCEwr6ChMCJgITCpYKDAJXAhIEsA4K%2FXQYGf5XDB4CBggEDRkBqRkYAowKDsgOC%2F4%2BCw4OCpgKDg4KmAoODgsBwgsOAAMAFwAXBJkEmQAPABsAJwAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgUXFhQPAQYmNRE0NgHj6tabW1ub1urWm1tbmwG%2F6MVycsXoxXJy%2Fov9ERH9EBgYBJlbm9bq1ptbW5vW6tabO3LF6MVycsXoxV2%2BDCQMvgwLFQGQFQsAAQAXABcEmQSwACgAAAE3NhYVERQGIyEiJj8BJiMiDgEUHgEyPgE1MxQOAiIuAjQ%2BAjMyA7OHBwsPCv6WCwQHhW2BdMVycsXoxXKWW5vW6tabW1ub1nXABCSHBwQL%2FpYKDwsHhUxyxejFcnLFdHXWm1tbm9bq1ptbAAAAAAIAFwABBJkEsAAaADUAAAE3NhYVERQGIyEiJj8BJiMiDgEVIzQ%2BAjMyEzMUDgIjIicHBiY1ETQ2MyEyFg8BFjMyPgEDs4cHCw8L%2FpcLBAeGboF0xXKWW5vWdcDrllub1nXAnIYHCw8LAWgKBQiFboJ0xXIEJIcHBAv%2BlwsPCweGS3LFdHXWm1v9v3XWm1t2hggFCgFoCw8LB4VMcsUAAAAKAGQAAASwBLAADwAfAC8APwBPAF8AbwB%2FAI8AnwAAEyEyFhURFAYjISImNRE0NgUhIgYVERQWMyEyNjURNCYFMzIWHQEUBisBIiY9ATQ2MyEyFh0BFAYjISImPQE0NgczMhYdARQGKwEiJj0BNDYzITIWHQEUBiMhIiY9ATQ2BzMyFh0BFAYrASImPQE0NjMhMhYdARQGIyEiJj0BNDYHMzIWHQEUBisBIiY9ATQ2MyEyFh0BFAYjISImPQE0Nn0EGgoPDwr75goPDwPA%2FK4KDw8KA1IKDw%2F9CDIKDw8KMgoPD9IBwgoPDwr%2BPgoPD74yCg8PCjIKDw%2FSAcIKDw8K%2Fj4KDw%2B%2BMgoPDwoyCg8P0gHCCg8PCv4%2BCg8PvjIKDw8KMgoPD9IBwgoPDwr%2BPgoPDwSwDwr7ggoPDwoEfgoPyA8K%2FK4KDw8KA1IKD2QPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKDwAAAAACAAAAAARMBLAAGQAjAAABNTQmIyEiBh0BIyIGFREUFjMhMjY1ETQmIyE1NDY7ATIWHQEDhHVT%2FtRSdmQpOzspA4QpOzsp%2FageFMgUHgMgyFN1dlLIOyn9qCk7OykCWCk7lhUdHRWWAAIAZAAABEwETAAJADcAABMzMhYVESMRNDYFMhcWFREUBw4DIyIuAScuAiMiBwYjIicmNRE%2BATc2HgMXHgIzMjc2fTIKD2QPA8AEBRADIUNAMRwaPyonKSxHHlVLBwgGBQ4WeDsXKC4TOQQpLUUdZ1AHBEwPCvvNBDMKDzACBhH%2BWwYGO1AkDQ0ODg8PDzkFAwcPAbY3VwMCAwsGFAEODg5XCAAAAwAAAAAEsASXACEAMQBBAAAAMh4CFREUBisBIiY1ETQuASAOARURFAYrASImNRE0PgEDMzIWFREUBisBIiY1ETQ2ITMyFhURFAYrASImNRE0NgHk6N6jYw8KMgoPjeT%2B%2BuSNDwoyCg9joyqgCAwMCKAIDAwCYKAIDAwIoAgMDASXY6PedP7UCg8PCgEsf9FyctF%2F%2FtQKDw8KASx03qP9wAwI%2FjQIDAwIAcwIDAwI%2FjQIDAwIAcwIDAAAAAACAAAA0wRHA90AFQA5AAABJTYWFREUBiclJisBIiY1ETQ2OwEyBTc2Mh8BFhQPARcWFA8BBiIvAQcGIi8BJjQ%2FAScmND8BNjIXAUEBAgkMDAn%2B%2FhUZ%2BgoPDwr6GQJYeAcUByIHB3h4BwciBxQHeHgHFAciBwd3dwcHIgcUBwMurAYHCv0SCgcGrA4PCgFeCg%2BEeAcHIgcUB3h4BxQHIgcHd3cHByIHFAd4eAcUByIICAAAAAACAAAA0wNyA90AFQAvAAABJTYWFREUBiclJisBIiY1ETQ2OwEyJTMWFxYVFAcGDwEiLwEuATc2NTQnJjY%2FATYBQQECCQwMCf7%2BFRn6Cg8PCvoZAdIECgZgWgYLAwkHHQcDBkhOBgMIHQcDLqwGBwr9EgoHBqwODwoBXgoPZAEJgaGafwkBAQYXBxMIZ36EaggUBxYFAAAAAAMAAADEBGID7AAbADEASwAAATMWFxYVFAYHBgcjIi8BLgE3NjU0JicmNj8BNgUlNhYVERQGJyUmKwEiJjURNDY7ATIlMxYXFhUUBwYPASIvAS4BNzY1NCcmNj8BNgPHAwsGh0RABwoDCQcqCAIGbzs3BgIJKgf9ggECCQwMCf7%2BFRn6Cg8PCvoZAdIECgZgWgYLAwkHHQcDBkhOBgMIHQcD7AEJs9lpy1QJAQYiBhQIlrJarEcJFAYhBb6sBgcK%2FRIKBwasDg8KAV4KD2QBCYGhmn8JAQEGFwcTCGd%2BhGoIFQYWBQAAAAANAAAAAASwBLAACQAVABkAHQAhACUALQA7AD8AQwBHAEsATwAAATMVIxUhFSMRIQEjFTMVIREjESM1IQURIREhESERBSM1MwUjNTMBMxEhETM1MwEzFSMVIzUjNTM1IzUhBREhEQcjNTMFIzUzASM1MwUhNSEB9GRk%2FnBkAfQCvMjI%2FtTIZAJY%2B7QBLAGQASz84GRkArxkZP1EyP4MyGQB9MhkyGRkyAEs%2FUQBLGRkZAOEZGT%2BDGRkAfT%2B1AEsA4RkZGQCWP4MZMgBLAEsyGT%2B1AEs%2FtQBLMhkZGT%2BDP4MAfRk%2FtRkZGRkyGTI%2FtQBLMhkZGT%2B1GRkZAAAAAAJAAAAAASwBLAAAwAHAAsADwATABcAGwAfACMAADcjETMTIxEzASMRMxMjETMBIxEzASE1IRcjNTMXIzUzBSM1M2RkZMhkZAGQyMjIZGQBLMjI%2FOD%2B1AEsyGRkyGRkASzIyMgD6PwYA%2Bj8GAPo%2FBgD6PwYA%2Bj7UGRkW1tbW1sAAAIAAAAKBKYEsAANABUAAAkBFhQHAQYiJwETNDYzBCYiBhQWMjYB9AKqCAj%2BMAgUCP1WAQ8KAUM7Uzs7UzsEsP1WCBQI%2FjAICAKqAdsKD807O1Q7OwAAAAADAAAACgXSBLAADQAZACEAAAkBFhQHAQYiJwETNDYzIQEWFAcBBiIvAQkBBCYiBhQWMjYB9AKqCAj%2BMAgUCP1WAQ8KAwYCqggI%2FjAIFAg4Aaj9RP7TO1M7O1M7BLD9VggUCP4wCAgCqgHbCg%2F9VggUCP4wCAg4AaoCvM07O1Q7OwAAAAABAGQAAASwBLAAJgAAASEyFREUDwEGJjURNCYjISIPAQYWMyEyFhURFAYjISImNRE0PwE2ASwDOUsSQAgKDwr9RBkSQAgFCgK8Cg8PCvyuCg8SixIEsEv8fBkSQAgFCgO2Cg8SQAgKDwr8SgoPDwoDzxkSixIAAAABAMj%2F%2FwRMBLAACgAAEyEyFhURCQERNDb6AyAVHf4%2B%2Fj4dBLAdFfuCAbz%2BQwR%2FFR0AAAAAAwAAAAAEsASwABUARQBVAAABISIGBwMGHwEeATMhMjY%2FATYnAy4BASMiBg8BDgEjISImLwEuASsBIgYVERQWOwEyNj0BNDYzITIWHQEUFjsBMjY1ETQmASEiBg8BBhYzITI2LwEuAQM2%2FkQLEAFOBw45BhcKAcIKFwY%2BDgdTARABVpYKFgROBBYK%2FdoKFgROBBYKlgoPDwqWCg8PCgLuCg8PCpYKDw%2F%2Bsf4MChMCJgILCgJYCgsCJgITBLAPCv7TGBVsCQwMCWwVGAEtCg%2F%2BcA0JnAkNDQmcCQ0PCv12Cg8PCpYKDw8KlgoPDwoCigoP%2FagOCpgKDg4KmAoOAAAAAAQAAABkBLAETAAdACEAKQAxAAABMzIeAh8BMzIWFREUBiMhIiY1ETQ2OwE%2BBAEVMzUEIgYUFjI2NCQyFhQGIiY0AfTIOF00JAcGlik7Oyn8GCk7OymWAgknM10ByGT%2Bz76Hh76H%2Fu9WPDxWPARMKTs7FRQ7Kf2oKTs7KQJYKTsIG0U1K%2F7UZGRGh76Hh74IPFY8PFYAAAAAAgA1AAAEsASvACAAIwAACQEWFx4BHwEVITUyNi8BIQYHBh4CMxUhNTY3PgE%2FAQEDIQMCqQGBFCgSJQkK%2Fl81LBFS%2Fnk6IgsJKjIe%2FpM4HAwaBwcBj6wBVKIEr%2FwaMioTFQECQkJXLd6RWSIuHAxCQhgcDCUNDQPu%2FVoByQAAAAADAGQAAAPwBLAAJwAyADsAAAEeBhUUDgMjITU%2BATURNC4EJzUFMh4CFRQOAgclMzI2NTQuAisBETMyNjU0JisBAvEFEzUwOyodN1htbDD%2BDCk7AQYLFyEaAdc5dWM%2BHy0tEP6Pi05pESpTPnbYUFJ9Xp8CgQEHGB0zOlIuQ3VONxpZBzMoAzsYFBwLEAkHRwEpSXNDM1s6KwkxYUopOzQb%2FK5lUFqBAAABAMgAAANvBLAAGQAAARcOAQcDBhYXFSE1NjcTNjQuBCcmJzUDbQJTQgeECSxK%2Fgy6Dq0DAw8MHxUXDQYEsDkTNSj8uTEoBmFhEFIDQBEaExAJCwYHAwI5AAAAAAL%2FtQAABRQEsAAlAC8AAAEjNC4FKwERFBYfARUhNTI%2BAzURIyIOBRUjESEFIxEzByczESM3BRQyCAsZEyYYGcgyGRn%2BcAQOIhoWyBkYJhMZCwgyA%2Bj7m0tLfX1LS30DhBUgFQ4IAwH8rhYZAQJkZAEFCRUOA1IBAwgOFSAVASzI%2FOCnpwMgpwACACH%2FtQSPBLAAJQAvAAABIzQuBSsBERQWHwEVITUyPgM1ESMiDgUVIxEhEwc1IRUnNxUhNQRMMggLGRMmGBnIMhkZ%2FnAEDiIaFsgZGCYTGQsIMgPoQ6f84KenAyADhBUgFQ4IAwH9dhYZAQJkZAEFCRUOAooBAwgOFSAVASz7gn1LS319S0sABAAAAAAEsARMAA8AHwAvAD8AABMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYyAlgVHR0V%2FagVHR0VA%2BgVHR0V%2FBgVHR0VAyAVHR0V%2FOAVHR0VBEwVHR0V%2B7QVHR0ETB0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR0ABAAAAAAEsARMAA8AHwAvAD8AABMhMhYdARQGIyEiJj0BNDYDITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NgMhMhYdARQGIyEiJj0BNDb6ArwVHR0V%2FUQVHR2zBEwVHR0V%2B7QVHR3dArwVHR0V%2FUQVHR2zBEwVHR0V%2B7QVHR0ETB0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR0ABAAAAAAEsARMAA8AHwAvAD8AAAE1NDYzITIWHQEUBiMhIiYBNTQ2MyEyFh0BFAYjISImEzU0NjMhMhYdARQGIyEiJgE1NDYzITIWHQEUBiMhIiYB9B0VAlgVHR0V%2FagVHf5wHRUD6BUdHRX8GBUdyB0VAyAVHR0V%2FOAVHf7UHRUETBUdHRX7tBUdA7ZkFR0dFWQVHR3%2B6WQVHR0VZBUdHf7pZBUdHRVkFR0d%2FulkFR0dFWQVHR0AAAQAAAAABLAETAAPAB8ALwA%2FAAATITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2MgRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dBEwdFWQVHR0VZBUd%2FtQdFWQVHR0VZBUd%2FtQdFWQVHR0VZBUd%2FtQdFWQVHR0VZBUdAAgAAAAABLAETAAPAB8ALwA%2FAE8AXwBvAH8AABMzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2ATMyFh0BFAYrASImPQE0NikBMhYdARQGIyEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2KQEyFh0BFAYjISImPQE0NgEzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2MmQVHR0VZBUdHQFBAyAVHR0V%2FOAVHR3%2B6WQVHR0VZBUdHQFBAyAVHR0V%2FOAVHR3%2B6WQVHR0VZBUdHQFBAyAVHR0V%2FOAVHR3%2B6WQVHR0VZBUdHQFBAyAVHR0V%2FOAVHR0ETB0VZBUdHRVkFR0dFWQVHR0VZBUd%2FtQdFWQVHR0VZBUdHRVkFR0dFWQVHf7UHRVkFR0dFWQVHR0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR0dFWQVHR0VZBUdAAAG%2F5wAAASwBEwAAwATACMAKgA6AEoAACEjETsCMhYdARQGKwEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2BQc1IzUzNQUhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2AZBkZJZkFR0dFWQVHR0VAfQVHR0V%2FgwVHR3%2B%2BqfIyAHCASwVHR0V%2FtQVHR0VAlgVHR0V%2FagVHR0ETB0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR36fUtkS68dFWQVHR0VZBUd%2FtQdFWQVHR0VZBUdAAAABgAAAAAFFARMAA8AEwAjACoAOgBKAAATMzIWHQEUBisBIiY9ATQ2ASMRMwEhMhYdARQGIyEiJj0BNDYFMxUjFSc3BSEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYyZBUdHRVkFR0dA2dkZPyuAfQVHR0V%2FgwVHR0EL8jIp6f75gEsFR0dFf7UFR0dFQJYFR0dFf2oFR0dBEwdFWQVHR0VZBUd%2B7QETP7UHRVkFR0dFWQVHchkS319rx0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR0AAAAAAgAAAMgEsAPoAA8AEgAAEyEyFhURFAYjISImNRE0NgkCSwLuHywsH%2F0SHywsBIT%2B1AEsA%2BgsH%2F12HywsHwKKHyz9RAEsASwAAwAAAAAEsARMAA8AFwAfAAATITIWFREUBiMhIiY1ETQ2FxE3BScBExEEMhYUBiImNCwEWBIaGhL7qBIaGkr3ASpKASXs%2FNJwTk5wTgRMGhL8DBIaGhID9BIaZP0ftoOcAT7%2B4AH0dE5vT09vAAAAAAIA2wAFBDYEkQAWAB4AAAEyHgEVFAcOAQ8BLgQnJjU0PgIWIgYUFjI2NAKIdcZzRkWyNjYJIV5YbSk8RHOft7eCgreCBJF4ynVzj23pPz4IIWZomEiEdVijeUjDgriBgbgAAAACABcAFwSZBJkADwAXAAAAMh4CFA4CIi4CND4BAREiDgEUHgEB4%2BrWm1tbm9bq1ptbW5sBS3TFcnLFBJlbm9bq1ptbW5vW6tab%2FG8DVnLF6MVyAAACAHUAAwPfBQ8AGgA1AAABHgYVFA4DBy4DNTQ%2BBQMOAhceBBcWNj8BNiYnLgInJjc2IyYCKhVJT1dOPiUzVnB9P1SbfEokP0xXUEm8FykoAwEbITEcExUWAgYCCQkFEikMGiACCAgFD0iPdXdzdYdFR4BeRiYEBTpjl1lFh3ZzeHaQ%2Ff4hS4I6JUEnIw4IBwwQIgoYBwQQQSlZtgsBAAAAAwAAAAAEywRsAAwAKgAvAAABNz4CHgEXHgEPAiUhMhcHISIGFREUFjMhMjY9ATcRFAYjISImNRE0NgkBBzcBA%2BhsAgYUFR0OFgoFBmz9BQGQMje7%2FpApOzspAfQpO8i7o%2F5wpbm5Azj%2BlqE3AWMD9XMBAgIEDw4WKgsKc8gNuzsp%2FgwpOzsptsj%2BtKW5uaUBkKW5%2Ftf%2BljKqAWMAAgAAAAAEkwRMABsANgAAASEGByMiBhURFBYzITI2NTcVFAYjISImNRE0NgUBFhQHAQYmJzUmDgMHPgY3NT4BAV4BaaQ0wyk7OykB9Ck7yLml%2FnClubkCfwFTCAj%2BrAcLARo5ZFRYGgouOUlARioTAQsETJI2Oyn%2BDCk7OymZZ6W5uaUBkKW5G%2F7TBxUH%2Fs4GBAnLAQINFjAhO2JBNB0UBwHSCgUAAAAAAgAAAAAEnQRMAB0ANQAAASEyFwchIgYVERQWMyEyNj0BNxUUBiMhIiY1ETQ2CQE2Mh8BFhQHAQYiLwEmND8BNjIfARYyAV4BXjxDsv6jKTs7KQH0KTvIuaX%2BcKW5uQHKAYsHFQdlBwf97QcVB%2FgHB2UHFQdvCBQETBexOyn%2BDCk7OylFyNulubmlAZCluf4zAYsHB2UHFQf97AcH%2BAcVB2UHB28HAAAAAQAKAAoEpgSmADsAAAkBNjIXARYGKwEVMzU0NhcBFhQHAQYmPQEjFTMyFgcBBiInASY2OwE1IxUUBicBJjQ3ATYWHQEzNSMiJgE%2BAQgIFAgBBAcFCqrICggBCAgI%2FvgICsiqCgUH%2FvwIFAj%2B%2BAgFCq%2FICgj%2B%2BAgIAQgICsivCgUDlgEICAj%2B%2BAgKyK0KBAf%2B%2FAcVB%2F73BwQKrcgKCP74CAgBCAgKyK0KBAcBCQcVBwEEBwQKrcgKAAEAyAAAA4QETAAZAAATMzIWFREBNhYVERQGJwERFAYrASImNRE0NvpkFR0B0A8VFQ%2F%2BMB0VZBUdHQRMHRX%2BSgHFDggV%2FBgVCA4Bxf5KFR0dFQPoFR0AAAABAAAAAASwBEwAIwAAEzMyFhURATYWFREBNhYVERQGJwERFAYnAREUBisBIiY1ETQ2MmQVHQHQDxUB0A8VFQ%2F%2BMBUP%2FjAdFWQVHR0ETB0V%2FkoBxQ4IFf5KAcUOCBX8GBUIDgHF%2FkoVCA4Bxf5KFR0dFQPoFR0AAAABAJ0AGQSwBDMAFQAAAREUBicBERQGJwEmNDcBNhYVEQE2FgSwFQ%2F%2BMBUP%2FhQPDwHsDxUB0A8VBBr8GBUIDgHF%2FkoVCA4B4A4qDgHgDggV%2FkoBxQ4IAAAAAQDIABYEMwQ2AAsAABMBFhQHAQYmNRE0NvMDLhIS%2FNISGRkEMv4OCx4L%2Fg4LDhUD6BUOAAIAyABkA4QD6AAPAB8AABMzMhYVERQGKwEiJjURNDYhMzIWFREUBisBIiY1ETQ2%2BsgVHR0VyBUdHQGlyBUdHRXIFR0dA%2BgdFfzgFR0dFQMgFR0dFfzgFR0dFQMgFR0AAAEAyABkBEwD6AAPAAABERQGIyEiJjURNDYzITIWBEwdFfzgFR0dFQMgFR0DtvzgFR0dFQMgFR0dAAAAAAEAAAAZBBMEMwAVAAABETQ2FwEWFAcBBiY1EQEGJjURNDYXAfQVDwHsDw%2F%2BFA8V%2FjAPFRUPAmQBthUIDv4gDioO%2FiAOCBUBtv47DggVA%2BgVCA4AAAH%2F%2FgACBLMETwAjAAABNzIWFRMUBiMHIiY1AwEGJjUDAQYmNQM0NhcBAzQ2FwEDNDYEGGQUHgUdFWQVHQL%2BMQ4VAv4yDxUFFQ8B0gIVDwHSAh0ETgEdFfwYFR0BHRUBtf46DwkVAbX%2BOQ4JFAPoFQkP%2Fj4BthQJDv49AbYVHQAAAQEsAAAD6ARMABkAAAEzMhYVERQGKwEiJjURAQYmNRE0NhcBETQ2A1JkFR0dFWQVHf4wDxUVDwHQHQRMHRX8GBUdHRUBtv47DggVA%2BgVCA7%2BOwG2FR0AAAIAZADIBLAESAALABsAAAkBFgYjISImNwE2MgEhMhYdARQGIyEiJj0BNDYCrgH1DwkW%2B%2B4WCQ8B9Q8q%2FfcD6BUdHRX8GBUdHQQ5%2FeQPFhYPAhwP%2FUgdFWQVHR0VZBUdAAEAiP%2F8A3UESgAFAAAJAgcJAQN1%2FqABYMX92AIoA4T%2Bn%2F6fxgIoAiYAAAAAAQE7%2F%2FwEKARKAAUAAAkBJwkBNwQo%2FdnGAWH%2Bn8YCI%2F3ZxgFhAWHGAAIAFwAXBJkEmQAPADMAAAAyHgIUDgIiLgI0PgEFIyIGHQEjIgYdARQWOwEVFBY7ATI2PQEzMjY9ATQmKwE1NCYB4%2BrWm1tbm9bq1ptbW5sBfWQVHZYVHR0Vlh0VZBUdlhUdHRWWHQSZW5vW6tabW1ub1urWm7odFZYdFWQVHZYVHR0Vlh0VZBUdlhUdAAAAAAIAFwAXBJkEmQAPAB8AAAAyHgIUDgIiLgI0PgEBISIGHQEUFjMhMjY9ATQmAePq1ptbW5vW6tabW1ubAkX%2BDBUdHRUB9BUdHQSZW5vW6tabW1ub1urWm%2F5%2BHRVkFR0dFWQVHQACABcAFwSZBJkADwAzAAAAMh4CFA4CIi4CND4BBCIPAScmIg8BBhQfAQcGFB8BFjI%2FARcWMj8BNjQvATc2NC8BAePq1ptbW5vW6tabW1ubAeUZCXh4CRkJjQkJeHgJCY0JGQl4eAkZCY0JCXh4CQmNBJlbm9bq1ptbW5vW6tabrQl4eAkJjQkZCXh4CRkJjQkJeHgJCY0JGQl4eAkZCY0AAgAXABcEmQSZAA8AJAAAADIeAhQOAiIuAjQ%2BAQEnJiIPAQYUHwEWMjcBNjQvASYiBwHj6tabW1ub1urWm1tbmwEVVAcVCIsHB%2FIHFQcBdwcHiwcVBwSZW5vW6tabW1ub1urWm%2F4xVQcHiwgUCPEICAF3BxUIiwcHAAAAAAMAFwAXBJkEmQAPADsASwAAADIeAhQOAiIuAjQ%2BAQUiDgMVFDsBFjc%2BATMyFhUUBgciDgUHBhY7ATI%2BAzU0LgMTIyIGHQEUFjsBMjY9ATQmAePq1ptbW5vW6tabW1ubAT8dPEIyIRSDHgUGHR8UFw4TARkOGhITDAIBDQ6tBx4oIxgiM0Q8OpYKDw8KlgoPDwSZW5vW6tabW1ub1urWm5ELHi9PMhkFEBQQFRIXFgcIBw4UHCoZCBEQKDhcNi9IKhsJ%2FeMPCpYKDw8KlgoPAAADABcAFwSZBJkADwAfAD4AAAAyHgIUDgIiLgI0PgEFIyIGHQEUFjsBMjY9ATQmAyMiBh0BFBY7ARUjIgYdARQWMyEyNj0BNCYrARE0JgHj6tabW1ub1urWm1tbmwGWlgoPDwqWCg8PCvoKDw8KS0sKDw8KAV4KDw8KSw8EmVub1urWm1tbm9bq1ptWDwqWCg8PCpYKD%2F7UDwoyCg%2FIDwoyCg8PCjIKDwETCg8AAgAAAAAEsASwAC8AXwAAATMyFh0BHgEXMzIWHQEUBisBDgEHFRQGKwEiJj0BLgEnIyImPQE0NjsBPgE3NTQ2ExUUBisBIiY9AQ4BBzMyFh0BFAYrAR4BFzU0NjsBMhYdAT4BNyMiJj0BNDY7AS4BAg2WCg9nlxvCCg8PCsIbl2cPCpYKD2eXG8IKDw8KwhuXZw%2B5DwqWCg9EZheoCg8PCqgXZkQPCpYKD0RmF6gKDw8KqBdmBLAPCsIbl2cPCpYKD2eXG8IKDw8KwhuXZw8KlgoPZ5cbwgoP%2Fs2oCg8PCqgXZkQPCpYKD0RmF6gKDw8KqBdmRA8KlgoPRGYAAwAXABcEmQSZAA8AGwA%2FAAAAMh4CFA4CIi4CND4BBCIOARQeATI%2BATQmBxcWFA8BFxYUDwEGIi8BBwYiLwEmND8BJyY0PwE2Mh8BNzYyAePq1ptbW5vW6tabW1ubAb%2FoxXJyxejFcnKaQAcHfHwHB0AHFQd8fAcVB0AHB3x8BwdABxUHfHwHFQSZW5vW6tabW1ub1urWmztyxejFcnLF6MVaQAcVB3x8BxUHQAcHfHwHB0AHFQd8fAcVB0AHB3x8BwAAAAMAFwAXBJkEmQAPABsAMAAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgcXFhQHAQYiLwEmND8BNjIfATc2MgHj6tabW1ub1urWm1tbmwG%2F6MVycsXoxXJyg2oHB%2F7ACBQIyggIagcVB0%2FFBxUEmVub1urWm1tbm9bq1ps7csXoxXJyxejFfWoHFQf%2BvwcHywcVB2oICE%2FFBwAAAAMAFwAXBJkEmQAPABgAIQAAADIeAhQOAiIuAjQ%2BAQUiDgEVFBcBJhcBFjMyPgE1NAHj6tabW1ub1urWm1tbmwFLdMVyQQJLafX9uGhzdMVyBJlbm9bq1ptbW5vW6tabO3LFdHhpAktB0P24PnLFdHMAAAAAAQAXAFMEsAP5ABUAABMBNhYVESEyFh0BFAYjIREUBicBJjQnAgoQFwImFR0dFf3aFxD99hACRgGrDQoV%2Ft0dFcgVHf7dFQoNAasNJgAAAAABAAAAUwSZA%2FkAFQAACQEWFAcBBiY1ESEiJj0BNDYzIRE0NgJ%2FAgoQEP32EBf92hUdHRUCJhcD8f5VDSYN%2FlUNChUBIx0VyBUdASMVCgAAAAEAtwAABF0EmQAVAAAJARYGIyERFAYrASImNREhIiY3ATYyAqoBqw0KFf7dHRXIFR3%2B3RUKDQGrDSYEif32EBf92hUdHRUCJhcQAgoQAAAAAQC3ABcEXQSwABUAAAEzMhYVESEyFgcBBiInASY2MyERNDYCJsgVHQEjFQoN%2FlUNJg3%2BVQ0KFQEjHQSwHRX92hcQ%2FfYQEAIKEBcCJhUdAAABAAAAtwSZBF0AFwAACQEWFAcBBiY1EQ4DBz4ENxE0NgJ%2FAgoQEP32EBdesKWBJAUsW4fHfhcEVf5VDSYN%2FlUNChUBIwIkRHVNabGdcUYHAQYVCgACAAAAAASwBLAAFQArAAABITIWFREUBi8BBwYiLwEmND8BJyY2ASEiJjURNDYfATc2Mh8BFhQPARcWBgNSASwVHRUOXvkIFAhqBwf5Xg4I%2FiH%2B1BUdFQ5e%2BQgUCGoHB%2FleDggEsB0V%2FtQVCA5e%2BQcHaggUCPleDhX7UB0VASwVCA5e%2BQcHaggUCPleDhUAAAACAEkASQRnBGcAFQArAAABFxYUDwEXFgYjISImNRE0Nh8BNzYyASEyFhURFAYvAQcGIi8BJjQ%2FAScmNgP2agcH%2BV4OCBX%2B1BUdFQ5e%2BQgU%2FQwBLBUdFQ5e%2BQgUCGoHB%2FleDggEYGoIFAj5Xg4VHRUBLBUIDl75B%2F3xHRX%2B1BUIDl75BwdqCBQI%2BV4OFQAAAAADABcAFwSZBJkADwAfAC8AAAAyHgIUDgIiLgI0PgEFIyIGFxMeATsBMjY3EzYmAyMiBh0BFBY7ATI2PQE0JgHj6tabW1ub1urWm1tbmwGz0BQYBDoEIxQ2FCMEOgQYMZYKDw8KlgoPDwSZW5vW6tabW1ub1urWm7odFP7SFB0dFAEuFB3%2BDA8KlgoPDwqWCg8AAAAABQAAAAAEsASwAEkAVQBhAGgAbwAAATIWHwEWHwEWFxY3Nj8BNjc2MzIWHwEWHwIeATsBMhYdARQGKwEiBh0BIREjESE1NCYrASImPQE0NjsBMjY1ND8BNjc%2BBAUHBhY7ATI2LwEuAQUnJgYPAQYWOwEyNhMhIiY1ESkBERQGIyERAQQJFAUFFhbEFQ8dCAsmxBYXERUXMA0NDgQZCAEPCj0KDw8KMgoP%2FnDI%2FnAPCjIKDw8KPQsOCRkFDgIGFRYfAp2mBwQK2woKAzMDEP41sQgQAzMDCgrnCwMe%2FokKDwGQAlgPCv6JBLAEAgIKDXYNCxUJDRZ2DQoHIREQFRh7LAkLDwoyCg8PCq8BLP7UrwoPDwoyCg8GBQQwgBkUAwgWEQ55ogcKDgqVCgSqnQcECo8KDgr8cg8KAXf%2BiQoPAZAAAAAAAgAAAAwErwSmACsASQAAATYWFQYCDgQuAScmByYOAQ8BBiY1NDc%2BATc%2BAScuAT4BNz4GFyYGBw4BDwEOBAcOARY2Nz4CNz4DNz4BBI0IGgItQmxhi2KORDg9EQQRMxuZGhYqCFUYEyADCQIQOjEnUmFch3vAJQgdHyaiPT44XHRZUhcYDhItIRmKcVtGYWtbKRYEBKYDEwiy%2Ft3IlVgxEQgLCwwBAQIbG5kYEyJAJghKFRE8Hzdff4U%2FM0o1JSMbL0QJGCYvcSEhHjZST2c1ODwEJygeW0AxJUBff1UyFAABAF0AHgRyBM8ATwAAAQ4BHgQXLgc%2BATceAwYHDgQHBicmNzY3PgQuAScWDgMmJy4BJyY%2BBDcGHgM3PgEuAicmPgMCjScfCic4R0IgBBsKGAoQAwEJEg5gikggBhANPkpTPhZINx8SBgsNJysiCRZOQQoVNU1bYC9QZwICBAUWITsoCAYdJzIYHw8YIiYHDyJJYlkEz0OAZVxEOSQMBzgXOB42IzElKRIqg5Gnl0o3Z0c6IAYWCwYNAwQFIDhHXGF1OWiqb0sdBxUknF0XNTQ8PEUiNWNROBYJDS5AQVUhVZloUSkAAAAAA%2F%2FcAGoE1ARGABsAPwBRAAAAMh4FFA4FIi4FND4EBSYGFxYVFAYiJjU0NzYmBwYHDgEXHgQyPgM3NiYnJgUHDgEXFhcWNj8BNiYnJicuAQIGpJ17bk85HBw6T257naKde25POhwcOU9uewIPDwYIGbD4sBcIBw5GWg0ECxYyWl%2BDiINfWjIWCwQMWv3%2FIw8JCSU4EC0OIw4DDywtCyIERi1JXGJcSSpJXGJcSS0tSVxiXEkqSVxiXEncDwYTOT58sLB8OzcTBg9FcxAxEiRGXkQxMEVeRSQSMRF1HiQPLxJEMA0EDyIPJQ8sSRIEAAAABP%2FcAAAE1ASwABQAJwA7AEwAACEjNy4ENTQ%2BBTMyFzczEzceARUUDgMHNz4BNzYmJyYlBgcOARceBBc3LgE1NDc2JhcHDgEXFhcWNj8CJyYnLgECUJQfW6l2WSwcOU9ue51SPUEglCYvbIknUGqYUi5NdiYLBAw2%2FVFGWg0ECxIqSExoNSlrjxcIB3wjDwkJJTgQLQ4MFgMsLQsieBRhdHpiGxVJXGJcSS0Pef5StVXWNBpacm5jGq0xiD8SMRFGckVzEDESHjxRQTkNmhKnbjs3EwZwJA8vEkQwDQQPC1YELEkSBAAAAAP%2FngAABRIEqwALABgAKAAAJwE2FhcBFgYjISImJSE1NDY7ATIWHQEhAQczMhYPAQ4BKwEiJi8BJjZaAoIUOBQCghUbJfryJRsBCgFZDwqWCg8BWf5DaNAUGAQ6BCMUNhQjBDoEGGQEKh8FIfvgIEdEhEsKDw8KSwLT3x0U%2FBQdHRT8FB0AAAABAGQAFQSwBLAAKAAAADIWFREBHgEdARQGJyURFh0BFAYvAQcGJj0BNDcRBQYmPQE0NjcBETQCTHxYAWsPFhgR%2FplkGhPNzRMaZP6ZERgWDwFrBLBYPv6t%2FrsOMRQpFA0M%2Bf75XRRAFRAJgIAJEBVAFF0BB%2FkMDRQpFDEOAUUBUz4AAAARAAAAAARMBLAAHQAnACsALwAzADcAOwA%2FAEMARwBLAE8AUwBXAFsAXwBjAAABMzIWHQEzMhYdASE1NDY7ATU0NjsBMhYdASE1NDYBERQGIyEiJjURFxUzNTMVMzUzFTM1MxUzNTMVMzUFFTM1MxUzNTMVMzUzFTM1MxUzNQUVMzUzFTM1MxUzNTMVMzUzFTM1A1JkFR0yFR37tB0VMh0VZBUdAfQdAQ8dFfwYFR1kZGRkZGRkZGRk%2FHxkZGRkZGRkZGT8fGRkZGRkZGRkZASwHRUyHRWWlhUdMhUdHRUyMhUd%2FnD9EhUdHRUC7shkZGRkZGRkZGRkyGRkZGRkZGRkZGTIZGRkZGRkZGRkZAAAAAMAAAAZBXcElwAZACUANwAAARcWFA8BBiY9ASMBISImPQE0NjsBATM1NDYBBycjIiY9ATQ2MyEBFxYUDwEGJj0BIyc3FzM1NDYEb%2FkPD%2FkOFZ%2F9qP7dFR0dFdECWPEV%2FamNetEVHR0VASMDGvkPD%2FkOFfG1jXqfFQSN5g4qDuYOCBWW%2FagdFWQVHQJYlhUI%2FpiNeh0VZBUd%2Fk3mDioO5g4IFZa1jXqWFQgAAAABAAAAAASwBEwAEgAAEyEyFhURFAYjIQERIyImNRE0NmQD6Ck7Oyn9rP7QZCk7OwRMOyn9qCk7%2FtQBLDspAlgpOwAAAAMAZAAABEwEsAAJABMAPwAAEzMyFh0BITU0NiEzMhYdASE1NDYBERQOBSIuBTURIRUUFRwBHgYyPgYmNTQ9AZbIFR3%2B1B0C0cgVHf7UHQEPBhgoTGacwJxmTCgYBgEsAwcNFB8nNkI2Jx8TDwUFAQSwHRX6%2BhUdHRX6%2BhUd%2FnD%2B1ClJalZcPigoPlxWakkpASz6CRIVKyclIRsWEAgJEBccISUnKhURCPoAAAAB%2F%2F8A1ARMA8IABQAAAQcJAScBBEzG%2Fp%2F%2Bn8UCJwGbxwFh%2Fp%2FHAicAAQAAAO4ETQPcAAUAAAkCNwkBBE392v3ZxgFhAWEDFf3ZAifH%2Fp8BYQAAAAAC%2F1EAZAVfA%2BgAFAApAAABITIWFREzMhYPAQYiLwEmNjsBESElFxYGKwERIRchIiY1ESMiJj8BNjIBlALqFR2WFQgO5g4qDuYOCBWW%2FoP%2BHOYOCBWWAYHX%2FRIVHZYVCA7mDioD6B0V%2FdkVDvkPD%2FkOFQGRuPkOFf5wyB0VAiYVDvkPAAABAAYAAASeBLAAMAAAEzMyFh8BITIWBwMOASMhFyEyFhQGKwEVFAYiJj0BIRUUBiImPQEjIiYvAQMjIiY0NjheERwEJgOAGB4FZAUsIf2HMAIXFR0dFTIdKh3%2B1B0qHR8SHQYFyTYUHh4EsBYQoiUY%2FiUVK8gdKh0yFR0dFTIyFR0dFTIUCQoDwR0qHQAAAAACAAAAAASwBEwACwAPAAABFSE1MzQ2MyEyFhUFIREhBLD7UMg7KQEsKTv9RASw%2B1AD6GRkKTs7Kcj84AACAAAAAAXcBEwADAAQAAATAxEzNDYzITIWFSEVBQEhAcjIyDspASwqOgH0ASz%2B1PtQASwDIP5wAlgpOzspyGT9RAK8AAEBRQAAA2sErwAbAAABFxYGKwERMzIWDwEGIi8BJjY7AREjIiY%2FATYyAnvmDggVlpYVCA7mDioO5g4IFZaWFQgO5g4qBKD5DhX9pxUO%2BQ8P%2BQ4VAlkVDvkPAAAAAQABAUQErwNrABsAAAEXFhQPAQYmPQEhFRQGLwEmND8BNhYdASE1NDYDqPkODvkPFf2oFQ%2F5Dg75DxUCWBUDYOUPKQ%2FlDwkUl5cUCQ%2FlDykP5Q8JFZWVFQkAAAAEAAAAAASwBLAACQAZAB0AIQAAAQMuASMhIgYHAwUhIgYdARQWMyEyNj0BNCYFNTMVMzUzFQSRrAUkFP1gFCQFrAQt%2FBgpOzspA%2BgpOzv%2Bq2RkZAGQAtwXLSgV%2FR1kOylkKTs7KWQpO8hkZGRkAAAAA%2F%2BcAGQEsARMAAsAIwAxAAAAMhYVERQGIiY1ETQDJSMTFgYjIisBIiYnAj0BNDU0PgE7ASUBFSIuAz0BND4CNwRpKh0dKh1k%2FV0mLwMRFQUCVBQdBDcCCwzIAqP8GAQOIhoWFR0dCwRMHRX8rhUdHRUDUhX8mcj%2B7BAIHBUBUQ76AgQQDw36%2FtT6AQsTKRwyGigUDAEAAAACAEoAAARmBLAALAA1AAABMzIWDwEeARcTFzMyFhQGBw4EIyIuBC8BLgE0NjsBNxM%2BATcnJjYDFjMyNw4BIiYCKV4UEgYSU3oPP3YRExwaEggeZGqfTzl0XFU%2BLwwLEhocExF2Pw96UxIGEyQyNDUxDDdGOASwFRMlE39N%2FrmtHSkoBwQLHBYSCg4REg4FBAgoKR2tAUdNfhQgExr7vgYGMT09AAEAFAAUBJwEnAAXAAABNwcXBxcHFycHJwcnBzcnNyc3Jxc3FzcDIOBO6rS06k7gLZubLeBO6rS06k7gLZubA7JO4C2bmy3gTuq0tOpO4C2bmy3gTuq0tAADAAAAZASwBLAAIQAtAD0AAAEzMhYdAQchMhYdARQHAw4BKwEiJi8BIyImNRE0PwI%2BARcPAREzFzMTNSE3NQEzMhYVERQGKwEiJjURNDYCijIoPBwBSCg8He4QLBf6B0YfHz0tNxSRYA0xG2SWZIjW%2Bv4%2BMv12ZBUdHRVkFR0dBLBRLJZ9USxkLR3%2BqBghMhkZJCcBkCQbxMYcKGTU1f6JZAF3feGv%2FtQdFf4MFR0dFQH0FR0AAAAAAwAAAAAEsARMACAAMAA8AAABMzIWFxMWHQEUBiMhFh0BFAYrASImLwImNRE0NjsBNgUzMhYVERQGKwEiJjURNDYhByMRHwEzNSchNQMCWPoXLBDuHTwo%2FrgcPCgyGzENYJEUNy09fP3pZBUdHRVkFR0dAl%2BIZJZkMjIBwvoETCEY%2FqgdLWQsUXYHlixRKBzGxBskAZAnJGRkHRX%2BDBUdHRUB9BUdZP6J1dSv4X0BdwADAAAAZAUOBE8AGwA3AEcAAAElNh8BHgEPASEyFhQGKwEDDgEjISImNRE0NjcXERchEz4BOwEyNiYjISoDLgQnJj8BJwUzMhYVERQGKwEiJjURNDYBZAFrHxZuDQEMVAEuVGxuVGqDBhsP%2FqoHphwOOmQBJYMGGw%2FLFRMSFv44AgoCCQMHAwUDAQwRklb9T2QVHR0VZBUdHQNp5hAWcA0mD3lMkE7%2BrRUoog0CDRElCkj%2BCVkBUxUoMjIBAgIDBQIZFrdT5B0V%2FgwVHR0VAfQVHQAAAAP%2FnABkBLAETwAdADYARgAAAQUeBBURFAYjISImJwMjIiY0NjMhJyY2PwE2BxcWBw4FKgIjIRUzMhYXEyE3ESUFMzIWFREUBisBIiY1ETQ2AdsBbgIIFBANrAf%2Bqg8bBoNqVW1sVAEuVQsBDW4WSpIRDAIDBQMHAwkDCgH%2BJd0PHAaCASZq%2FqoCUGQVHR0VZBUdHQRP5gEFEBEXC%2F3zDaIoFQFTTpBMeQ8mDXAWrrcWGQIFAwICAWQoFf6tWQH37OQdFf4MFR0dFQH0FR0AAAADAGEAAARMBQ4AGwA3AEcAAAAyFh0BBR4BFREUBiMhIiYvAQMmPwE%2BAR8BETQXNTQmBhURHAMOBAcGLwEHEyE3ESUuAQMhMhYdARQGIyEiJj0BNDYB3pBOAVMVKKIN%2FfMRJQoJ5hAWcA0mD3nGMjIBAgIDBQIZFrdT7AH3Wf6tFSiWAfQVHR0V%2FgwVHR0FDm5UaoMGGw%2F%2BqgemHA4OAWsfFm4NAQxUAS5U1ssVExIW%2FjgCCgIJAwcDBQMBDBGSVv6tZAElgwYb%2FQsdFWQVHR0VZBUdAAP%2F%2FQAGA%2BgFFAAPAC0ASQAAASEyNj0BNCYjISIGHQEUFgEVFAYiJjURBwYmLwEmNxM%2BBDMhMhYVERQGBwEDFzc2Fx4FHAIVERQWNj0BNDY3JREnAV4B9BUdHRX%2BDBUdHQEPTpBMeQ8mDXAWEOYBBRARFwsCDQ2iKBX9iexTtxYZAgUDAgIBMjIoFQFTWQRMHRVkFR0dFWQVHfzmalRubFQBLlQMAQ1uFh8BawIIEw8Mpgf%2Bqg8bBgHP%2Fq1WkhEMAQMFAwcDCQIKAv44FhITFcsPGwaDASVkAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEBJSYGHQEhIgYdARQWMyEVFBY3JTY0AeLs1ptbW5vW7NabW1ubAob%2B7RAX%2Fu0KDw8KARMXEAETEASaW5vW7NabW1ub1uzWm%2F453w0KFYkPCpYKD4kVCg3fDSYAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgENAQYUFwUWNj0BITI2PQE0JiMhNTQmAeLs1ptbW5vW7NabW1ubASX%2B7RAQARMQFwETCg8PCv7tFwSaW5vW7NabW1ub1uzWm%2BjfDSYN3w0KFYkPCpYKD4kVCgAAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEBAyYiBwMGFjsBERQWOwEyNjURMzI2AeLs1ptbW5vW7NabW1ubAkvfDSYN3w0KFYkPCpYKD4kVCgSaW5vW7NabW1ub1uzWm%2F5AARMQEP7tEBf%2B7QoPDwoBExcAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEFIyIGFREjIgYXExYyNxM2JisBETQmAeLs1ptbW5vW7NabW1ubAZeWCg%2BJFQoN3w0mDd8NChWJDwSaW5vW7NabW1ub1uzWm7sPCv7tFxD%2B7RAQARMQFwETCg8AAAMAGAAYBJgEmAAPAJYApgAAADIeAhQOAiIuAjQ%2BASUOAwcGJgcOAQcGFgcOAQcGFgcUFgcyHgEXHgIXHgI3Fg4BFx4CFxQGFBcWNz4CNy4BJy4BJyIOAgcGJyY2NS4BJzYuAQYHBicmNzY3HgIXHgMfAT4CJyY%2BATc%2BAzcmNzIWMjY3LgMnND4CJiceAT8BNi4CJwYHFB4BFS4CJz4BNxYyPgEB5OjVm1xcm9Xo1ZtcXJsBZA8rHDoKDz0PFD8DAxMBAzEFCRwGIgEMFhkHECIvCxU%2FOR0HFBkDDRQjEwcFaHUeISQDDTAMD0UREi4oLBAzDwQBBikEAQMLGhIXExMLBhAGKBsGBxYVEwYFAgsFAwMNFwQGCQcYFgYQCCARFwkKKiFBCwQCAQMDHzcLDAUdLDgNEiEQEgg%2FKhADGgMKEgoRBJhcm9Xo1ZtcXJvV6NWbEQwRBwkCAwYFBycPCxcHInIWInYcCUcYChQECA4QBAkuHgQPJioRFRscBAcSCgwCch0kPiAIAQcHEAsBAgsLIxcBMQENCQIPHxkCFBkdHB4QBgEBBwoMGBENBAMMJSAQEhYXDQ4qFBkKEhIDCQsXJxQiBgEOCQwHAQ0DBAUcJAwSCwRnETIoAwEJCwsLJQcKDBEAAAAAAQAAAAIErwSFABYAAAE2FwUXNxYGBw4BJwEGIi8BJjQ3ASY2AvSkjv79kfsGUE08hjv9rA8rD28PDwJYIk8EhVxliuh%2BWYcrIgsW%2FawQEG4PKxACV2XJAAYAAABgBLAErAAPABMAIwAnADcAOwAAEyEyFh0BFAYjISImPQE0NgUjFTMFITIWHQEUBiMhIiY9ATQ2BSEVIQUhMhYdARQGIyEiJj0BNDYFIRUhZAPoKTs7KfwYKTs7BBHIyPwYA%2BgpOzsp%2FBgpOzsEEf4MAfT8GAPoKTs7KfwYKTs7BBH%2B1AEsBKw7KWQpOzspZCk7ZGTIOylkKTs7KWQpO2RkyDspZCk7OylkKTtkZAAAAAIAZAAABEwEsAALABEAABMhMhYUBiMhIiY0NgERBxEBIZYDhBUdHRX8fBUdHQI7yP6iA4QEsB0qHR0qHf1E%2FtTIAfQB9AAAAAMAAABkBLAEsAAXABsAJQAAATMyFh0BITIWFREhNSMVIRE0NjMhNTQ2FxUzNQEVFAYjISImPQEB9MgpOwEsKTv%2BDMj%2BDDspASw7KcgB9Dsp%2FBgpOwSwOylkOyn%2BcGRkAZApO2QpO2RkZP1EyCk7OynIAAAABAAAAAAEsASwABUAKwBBAFcAABMhMhYPARcWFA8BBiIvAQcGJjURNDYpATIWFREUBi8BBwYiLwEmND8BJyY2ARcWFA8BFxYGIyEiJjURNDYfATc2MgU3NhYVERQGIyEiJj8BJyY0PwE2MhcyASwVCA5exwcHaggUCMdeDhUdAzUBLBUdFQ5exwgUCGoHB8deDgj%2BL2oHB8deDggV%2FtQVHRUOXscIFALLXg4VHRX%2B1BUIDl7HBwdqCBQIBLAVDl7HCBQIagcHx14OCBUBLBUdHRX%2B1BUIDl7HBwdqCBQIx14OFf0maggUCMdeDhUdFQEsFQgOXscHzl4OCBX%2B1BUdFQ5exwgUCGoHBwAAAAYAAAAABKgEqAAPABsAIwA7AEMASwAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JiQyFhQGIiY0JDIWFAYjIicHFhUUBiImNTQ2PwImNTQEMhYUBiImNCQyFhQGIiY0Advy3Z9fX5%2Fd8t2gXl6gAcbgv29vv%2BC%2Fb2%2F%2BLS0gIC0gAUwtICAWDg83ETNIMykfegEJ%2FoctICAtIAIdLSAgLSAEqF%2Bf3fLdoF5eoN3y3Z9Xb7%2Fgv29vv%2BC%2FBiAtISEtICAtIQqRFxwkMzMkIDEFfgEODhekIC0gIC0gIC0gIC0AAf%2FYAFoEuQS8AFsAACUBNjc2JicmIyIOAwcABw4EFx4BMzI3ATYnLgEjIgcGBwEOASY0NwA3PgEzMhceARcWBgcOBgcGIyImJyY2NwE2NzYzMhceARcWBgcBDgEnLgECIgHVWwgHdl8WGSJBMD8hIP6IDx4eLRMNBQlZN0ozAiQkEAcdEhoYDRr%2Bqw8pHA4BRyIjQS4ODyw9DQ4YIwwod26La1YOOEBGdiIwGkQB%2F0coW2tQSE5nDxE4Qv4eDyoQEAOtAdZbZWKbEQQUGjIhH%2F6JDxsdNSg3HT5CMwIkJCcQFBcMGv6uDwEcKQ4BTSIjIQEINykvYyMLKnhuiWZMBxtAOU6%2BRAH%2FSBg3ISSGV121Qv4kDwIPDyYAAAACAGQAWASvBEQAGQBEAAABPgIeAhUUDgMHLgQ1ND4CHgEFIg4DIi4DIyIGFRQeAhcWFx4EMj4DNzY3PgQ1NCYCiTB7eHVYNkN5hKg%2BPqeFeEM4WnZ4eQEjIT8yLSohJyktPyJDbxtBMjMPBw86KzEhDSIzKUAMBAgrKT8dF2oDtURIBS1TdkA5eYB%2FslVVsn%2BAeTlAdlMtBUgtJjY1JiY1NiZvTRc4SjQxDwcOPCouGBgwKEALBAkpKkQqMhNPbQACADn%2F8gR3BL4AFwAuAAAAMh8BFhUUBg8BJi8BNycBFwcvASY0NwEDNxYfARYUBwEGIi8BJjQ%2FARYfAQcXAQKru0KNQjgiHR8uEl%2F3%2FnvUaRONQkIBGxJpCgmNQkL%2B5UK6Qo1CQjcdLhJf9wGFBL5CjUJeKmsiHTUuEl%2F4%2FnvUahKNQrpCARv%2BRmkICY1CukL%2B5UJCjUK7Qjc3LxFf%2BAGFAAAAAAMAyAAAA%2BgEsAARABUAHQAAADIeAhURFAYjISImNRE0PgEHESERACIGFBYyNjQCBqqaZDo7Kf2oKTs8Zj4CWP7%2FVj09Vj0EsB4uMhX8Ryk7OykDuRUzLar9RAK8%2FRY9Vj09VgABAAAAAASwBLAAFgAACQEWFAYiLwEBEScBBRMBJyEBJyY0NjIDhgEbDx0qDiT%2B6dT%2BzP7oywEz0gEsAQsjDx0qBKH%2B5g8qHQ8j%2FvX%2B1NL%2BzcsBGAE01AEXJA4qHQAAAAADAScAEQQJBOAAMgBAAEsAAAEVHgQXIy4DJxEXHgQVFAYHFSM1JicuASczHgEXEScuBDU0PgI3NRkBDgMVFB4DFxYXET4ENC4CArwmRVI8LAKfBA0dMydAIjxQNyiym2SWVygZA4sFV0obLkJOMCAyVWg6HSoqFQ4TJhkZCWgWKTEiGBkzNwTgTgUTLD9pQiQuLBsH%2Fs0NBxMtPGQ%2Bi6oMTU8QVyhrVk1iEAFPCA4ZLzlYNkZwSCoGTf4SARIEDh02Jh0rGRQIBgPQ%2FsoCCRYgNEM0JRkAAAABAGQAZgOUBK0ASgAAATIeARUjNC4CIyIGBwYVFB4BFxYXMxUjFgYHBgc%2BATM2FjMyNxcOAyMiLgEHDgEPASc%2BBTc%2BAScjNTMmJy4CPgE3NgIxVJlemSc8OxolVBQpGxoYBgPxxQgVFS02ImIWIIwiUzUyHzY4HCAXanQmJ1YYFzcEGAcTDBEJMAwk3aYXFQcKAg4tJGEErVCLTig%2FIhIdFSw5GkowKgkFZDKCHj4yCg8BIh6TExcIASIfBAMaDAuRAxAFDQsRCjePR2QvORQrREFMIVgAAAACABn%2F%2FwSXBLAADwAfAAABMzIWDwEGIi8BJjY7AREzBRcWBisBESMRIyImPwE2MgGQlhUIDuYOKg7mDggVlsgCF%2BYOCBWWyJYVCA7mDioBLBYO%2Bg8P%2Bg4WA4QQ%2BQ4V%2FHwDhBUO%2BQ8AAAQAGf%2F%2FA%2BgEsAAHABcAGwAlAAABIzUjFSMRIQEzMhYPAQYiLwEmNjsBETMFFTM1EwczFSE1NyM1IQPoZGRkASz9qJYVCA7mDioO5g4IFZbIAZFkY8jI%2FtTIyAEsArxkZAH0%2FHwWDvoPD%2FoOFgOEZMjI%2FRL6ZJb6ZAAAAAAEABn%2F%2FwPoBLAADwAZACEAJQAAATMyFg8BBiIvASY2OwERMwUHMxUhNTcjNSERIzUjFSMRIQcVMzUBkJYVCA7mDioO5g4IFZbIAljIyP7UyMgBLGRkZAEsx2QBLBYO%2Bg8P%2Bg4WA4SW%2BmSW%2BmT7UGRkAfRkyMgAAAAEABn%2F%2FwRMBLAADwAVABsAHwAAATMyFg8BBiIvASY2OwERMwEjESM1MxMjNSMRIQcVMzUBkJYVCA7mDioO5g4IFZbIAlhkZMhkZMgBLMdkASwWDvoPD%2FoOFgOE%2FgwBkGT7UGQBkGTIyAAAAAAEABn%2F%2FwRMBLAADwAVABkAHwAAATMyFg8BBiIvASY2OwERMwEjNSMRIQcVMzUDIxEjNTMBkJYVCA7mDioO5g4IFZbIArxkyAEsx2QBZGTIASwWDvoPD%2FoOFgOE%2FgxkAZBkyMj7tAGQZAAAAAAFABn%2F%2FwSwBLAADwATABcAGwAfAAABMzIWDwEGIi8BJjY7AREzBSM1MxMhNSETITUhEyE1IQGQlhUIDuYOKg7mDggVlsgB9MjIZP7UASxk%2FnABkGT%2BDAH0ASwWDvoPD%2FoOFgOEyMj%2BDMj%2BDMj%2BDMgABQAZ%2F%2F8EsASwAA8AEwAXABsAHwAAATMyFg8BBiIvASY2OwERMwUhNSEDITUhAyE1IQMjNTMBkJYVCA7mDioO5g4IFZbIAyD%2BDAH0ZP5wAZBk%2FtQBLGTIyAEsFg76Dw%2F6DhYDhMjI%2FgzI%2FgzI%2FgzIAAIAAAAABEwETAAPAB8AAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmAV4BkKK8u6P%2BcKW5uQJn%2FgwpOzspAfQpOzsETLuj%2FnClubmlAZClucg7Kf4MKTs7KQH0KTsAAAAAAwAAAAAETARMAA8AHwArAAABITIWFREUBiMhIiY1ETQ2BSEiBhURFBYzITI2NRE0JgUXFhQPAQYmNRE0NgFeAZClubml%2FnCju7wCZP4MKTs7KQH0KTs7%2Fm%2F9ERH9EBgYBEy5pf5wpbm5pQGQo7vIOyn%2BDCk7OykB9Ck7gr4MJAy%2BDAsVAZAVCwAAAAADAAAAAARMBEwADwAfACsAAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmBSEyFg8BBiIvASY2AV4BkKO7uaX%2BcKW5uQJn%2FgwpOzspAfQpOzv%2BFQGQFQsMvgwkDL4MCwRMvKL%2BcKW5uaUBkKO7yDsp%2FgwpOzspAfQpO8gYEP0REf0QGAAAAAMAAAAABEwETAAPAB8AKwAAASEyFhURFAYjISImNRE0NgUhIgYVERQWMyEyNjURNCYFFxYGIyEiJj8BNjIBXgGQpbm5pf5wo7u5Amf%2BDCk7OykB9Ck7O%2F77vgwLFf5wFQsMvgwkBEy5pf5wo7u8ogGQpbnIOyn%2BDCk7OykB9Ck7z%2F0QGBgQ%2FREAAAAAAgAAAAAFFARMAB8ANQAAASEyFhURFAYjISImPQE0NjMhMjY1ETQmIyEiJj0BNDYHARYUBwEGJj0BIyImPQE0NjsBNTQ2AiYBkKW5uaX%2BcBUdHRUBwik7Oyn%2BPhUdHb8BRBAQ%2FrwQFvoVHR0V%2BhYETLml%2FnCluR0VZBUdOykB9Ck7HRVkFR3p%2FuQOJg7%2B5A4KFZYdFcgVHZYVCgAAAQDZAAID1wSeACMAAAEXFgcGAgclMhYHIggBBwYrAScmNz4BPwEhIicmNzYANjc2MwMZCQgDA5gCASwYEQ4B%2Fvf%2B8wQMDgkJCQUCUCcn%2FtIXCAoQSwENuwUJEASeCQoRC%2F5TBwEjEv7K%2FsUFDwgLFQnlbm4TFRRWAS%2FTBhAAAAACAAAAAAT%2BBEwAHwA1AAABITIWHQEUBiMhIgYVERQWMyEyFh0BFAYjISImNRE0NgUBFhQHAQYmPQEjIiY9ATQ2OwE1NDYBXgGQFR0dFf4%2BKTs7KQHCFR0dFf5wpbm5AvEBRBAQ%2FrwQFvoVHR0V%2BhYETB0VZBUdOyn%2BDCk7HRVkFR25pQGQpbnp%2FuQOJg7%2B5A4KFZYdFcgVHZYVCgACAAAAAASwBLAAFQAxAAABITIWFREUBi8BAQYiLwEmNDcBJyY2ASMiBhURFBYzITI2PQE3ERQGIyEiJjURNDYzIQLuAZAVHRUObf7IDykPjQ8PAThtDgj%2B75wpOzspAfQpO8i7o%2F5wpbm5pQEsBLAdFf5wFQgObf7IDw%2BNDykPAThtDhX%2B1Dsp%2FgwpOzsplMj%2B1qW5uaUBkKW5AAADAA4ADgSiBKIADwAbACMAAAAyHgIUDgIiLgI0PgEEIg4BFB4BMj4BNCYEMhYUBiImNAHh7tmdXV2d2e7ZnV1dnQHD5sJxccLmwnFx%2FnugcnKgcgSiXZ3Z7tmdXV2d2e7ZnUdxwubCcXHC5sJzcqBycqAAAAMAAAAABEwEsAAVAB8AIwAAATMyFhURMzIWBwEGIicBJjY7ARE0NgEhMhYdASE1NDYFFTM1AcLIFR31FAoO%2FoEOJw3%2BhQ0JFfod%2FoUD6BUd%2B7QdA2dkBLAdFf6iFg%2F%2BVg8PAaoPFgFeFR38fB0V%2BvoVHWQyMgAAAAMAAAAABEwErAAVAB8AIwAACQEWBisBFRQGKwEiJj0BIyImNwE%2BAQEhMhYdASE1NDYFFTM1AkcBeg4KFfQiFsgUGPoUCw4Bfw4n%2FfkD6BUd%2B7QdA2dkBJ7%2BTQ8g%2BhQeHRX6IQ8BrxAC%2FH8dFfr6FR1kMjIAAwAAAAAETARLABQAHgAiAAAJATYyHwEWFAcBBiInASY0PwE2MhcDITIWHQEhNTQ2BRUzNQGMAXEHFQeLBwf98wcVB%2F7cBweLCBUH1APoFR37tB0DZ2QC0wFxBweLCBUH%2FfMICAEjCBQIiwcH%2FdIdFfr6FR1kMjIABAAAAAAETASbAAkAGQAjACcAABM3NjIfAQcnJjQFNzYWFQMOASMFIiY%2FASc3ASEyFh0BITU0NgUVMzWHjg4qDk3UTQ4CFtIOFQIBHRX9qxUIDtCa1P49A%2BgVHfu0HQNnZAP%2Fjg4OTdRMDyqa0g4IFf2pFB4BFQ7Qm9T9Oh0V%2BvoVHWQyMgAAAAQAAAAABEwEsAAPABkAIwAnAAABBR4BFRMUBi8BByc3JyY2EwcGIi8BJjQ%2FAQEhMhYdASE1NDYFFTM1AV4CVxQeARUO0JvUm9IOCMNMDyoOjg4OTf76A%2BgVHfu0HQNnZASwAgEdFf2rFQgO0JrUmtIOFf1QTQ4Ojg4qDk3%2BWB0V%2BvoVHWQyMgACAAT%2F7ASwBK8ABQAIAAAlCQERIQkBFQEEsP4d%2Fsb%2BcQSs%2FTMCq2cBFP5xAacDHPz55gO5AAAAAAIAAABkBEwEsAAVABkAAAERFAYrAREhESMiJjURNDY7AREhETMHIzUzBEwdFZb9RJYVHR0V%2BgH0ZMhkZAPo%2FK4VHQGQ%2FnAdFQPoFB7%2B1AEsyMgAAAMAAABFBN0EsAAWABoALwAAAQcBJyYiDwEhESMiJjURNDY7AREhETMHIzUzARcWFAcBBiIvASY0PwE2Mh8BATYyBEwC%2FtVfCRkJlf7IlhUdHRX6AfRkyGRkAbBqBwf%2BXAgUCMoICGoHFQdPASkHFQPolf7VXwkJk%2F5wHRUD6BQe%2FtQBLMjI%2Fc5qBxUH%2FlsHB8sHFQdqCAhPASkHAAMAAAANBQcEsAAWABoAPgAAAREHJy4BBwEhESMiJjURNDY7AREhETMHIzUzARcWFA8BFxYUDwEGIi8BBwYiLwEmND8BJyY0PwE2Mh8BNzYyBExnhg8lEP72%2FreWFR0dFfoB9GTIZGQB9kYPD4ODDw9GDykPg4MPKQ9GDw%2BDgw8PRg8pD4ODDykD6P7zZ4YPAw7%2B9v5wHRUD6BQe%2FtQBLMjI%2FYxGDykPg4MPKQ9GDw%2BDgw8PRg8pD4ODDykPRg8Pg4MPAAADAAAAFQSXBLAAFQAZAC8AAAERISIGHQEhESMiJjURNDY7AREhETMHIzUzEzMyFh0BMzIWDwEGIi8BJjY7ATU0NgRM%2FqIVHf4MlhUdHRX6AfRkyGRklmQVHZYVCA7mDioO5g4IFZYdA%2Bj%2B1B0Vlv5wHRUD6BQe%2FtQBLMjI%2FagdFfoVDuYODuYOFfoVHQAAAAADAAAAAASXBLAAFQAZAC8AAAERJyYiBwEhESMiJjURNDY7AREhETMHIzUzExcWBisBFRQGKwEiJj0BIyImPwE2MgRMpQ4qDv75%2Fm6WFR0dFfoB9GTIZGTr5g4IFZYdFWQVHZYVCA7mDioD6P5wpQ8P%2Fvf%2BcB0VA%2BgUHv7UASzIyP2F5Q8V%2BhQeHhT6FQ%2FlDwADAAAAyASwBEwACQATABcAABMhMhYdASE1NDYBERQGIyEiJjURExUhNTIETBUd%2B1AdBJMdFfu0FR1kAZAETB0VlpYVHf7U%2FdoVHR0VAib%2B1MjIAAAGAAMAfQStBJcADwAZAB0ALQAxADsAAAEXFhQPAQYmPQEhNSE1NDYBIyImPQE0NjsBFyM1MwE3NhYdASEVIRUUBi8BJjQFIzU7AjIWHQEUBisBA6f4Dg74DhX%2BcAGQFf0vMhUdHRUyyGRk%2FoL3DhUBkP5wFQ73DwOBZGRkMxQdHRQzBI3mDioO5g4IFZbIlhUI%2FoUdFWQVHcjI%2FcvmDggVlsiWFQgO5g4qecgdFWQVHQAAAAACAGQAAASwBLAAFgBRAAABJTYWFREUBisBIiY1ES4ENRE0NiUyFh8BERQOAg8BERQGKwEiJjURLgQ1ETQ%2BAzMyFh8BETMRPAE%2BAjMyFh8BETMRND4DA14BFBklHRXIFR0EDiIaFiX%2B4RYZAgEVHR0LCh0VyBUdBA4iGhYBBwoTDRQZAgNkBQkVDxcZAQFkAQUJFQQxdBIUH%2FuuFR0dFQGNAQgbHzUeAWcfRJEZDA3%2BPhw%2FMSkLC%2F5BFR0dFQG%2FBA8uLkAcAcICBxENCxkMDf6iAV4CBxENCxkMDf6iAV4CBxENCwABAGQAAASwBEwAMwAAARUiDgMVERQWHwEVITUyNjURIREUFjMVITUyPgM1ETQmLwE1IRUiBhURIRE0JiM1BLAEDiIaFjIZGf5wSxn%2BDBlL%2FnAEDiIaFjIZGQGQSxkB9BlLBEw4AQUKFA78iBYZAQI4OA0lAYr%2BdiUNODgBBQoUDgN4FhkBAjg4DSX%2BdgGKJQ04AAAABgAAAAAETARMAAwAHAAgACQAKAA0AAABITIWHQEjBTUnITchBSEyFhURFAYjISImNRE0NhcVITUBBTUlBRUhNQUVFAYjIQchJyE3MwKjAXcVHWn%2B2cj%2BcGQBd%2F4lASwpOzsp%2FtQpOzspASwCvP5wAZD8GAEsArwdFf6JZP6JZAGQyGkD6B0VlmJiyGTIOyn%2BDCk7OykB9Ck7ZMjI%2FveFo4XGyMhm%2BBUdZGTIAAEAEAAQBJ8EnwAmAAATNzYWHwEWBg8BHgEXNz4BHwEeAQ8BBiIuBicuBTcRohEuDosOBhF3ZvyNdxEzE8ATBxGjAw0uMUxPZWZ4O0p3RjITCwED76IRBhPCFDERdo78ZXYRBA6IDi8RogEECBUgNUNjO0qZfHNVQBAAAAACAAAAAASwBEwAIwBBAAAAMh4EHwEVFAYvAS4BPQEmIAcVFAYPAQYmPQE%2BBRIyHgIfARUBHgEdARQGIyEiJj0BNDY3ATU0PgIB%2FLimdWQ%2FLAkJHRTKFB2N%2FsKNHRTKFB0DDTE7ZnTKcFImFgEBAW0OFR0V%2B7QVHRUOAW0CFiYETBUhKCgiCgrIFRgDIgMiFZIYGJIVIgMiAxgVyAQNJyQrIP7kExwcCgoy%2FtEPMhTUFR0dFdQUMg8BLzIEDSEZAAADAAAAAASwBLAADQAdACcAAAEHIScRMxUzNTMVMzUzASEyFhQGKwEXITcjIiY0NgMhMhYdASE1NDYETMj9qMjIyMjIyPyuArwVHR0VDIn8SokMFR0dswRMFR37UB0CvMjIAfTIyMjI%2FOAdKh1kZB0qHf7UHRUyMhUdAAAAAwBkAAAEsARMAAkAEwAdAAABIyIGFREhETQmASMiBhURIRE0JgEhETQ2OwEyFhUCvGQpOwEsOwFnZCk7ASw7%2FRv%2B1DspZCk7BEw7KfwYA%2BgpO%2F7UOyn9RAK8KTv84AGQKTs7KQAAAAAF%2F5wAAASwBEwADwATAB8AJQApAAATITIWFREUBiMhIiY1ETQ2FxEhEQUjFTMRITUzNSMRIQURByMRMwcRMxHIArx8sLB8%2FUR8sLAYA4T%2BDMjI%2FtTIyAEsAZBkyMhkZARMsHz%2BDHywsHwB9HywyP1EArzIZP7UZGQBLGT%2B1GQB9GT%2B1AEsAAAABf%2BcAAAEsARMAA8AEwAfACUAKQAAEyEyFhURFAYjISImNRE0NhcRIREBIzUjFSMRMxUzNTMFEQcjETMHETMRyAK8fLCwfP1EfLCwGAOE%2FgxkZGRkZGQBkGTIyGRkBEywfP4MfLCwfAH0fLDI%2FUQCvP2oyMgB9MjIZP7UZAH0ZP7UASwABP%2BcAAAEsARMAA8AEwAbACMAABMhMhYVERQGIyEiJjURNDYXESERBSMRMxUhESEFIxEzFSERIcgCvHywsHz9RHywsBgDhP4MyMj%2B1AEsAZDIyP7UASwETLB8%2Fgx8sLB8AfR8sMj9RAK8yP7UZAH0ZP7UZAH0AAAABP%2BcAAAEsARMAA8AEwAWABkAABMhMhYVERQGIyEiJjURNDYXESERAS0BDQERyAK8fLCwfP1EfLCwGAOE%2Fgz%2B1AEsAZD%2B1ARMsHz%2BDHywsHwB9HywyP1EArz%2BDJaWlpYBLAAAAAX%2FnAAABLAETAAPABMAFwAgACkAABMhMhYVERQGIyEiJjURNDYXESERAyERIQcjIgYVFBY7AQERMzI2NTQmI8gCvHywsHz9RHywsBgDhGT9RAK8ZIImOTYpgv4Mgik2OSYETLB8%2Fgx8sLB8AfR8sMj9RAK8%2FagB9GRWQUFUASz%2B1FRBQVYAAAAF%2F5wAAASwBEwADwATAB8AJQApAAATITIWFREUBiMhIiY1ETQ2FxEhEQUjFTMRITUzNSMRIQEjESM1MwMjNTPIArx8sLB8%2FUR8sLAYA4T%2BDMjI%2FtTIyAEsAZBkZMjIZGQETLB8%2Fgx8sLB8AfR8sMj9RAK8yGT%2B1GRkASz%2BDAGQZP4MZAAG%2F5wAAASwBEwADwATABkAHwAjACcAABMhMhYVERQGIyEiJjURNDYXESERBTMRIREzASMRIzUzBRUzNQEjNTPIArx8sLB8%2FUR8sLAYA4T9RMj%2B1GQCWGRkyP2oZAEsZGQETLB8%2Fgx8sLB8AfR8sMj9RAK8yP5wAfT%2BDAGQZMjIyP7UZAAF%2F5wAAASwBEwADwATABwAIgAmAAATITIWFREUBiMhIiY1ETQ2FxEhEQEHIzU3NSM1IQEjESM1MwMjNTPIArx8sLB8%2FUR8sLAYA4T%2BDMdkx8gBLAGQZGTIx2RkBEywfP4MfLCwfAH0fLDI%2FUQCvP5wyDLIlmT%2BDAGQZP4MZAAAAAMACQAJBKcEpwAPABsAJQAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgchFSEVISc1NyEB4PDbnl5entvw255eXp4BxeTCcXHC5MJxcWz%2B1AEs%2FtRkZAEsBKdentvw255eXp7b8NueTHHC5MJxccLkwtDIZGTIZAAAAAAEAAkACQSnBKcADwAbACcAKwAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgcVBxcVIycjFSMRIQcVMzUB4PDbnl5entvw255eXp4BxeTCcXHC5MJxcWwyZGRklmQBLMjIBKdentvw255eXp7b8NueTHHC5MJxccLkwtBkMmQyZGQBkGRkZAAAAv%2Fy%2F50EwgRBACAANgAAATIWFzYzMhYUBisBNTQmIyEiBh0BIyImNTQ2NyY1ND4BEzMyFhURMzIWDwEGIi8BJjY7ARE0NgH3brUsLC54qqp4gB0V%2FtQVHd5QcFZBAmKqepYKD4kVCg3fDSYN3w0KFYkPBEF3YQ6t8a36FR0dFfpzT0VrDhMSZKpi%2FbMPCv7tFxD0EBD0EBcBEwoPAAAAAAL%2F8v%2BcBMMEQQAcADMAAAEyFhc2MzIWFxQGBwEmIgcBIyImNTQ2NyY1ND4BExcWBisBERQGKwEiJjURIyImNzY3NjIB9m62LCsueaoBeFr%2Bhg0lDf6DCU9xVkECYqnm3w0KFYkPCpYKD4kVCg3HGBMZBEF3YQ%2BteGOkHAFoEBD%2Bk3NPRWsOExNkqWP9kuQQF%2F7tCg8PCgETFxDMGBMAAAABAGQAAARMBG0AGAAAJTUhATMBMwkBMwEzASEVIyIGHQEhNTQmIwK8AZD%2B8qr%2B8qr%2B1P7Uqv7yqv7yAZAyFR0BkB0VZGQBLAEsAU3%2Bs%2F7U%2FtRkHRUyMhUdAAAAAAEAeQAABDcEmwAvAAABMhYXHgEVFAYHFhUUBiMiJxUyFh0BITU0NjM1BiMiJjU0Ny4BNTQ2MzIXNCY1NDYCWF6TGll7OzIJaUo3LRUd%2FtQdFS03SmkELzlpSgUSAqMEm3FZBoNaPWcfHRpKaR77HRUyMhUd%2Bx5pShIUFVg1SmkCAhAFdKMAAAAGACcAFASJBJwAEQAqAEIASgBiAHsAAAEWEgIHDgEiJicmAhI3PgEyFgUiBw4BBwYWHwEWMzI3Njc2Nz4BLwEmJyYXIgcOAQcGFh8BFjMyNz4BNz4BLwEmJyYWJiIGFBYyNjciBw4BBw4BHwEWFxYzMjc%2BATc2Ji8BJhciBwYHBgcOAR8BFhcWMzI3PgE3NiYvASYD8m9PT29T2dzZU29PT29T2dzZ%2Fj0EBHmxIgQNDCQDBBcGG0dGYAsNAwkDCwccBAVQdRgEDA0iBAQWBhJROQwMAwkDCwf5Y4xjY4xjVhYGElE6CwwDCQMLBwgEBVB1GAQNDCIEjRcGG0dGYAsNAwkDCwcIBAR5sSIEDQwkAwPyb%2F7V%2FtVvU1dXU28BKwErb1NXVxwBIrF5DBYDCQEWYEZHGwMVDCMNBgSRAhh1UA0WAwkBFTpREgMVCyMMBwT6Y2OMY2MVFTpREQQVCyMMBwQCGHVQDRYDCQEkFmBGRxsDFQwjDQYEASKxeQwWAwkBAAAABQBkAAAD6ASwAAwADwAWABwAIgAAASERIzUhFSERNDYzIQEjNQMzByczNTMDISImNREFFRQGKwECvAEstP6s%2FoQPCgI%2FASzIZKLU1KJktP51Cg8DhA8KwwMg%2FoTIyALzCg%2F%2B1Mj84NTUyP4MDwoBi8jDCg8AAAAABQBkAAAD6ASwAAkADAATABoAIQAAASERCQERNDYzIQEjNRMjFSM1IzcDISImPQEpARUUBisBNQK8ASz%2Bov3aDwoCPwEsyD6iZKLUqv6dCg8BfAIIDwqbAyD9%2BAFe%2FdoERwoP%2FtTI%2FHzIyNT%2BZA8KNzcKD1AAAAAAAwAAAAAEsAP0AAgAGQAfAAABIxUzFyERIzcFMzIeAhUhFSEDETM0PgIBMwMhASEEiqJkZP7UotT9EsgbGiEOASz9qMhkDiEaAnPw8PzgASwB9AMgyGQBLNTUBBErJGT%2BogHCJCsRBP5w%2FnAB9AAAAAMAAAAABEwETAAZADIAOQAAATMyFh0BMzIWHQEUBiMhIiY9ATQ2OwE1NDYFNTIWFREUBiMhIic3ARE0NjMVFBYzITI2AQc1IzUzNQKKZBUdMhUdHRX%2B1BUdHRUyHQFzKTs7Kf2oARP2%2Fro7KVg%2BASw%2BWP201MjIBEwdFTIdFWQVHR0VZBUdMhUd%2BpY7KfzgKTsE9gFGAUQpO5Y%2BWFj95tSiZKIAAwBkAAAEvARMABkANgA9AAABMzIWHQEzMhYdARQGIyEiJj0BNDY7ATU0NgU1MhYVESMRMxQOAiMhIiY1ETQ2MxUUFjMhMjYBBzUjNTM1AcJkFR0yFR0dFf7UFR0dFTIdAXMpO8jIDiEaG%2F2oKTs7KVg%2BASw%2BWAGc1MjIBEwdFTIdFWQVHR0VZBUdMhUd%2BpY7Kf4M%2FtQkKxEEOykDICk7lj5YWP3m1KJkogAAAAP%2FogAABRYE1AALABsAHwAACQEWBiMhIiY3ATYyEyMiBhcTHgE7ATI2NxM2JgMVMzUCkgJ9FyAs%2BwQsIBcCfRZARNAUGAQ6BCMUNhQjBDoEGODIBK37sCY3NyYEUCf%2BTB0U%2FtIUHR0UAS4UHf4MZGQAAAAACQAAAAAETARMAA8AHwAvAD8ATwBfAG8AfwCPAAABMzIWHQEUBisBIiY9ATQ2EzMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2ITMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2ITMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBqfoKDw8K%2BgoPDwr6Cg8PCvoKDw8BmvoKDw8K%2BgoPD%2Fzq%2BgoPDwr6Cg8PAZr6Cg8PCvoKDw8BmvoKDw8K%2BgoPD%2Fzq%2BgoPDwr6Cg8PAZr6Cg8PCvoKDw8BmvoKDw8K%2BgoPDwRMDwqWCg8PCpYKD%2F7UDwqWCg8PCpYKDw8KlgoPDwqWCg%2F%2B1A8KlgoPDwqWCg8PCpYKDw8KlgoPDwqWCg8PCpYKD%2F7UDwqWCg8PCpYKDw8KlgoPDwqWCg8PCpYKDw8KlgoPAAAAAwAAAAAEsAUUABkAKQAzAAABMxUjFSEyFg8BBgchJi8BJjYzITUjNTM1MwEhMhYUBisBFyE3IyImNDYDITIWHQEhNTQ2ArxkZAFePjEcQiko%2FPwoKUIcMT4BXmRkyP4%2BArwVHR0VDIn8SooNFR0dswRMFR37UB0EsMhkTzeEUzMzU4Q3T2TIZPx8HSodZGQdKh3%2B1B0VMjIVHQAABAAAAAAEsAUUAAUAGQArADUAAAAyFhUjNAchFhUUByEyFg8BIScmNjMhJjU0AyEyFhQGKwEVBSElNSMiJjQ2AyEyFh0BITU0NgIwUDnCPAE6EgMBSCkHIq%2F9WrIiCikBSAOvArwVHR0VlgET%2FEoBE5YVHR2zBEwVHftQHQUUOykpjSUmCBEhFpGRFiERCCb%2BlR0qHcjIyMgdKh39qB0VMjIVHQAEAAAAAASwBJ0ABwAUACQALgAAADIWFAYiJjQTMzIWFRQXITY1NDYzASEyFhQGKwEXITcjIiY0NgMhMhYdASE1NDYCDZZqapZqty4iKyf%2BvCcrI%2F7NArwVHR0VDYr8SokMFR0dswRMFR37UB0EnWqWamqW%2Fus5Okxra0w6Of5yHSodZGQdKh3%2B1B0VMjIVHQAEAAAAAASwBRQADwAcACwANgAAATIeARUUBiImNTQ3FzcnNhMzMhYVFBchNjU0NjMBITIWFAYrARchNyMiJjQ2AyEyFh0BITU0NgJYL1szb5xvIpBvoyIfLiIrJ%2F68Jysj%2Fs0CvBUdHRUNivxKiQwVHR2zBEwVHftQHQUUa4s2Tm9vTj5Rj2%2BjGv4KOTpMa2tMOjn%2Bch0qHWRkHSod%2FtQdFTIyFR0AAAADAAAAAASwBRIAEgAiACwAAAEFFSEUHgMXIS4BNTQ%2BAjcBITIWFAYrARchNyMiJjQ2AyEyFh0BITU0NgJYASz%2B1CU%2FP00T%2Fe48PUJtj0r%2BogK8FR0dFQ2K%2FEqJDBUdHbMETBUd%2B1AdBLChizlmUT9IGVO9VFShdksE%2FH4dKh1kZB0qHf7UHRUyMhUdAAIAyAAAA%2BgFFAAPACkAAAAyFh0BHgEdASE1NDY3NTQDITIWFyMVMxUjFTMVIxUzFAYjISImNRE0NgIvUjsuNv5wNi5kAZA2XBqsyMjIyMh1U%2F5wU3V1BRQ7KU4aXDYyMjZcGk4p%2Fkc2LmRkZGRkU3V1UwGQU3UAAAMAZP%2F%2FBEwETAAPAC8AMwAAEyEyFhURFAYjISImNRE0NgMhMhYdARQGIyEXFhQGIi8BIQcGIiY0PwEhIiY9ATQ2BQchJ5YDhBUdHRX8fBUdHQQDtgoPDwr%2B5eANGiUNWP30Vw0mGg3g%2Ft8KDw8BqmQBRGQETB0V%2FgwVHR0VAfQVHf1EDwoyCg%2FgDSUbDVhYDRslDeAPCjIKD2RkZAAAAAAEAAAAAASwBEwAGQAjAC0ANwAAEyEyFh0BIzQmKwEiBhUjNCYrASIGFSM1NDYDITIWFREhETQ2ExUUBisBIiY9ASEVFAYrASImPQHIAyBTdWQ7KfopO2Q7KfopO2R1EQPoKTv7UDvxHRVkFR0D6B0VZBUdBEx1U8gpOzspKTs7KchTdf4MOyn%2B1AEsKTv%2BDDIVHR0VMjIVHR0VMgADAAEAAASpBKwADQARABsAAAkBFhQPASEBJjQ3ATYyCQMDITIWHQEhNTQ2AeACqh8fg%2F4f%2FfsgIAEnH1n%2BrAFWAS%2F%2Bq6IDIBUd%2FHwdBI39VR9ZH4MCBh9ZHwEoH%2F5u%2FqoBMAFV%2FBsdFTIyFR0AAAAAAgCPAAAEIQSwABcALwAAAQMuASMhIgYHAwYWMyEVFBYyNj0BMzI2AyE1NDY7ATU0NjsBETMRMzIWHQEzMhYVBCG9CCcV%2FnAVJwi9CBMVAnEdKh19FROo%2Fa0dFTIdFTDILxUdMhUdAocB%2BhMcHBP%2BBhMclhUdHRWWHP2MMhUdMhUdASz%2B1B0VMh0VAAAEAAAAAASwBLAADQAQAB8AIgAAASERFAYjIREBNTQ2MyEBIzUBIREUBiMhIiY1ETQ2MyEBIzUDhAEsDwr%2Bif7UDwoBdwEsyP2oASwPCv12Cg8PCgF3ASzIAyD9wQoPAk8BLFQKD%2F7UyP4M%2FcEKDw8KA7YKD%2F7UyAAC%2F5wAZAUUBEcARgBWAAABMzIeAhcWFxY2NzYnJjc%2BARYXFgcOASsBDgEPAQ4BKwEiJj8BBisBIicHDgErASImPwEmLwEuAT0BNDY7ATY3JyY2OwE2BSMiBh0BFBY7ATI2PQE0JgHkw0uOakkMEhEfQwoKGRMKBQ8XDCkCA1Y9Pgc4HCcDIhVkFRgDDDEqwxgpCwMiFWQVGAMaVCyfExwdFXwLLW8QBxXLdAFF%2BgoPDwr6Cg8PBEdBa4pJDgYKISAiJRsQCAYIDCw9P1c3fCbqFB0dFEYOCEAUHR0UnUplNQcmFTIVHVdPXw4TZV8PCjIKDw8KMgoPAAb%2FnP%2FmBRQEfgAJACQANAA8AFIAYgAAASU2Fh8BFgYPASUzMhYfASEyFh0BFAYHBQYmJyYjISImPQE0NhcjIgYdARQ7ATI2NTQmJyYEIgYUFjI2NAE3PgEeARceAT8BFxYGDwEGJi8BJjYlBwYfAR4BPwE2Jy4BJy4BAoEBpxMuDiAOAxCL%2FCtqQ0geZgM3FR0cE%2F0fFyIJKjr%2B1D5YWLlQExIqhhALIAsSAYBALS1ALf4PmBIgHhMQHC0aPzANITNQL3wpgigJASlmHyElDR0RPRMFAhQHCxADhPcICxAmDyoNeMgiNtQdFTIVJgeEBBQPQ1g%2ByD5YrBwVODMQEAtEERzJLUAtLUD%2B24ITChESEyMgAwWzPUkrRSgJL5cvfRxYGyYrDwkLNRAhFEgJDAQAAAAAAwBkAAAEOQSwAFEAYABvAAABMzIWHQEeARcWDgIPATIeBRUUDgUjFRQGKwEiJj0BIxUUBisBIiY9ASMiJj0BNDY7AREjIiY9ATQ2OwE1NDY7ATIWHQEzNTQ2AxUhMj4CNTc0LgMjARUhMj4CNTc0LgMjAnGWCg9PaAEBIC4uEBEGEjQwOiodFyI2LUAjGg8KlgoPZA8KlgoPrwoPDwpLSwoPDwqvDwqWCg9kD9cBBxwpEwsBAQsTKRz%2B%2BQFrHCkTCwEBCxMpHASwDwptIW1KLk0tHwYGAw8UKDJOLTtdPCoVCwJLCg8PCktLCg8PCksPCpYKDwJYDwqWCg9LCg8PCktLCg%2F%2B1MgVHR0LCgQOIhoW%2FnDIFR0dCwoEDiIaFgAAAwAEAAIEsASuABcAKQAsAAATITIWFREUBg8BDgEjISImJy4CNRE0NgQiDgQPARchNy4FAyMT1AMMVnokEhIdgVL9xFKCHAgYKHoCIIx9VkcrHQYGnAIwnAIIIClJVSGdwwSuelb%2BYDO3QkJXd3ZYHFrFMwGgVnqZFyYtLSUMDPPzBQ8sKDEj%2FsIBBQACAMgAAAOEBRQADwAZAAABMzIWFREUBiMhIiY1ETQ2ARUUBisBIiY9AQHblmesVCn%2BPilUrAFINhWWFTYFFKxn%2FgwpVFQpAfRnrPwY4RU2NhXhAAACAMgAAAOEBRQADwAZAAABMxQWMxEUBiMhIiY1ETQ2ARUUBisBIiY9AQHbYLOWVCn%2BPilUrAFINhWWFTYFFJaz%2FkIpVFQpAfRnrPwY4RU2NhXhAAACAAAAFAUOBBoAFAAaAAAJASUHFRcVJwc1NzU0Jj4CPwEnCQEFJTUFJQUO%2FYL%2Bhk5klpZkAQEBBQQvkwKCAVz%2Bov6iAV4BXgL%2F%2FuWqPOCWx5SVyJb6BA0GCgYDKEEBG%2F1ipqaTpaUAAAMAZAH0BLADIAAHAA8AFwAAEjIWFAYiJjQkMhYUBiImNCQyFhQGIiY0vHxYWHxYAeh8WFh8WAHofFhYfFgDIFh8WFh8WFh8WFh8WFh8WFh8AAAAAAMBkAAAArwETAAHAA8AFwAAADIWFAYiJjQSMhYUBiImNBIyFhQGIiY0Aeh8WFh8WFh8WFh8WFh8WFh8WARMWHxYWHz%2ByFh8WFh8%2FshYfFhYfAAAAAMAZABkBEwETAAPAB8ALwAAEyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2fQO2Cg8PCvxKCg8PCgO2Cg8PCvxKCg8PCgO2Cg8PCvxKCg8PBEwPCpYKDw8KlgoP%2FnAPCpYKDw8KlgoP%2FnAPCpYKDw8KlgoPAAAABAAAAAAEsASwAA8AHwAvADMAAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmBSEyFhURFAYjISImNRE0NhcVITUBXgH0ory7o%2F4Mpbm5Asv9qCk7OykCWCk7O%2F2xAfQVHR0V%2FgwVHR1HAZAEsLuj%2FgylubmlAfSlucg7Kf2oKTs7KQJYKTtkHRX%2B1BUdHRUBLBUdZMjIAAAAAAEAZABkBLAETAA7AAATITIWFAYrARUzMhYUBisBFTMyFhQGKwEVMzIWFAYjISImNDY7ATUjIiY0NjsBNSMiJjQ2OwE1IyImNDaWA%2BgVHR0VMjIVHR0VMjIVHR0VMjIVHR0V%2FBgVHR0VMjIVHR0VMjIVHR0VMjIVHR0ETB0qHcgdKh3IHSodyB0qHR0qHcgdKh3IHSodyB0qHQAAAAYBLAAFA%2BgEowAHAA0AEwAZAB8AKgAAAR4BBgcuATYBMhYVIiYlFAYjNDYBMhYVIiYlFAYjNDYDFRQGIiY9ARYzMgKKVz8%2FV1c%2FP%2F75fLB8sAK8sHyw%2FcB8sHywArywfLCwHSodKAMRBKNDsrJCQrKy%2FsCwfLB8fLB8sP7UsHywfHywfLD%2B05AVHR0VjgQAAAH%2FtQDIBJQDgQBCAAABNzYXAR4BBw4BKwEyFRQOBCsBIhE0NyYiBxYVECsBIi4DNTQzIyImJyY2NwE2HwEeAQ4BLwEHIScHBi4BNgLpRRkUASoLCAYFGg8IAQQNGyc%2FKZK4ChRUFQu4jjBJJxkHAgcPGQYGCAsBKhQaTBQVCiMUM7YDe7YsFCMKFgNuEwYS%2FtkLHw8OEw0dNkY4MhwBIBgXBAQYF%2F7gKjxTQyMNEw4PHwoBKBIHEwUjKBYGDMHBDAUWKCMAAAAAAgAAAAAEsASwACUAQwAAASM0LgUrAREUFh8BFSE1Mj4DNREjIg4FFSMRIQEjNC4DKwERFBYXMxUjNTI1ESMiDgMVIzUhBLAyCAsZEyYYGcgyGRn%2BcAQOIhoWyBkYJhMZCwgyA%2Bj9RBkIChgQEWQZDQzIMmQREBgKCBkB9AOEFSAVDggDAfyuFhkBAmRkAQUJFQ4DUgEDCA4VIBUBLP0SDxMKBQH%2BVwsNATIyGQGpAQUKEw%2BWAAAAAAMAAAAABEwErgAdACAAMAAAATUiJy4BLwEBIwEGBw4BDwEVITUiJj8BIRcWBiMVARsBARUUBiMhIiY9ATQ2MyEyFgPoGR4OFgUE%2Ft9F%2FtQSFQkfCwsBETE7EkUBJT0NISf%2B7IZ5AbEdFfwYFR0dFQPoFR0BLDIgDiIKCwLr%2FQ4jFQkTBQUyMisusKYiQTIBhwFW%2Fqr942QVHR0VZBUdHQADAAAAAASwBLAADwBHAEoAABMhMhYVERQGIyEiJjURNDYFIyIHAQYHBgcGHQEUFjMhMjY9ATQmIyInJj8BIRcWBwYjIgYdARQWMyEyNj0BNCYnIicmJyMBJhMjEzIETBUdHRX7tBUdHQJGRg0F%2FtUREhImDAsJAREIDAwINxAKCj8BCjkLEQwYCAwMCAE5CAwLCBEZGQ8B%2FuAFDsVnBLAdFfu0FR0dFQRMFR1SDP0PIBMSEAUNMggMDAgyCAwXDhmjmR8YEQwIMggMDAgyBwwBGRskAuwM%2FgUBCAAABAAAAAAEsASwAAMAEwAjACcAAAEhNSEFITIWFREUBiMhIiY1ETQ2KQEyFhURFAYjISImNRE0NhcRIREEsPtQBLD7ggGQFR0dFf5wFR0dAm0BkBUdHRX%2BcBUdHUcBLARMZMgdFfx8FR0dFQOEFR0dFf5wFR0dFQGQFR1k%2FtQBLAAEAAAAAASwBLAADwAfACMAJwAAEyEyFhURFAYjISImNRE0NgEhMhYVERQGIyEiJjURNDYXESEREyE1ITIBkBUdHRX%2BcBUdHQJtAZAVHR0V%2FnAVHR1HASzI%2B1AEsASwHRX8fBUdHRUDhBUd%2FgwdFf5wFR0dFQGQFR1k%2FtQBLP2oZAAAAAACAAAAZASwA%2BgAJwArAAATITIWFREzNTQ2MyEyFh0BMxUjFRQGIyEiJj0BIxEUBiMhIiY1ETQ2AREhETIBkBUdZB0VAZAVHWRkHRX%2BcBUdZB0V%2FnAVHR0CnwEsA%2BgdFf6ilhUdHRWWZJYVHR0Vlv6iFR0dFQMgFR3%2B1P7UASwAAAQAAAAABLAEsAADABMAFwAnAAAzIxEzFyEyFhURFAYjISImNRE0NhcRIREBITIWFREUBiMhIiY1ETQ2ZGRklgGQFR0dFf5wFR0dRwEs%2FqIDhBUdHRX8fBUdHQSwZB0V%2FnAVHR0VAZAVHWT%2B1AEs%2FgwdFf5wFR0dFQGQFR0AAAAAAgBkAAAETASwACcAKwAAATMyFhURFAYrARUhMhYVERQGIyEiJjURNDYzITUjIiY1ETQ2OwE1MwcRIRECWJYVHR0VlgHCFR0dFfx8FR0dFQFelhUdHRWWZMgBLARMHRX%2BcBUdZB0V%2FnAVHR0VAZAVHWQdFQGQFR1kyP7UASwAAAAEAAAAAASwBLAAAwATABcAJwAAISMRMwUhMhYVERQGIyEiJjURNDYXESERASEyFhURFAYjISImNRE0NgSwZGT9dgGQFR0dFf5wFR0dRwEs%2FK4DhBUdHRX8fBUdHQSwZB0V%2FnAVHR0VAZAVHWT%2B1AEs%2FgwdFf5wFR0dFQGQFR0AAAEBLAAwA28EgAAPAAAJAQYjIiY1ETQ2MzIXARYUA2H%2BEhcSDhAQDhIXAe4OAjX%2BEhcbGQPoGRsX%2FhIOKgAAAAABAUEAMgOEBH4ACwAACQE2FhURFAYnASY0AU8B7h0qKh3%2BEg4CewHuHREp%2FBgpER0B7g4qAAAAAAEAMgFBBH4DhAALAAATITIWBwEGIicBJjZkA%2BgpER3%2BEg4qDv4SHREDhCod%2FhIODgHuHSoAAAAAAQAyASwEfgNvAAsAAAkBFgYjISImNwE2MgJ7Ae4dESn8GCkRHQHuDioDYf4SHSoqHQHuDgAAAAACAAgAAASwBCgABgAKAAABFQE1LQE1ASE1IQK8%2FUwBnf5jBKj84AMgAuW2%2Fr3dwcHd%2B9jIAAAAAAIAAABkBLAEsAALADEAAAEjFTMVIREzNSM1IQEzND4FOwERFAYPARUhNSIuAzURMzIeBRUzESEEsMjI%2FtTIyAEs%2B1AyCAsZEyYYGWQyGRkBkAQOIhoWZBkYJhMZCwgy%2FOADhGRkASxkZP4MFSAVDggDAf3aFhkBAmRkAQUJFQ4CJgEDCA4VIBUBLAAAAgAAAAAETAPoACUAMQAAASM0LgUrAREUFh8BFSE1Mj4DNREjIg4FFSMRIQEjFTMVIREzNSM1IQMgMggLGRMmGBlkMhkZ%2FnAEDiIaFmQZGCYTGQsIMgMgASzIyP7UyMgBLAK8FSAVDggDAf3aFhkCAWRkAQUJFQ4CJgEDCA4VIBUBLPzgZGQBLGRkAAABAMgAZgNyBEoAEgAAATMyFgcJARYGKwEiJwEmNDcBNgK9oBAKDP4wAdAMChCgDQr%2BKQcHAdcKBEoWDP4w%2FjAMFgkB1wgUCAHXCQAAAQE%2BAGYD6ARKABIAAAEzMhcBFhQHAQYrASImNwkBJjYBU6ANCgHXBwf%2BKQoNoBAKDAHQ%2FjAMCgRKCf4pCBQI%2FikJFgwB0AHQDBYAAAEAZgDIBEoDcgASAAAAFh0BFAcBBiInASY9ATQ2FwkBBDQWCf4pCBQI%2FikJFgwB0AHQA3cKEKANCv4pBwcB1woNoBAKDP4wAdAAAAABAGYBPgRKA%2BgAEgAACQEWHQEUBicJAQYmPQE0NwE2MgJqAdcJFgz%2BMP4wDBYJAdcIFAPh%2FikKDaAQCgwB0P4wDAoQoA0KAdcHAAAAAgDZ%2F%2FkEPQSwAAUAOgAAARQGIzQ2BTMyFh8BNjc%2BAh4EBgcOBgcGIiYjIgYiJy4DLwEuAT4EHgEXJyY2A%2BiwfLD%2BVmQVJgdPBQsiKFAzRyorDwURAQQSFyozTSwNOkkLDkc3EDlfNyYHBw8GDyUqPjdGMR%2BTDA0EsHywfLDIHBPCAQIGBwcFDx81S21DBxlLR1xKQhEFBQcHGWt0bCQjP2hJNyATBwMGBcASGAAAAAACAMgAFQOEBLAAFgAaAAATITIWFREUBisBEQcGJjURIyImNRE0NhcVITX6AlgVHR0Vlv8TGpYVHR2rASwEsB0V%2FnAVHf4MsgkQFQKKHRUBkBUdZGRkAAAAAgDIABkETASwAA4AEgAAEyEyFhURBRElIREjETQ2ARU3NfoC7ic9%2FUQCWP1EZB8BDWQEsFEs%2FFt1A7Z9%2FBgEARc0%2FV1kFGQAAQAAAAECTW%2FDBF9fDzz1AB8EsAAAAADQdnOXAAAAANB2c5f%2FUf%2BcBdwFFAAAAAgAAgAAAAAAAAABAAAFFP%2BFAAAFFP9R%2FtQF3AABAAAAAAAAAAAAAAAAAAAAowG4ACgAAAAAAZAAAASwAAAEsABkBLAAAASwAAAEsABwAooAAAUUAAACigAABRQAAAGxAAABRQAAANgAAADYAAAAogAAAQQAAABIAAABBAAAAUUAAASwAGQEsAB7BLAAyASwAMgB9AAABLD%2F8gSwAAAEsAAABLD%2F8ASwAAAEsAAOBLAACQSwAGQEsP%2FTBLD%2F0wSwAAAEsAAABLAAAASwAAAEsAAABLAAJgSwAG4EsAAXBLAAFwSwABcEsABkBLAAGgSwAGQEsAAMBLAAZASwABcEsP%2BcBLAAZASwABcEsAAXBLAAAASwABcEsAAXBLAAFwSwAGQEsAAABLAAZASwAAAEsAAABLAAAASwAAAEsAAABLAAAASwAAAEsAAABLAAZASwAMgEsAAABLAAAASwADUEsABkBLAAyASw%2F7UEsAAhBLAAAASwAAAEsAAABLAAAASwAAAEsP%2BcBLAAAASwAAAEsAAABLAA2wSwABcEsAB1BLAAAASwAAAEsAAABLAACgSwAMgEsAAABLAAnQSwAMgEsADIBLAAyASwAAAEsP%2F%2BBLABLASwAGQEsACIBLABOwSwABcEsAAXBLAAFwSwABcEsAAXBLAAFwSwAAAEsAAXBLAAFwSwABcEsAAXBLAAAASwALcEsAC3BLAAAASwAAAEsABJBLAAFwSwAAAEsAAABLAAXQSw%2F9wEsP%2FcBLD%2FnwSwAGQEsAAABLAAAASwAAAEsABkBLD%2F%2FwSwAAAEsP9RBLAABgSwAAAEsAAABLABRQSwAAEEsAAABLD%2FnASwAEoEsAAUBLAAAASwAAAEsAAABLD%2FnASwAGEEsP%2F9BLAAFgSwABYEsAAWBLAAFgSwABgEsAAABMQAAASwAGQAAAAAAAD%2F2ABkADkAyAAAAScAZAAZABkAGQAZABkAGQAZAAAAAAAAAAAAAADZAAAAAAAOAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAMAZABkAAAAEAAAAAAAZP%2Bc%2F5z%2FnP%2Bc%2F5z%2FnP%2Bc%2F5wACQAJ%2F%2FL%2F8gBkAHkAJwBkAGQAAAAAAGT%2FogAAAAAAAAAAAAAAAADIAGQAAAABAI8AAP%2Bc%2F5wAZAAEAMgAyAAAAGQBkABkAAAAZAEs%2F7UAAAAAAAAAAAAAAAAAAABkAAABLAFBADIAMgAIAAAAAADIAT4AZgBmANkAyADIAAAAKgAqACoAKgCyAOgA6AFOAU4BTgFOAU4BTgFOAU4BTgFOAU4BTgFOAU4BpAIGAiICfgKGAqwC5ANGA24DjAPEBAgEMgRiBKIE3AVcBboGcgb0ByAHYgfKCB4IYgi%2BCTYJhAm2Cd4KKApMCpQK4gswC4oLygwIDFgNKg1eDbAODg5oDrQPKA%2BmD%2BYQEhBUEJAQqhEqEXYRthIKEjgSfBLAExoTdBPQFCoU1BU8FagVzBYEFjYWYBawFv4XUhemGAIYLhhqGJYYsBjgGP4ZKBloGZQZxBnaGe4aNhpoGrga9hteG7QcMhyUHOIdHB1EHWwdlB28HeYeLh52HsAfYh%2FSIEYgviEyIXYhuCJAIpYiuCMOIyIjOCN6I8Ij4CQCJDAkXiSWJOIlNCVgJbwmFCZ%2BJuYnUCe8J%2FgoNChwKKwpoCnMKiYqSiqEKworeiwILGgsuizsLRwtiC30LiguZi6iLtgvDi9GL34vsi%2F4MD4whDDSMRIxYDGuMegyJDJeMpoy3jMiMz4zaDO2NBg0YDSoNNI1LDWeNeg2PjZ8Ntw3GjdON5I31DgQOEI4hjjIOQo5SjmIOcw6HDpsOpo63jugO9w8GDxQPKI8%2BD0yPew%2BOj6MPtQ%2FKD9uP6o%2F%2BkBIQIBAxkECQX5CGEKoQu5DGENCQ3ZDoEPKRBBEYESuRPZFWkW2RgZGdEa0RvZHNkd2R7ZH9kgWSDJITkhqSIZIzEkSSThJXkmESapKAkouSlIAAQAAARcApwARAAAAAAACAAAAAQABAAAAQAAuAAAAAAAAABAAxgABAAAAAAATABIAAAADAAEECQAAAGoAEgADAAEECQABACgAfAADAAEECQACAA4ApAADAAEECQADAEwAsgADAAEECQAEADgA%2FgADAAEECQAFAHgBNgADAAEECQAGADYBrgADAAEECQAIABYB5AADAAEECQAJABYB%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%2FtQAyAAAAAAAAAAAAAAAAAAAAAAAAAAABFwAAAQIBAwADAA0ADgEEAJYBBQEGAQcBCAEJAQoBCwEMAQ0BDgEPARABEQESARMA7wEUARUBFgEXARgBGQEaARsBHAEdAR4BHwEgASEBIgEjASQBJQEmAScBKAEpASoBKwEsAS0BLgEvATABMQEyATMBNAE1ATYBNwE4ATkBOgE7ATwBPQE%2BAT8BQAFBAUIBQwFEAUUBRgFHAUgBSQFKAUsBTAFNAU4BTwFQAVEBUgFTAVQBVQFWAVcBWAFZAVoBWwFcAV0BXgFfAWABYQFiAWMBZAFlAWYBZwFoAWkBagFrAWwBbQFuAW8BcAFxAXIBcwF0AXUBdgF3AXgBeQF6AXsBfAF9AX4BfwGAAYEBggGDAYQBhQGGAYcBiAGJAYoBiwGMAY0BjgGPAZABkQGSAZMBlAGVAZYBlwGYAZkBmgGbAZwBnQGeAZ8BoAGhAaIBowGkAaUBpgGnAagBqQGqAasBrAGtAa4BrwGwAbEBsgGzAbQBtQG2AbcBuAG5AboBuwG8Ab0BvgG%2FAcABwQHCAcMBxAHFAcYBxwHIAckBygHLAcwBzQHOAc8B0AHRAdIB0wHUAdUB1gHXAdgB2QHaAdsB3AHdAd4B3wHgAeEB4gHjAeQB5QHmAecB6AHpAeoB6wHsAe0B7gHvAfAB8QHyAfMB9AH1AfYB9wH4AfkB%2BgH7AfwB%2FQH%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%3D%29%20format%28%27truetype%27%29%2Curl%28data%3Aimage%2Fsvg%2Bxml%3Bbase64%2CPD94bWwgdmVyc2lvbj0iMS4wIiBzdGFuZGFsb25lPSJubyI%2FPgo8IURPQ1RZUEUgc3ZnIFBVQkxJQyAiLS8vVzNDLy9EVEQgU1ZHIDEuMS8vRU4iICJodHRwOi8vd3d3LnczLm9yZy9HcmFwaGljcy9TVkcvMS4xL0RURC9zdmcxMS5kdGQiID4KPHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPgo8bWV0YWRhdGE%2BPC9tZXRhZGF0YT4KPGRlZnM%2BCjxmb250IGlkPSJnbHlwaGljb25zX2hhbGZsaW5nc3JlZ3VsYXIiIGhvcml6LWFkdi14PSIxMjAwIiA%2BCjxmb250LWZhY2UgdW5pdHMtcGVyLWVtPSIxMjAwIiBhc2NlbnQ9Ijk2MCIgZGVzY2VudD0iLTI0MCIgLz4KPG1pc3NpbmctZ2x5cGggaG9yaXotYWR2LXg9IjUwMCIgLz4KPGdseXBoIGhvcml6LWFkdi14PSIwIiAvPgo8Z2x5cGggaG9yaXotYWR2LXg9IjQwMCIgLz4KPGdseXBoIHVuaWNvZGU9IiAiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDAzOyIgaG9yaXotYWR2LXg9IjEzMDAiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDA0OyIgaG9yaXotYWR2LXg9IjQzMyIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeDIwMDU7IiBob3Jpei1hZHYteD0iMzI1IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAwNjsiIGhvcml6LWFkdi14PSIyMTYiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDA3OyIgaG9yaXotYWR2LXg9IjIxNiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeDIwMDg7IiBob3Jpei1hZHYteD0iMTYyIiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAwOTsiIGhvcml6LWFkdi14PSIyNjAiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDBhOyIgaG9yaXotYWR2LXg9IjcyIiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAyZjsiIGhvcml6LWFkdi14PSIyNjAiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3gyNmZhOyIgZD0iTTc3NCAxMTkzLjVxMTYgLTkuNSAyMC41IC0yN3QtNS41IC0zMy41bC0xMzYgLTE4N2w0NjcgLTc0NmgzMHEyMCAwIDM1IC0xOC41dDE1IC0zOS41di00MmgtMTIwMHY0MnEwIDIxIDE1IDM5LjV0MzUgMTguNWgzMGw0NjggNzQ2bC0xMzUgMTgzcS0xMCAxNiAtNS41IDM0dDIwLjUgMjh0MzQgNS41dDI4IC0yMC41bDExMSAtMTQ4bDExMiAxNTBxOSAxNiAyNyAyMC41dDM0IC01ek02MDAgMjAwaDM3N2wtMTgyIDExMmwtMTk1IDUzNHYtNjQ2eiAiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDIyOyIgZD0iTTYwMCAxMjAwdi00MDBxMCAtNDEgMjkuNSAtNzAuNXQ3MC41IC0yOS41aDMwMHYtNjUwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC04MDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djExMDBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41aDQ1MHpNMTAwMCA4MDBoLTI1MHEtMjEgMCAtMzUuNSAxNC41dC0xNC41IDM1LjV2MjUweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwMjM7IiBkPSJNNjAwIDExNzdxMTE3IDAgMjI0IC00NS41dDE4NC41IC0xMjN0MTIzIC0xODQuNXQ0NS41IC0yMjR0LTQ1LjUgLTIyNHQtMTIzIC0xODQuNXQtMTg0LjUgLTEyM3QtMjI0IC00NS41dC0yMjQgNDUuNXQtMTg0LjUgMTIzdC0xMjMgMTg0LjV0LTQ1LjUgMjI0dDQ1LjUgMjI0dDEyMyAxODQuNXQxODQuNSAxMjN0MjI0IDQ1LjV6TTYwMCAxMDI3cS0xMTYgMCAtMjE0LjUgLTU3dC0xNTUuNSAtMTU1LjV0LTU3IC0yMTQuNXQ1NyAtMjE0LjUgdDE1NS41IC0xNTUuNXQyMTQuNSAtNTd0MjE0LjUgNTd0MTU1LjUgMTU1LjV0NTcgMjE0LjV0LTU3IDIxNC41dC0xNTUuNSAxNTUuNXQtMjE0LjUgNTd6TTUyNSA5MDBoNTBxMTAgMCAxNy41IC03LjV0Ny41IC0xNy41di0yNzVoMTc1cTEwIDAgMTcuNSAtNy41dDcuNSAtMTcuNXYtNTBxMCAtMTAgLTcuNSAtMTcuNXQtMTcuNSAtNy41aC0yNTBxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXYzNTBxMCAxMCA3LjUgMTcuNXQxNy41IDcuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDI0OyIgZD0iTTEzMDAgMGgtNTM4bC00MSA0MDBoLTI0MmwtNDEgLTQwMGgtNTM4bDQzMSAxMjAwaDIwOWwtMjEgLTMwMGgxNjJsLTIwIDMwMGgyMDh6TTUxNSA4MDBsLTI3IC0zMDBoMjI0bC0yNyAzMDBoLTE3MHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDI1OyIgZD0iTTU1MCAxMjAwaDIwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtNDUwaDE5MXEyMCAwIDI1LjUgLTExLjV0LTcuNSAtMjcuNWwtMzI3IC00MDBxLTEzIC0xNiAtMzIgLTE2dC0zMiAxNmwtMzI3IDQwMHEtMTMgMTYgLTcuNSAyNy41dDI1LjUgMTEuNWgxOTF2NDUwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXpNMTEyNSA0MDBoNTBxMTAgMCAxNy41IC03LjV0Ny41IC0xNy41di0zNTBxMCAtMTAgLTcuNSAtMTcuNXQtMTcuNSAtNy41IGgtMTA1MHEtMTAgMCAtMTcuNSA3LjV0LTcuNSAxNy41djM1MHEwIDEwIDcuNSAxNy41dDE3LjUgNy41aDUwcTEwIDAgMTcuNSAtNy41dDcuNSAtMTcuNXYtMTc1aDkwMHYxNzVxMCAxMCA3LjUgMTcuNXQxNy41IDcuNXoiIC8%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%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%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDQyOyIgZD0iTTUwMCAxMjAwbDY4MiAtNjgycTggLTggOCAtMTh0LTggLTE4bC00NjQgLTQ2NHEtOCAtOCAtMTggLTh0LTE4IDhsLTY4MiA2ODJsMSA0NzVxMCAxMCA3LjUgMTcuNXQxNy41IDcuNWg0NzR6TTgwMCAxMjAwbDY4MiAtNjgycTggLTggOCAtMTh0LTggLTE4bC00NjQgLTQ2NHEtOCAtOCAtMTggLTh0LTE4IDhsLTU2IDU2bDQyNCA0MjZsLTcwMCA3MDBoMTUwek0zMTkuNSAxMDI0LjVxLTI5LjUgMjkuNSAtNzEgMjkuNXQtNzEgLTI5LjUgdC0yOS41IC03MS41dDI5LjUgLTcxLjV0NzEgLTI5LjV0NzEgMjkuNXQyOS41IDcxLjV0LTI5LjUgNzEuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDQzOyIgZD0iTTMwMCAxMjAwaDgyNXE3NSAwIDc1IC03NXYtOTAwcTAgLTI1IC0xOCAtNDNsLTY0IC02NHEtOCAtOCAtMTMgLTUuNXQtNSAxMi41djk1MHEwIDEwIC03LjUgMTcuNXQtMTcuNSA3LjVoLTcwMHEtMjUgMCAtNDMgLTE4bC02NCAtNjRxLTggLTggLTUuNSAtMTN0MTIuNSAtNWg3MDBxMTAgMCAxNy41IC03LjV0Ny41IC0xNy41di05NTBxMCAtMTAgLTcuNSAtMTcuNXQtMTcuNSAtNy41aC04NTBxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXY5NzUgcTAgMjUgMTggNDNsMTM5IDEzOXExOCAxOCA0MyAxOHoiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDQ5OyIgZD0iTTg3NyAxMjAwbDIgLTU3cS04MyAtMTkgLTExNiAtNDUuNXQtNDAgLTY2LjVsLTEzMiAtODM5cS05IC00OSAxMyAtNjl0OTYgLTI2di05N2gtNTAwdjk3cTE4NiAxNiAyMDAgOThsMTczIDgzMnEzIDE3IDMgMzB0LTEuNSAyMi41dC05IDE3LjV0LTEzLjUgMTIuNXQtMjEuNSAxMHQtMjYgOC41dC0zMy41IDEwcS0xMyAzIC0xOSA1djU3aDQyNXoiIC8%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDY5OyIgZD0iTTI1MCAxMTAwaDEwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtNDM4bDQ2NCA0NTNxMTUgMTQgMjUuNSAxMHQxMC41IC0yNXYtMTAwMHEwIC0yMSAtMTAuNSAtMjV0LTI1LjUgMTBsLTQ2NCA0NTN2LTQzOHEwIC0yMSAtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtMTAwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXYxMDAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDcwOyIgZD0iTTUwIDExMDBoMTAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di00MzhsNDY0IDQ1M3ExNSAxNCAyNS41IDEwdDEwLjUgLTI1di00MzhsNDY0IDQ1M3ExNSAxNCAyNS41IDEwdDEwLjUgLTI1di0xMDAwcTAgLTIxIC0xMC41IC0yNXQtMjUuNSAxMGwtNDY0IDQ1M3YtNDM4cTAgLTIxIC0xMC41IC0yNXQtMjUuNSAxMGwtNDY0IDQ1M3YtNDM4cTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC0xMDBxLTIxIDAgLTM1LjUgMTQuNSB0LTE0LjUgMzUuNXYxMDAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDcxOyIgZD0iTTEyMDAgMTA1MHYtMTAwMHEwIC0yMSAtMTAuNSAtMjV0LTI1LjUgMTBsLTQ2NCA0NTN2LTQzOHEwIC0yMSAtMTAuNSAtMjV0LTI1LjUgMTBsLTQ5MiA0ODBxLTE1IDE0IC0xNSAzNXQxNSAzNWw0OTIgNDgwcTE1IDE0IDI1LjUgMTB0MTAuNSAtMjV2LTQzOGw0NjQgNDUzcTE1IDE0IDI1LjUgMTB0MTAuNSAtMjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA3MjsiIGQ9Ik0yNDMgMTA3NGw4MTQgLTQ5OHExOCAtMTEgMTggLTI2dC0xOCAtMjZsLTgxNCAtNDk4cS0xOCAtMTEgLTMwLjUgLTR0LTEyLjUgMjh2MTAwMHEwIDIxIDEyLjUgMjh0MzAuNSAtNHoiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDc5OyIgZD0iTTg4NSA5MDBsLTM1MiAtMzUzbDM1MiAtMzUzbC0xOTcgLTE5OGwtNTUyIDU1Mmw1NTIgNTUweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwODA7IiBkPSJNMTA2NCA1NDdsLTU1MSAtNTUxbC0xOTggMTk4bDM1MyAzNTNsLTM1MyAzNTNsMTk4IDE5OHoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDgzOyIgZD0iTTYwMCAxMTc3cTExNyAwIDIyNCAtNDUuNXQxODQuNSAtMTIzdDEyMyAtMTg0LjV0NDUuNSAtMjI0dC00NS41IC0yMjR0LTEyMyAtMTg0LjV0LTE4NC41IC0xMjN0LTIyNCAtNDUuNXQtMjI0IDQ1LjV0LTE4NC41IDEyM3QtMTIzIDE4NC41dC00NS41IDIyNHQ0NS41IDIyNHQxMjMgMTg0LjV0MTg0LjUgMTIzdDIyNCA0NS41ek03NDEuNSA5MTNxLTEyLjUgMCAtMjEuNSAtOWwtMTIwIC0xMjBsLTEyMCAxMjBxLTkgOSAtMjEuNSA5IHQtMjEuNSAtOWwtMTQxIC0xNDFxLTkgLTkgLTkgLTIxLjV0OSAtMjEuNWwxMjAgLTEyMGwtMTIwIC0xMjBxLTkgLTkgLTkgLTIxLjV0OSAtMjEuNWwxNDEgLTE0MXE5IC05IDIxLjUgLTl0MjEuNSA5bDEyMCAxMjBsMTIwIC0xMjBxOSAtOSAyMS41IC05dDIxLjUgOWwxNDEgMTQxcTkgOSA5IDIxLjV0LTkgMjEuNWwtMTIwIDEyMGwxMjAgMTIwcTkgOSA5IDIxLjV0LTkgMjEuNWwtMTQxIDE0MXEtOSA5IC0yMS41IDl6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA4NDsiIGQ9Ik02MDAgMTE3N3ExMTcgMCAyMjQgLTQ1LjV0MTg0LjUgLTEyM3QxMjMgLTE4NC41dDQ1LjUgLTIyNHQtNDUuNSAtMjI0dC0xMjMgLTE4NC41dC0xODQuNSAtMTIzdC0yMjQgLTQ1LjV0LTIyNCA0NS41dC0xODQuNSAxMjN0LTEyMyAxODQuNXQtNDUuNSAyMjR0NDUuNSAyMjR0MTIzIDE4NC41dDE4NC41IDEyM3QyMjQgNDUuNXpNNTQ2IDYyM2wtODQgODVxLTcgNyAtMTcuNSA3dC0xOC41IC03bC0xMzkgLTEzOXEtNyAtOCAtNyAtMTh0NyAtMTggbDI0MiAtMjQxcTcgLTggMTcuNSAtOHQxNy41IDhsMzc1IDM3NXE3IDcgNyAxNy41dC03IDE4LjVsLTEzOSAxMzlxLTcgNyAtMTcuNSA3dC0xNy41IC03eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwODU7IiBkPSJNNjAwIDExNzdxMTE3IDAgMjI0IC00NS41dDE4NC41IC0xMjN0MTIzIC0xODQuNXQ0NS41IC0yMjR0LTQ1LjUgLTIyNHQtMTIzIC0xODQuNXQtMTg0LjUgLTEyM3QtMjI0IC00NS41dC0yMjQgNDUuNXQtMTg0LjUgMTIzdC0xMjMgMTg0LjV0LTQ1LjUgMjI0dDQ1LjUgMjI0dDEyMyAxODQuNXQxODQuNSAxMjN0MjI0IDQ1LjV6TTU4OCA5NDFxLTI5IDAgLTU5IC01LjV0LTYzIC0yMC41dC01OCAtMzguNXQtNDEuNSAtNjN0LTE2LjUgLTg5LjUgcTAgLTI1IDIwIC0yNWgxMzFxMzAgLTUgMzUgMTFxNiAyMCAyMC41IDI4dDQ1LjUgOHEyMCAwIDMxLjUgLTEwLjV0MTEuNSAtMjguNXEwIC0yMyAtNyAtMzR0LTI2IC0xOHEtMSAwIC0xMy41IC00dC0xOS41IC03LjV0LTIwIC0xMC41dC0yMiAtMTd0LTE4LjUgLTI0dC0xNS41IC0zNXQtOCAtNDZxLTEgLTggNS41IC0xNi41dDIwLjUgLTguNWgxNzNxNyAwIDIyIDh0MzUgMjh0MzcuNSA0OHQyOS41IDc0dDEyIDEwMHEwIDQ3IC0xNyA4MyB0LTQyLjUgNTd0LTU5LjUgMzQuNXQtNjQgMTh0LTU5IDQuNXpNNjc1IDQwMGgtMTUwcS0xMCAwIC0xNy41IC03LjV0LTcuNSAtMTcuNXYtMTUwcTAgLTEwIDcuNSAtMTcuNXQxNy41IC03LjVoMTUwcTEwIDAgMTcuNSA3LjV0Ny41IDE3LjV2MTUwcTAgMTAgLTcuNSAxNy41dC0xNy41IDcuNXoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDg4OyIgZD0iTTYwMCAxMTc3cTExNyAwIDIyNCAtNDUuNXQxODQuNSAtMTIzdDEyMyAtMTg0LjV0NDUuNSAtMjI0dC00NS41IC0yMjR0LTEyMyAtMTg0LjV0LTE4NC41IC0xMjN0LTIyNCAtNDUuNXQtMjI0IDQ1LjV0LTE4NC41IDEyM3QtMTIzIDE4NC41dC00NS41IDIyNHQ0NS41IDIyNHQxMjMgMTg0LjV0MTg0LjUgMTIzdDIyNCA0NS41ek02MDAgMTAyN3EtMTE2IDAgLTIxNC41IC01N3QtMTU1LjUgLTE1NS41dC01NyAtMjE0LjV0NTcgLTIxNC41IHQxNTUuNSAtMTU1LjV0MjE0LjUgLTU3dDIxNC41IDU3dDE1NS41IDE1NS41dDU3IDIxNC41dC01NyAyMTQuNXQtMTU1LjUgMTU1LjV0LTIxNC41IDU3ek03NTkgODIzbDY0IC02NHE3IC03IDcgLTE3LjV0LTcgLTE3LjVsLTEyNCAtMTI0bDEyNCAtMTI0cTcgLTcgNyAtMTcuNXQtNyAtMTcuNWwtNjQgLTY0cS03IC03IC0xNy41IC03dC0xNy41IDdsLTEyNCAxMjRsLTEyNCAtMTI0cS03IC03IC0xNy41IC03dC0xNy41IDdsLTY0IDY0IHEtNyA3IC03IDE3LjV0NyAxNy41bDEyNCAxMjRsLTEyNCAxMjRxLTcgNyAtNyAxNy41dDcgMTcuNWw2NCA2NHE3IDcgMTcuNSA3dDE3LjUgLTdsMTI0IC0xMjRsMTI0IDEyNHE3IDcgMTcuNSA3dDE3LjUgLTd6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA4OTsiIGQ9Ik02MDAgMTE3N3ExMTcgMCAyMjQgLTQ1LjV0MTg0LjUgLTEyM3QxMjMgLTE4NC41dDQ1LjUgLTIyNHQtNDUuNSAtMjI0dC0xMjMgLTE4NC41dC0xODQuNSAtMTIzdC0yMjQgLTQ1LjV0LTIyNCA0NS41dC0xODQuNSAxMjN0LTEyMyAxODQuNXQtNDUuNSAyMjR0NDUuNSAyMjR0MTIzIDE4NC41dDE4NC41IDEyM3QyMjQgNDUuNXpNNjAwIDEwMjdxLTExNiAwIC0yMTQuNSAtNTd0LTE1NS41IC0xNTUuNXQtNTcgLTIxNC41dDU3IC0yMTQuNSB0MTU1LjUgLTE1NS41dDIxNC41IC01N3QyMTQuNSA1N3QxNTUuNSAxNTUuNXQ1NyAyMTQuNXQtNTcgMjE0LjV0LTE1NS41IDE1NS41dC0yMTQuNSA1N3pNNzgyIDc4OGwxMDYgLTEwNnE3IC03IDcgLTE3LjV0LTcgLTE3LjVsLTMyMCAtMzIxcS04IC03IC0xOCAtN3QtMTggN2wtMjAyIDIwM3EtOCA3IC04IDE3LjV0OCAxNy41bDEwNiAxMDZxNyA4IDE3LjUgOHQxNy41IC04bDc5IC03OWwxOTcgMTk3cTcgNyAxNy41IDd0MTcuNSAtN3oiIC8%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDk2OyIgZD0iTTg1MCAxMjAwaDMwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtMzAwcTAgLTIxIC0xMC41IC0yNXQtMjQuNSAxMGwtOTQgOTRsLTI0OSAtMjQ5cS04IC03IC0xOCAtN3QtMTggN2wtMTA2IDEwNnEtNyA4IC03IDE4dDcgMThsMjQ5IDI0OWwtOTQgOTRxLTE0IDE0IC0xMCAyNC41dDI1IDEwLjV6TTM1MCAwaC0zMDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djMwMHEwIDIxIDEwLjUgMjV0MjQuNSAtMTBsOTQgLTk0bDI0OSAyNDkgcTggNyAxOCA3dDE4IC03bDEwNiAtMTA2cTcgLTggNyAtMTh0LTcgLTE4bC0yNDkgLTI0OWw5NCAtOTRxMTQgLTE0IDEwIC0yNC41dC0yNSAtMTAuNXoiIC8%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTE1OyIgZD0iTTQwNCAxMDAwaDc0NnEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtNTUxaDE1MHEyMSAwIDI1IC0xMC41dC0xMCAtMjQuNWwtMjMwIC0yNDlxLTE0IC0xNSAtMzUgLTE1dC0zNSAxNWwtMjMwIDI0OXEtMTQgMTQgLTEwIDI0LjV0MjUgMTAuNWgxNTB2NDAxaC0zODF6TTEzNSA5ODRsMjMwIC0yNDlxMTQgLTE0IDEwIC0yNC41dC0yNSAtMTAuNWgtMTUwdi00MDBoMzg1bDIxNSAtMjAwaC03NTBxLTIxIDAgLTM1LjUgMTQuNSB0LTE0LjUgMzUuNXY1NTBoLTE1MHEtMjEgMCAtMjUgMTAuNXQxMCAyNC41bDIzMCAyNDlxMTQgMTUgMzUgMTV0MzUgLTE1eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxMTY7IiBkPSJNNTYgMTIwMGg5NHExNyAwIDMxIC0xMXQxOCAtMjdsMzggLTE2Mmg4OTZxMjQgMCAzOSAtMTguNXQxMCAtNDIuNWwtMTAwIC00NzVxLTUgLTIxIC0yNyAtNDIuNXQtNTUgLTIxLjVoLTYzM2w0OCAtMjAwaDUzNXEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXQtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtNTB2LTUwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41dC0zNS41IDE0LjV0LTE0LjUgMzUuNXY1MGgtMzAwdi01MCBxMCAtMjEgLTE0LjUgLTM1LjV0LTM1LjUgLTE0LjV0LTM1LjUgMTQuNXQtMTQuNSAzNS41djUwaC0zMXEtMTggMCAtMzIuNSAxMHQtMjAuNSAxOWwtNSAxMGwtMjAxIDk2MWgtNTRxLTIwIDAgLTM1IDE0LjV0LTE1IDM1LjV0MTUgMzUuNXQzNSAxNC41eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxMTc7IiBkPSJNMTIwMCAxMDAwdi0xMDBoLTEyMDB2MTAwaDIwMHEwIDQxIDI5LjUgNzAuNXQ3MC41IDI5LjVoMzAwcTQxIDAgNzAuNSAtMjkuNXQyOS41IC03MC41aDUwMHpNMCA4MDBoMTIwMHYtODAwaC0xMjAwdjgwMHoiIC8%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%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%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%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%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%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%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%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTYyOyIgZD0iTTc5MyAxMTgybDkgLTlxOCAtMTAgNSAtMjdxLTMgLTExIC03OSAtMjI1LjV0LTc4IC0yMjEuNWwzMDAgMXEyNCAwIDMyLjUgLTE3LjV0LTUuNSAtMzUuNXEtMSAwIC0xMzMuNSAtMTU1dC0yNjcgLTMxMi41dC0xMzguNSAtMTYyLjVxLTEyIC0xNSAtMjYgLTE1aC05bC05IDhxLTkgMTEgLTQgMzJxMiA5IDQyIDEyMy41dDc5IDIyNC41bDM5IDExMGgtMzAycS0yMyAwIC0zMSAxOXEtMTAgMjEgNiA0MXE3NSA4NiAyMDkuNSAyMzcuNSB0MjI4IDI1N3Q5OC41IDExMS41cTkgMTYgMjUgMTZoOXoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTY1OyIgZD0iTTYwMCAxMTg2cTExOSAwIDIyNy41IC00Ni41dDE4NyAtMTI1dDEyNSAtMTg3dDQ2LjUgLTIyNy41dC00Ni41IC0yMjcuNXQtMTI1IC0xODd0LTE4NyAtMTI1dC0yMjcuNSAtNDYuNXQtMjI3LjUgNDYuNXQtMTg3IDEyNXQtMTI1IDE4N3QtNDYuNSAyMjcuNXQ0Ni41IDIyNy41dDEyNSAxODd0MTg3IDEyNXQyMjcuNSA0Ni41ek02MDAgMTAyMnEtMTE1IDAgLTIxMiAtNTYuNXQtMTUzLjUgLTE1My41dC01Ni41IC0yMTJ0NTYuNSAtMjEyIHQxNTMuNSAtMTUzLjV0MjEyIC01Ni41dDIxMiA1Ni41dDE1My41IDE1My41dDU2LjUgMjEydC01Ni41IDIxMnQtMTUzLjUgMTUzLjV0LTIxMiA1Ni41ek02MDAgNzk0cTgwIDAgMTM3IC01N3Q1NyAtMTM3dC01NyAtMTM3dC0xMzcgLTU3dC0xMzcgNTd0LTU3IDEzN3Q1NyAxMzd0MTM3IDU3eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxNjY7IiBkPSJNNDUwIDEyMDBoMjAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di0zNTBoMjQ1cTIwIDAgMjUgLTExdC05IC0yNmwtMzgzIC00MjZxLTE0IC0xNSAtMzMuNSAtMTV0LTMyLjUgMTVsLTM3OSA0MjZxLTEzIDE1IC04LjUgMjZ0MjUuNSAxMWgyNTB2MzUwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXpNNTAgMzAwaDEwMDBxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV2LTI1MGgtMTEwMHYyNTBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41eiBNOTAwIDIwMHYtNTBoMTAwdjUwaC0xMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTE2NzsiIGQ9Ik01ODMgMTE4MmwzNzggLTQzNXExNCAtMTUgOSAtMzF0LTI2IC0xNmgtMjQ0di0yNTBxMCAtMjAgLTE3IC0zNXQtMzkgLTE1aC0yMDBxLTIwIDAgLTMyIDE0LjV0LTEyIDM1LjV2MjUwaC0yNTBxLTIwIDAgLTI1LjUgMTYuNXQ4LjUgMzEuNWwzODMgNDMxcTE0IDE2IDMzLjUgMTd0MzMuNSAtMTR6TTUwIDMwMGgxMDAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di0yNTBoLTExMDB2MjUwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXogTTkwMCAyMDB2LTUwaDEwMHY1MGgtMTAweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxNjg7IiBkPSJNMzk2IDcyM2wzNjkgMzY5cTcgNyAxNy41IDd0MTcuNSAtN2wxMzkgLTEzOXE3IC04IDcgLTE4LjV0LTcgLTE3LjVsLTUyNSAtNTI1cS03IC04IC0xNy41IC04dC0xNy41IDhsLTI5MiAyOTFxLTcgOCAtNyAxOHQ3IDE4bDEzOSAxMzlxOCA3IDE4LjUgN3QxNy41IC03ek01MCAzMDBoMTAwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtMjUwaC0xMTAwdjI1MHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjV6TTkwMCAyMDB2LTUwaDEwMHY1MCBoLTEwMHoiIC8%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%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%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%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%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%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjIxOyIgZD0iTTQ4MCAxMTY1bDY4MiAtNjgzcTMxIC0zMSAzMSAtNzUuNXQtMzEgLTc1LjVsLTEzMSAtMTMxaC00ODFsLTUxNyA1MThxLTMyIDMxIC0zMiA3NS41dDMyIDc1LjVsMjk1IDI5NnEzMSAzMSA3NS41IDMxdDc2LjUgLTMxek0xMDggNzk0bDM0MiAtMzQybDMwMyAzMDRsLTM0MSAzNDF6TTI1MCAxMDBoODAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di01MGgtOTAwdjUwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjI3OyIgZD0iTTYyNSAxMjAwaDE1MHExMCAwIDE3LjUgLTcuNXQ3LjUgLTE3LjV2LTEwOXE3OSAtMzMgMTMxIC04Ny41dDUzIC0xMjguNXExIC00NiAtMTUgLTg0LjV0LTM5IC02MXQtNDYgLTM4dC0zOSAtMjEuNWwtMTcgLTZxNiAwIDE1IC0xLjV0MzUgLTl0NTAgLTE3LjV0NTMgLTMwdDUwIC00NXQzNS41IC02NHQxNC41IC04NHEwIC01OSAtMTEuNSAtMTA1LjV0LTI4LjUgLTc2LjV0LTQ0IC01MXQtNDkuNSAtMzEuNXQtNTQuNSAtMTZ0LTQ5LjUgLTYuNSB0LTQzLjUgLTF2LTc1cTAgLTEwIC03LjUgLTE3LjV0LTE3LjUgLTcuNWgtMTUwcS0xMCAwIC0xNy41IDcuNXQtNy41IDE3LjV2NzVoLTEwMHYtNzVxMCAtMTAgLTcuNSAtMTcuNXQtMTcuNSAtNy41aC0xNTBxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXY3NWgtMTc1cS0xMCAwIC0xNy41IDcuNXQtNy41IDE3LjV2MTUwcTAgMTAgNy41IDE3LjV0MTcuNSA3LjVoNzV2NjAwaC03NXEtMTAgMCAtMTcuNSA3LjV0LTcuNSAxNy41djE1MCBxMCAxMCA3LjUgMTcuNXQxNy41IDcuNWgxNzV2NzVxMCAxMCA3LjUgMTcuNXQxNy41IDcuNWgxNTBxMTAgMCAxNy41IC03LjV0Ny41IC0xNy41di03NWgxMDB2NzVxMCAxMCA3LjUgMTcuNXQxNy41IDcuNXpNNDAwIDkwMHYtMjAwaDI2M3EyOCAwIDQ4LjUgMTAuNXQzMCAyNXQxNSAyOXQ1LjUgMjUuNWwxIDEwcTAgNCAtMC41IDExdC02IDI0dC0xNSAzMHQtMzAgMjR0LTQ4LjUgMTFoLTI2M3pNNDAwIDUwMHYtMjAwaDM2M3EyOCAwIDQ4LjUgMTAuNSB0MzAgMjV0MTUgMjl0NS41IDI1LjVsMSAxMHEwIDQgLTAuNSAxMXQtNiAyNHQtMTUgMzB0LTMwIDI0dC00OC41IDExaC0zNjN6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTIzMDsiIGQ9Ik0yMTIgMTE5OGg3ODBxODYgMCAxNDcgLTYxdDYxIC0xNDd2LTQxNnEwIC01MSAtMTggLTE0Mi41dC0zNiAtMTU3LjVsLTE4IC02NnEtMjkgLTg3IC05My41IC0xNDYuNXQtMTQ2LjUgLTU5LjVoLTU3MnEtODIgMCAtMTQ3IDU5dC05MyAxNDdxLTggMjggLTIwIDczdC0zMiAxNDMuNXQtMjAgMTQ5LjV2NDE2cTAgODYgNjEgMTQ3dDE0NyA2MXpNNjAwIDEwNDVxLTcwIDAgLTEzMi41IC0xMS41dC0xMDUuNSAtMzAuNXQtNzguNSAtNDEuNSB0LTU3IC00NXQtMzYgLTQxdC0yMC41IC0zMC41bC02IC0xMmwxNTYgLTI0M2g1NjBsMTU2IDI0M3EtMiA1IC02IDEyLjV0LTIwIDI5LjV0LTM2LjUgNDJ0LTU3IDQ0LjV0LTc5IDQydC0xMDUgMjkuNXQtMTMyLjUgMTJ6TTc2MiA3MDNoLTE1N2wxOTUgMjYxeiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUyMzE7IiBkPSJNNDc1IDEzMDBoMTUwcTEwMyAwIDE4OSAtODZ0ODYgLTE4OXYtNTAwcTAgLTQxIC00MiAtODN0LTgzIC00MmgtNDUwcS00MSAwIC04MyA0MnQtNDIgODN2NTAwcTAgMTAzIDg2IDE4OXQxODkgODZ6TTcwMCAzMDB2LTIyNXEwIC0yMSAtMjcgLTQ4dC00OCAtMjdoLTE1MHEtMjEgMCAtNDggMjd0LTI3IDQ4djIyNWgzMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTIzMjsiIGQ9Ik00NzUgMTMwMGg5NnEwIC0xNTAgODkuNSAtMjM5LjV0MjM5LjUgLTg5LjV2LTQ0NnEwIC00MSAtNDIgLTgzdC04MyAtNDJoLTQ1MHEtNDEgMCAtODMgNDJ0LTQyIDgzdjUwMHEwIDEwMyA4NiAxODl0MTg5IDg2ek03MDAgMzAwdi0yMjVxMCAtMjEgLTI3IC00OHQtNDggLTI3aC0xNTBxLTIxIDAgLTQ4IDI3dC0yNyA0OHYyMjVoMzAweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUyMzM7IiBkPSJNMTI5NCA3NjdsLTYzOCAtMjgzbC0zNzggMTcwbC03OCAtNjB2LTIyNGwxMDAgLTE1MHYtMTk5bC0xNTAgMTQ4bC0xNTAgLTE0OXYyMDBsMTAwIDE1MHYyNTBxMCA0IC0wLjUgMTAuNXQwIDkuNXQxIDh0MyA4dDYuNSA2bDQ3IDQwbC0xNDcgNjVsNjQyIDI4M3pNMTAwMCAzODBsLTM1MCAtMTY2bC0zNTAgMTY2djE0N2wzNTAgLTE2NWwzNTAgMTY1di0xNDd6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTIzNDsiIGQ9Ik0yNTAgODAwcTYyIDAgMTA2IC00NHQ0NCAtMTA2dC00NCAtMTA2dC0xMDYgLTQ0dC0xMDYgNDR0LTQ0IDEwNnQ0NCAxMDZ0MTA2IDQ0ek02NTAgODAwcTYyIDAgMTA2IC00NHQ0NCAtMTA2dC00NCAtMTA2dC0xMDYgLTQ0dC0xMDYgNDR0LTQ0IDEwNnQ0NCAxMDZ0MTA2IDQ0ek0xMDUwIDgwMHE2MiAwIDEwNiAtNDR0NDQgLTEwNnQtNDQgLTEwNnQtMTA2IC00NHQtMTA2IDQ0dC00NCAxMDZ0NDQgMTA2dDEwNiA0NHoiIC8%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjUwOyIgZD0iTTg2NSA1NjVsLTQ5NCAtNDk0cS0yMyAtMjMgLTQxIC0yM3EtMTQgMCAtMjIgMTMuNXQtOCAzOC41djEwMDBxMCAyNSA4IDM4LjV0MjIgMTMuNXExOCAwIDQxIC0yM2w0OTQgLTQ5NHExNCAtMTQgMTQgLTM1dC0xNCAtMzV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTI1MTsiIGQ9Ik0zMzUgNjM1bDQ5NCA0OTRxMjkgMjkgNTAgMjAuNXQyMSAtNDkuNXYtMTAwMHEwIC00MSAtMjEgLTQ5LjV0LTUwIDIwLjVsLTQ5NCA0OTRxLTE0IDE0IC0xNCAzNXQxNCAzNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjUyOyIgZD0iTTEwMCA5MDBoMTAwMHE0MSAwIDQ5LjUgLTIxdC0yMC41IC01MGwtNDk0IC00OTRxLTE0IC0xNCAtMzUgLTE0dC0zNSAxNGwtNDk0IDQ5NHEtMjkgMjkgLTIwLjUgNTB0NDkuNSAyMXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjUzOyIgZD0iTTYzNSA4NjVsNDk0IC00OTRxMjkgLTI5IDIwLjUgLTUwdC00OS41IC0yMWgtMTAwMHEtNDEgMCAtNDkuNSAyMXQyMC41IDUwbDQ5NCA0OTRxMTQgMTQgMzUgMTR0MzUgLTE0eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUyNTQ7IiBkPSJNNzAwIDc0MXYtMTgybC02OTIgLTMyM3YyMjFsNDEzIDE5M2wtNDEzIDE5M3YyMjF6TTEyMDAgMGgtODAwdjIwMGg4MDB2LTIwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjU1OyIgZD0iTTEyMDAgOTAwaC0yMDB2LTEwMGgyMDB2LTEwMGgtMzAwdjMwMGgyMDB2MTAwaC0yMDB2MTAwaDMwMHYtMzAwek0wIDcwMGg1MHEwIDIxIDQgMzd0OS41IDI2LjV0MTggMTcuNXQyMiAxMXQyOC41IDUuNXQzMSAydDM3IDAuNWgxMDB2LTU1MHEwIC0yMiAtMjUgLTM0LjV0LTUwIC0xMy41bC0yNSAtMnYtMTAwaDQwMHYxMDBxLTQgMCAtMTEgMC41dC0yNCAzdC0zMCA3dC0yNCAxNXQtMTEgMjQuNXY1NTBoMTAwcTI1IDAgMzcgLTAuNXQzMSAtMiB0MjguNSAtNS41dDIyIC0xMXQxOCAtMTcuNXQ5LjUgLTI2LjV0NCAtMzdoNTB2MzAwaC04MDB2LTMwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjU2OyIgZD0iTTgwMCA3MDBoLTUwcTAgMjEgLTQgMzd0LTkuNSAyNi41dC0xOCAxNy41dC0yMiAxMXQtMjguNSA1LjV0LTMxIDJ0LTM3IDAuNWgtMTAwdi01NTBxMCAtMjIgMjUgLTM0LjV0NTAgLTE0LjVsMjUgLTF2LTE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+
+
+<h1 class="title toc-ignore">Laundry, Energy & Time (ER&SS submission)</h1>
+<h4 class="author"><em>Ben Anderson (<a href="mailto:b.anderson@soton.ac.uk/@dataknut">b.anderson@soton.ac.uk/@dataknut</a>) [Energy & Climate Change, Faculty of Engineering & Environment, University of Southampton]</em></h4>
+<h4 class="date"><em>Last run at: 2016-08-01 12:14:07</em></h4>
+
+</div>
+
+<div id="TOC">
+<ul>
+<li><a href="#data-analysis-for-laundry-paper"><span class="toc-section-number">1</span> Data analysis for ‘Laundry’ paper:</a></li>
+<li><a href="#mtus-codes-of-interest"><span class="toc-section-number">2</span> MTUS codes of interest:</a></li>
+<li><a href="#analysing-loaded-data"><span class="toc-section-number">3</span> Analysing loaded data</a><ul>
+<li><a href="#efsfes-data"><span class="toc-section-number">3.1</span> EFS/FES data</a></li>
+<li><a href="#mtus-episodes"><span class="toc-section-number">3.2</span> MTUS episodes</a></li>
+<li><a href="#switch-to-survey-analysis-of-half-hour-sampled-data"><span class="toc-section-number">3.3</span> Switch to survey analysis of half hour sampled data</a></li>
+</ul></li>
+</ul>
+</div>
+
+<div id="data-analysis-for-laundry-paper" class="section level1">
+<h1><span class="header-section-number">1</span> Data analysis for ‘Laundry’ paper:</h1>
+<p>Use MTUS World 6 time-use data (UK subset) to examine: * distributions of laundry in 1985 & 2005 * changing laundry practice * data source: www.timeuse.org/mtus * data already in long format (but episodes) processed using DEMAND-BA-MTUS-W6-Laundry-Change-Over-Time-v2.0-adult-data-processing.R</p>
+<p>Uses FES/EFS/LCFS to examine: * historical ownership of washing machines/tumble dryers * Data source: <a href="http://discover.ukdataservice.ac.uk/series/?sn=200016" class="uri">http://discover.ukdataservice.ac.uk/series/?sn=200016</a></p>
+<p>Uses SPRG water practices survey: * reported laundry practices * data source: <a href="http://www.sprg.ac.uk/projects-fellowships/patterns-of-water" class="uri">http://www.sprg.ac.uk/projects-fellowships/patterns-of-water</a></p>
+<p>This work was funded by RCUK through the End User Energy Demand Centres Programme via the “DEMAND: Dynamics of Energy, Mobility and Demand” Centre (www.demand.ac.uk, gow.epsrc.ac.uk/NGBOViewGrant.aspx?GrantRef=EP/K011723/1)</p>
+<blockquote>
+<p>This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License (<a href="http://choosealicense.com/licenses/gpl-2.0/" class="uri">http://choosealicense.com/licenses/gpl-2.0/</a>), or (at your option) any later version.</p>
+</blockquote>
+<blockquote>
+<p>This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.</p>
+</blockquote>
+</div>
+<div id="mtus-codes-of-interest" class="section level1">
+<h1><span class="header-section-number">2</span> MTUS codes of interest:</h1>
+<p>1983/4/7: Main/Sec21 Laundry, ironing, clothing repair * 0701 Wash clothes, hang out / bring in washing * 0702 Iron clothes * 0801 Repair, upkeep of clothes so may over-estimate laundry</p>
+<p>2005: Main/Sec21 Laundry, ironing, clothing repair * Pact=7 (washing clothes)</p>
+<pre><code>## [1] "Feedback: Loading ~/Data/Family Expenditure Survey/1985/stata8//hchars.dta"</code></pre>
+<pre><code>## [1] "Feedback: Loading ~/Data/Expenditure and Food Survey/2004-2005/stata/dvhh.dta"</code></pre>
+<pre><code>## [1] "Loading via gunzip -c ~/Data/MTUS/World_6/processed/MTUSW6UKsurveyCore_DT.csv.gz"</code></pre>
+<pre><code>## [1] "Feedback: Done loading TU survey data"</code></pre>
+<pre><code>## [1] "Loading via gunzip -c ~/Data/MTUS/World_6/processed/MTUSW6UKdiaryEps_DT.csv.gz"</code></pre>
+<pre><code>##
+Read 60.8% of 1364047 rows
+Read 1364047 rows and 17 (of 17) columns from 0.299 GB file in 00:00:03</code></pre>
+<pre><code>## [1] "Feedback: Done loading TU episodes data"</code></pre>
+<pre><code>## [1] "Loading via gunzip -c ~/Data/MTUS/World_6/processed/MTUSW6UKdiarySampled_DT.csv.gz"</code></pre>
+<pre><code>##
+Read 0.0% of 8164512 rows
+Read 9.8% of 8164512 rows
+Read 20.3% of 8164512 rows
+Read 30.1% of 8164512 rows
+Read 38.6% of 8164512 rows
+Read 46.5% of 8164512 rows
+Read 56.1% of 8164512 rows
+Read 65.4% of 8164512 rows
+Read 74.6% of 8164512 rows
+Read 81.9% of 8164512 rows
+Read 89.8% of 8164512 rows
+Read 98.1% of 8164512 rows
+Read 8164512 rows and 21 (of 21) columns from 1.412 GB file in 00:00:17</code></pre>
+<pre><code>## [1] "Feedback: Done loading TU sampled data"</code></pre>
+<pre><code>## [1] "Loading via gunzip -c ~/Data/MTUS/World_6/processed/MTUSW6UK_halfhours_laundry_DT.csv.gz"</code></pre>
+<pre><code>##
+Read 63.2% of 2721504 rows
+Read 2721504 rows and 20 (of 20) columns from 0.190 GB file in 00:00:03</code></pre>
+<pre><code>## [1] "Feedback: Done loading TU sampled laundry data aggregated to half hour level"</code></pre>
+</div>
+<div id="analysing-loaded-data" class="section level1">
+<h1><span class="header-section-number">3</span> Analysing loaded data</h1>
+<div id="efsfes-data" class="section level2">
+<h2><span class="header-section-number">3.1</span> EFS/FES data</h2>
+<pre><code>##
+## not recorded washing machine
+## 0.1699943 0.8300057</code></pre>
+<pre><code>## [1] "Feedback: Loading ~/Data/Family Expenditure Survey/1985/stata8//hchars.dta"</code></pre>
+<pre><code>##
+## not recorded washing machine
+## 0.1699943 0.8300057</code></pre>
+<pre><code>##
+## washing machine no washing machine present
+## 0.95013239 0.04986761</code></pre>
+<pre><code>##
+## tumble drier tumble drier not present
+## 0.5854663 0.4145337</code></pre>
+</div>
+<div id="mtus-episodes" class="section level2">
+<h2><span class="header-section-number">3.2</span> MTUS episodes</h2>
+<pre><code>## [1] "Feedback: Writing results to ~/Dropbox/Work/DraftPapers/The Time and Timing of Demand - Laundry/results/"</code></pre>
+<table>
+<caption>n episodes in total per year</caption>
+<thead>
+<tr class="header">
+<th align="left">ba_survey</th>
+<th align="right">N</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">1974</td>
+<td align="right">301586</td>
+</tr>
+<tr class="even">
+<td align="left">1985</td>
+<td align="right">474096</td>
+</tr>
+<tr class="odd">
+<td align="left">1995</td>
+<td align="right">28165</td>
+</tr>
+<tr class="even">
+<td align="left">2000</td>
+<td align="right">464164</td>
+</tr>
+<tr class="odd">
+<td align="left">2005</td>
+<td align="right">96036</td>
+</tr>
+</tbody>
+</table>
+<pre><code>## ba_survey
+## laundry_p 1974 1985 1995 2000 2005
+## 0 300350 466018 27647 454161 94660
+## 1 1236 8078 518 10003 1376</code></pre>
+<pre><code>## ba_survey
+## laundry_s 1974 1985 1995 2000 2005
+## 0 300936 471850 28165 462685 95898
+## 1 650 2246 0 1479 138</code></pre>
+<pre><code>## ba_survey
+## laundry_all 1974 1985 1995 2000 2005
+## 0 299700 463889 27647 452745 94522
+## 1 1886 10207 518 11419 1514</code></pre>
+<pre><code>## [1] "Feedback: # n episodes by duration - to show how recording period varies things"</code></pre>
+<pre><code>## ba_survey
+## time 1974 1985 1995 2000 2005
+## 5 0 5833 0 0 0
+## 10 29987 6853 0 186507 20482
+## 15 0 197190 5129 0 0
+## 20 17382 2673 0 74010 14714
+## 25 0 612 0 0 0
+## 30 146905 81790 7060 52123 17602
+## 35 0 285 0 0 0
+## 40 223 1023 0 29261 4769
+## 45 0 43099 1721 0 0
+## 50 4557 599 0 19905 3442
+## 55 0 124 0 0 0
+## 60 30764 27370 3857 16775 7463
+## 65 0 65 0 0 0
+## 70 20 246 0 9689 2002
+## 75 0 16343 577 0 0
+## 80 1845 227 0 6852 1422
+## 85 0 27 0 0 0
+## 90 11858 11943 1178 6088 2915
+## 95 0 17 0 0 0
+## 100 18 114 0 4333 1013
+## 105 0 9370 416 0 0
+## 110 1024 74 0 4138 963
+## 115 0 1 0 0 0
+## 120 7881 8490 1406 4479 2684
+## 125 0 3 0 0 0
+## 130 10 55 0 2893 711
+## 135 0 6413 319 0 0
+## 140 748 34 0 2422 624
+## 150 6754 5342 693 2754 1556
+## 155 0 1 0 0 0
+## 160 15 30 0 2054 525
+## 165 0 5456 244 0 0
+## 170 819 22 0 2087 558
+## 175 0 3 0 0 0
+## 180 7354 4919 963 3877 1826
+## 190 27 8 0 1702 517
+## 195 0 4256 191 0 0
+## 200 924 7 0 1732 399
+## 210 7437 3824 606 2274 1332
+## 220 21 4 0 1649 345
+## 225 0 4103 211 0 0
+## 230 811 1 0 1878 356
+## 240 7255 4178 815 2910 1339
+## 250 6 3 0 1750 373
+## 255 0 3372 156 0 0
+## 260 578 2 0 1570 313
+## 270 5926 2852 511 1999 979
+## 280 5 1 0 1466 273
+## 285 0 3105 150 0 0
+## 290 357 0 0 1483 346
+## 300 3931 3005 636 2437 1150
+## 310 2 1 0 1357 249
+## 315 0 2138 119 0 0
+## 320 193 1 0 1025 225
+## 330 1887 1549 308 1276 646
+## 340 1 1 0 756 159
+## 345 0 1230 78 0 0
+## 350 152 0 0 633 177
+## 360 986 1065 340 1741 598
+## 370 2 0 0 850 78
+## 375 0 598 42 0 0
+## 380 90 0 0 320 62
+## 390 494 343 88 399 178
+## 400 1 0 0 229 38
+## 405 0 276 21 0 0
+## 410 60 0 0 206 50
+## 420 323 226 84 325 154
+## 430 5 0 0 176 31
+## 435 0 149 12 0 0
+## 440 49 0 0 99 19
+## 450 240 119 37 146 49
+## 460 0 0 0 111 18
+## 465 0 94 13 0 0
+## 470 43 0 0 91 14
+## 480 386 194 40 175 74
+## 490 1 0 0 127 15
+## 495 0 103 10 0 0
+## 500 46 0 0 71 17
+## 510 372 77 21 97 35
+## 520 0 0 0 78 13
+## 525 0 83 6 0 0
+## 530 56 0 0 65 6
+## 540 258 88 21 110 36
+## 550 0 0 0 81 6
+## 555 0 70 8 0 0
+## 560 49 0 0 55 11
+## 570 116 45 12 41 19
+## 580 0 0 0 39 5
+## 585 0 31 6 0 0
+## 590 16 0 0 34 7
+## 600 83 36 23 60 14
+## 610 0 0 0 38 7
+## 615 0 28 0 0 0
+## 620 14 0 0 21 4
+## 630 52 18 5 28 8
+## 640 0 0 0 19 1
+## 645 0 17 3 0 0
+## 650 24 0 0 17 0
+## 660 36 18 5 21 3
+## 670 0 0 0 18 1
+## 675 0 17 1 0 0
+## 680 8 0 0 9 2
+## 690 26 9 5 9 3
+## 700 0 0 0 7 1
+## 705 0 9 0 0 0
+## 710 8 0 0 10 0
+## 720 25 9 6 27 4
+## 730 0 0 0 9 1
+## 735 0 9 1 0 0
+## 740 3 0 0 7 3
+## 750 2 8 1 5 1
+## 760 0 0 0 3 0
+## 765 0 4 2 0 0
+## 770 1 0 0 3 0
+## 780 11 5 1 11 0
+## 790 0 0 0 4 0
+## 795 0 1 0 0 0
+## 800 0 0 0 5 0
+## 810 2 5 2 0 1
+## 820 0 0 0 3 0
+## 825 0 2 0 0 0
+## 830 0 0 0 2 0
+## 840 5 1 0 3 0
+## 850 0 0 0 1 0
+## 855 0 2 0 0 0
+## 860 2 0 0 2 0
+## 870 3 3 1 1 0
+## 880 0 0 0 1 0
+## 885 0 2 0 0 0
+## 890 1 0 0 2 0
+## 900 0 4 1 1 0
+## 915 0 2 0 0 0
+## 930 2 2 1 1 0
+## 945 0 3 0 0 0
+## 960 2 10 1 1 0
+## 975 0 7 0 0 0
+## 980 1 0 0 0 0
+## 990 0 3 0 0 0
+## 1000 0 0 0 1 0
+## 1005 0 2 0 0 0
+## 1010 0 0 0 1 0
+## 1020 2 4 0 0 0
+## 1050 1 2 0 1 0
+## 1065 0 1 0 0 0
+## 1070 0 0 0 1 0
+## 1080 2 1 1 1 0
+## 1095 0 1 0 0 0
+## 1125 0 1 0 0 0
+## 1140 0 1 0 0 0
+## 1170 0 1 0 0 0</code></pre>
+<pre><code>## [1] "Feedback: # n episodes of laundry as a primary act by duration (to show how recording period varies things)"</code></pre>
+<pre><code>## ba_survey
+## time 1974 1985 1995 2000 2005
+## 5 0 73 0 0 0
+## 10 0 81 0 5347 324
+## 15 0 3865 91 0 0
+## 20 51 27 0 1815 209
+## 25 0 4 0 0 0
+## 30 859 1512 152 961 283
+## 35 0 3 0 0 0
+## 40 0 11 0 550 79
+## 45 0 893 39 0 0
+## 50 14 4 0 415 67
+## 55 0 0 0 0 0
+## 60 173 659 124 332 175
+## 65 0 0 0 0 0
+## 70 0 4 0 180 41
+## 75 0 371 15 0 0
+## 80 4 1 0 127 24
+## 85 0 0 0 0 0
+## 90 68 213 22 84 64
+## 95 0 0 0 0 0
+## 100 0 0 0 58 13
+## 105 0 144 3 0 0
+## 110 0 0 0 40 16
+## 115 0 0 0 0 0
+## 120 33 85 37 31 30
+## 125 0 0 0 0 0
+## 130 0 0 0 19 3
+## 135 0 52 3 0 0
+## 140 1 1 0 12 6
+## 150 12 24 8 9 14
+## 155 0 0 0 0 0
+## 160 0 0 0 8 3
+## 165 0 16 1 0 0
+## 170 1 0 0 3 5
+## 175 0 0 0 0 0
+## 180 6 14 8 5 8
+## 190 0 0 0 2 1
+## 195 0 7 2 0 0
+## 200 0 0 0 0 0
+## 210 5 4 8 1 7
+## 220 0 0 0 2 0
+## 225 0 4 0 0 0
+## 230 0 0 0 0 1
+## 240 3 3 4 0 1
+## 250 0 0 0 1 0
+## 255 0 2 0 0 0
+## 260 0 0 0 0 0
+## 270 2 0 1 1 1
+## 280 0 0 0 0 0
+## 285 0 0 0 0 0
+## 290 0 0 0 0 0
+## 300 4 1 0 0 0
+## 310 0 0 0 0 0
+## 315 0 0 0 0 0
+## 320 0 0 0 0 0
+## 330 0 0 0 0 0
+## 340 0 0 0 0 0
+## 345 0 0 0 0 0
+## 350 0 0 0 0 0
+## 360 0 0 0 0 0
+## 370 0 0 0 0 0
+## 375 0 0 0 0 0
+## 380 0 0 0 0 0
+## 390 0 0 0 0 0
+## 400 0 0 0 0 0
+## 405 0 0 0 0 0
+## 410 0 0 0 0 0
+## 420 0 0 0 0 0
+## 430 0 0 0 0 0
+## 435 0 0 0 0 0
+## 440 0 0 0 0 0
+## 450 0 0 0 0 1
+## 460 0 0 0 0 0
+## 465 0 0 0 0 0
+## 470 0 0 0 0 0
+## 480 0 0 0 0 0
+## 490 0 0 0 0 0
+## 495 0 0 0 0 0
+## 500 0 0 0 0 0
+## 510 0 0 0 0 0
+## 520 0 0 0 0 0
+## 525 0 0 0 0 0
+## 530 0 0 0 0 0
+## 540 0 0 0 0 0
+## 550 0 0 0 0 0
+## 555 0 0 0 0 0
+## 560 0 0 0 0 0
+## 570 0 0 0 0 0
+## 580 0 0 0 0 0
+## 585 0 0 0 0 0
+## 590 0 0 0 0 0
+## 600 0 0 0 0 0
+## 610 0 0 0 0 0
+## 615 0 0 0 0 0
+## 620 0 0 0 0 0
+## 630 0 0 0 0 0
+## 640 0 0 0 0 0
+## 645 0 0 0 0 0
+## 650 0 0 0 0 0
+## 660 0 0 0 0 0
+## 670 0 0 0 0 0
+## 675 0 0 0 0 0
+## 680 0 0 0 0 0
+## 690 0 0 0 0 0
+## 700 0 0 0 0 0
+## 705 0 0 0 0 0
+## 710 0 0 0 0 0
+## 720 0 0 0 0 0
+## 730 0 0 0 0 0
+## 735 0 0 0 0 0
+## 740 0 0 0 0 0
+## 750 0 0 0 0 0
+## 760 0 0 0 0 0
+## 765 0 0 0 0 0
+## 770 0 0 0 0 0
+## 780 0 0 0 0 0
+## 790 0 0 0 0 0
+## 795 0 0 0 0 0
+## 800 0 0 0 0 0
+## 810 0 0 0 0 0
+## 820 0 0 0 0 0
+## 825 0 0 0 0 0
+## 830 0 0 0 0 0
+## 840 0 0 0 0 0
+## 850 0 0 0 0 0
+## 855 0 0 0 0 0
+## 860 0 0 0 0 0
+## 870 0 0 0 0 0
+## 880 0 0 0 0 0
+## 885 0 0 0 0 0
+## 890 0 0 0 0 0
+## 900 0 0 0 0 0
+## 915 0 0 0 0 0
+## 930 0 0 0 0 0
+## 945 0 0 0 0 0
+## 960 0 0 0 0 0
+## 975 0 0 0 0 0
+## 980 0 0 0 0 0
+## 990 0 0 0 0 0
+## 1000 0 0 0 0 0
+## 1005 0 0 0 0 0
+## 1010 0 0 0 0 0
+## 1020 0 0 0 0 0
+## 1050 0 0 0 0 0
+## 1065 0 0 0 0 0
+## 1070 0 0 0 0 0
+## 1080 0 0 0 0 0
+## 1095 0 0 0 0 0
+## 1125 0 0 0 0 0
+## 1140 0 0 0 0 0
+## 1170 0 0 0 0 0</code></pre>
+<pre><code>## [1] "% episodes that are laundry_all in 1985 = 2.15293948904863"</code></pre>
+<pre><code>## [1] "% episodes that are laundry_all in 2005 = 1.57649214877754"</code></pre>
+<pre><code>## [1] "Feedback: Plotting 1985 all episodes start"</code></pre>
+<p><img 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qmHsA8BBBBAAAEEEEAgHQQybAD61FNPpQMnh0QAAQQQQAABBBAIJxDzQ/A+n8927NjhUjCFu1j2I4AAAggggAACCKS/QEwGoEoW36dPH1eSUwuRChcu7Fa+K89jlSpVrHfv3rZ///701+UMEEAAAQQQQAABBBIIxNwQ/IYNG6xevXqWJUsWa9eunZUpU8YKFCjg7isR/bp162zixIk2adIkl6qpbNmyCS6aDQgggAACCCCAAALpJxBzAagq2ZQqVcpVOUqsHvyAAQOsadOmNmbMGOvXr1/66XJkBBBAAAEEEEAAgQQCMTcEv3TpUldeMbHgU1eYLVs269Spk02dOjXBBbMBAQQQQAABBBBAIH0FYi4AVR34efPmhVWbNWuWFS9ePOzjeAACCCCAAAIIIIBAZAVibgi+ffv2piB0+/bt1qFDB9McT5XeVN1vzQFdv369jR071qZMmeKG6SPLydEQQAABBBBAAAEEwgnEXABatWpVW7ZsmXXt2tVVOjp+/HiCa2zUqJFNnz7dGjRokGAfGxBAAAEEEEAAAQTSVyDmAlBxqfb7V199ZUePHrWNGze6Xs9jx45ZsWLFrESJEq5HNH1ZOToCCCCAAAIIIIBAYgIxGYAGLkY5QBWM6ouGAAIIIIAAAgggEBsCMbcIKTZYOUsEEEAAAQQQQACBxAQIQBOTYTsCCCCAAAIIIIBAmggQgKYJKy+KAAIIIIAAAgggkJgAAWhiMmxHAAEEEEAAAQQQSBMBAtA0YeVFEUAAAQQQQAABBBITIABNTIbtCCCAAAIIIIAAAmkiQACaJqy8KAIIIIAAAggggEBiAgSgicmwHQEEEEAAAQQQQCBNBAhA04SVF0UAAQQQQAABBBBITIAANDEZtiOAAAIIIIAAAgikiQABaJqw8qIIIIAAAggggAACiQkkOwD1+Xxxr/Hnn3/ap59+alu3bo3bxg0EEEAAAQQQQAABBJIjkKwAdMSIEVahQgX3emvWrLHzzz/fWrVqZSVLlrSpU6cm5zg8BgEEEEAAAQQQQAABJxA2AF29erX17NnTGjRoYOoFHTx4sGXNmtXmz59v3bp1s9tvvx1KBBBAAAEEEEimwLFjx2zhwoU2adIk+/HHH033aQhkNoGs4S54wYIFVrZsWRs5cqR76Oeff25t2rSx2rVrW6lSpWzYsGG2bds2K1q0aLiXYj8CCCCAAAKZWmDXrl3WuHFjO3LkiJUvX97mzp1rO3bssIMHD1rOnDkztQ0Xn7kEwvaA6j9LoUKFnMqyZctsy5YtdtVVV7n7+g+k3tBcuXJlLjWuFgEEEEAAgRMUOHDggBUsWNCqVatmv/76q+sB1ZqKevXqWeXKlU37aQhkFoGwAWiVKlVs0aJF9vXXX9sLL7zgPqHp09vff/9t/fv3t1q1atkZZ5yRWby4TgQQQAABBFIk8NZbb1nNmjVt1KhR8Z6vXlCts/jggw/ibecOAhlZIOwQfMOGDa158+Z22WWXOYfhw4dbnjx5rHPnzqbh+Ndffz0j+3BtCCCAAAIIpIrA0qVLrWvXrp6vdfnll5v20xDILAJhA1BB6FPZ8uXLLW/evFaiRAlnowVIQ4cOpfczs7xTuE4EEEAAgZMS0Gjhzp07PV9j9+7d/D31lGFjRhUIOwSvC8+SJYtVrFjR8ufPb5oHun//frvwwgv5z5JR3xVcFwIIIIBAqgs0a9bMXnnlFbfgKPjF9+zZY0899ZQ1bdo0eDO3EcjQAskKQDdt2mTXXnutnX766aY5oeoN7dOnj91///0J/iNlaC0uDgEEEEAAgRQKNGrUyDp16mS5c+e2adOm2fr1623y5Mmuc+e5556zOnXqpPCVeRoCsScQdgj+6NGj1rJlS/vnn39syJAh9uijj7qrrF+/vnXp0sUtRmIeaOz94DljBBBAAIHIC/Tr188VcVGP5+bNm93t9957zzp06BD5k+GICKSjQNgAdObMme4/iRLSaw5o37593em2bdvWDcHfcsstLkG9hulpCCCAAAIIIJC0wG233Wb6oiGQmQXCDsH/9ttvbv6ngs/QdvHFF7t68BpGoCGAAAIIIIAAAgggkByBsAFo6dKlbd68ea5SQ+gLjh8/3iWiL168eOgu7iOAAAIIIIAAAggg4CkQdgheucmUekmr9+677z47fvy4qVdUOUBVnrN9+/Z22mmneb44GxFAIGMLqIa1qqWdeeaZli1btkQv9pNPPrFx48a5sr36UNujRw+rXr16oo9nBwIIIIBAxhYI2wOqle8ff/yx/fvvvy7YVAqmjh072jPPPOPKh7300ksZW4irQwCBBAL6fXD33XdbgQIFXFlB5TccNGhQgsdpgyqmtWvXzlq0aOFulylTxmrUqGHffPON5+PZiAACCCCQ8QXC9oCKQDVqVY5z8eLFrvdTPZ7KC3rBBRdkfCGuEAEEEgho/rdW8G7YsMEFoUqirV7Q6dOn24wZM1zuYD3piy++sMcee8zlDlbqGTXVvb7yyitdzsOffvrJzj77bLedfxBAAAEEMo9A2B7QAMUpp5xi+qOjIXflBCX4DMjwHYHMJaDh9CVLltj27dtd8KmrV0+oz+czJdT+9NNP40A0evLqq6+6vIdxG/03LrnkEmvTpo19+eWXwZu5jQACCCCQSQQ8e0BVelPzO5PbZs2aldyH8jgEEIhxgTlz5ricwF6Xoek52t+qVSu3e8eOHS7PoddjtXjxzz//9NrFNgQQQACBDC7g2QOaK1cuK1KkSNyX/ojMnj3b9XCcf/75Lh+ohuS1LVAbPoM7cXkIIPD/AurpTCzvb+j2cuXKuSwaXngTJ040/T6hIYAAAghkPgHPHtDmzZubvtS2bt1qlSpVstBKDVr92q1bN1eWM/OxccUIZF6BSy+91AYPHmw9e/ZMgHDvvffapEmT4rbfeeeddt5557kpOzfddFPcdk3jWbFihVuYFLeRGwgggAACmUbAMwANvnrVqy1YsKCb+xm8XSlXVJazVKlSLrVK0aJFg3dzGwEEMqhA69atbdiwYa4XdO/eva4imr5r+4UXXujmdgYuXT2gK1eutPLly9vYsWPdd42e/PXXX26+aM6cOQMP5TsCCCCAQCYSCBuAKr3Kli1b3B+L/Pnzx6P5/vvvTX9AvKokxXsgdxBAICYEDh8+bCq/u2bNGitWrJgpD7BWtwc3LUj8+uuvXe3qwoULW6FChUyr4FXLWqvgQ5uG2Xfu3GlfffWVW7jUqFEj01eOHDlCH8p9BBBAAIFMIhA2AG3cuLHLAdqlSxfr16+fqUfj0KFDbl5Xnz593FA9vRiZ5N3CZWZoAfVi1q1b1/Lly2cXXXSRS6F03XXX2erVq93/+9CLV4/mW2+95aqkKQhNqiCFgli9Fg0BBBBAAAEJhA1A8+TJY99++60bXlM+0OCm5NLvvPNO8CZuI4BADAqo51OBp4LECRMmxF2B/n9rDqeqn5177rlx2wM3FHRSijegkT7f169f7+bob9y40WUcuP76693PLH3OhqMigAACyRPwXAUf+tRq1arZqlWrbOHChTZq1Ci3yEAJqJWuid7PUC3uIxB7Ap999plVqFAhXvCpq7jllltMIx0nkpYt9q4+ds9Y82s171Y5WZWnWWmtNOXhu+++i92L4swRQCBTCITtAQ0oZM+e3aVlUh3ns846i/RLARi+I5ABBDTMrpXpXk2Vi15//XWvXWxLR4E//vjDZRdQj3Xw9Abd1s/s559/dh8q0vEUOTQCCCCQqECyekDXrl3rFiOoZJ4WJagKkobm77vvPpcbNNFXZwcCCMSEgOZwbtq0yfNctV37adEloDyqN9xwQ7zgU2dYv359e+ihh2zMmDHRdcKcDQIIIBAkEDYAPXr0qKtqonlGGor75ptvbP78+fbEE0/Y6NGj7eGHHw56ufS9+e+//7r61Ol7FhwdgdgTaNKkials5oIFC+Kd/L59++yuu+5yc8Dj7eBOugvog0HNmjU9z0PTKTZv3uy5j41pI6BRhAcffNB9IFA+XGWJoSGAQOICYQNQpWTRUI7miD377LPu03Xt2rVdMPrII4+4VbCJv3za7Nm2bZsNHz7crcrXHCi1vn37mnKRqjKT0scE16NOm7PgVRHIOALnnHOOC0BVo71///5u4eG7777rarw/88wzcYUpMs4Vx/6V6Hfd8uXLPS9E8/WpUudJkyYb9TdSc2+zZs1qWgSm9GT6O/nhhx+myfF4UQQyhIC/rF6SbciQIT5/ShbPx/gnvvv8CD7/EL3n/rTY6K/M5CtTpozP/x/d5y8Z6vPnJvX556f5/DkFff48hL6hQ4e68z311FN9/l/CaXEK7jVPP/10d9w0OwAvjEA6COj/zM033+zzD+P6/H9IfR999FE6nAWHTI6Afw6ozz81wjdu3Lh4D58zZ477vfzrr7/G286dtBHQz0F/B/2dHvEO8Pvvv7vt/hHDeNtP9I5e54UXXvD5e1fd35w9e/Z4voR/BNA3aNAgX4MGDXz+hcO+jh07+vzVxjwfy8aML+DPWuRbunRpVF9o2EVISrHivwjThPeSJUvGC7q/+OIL8wd6rucx3o40vOP/j+gqr2gVvhZG9ejRw+644w43JUB5StVUIlCfPlUuMDilTBqeFi+NQIYQUP5PUqvFxo9SPZxz58511aX0u1hzPzUMrAVjGv7VMDwt7QUmT57sht1btmwZ72Bly5a1559/3t5//3339yjezmTe0ZS3Nm3auLm+GqXQqIT+3inrgXpZg1vDhg3dPO7XXnvNzdlW76vWa2haTWJTNYKfz20EIi0Qdgj+qquucpVQlIhegeiRI0fs77//Nv2nU8Cn8nuRTMX0ww8/WKdOndwwu5JbaxqAmoY9gpvSx6jWNA0BBBCINQGVK/X3RFudOnXsmmuucXk+va5Bw776MK5gU7+fVa1u8eLFVqtWLa+Hsy0NBDQXNzRHduAwCkITW9wXeExi33/88Ufz92bal19+aS+//LI98MADbg2G5pkqoNT87EB77rnn3D5VMFOVsSpVqpimznz++eem+d0qMkFDINoEwvaAqhSnFif4u/NN+UCzZMkSt/Jdb/QRI0ZE9Jr0H+unn36KO2bFihVdT2doOdAlS5Yk+IQY9yRuIIAAAlEqoN5LjeCo9+zuu+92AYyKfigo9U8xSnDWyk4S+CCeYCcb0lygVKlSNmXKFM/jqAdTqQtT0l599VXXyaP8rsFNweayZctcJ5CyIKipQ0jrNUJb8+bNXVWzefPmWbNmzUJ3cx+BdBUIG4Dq7PTLUJOs1fuoRT+q4axP3OnRra+eTg01+Cc22FNPPeWmBdx///1xiPq0qf+gb7/9tuk/MA0BBBCIFYEtW7a437ezZ8+2yy67LO60NfKkVFj6ndu+ffu47dxIfwGNAmoRrP4eKTtMoE2fPt38aygSXSgWeFxi31XZKji/a/Dj1BmkzDSB5p8XmmiHi4bqtZ+GQLQJhB2CD5ywhtn1C/HOO+90n6T8i4FMX5FuqlWtCkwKhlUeMLSpPvUrr7xi3bt3t1tvvTV0N/cRQACBqBVQL1bbtm3jBZ86WZU81WgT2T2i70dXoEABVyVQQajyZCtbzO23327+RbFuOoTmYaakaf2FfwGS51M13SI4y4GqYc2aNSvBY9VRozmolSpVSrCPDQikt0CyAlD94gtMaNccE807atWqlet9nDp1asSvQcfWEISqfYQ2TRVQL4KGrzRdgIYAAgjEisCuXbtMQ+peTenldu7c6bWLbeksoOqAu3fvdouFtEaiatWqLvisXr16is9MAaymYIR29Lz11ltuXqjmdgaaisIo92jwVAAFnxqt1PuJADQgxfdoEgg7BK9VlVpVrkVIekNrZblynSkZ/fjx490nPa2Qj3TT6nt96Zz0S1m39UlUnxppCCCAQCwKlC9f3o3weJ27ej+1nxadAloAphXqqdWuuOIKV+xFHzwGDhxommuqFe2aB6ye0YIFC8YdStkrNEdY37U2Q89RhgTdptc8jokbUSYQtgdUb3it5Bs5cqTrUdSqOqWF0LxQlXvTnEslho9kU4UPVWXSf0gNTWmOi1bEayGSFin17t3b9u/fH8lT4lgIIIDASQso6NB8T61+D27+fKz20ksvWa9evYI3czuDC2gamQJJpV1SIJk7d25TaWz9TQ5tNWrUcL2wSk145ZVX2qRJk0zpudRhREMgGgXCvjM1JBSoA61hbw1vKzWTmn5R6s3tTwgfsWtTyhENvWt4XStD/UnpXc+n7msIZN26daYayfrP99VXX3n+R43YyXIgBBBA4AQE9IH622+/dYGGhnW1wEUf8rUyXvP+zj333BN4NR6aEQT0985rupnXtakXtkWLFl672IZA1AmEDUDVo6h6719//bUbDtBipMaNG7tcoCrZp3xzStUUqaYpAOr51GR9JaL3agMGDLCmTZvamDFjXBoLr8ewDQEEEIhGAX2gP3r0qPudq4WW6hXVvD+N8tAQQACBjCIQNgBVyiPlEtMKeDXVYM+TJ4917tzZJblV1Y1INvUCKMl8YsGnziVbtmzWyZ+sPlAvPpLnx7EQQACBkxXQ7zDN39MXDQEEEMiIAmEDUF200h4tX77czbEMpH7o1q2bmwwdyd5PnYvmRimprtJcJNWUkiIlC5LU06v5ruHa4cOH7c8//wz3MPYjgEAKBI4fP27+2tZuceEpp4Sdqp6CI/AUBBBAAIH0FEhWAKr5lao4FNw04Tk9mpIwKwjVpGylqdBkbA1N6Y+U5oAqOa9ygSodhVdliHDnrJQVKn0Xrqk6VCTnvoY7H/YjkBEEtHhQK4lVfvDgwYNuzqNK/l577bUZ4fK4BgQQQACB/xfwDECV5F09iOplVAWFDz/8MEkwrYaPVFN+NS2G6tq1qxuKV09JaNOwlapQqI7uiTaVTUtO6TTZnH766Sf68jweAQQSEdC8R03vUX5D5RvWggqtANb/Y1WUUZ5DGgIIIIBAxhBINABVOUutwFSPom4n1SIZgOo8tBJUK9z1B0vlynSOx44dc7nPNEWAyfpJ/bTYh0B0CqiU4TnnnON6PwNneOmll7rMG6omo7KEym9IQwABBBCIfQHPAPSee+4xfamp6tFff/0VlVeqlCUKRr1Sk+zbt8+liNKqfRoCCES/gOqfv/vuuwlOVOmIlNfwm2++cZVmEjyADQgggAACMSdwQrP7lfdzyZIllh6Vj05UVnM5tVqehgACsSGgEY3EsltoVfg///wTGxfCWSKAAAIIhBVIVgC6Y8cOa9asmZvzqNq2Cu5UfUjzsqK1de/enYS80frD4bwQ8BCoW7euy90buuvQoUP2/vvvJ6gOdODAATdSo1EaZbxo1aqVrVy5MvTp3EcAAQQQiEIBzyH44PNUr8TFF19sOXLkcCUutcBHq+JVC/7JJ590K8+ffvrp4KdExW3NJ6MhgEDsCKi8ruZ4KpuFMlno94w+/OrDrlbGq+pZoKk3VIsAVZVNZSrz5ctno0aNMs0VVdUgFcigIYAAAghEr0DYADRQh1arUoMXAKg6h1asqhqS0qSkV64+n89nO3fudPkCCxQoEL3SnBkCCCQpoLmeyrpRoUIFt5CwaNGiLt2aFjkOHDgw3nM1R12LDadOnRq3/YknnjBVblMpQpXsZf53HI27od+VWlD63nvvuYVdGsnq2bOnqd44DQEEEIi0QNgh+NWrV7tqHMHBZ+Akb7jhBhf8qVZxJNvmzZtNvSWl/CU5tRBJPST6Y5Q3b173B6h3796mfII0BBCILQH9H1Zmi19//dUmTZrk5puHBp+6ImXBUEGK0KYcvgqsfvrpp9Bdmf6+cqk+88wzbprD2rVr3RQqVbQbNGhQprcBAAEEIi8QtgdUaVDUs6BfWMFDYDrV0aNHuyBQv/Aj1dSzUa9ePTc8165dO3dO6vnUcJ2G7tatW2cTJ050f7z0R0qJ6mkIIBA7AqeeeqqpN1RfiTVludCwu1dT/lA+gMaXmTBhgpuqoOpSgdEqlVneu3evlStXznRbU61oCCCAQKQEwgagCu4U8KkSkoJRJYnW3KsFCxa4lClt2rSJ9wlawzmFChVKs/MfPHiwC3pV5SixFbMDBgywpk2buk/6mh5AQwCBjCWgxZCffvqpmxsafGUKTPW7QcPMtP8EZPLqq6/GBZ+BPSqlrN/ZKtxBABpQ4TsCCERCIGwAqqpDqoqkoW5N7tdXoGlhkkpe6ivQrr766jQNQJcuXerSKyUWfOo8lLKlU6dONnz4cDc/NXBufEcAgYwh8Nhjj5lWzauXVHM+1bQqXgGVhpWLFCmSMS40la5CPcKy8Wqayy87WkKBzz//3NTpsWLFClNnjKpx3XnnnW7ELeGj2YIAAiciEDYA1SpTDdNES1MdeM39UinMpJqCZqVmoSGAQMYT0O+BH3/80dQTqp47DbsvXLjQlH5t2LBhGe+CT/KKZPTZZ59Z+/btE7zS888/76ZTJdiRyTd8/PHHphE+ZVnQ6N/WrVutUqVKrmNDHTNZs4b985nJBbl8BJIWOKH/QfqUrNXwmguqXsakeiGTPmzK9+oXqP74bN++3Tp06ODmeGoBkuY1aQ6oynIqhYt6ZTXsREMAgYwpUK1aNTfXc/HixaZcoZUrV05y3mjGVEjeVXXp0sXUa6ypSV9++aV70vHjx+3CCy90HQwtW7ZM3gtlkkdpYa2CT+WVVZ5ZNf2dUSYBve/efvttu+222zKJBpeJQNoIJCsA1X9GDT1oVaqa5n8qMbQ+ASrfZq5cudLm7DxetWrVqqZPn127dnVD8folGtqUq1Rzmho0aBC6i/sIIJCBBHLnzu16pzLQJaXJpSi7gHKqFixY0GUNUbCuTAHqGVVPshZ+0f4TUNlXZQ0IBJ//7TF79tln3boHAtBglf/dPnjwoOtNV/pGdVJpBFUdRYGFbwmfwZbMLBA2AFUien06VuJnVT569NFHnVf9+vVNn6r//vtve/311yNqqNrvWuGuc1PKFvV6Hjt2zOUpLVGihPukGtET4mAIIIBAlAsoWNciLWU0UadCKX8aO30RHCT8wWnOrIJ2r6a5tMyZTSijGOGcc85xRSA6duzo/j7ffPPNrpNq+fLlLiBN+Cy2ZGaBsAGohrGVd1P5QPUfsm/fvs6rbdu2blK76q1rWEJpkCLdtDBKwai+aAgggAACSQto1Oq8885zX0k/MnPvveiii1zxA42whQboI0aMsJo1a2ZuoJCrVwygTilZffHFF3F7FYAqzdfIkSPd/Oy4HTF4Q9P6xo0b54o4KNDu1q2b1ahRIwavJHpOOWwi+t9++82lYPL6NKjhG03MVg8kDQEEEEAAgYwgoMVtmuupqQkaaQu0F154waX4CowEBrZn9u/qWdfUOK+iNFo0rHSIClJjtb300kumDD+XX365Pf74466nVx9SNNWAlnKBsD2gpUuXdqvONX8oNL/n+PHj3TxQVpun/AfAMxFAIDIC+gOooVUNFeoDdWjPVmTOgqPEioAKmijg0GJblZ7etm2bHT582C3EJc1X/J+ibFSQRvM+Q5sqFR45csQtFIzkepHQ80jp/RkzZrg1MLt27XKpuPQ6l112mcuJ3qxZM1u0aJEpTqKduEDYAFT/ATWvUtD33XefaUhCvaLKj6Zuda1K11A4DQEEEIhWgSVLlrik9VrVrF4tLcZRdSCG0KL1J5b+56UPKHPmzHFDrnrfKNWXCrLw9y7hz0aluv/44w+3JiQ03+zvv//uFiqnR9achGd64lu0+Hro0KFxwWfgFWrVqmWqxjh58mS75557Apv5fgICYYfgVfVI+dBUwk3BpnoQNMFYNYVVIUld04G2Z88e97jAfb4jgAAC6S2gaUIaUlXFHy2a1O8pVUvTEJo+SNMQSEpAwZU6YjQkT/DpLaU4Qavd9f8seKhd0xc0B1RZdGI104JGfxMrN67RX+2npUwgbACql1XKDnUz//DDDy7H5ocffmha1abANLgesyqTaDsNAQQQiBYB/WHUnL277747brHkdddd5yq8KXG9PlzTEEDg5AS0OEsjC+o51jzJhx56yMqXL+9GHh5++OGTe/F0fHbZsmVt/vz5nmfwySefsAjaUyZ5G8MOwQdeRm8qLTrSFw0BBBCIBQHN9/zuu+9s6tSpCU63YcOG7o/lunXr+COSQMdcT5Z6sLTARB0NVP7xQGJTnIB6OFWqWykStfBI8z2VL/ySSy6Je0ws3lDpVQWhqoKlVf2BplzkyqHbunXrwCa+n6BAsgPQE3xdHo4AAghEhYA+PCeWJi6pfVFx8ul0EpqqoBR7Kuih/KGa+//II4+4dQDpdEocNkYEtGBLXxmlqfKj0lAqfZkWXmsesEaElZ5SC5M0/YCWMoFkDcGn7KV5FgIIIJC+Auq1U87G1157LcGJqCSl0sYopx/tPwGtWFaWABX32Llzp/3555+u+t2gQYPsgQce+O+B3IppAc3V1M9YK/uD523G9EWl0clrHqvmet50002u3K8WHS1dujTBwqQ0OnyGfVl6QDPsj5YLQwABCSh3o1a7q1dPc9HUGzpt2jSX2UNDhQwtx3+fPP/88663JzihuIYg16xZ44YiA0OS8Z/FvVgS0Aevzp07u6FyzYFWyVHN4dQ6Dpq3gOa33njjjd472ZoiAXpAU8TGkxBAIFYEtDJXJXvfeecdN5fxzDPPtJ49e9pnn31mderUiZXLiNh5zp49215++eUEx9NQfPPmzU37abEroA9iJUuWdEPJGkJWD+gTTzzhstp88MEHsXthnHnMCdADGnM/Mk4YAQROVEB/cJXLMZAqrkCBAiSiTwRRPcSJzZlNbHsiL8XmKBS46667rGXLljZkyJC4s1Np7cWLF9tVV13l9uXIkSNuHzcQSCsBekDTSpbXRQCBqBJQ8KRk4oFUMVF1clF0Msrv/MYbbyQ4o4MHD9qbb77pesoS7GRDzAgouf4rr7yS4Hw1UqAFNz/99FOCfWxAIC0ECEDTQpXXRAABBGJUQEnDNRRboUKFuDroqnKjIfgePXq43I4xemmctl9Acz4TS6ivrBCZKS/uc8895+Y1q0qTgm8V2KFFTiBsAKohq3HjxrlaruFOa8yYMe6HGO5x7EcAgcgKKB+mVjRrhTMrXiNrH2tH0wp4vU/UU6zbmr5QtWpVe/LJJ+NVvou16+J8/yegec+jRo1KwKES28rjqXyXmaFpnrOS5X/00UeuwqOqoimBvio90iIjEHYOqOqc6geiT8CqCZ9Uo65yUjrsQyB9BDTX68UXX3RBxYEDB9zCG/2/TqwXJH3OkqNGk4DeG19//bUdOnTI/vrrLytcuLBly5Ytmk6Rc0mhQP/+/V3vtj5k6EOFmioYKr+lAtM8efK4bRn5HyXLVxU0LcAK1KjX9av3VymXFIy2aNEiIxNExbWF7QHVp2C1vXv3RsUJcxIIIJB8gQkTJrjk4aoEpHyOu3fvdn9oVMNYt2kIJCagObOqZqP3CsFnYkqxt/2CCy6wLVu2uHmg+mChFFuqda80TErNlBnajBkzTIF4IPgMXLOmICgw1f6Taaq+pt7VO+64w4YOHeo+xJ3M62XU54btAdXQS/v27e3SSy91Jaf0Zg39od13330Z1YfrQiBmBZTr74YbbnDVOrTqW03/d/ULUcPxyo+pX8I0BBDIXAJnnXWWbdu2zbZv3+56uc8+++xM9SFDPftFihTx/KHnzJnTtOAupU29yL169XJFG9Sbqk4A3VfQL3fafwJhA9AlS5bYJ5984p6huaBejQDUS4VtCKSvwDfffOMSJweCz+CzUSoWqtoEi/x3W3Nk1WM8d+5cl45IvUMZqbTgf1fKrcwsoNrtxYoVy5QEqk+vjA4KDEPbgAEDrG/fvqGbk3VfxRtuu+0227p1qxUtWtQ9p0uXLvboo49a/fr1XYYBLeaj/U8g7BB8kyZNTPPGkvoCEwEEok9AgVRieRs11ERLKCAzJVtXYm7NhVNvSKNGjdx8MBZvJfRiCwKxKKC8p5rbrDrugVX/x48ft8suu8wVrVDJzZS00aNHu3m0geAz8BoaadIaGoo4BET+9z3Rv0KaL9ahQweHpmH33r17u5Vi8Z/OPQQQiFYBldWbPn26K0EZeo4atdAnclp8geuuu86mTJliP/zwgyvb+dhjj7k/UNqmMp40BBCIfQEtslu4cKGLb5R0v3bt2m6hnW7v378/xdMRNm/ebJUrV/YEUloz7af9J+A5BK9PAprzuWHDBjc5WSvgtYpW3crvv//+f8/mFgIIRK1AqVKl3HCQUukoxcq5555rx44dcxPjlW5FK0Fp/wloXphSsmjBZXDPsXqLVcpTq2TVk6GhSxoCCMS2gP4fr1ixws3N1Hx59VBqjubJjA5pSsMvv/xiF110UQIcTenR6ArtPwHPAFRzx1atWmVK1dKsWTP3aAWg6gUdPHiwWxX530twCwEEolVg4MCBptrn6u3UL1wFV5r/pEVIWuFM+09AizIUtJ9xxhn/bfz/W1oJLj/V0VY1JRoCCMS+gD5o6v+2vlKjde3a1Zo2bermjCt/bqDdfffdLjDVED/tPwHPAPT33393f5wCwaceruF4BaBr1qxJtR/Wf6fBLQQQSCuB+++/33r27OmCTgVXmtd4Mp/y0+o80/t1lZJGU4/UEyqj4KaAXcn81ZtMQwABBLwEtGbmrbfeMmUVUBomBaHq+Zw1a5ZLe8eH/vhqnnNA9Sk/9BdtIB+oegloCCAQWwLK46jhJa3AJPj0/tnJRsmn1UMc2tq1a+fS0WEXKsN9BBAIFujUqZMtWrTIFfrQEL+mM65cuZKRk2Ck/7/t2QPqtdoz8ItX80NpCCCAQEYU0CrWQoUKuQ/gmnakObPapp6LV199NSNeMteEAAKpLKCqkFSGDI/qGYCGfxqPQAABBDKegFbB7tmzx959912bN2+eu0BlDFAPKA0BBBBAIPUEEg1Ad+3a5XJihR7q6aeftpEjR8bbPGfOnHj3uYMAAgjEqoAWG2kYTV80BBCILQGN0qq2vTJ/KB9ntWrVTB8sadEn4BmAakVYzZo14xK0Bk67Xr167mYgcWtgO98RQAABBBBAAIH0FDhy5Ii1bNnSBaCKYTT3UsGoSo5qkSEtugQ8A1DVj9YXDQEEEEAAAQQQiHYBdYxpIWHp0qVd3t5ALt9HHnnE1X1Xrs/USrcU7Raxcn6eq+DDnbw+TRw+fDjcw9iPAAIIIIBAVAn8/PPPLq3gBRdcYLVq1bJXXnnFWFwbVT+iFJ2MCmsoANXQeyD41Auptvs999xjL7/8copelyelnUCiAaiqH912222uBnzg8MpnpbKcmlehGqrt27d39VQD+/mOAAIIIIBAtAqsXr3alUpUucRJkybZkCFDXHCiAgRHjx6N1tPOkOelcreqDKSh8TJlyrgKbSfzM1i6dKkpEbxX03G0nxZdAp4B6I8//mjlypWz2bNnu8opOuXdu3db27ZtXeCpdCRKbq1PHApSaQgggAACCESzwMGDB+388893icL79Oljqs1dp04dU9pBpd567bXXovn0M9S5/frrr673WcVuVLpy+vTpNnPmTMuePburNpaSi1WRjb/++svzqVpU7VXhzPPBbIyYgGcAqrx3DRo0MFVEUm1TNdVIVhA6btw4u/POO+3ZZ5+1N954w23XD5eGAAIIIIBAtAosWbLEqlev7pndYNSoUfb6669H66lnqPPS9L0LL7zQxRIqUake0HPPPdfWrl1rrVq1sl69eqXoehs3bmwTJkywdevWxXu+pleokqNKZNKiSyDBIiT1bioAVf1opS8ItM2bN5uqqWjYPbTpU6R6RzXXgoYAAggggEC0CSi/q6qBeTVV+lMFQFraC2gObqVKlTwXOr/wwgvWsGHDFJ2EpgeqF1vD+WPGjHEViDZs2GCtW7d2ZTFJq5Yi1jR9UoIAVL2byutZoECBuEm7W7dutfLly9tLL71kXbp0iTuhb7/91r1Z1H2umqc0BBBAAAEEolFAQc/333/v5nqedtpp8U5R80HVK0dLe4EDBw4kWpZSJcD37duX4pO48cYb3WsPHjzY+vbt61a/P/roo6ZiErToE0gQgGr12E033eR6NIsUKeI+qQwfPtzVj1ZqJiVpVq6tL7/80pTeoGrVqnbOOedE35VxRggggAACCPy/wNlnn+2GYVVWVcPAWbP+78+fOlLuvfdeV78brLQX0AeBZcuWuYpj+fLli3fAt99+2y6++OJ42070TpMmTUxftOgXSBCA6pQ1V6J79+7Wv39/++eff9yq98mTJ8dN4tWnRc2p0MrBL774IvqvkjNEAAEEEMiwAlp8onRKKp+qqjfXXHON3Xzzza7jJPii33nnHQtMJ1N5VQ3Lq1d0xowZ1O4OhkrD25red8cdd7ieSq0f0Wir2rRp09ziZg3RhzalfnzrrbdcUnktJmrRooVdddVVoQ/jfowJeAag6uV8/vnn7ZlnnrFt27a5QDP4uho1amTz5893q9hOOcVzHVPww7mNAAIIIICAEzh27Jhb4KqRtPPOO8/UI+nVFJxoXcHXX3/tdl966aWmUtDK9RjcDh065IIYrU9QvkfdV3D51FNP2apVq9zahcDj9fdq1qxZLleksr3kyZPHateuneiQcOB5fE9dgeeee870PlAwqjmf+/fvd4uHNP0vdCrEzp073RQ/faBQz+aff/7pvj/xxBPWr1+/1D0xXi2iAp4BaOAM9ElSvZyBptVkWhmvN4vyqKV38Kn0GcEJZwPnqe/6JaSWM2dO951/EEAAAQTSV0C5pBUoKujU3xcNxb755pvx1hboDDVEroVBt9xyi40YMcL9rendu7dLA6gARGmT1FT9RqmVrrjiChs7dqzbpn/0t0GJ5rUoRUFpaFOaQX3R0k/gxRdfdNP49B7Q3+kqVaok+DCiv+P6WWsOpxYoBdrtt9/u3gv6AKPRWFqMCvj/oyZoCxYs8Pm7t33+H37cPv+nUJ+/xJXPf5nuy/+G8fk/gfj8QWncYyJ1wz+M4vMHxj7/RHLfJZdc4vPP4UlwaH86B5//U3CC7am1wZ+I3+dP25FaL8frIIAAAhlawF+X2/3t+OSTT+KuM7DNv8A1bps/qPTVr1/f56/lHbctcMMfePj8PWY+PUZty5YtPv+QrOffoY0bN/r8RVMCT+V7DAr4c437/D2inmeu99GVV17puY+NPp+/k9DnT74f1RSe4+c7duxw8zE0/1NNc2Y0p0Y9nk8++aTL33X11VfboEGD3DxR96AI/aO5OvpUrIVPDzzwgOlcNTRDma0I/QA4DAIIIBAioBzRWg+gPNHLly8P2fu/u8qwol4s/S0JNPVealRNQ+tKFK+mofnFixebektDm3pDFy5cGDfCpQwtyiHpNRKmut8axvf/BQ59Ge7HiIBiD+Vu9WrqwfZ/APHaxbYYEfAMQEPP/YMPPnA50lT5SKkNtBr+ww8/NM3B0FyOSNbR1ZCKJh9rrojmqKruq9IsaNGUJpjTEEAAAQQiJ6A5lcq9qEVACkIrVqzo5mBqeDy4LVq0yLNUovI3lihRwvy9oe7hCkRV6jk0VZJ2auW6Vk4HcnZqipj+Bii1T2hToKqOCgLQUJkTu79mzRr3oSF//vxuzqwSvvt7l0/sRVL4aP18EyuhqfeT3je02BVIVgCqlYL+IZEE6ZZUhlPzQVXBIFJNiWVV1zXQ9MlXk82VEkrzQlTOi4YAAghEQkALKfRhXKWJH3/8cVNKn8zUVF1I8y8///xzl5pP8zA1cjZx4kTXqxlsoYBSczu9mub6qQyjmnJB6rEq1xjaVqxY4YLNwBxQraC+7rrr3GKm4I6QvXv3mn96lqnkZnqvVQi9hli6r7/96mHW/ExVGFKPswrPKLBXAJjWTT9DLYq+9tpr4x1q06ZNbiTUa35vvAdyJ6oFkgxAA58yNWSirvDQplWEaposHqmm0qD6xB3alDJKPbN6o2pSMw0BBBBISwEFWipZrOpxKieoP5T6oK7FMpmlaUqWMqbougNNDgrMR44caeo9CzRZea1a1gibVqsHFgWpl1MBvVZDa8FRoGk4XfXbNfIVyOGpfSqhqeF2HVf23bp1c0GTjqVOElrKBNRz3KZNG7v11ltdJ496ntUzLVelRNKq9OCgP2VHSfpZqr44e/ZsCxQK0LQ/Tb1TQKwPO5oKSIthAa8Zqv5hFDdZ3N+76PN/+vFdf/31Pv8bwef/j+8efvToUd/QoUN9/v/0Pn93vNdLpNm28ePH+/y/fHz+Xyw+fwAc7zj+X3o+f7Ds8/9H8fmHdViEFE+HOwggkFoCWnzpH3b2+VMCxXtJ//Cx+905bNiweNsz6h39ffD3jHlenj948QUvOPL3WjmbZs2a+fzBpE9W/kDGbfvpp58SvIZ/vqj7Xe8v8+zzdyz4/L2dPn/Am+BxgQ3+HKA+fwUcn+x/+eWXwGa+p1BAf+f9mQp8/jm5nq+gBV76mUaiaUG0v7ym7+GHH/YNHDjQ5/V+icR5xNIxYmERUlav2FmfKtTdrrkX+tIwi2roBnoWVSpLlSM0OVgpNCLZ/MGwrV692i1+Ug634Hr1+lQ8YcIE02R3VVRIbPJyJM+XYyGAQMYT0BCgpgP5/0jHuzilk/nhhx9cNTnNS8/oTUnB1UupuXqhTUYaTg809VLqb0fHjh1dD6WG41X1RouNlNYvtCn1jka1lHNaTaWgNQKWWNPQsL5oqSOgObz+gMtzLq6OoCkTWjAWiaaUXarQSMtYAp4BqC5Rv1D05U9nFHfFGlZR02Rkzf9Q8Jce82s010rd8JqfEtr0n0LDAwpCVT2BhgACCKS2gILPunXrev7+09DxH3/8kdqHjMrXU9W8zp07m7/HMd75vffeey4Qv+iii+Jt1xCuv1c03rak7ijgDJ3/l9Tj2Zd6Agr6lEv1448/ttatW8d74e+++85loFF5UxoCKRVIcg5o6ItqPoaaUh8oAWx6BJ+Bc9J/Dv8QgPuEplRMSgMS3GrVqmUtW7YM3sRtBBBAIFUESpYs6eYter2YVmWrty+0Kd1Qjx493LxR/W5SRo9Yb5oDqrmwWgz6zTffuIVDzz77rOut0oIhBZy02BXQz1LzQL/88su4i9BIqHqalXIreC5u3AO4gUAyBRINQLW6Xb9cNOSt3Gvqildai0qVKrnUBxpaUZe4hlQi3bQgSqsb1UOr1ZJaAKCSXjonTU7WRHSdPw0BBBBICwGlf9GCGNWkDm5alKHfQcpVHNzUY6pFNgrIlPNS+zVKoxEm/W6N1aYARIGmFqb45+fZjTfe6IJQ/xw9K1++fKxeFuf9/wJKefj999+bf96u6w3V33+lYdIoIx08vE1OViCLJtWGvojmfij9gVa5K6ebhleU//P99993n+y1Kk6fgkaPHm3+CeJuFWLoa6TVff0ir1evnvvErXq/yj+nVBz6BK5eUM1dVQoQXZbylirHXFo0zT9VKTGlfqIhgEDmE1CWEH3oVaD14IMPug+9mqeo0ZfgspD6MKzfF/7FMS5fcUBKwapGcR566CFXajCwne8IRJuAermVqUDfVTxAI5C06BbQB2H/wi33gThaz9RzDujkyZPd4qOff/7ZfcrXJ6DatWu728EpkDTXqUuXLu4Xa6TekP5Vjq7nU/k+A3njQnEHDBhgmpskfK+0H6GP5z4CCCBwogJagKOFNPog7i9V7OpYqzCHPpQHN40caS5k6KIkTWHSoknNadeCGxoC0Sqgnm51RtEQSE0BzyF4fdLRHA8NMampN1SfetTjGNw0N0Sf4tUrGammVfnKP5ZY8Knz0FzVTp062dSpUyN1WhwHAQQyoYB+D911110u64amKoUGnyLRKvHERmI0V1T5LWkIIIBAZhPwDEC18lC1eFVNQk1ltzSxXglhg1ugUoXXhPvgx6XmbQXG/nxvYV9SPbUpOS9NP1CvRrgvDfF7zF4Ie148AAEEMpeAPrwvWLDA8/fFjBkzXEqizCXC1SKAAAL+0rpeCJpcrDlN+sV5+eWX25QpU9ykeZVb0ypOTaBXSS5VnFBZzEiudGzfvr3rnVWKpQ4dOrieBS1A0nCW5oCuX7/ezb/SOaekLKfmud5xxx1eLPG2KUDVsWgIIIBAUgLKcamsIUpZE5yeSQt1VEows5XvTMqKfQggkHkEPBch6fKVMkRBqHo+GzZsaJp7qXxgmvPpr2DhhDQvVAt+kkoOnBaUOreuXbvanDlzPEuBNWrUyNWG13mnVWMRUlrJ8roIZDwBJazXYkk1LeLUsLu/4pybA6r56jQEEEAgNQViYRFSogFoYhDq+VPlCvU66gJVfze9mn6pa3qAeiKVJF+BsNKj6NzSuhGAprUwr4+dQE/bAAA3WklEQVRAxhLQfHn97lRWEf3+0OiSfl/REEAAgdQWiIUA1HMIPhRCw90a3tacR6Ud0S/O9Aw8A+enHKD+WsTMoQqA8B0BBKJWQNOEGjRo4L6i9iQ5MQQQQCBCAp6LkALHVp5LfUJXrjqtiFcaBt1XKU6VR1NFpGhtSpB/6NChaD09zgsBBBBAAAEEMrmA4iitPXn99dddifPMxJFoAKr6vsqhqd5O5bZT3WNVHFL5rYEDB7o665pcr2Tv0dg04T+0Gkk0nifnhAACCCCAAAKZT0DrarTY+9NPP3XB58UXX+wWdisbT2ZonnNA165d64a1ly9fHq+cmkrHbdu2zT755BNn89hjj7kEzJrXFG3tiSeecKXvVC40LRpzQNNClddEAAEEEEAg4wt88803bjqOCv6oqI+apjlqeqPK9T766KMnhRALc0A9e0CnTZtmNWvWjBd8SqJjx46mfVqIpKaa66qStHPnTnc/mv556qmnXK36aDonzgUBBBBAAAEEEFCQOWrUqLjgUyIqKa4F1SrpmxnSPHoGoCoxp0T0oRWOlFdTXcNCUtu0aZOrSJQrVy53Pz3+0SeGHTt2uEVS6XF8jokAAggggAACCJyIwMqVK+2qq65K8BT1gNaqVcsChX4SPCADbfAMQJs0aeJy1rVo0cKGDx/uglHVKx46dKhdc801LujUMPz1119vekykA9DNmzdbnz59XE14rYTX3FSlXtIKfXU7q2d2//79GejHxKUggAACCCCAQEYRUMyigj5eTb2fkUgn6XXsSG7zTMOkC9fkWNVcV+WjQFPC5JEjR7q7Y8eOtSuvvNIeeuihwO6IfFevbL169VwvrGrTlylTxgXL6pVVqqh169a55PiTJk1yC6QSq8EckZPlIAgggAACCCCAQIiAOu9uvPFGV+wneNegQYNcruCqVasGb86Qtz0DUF2paq5rEZK6gRWlK9+myskFmlbDZ82a6NMDD0v176rIVKpUKVdmM3v27J6vP2DAAFOwPGbMGLeS3/NBbEQAAQQQQAABBNJB4Mknn3RlztV5NnnyZJfe8rPPPrNnn33WlezNkSNHOpxVZA/pOQQfOAUNb1erVs2aNWsWL/jU/vQIPnXcpUuXup7ZxIJPPSZbtmzWqVMnmzp1qu7SEEAAAQQQQACBqBHQXM9FixaZejw1vfG+++6zvXv3ujLomaVCWuS7ME/yx6+e2Xnz5tntt9+e5CvNmjXLihcvnuRj2IkAAggggAACCKSHgHo/H3jgAfeVHsdP72PGXADavn17Nz1A5UE7dOhgmuOpOasqc6c5oJq8q/mpU6ZMccP06Q3M8RFAAAEEEEAAAQTiC3gGoJpD+cwzz8R/ZBL3Dh48mMTe1N2libnLli2zrl27ukpHx48fT3CARo0a2fTp06m5nECGDQgggAACCCCAQPoLeAagCu6U83P27NnWrVs3t9I8/U/1vzPQgiiVAD169Kht3LjR9XoqeWuxYsVcrfrMkL7gPw1uIYAAAggggAACsSXgGYAqgFMPYsOGDW3hwoUuK78mzEZb0yIpBaP6oiGAAAIIIIAAAgjEhkCiq+C1yl1lojTcrZRLNAQQQAABBBBAAAEEUkPAswc08MLK+7lgwQK3wCewje8IIIAAAggggAACCJyMQJIBqF64cuXKJ/P6PBcBBBBAAAEEEEAAgXgCYQPQeI/mDgIIRI3AypUrbfHixZY7d25XnrZgwYJRc26cCAIIIIAAAkkJJCsA1R+5ESNG2KpVq2z//v0JXk/ViWgIIBAZAZ/PZzfddJOp2IIWCv7111/WunVrlxni8ssvj8xJcBQEEEAAAQROQiBsALp582YL/FFTfs0LL7zwJA7HUxFA4GQFVBpXZWaVeixQEveHH36wWrVq2YwZM0z/T2kIIIAAAghEs0DYAHTOnDl25MgR27JlixUoUCCar4VzQyDDC2zatMkFn4cOHYoLPnXRNWvWtPfee8+ee+45AtAM/y7gAhFAAIHYF0g0DVPg0vSHrkKFCgSfARC+I5COApoG06RJE8uRI0eCs7jyyivt119/TbCdDQgggAACCESbQNgA9IorrrDff//dtOCBhgAC6SuQJ08e27Ztm+dJbN++3fLmzeu5j40IIIAAAghEk4DnELyqH6kMZ6BVq1bN6tata23atLHSpUvHG/rTYx588MHAQ/mOAAJpKKC0aDt27HBFIrp06RLvSJUqVbL77rsv3jbuIIAAAgggEI0CngHo999/b/37909wvh988EGCbdpAAOrJwkYEUl1AQ+/Tpk1ziwGVfeK2226zvXv32osvvmglSpSwwYMHp/oxeUEEEEAAAQRSW8BzCL579+7uj5r+sCXnK7VPitdDAIHEBSpWrGgbNmywAwcOuHRMDz/8sFWpUsXWrFlD1bLE2diDAAIIIBBFAp49oFF0fpwKAgh4CJx99tk2evRojz1sQgABBBBAIPoFkhWAkog++n+QnCECCCCAAAIIIBArAmEDUBLRx8qPkvNEAAEEEEAAAQRiQyBsAEoi+tj4QXKWCCCAAAIIIIBArAh4LkIKPnkS0QdrcBsBBBBAAAEEEEDgZAXCBqAkoj9ZYp6PAAIIIIAAAgggECzgOQRPIvpgIm4jgAACCCCAAAIIpKaAZwBKIvrUJOa1EEAAAQQQQAABBIIFPANQJaLXFw0BBBBAAAEEEEAAgdQWCDsHdO7cudarVy/76aefUvvYvB4CCCCAAAIIIIBAJhQIG4Dmy5fP1Z6uWrWq6Wvo0KG2Y8eOTEjFJSOAAAIIIIAAAgikhkDYALRSpUq2fPly08KkSy+91Pr372/Fixe3Vq1a2ccff2zHjh1LjfPgNRBAAAEEEEAAAQQyiUDYADTgcNFFF9mwYcNsy5Yt9tFHH9mZZ55pnTp1smLFitm9995rK1euDDyU7wgggAACCCCAAAIIJCqQ7AA08AobN260JUuW2I8//mh///23lSlTxhYsWGAVKlSwxx9/PPAwviOAAAIIIIAAAggg4CnguQo+9JE7d+60CRMm2HvvvWdK0VSkSBG76aab3P2KFSu6h3/44Yd23XXXWevWra169eqhL8F9BBBAAAEEEEAAAQScQNgA9JtvvrFGjRq5Bzdv3tw+++wza9q0qWXNGv+pzZo1c4/Zvn07tAgggAACCCCAAAIIJCoQP4r0eFiuXLls0KBB1qFDBytUqJDHI/63KXv27LZt2zbXO5rog9iBAAIIIIAAAgggkOkFwgagWnykL7W9e/famjVrTKmZzjnnHDv11FPjANUjqqF5GgIIIIAAAggggAACSQkkaxHS6tWr3bC7As8aNWpY2bJlLX/+/C4l0z///JPU67MvGQKa5vDII4+46lNvvfWWHT58OBnP4iEIIIAAAggggEBsCoQNQPft22ea3/nHH3/YqFGjTMHS6NGjXUA6cOBAFzjF5qVHx1kPGDDA2rVrZ5rqcP7559tzzz1nOXPmdBkGouMMOQsEEEAAAQQQQCB1BcIOwc+cOdM2bNhgq1atcimXdPj69evbrbfeal988YW1adPGnn76adMcUNqJCSigf/TRR12wmSdPHvfke+65x66++moX9M+ZMyfBYq8TOwKPRgABBBBAAAEEok8gbA/o1q1brW7dunHBZ/AlXHHFFa4SkhYf0U5c4LXXXrPp06dbIPgMvMLkyZNt06ZNtmLFisAmviOAAAIIIIAAAhlGIGwAetlll9nPP//sFh+FXrVKcZYqVcotSArdx/3wAqoqdeGFF3o+sHLlyq7n2XMnGxFAAAEEEEAAgRgW8ByCX7RokWn4N9DOO+88U034hg0bWpMmTdwcRVVCevPNN+2BBx4IPIzvJyigMqaa2nDWWWcleKYS/mtqAw0BBBBAAAEEEMhoAp4B6Pz5861fv37xrlUpl7QASV+Bpnmfqg/fv3//wCa+n4BAly5dXFB/8OBBF9QHnqqpDTt27HDlTQPb+I4AAggggAACCGQUAc8AtEePHqavE2179uxx8xmD84Oe6Gtkpsd37drVlOJKK+C1+l1prqZOnWrLly+3AwcOWLZs2TITB9eKAAIIIIAAAplEIOwc0BNx0GIlBU+05Au88MILbiGSFnJp6kPjxo3tt99+c0Fp8l+FRyKAAAIIIIAAArEj4NkDGjunnzHOVEGnvmgIIIAAAggggEBmEEjVHtDMAMY1IoAAAggggAACCJycQMwFoKpLrzmTyfnq2LHjyenwbAQQQAABBBBAAIFUF4i5IfghQ4ZY27Zt7ejRo/bYY4/ZKackHkMrfRQNAQQQQAABBBBAILoEYi4AVRnQb7/91qpWrWr//vuv3X///dElytkggAACCCCAAAIIJCmQePdhkk9L353q2VSS9meeecZ27dqVvifD0RFAAAEEEEAAAQROSCDmekADV3fvvfda9erVXS9oYBvfEUAAAQQQQAABBKJfIGYDUCW7V2lQGgIIIIAAAggggEBsCSR7CN7n88Vd2Z9//mmffvqpbd26NW6bbowZM8bKlCkTb1ta39F5qWzl7t270/pQvD4CCCCAAAIIIIBAKggkKwAdMWJEXF3yNWvW2Pnnn2+tWrWykiVLutKRgfOoUaOG5c6dO3A3zb5v3rzZ+vTpY6VKlbLTTjvNChcubGeeeablzZvXqlSpYr1797b9+/en2fF5YQQQQAABBBBAAIGUC4Qdglet8p49e1qXLl1MvY2DBw+2rFmz2vz58238+PF2++232x9//JHyMzjBZ27YsMHq1atnWbJksXbt2rke1wIFCrj76gVdt26dTZw40SZNmmRfffWVlS1b9gSPwMMRQAABBBBAAAEE0lIgbAC6YMECF8SNHDnSncfnn39ubdq0sdq1a7seyGHDhpnqmBctWjQtzzPutRUAq+dz5syZlj179rjtwTcGDBhgTZs2dVMC+vXrF7yL2wgggAACCCCAAALpLBB2CF5pjgoVKuROc9myZbZlyxa76qqr3P0jR4643lBVJYpUW7p0qd18882JBp86j2zZslmnTp3iTQ+I1PlxHAQQQAABBBBAAIGkBcL2gGpO5cMPP2xff/21jR492nLmzGmNGze2v//+2/r372+1atWyM844I+mjpOLeOnXq2Lx589zQf1IvO2vWLCtevHhSD/Hct2rVKps9e7bnvuCNx44dcwbB27iNAAIIIIAAAgggEF4gbACqVEfNmze3yy67zL3a8OHDLU+ePNa5c2fTcPzrr78e/iip+Ij27dubgtDt27dbhw4d3PQALUBSSU7NAV2/fr2NHTvWpkyZ4obpT/TQeo2ffvop7NNUhUnlQGkIIIAAAggggAACJyaQxb+w6L/8Sok8Vw9Zvny55ciRw/X6FSxY0JSKqVy5chHt/Qyc3u+//25du3a1OXPm2PHjxwOb4743atTIHnnkkTTNE6og/MUXXwzbExt3UtxAAAEEEEAAAQQiIKDRa6XG1PdobWF7QHXiO3futAceeMBmzJhh//zzj7sWzQvV0HyvXr0ifm3nnnuuW+GuHsiNGze6Xk8NiRcrVsxKlCjhUjJF/KQ4IAIIIIAAAggggECyBMIGoAryLr74Ytf7qfya6l1UCiSlYXryySfdsLfqsqdHUw5QBaP6Cm379u1zC6Q0Z5WGAAIIIIAAAgggED0CYQPQuXPnuvmWSkCvHsZAu+KKK9xcUC1EUqojzcGMpnb22We7xVIffPBBNJ0W54IAAggggAACCGR6gbBRoxLRq9czOPgMqN1www1ueH7Tpk2BTVHzvXv37taiRYuoOR9OBAEEEEAAAQQQQOB/AmED0EsvvdS+//57W7t2bQIzpWVSUnj1NkZbe+qpp+ymm26KttPifBBAAAEEEEAAgUwvEHYIXmUuVfqyYsWKpmC0SZMmdvrpp5sqJL377ruuKtKgQYPiIG+99da4xPVxG9Pwhlboa5HUqaeeajpXGgKxLqCFfppnrfc0DQEEEEAAgYwoEDYAVfUjJXXXgh/1hOor0JSWSfk29RVoV199dZoHoJs3bzaVAJ0wYYLpdmBlvhLiq0dWUwY0L1WBMg2BWBHQwr4+ffrYwoUL3QK6+vXr2/vvv2/58+ePlUvgPBFAAAEEEEiWQNgAVGU39+7dm6wXi8SDNmzY4Hpk1UPUrl07K1OmjOv51H0lkV+3bp1NnDjRJk2a5FI1lS1bNhKnxTEQOCmBlStXWt26de2tt95ylbiUfeKee+5x7+3ffvvNM9PDSR2QJyOAAAIIIJCOAmED0OBzO3DggGk1vII+1VvPnj178O6I3B48eLDr5Zw5c2aixx8wYIA1bdrUJWFVTygNgWgWUFWtatWq2TvvvGM333yzO9WsWbO60reaX92tWzebOnWqG5aP5uvg3BBAAAEEEEiuQNhFSHohrXK/9tpr3ZC2suqrKpKGCu+//347ePBgco+VKo9bunSp+yOdVPCr4LhTp07uj3aqHJQXQSANBdSrX7hw4bjgM/hQ+n82b948U5BKQwABBBBAIKMIhA1ANRTYsmVLUzqmIUOGWK5cudy1a37am2++affee29ELVQHXn+QwzXNWy1evHi4h7EfgXQXUBWvxD5Q6cNUYI5zup8oJ4AAAggggEAqCYQdgtdQtxb6KADNmzev9e3b1x26bdu2rg78LbfcYlqJrjmYkWjt27c3BaHbt2+3Dh06mOZ4nnnmmS4RvuaArl+/3saOHesWRuncaQhEu4CmtOzfv9/Uu1+1atV4pztixAirXr26W5QUbwd3EEAAAQQQiGGBsAGoFkAoBZOCz9CmEp1bt251QV/p0qVDd6fJff2B1sr8rl27moLf48ePJziOVsFPnz7dGjRokGAfGxCINgH1cj777LNuHqjSm9WsWdOd4meffWY9e/a0FStWRNspcz4IIIAAAgiclEDYAFSBpYa8d+zYkSC90vjx413PTKSHulX7/auvvjJND9i4caMLgDWMqWpNJUqUcD2iJ6XCkxGIsIAWH+lD3iWXXGLlypUzvZ81sjB79mwrX758hM+GwyGAAAIIIJC2AmED0Msvv9wFdc2aNbP77rvP9TiqV/Tzzz+3kSNHmobElSM0PZqOq2BUXzQEYl3gmmuuscOHD7uqY+oVLeXPaUsy+lj/qXL+CCCAAAJeAmEDUCVz//jjj92qcgWbah07dnTfW7VqZS+99JK7zT8IIHDyAvpQRY/nyTvyCggggAAC0S0QNgDV6VeuXNkWLVpkixcvNvV+6o+k5oVecMEF0X11GezslIpHmQe0uErTD7QYq0ePHpYzZ84MdqVcDgIIIIAAAghkZIFkBaACOOWUU0yLjvRFi7yA5gNqAZbKn/bu3dt9V5L9hx56yP766y/Lly9f5E+KIyKAAAIIIIAAAikQ8AxAlyxZYnPnzk32y6kXjpa2Ar169XJVqIIT/2sKxPXXX++KBEybNo35gmn7I+DVEUAAAQQQQCCVBDwD0Dlz5rieteQegwA0uVIpf9yECRNM9cJD27hx46xIkSK2c+dO9z10P/cRQAABBBBAAIFoE/CshKTetiNHjiT7K9ouKqOdj3Kd7tq1y0qWLJng0jQ1QvXCt2zZkmAfGxBAAAEEEEAAgWgU8OwBZQg++n5UCjJ//PFHq1GjRryT05C8titlDw0BBBBAAAEEEIgFAc8AlCH46PrRqZeze/fudtFFF9nff/9tefLkcSeontHcuXNbmzZtLH/+/NF10pwNAggggAACCCCQiIBnAKoheH3RokdAJRlVjeqMM85w5RmzZ8/u6t23bNnSJk6cGD0nypkggAACCCCAAAJhBDzngIZ5TrzdSgFEi4zAM888Y5oeoeF49XgOHz7cPv30U8uSJUtkToCjIIAAAggggAACqSDg2QMa+rpKfP7KK6+4ldZKhq6m75p/uHr1ardYKfQ53E8bAeUC1RcNAQQQQAABBBCIVYGwPaB//PGHaZh3/vz5pvrUy5Yts6JFi9qePXvsl19+sYEDB8bqtXPeCCCAAAIIIIAAAukgELYH9NtvvzUtdlmzZo3r9SxUqJCNGTPGLX656667bO3atelw2hwSAQQQQAABBBBAIFYFwvaAbt261WrVqmWnn3665c2b18466yxbsGCBm3fYt29fe+ONN+zYsWOxev2cNwIIIIAAAggggECEBcIGoKVLl7Z169bFnVb58uVt3rx57n4g9c+mTZvi9nMDAQQQQAABBBBAAIGkBMIGoLVr13bzPTt16mRa8a77GoLXXNBBgwaZhuRJgp4UMfsQQAABBBBAAAEEggXCBqBacDRq1CibOnWqbdu2zTTvU3NCq1SpYv369bOuXbuSBihYlNsIIIAAAggggAACSQqEXYSkZ7dr186uueYaO/XUU92XSj/OmDHDlYUsU6ZMkgdgJwIIIIAAAggggAACwQLJCkD1hNNOOy3uefny5XNBadwGbiCAAAIIIIAAAgggkEyBsEPwyXwdHoYAAggggAACCCCAQLIEkt0DmqxX40FRJ3DkyBF3TqodT4u8gCqGTZo0yWbPnm0+n8/q169vN954o51yCp/9Iv/T4IgIIIAAAtEiwF/BaPlJpPJ5fPfddy5/q6ZLKF1W3bp1jXRZqYycjJe76qqrbPDgwVapUiVXQrVz584ul+7Ro0eT8WweggACCCCAQMYUIADNgD9XVaeqU6eO3XnnnXbw4EHbv3+/3X333VayZElXRCADXnJUXpIyRKh4w8KFC52/fh7qkdaHguHDh0flOXNSCCCAAAIIREKAIfhIKEf4GOp1e/nll+3WW291R86SJYt16NDBDh06ZN27d7cffvghwmeU+Q6nofdx48Z5lqpVFonChQu7lGa5cuXKfDhcMQIIIIBAphegBzSDvQV27dpl+urWrVuCK+vSpYutWLHC9u3bl2AfG1JXQMbZsmWzggULJnjh3Llzm/Lrbt++PcE+NiCAAAIIIJAZBAhAM9hPWQtdElvgop5Q7VMhAVraCuTJk8eOHTvmijeEHunvv/+29evXW5EiRUJ3cR8BBBBAAIFMIUAAmsF+zAUKFHDlUSdPnpzgykaPHm3FihWzM844I8E+NqSugIo23HHHHXbFFVe41e/Br655oW3atDGG34NVuI0AAgggkJkEmAOawX7a6uF88cUXrVmzZvbhhx/atdde667ws88+Mw3B//rrr5ROjdDPfNCgQbZ06VLX6zxgwADLmjWrS8nUuHFj++CDDyJ0FhwGAQQQQACB6BMgAI2+n8lJn1HTpk1t8eLFpsVIPXr0cD1wWnk9bdo0q1Chwkm/Pi+QPAF9GJg5c6ZNnTrV5syZY1qY9Mgjj1jz5s35EJA8Qh6FAAIIIJBBBQhAM+gPtnr16m6Rixa6aM7nWWedlejc0AxKEDWX1aRJE9MXDQEEEEAAAQT+J0AAmoHfCeqBU+BJQwABBBBAAAEEokmARUjR9NPgXBBAAAEEEEAAgUwgQACaCX7IXCICCCCAAAIIIBBNAgSg0fTT4FxiQkDlNJ955hm77LLL7OKLL3YLvXbu3BkT585JIoAAAgggEA0CzAGNhp9COp/DX3/9Za+++qrNnz/f5aZU7krlsFTielp8AS3oOuecc+zcc8+1p556ylTVaMiQIS73qpLLax8NAQQQQAABBJIWIABN2ifD7z1w4IApeX3Hjh1d0Hnw4EG78cYbbeTIkbZo0SJTQnXafwLdu3d3aa2+/fbbuI3jx4+3ihUrupXuy5YtcyU443ZyAwEEEEAAAQQSCDAEn4Ak82xQXsoyZcrYlVdeae+++661bNnSbrjhBhdgqVd01KhRmQcjmVeqhP7qKQ5tjz/+uB09etTWrFkTuov7CCCAAAIIIBAiQAAaApKZ7m7evNnVK1ei9NCmBOr9+/cP3Zzp7+/Zs8eKFi3q6aCUV7t37/bcx0YEEEAAAQQQ+E+AAPQ/i0x3a9u2bXbeeed5zvXUXEYlsQ9tGzdutAcffNBVWWrbtm2m6yW94IIL7JtvvgllMU1dmDdvnvNMsJMNCCCAAAIIIBBPIOYDUJ/PZzt27KDnKd6PNXl3FGQuX77cDh8+nOAJP/zwg5UqVSredgWseo6Gmu+//35XZ/62226zNm3auGH7eA/OoHceeOABa9asma1duzbuCv/55x+3GElzZwsWLBi3nRsIIIAAAggg4C0QkwGoho779OnjAqTTTjvNChcubGeeeablzZvXqlSpYr1797b9+/d7XzFb4wSKFCni5n0qaFIgH2h79+61evXquZ7OwLZDhw65qkpPPPGEDR061Bo3buwWK2keqXoEX3zxxcBD476vWrXKevbsaapNf9NNN9nHH38cty/abii10n333ecWE5UsWdIFmT/++GOC02zXrp2NGzfOypYta7rdqVMnK126tHXo0MHGjh2b4PFsQAABBBBAAIGEAjG3Cn7Dhg0uOFKKIAUAWkSjVdy6r/l369ats4kTJ9qkSZPsq6++coFCwstmS0DgnXfecUGXynZqaF2BplZ1K89l586dAw+zJUuWuMf169cvbptu6HkfffSR+0CgwD/QVq5caRqufvTRR+3ee+81fWhQT+ljjz1mTz/9dOBhUfFdqZXOPvts94Hm/fffd72Yeg/VqFHDNBdWaamCmxZqXXrppfb11187L/UGX3jhhcEP4TYCCCCAAAIIJCEQcwHo4MGDXaCgwCB79uyelzZgwADX6zZmzBgLDZg8n5CJNyrNkoLF2bNn2/fff2/FihVzgXulSpXiqWg+6Pnnnx9vW+COgrc///wzcNd27drlgk8FcxqWDjTdDvRSX3vttYHN7vuHH35oL7/8sukDRokSJVyPadeuXeM9Jq3uKEhWD/CCBQviDqGeWwWgV199tSm/Z/78+eP26Yacgq8t3k7uIIAAAggggECSAjEXgC5dutRuueWWRINPXW22bNnc0Ojw4cMJQJP88f+3s2HDhqavxFq5cuVcgKpALTRBvXoCSwXNF501a5ZL7RQaoOXMmdPefvttmzBhgps/GjiWkuDffffd9sknn1jVqlVdKiP1Oq5YscIN9wcel1bf9WFG6ZVCm6Yh1KxZ0123phHQEEAAAQQQQCB1BGIuAK1Tp45bbXz77bcnKaAgqHjx4kk+hp3JF6hQoYIbgtfcx+AFOL///rsL9mfMmBH3YlqsVL58+bj7wTfUW6r9gaZ5lgo+1cOqubxqWuikhVF6rILia665JvBw913zTnUOmzZtcoGvHq+pACltf//9d4IezsBr5cuXj/nEAQy+I4AAAgggkEoCMReAtm/f3hSEKmDRwg8FRFqApABEc0A1XKrFIFOmTHHz91LJKdO/jHw///xzO/300+2MM84wDY+r/rnmSmr4vFGjRnFGKlOZ2IIcDfVr3m6gTZs2zS0aCwSfge2aXqEFT/o5BgegWiDVokUL93PWcVSRSIupNPc3a9aUvZ011P7pp5/GW3Sl8zh27Ji7voEDBwZOi+8IIIAAAgggkAoCKe82SoWDp+QlNESrcodKBaSheAWjmpuoIeJatWrZ9ddf73IyTp8+3Ro0aJCSQ/CcRAQUFGq1uBYdKRn7JZdcYj/99FO84XQ99fLLL3eLc5o3bx7vlZQnU0Flr1694rar91EfILyatmt/oGmBlHoktWpf+UjVy63zUTnRunXruoAx8NgT+f7QQw+5RVSaHhBoel0d6+abb3Z13wPb+Y4AAggggAACJy+Qsi6jkz/uSb2Cer60wl1BqBatKAjSSmYFolrAklhAc1IH5clOQD2h6u0M7vEMpVGgqp5J9ZTqZ3Xddde54XLNs5w7d66b5xl4jj5QvPnmm/bwww8HNsV9f/31113C+8AGPU6LoxQAB1og+4HmjGoOqTIjnGirXLmyKWWU3j8vvfSSC66VB1XnPXr06BN9OR6PAAIIIIAAAmEEYjIAVUqfYcOGucUsuq1E4GoKeLQYRsGRVr9ruJiWPgJ58uRxvaBffvmlrV692k2VUHYCfUAIbq1atTINcSuN0S+//BK3S72kGq4Pzh26cOFCl6sz7kFBN5o0aeJWsackANXLqCKUelK1yE3D/Dof5QOlIYAAAggggEDqC8RcAEoe0NR/E6TVK+bIkcNat26d5Murt1Tpj/RYpWjSh4c1a9a4DxVarKRtgaYPFIkVGNBQfe7cuQMPTdH3XLlyuSkdKXoyT0IAAQQQQACBZAvEXABKHtBk/2xj5oEKQtWLrbm96/2LyJS9QEPzqnIV3DTM3rdvX7vzzjvjLThSUKrE+Rr2pyGAAAIIIIBA9AvE3CIkDZFqYYiClsRaIA/o1KlTE3sI26NMQAnxq1Wr5npMlXszNPjU6bZt29aqV6/u8rwqfdO+fftc0Knhfs0h1UIkGgIIIIAAAghEv0DM9YCSBzT631RpeYaqbqWcpKotr/m/mqf5yiuvuFyiaXlcXhsBBBBAAAEEUk8g5gLQtM4D+sUXX7jh3HDEBw8etC1btoR7GPvTQEBpk/RFQwABBBBAAIHYFIi5ADSQB1SJ0JUHVOmXQpsWsqQ0D6hyWyoVT7jWsWNHVys83OPYjwACCCCAAAIIIBBfIOYCUJ1+cB5QJSTXwhVVrSlWrNhJ5wFVknN9hWt6DPlGwymxHwEEEEAAAQQQSCgQkwFo4DK0UEXBqL5oCCCAAAIIIIAAArEhEHOr4JPLqhXSKt1IQwABBBBAAAEEEIgugQwbgJ599tlujmh0cXM2CCCAAAIIIIAAAjE9BJ/Uj6979+5Wrly5pB5yUvuOHDniSkUuXrw40dfZvn27zZw5k5KOiQpF3w71mu/evdslw4++s+OMvARUiEBfRYsW9drNtigUUOWyw4cPW+HChaPw7DglL4E9e/ZY/vz5rX79+l672RZlAvo7Fu0ti8/fov0ko/H8vv/+e3v77bfjVeQJPU+VDVUAyhzVUJnova968H/++aeVLl06ek+SM4snoGBGfxw16kGLDYG//vrLlMpOVc9osSGwc+dOVzL56quvjo0TzuRnWapUKevVq5epyEu0tpgPQBU/6z+GkAsUKBBVzkuWLLEuXbqYqvbQYkNg9uzZ9vTTT9usWbNi44Q5S/voo49s7NixNmnSJDRiRGD06NE2b948GzVqVIycMaf54osvuuIfL7zwAhgIpIpATM4BVQWcPn36mCJ8rYTXMI5SIuXNm9eqVKlivXv3dkNyqSLEiyCAAAIIIIAAAgikqkDMzQHVsHa9evUsS5Ys1q5dOytTpozr+dR9zXlYt26dTZw40fWGfPXVV1a2bNlUBePFEEAAAQQQQAABBE5OIOYC0MGDB7ueT82tzJ49u+fVDxgwwJo2bWqqG96vXz/Px7ARAQQQQAABBBBAIH0EYm4IfunSpXbzzTcnGnyKMVu2bNapUyebOnVq+qhyVAQQQAABBBBAAIFEBWIuAK1Tp46bvJ7oFf3/Di0iYYVlOCX2I4AAAggggAACkReIuSH49u3bm4JQ5djs0KGDm+OpBUinnHKKmwOquvBaETtlyhSXAinypBwRAQQQQAABBBBAICmBmAtAq1atasuWLbOuXbu6SkfHjx9PcH2NGjWy6dOnW4MGDRLsYwMCCCCAAAIIIIBA+grEXAAqLiV21wr3o0eP2saNG029nseOHfu/9u47xorqC+D4WVgE6b2sCwsaIIAEDTUEpZpAIKICf1ACoUmkmEAo0kIJYDQQIAEFDBj/gaigYCHSEgIEAgmhSAQUkaIISm/Sxzn393vje/t2gWybO/d9b7LuzLx5b879HB973syd+yQjI0MyMzPNlEzhsnJ0BBBAAAEEEEAAgdwEIj8RfW4ds2G7ftXc2bNnC/UrQW3op0sx6Dch6fAOnd6LFg0B/SYk/WadrKysaARMlCZf+l7TEwa0aAjoF77oiZ5atWpFI2CitF6AAtT6FBEgAggggAACCCDglkDk7oJ3i5/eIIAAAggggAACqSdAAZp6OafHCCCAAAIIIIBAqAIUoKHyc3AEEEAAAQQQQCD1BChAUy/n9BgBBBBAAAEEEAhVgAI0VH4OjgACCCCAAAIIpJ4ABWjq5ZweI4AAAggggAACoQpQgIbKz8ERQAABBBBAAIHUE6AATb2c02MEEEAAAQQQQCBUAQrQUPk5OAIIIIAAAgggkHoCFKCpl3N6jAACCCCAAAIIhCpAAVrI/J7nFfIRePn8Cty7d08ePnyY68uQw1xpQnng0aNHTzwuOXsiUZHu8DT5eJp9ijRoDvZEAXL2RCJ2eIwABehjcPL60M2bN2XSpElSv359qVy5srz11lty6dKlvL4czytEgd9++00yMjLkhx9+SDrKZ599Jh06dJDSpUtLq1atZPv27Un7sKFoBG7duiXjx483uUpPT5esrCyZN2+ePHjwICEAcpbAEerK/fv3ZeHChdKmTRspW7astG/fXvbu3ZsUEzlLIrFig77nXnjhBRk9enRSPOQsiYQNeRCgAM0D2pOeMmXKFFm3bp18/PHHsmHDBjl58qR06dJF+LT4JLmifVyLz9dffz3HDwc7duyQt99+W3r37i179uyR5s2bS7du3eTw4cNFGyRHMwIjR46UVatWmT+Gu3fvlgEDBsiMGTNk1qxZgRA5CyisWFixYoXJz8CBA2XLli1Sq1Yt6dy5sxw7diyIj5wFFNYtTJw40fztyh4YOcsuwnqeBfyiiFaAAn6B4hUrVsxbv3598Ko//fSTXof3/LNswTYWwhX46KOPvDJlyngNGzY0ufnuu+8SAmrUqJHXv3//hG0vvviiN2TIkIRtrBS+wNWrV817asKECQkH69Wrl1e9evVgGzkLKEJfuH37tlezZk3PL2KCWK5du2bec3Pnzg22kbOAwqqFrVu3ev7VO8//0OCNGjUqITZylsDBSj4EOAOa59I95yfqJ/1nnnnGnC2L7eG/YaVBgwby/fffxzbxO2QB/4+gjBkzRjZu3JgUye+//y5Hjx6VN998M+Gxnj175rh/wk6sFLiAjs/VqwnvvPNOwmvXq1dPbty4Ya4skLMEmtBXnn32Wfnxxx9l2rRpQSxXrlyRu3fvSokSJcw2chbQWLWg76mhQ4ea4RNVqlSRtLS0ID5yFlCwUAACFKAFgBj/EidOnJBq1aqZIjR+u44zvHDhQvwmlkMUOHDggLz//vtJedKQfvnlFxPZc889lxChrv/999/yNDfBJDyRlXwJ6DhqHQ6hBWesaQ7WrFkjrVu3Nn8gyVlMxp7fVatWlXLlyomOBdXLtvoBQv9t7NOnjwmSnNmTq/hIxo0bJ40bNxYdOpG9kbPsIqznRyA9P0/muckC169fNzceZX+kUqVKFKDZUUJc1z+EuTU9A6BNP/3HN82hno3TG8oe9/z457BcOAKTJ0+WP//8U7766itzAHJWOM4F8ap6I5LelKlt5cqVUrduXbNMzgyDVf/RmzG/+OILOXLkSI5xkbMcWdiYRwHOgOYRLren6eUlfwxo0sN6GUOn+6HZL6B3WWuLv/QUHzV5jNco+uU5c+bIhx9+KB988IGZnUAjIGdFn4enPWLfvn3NzXv+GF5zFlTzp42cPa1g0eznj7WWYcOGmfdW7dq1czwoOcuRhY15FEiulPL4QjztfwL+wHvRsU7Zm24rX7589s2sWyigd+tq03+Q41ssr+QxXqVol7WImT59uik+dVqmWCNnMQn7fmsx07RpU1PYdO/eXfwbAM24XXJmV670vaX3L+j0gTrlnP7olII67lOX79y5Y2Yy0Kj5t9Gu3EU1Gi7BF3Dm9B9VHSeol2qLFy8evPr58+fNnJLBBhasFdAPEdr0Em980xzGxrXFb2e5aAS04NTLucuXLzdjQuOPSs7iNcJf9u94l127dkm7du2kQoUKQUA6Hd3XX39tihpyFrBYsXDw4EHRqel0qqz4durUKfFndRH9Tc7iZVjOrwBnQPMrmO35+ubVCXz1E2Os6Tygeld1p06dYpv4bbGAfoho0qRJ0qwF/lRN5DCkvOkldy0+V69enVR8akjkLKTE5HJY/bDWo0cPM3tB/C46E4h+sYPelEnO4mXCX/7222/l9OnTCT86e8ugQYPMtszMTHIWfprciiAfUzjx1FwEOnbs6OmckcePH/f8yxeeX3h6/pkAz79zN5dnsDksgbNnz+Y4D6g/7Y9XqlQpb+3atZ7OX+h/645Z//XXX8MKNWWPq+8hv2jxWrRo4fmTmyf9+GNyjQ05s+t/Ef+bj7waNWp4/pdxeH/88Yc3c+ZMz78q5PlDKIJAyVlAYeWC/h3zvwkpITZylsDBSj4EdCwOrYAF9A9m27ZtTWHjD9r2XnvtNU8no6fZJ5BbAep/xaPnT0fi+TeVmTzq5Mv+18/Z14EUiGjx4sUmB/5H/xx/6wcEbeTMrv8Zzp0753Xt2jXIWcmSJb2pU6d6sQ8M5MyufOUUTU4FKO+znKTYlheBNH2SW+d07enNxYsXzR3xOo8hLZoCOvD+r7/+kjp16kSzAykYNTmzK+mXL1824+Kff/75YBL67BGSs+wi9q+TM/tzZHuEFKC2Z4j4EEAAAQQQQAABxwS4CcmxhNIdBBBAAAEEEEDAdgEKUNszRHwIIIAAAggggIBjAhSgjiWU7iCAAAIIIIAAArYLUIDaniHiQwABBBBAAAEEHBOgAHUsoXQHAQQQQAABBBCwXYAC1PYMER8CCCCAAAIIIOCYAAWoYwmlOwgggAACCCCAgO0CFKC2Z4j4EEAAAQQQQAABxwQoQB1LKN1BAAEEEEAAAQRsF6AAtT1DxIcAAggggAACCDgmQAHqWELpDgIIIIAAAgggYLsABajtGSI+BBBAAAEEEEDAMQEKUMcSSncQQAABBBBAAAHbBShAbc8Q8SGAAAIIIIAAAo4JUIA6llC6gwACCCCAAAII2C5AAWp7hogPAQQQQAABBBBwTIAC1LGE0h0EEEAAAQQQQMB2AQpQ2zNEfAgggAACCCCAgGMCFKCOJZTuIIAAAggggAACtgtQgNqeIeJDAAEEEEAAAQQcE6AAdSyhdAcBBBBAAAEEELBdgALU9gwRHwIIIIAAAggg4JgABahjCaU7CCCAAAIIIICA7QIUoLZniPgQQAABBBBAAAHHBChAHUso3UEAAQQQQAABBGwXoAC1PUPEhwAC1gocOXJE0tLS5PPPP893jMWLF5dly5bl+3V4AQQQQCAKAhSgUcgSMSKAAAIIIIAAAg4JUIA6lEy6ggACCCCAAAIIREGAAjQKWSJGBBCIjED79u1l06ZNMnr0aKldu7b5GT9+vNy/fz/ow6lTp2TIkCFSs2ZNad68uWzevDl4LLZw8+ZNGTlypNStW1eqVq0qb7zxhpw+fTr2sHl9Pdb169eDbbNnz5ZXXnlFLl68GGxjAQEEELBRgALUxqwQEwIIRFbg4MGDMnToUDl8+LBMnz5devToIQsWLJCFCxeaPt27d0969+4t+/fvlyVLlph9+/fvL48ePQr67HmedO7cWdatWyeDBg2STz75RK5cuSItW7aUy5cvm/1GjRole/fuNcfQDbt375ZZs2aZ19aClYYAAgjYLJBuc3DEhgACCERRICMjQ3bs2BGErkXpli1bZOLEifLpp5+a4vTkyZOSmZlp9qlcubL07ds32F9vatq3b59s3LhRunXrZrZ37dpVqlWrJvPnz5d58+ZJo0aNZObMmTJt2jTz3MGDB0uHDh3k3XffDV6HBQQQQMBWAQpQWzNDXAggEFkBLQTjW/369eX48eNmkxajetk9Vnzqxl69ekmxYv9dkNq5c6dUqFBBypUrZ85sxl6rWbNmCesTJkwwZ0m7dOki6enppsjVu/JpCCCAgO0CFKC2Z4j4EEAgcgLZL4GXLFkyuMT+888/m7Gf8Z0qUaKEVK9ePdikY0SvXbtmxnMGG/+/oGNCY02nbho7dqzoJXz9qVOnTuwhfiOAAAJWC/z3kdvqMAkOAQQQiI7A485CZmVlBeM443t048aNYLVKlSqi+z18+NAUrjo+NPajl+5j7Z9//jHjPnXfNWvWJJwdje3DbwQQQMBGAQpQG7NCTAgg4KzASy+9JAcOHJCrV68GfTx06JDcunUrWG/SpIm5411vMtJiVn/0xqR+/fqZMaCxHXX857lz58x401dffVV0HKgWpTQEEEDAdgEKUNszRHwIIOCUgF4q15uOtFg8f/68nDhxQoYPH57QxxEjRoieBdU73b/55htTZL733nvy5Zdfio731KZ3vS9atEjmzJljLr0vX77cFK1alNIQQAAB2wUoQG3PEPEhgIBTAlpYrl+/Xs6cOSN6t3zjxo3Nne66HGsVK1aUrVu3mtWePXuaG5a2bdsmS5culZdfflnu3LljCli9mWnMmDFmvwYNGsjUqVNNUarFKQ0BBBCwWSDNv6zj2RwgsSGAAAKuCly4cMHc6V66dOlcu6iX6rXg1EnraQgggIArAhSgrmSSfiCAAAIIIIAAAhER4BJ8RBJFmAgggAACCCCAgCsCFKCuZJJ+IIAAAggggAACERGgAI1IoggTAQQQQAABBBBwRYAC1JVM0g8EEEAAAQQQQCAiAhSgEUkUYSKAAAIIIIAAAq4IUIC6kkn6gQACCCCAAAIIRESAAjQiiSJMBBBAAAEEEEDAFQEKUFcyST8QQAABBBBAAIGICFCARiRRhIkAAggggAACCLgiQAHqSibpBwIIIIAAAgggEBEBCtCIJIowEUAAAQQQQAABVwQoQF3JJP1AAAEEEEAAAQQiIkABGpFEESYCCCCAAAIIIOCKAAWoK5mkHwgggAACCCCAQEQEKEAjkijCRAABBBBAAAEEXBGgAHUlk/QDAQQQQAABBBCIiAAFaEQSRZgIIIAAAggggIArAhSgrmSSfiCAAAIIIIAAAhERoACNSKIIEwEEEEAAAQQQcEWAAtSVTNIPBBBAAAEEEEAgIgIUoBFJFGEigAACCCCAAAKuCFCAupJJ+oEAAggggAACCERE4F8TiOhMEd9rigAAAABJRU5ErkJggg==" alt /><!-- --></p>
+<pre><code>## [1] "Feedback: Plotting 1985 laundry episodes start"</code></pre>
+<p><img 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" alt /><!-- --></p>
+<pre><code>## [1] "Feedback: Plotting 2005 all episodes start"</code></pre>
+<p><img 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7Zx40YX9VRveL8GqCKiGTXlDhYoUCDs7qrCV9QVi14Cen8U0f7333/t77//diL05H5G7/vFyCAAAQhAIH4IxKQD6n97pAEqZ1QPDAIpEZDTmT9//pRWsxwCEIAABCAAAY8J5NgQnnI0FfXCIAABCEAAAhCAAASii0COdUCV90fHm+j6sDEaCEAAAhCAAAQgIAIxPQWf2lvYo0cPK1euXGqbsA4CEIAABCAAAQhAIBsI5FgHdNCgQdmAk1NCAAIQgAAEIAABCIQjEPNT8JLX2bVrl9MADXexrIcABCAAAQhAAAIQyH4CMemAbtmyxfr16+d6wqsSvlixYibpJek8Vq5c2Xr37m0HDhzIfrqMAAIQgAAEIAABCEAgGYGYm4LfsGGD1alTx6TD2aZNGytTpowVLlzYvVYnpHXr1tmECRNs4sSJTiu0bNmyyS6aBRCAAAQgAAEIQAAC2Ucg5hxQdbIpVaqUa7N50kknhSQ3ePBga9q0qY0ZM8YGDhwYchsWQgACEIAABCAAAQhkD4GYm4JfunSpa6+YkvMpjLlz57ZOnTrZ1KlTs4cqZ4UABCAAAQhAAAIQSJFAzDmgtWvXtnnz5qV4Qf4VM2fOtLPOOsv/kr8QgAAEIAABCEAAAlFCIOam4Nu1a2dyQnfs2GHt27c35XiqAEl9v5UDun79ehs/frxNmTLFTdNHCWeGAQEIQAACEIAABCDw/wnEnANapUoVW7ZsmXXr1s11Ojp27FiyN/PKK6+0adOmWb169ZKtYwEEIAABCEAAAhCAQPYSiDkHVLjOPfdcV+F+5MgR27hxo4t6Hj161M4880wrUaKEi4hmL1bODgEIQAACEIAABCCQEoGYdED9FyMNUDmjemAQgAAEIAABCEAAArFBIOaKkGIDK6OEAAQgAAEIQAACEEiJAA5oSmRYDgEIQAACEIAABCAQEQI4oBHBykEhAAEIQAACEIAABFIigAOaEhmWQwACEIAABCAAAQhEhAAOaESwclAIQAACEIAABCAAgZQI4ICmRIblEIAABCAAAQhAAAIRIYADGhGsHBQCEIAABCAAAQhAICUCOKApkWE5BCAAAQhAAAIQgEBECOCARgQrB4UABCAAAQhAAAIQSIkADmhKZFgOAQhAAAIQgAAEIBARAjigEcHKQSEAAQhAAAIQgAAEUiKQZgc0ISEh8Rg7d+60Tz/91LZt25a4jCcQgAAEIAABCEAAAhBIC4E0OaCvvPKKnX/++e54a9assQoVKliLFi3s7LPPtqlTp6blPGwDAQhAAAIQgAAEIAABRyCsA7pq1Srr2bOn1atXzxQFHTJkiOXKlcvmz59v3bt3t65du4ISAhCAAAQgAAEIQAACaSYQ1gFduHChlS1b1kaMGGHHHXecTZo0yVq1amW1atWy+++/3zZv3mzbt29P8wnZEAIQgAAEIAABCEAgvgmEdUB///13K1q0qKO0bNky27p1qzVp0sS9Pnz4sIuG5s2bN74pcvUQgAAEIAABCEAAAmkmENYBrVy5si1atMhmz55tw4YNs5NPPtkaNWpkf/75pz3xxBNWs2ZNK1iwYJpPyIYQgAAEIAABCEAAAvFNIFe4y69fv75dc801dsUVV7hNhw8fbgUKFLDOnTu76fiRI0eGOwTrIQABCEAAAhCAAAQgkEggrAOqLT/44ANbsWKFFSpUyEqUKOF2VgHS888/T/QzESVPIAABCEAAAhCAAATSQiDsFLwOouKjSpUq2amnnmrKAz1w4IBdcMEFOJ9pIcw2EIAABCAAAQhAAAJJCKTJAVWle+vWrS1//vymnFBFQ/v162d9+vSxgwcPJjkgLyAAAQhAAAIQgAAEIJAagbAO6JEjR+zaa6816YE+99xz5q94r1u3rr3++ut2zz33pHZ81kEAAhCAAAQgAAEIQCAJgbA5oNOnT7ctW7Y4B1Q5oP3793cHuP76690UfMeOHZ1AvabpMQhAAAIQgAAEIAABCIQjEDYCunr1apf/Kecz2C6++GLXD379+vXBq3gNAQhAAAIQgAAEIACBkATCOqClS5e2efPm2a5du5Id4L333nNC9GeddVayddmx4K+//rJNmzZlx6k5JwQgAAEIQAACEIBAGgmEdUAbNGjgpJeaNWtm7777rh07dswUFX3kkUfco127dnbiiSem8XSR3eyjjz6yqlWrRvYkHB0CEIAABCAAAQhAIFMEwuaAqvL9448/tk6dOpmcTVmHDh3c3xYtWtgLL7zgnnv1z9ChQy2lKf9ff/3VFAW966673HCqVavmBPO9GhvngQAEIAABCEAAAhAITyCsA6pDXHTRRa4d5+LFi130UxFP6YJWrFgx/BmyeAuNQVP/0iQNnvrfu3evHT161LUN1WlPOumkLD47h4MABCAAAQhAAAIQyCyBNDmgOsnxxx9vKjrSIztt/PjxziEePHiw3Xzzzda7d283No1p7Nix1qtXL/vpp5+yc4icGwIQgAAEIAABCEAgFQIhHVC13hwxYkQquyVdNXPmzKQLIvhKjvADDzxgjRs3tvbt29vnn39uY8aMsXPOOSeCZ+XQEIAABCAAAQhAAAJZRSBkEZLE5k8//fTEhyrgv/76a6f3WaFCBdcTftGiRW6Zvzd8Vg0orcepXr26/fDDDy4VQCkCb7/9dlp3ZTsIQAACEIAABCAAgWwkEDICes0115gesm3bttmFF15o48aNcxFH/1iVa9m9e3fXltO/zOu/cpRfeeUVu/rqq12x0cknn+z1EDgfBCAAAQhAAAIQgEA6CYSMgAYe48svv7TTTjstsQLevy537tz20EMPOY3Q7du3+xdny185oMr7rFWrllWpUiVbxsBJIQABCEAAAhCAAATSRiBkBDRw14IFC9rWrVtNFeaqPA+0BQsWmKKOobokBW4XyecJCQm2e/duJ4gvnVIMAhCAAAQgAAEIQCC6CYSNgDZq1Mj+/fdf69Kli4syHjp0yP744w9X/NOvXz83Ve/11Ld60+vcpUqVciL4xYoVsyJFijhHuHLlyq4y/sCBA9FNntFBAAIQgAAEIACBOCUQNgJaoEAB++abb6xly5ZO/iiQU5s2bTwv/tmwYYPVqVPHjjvuONP5y5QpY4ULF3av9+zZY+vWrbMJEybYxIkTbcaMGVa2bNnAIfMcAhCAAAQgAAEIQCCbCYR1QDU+tbdUlyHlWS5btsxOOeUUq1GjhpUsWdLz4Q8ZMsRFPqdPn56i0Lw0Qps2berkmQYOHOj5GDkhBCAAAQhAAAIQgEDKBMJOwft3VVchSTOVLl3azj//fNcf3r/Oy79Lly51AvSpdTlSgZRah06dOtXLoXEuCEAAAhCAAAQgAIE0EEhTBHTt2rV26623Ot1P/zElgdStWzcbNmyYm/72L4/039q1a7vK+65du6Z6KonjB7fqTHWH/7/y+++/t/fffz/spocPH7bff/897HZsAAEIQAACEIAABCCQlEBYB/TIkSPWokULU1GPCn8keZQrVy6bM2eOPfnkk64I6Kmnnkp61Ai+ateunckJ3bFjh9MlVY6nCpDUIUk5oOvXrze165wyZYppmj69Jse6ePHiYXdTDuoJJ5wQdjs2gAAEIAABCEAAAhBISiCsAyonTrmfelxwwQWJe0tzU06YIqBeOqDS+VQeqqKvHTt2tGPHjiWOyf/kyiuvtGnTplm9evX8i9L8t1KlSq67UrgdBgwY4HJhw23HeghAAAIQgAAEIACBpATCOqCrVq1yBUeBzqf/EMqzVFRUlefKDfXKzj33XFfhrujsxo0bXdRTnZnOPPNMl5uqiCgGAQhAAAIQgAAEIBCdBMIWISmPUoU/mzZtSnYFn3/+uZuGPuOMM5Kt82LBiSeeaHJGFfFU1bs0QP3O5/79++3vv//2YhicAwJxSUCKFBUqVLB8+fK5v0OHDo1LDlw0BCAAAQikn0BYB7RJkybOqZMQvRxRFd/8+eefNnnyZJPEkfRBvRaiT8tlSiJKU/QYBCCQ9QRefPFFu++++0zdx9SJ7J133rG+fftajx49sv5kHBECEIAABHIcgbBT8GrF+fHHH1uHDh2cHqjyPtX+UqbI4yuvvBKVUPRDWK5cuagcG4OCQCwT+PLLL61nz56mFBhJnsmqV6/uXuv/XPPmza1x48axfImMHQIQgAAEIkwgrAOq86vgSEVI3333na1cudLy5MnjtEAvueSSCA8v44cfNGhQxndmTwhAIEUCKvB7+umnE51P/4ZyRu+44w776quvcED9UPgLAQhAAAIhCaTJAdWemma/4oor3GPnzp327bff2rZt29IkWRTyzFm0UNFYTQFKEkktOTEIQCCyBA4ePOjyPkOdJX/+/K4oMNQ6lkEAAhCAAAT8BMLmgGpDTbOr+5FszZo1ruBA2qBnn312tnQb2rJli6u+L1WqlNMhLVasmMtTLVSokCtE6t27t9MtdQPmHwhAIEsJ1KxZ0yZNmhTymJJku/TSS0OuYyEEIAABCEDATyBsBFQyTMr3UhGSoo2qfJUQ/fz58+29994zdSQKVSHvP0FW/92wYYPVqVPHaZC2adPGypQp4yKfyk2VEL0koSZMmGATJ050Uk0SqscgAIGsI9C2bVvXhEI3gPr/5s8Ll+7u5s2bTc0iMAhAAAIQgEBqBMI6oAsXLjQ5cSNGjHDHUeSjVatWLi9UP0Cqht2+fbt5JcUkB1jnlUB+Sv3gBw8e7GSZxowZ4yr1UwPAOghAIH0ElAO+fPlyp/2rzmHKBV+xYoVVq1bN/vrrr2S5oek7OltDAAIQgEA8EAjrgKrfedGiRR0LdSDaunWrSZpJJkkmRUP1I+SVSQpK8kopOZ8ah4ohJJI/fPhwHFCv3hjOE1cE9H9MTSA0+6G/55xzjmsCoZa4GAQgAAEIQCAcgbC/FhJ3X7Rokc2ePdu13VQxUqNGjZwW6BNPPGHKB5NUk1emPvDz5s0Le7qZM2eaRPQxCEAgMgTkbMrxrFu3rkl3F+czMpw5KgQgAIGcSCBsBLR+/fp2zTXXuOp3AVBUsUCBAta5c2dXiDBy5EhPuSi/TE7ojh07rH379i49QN2P9OOnHND169fb+PHjbcqUKW6a3tPBcTIIQAACEIAABCAAgbAEwjqgOsIHH3zgcrxUZV6iRAl30O7du9vzzz/vafRTJ65SpYopFaBbt25uKv7YsWNuPIH/SCBfWoUqisAgAAEIQAACEIAABKKLQJocUFW5VqpUKcnI1fkku0z932fMmOE6ryj/TFHPo0eP2plnnukcZH8/+OwaH+eFAAQgAAEIZAWBv//+26XBqdi3fPnyduGFF5LukhVgOUa2EwjpgKrjkXIoJbG0d+9e+/DDD1Md6P3335/q+kitPPHEE03OqB4YBCAAAQhAICcRkMyZai5OO+00l2ctFRrVYcgZ1e8fBoFYJpCiA6pWey1btnTRRT1PzbLLAU1tTKyDAAQgAAEIxCoB1TlI53ro0KGm5ip+k+RhgwYNbNasWU6Fxr+cvxCINQIhq+Dvuusu++OPP1zHI0ku6Xlqj1i7aMYLAQhAAAIQiGYCzz33nP3vf/9L4nxqvIp+SgJRaWgYBGKZQEgHNKUL0od+yZIlnnY+SmksLIcABCAAAQjkVAI//vijK7QNdX2KgKoYF4NALBNIkwO6a9cua9asmeXPn991O5Hmn/qv6w4NgwAEIAABCEAgawlIX3v37t0hD7pz507PFWhCDoSFEMgEgbAO6JEjR+ziiy+2tWvXuqmAr776yulr9ujRwwYMGGCPPPJIJk7PrhCAAAQgAAEIBBNo2rSpy/8MlhpctWqVjR492hUnBe/DawjEEoGQRUiBFzB37lwn+r5mzRonc+Rf17BhQydIr25IAwcORBbCD4a/EIAABCAAgUwSUDvpqVOn2gknnOBUadR1bP78+XbTTTfZxIkTXYFSJk/B7hDIVgJhHVDdbUnYXRqbwda2bVvr1auXbd682UlEBK/nNQQgAAEIQAACGSPw3nvvWa1atdxMo6riS5cubV988YVdddVVGTsge0EgigiEdUAvv/xye/TRR90UvCQhAu3NN9+0UqVK4XwGQuE5BCAAAQhAIIsI9OzZ0/TAIJDTCIR1QAsXLmx16tRxnZDkjOrOS8VICxcutLFjx1qrVq3smWeeSeRyyy23WNGiRRNf8wQCEIgdAvo/rRa7EsAuXry4aZaDPO/Yef8YKQQgAIFYIRDWAZXUg7oiqevCggUL3MN/cXny5LEpU6a4h3/Z1VdfjQPqh8FfCMQQATmeSqlRoWGNGjVcE4qqVavavHnzXC5aZi7l4MGDpg5r27Ztc53LqlWr5nLbMnNM9oUABCAAgdglENYBlRD9vn37YvcKGTkEIBCWwE8//eScT8m7+GcwqlSpYqrAVf9pVd2qKCIjtn79elexK+k2pex8/vnntn//fpNTqptYDAIQgAAE4o9AWBmmQCR//fWXE789cOCA68QQuI7nEIBA7BJQVxVJq/mdT/+VHHfccS4H/Msvv/QvStdff+HEbbfd5iKp48ePdze0cmrr1q1rknnDvCegG4tgeR/vR8EZIQCBeCaQJgdUVe6tW7d2uZ+VK1e2FStWWL9+/axPnz4uihHPALl2COQEAopGKrc7lEkQWzefGbHhw4dbmzZtrG/fvkl2X7lypYt+ZtSxTXIwXqSJwL///uu+t0uUKGEnn3yyk/F59tln07QvG0EAAhDIagJhHVBFKK699lqTHJM6H+XNm9eNQdGL119/3e65556sHhPHgwAEPCZQvXp1mzZtWsizKjf0kksuCbku3EJN7Uu3MJTVq1fPli9fHmoVyyJAQEWkb7/9tn3zzTd26NAh++yzz1xzEQUSMAhAAAJeEwjrgE6fPt22bNliEqSXs5kr1/+ljV5//fX24YcfunyuhIQEr8fN+SAAgSwkIK3fAgUKmKbcFSnzW/fu3W3WrFl29913+xel62+hQoVSbCeo6XmdE4s8gXHjxjkR8+3bt7s8XL3PF110kXNE9T0ugXMMAhCAgJcEwjqgq1evdhJM+iEJNrXoVFWrigwwCEAgdgmo24puNitWrOhuMuWQnnvuufb999+7Tmiahs+IqZ2gpnn/+eefJLtrRkUzKAhqJ8ESsRdSMhk1alSy45900knWoUMHUw4wBgEIQMBLAmGr4NV5QTIsu3btSlagoC4NioieddZZXo6Zc0EAAhEgICdU+d0bN2603377zemAygnNnTt3hs/2v//9z0VQdQy1FSzlq4KXU+tvJ6jjY5EnoCn3lBQHtPzw4cORHwRngAAEIBBAIKwD2qBBA1PSerNmzezee+91lZOKik6aNMlGjBhh7dq1cxqhAcfkKQQgEMMESpYsmaXdzV577TWT7ufTTz/tZkzkhMoZlcRbKFMu6qeffmp//PGHXXDBBda1a9dkN7+h9mNZygRq165tH3zwgYt2Bm81YMAAlw8avJzX6ScgZQGlpOlmDoMABFInEHYKXpWxH3/8scsLk7MpCSZN2Tz++OOuQ9ILL7yQeIa9e/cmyR9LXMETCEAgrgl069bNNbT45ZdfXC/rlJzPJ5980ilulCtXzpo3b+7E66UfumnTprjml9mL79y5s8vzrFChQqL8kqKi6jOuGSw1EMEyR2DIkCFOWUCFuuoi9uqrr2bugOwNgRxOIGwEVNevZPVFixbZ4sWLTdFPdUWqVKmSyxcL5HPZZZeZpuUvvPDCwMU8hwAEIBCWgL47HnzwQfvzzz8Ti5M0hT948GBTxbwq6vPlyxf2OGyQnICcoq1bt7rvcgUVlOurPFw5+XPmzEm+A0vSRUD5tffdd5/TyVbUXtKFmkl46623XPfA448PG+tJ1/nYGAI5gUCaHFBdqP4DqehIDwwCEIBAVhNQpbamiYMr4+WUSi9U8kEpRU6zeiw58XgKHCjHd/fu3U7ZRA5SkSJFcuKlenpNajGrRgvS0pW+quzss892U/FKPZH01S233OLpmDgZBGKBALdlsfAuMUYIxAEBRejUISmUaUpecnBY5ggokKCUhqpVq+J8Zg5l4t5SGFCjBb/zmbjC96Rnz54oDAQC4TkEAgjggAbA4CkEIJB9BBQ1WrJkScgBSCJKxUsYBKKNQDiFAdrNRts7xniihQAOaLS8E4wDAnFOQKL3mqpcu3ZtEhIqkNmwYYMrekyyghcQiAICqn2QKkyohiz9+/c3rccgAIHkBNKcA5p81+hbog4u6vSBLmn0vTeMCALhCDRu3NjeffddK1u2rOu8pEpidehZs2aN7d+/H7m3cABZny0E1LThzDPPdHUS/jxQ/RZdd9119uuvv9rtt9+eLePipBCIdgIxGQGVkzl8+HAbOHCgrVy50jHWneYZZ5zhNEv1ZSAdQQwCEIgtAm3btnXV7vo/LFm3G264walvqHIbg0A0ElBb088//9waNWpkUhtQfq3ybI8ePWr79u0zdZvCIACB5ARiLgIq51NTGurWoqpO6ZBK4PqZZ54x9adXlb6qafV8wYIFVqNGjeRXzRIIQCBqCUjGRg8MArFCQE6oGiioeYI0a3UDVbhwYRcVjZVrYJwQ8JpAzEVAhw0bZupLrZwwOaHqNS0JDGmwyfFU1aFa/ckRlTAwBgEIQAACEPCCwKmnnuq0Vk877TScTy+Ac46YJhDWAdU0mPKy0tIreMyYMa4TRCSJSHOtU6dO7g5TGnbSCJTdeOONSU7bsWNHU9cVDAIQgAAEIAABCEAgugiEdUAnT57s+r3v2rUr7MirV68e8U4llStXth9//DFxLOrIpEhnoUKFEpfpieRclIeDQQACEIAABCAAAQhEF4GwOaCaSpApmbpEiRLZPnpFOuvXr+8kLwYNGuQ6TvTp0ydxXGqBppzQ0aNH04s3kQpP4pnAsWPH7IcffrDly5ebpgjr1KmDCHk8fyC4dghAAAJRQCCsA1qlShUXAb388sutZcuWTiIluKrv3nvv9exSVICkdn0PPfSQ60sv8epAGz9+vL388sumMdH+LJAMz+ORgCpxmzVr5gojateu7fKmW7RoYUuXLjXNJmAQgAAEIACB7CBwnE88NyG1E0+dOtVVlKe2zV9//ZXa6oisk86aHqqEDzS16zvhhBOcJFPg8qx+rn7Vzz77rHXt2jWrD83xIJAlBPRfW3nSuXLlsh07dpgqdWWfffaZ0yhUVFSSMRgEIBB/BJRe9+WXX9qBAwesZs2aprqJPHnyxB+IHHrFCjCoLieaAw1hc0Cvuuoqk4OZ2iM73j85mXI+9SOr/NQ9e/a4YUiEXnqgGATinYDUICSMvXPnzkTnU0yuvfZaJ1v23HPPxTsirh8CcUngnnvusTvuuMNKly5tmhl5/vnnXS/7P//8My55cNHZQyBFB1Q/Wu3bt3d5n+pM0rt3b3enlD3DTHpWRTn79etnpXy9oeWEqthIkR4VIsnbj6axJh05ryDgHQGpQLRr1y7kCRs2bGg///xzyHUshAAEci6BoUOHOv1sdRjr1auX3XrrrU4xRi1vmzdv7mYWc+7Vc2XRRCBkDqiKFpTzKa3NBg0auPwxTTdv27bN3nnnnWwdv78ntKYT27Rp42SfJPir14qCrlu3ziZMmGATJ060GTNmuJzVbB0wJ4dANhHQDZluJEOZpuRVkIRBAALxReCNN96whQsXWu7cuZNcuLo5SUB/7dq1Vq5cuSTreAGBSBAI6YDOmTPH9bBVjogKGGRyQBVZlORRdvZa1/kV+Zw+fXqKLc4GDx7sBOqV/6B2nRgE4pFA3bp1XV6X8ryaNGmSiEA3mPp/rS5iGAQgEF8EFEiqUKFCyIs+//zzTUoyOKAh8bAwiwmEnIL/7bffXE9bv/Opc2o6XqawfXaaqndvvvnmFJ1PjU13dhKrVwEVBoF4JaC0FDmfyuMeMGCAk2LS/wkpRygH7O67745XNFF/3VIvUCDg9ddft08//dTUEASDQFYQ0P//FStWJDuU6inmzp3rAjzJVrIAAhEgEDICqkTkYGF3vx6oerFnpylhet68eWGrz2fOnJmhSK2uPS3XqCiSfiQwCEQzgUsvvdTlej711FOuZa2m3dW2Fuczet+1v//+26U+qYBM1cmrV682SWf9+uuvVr58+egdOCOLCQLdunVzhUf6fJ188smJY5bOt74fzjnnnMRlPIFAJAmEdEBDKTMdf/z/BUvleGWnqahCTqhy2BSVVYGUIj0an3JA169fb9ICnTJlipumT+9YNbX/wAMPhN1NPxJpcVTDHogNIBBhAppWUzoKFv0EdFObN29eq1GjRpKOb2PHjnXTpitXrkxx+jT6r44RRgOBu+66y+V56nOmFDVJCn7xxRemINOiRYvoYR8Nb1KcjCGkAxrN1y5h/GXLlpnu4qRbFsohvvLKK23atGlWr169dF9Kq1atTI9wpv+0wSL44fZhPQQgAIHUCHz99dd2+umnmyS0Au2mm25yEdCXXnrJhg8fHriK5xBINwHVdEiOTb+TCtzot7R169bJCpPSfWB2gEA6CKTogP7+++92xRVXJDvUY489ZiNGjEiyfNasWUleR/rFueee6yrcjxw54jq7KOqpyIEq+DSNoIgoBgEIQCDWCCg3LzXprCeeeCLWLonxhiDwzz//mKLZmzZtcjmXSq2QtrWXpt/3UL/xXo6Bc8U3gZAOqKrcL7nkkmR6YOohLVMHomgwaYDKGdUDgwAEIBDrBJSDFxz99F+TqpclOYfFNgEVlDVt2tSlcCk9RsVmmslTJDK4zXV6r3Tfvn22YMECUy3DBRdcYBUrVkzvIdgeAp4RCOmAtm3b1vSIZdu/f79rQRiYZB3L18PYIQCBnE9ADQJUICadRhUg+U0RM+W8jxs3zr+IvzFIQLUDusmQhvW3336beAWVKlWyatWq2eLFizPcDvO7775zjq2asag5yw033ODaaL///vueR1cTL4wnEEiFwP9VFqWyQahVKgA6dOhQqFVRs6xkyZIuryVqBsRAIAABCIQhoBQiyS5JvUDKBXJIPvnkExf5VPqTXw4vzGFYHaUERo8ebRdddJF98MEHSUaormTSt5azmBFT1zPdsLz99tsmBZj33nvPzVSqIcvjjz+ekUOyDwQiTiBFB1S5KWrRpR7wfpNGmKrO1Ws9f/78Llfpjz/+8K+Oqr89evRwbcWialAMBgIQgEAYAsrLUzXyjz/+aLfffrvTAlWv7ocffjjMnqyOdgK6odBvUyhr1KiRLVmyJNSqsMvkZOrzcc011yRuK2UYpctJS3b58uWJy3kCgWghEHIK/ocffnBSR8oFVU5Jvnz5XH7K9ddfb8WLF7dXX33VyR299dZbzknVXVZ2mSSjdu/e7aYYAvOjBg0alF1D4rwQgAAEMkWgevXq9u6772bqGOwcfQQUuNFvaihT3qZ+azNiP/30kz344IPJdpUTKjUYrVdOKAaBaCIQ0gF988033YdWXVPUY1320UcfOSdUFe9KnJZJj/O6664zVcx7WXm+ZcsWe/HFF910hZ4rP0pWsGBBN40hGSbpm+k/OwYBCEBABHbt2mXffPON+77QdKXSdDAIeElAXckUAe3evXuSXM8DBw5Y//79XUFSRsajxjH6fIcyFa9JNhCDQLQRSOaAKropB1QOZdWqVRPHK0dPLS5DSYTIEVV0VD3YI20bNmwwVePLMVYid5kyZVx+lF6rinDdunU2YcIEU1R2xowZLmUg0mPi+BCAQHQTeOWVV1yESNEgfY+pQKNXr14mPUQMAl4RkAPavHlz14FIN0Pqua7pcRWfaQq9bt26GRqKjnvHHXeYckEDTQ1ZlBP64YcfBi7mOQSig4BvCjuJ+eQgEnxfzgm+3KMEX2TRPXz5oAm+qYGE1157LXGZ1vmioQk+xy/Bp8OZ4Ms1SXKcSL3w3Tkm+BzQBF8RVIqn8OmDJvj+Qyc8+uijKW6T2RW+6GrCyJEjM3sY9ocABCJMwFfUk+D7tnXfU/5T+aZB3bJIfkf4z8VfCAQT8DUTSPBVvSf4mg4k+NItEkaNGhW8Sbpe6/fYFzByn2mfrFOCzxFN8M0Sute+fOJ0HYuNcwYBX7FbwtKlS6P6YpJFQBVJVNcNRTTVkePCCy90nTeUSyJpJonlHj582LXuUs6JOhN52TvWB9RVt6eml6YIR6dOndy4NRWPQQAC8UlA+opqrfvVV18l+Z5Suo5mdfT91q9fP9f+Mj4JcdXZQUDtMPXIKtPvsgqcVIx03333mWQIlSonOS/lE2OhCSh9T1FjNbNRZ0PJYcl/wLwhkMwB1Wklkqs8FXXd0BukqvfJkye7HEut1/S25EBK+WQjPv/8cy3yzDTdP2/ePOvatWuq59S0g4qoMAhAIH4JyAFdu3atKS882NQ5TUWVGzdutPPOOy94Na8hEFMEFDx65JFH3COmBp5Ng5WTLl9HN6KSxpKA/86dO53yT968ebNpVPF12pAOqO6mhg4d6u6mtm/f7hzNQCz6Mp8/f77THVNk1EtTDqqcUGmRygmWLJTyVTUO5YDqTmb8+PGm3Jfp06d7OTTOBQEIRBkB/SirY5q6z5xyyilJRuebm3LdaBQNxSAAgfghoFlc/b9v1qyZK0z0X3njxo1dXcnq1asp3PJDieDfVL3HPHnyJHE+FU1YtWqVbd682d0xeO18ioOm/JctW2bqA9+xY0fnjFaoUMElc6uy9cYbb7SDBw/atGnTXCV/BNlxaAhAIMoJ6DtKPyq33HJLspFqFkXrlWoUyyatR8nsfPbZZ05H8ujRo7F8OYwdAhEnIIkzFYBpZjfQ5DeoyHns2LGBi3keIQIhI6Bq6eVLzncdOOSEytSvVnmVqjKXqcVl3759bcCAAYlSTW6FB/+o97sq3OWEavpMUU996WpKTZ1EvJSE8uByOQUEIJBBAoqASohb0U/dqKqVpSKi6hjzxhtvuFkTzfjEqkm+R9OIahyim3P1kd+6davLAUSGLlbfVcYdaQIS/E8pB1f/nzTDi0WeQEgHVHpiX375ZaK+pnIkpPcpx04Op77IlQf6zDPPuITd7OrQoR8SOaN6YBCAAARCEZBGoloH63tKUjW6cb3sssvczav6cseq6Tqk7ygJH92E+61Vq1Yu/12SdcFpB/5t+AuBSBGQ8yaJKd3YKV1PvemjzXRzprScUKbujvrOwCJPINUpeP/p1bdWXRoUdZRYrqrhpSumKOnTTz9tmprHIAABCEQrAalmDBkyxLW4VAqP9I5V9RrLpu/g0qVLJ8t1V9MQRXFG+/qOYxDwkoBS4Dp37uxE8ZWqp6j8Pffc4+UQ0nQu6ab6ZCWdXxO4w99//+1mdps0aRK4mOcRIhAyAhp8Lt0pSCA3WG5JveIlxaQqU6KQwdR4DQEIQCByBCRJp446oUwOqJRAMAh4RUA1GQpWSTnHn9ai5jSqKJeSzv333+/VUMKeR/6MpuAV6dRsr+TYfv31V6tfv75rUIEDGhZhlmyQqgOqqKdC1Zp+f+edd5KdUD3jZaeddlqydSyAAAQgAIHIEdB3s5Q/QpmCBrRfDEWGZZEg8Ndff9mYMWNM09d+51PnUa3IypUrnWOnmpHAdZEYR3qOKX1gOcaSrlKaoWQbR4wYYd26dUvPYdg2EwRSnYJXQY8qxZTrqXwi6YLKVPDzwgsvWJcuXaxRo0bkGWXiDWBXCEAAAhkhoCinCqn0ox9okpjRtKemGTEIeEFADpy0dEPlHKtmRMEsfS6jzaSOIbF+pQvoL86nt+9QSAf06quvdtXuyiWS1qbyIiTYrNwpmQRc9QUn+RJVmGIQgAAEIOAtgUsuucR1eSpcuLBrCCIn4OuvvzYpl0jH+ZprrvFsQCry0ozY7NmzXf6fZyfmRFFBoFixYrZt2zYXnAoe0O7du12xsqKhGAQCCaQ4BV/K1+VIjxYtWiRu79eXU+Wor7+s+XrPOh29xA14AgEIQAACnhHo2bOnUydRC0b/NOIrr7ziqv29GoRyURW0UCqWfhvkhKo+wNff3KshcJ5sJiBRdylLtG7d2j799NMkoylatKjddtttnss1JhkEL6KSQIoOaPBoFfWU5pyinhJvpr9sMCFeQwACEPCeQIcOHUyP7DDpQisQIeFu/xg03ariDikPvPTSS9kxrFTPqZk8qbeod7rGedNNN9mdd95JMCVVaqmvlE8gcXfx1FT8k08+6YqRRo4cmZhbmfoRWBuPBEJOwQuEop26g7344otdIZLucCT0rukd5YWqMGnFihXxyIxrhgAEIAABHwEVcqi4xO98Cop+K9SNTg6JBPKjydTJT7qU6v398ccf23PPPWc9evRwCi/SVcUyTkDvu1IxVBuiRg/vv/++m0HVTYoaQmAQCCYQMgIqXU/lfqpN1bXXXmvqv648o3z58jnxVgnVf/HFF1atWjWnpSX5BQwCEIAABOKLgLrm+YtTA69c+X61a9d2eaHRorcqp1gFMW+++WaS1qwJCQluRk+6kHJGsYwTUNRbNyR6YBAIRyCkAzp37lybMmWK6culUqVKIY+hO19Nr2gqAwc0JCIWQgACESawY8cO0/eVZGBq1KiR4vdVhIeRIw+/b98+x1ZFJHLcatasmWyaWkEJpWeFMi2XBmS0mNovKmiiyudgk5qApuJxQIPJ8BoCkSMQcgr+xx9/dHevKTmf/uEo0VwaX+rHjkEAAhDwkoCmUMuUKWPjx493XdouuOACVwRBZ7bMvwv6DVBzEQUZ5OBLmFvOZLCUjlothhIYVyGSHqrUjxaTQ600slCmAirlrmIQgIB3BEI6oLVq1bLly5eb/sOmZrNmzXLCspJgwCAAAQh4RWDOnDmmnucLFixwuXwSwVYHlokTJ9qgQYO8GkaOPM9vv/3mWiiqXenUqVOd1qicM0nxyeEUZ79p+l2dZKT/qP2kSTpu3Di74oornOOqopRoMd2gfPvttxYq11NtTdUNB4MABLwjEHIKXoVHkk7QX0U5dRerO0R13lCHDX8OqDRA1QpOhUkYBCAAAa8IyMlUr/NAp0FdVlQEoY4mkn1JKdrl1Rhj9TzDhg1z7QglqRNo/or3efPmWb169dwqRUVVsKrfATUlkaOqIp8ZM2ZYnTp1AnfP9uclS5a0Zs2aue48iuTmyvV/P3+6mbn33ntdVXy2D5IBQCCOCIR0QHX9iiz079/fBgwY4ITog5lII1Tac5KvwCAAAQh4SeCnn34K2elHRRDK8/v5559xQDP4hohdqMIiHU5aj2Lvd0C1TI6cCnhiwXTToq43uXPndukamuVTB5yvvvrKfW5i4RoYIwRyCoEUHVBVMaoFp4qMtm/f7jojKRld0+2KMOgh7S8MAhCAgNcEJHiumRjpEgebHAytxzJGQOx27twZcmexVXV7rJp+s2bOnGmrV682FSVpVk8pZ3xeYvUdZdyxTCBFB9R/UdLvUu6P/qMGCtH71/MXAhCAgNcE1HlH2pPqwhNoumH+5ZdfTPl+WMYING/e3EVAW7ZsmSTIoJx/dbmJlWhnalcvLWs9MAhAIPsIpOiAKq9H0xXqZKAvdMmcyDR1cc4559j555/vuh3oLwYBCEDASwJPPfWUcyB0gzx9+nRXBKOqeE0dS/ycvPSMvxvK+1dPeeXUfv755y6VQXmS99xzj82fPz9k1DnjZ0t5z3///ddUHCTHV1qdmvZv27ZtEqc45b1ZA4HsIyAlDuUZK8VD9TP+fOPsG1F0njmkA4oQfXS+WYwKAhD4PwK6EV67dq1zOAcOHOhmZ1Q0qU43JUqUAFMmCUjaSvmeL774okt1kA6o6gKkBeqVaWpcTvDNN9/s/kqnU/rTqrbX+4/FDwEpLWh2Q90X1XFJRW/SbI3GNEApQah5j+TL/N2hHnzwQevTp0/8vGFpvVLfnWUy891xJvgEhhN8UkzJ1gUuGD58eELFihUDF8XNc19KQoIvOhw318uFQgACEPCKgM/ZTPAVlCU7XdmyZROeffbZZMtZkHMJTJs2LcHnzyS88847Cb687wRfkZx7fcYZZyT4ooxRdeF///23G9uNN96YODZfvrFb1q9fP0/H6ms3m+BLUfL0nOk9WcgqIoTo0+q+sx0EIAABCGQlAaV/TZgwwRW/Bh/3hx9+sEceeSSZIH7wdrzOGQRU+Ny4cWPXlfF///ufm85W2p/P0XGpgNGWj6wUoKpVq9p7771nJ554onsT1NBBXcHUbUuzNth/BEI6oAjR/weIZxCAAAQg4B2BPXv2uKlLidsHm6Y0CxcubHJMsJxPQJqzV111ldMkD77aIUOG2Lvvvhu8OFtfK1f6ueeeSzYGFXFLg1bT8th/BELmgCJE/x8gnkEAAhBIjcCyZctchEbSdSqUIQc1NVrh1xUpUsTl9MrJVAFHoCm/TrKAapTihakYa+jQoa7ltBzf6667zh5++GEvTs05fASkvBPqRkRw1GUrGtunKjobyrQ8pXWhto+HZSEdUF04QvTx8PZzjRCAQEYJ6Mekffv29s0337guQPqxlDSUojKq1sYyRkAVw126dLH69eubnHspHcjEW07gLbfckji9mbEzpG0vdXNS69E333zTtRaV4ysNVBXEqM99NBbApO3KYmerGjVquOIdpWUEF56NGDHCdWmMpqvRDajGdYWvFW2gqRp+7NixpoJJLICA7z91quariE/YunVrgi8UnuDTgEvw9dJN2LhxY4JPIiPV/XL6SoqQcvo7zPVBIHUCvmiYKy7w9UZP3HDNmjVumU8+KHEZT9JPQL8vl19+uWM5ePDghCeffDKhevXqCddcc036D5aBPbZt2+bO/f333yfb25fTRyFUMiqRW+C7yXPvhQp8/ObL/XTL5It4Zb5oeEK3bt3cZ9BXge+KoYLP7YvQu3H58lQTfG2B3Wp/EdJDDz0UvHlEX8dsEVKAf+qeSohed37XXnutXXrppXb22We7uz/fF29UhsCDx89rCEAAAllJQF2YJMoufWRJBfmtTJkyNnnyZDdt61/G3/QTUHRRUcYpU6bY3r173UPRo88++yz9B8vAHtIebdGihSkCF2wvvPCCffHFF8GLeR0hAooc+m48TCkujRo1ssqVK9tjjz3mJJnki3hhb731ljVo0MAqVapkPifU1PJXzxctWpTk9EoXkP6nOkaqu5avUt/q1q1r0i1W63IsKYEUp+DVD/j22293uU1quynNrZ49eyaZdpBIsPSufG580qN69Ern9U/PBJ/Sd7fkFulDi0EAAhDISgLSG61Tp47lzZs32WF9kTv345hsBQvSTaBp06amh9emquWU8kyVBqD18WKa/lYFt1IS1BxAn/u77rrLkzQIMdZv/KRJk8wXlXZNcZT7qU5ncgK9MBUWde7c2Xwzwa4rpM4ph1jFUSoskjJDYN63qt/V7lWOqHJU9XlBiD70OxWyCl65TILrC2+7/Avd1ffu3dtB94WVQx/Jw6Vjxoyx0qVLu24nqthXpVywyTHu2LFj8GJeQwACEMg0Af2o6AcxlO3YsYPe4qHAxNAyRdkUBQ0VXHn77bddFC6GLifDQ9X1+7RXzafBaa1bt3Y5z6+88opz/uQneGmaiVUU0peK4Znzqet7//33TRX3On+gNWzY0M0KyzkONjnN6samSCjOZzCd/16HdEA17aEvUXn20rVSqzv9Z1QbNiXX6y4ou+yrr75yjqXagfbt29d16VDE4aWXXsquIXFeCEAgzghI20+RjlDTalqnamksdglICUaOl1IBAp1QFSSpyESdbeLBNAuqaK+igD5xdbv++utdJ6omTZo4ZzSQTU7loRtN/Z8OZaVKlXKR0VDrWBaeQMgp+PXr17tcT8lh+E1O3tSpU90diKbis8vhk/CsPvwai0y5IP3793cpAgUKFCDq6X/D+AsBCGSYgKYdJRotB8TviAQeTBW5ygPUD5AqtdUnXfmgal2plKXnn38+cHOexxgB/7Sv8vz0GVA+qKrgN2zY4NIrSpYsGWNXlP7hqiW30ux8RTTJdlYurqJ7qu5OSSYp2U4xukAzwN999537DARfgoJ1t912W/BiXqeRQEgH9Mwzz7TFixe7/AUJ//pNBUjqEdymTRs3Ba7tvDZ9AQROreuLYtCgQS4q27VrV/flL+kMDAIQgEBGCGjK7d577zVfO2JToeWWLVvc7I+m/gJNszAqRtJNcK9evUxi05oinDhxYpJc+cB9eB47BDR1+uuvv7rHL7/8YgrI6DMQKu83dq4q7SNVHYVuxIK1WHUERf9VYLNz584c74Deeuut5ms57iSfdCPiN83A+lSBEoNh/uX8TTuBkA6oEmuV4KtEW4XglU/pt5YtW9rrr7/uknKrVavmX+zZXzm9SvBVEnSgKVVg06ZNLk9F0wUYBCAAgfQSkPC40oykg1yzZk23+yf4oe/IAAA6u0lEQVSffOKqoaX/qLaAgaYfZ1VFYzmXQIUKFUyPeDPlMOomTBHQcuXKJbl8NQTQckX7c7qdd955bpbDJ2vk/Au1AlV64k8//eQc8MAgXU5nkdXXFzIHVAn2H3/8sWkqvl+/fsnO2alTJ5MswW+//ZZsXaQXyBlW0q+inUuWLElyOr9gsMRg9eHAIAABCKSVgHLbleM2bdq0ROdT+yrq8dFHH7nvwnjIeUsrL7bL2QQkLyb1m/Lly1tg8bGm5uUjKCdUDmo82IUXXuhmQlScLS433HCDc0pTUkqIByZZcY0hI6A6sJw4TXerEj6UaRpcb4amq7w0feglgaKIp3I+q1atmnh6TZloPIrajh492rIjQps4GJ5AAAIxRUDFBn6tweCBa+ZHN78HDx6Mmx/dYAa8jj8CPvF0p/ag/xdKS5HzpfxPpeGpMj6eTLOv6tCFZR2B43x39Nkj4pnJa9AdmQSKTz/9dFPPYP3H0F2Z3xYuXOgq+SWeHwmT8/vss8+6SGwkjs8xIQABbwlI+UP6gsrrDDZFRzXV9vvvvzt5leD1vIZATiagKWelviknWjqgemDRTUBSYpKs1N9otRQjoIEDVkGStL+UkB1K+2vp0qWBm0f8uYoCVG2qaKee6z+FTD8QqkpVEZK6ZvhzuCI+IE4AAQjEPAFV9eqhL+2bb745yfUo712zLV6JXyc5OS8gkM0ENJvIjGI2vwk58PRhHVA5eKrslMmxU4QgO01pAbr7UvW7pgEkkaDIp17v2bPH1q1bZxMmTHCVqOrcIAkVDAIQgEA4AvoOUW67blxV0KgqV0U+hw8f7vI/FRnVNhgEIAABCGSeQFgHVAL0aimlNlSBU9yZP3XGjqCOBKV8UU6J46cUjRg8eLBr36ZIhiKhGAQgAIG0ELjkkktc7rty3JVnLofzsssus+XLl4eUo0nLMdkGAhCAAASSEwhZBR+4mbTAJDsQDc6nxqXpfk2PpeR8ahuJRHfyVer7xeq1DIMABCCQFgISGZcck/o4S2hbVfESJMcgAAEIQCDrCIR1QNXvVHJLK1euzLqzZuJItWvXDtn7PfiQSpiOB42y4OvmNQQgkDUEpKpBH+esYRnpo6hOQaos6sqjwlSppYSqV4j0ODg+BCCQdgIhp+C///57FwHwH0bJ95qGatWqleuAFPylfN999/k3jfhfSaHICVXFavv27V2OpzpUqF2ackClXapuTWqRpWn69Jra6z399NNhd5Mci9ISMAhAAAIQyD4CCpDUqFHD5eq+9957TrNSs2RSKtF3dPHixbNvcJwZAhBIkUBIGSYl3T/88MMp7hS8QtNUXpq+cLp162bKT5UobrCpWOrBBx+0+vXrB68K+1qObVqivU2bNrWnnnrK7r777rDHZAMIQAACEMh6AlJAUcrViBEj3G9C4BnULU861p9++inFY4FgeB4XBGJBhimkAxor786RI0fcF4yinupZK6HYEiVKuJ69kb4GdEAjTZjjQwACEEidgNpBqj2q1E+CTTrRKljVzJhmyDAIxBOBWHBAQ07Bx8qbdOKJJ9q5557rHrEyZsYJAQhAAAJZQ0BFsgoGhLK8efO66fgY7bUS6pJYBoEcRSBNDmi0CdGn5R3Yv3+/KyBQCzEMAhCAAARyHoFy5cq5VpFr1qxJpvnsb8esLnkYBCAQfQTCOqDRJkSfVoSSUmnUqJF98MEHad2F7SAAAQhAIIYIKMCgfuWaCZMTqsYkMtUHqE7A6y59MYSOoUIg2wmEdUD1HzmahOjTSqxHjx6mu2MMAhCAAARyLoF77rnHlI5VpUoVO++880y1Adu3b7cvv/wyqvtg59x3hCuDQNoIhHVAo02IPm2XZTZo0KC0bsp2EIAABCAQwwTuvPNOu+mmm2zFihXOGVXzlNSalcTwpTJ0COQYAmEdUAnR33vvvU6aSHeX0WZKMN+9e7cpzydaujVFGyPGAwEIQCBaCKimYMaMGa5AqFatWibZPLU8zaypGKlmzZqZPQz7QwACHhEI6YBGsxC9uCgv9cUXX7T333/fPZcWnKxgwYJOdkNfaOoBnz9/frecfyCQ0wgsWLDAHnvsMdOPeb58+Vy+s/R7pYmIQSBaCQwYMMBpdnbo0MHy5MnjJJRKly5tq1atoutUtL5pERqXOlWNGzfOvvvuO/cd1qRJE7vmmmsidDYOG40EQuqARrMQ/YYNG6xOnTrujrlNmzYu6VyRT91BS+9NenATJkwwRUZ1l122bNmIcEcHNCJYOWgaCCxbtszltkl8+7rrrnP9yuvVq+e6g0n9gRuvNEBkE88JvPrqq6ap8uDPqL6/77//fvOyo57nF88JkxA4dOiQ++2+5JJLrHnz5u4z0atXL7vlllvszTffTLItLzJGIBZ0QEM6oBm7XG/2UneLH3/80bXZTCnHR6L06lSk9qGKhEbCcEAjQZVjhiOgFrCKeE6cONG1xg3cXtEDNWKQY4pBINoIlC9f3saMGWOXXnppkqGpyLVYsWImUXn9xXI2gX///ddOOeUUq1ixoot++q9Wv9v6TR8yZIj17t3bv5i/GSQQCw5o2PYQc+fONd2ZyOmLBpOshvr8puR8aoyahuzUqZNNnTo1GobMGCCQZQT0/7B69erJnE+dYOjQoe7GLMtOxoEgkIUENHulnu3Bpu9yOSPqaIflfAJr1651nak09R5o+t1euHAhEdBAKDn8eVgHVHcqkrOQxIUezz//vO3atSvbsNSuXdvmzZsX9vwzZ860s846K+x2bACBWCKgvKlChQqFHLKWaz0WnQT03mh6UTf0jz76qPuxjc6RRmZUxYsXNzmhwaZ0KeWAqpUylvMJbNq0yS6++OKQF6oo+datW0OuY2HOIxDWAb3wwgudtIUKky6//HJ74oknnGPXokUL+/jjj10Pdi+xtGvXzonLa4pdCczffvut+/L67bffXDhfwvPKixs7dqz17NnTy6FxLghEnMBFF11kS5YssT///DPZuTS9GSrClGxDFnhOQKkTmhKbNGmSK5Q8duyYm4pWMWW8WNu2bU25ysGmVCm1zZSDiuV8Auecc44pjz2ULV++3M4+++xQq1iWEwn47j7TZT6R3wTfl2hC586dE3xV5wmnnXZags/RS/jll1/SdZzMbOzLFUpo0KBBwvHHH5/ge0+SPXxV8Am+CGhmThF2X1+hR8LIkSPDbscGEMhqAr4ImvvM7927N/HQkydPdst8OoiJy3gSHQT0nanvKV+Vb5IB+WaS3PIPP/wwyfKc+sKnVpLgu0Fy1+y7WUrwFYsm3HDDDQk+EfkEn4OeUy+b6woi4MsBTdBvtE+CK8ka3wyB+2y8/PLLSZan98W7776b4IuwJhQpUiTBpweb4KsDSe8hcsT2vmBFgi9lMaqvJaQMU2qO9saNG10E5ocffnBRGFWxKW9Dd/JqiSZpmEib2q6pwl0dLzQe5Q4pgVlTOCrC8H3wIj0Ejg+BbCPw7LPPmiJqSo+RTu++ffvc/wHlayuXDosuAosWLXLRveCcdN/Nu3300Ucm1ZHWrVtH16AjMBppNSvvT2kISuv666+/TClVo0aNMrXUxOKDgC9wZJ9++qkrptTvtXTGlZ7y2muvWf/+/Z1SQkZJ+IJCrgXrlClTnCasUj6qVatms2fPdvnxWaE3m9GxsV9yAmlyQCX0Ls1NTXlLf/D00093XSf0ulKlSu6ovrt4893NWsuWLd0bnvxUWb9E7dfkjOqBQSCeCKjSXQoPmsqS7JKmdzWNiUUfAf0IXnHFFSEHphQn6RrHi8kB6NKli3vEyzVznckJ6LtKQSOlzCm9T6/feeedkCkayfcOveTXX391zqf+P/nziSXx5QsBmnJLdaNz2223hd6ZpdlCIKwDOmfOHNepQqOTzMtnn33mJI5y5Uq6a7NmzdwF7NixI1suhJNCIN4I6EawUaNG8XbZMXe9ivL4UpRCjnvlypWJP5YhN2AhBHIoAfkQqunQIytMs6JyMP3OZ+AxNTMrfXAc0EAq2f88qRcZYjy6M3nmmWesffv2VrRo0RBb/N8iSWls377dRUdT3IgVEIAABOKMgCp+lS4kDeOXXnop8eo1BS0RbhWPYRCAQOYIaBpf+tyhTF0StR6LLgJhHVBV1fora5VrtmbNGpd7pko25fT4TXczishgEIAABCDwHwHdnM+aNcuJrEvH2FfAaUpreuqpp0z5vDfddNN/G/MMAhDIEAH5KconDWXKs/b7MaHWsyx7CISVYdKwpNEm2SMVPUgEW+0tTz31VCfJ5O/Dnj3D56wQgAAEop+AZo8knaX2wdIx1myR8t+kCYpBAAKZJ6A8axX2Kc840C/p16+fffHFFyk6p5k/M0fIKIGwEVD17VV+Z548eeyNN96wcuXKmTQ3VdH55JNPugpcTdFjEIAABCCQMgFND6JNnDIf1kAgMwRUXS91BRVkqquST+rJNT6QwoIKkxQ0w6KLQFgHdPr06e5NVIVZmTJl3Ojr1q1rt9xyi33++eeuJaASfFNrjRldl8xoIAABCEAAAhDIaQSUFigxeylP+PTC7YwzzrAKFSo4hzSnXWtOuJ6wDui2bdtMnSr8zmfgRUuDUFIKmk5STigGAQhAAAIQgAAEspOA/BF8kux8B9J27rA5oMqr+Omnn1zxUfAh1YqzVKlSvNHBYHgNAQhAAAIQgAAEIJAigZARUHXuUNWm3yTiKsHk+vXr21VXXeW6VqgT0uuvv259+/b1b8ZfCEAAAhCAAAQgAAEIhCUQ0gGdP3++67ISuLdyKyRKr4fflPepFpxPPPGEfxF/IQABCEAAAhCAAAQgkCqBkA7o3XffbXqk1/bu3euEYAP1QdN7DLaHAAQgAAEIQAACEMjZBMLmgKbn8lWstGLFivTswrYQgAAEIAABCEAAAnFGIEsd0Dhjx+VCAAIQgAAEIAABCGSAAA5oBqCxCwQgAAEIQAACEIBAxgnggGacHXtCAAIQgAAEIAABCGSAAA5oBqCxCwQgAAEIQAACEIBAxgnggGacHXtCAAIQgAAEIAABCGSAAA5oBqCxCwQgAAEIQAACEIBAxgnggGacHXtCAAIQgAAEIACBDBP4999/bdeuXbZu3To7evRoho8TizvigMbiu8aYIQABCEAAAhCIaQJr1661iy++2CpWrGgNGjSwQoUK2ahRo2L6mtIz+DQ7oAkJCYnH3blzp3366ae2bdu2xGV6MmbMGCtTpkySZbyAAAQgAAEIQAACEPiPwO7du61s2bLWsmXLxAjo999/b7fddpsNGTLkvw1z8LM0OaCvvPKKnX/++Q7DmjVrrEKFCtaiRQs7++yzberUqYl4qlevbvny5Ut8zRMIQAACEIAABCAAgaQEevXqZTfddJM98sgjdtxxx7mVlSpVsi1bttjgwYNtz549SXfIga9C9oIPvM5Vq1ZZz549rUuXLqYoqDzzXLly2fz58+29996zrl272qZNmwJ34TkEIJABAsoFmjx5sn3zzTd24oknuikZTctgEIAABCCQswjoe37mzJnJLurMM8900/KLFi2yxo0bJ1ufkxaEjYAuXLjQhYlHjBjhvPRJkyZZq1atrFatWnb//ffb5s2bbfv27TmJCdcCAc8JHDt2zJo0aWJPPfWUFS5c2DmgDRs2tPbt27sbP88HxAkhAAEIQCBiBE444QT7559/Qh5fy7U+p1vYCOjvv/9uRYsWdRyWLVtmW7dudT+UWnD48GEXDc2bN29O58T1QSCiBBTpXLx4se3fvz/xPA8++KDlzp3bpbw8+uijict5AgEIQAACsU2gfv369txzz5lSHANtxYoV9vXXX9snn3wSuDhHPg8bAa1cubIpFDx79mwbNmyYnXzyydaoUSP7888/7YknnrCaNWtawYIFcyQcLgoCXhDYu3ev+/8lKY5AU6rLr7/+aq+++qopQopBAAIQgEDOIDBo0CB788037eGHH7ZDhw65i5KfpTzQiRMnxoVfFTYCKi/9mmuusSuuuMIBGj58uBUoUMA6d+5smo4fOXJkzvg0cBUQyCYCSmO58MILLU+ePMlGUL58efvjjz/cVI3yQjEIQAACEIh9AsWLF3ff7dddd51Lu9IVnXPOOfbOO++4NMfYv8LwVxDWAdUhPvjgA1NYWD+Qinyq6Kh79+72/PPPe+6l16hRw40l/KWZexPHjRuXlk3ZBgLZRkBJ5xs3bnQixJpyDzQtlzZc8PLAbXgOAQhAAAKxR0Azyl9++aULMEiEXj7W8ceHnZiOvQtNYcRpckClV9W3b1/76quvEpNmlRf6wAMPmKQEvDTlTFx//fV25MgRF7pO7c1S9AiDQLQTOPXUU61evXruIXWJQNMdcZ8+fRJlOgLX8RwCEIAABGKbgCSYFGCIxyBDWAdUjp6U+uWZ9+7d26688kr3Y6gfygEDBjitqscee8yzT0DdunWdTE2VKlVMsjX6ccYgEMsE9AUkSTMV82laRv+f9P9O6S1KgYkXUeJYfg8ZOwQgAAEIpI9AWAd07ty5tmPHDpMAvaYK/SaJGOWCqhBp4MCBnoaNFdnUj7Qc4FtvvdWKFCniHxZ/IRCTBDQVoymY0aNHO41d5XtqhuGGG26Iyeth0BCAAAQgAIHUCIR1QCVEr6hnoPPpP2Dbtm3dFLyKKEqWLOlf7Mnfe+65x6pVq+aioJ6ckJNAIMIEVPWuGyo9MAhAAAIQgEBOJhA22/Xyyy+3BQsW2Nq1a5NxkIRAqVKlPHc+NRCJtGp6slixYsnGxQIIQAACEIAABCAAgeglEDYCqq4sderUcdpUckavuuoqy58/v6lD0tixY12l+TPPPJN4hbfcckuicH3iwgg+UXtQFUnJIdVYMQhAAAIQgAAEIACB6CYQ1gFV9yP1K1VOmiKhevhNhUlTpkxxD/+yq6++OuIO6JYtW+zFF1+0999/3/Tc385KgviKyCplQHmpcpQxCEAAAhCAAAQgAIHoIhDWAVV/6n379kXNqDds2OAisqocbtOmjZUpU8ZFPvV6z549tm7dOpswYYLrJDBjxgzXxz5qBp/CQMaPH29Tp051GqvVq1e3Hj16mKR5MAhAAAIQgAAEIJATCYR1QAMv+q+//nLV8HL6pFl10kknBa725LkkaRTlnD59eornHzx4sDVt2tTGjBnjIqGeDCyDJ5G+qiK5asslp/O1116z/v3729atW50kTwYPy24QgAAEIAABCEAgagmELULSyFXl3rp1azelrd7w6orUr18/p8F58OBBTy9u6dKldvPNN6fofGowco47derkooqeDi6dJxs2bJgNHTrU1q9f78arllxKaZC2aePGjZ0WZDoPyeYQgAAEIAABCEAg6gmEjYBKEPvaa691eZbqQvTQQw+5i5IgfJcuXdy0sZf94GvXrm3z5s2zrl27pgpXeatnnXVWqtuEWqlq/8A811DbaJk0Gw8cOJDS6jQtV+RT1xLczUlRXk3J//zzz1a1atU0HYuNIAABCEAAAhCAQKwQCOuAaqpbhT7SA1VPak0Py9QOU0U/HTt2NFWiKwfTC2vXrp3JCZU4fvv27V2Op4To5cQpB1TRROVUKpKosafXFO2dPHly2N3UhSmz0V9NsyudIJSVLl3aTcPjgIaiwzIIQAACEIAABGKZQFgHdPXq1U6CSc5nsKlF57Zt25zTJ4fJC1MLTlXmd+vWzTm/x44dS3ZaVcFPmzbN9dZOtjLMAklN6RHOPvvss0xrkMr5XLx4cUiR/y+++MKefvrpcMNgPQQgAAEIQAACEIg5AmEdUDmWmibetWtXMnkl9a9W95aMTHVnhtS5555rqnBXesDGjRudA6wpcXVrKlGiRMy05uzZs6dLbwguODrttNPcNZx33nmZwcS+EIAABCAAAQhAICoJhHVAGzRo4Jy6Zs2a2b333muKOCoqOmnSJBsxYoRpSlwaodlhOq+cUT2Cbf/+/c45Vo/taDXJSKnqXY5z9+7dXRW8HGv1up89e7ZnaQ3RyodxQQACEIAABCCQMwmErYKXmPvHH3/seq7L2VThTYcOHezxxx93epwvvPBCVJJRb3rlp0a73XbbbbZ8+XKXy6rq/QcffNDmzJnjKvmjfeyMDwIQgAAEIAABCGSEQNgIqA560UUX2aJFi1y+oqKfijxWqlTJKlasmJFzerKPxNzLlSvnybkyexKx1AODAAQgAAEIQAAC8UAgTQ6oQKjKXEVHesSCSdgdgwAEIAABCEAAAhCIPgIhHdAlS5bY3Llz0zzau+++O83bZvWGkoDavXu3nXDCCa4lZ1Yfn+NBAAIQgAAEIAABCGQtgZAO6KxZs+z+++9P85m8dkClS/riiy+6FpZ6/s8//7ixSpdU0kaSYRo4cKDr3JTmi2BDCEAAAhCAAAQgAAFPCIR0QHv16mV6RKNt2LDBFT9J+F5V5OpLX7hwYVcxLiH6devW2YQJE2zixIlOqqls2bLReBmMCQIQgAAEIAABCMQtgZAOaDRPwatNpaKc6nJ00kknhXzjBg8ebE2bNrUxY8a4SGjIjVgIAQhAAAIQgAAEIJAtBEI6oNE8Bb906VInr5SS8ymKkjPq1KmTDR8+HAc0Wz5WnBQCEIAABCAAAQikTCCkDqim3w8fPpzmR8qHz/o16gOvzkzhbObMmZ53aAo3JtZDAAIQgAAEIAABCJiFjICmB8wff/zhOvikZ5/MbCsxfDmhO3bssPbt2zsB9yJFijiZKOWArl+/3saPH29Tpkxx0/SZORf7QgACEIAABCAAAQhkPYE0OaDKt3z55Zed3NG///7rRqG/Bw8etFWrVrlIadYPLfQRq1SpYsuWLbNu3bq5qXi1Bg02VcFPmzbN6tWrF7yK1xCAAAQgAAEIQAAC2UwgrAO6adMmu/baa61AgQKuW48KlBo3bmw///yzcz6HDRvm+SWo97t6ph85csQ2btzoop5Hjx51PdVLlChhiohiEIAABCAAAQhAAALRSSCsA/rNN9+Yooxr1qxx/eCLFi3qqsvz5ctnd9xxh61duzbbrkwtQeWM6oFBAAIQgAAEIAABCMQGgZBFSIFD37Ztm9WsWdOJuhcqVMiKFy9uCxcudLqb/fv3t1GjRpmijxgEIAABCEAAAhCAAATSQiCsA1q6dGkn7u4/2HnnnZdYhX7qqae6xZs3b/av5i8EIAABCEAAAhCAAARSJRDWAa1Vq5bt3bvX6Wqq4l2vJfCuQqBnnnnGNCUvYXgMAhCAAAQgAAEIQAACaSEQ1gE944wz7I033rCpU6fa9u3bXd6nckIrV67sRN5Vja62mBgEIBCawMqVK+3VV191j59++in0RiyFAAQgAAEIxBGBsA6oWKjnuqrNy5cvb6effrr98MMP9sEHH9jq1avtkUceiSNcXCoE0kegT58+Vr9+fVMHL80aXHTRRda2bdv0HYStIQABCEAAAjmMQNgqeP/1quLcb6eccopzSv2v+QsBCCQn8Pzzz9u4ceOcgoRUI2QvvPCCqY2scquffPLJ5DuxBAIQgAAEIBAHBNIUAY0DDlwiBLKUgBo1yMGcM2eO+Z1PnUA3clKW0JT8oUOHsvScHAwCEIAABCAQKwRwQGPlnWKcMUVg//79Tp5MaSvBprxqKUgopxqDAAQgAAEIxCMBHNB4fNe55ogTyJ8/v+vUdeDAgWTnUgevnTt30rErGRkWQAACEIBAvBDAAY2Xd5rr9JRArly5rHnz5q6NbfCJq1Wr5oqRAqfmg7fhNQQgAAEIQCAnE8ABjaF3d9OmTdayZUsXOStQoIA1bNjQKRHE0CXE1VBff/11+/rrr61EiRI2ZcoUJ2V2ww032M8//2yzZ8+244/nv19cfSC4WAhAAAIQSCTAL2Aiiuh+opzCkiVLuuppyV8pf7Bx48ZOGmv+/PnRPfg4HZ0inJpuv/vuu2348OGmqnjJMO3bt88VI8UpFi4bAhCAAAQgYGmWYYJV9hFISEiwdu3a2XXXXWfPPvts4kD69etnZcuWtfbt27tIqKZ9MW8IqMp969atdvDgQXdTEChTFjiC3Llz23333ecegct5DgEIQAACEIhnAkRAY+DdV+epuXPn2ttvv51stK1bt3ZTuWvXrk22jgWRISBR+UqVKrm2tFdffbWTWRo/fnxkTsZRIQABCEAAAjmQACGzGHlTpRl58sknhxxtnjx57PDhwyHXsTBrCSgPt2rVqjZq1Ci79dZb3cHlkGrZ3r17rXv37ll7Qo4GAQhAAAIQyIEEiIDGwJt6wgknWPXq1V370+DhqqBlxYoVVq5cuSSrlDP6+OOPW5MmTdxDoujKR8QyR+D222+3Bx54INH51NGqVKliq1atsv79+9tff/2VuROwNwQgAAEIQCAOCOCAxsibPHjwYLvpppvso48+Shzxb7/9ZhdccIGNHj3aFAX129GjR61gwYK2ZMkSu/fee61Xr16mimy1gAylS+nfj7/hCXz77bfWt2/fZBvqBkDtNXVDgEEAAhCAAAQgkDoBpuBT5xM1a+vVq2ea6lW0rUKFCs7h3Lx5s6uu7tixY+I4VbCk6vgyZcrYxIkTE5evWbPGRUI7depkH374oR133HGJ63iSdgIqKkopkizHn0KwtLNkSwhAAAIQiF8COKAx9N5XrlzZ5XpqulfOznnnnZcsL1S5ogsWLHD5iMGX9sknn1jRokWdA6VoKJZ+AroRGDJkiA0dOjTJzjNnzrQff/zRFSclWcELCEAAAhCAAASSEcABTYYkuhdI7kfT7inZnj17nFB9KAdTRUx58+a1P//80zmiKR2D5SkTkON5zjnn2Lp162zcuHEuEv3pp5+6BgHz5s1zaQ4p780aCEAAAhCAAAREgBzQHPY5KFasmNOm3LJlS7IrU86o9CtPPfXUZOtYkDYCagYgB165tIomK/f2sccesy+++MJq166dtoOwFQQgAAEIQCDOCRABzWEfAOUo3nXXXa79oyqyFfGUSTBdhTJPP/00eYqZfM/VBnXq1KnOmVfOrZhjEIAABCAAAQiknQAOaNpZxcyWgwYNsl27djmB9G7dupmE7CdPnmw9e/akI08WvYsq4qLgKItgchgIQAACEIg7AkzB59C3/NVXX7Xly5e7qvlq1arZ9OnTXS/yrLhcObSagpb4vSKAGAQgAAEIQAACEEgPgZiPgMoB2r17t0msvXDhwum59hy/rdpF6pGVpnagErjfuXOnq8S/7LLLbNKkSUl0SLPyfBwLAhCAAAQgAIGcRyAmI6AqsOnXr5+VKlXKVBWuwpsiRYpYoUKFTFJFvXv3RnA9Ap/VMWPGmHREx44da/v27TNV3NeoUcOx37FjRwTO6O0hdTMjeSsVGalYC4MABCAAAQhAIDIEYi4CumHDBqtTp44TUm/Tpo0TXFfkUzl5cogkjzNhwgQnwj5jxgwrW7ZsZMjlsKNKqP67775zkUxVc59++ulJrvCPP/4wCd5rO4ncy1QBrhafv//+u4uKDh8+PMk+sfRCUXR1mpo9e7YrKtJnSnqfrVu3jqXLYKwQgAAEIACBmCAQcw6onAJFPpXTGErrUtTVtrJp06amiN3AgQNj4o3IzkHeeeedzmGvX7++q5Zv1aqVe62/flMLSvWV9zuf/uX6q+Im3QzEqkkhQJJKcrDlaOtztWjRIiertHHjRtfONFavjXFDAAIQgAAEopFAzE3Bqx3lzTffnKLzKciSxdFUsaRysNQJ3HbbbaaCJTla7733nn322Wf2yy+/2PXXX5+klaemp48/PvTHRdXgKU1Za7mcun/++SdqC5YeeOAB52yOHj068XOl1IL169e7yK6ioxgEIAABCEAAAllHILRHkXXHz/IjaXpYHWfCmVojnnXWWeE2i+v1cgxHjRqVGPXzw1CLT8k2STPUb5dccokpCqrio2B7+OGHrW7dusGL7ZlnnrGzzz7b6Y8WLFjQObUpOaraWZHItWvXur9eVtfPmjXLXn755WTjP/PMM126x/z585OtYwEEIAABCEAAAhknEHNT8O3atXPRKhW9tG/f3uV4qgBJ0TnlgCpqNX78eJsyZYqbps84mpy/p5y9iy++2E455ZRkF9uwYUO78cYbE5drirpPnz4uN3TFihVWsWJFpy/64IMPupxbsQ80pT+oUGzlypVWoUIFkyh+jx493Psleaj8+fMnbi6nVOL52qd48eK2adMmU891RbBTirom7pwFT3T+lMTkdX7JTmEQgAAEIAABCGQdgZhzQKtUqWLLli0zCawrZy+Uc3DllVfatGnTnBOTdahy3pEUlUxpetkvZB941Q899JBzVuUcqq+8KuHLly/vpu8D23uuXr3avTf79+9PdDTz5ctnb775prVo0cJeeuklu//++xMPraj2999/7yKxUjI4dOiQ208FZHJgg3N95TDKed68ebPLB1Zv9sw4qpdffrmNGDHCgouoFO1Vn3etwyAAAQhAAAIQyDoCMTcFr0s/99xzTRXuf//9t8nZ+eqrr1zEU/mhcqj0WgU1WOoEVMwlGSsVdgWbpuGbNWsWvNi6d+9uksGaO3euq4jXtLym2QNNleSdO3dOdD4D191xxx3uvfMv03ul6ns5lXI+ZaquV86o3mdFRQNNTq/eW0Vo1YNd0VU5oNo+oybHWk6xCqk0Dpk+V1ICUJX/GWeckdFDsx8EIAABCEAAAiEIxFwENPAa5DwpSibHBSH6QDJpe65pZ6UqiKGmxf36qS+88ILLw1R+aCjTfiVLlgy1yi1T/qbej1Cm5YH5nd98840NGDDAyWgFby9ZJKkddO3a1a3SDYfSBVq2bGlz5sxxy3QspWBIEF/HSmkqPfjYga+VKyzHtmbNmu74Op6isHLMlXaAQQACEIAABCCQtQRiMgKKEH3WfQgkq6R8Wk1zK6f23nvvdVJLarWZkhMZ7uzSaVURk1p1Blvfvn1dYY9/ufRb/VFH/zL/X0U1A6fWX3/9dbvwwgvto48+8m+SqP+qnNJPPvkkcXl6nygd4eeff3b5p4ruShUA5zO9FNkeAhCAAAQgkDYCMRcBRYg+bW9serZSJ6mRI0emZ5dUt1WBkqKUmkpXnqYijEeOHLEOHTqY0iQ0be+3Bg0auHze/v37J3N4u3TpkmQKXnmicpBD2VVXXWULFy7MlB6pnF1FWEMVZYU6J8sgAAEIQAACEMgYgZhzQBGiz9gb7fVeyqlUakS5cuVcq1R1S1JkVNXyckz9pgKg6tWru8jml19+6fJJtY2cVaUGKCrrN0U5FZkNZWqfqUInDAIQgAAEIACB6CcQc1PwCNFH/4fKP8InnnjC9u7d6/I1t27d6qblA6vl/duNGzfORUwvuugi0/rSpUs7R1SC+IFT8Co8UkV6cMGRnNLHH3/cGjdu7D8kfyEAAQhAAAIQiGICMeeAIkQfxZ+mEENToZgKlgoUKJDEmQzeVM6qFAyk4yqB/Ndeey1ZQZG6M1WrVs0t/+GHH0wyTyo80rHVzUiFSBgEIAABCEAAAtFPIOam4CMtRK9o3O233x72nZOw+rp168JuxwZpJ6CiJ78UU0p7SZbp/PPPN1XIqxhNElDqYqR+9hgEIAABCEAAArFB4DifjE1CbAz1v1H+9ttvrnBFLRRTEqJXh56MaIFqejdU9fZ/Z/+/ZxJjf+6550K2oAzeltcQgAAEIAABCEDAKwKVK1d2Rbz6G60WcxFQgfQL0auyWnI5mrY9evSoqXd3iRIlnC5kRoHnypXL9Ahn0ptMy3bhjsN6CEAAAhCAAAQgEG8EwntaUUxE+YVyRvXAIAABCEAAAhCAAARig0DMFSGlFasKVNQ5B4MABCAAAQhAAAIQiC4COdYBVeV1x44do4s2o4EABCAAAQhAAAIQsJiegk/t/evRo4cTQU9tG9ZBAAIQgAAEIAABCHhPIMc6oIMGDYooTVXKf/3117Z48eIUz6Me69OnT3dSQSluxIqoIqC0DXViUvtQLDYIqBGBHmeccUZsDJhRmjqXHTp0yHVJA0dsEFBTETUKqVu3bmwMOM5H+f/au/fYKKovgOOnpVBFAW0pQi20gmB4GDH4CkGLUhMaiajAH1ADSlUiD40G8AFEqljjA1GjKBpQ/1CCUsRXgzyMQcVgYoqVqCgiD0VA5A1iK53fnPv77fx2uy0lbeneO/u9Sd3Z2cec+zkue3bmzh39HrO9OV+A6ixSOoG5ziGZkZHRYt7z58+XN95446Rnwut16/XKTfVdPrLFgmVDpyyg87vu2bPHfEGe8ot4YkIFtJjRL0e9gAHNDQHN1bFjx/ih50a6TJT6PauXUc7KynIo6uQN9d5775V+/fpZDeDkPKA6AfkLL7wgS5YsMZORRy7N2L59e8nLy5OCggIpKSkRvXZ4IltFRYUUFxeLXrWH5oaA7tV+7LHH5NNPP3UjYKKUZcuWyVtvvSVlZWVoOCKwaNEi+fLLL2XhwoWOREyYzz77rPm+nTt3LhgINIuAc3tAda/ioEGDJCUlRUaNGiXdu3c3ez71vu5y1qsTLV261HwZrVmzRnr06NEsULwJAggggAACCCCAQPMIOFeAPv3002Yvp46tTE9Pr1OhtLRUCgsLzVUAdE8oDQEEEEAAAQQQQMAeAeemYdIxlWPHjq23+FRavUrRbbfdJitWrLBHmkgQQAABBBBAAAEEjIBzBejAgQPN2KGG8qdj+DiTuSElHkcAAQQQQAABBFpewLlD8GPGjBEtQnWKo6KiIjPGMzMzU1JTU80YUL0uvJ6QUF5ebqZAanlStogAAggggAACCCBwMgHnCtD+/ftLZWWlTJgwwVzpqKamJq5/ehb8ypUrJT8/P+4xViCAAAIIIIAAAggkVsC5AlS5LrzwQtEz3KuqqmT79u2iez2rq6slOztbcnJyRPeI0hBAAAEEEEAAAQTsFHCyAI1QtmnTxhSjWpDSEEAAAQQQQAABBNwQcHIiejdoxVxqbseOHVyT3pWE+XHqlZB0fLHOL0tzQ0CvhKRX1snNzXUjYKI0+dLPmh6xorkhoFdC0iONXbp0cSNgorRegALU+hQRIAIIIIAAAgggEC4B56ZhChc/vUEAAQQQQAABBJJPgAI0+XJOjxFAAAEEEEAAgYQKUIAmlJ+NI4AAAggggAACySdAAZp8OafHCCCAAAIIIIBAQgUoQBPKz8YRQAABBBBAAIHkE6AATb6c02MEEEAAAQQQQCChAhSgCeVn4wgggAACCCCAQPIJUIAmX87pMQIIIIAAAgggkFABCtCE8rNxBBBAAAEEEEAg+QQoQJMv5/QYAQQQQAABBBBIqAAF6Gnm9zzvNG+Bt2+qQFVVlZw4caLetyGH9dIk5IGampoGt0vOGiRq0SecSj5O5TktGjQba1CAnDVIxBNOIkABehKcxj505MgReeCBB6Rnz56SkZEht9xyi/z111+NfTtedxoFfv31V8nOzpYVK1bEbeXNN9+UwYMHS9u2beWKK66Qzz77LO45rGgZgaNHj8rUqVNNrtLS0iQ3N1dKS0vl33//jQmAnMVwJPROdXW1zJs3T6666io5++yzJT8/X9avXx8XEzmLI7FihX7mevToIZMnT46Lh5zFkbCiEQIUoI1Aa+glDz/8sJSVlcnLL78s77//vmzZskUKCgqEX4sNybXs41p83njjjXX+OFi7dq3cddddMnLkSPnqq69kwIABUlhYKJWVlS0bJFszAhMnTpRFixaZL8N169bJrbfeKo888oiUlJQEQuQsoLBi4dVXXzX5GTt2rKxatUq6dOkiQ4YMkR9//DGIj5wFFNYtTJ8+3Xx31Q6MnNUW4X6jBfyiiNaMAn6B4qWmpnrLly8P3vX777/X4/Cev5ctWMdCYgXmz5/vnXXWWd5FF11kcvPRRx/FBNS7d2+vqKgoZl2/fv288ePHx6zjzukXOHDggPlMTZs2LWZjI0aM8Dp16hSsI2cBRcIXjh075nXu3Nnzi5ggloMHD5rP3OOPPx6sI2cBhVULq1ev9vyjd57/o8GbNGlSTGzkLIaDO00QYA9oo0v3ul+ov/TbtGlj9pZFnuF/YKVXr17y8ccfR1Zxm2AB/0tQpkyZIuXl5XGR/Pbbb/LDDz/IzTffHPPY8OHD63x+zJO40+wCOj5XjybcfffdMe99wQUXyOHDh82RBXIWQ5PwO2eeeaZ89913MnPmzCCW/fv3yz///COtW7c268hZQGPVgn6miouLzfCJzMxMSUlJCeIjZwEFC80gQAHaDIjRb7F582bJysoyRWj0eh1nuHv37uhVLCdQoKKiQp544om4PGlIP//8s4ns/PPPj4lQ7//5559yKifBxLyQO00S0HHUOhxCC85I0xwsXrxYrrzySvMFSc4iMvbcduzYUdq1ayc6FlQP2+oPCP23cdSoUSZIcmZPrqIjuf/++6VPnz6iQydqN3JWW4T7TRFIa8qLeW28wKFDh8yJR7UfOffccylAa6Mk8L5+EdbXdA+ANv31H900h7o3Tk8oO9nro1/D8ukReOihh+SPP/6QZcuWmQ2Qs9Pj3Bzvqici6UmZ2hYuXCh5eXlmmZwZBqv+oydjvvPOO7Jx48Y64yJndbKwspEC7AFtJFx9L9PDS/4Y0LiH9TCGTvdDs19Az7LWFn3oKTpq8hit0fLLc+bMkaeeekqefPJJMzuBRkDOWj4Pp7rF0aNHm5P3/DG8Zi+o5k8bOTtVwZZ5nj/WWu644w7z2eratWudGyVndbKwspEC8ZVSI9+Il/1XwB94LzrWqXbTde3bt6+9mvsWCujZutr0H+ToFskreYxWadllLWJmzZplik+dlinSyFlEwr5bLWYuvvhiU9jccMMN4p8AaMbtkjO7cqWfLT1/QacP1Cnn9E+nFNRxn7p8/PhxM5OBRs2/jXblztVoOATfzJnTf1R1nKAeqm3VqlXw7rt27TJzSgYrWLBWQH9EaNNDvNFNcxgZ1xa9nuWWEdCCUw/nLliwwIwJjd4qOYvWSPyyf8a7fPHFFzJo0CDp0KFDEJBOR/fee++ZooacBSxWLGzYsEF0ajqdKiu6bd26VfxZXURvyVm0DMtNFWAPaFMFa71eP7w6ga/+Yow0nQdUz6q+7rrrIqu4tVhAf0T07ds3btYCf6omcpigvOkhdy0+33777bjiU0MiZwlKTD2b1R9rw4YNM7MXRD9FZwLRCzvoSZnkLFom8csffvihbNu2LeZPZ28ZN26cWZeTk0POEp+mcEXQhCmceGk9Atdee62nc0Zu2rTJ8w9feH7h6fl7Ajz/zN16XsHqRAns2LGjznlA/Wl/vDPOOMNbunSpp/MX+lfdMfd/+eWXRIWatNvVz5BftHiXXXaZ509uHvfnj8k1NuTMrv9F/Csfeeedd57nX4zD+/33373Zs2d7/lEhzx9CEQRKzgIKKxf0e8y/ElJMbOQshoM7TRDQsTi0ZhbQL8yBAweawsYftO1df/31nk5GT7NPoL4C1L/Eo+dPR+L5J5WZPOrky/7l5+zrQBJE9Pzzz5sc+D/967zVHwjayJld/zPs3LnTGzp0aJCz9PR0b8aMGV7kBwM5sytfdUVTVwHK56wuKdY1RiBFXxSufbr29Gbv3r3mjHidx5DmpoAOvN+zZ49069bNzQ4kYdTkzK6k79u3z4yL7969ezAJfe0IyVltEfvvkzP7c2R7hBSgtmeI+BBAAAEEEEAAgZAJcBJSyBJKdxBAAAEEEEAAAdsFKEBtzxDxIYAAAggggAACIROgAA1ZQukOAggggAACCCBguwAFqO0ZIj4EEEAAAQQQQCBkAhSgIUso3UEAAQQQQAABBGwXoAC1PUPEhwACCCCAAAIIhEyAAjRkCaU7CCCAAAIIIICA7QIUoLZniPgQQAABBBBAAIGQCVCAhiyhdAcBBBBAAAEEELBdgALU9gwRHwIIIIAAAgggEDIBCtCQJZTuIIAAAggggAACtgtQgNqeIeJDAAEEEEAAAQRCJkABGrKE0h0EEEAAAQQQQMB2AQpQ2zNEfAgggAACCCCAQMgEKEBDllC6gwACCCCAAAII2C5AAWp7hogPAQQQQAABBBAImQAFaMgSSncQQAABBBBAAAHbBShAbc8Q8SGAAAIIIIAAAiEToAANWULpDgIIIIAAAgggYLsABajtGSI+BBBAAAEEEEAgZAIUoCFLKN1BAAEEEEAAAQRsF6AAtT1DxIcAAggggAACCIRMgAI0ZAmlOwgggAACCCCAgO0CFKC2Z4j4EEAAAQQQQACBkAlQgIYsoXQHAQQQQAABBBCwXYAC1PYMER8CCFgrsHHjRklJSZElS5Y0OcZWrVrJK6+80uT34Q0QQAABFwQoQF3IEjEigAACCCCAAAIhEqAADVEy6QoCCCCAAAIIIOCCAAWoC1kiRgQQcEYgPz9fPvnkE5k8ebJ07drV/E2dOlWqq6uDPmzdulXGjx8vnTt3lgEDBsjKlSuDxyILR44ckYkTJ0peXp507NhRbrrpJtm2bVvkYfP+uq1Dhw4F6x599FG5+uqrZe/evcE6FhBAAAEbBShAbcwKMSGAgLMCGzZskOLiYqmsrJRZs2bJsGHDZO7cuTJv3jzTp6qqKhk5cqR888038uKLL5rnFhUVSU1NTdBnz/NkyJAhUlZWJuPGjZPXXntN9u/fL5dffrns27fPPG/SpEmyfv16sw1dsW7dOikpKTHvrQUrDQEEELBZIM3m4IgNAQQQcFEgOztb1q5dG4SuRemqVatk+vTp8vrrr5vidMuWLZKTk2Oek5GRIaNHjw6eryc1ff3111JeXi6FhYVm/dChQyUrK0ueeeYZKS0tld69e8vs2bNl5syZ5rW33367DB48WO65557gfVhAAAEEbBWgALU1M8SFAALOCmghGN169uwpmzZtMqu0GNXD7pHiU1eOGDFCUlP/f0Dq888/lw4dOki7du3Mns3Ie11yySUx96dNm2b2khYUFEhaWpopcvWsfBoCCCBguwAFqO0ZIj4EEHBOoPYh8PT09OAQ+08//WTGfkZ3qnXr1tKpU6dglY4RPXjwoBnPGaz834KOCY00nbrpvvvuEz2Er3/dunWLPMQtAgggYLXA/39yWx0mwSGAAALuCJxsL2Rubm4wjjO6R4cPHw7uZmZmij7vxIkTpnDV8aGRPz10H2l///23Gfepz128eHHM3tHIc7hFAAEEbBSgALUxK8SEAAKhFejfv79UVFTIgQMHgj5+++23cvTo0eB+3759zRnvepKRFrP6pycmjRkzxowBjTxRx3/u3LnTjDe95pprRMeBalFKQwABBGwXoAC1PUPEhwACoRLQQ+V60pEWi7t27ZLNmzfLnXfeGdPHCRMmiO4F1TPdP/jgA1NkPvjgg/Luu++KjvfUpme9P/fcczJnzhxz6H3BggWmaNWilIYAAgjYLkABanuGiA8BBEIloIXl8uXLZfv27aJny/fp08ec6a7LkXbOOefI6tWrzd3hw4ebE5bWrFkjL730klx66aVy/PhxU8DqyUxTpkwxz+vVq5fMmDHDFKVanNIQQAABmwVS/MM6ns0BEhsCCCAQVoHdu3ebM93btm1bbxf1UL0WnDppPQ0BBBAIiwAFaFgyST8QQAABBBBAAAFHBDgE70iiCBMBBBBAAAEEEAiLAAVoWDJJPxBAAAEEEEAAAUcEKEAdSRRhIoAAAggggAACYRGgAA1LJukHAggggAACCCDgiAAFqCOJIkwEEEAAAQQQQCAsAhSgYckk/UAAAQQQQAABBBwRoAB1JFGEiQACCCCAAAIIhEWAAjQsmaQfCCCAAAIIIICAIwIUoI4kijARQAABBBBAAIGwCFCAhiWT9AMBBBBAAAEEEHBEgALUkUQRJgIIIIAAAgggEBYBCtCwZJJ+IIAAAggggAACjghQgDqSKMJEAAEEEEAAAQTCIkABGpZM0g8EEEAAAQQQQMARAQpQRxJFmAgggAACCCCAQFgEKEDDkkn6gQACCCCAAAIIOCJAAepIoggTAQQQQAABBBAIiwAFaFgyST8QQAABBBBAAAFHBChAHUkUYSKAAAIIIIAAAmERoAANSybpBwIIIIAAAggg4IgABagjiSJMBBBAAAEEEEAgLAIUoGHJJP1AAAEEEEAAAQQcEfgPcYANUzgY2YsAAAAASUVORK5CYII=" alt /><!-- --></p>
+<pre><code>## [1] "Feedback: Plotting 2005 laundry episodes start"</code></pre>
+<p><img 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" 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" alt /><!-- --></p>
+<pre><code>## [1] "Feedback: Saving episodes results into: ~/Dropbox/Work/DraftPapers/The Time and Timing of Demand - Laundry/results/"</code></pre>
+<table>
+<caption>Main acts before laundry in 1985</caption>
+<thead>
+<tr class="header">
+<th align="left">V1</th>
+<th align="right">N</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">laundry, ironing, clothing repair</td>
+<td align="right">16.07</td>
+</tr>
+<tr class="even">
+<td align="left">cleaning</td>
+<td align="right">10.72</td>
+</tr>
+<tr class="odd">
+<td align="left">meals or snacks in other places</td>
+<td align="right">10.66</td>
+</tr>
+<tr class="even">
+<td align="left">set table, wash/put away dishes</td>
+<td align="right">10.32</td>
+</tr>
+<tr class="odd">
+<td align="left">food preparation, cooking</td>
+<td align="right">8.60</td>
+</tr>
+<tr class="even">
+<td align="left">wash, dress, care for self</td>
+<td align="right">7.02</td>
+</tr>
+</tbody>
+</table>
+<table>
+<caption>Main acts after laundry in 1985</caption>
+<thead>
+<tr class="header">
+<th align="left">V1</th>
+<th align="right">N</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">laundry, ironing, clothing repair</td>
+<td align="right">16.07</td>
+</tr>
+<tr class="even">
+<td align="left">cleaning</td>
+<td align="right">10.72</td>
+</tr>
+<tr class="odd">
+<td align="left">meals or snacks in other places</td>
+<td align="right">10.66</td>
+</tr>
+<tr class="even">
+<td align="left">set table, wash/put away dishes</td>
+<td align="right">10.32</td>
+</tr>
+<tr class="odd">
+<td align="left">food preparation, cooking</td>
+<td align="right">8.60</td>
+</tr>
+<tr class="even">
+<td align="left">wash, dress, care for self</td>
+<td align="right">7.02</td>
+</tr>
+</tbody>
+</table>
+<table>
+<caption>Main acts after laundry in 2005</caption>
+<thead>
+<tr class="header">
+<th align="left">V1</th>
+<th align="right">N</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">meals or snacks in other places</td>
+<td align="right">20.64</td>
+</tr>
+<tr class="even">
+<td align="left">cleaning</td>
+<td align="right">13.23</td>
+</tr>
+<tr class="odd">
+<td align="left">food preparation, cooking</td>
+<td align="right">12.14</td>
+</tr>
+<tr class="even">
+<td align="left">wash, dress, care for self</td>
+<td align="right">8.07</td>
+</tr>
+<tr class="odd">
+<td align="left">laundry, ironing, clothing repair</td>
+<td align="right">6.69</td>
+</tr>
+<tr class="even">
+<td align="left">watch TV, video, DVD, streamed film</td>
+<td align="right">5.89</td>
+</tr>
+</tbody>
+</table>
+<table>
+<caption>Main acts after laundry in 2005</caption>
+<thead>
+<tr class="header">
+<th align="left">V1</th>
+<th align="right">N</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">food preparation, cooking</td>
+<td align="right">16.42</td>
+</tr>
+<tr class="even">
+<td align="left">cleaning</td>
+<td align="right">12.35</td>
+</tr>
+<tr class="odd">
+<td align="left">watch TV, video, DVD, streamed film</td>
+<td align="right">10.17</td>
+</tr>
+<tr class="even">
+<td align="left">meals or snacks in other places</td>
+<td align="right">7.49</td>
+</tr>
+<tr class="odd">
+<td align="left">laundry, ironing, clothing repair</td>
+<td align="right">6.69</td>
+</tr>
+<tr class="even">
+<td align="left">wash, dress, care for self</td>
+<td align="right">6.69</td>
+</tr>
+</tbody>
+</table>
+<table>
+<caption>Main acts after early weekday morning laundry in 2005</caption>
+<thead>
+<tr class="header">
+<th align="left">V1</th>
+<th align="right">N</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">cleaning</td>
+<td align="right">21.78</td>
+</tr>
+<tr class="even">
+<td align="left">food preparation, cooking</td>
+<td align="right">12.38</td>
+</tr>
+<tr class="odd">
+<td align="left">wash, dress, care for self</td>
+<td align="right">7.92</td>
+</tr>
+<tr class="even">
+<td align="left">laundry, ironing, clothing repair</td>
+<td align="right">7.43</td>
+</tr>
+<tr class="odd">
+<td align="left">travel to/from work</td>
+<td align="right">7.43</td>
+</tr>
+<tr class="even">
+<td align="left">meals or snacks in other places</td>
+<td align="right">6.93</td>
+</tr>
+</tbody>
+</table>
+<table>
+<caption>Main acts 2 after early weekday morning laundry in 2005</caption>
+<thead>
+<tr class="header">
+<th align="left">V1</th>
+<th align="right">N</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">food preparation, cooking</td>
+<td align="right">10.89</td>
+</tr>
+<tr class="even">
+<td align="left">meals or snacks in other places</td>
+<td align="right">9.90</td>
+</tr>
+<tr class="odd">
+<td align="left">laundry, ironing, clothing repair</td>
+<td align="right">9.41</td>
+</tr>
+<tr class="even">
+<td align="left">paid work-main job (not at home)</td>
+<td align="right">8.42</td>
+</tr>
+<tr class="odd">
+<td align="left">cleaning</td>
+<td align="right">7.43</td>
+</tr>
+<tr class="even">
+<td align="left">other travel</td>
+<td align="right">5.94</td>
+</tr>
+</tbody>
+</table>
+<table>
+<caption>Main acts 3 after early weekday morning laundry in 2005</caption>
+<thead>
+<tr class="header">
+<th align="left">V1</th>
+<th align="right">N</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">shop, person/hhld care travel</td>
+<td align="right">10.40</td>
+</tr>
+<tr class="even">
+<td align="left">food preparation, cooking</td>
+<td align="right">9.41</td>
+</tr>
+<tr class="odd">
+<td align="left">meals or snacks in other places</td>
+<td align="right">8.91</td>
+</tr>
+<tr class="even">
+<td align="left">other travel</td>
+<td align="right">8.91</td>
+</tr>
+<tr class="odd">
+<td align="left">purchase goods</td>
+<td align="right">8.42</td>
+</tr>
+<tr class="even">
+<td align="left">paid work-main job (not at home)</td>
+<td align="right">7.43</td>
+</tr>
+</tbody>
+</table>
+<table>
+<caption>Main acts before evening peak weekday laundry in 2005</caption>
+<thead>
+<tr class="header">
+<th align="left">V1</th>
+<th align="right">N</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">meals or snacks in other places</td>
+<td align="right">32.16</td>
+</tr>
+<tr class="even">
+<td align="left">food preparation, cooking</td>
+<td align="right">11.70</td>
+</tr>
+<tr class="odd">
+<td align="left">cleaning</td>
+<td align="right">8.77</td>
+</tr>
+<tr class="even">
+<td align="left">physical, medical child care</td>
+<td align="right">7.02</td>
+</tr>
+<tr class="odd">
+<td align="left">watch TV, video, DVD, streamed film</td>
+<td align="right">7.02</td>
+</tr>
+<tr class="even">
+<td align="left">other travel</td>
+<td align="right">5.85</td>
+</tr>
+</tbody>
+</table>
+<table>
+<caption>Main acts before evening peak weekday laundry in 2005</caption>
+<thead>
+<tr class="header">
+<th align="left">V1</th>
+<th align="right">N</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">watch TV, video, DVD, streamed film</td>
+<td align="right">25.15</td>
+</tr>
+<tr class="even">
+<td align="left">food preparation, cooking</td>
+<td align="right">15.79</td>
+</tr>
+<tr class="odd">
+<td align="left">meals or snacks in other places</td>
+<td align="right">9.36</td>
+</tr>
+<tr class="even">
+<td align="left">cleaning</td>
+<td align="right">7.02</td>
+</tr>
+<tr class="odd">
+<td align="left">wash, dress, care for self</td>
+<td align="right">6.43</td>
+</tr>
+<tr class="even">
+<td align="left">laundry, ironing, clothing repair</td>
+<td align="right">5.85</td>
+</tr>
+</tbody>
+</table>
+<pre><code>## [1] "Feedback: Done analysing episode file"</code></pre>
+</div>
+<div id="switch-to-survey-analysis-of-half-hour-sampled-data" class="section level2">
+<h2><span class="header-section-number">3.3</span> Switch to survey analysis of half hour sampled data</h2>
+<pre><code>## [1] "Feedback: Join survey to derived half hour data"</code></pre>
+<pre><code>## ba_survey ba_diarypid diarypid pid ba_pid r_month r_dow r_hour
+## 1: 1985 14899 316349 193929 2609 10 Friday 0
+## 2: 1985 14899 316349 193929 2609 10 Friday 0
+## 3: 1985 14899 316349 193929 2609 10 Friday 1
+## 4: 1985 14899 316349 193929 2609 10 Friday 1
+## 5: 1985 14899 316349 193929 2609 10 Friday 2
+## 6: 1985 14899 316349 193929 2609 10 Friday 2
+## st_halfhour N_obs N_laundry_p N_laundry_ph N_laundry_photh
+## 1: 00:00 3 0 0 0
+## 2: 00:30 3 0 0 0
+## 3: 01:00 3 0 0 0
+## 4: 01:30 3 0 0 0
+## 5: 02:00 3 0 0 0
+## 6: 02:30 3 0 0 0
+## N_laundry_psh N_laundry_poth Any_laundry_p Any_laundry_ph
+## 1: 0 0 0 0
+## 2: 0 0 0 0
+## 3: 0 0 0 0
+## 4: 0 0 0 0
+## 5: 0 0 0 0
+## 6: 0 0 0 0
+## Any_laundry_photh Any_laundry_psh Any_laundry_poth countrya
+## 1: 0 0 0 United Kingdom
+## 2: 0 0 0 United Kingdom
+## 3: 0 0 0 United Kingdom
+## 4: 0 0 0 United Kingdom
+## 5: 0 0 0 United Kingdom
+## 6: 0 0 0 United Kingdom
+## survey swave msamp hldid persid id
+## 1: 1983 not longitudinal study/case 1 sample 1 4.940656e-324 1
+## 2: 1983 not longitudinal study/case 1 sample 1 4.940656e-324 1
+## 3: 1983 not longitudinal study/case 1 sample 1 4.940656e-324 1
+## 4: 1983 not longitudinal study/case 1 sample 1 4.940656e-324 1
+## 5: 1983 not longitudinal study/case 1 sample 1 4.940656e-324 1
+## 6: 1983 not longitudinal study/case 1 sample 1 4.940656e-324 1
+## diary mtus_day mtus_month mtus_year empstat occup
+## 1: 1st diary day Wednesday November 1983 part-time missing
+## 2: 1st diary day Wednesday November 1983 part-time missing
+## 3: 1st diary day Wednesday November 1983 part-time missing
+## 4: 1st diary day Wednesday November 1983 part-time missing
+## 5: 1st diary day Wednesday November 1983 part-time missing
+## 6: 1st diary day Wednesday November 1983 part-time missing
+## urban badcase sex hhtype income
+## 1: urban/suburban good case Woman Married/cohabiting couple alone missing
+## 2: urban/suburban good case Woman Married/cohabiting couple alone missing
+## 3: urban/suburban good case Woman Married/cohabiting couple alone missing
+## 4: urban/suburban good case Woman Married/cohabiting couple alone missing
+## 5: urban/suburban good case Woman Married/cohabiting couple alone missing
+## 6: urban/suburban good case Woman Married/cohabiting couple alone missing
+## propwt i.ba_survey i.ba_pid age ba_age_cohort ba_age_r ba_nchild
+## 1: 1.07194 1985 2599 26 (1.95e+03,1.96e+03] (25,35] 0
+## 2: 1.07194 1985 2599 26 (1.95e+03,1.96e+03] (25,35] 0
+## 3: 1.07194 1985 2599 26 (1.95e+03,1.96e+03] (25,35] 0
+## 4: 1.07194 1985 2599 26 (1.95e+03,1.96e+03] (25,35] 0
+## 5: 1.07194 1985 2599 26 (1.95e+03,1.96e+03] (25,35] 0
+## 6: 1.07194 1985 2599 26 (1.95e+03,1.96e+03] (25,35] 0
+## ba_npeople
+## 1: 2
+## 2: 2
+## 3: 2
+## 4: 2
+## 5: 2
+## 6: 2</code></pre>
+<pre><code>## ba_survey ba_diarypid diarypid pid ba_pid r_month r_dow r_hour
+## 1: NA 58940 NA NA NA NA NA NA
+## 2: NA 58941 NA NA NA NA NA NA
+## 3: NA 58942 NA NA NA NA NA NA
+## 4: NA 58943 NA NA NA NA NA NA
+## 5: NA 58944 NA NA NA NA NA NA
+## 6: NA 58945 NA NA NA NA NA NA
+## st_halfhour N_obs N_laundry_p N_laundry_ph N_laundry_photh
+## 1: NA NA NA NA NA
+## 2: NA NA NA NA NA
+## 3: NA NA NA NA NA
+## 4: NA NA NA NA NA
+## 5: NA NA NA NA NA
+## 6: NA NA NA NA NA
+## N_laundry_psh N_laundry_poth Any_laundry_p Any_laundry_ph
+## 1: NA NA NA NA
+## 2: NA NA NA NA
+## 3: NA NA NA NA
+## 4: NA NA NA NA
+## 5: NA NA NA NA
+## 6: NA NA NA NA
+## Any_laundry_photh Any_laundry_psh Any_laundry_poth countrya
+## 1: NA NA NA United Kingdom
+## 2: NA NA NA United Kingdom
+## 3: NA NA NA United Kingdom
+## 4: NA NA NA United Kingdom
+## 5: NA NA NA United Kingdom
+## 6: NA NA NA United Kingdom
+## survey swave msamp hldid persid id
+## 1: 2005 not longitudinal study/case 1 sample 0 9.906327e-312 1
+## 2: 2005 not longitudinal study/case 1 sample 0 9.906327e-312 1
+## 3: 2005 not longitudinal study/case 1 sample 0 9.906327e-312 1
+## 4: 2005 not longitudinal study/case 1 sample 0 9.906327e-312 1
+## 5: 2005 not longitudinal study/case 1 sample 0 9.906327e-312 1
+## 6: 2005 not longitudinal study/case 1 sample 0 9.906327e-312 1
+## diary mtus_day mtus_month mtus_year empstat
+## 1: 1st diary day Saturday June 2005 not in paid work
+## 2: 1st diary day Saturday June 2005 not in paid work
+## 3: 1st diary day Tuesday June 2005 not in paid work
+## 4: 1st diary day Tuesday June 2005 part-time
+## 5: 1st diary day Tuesday June 2005 full-time
+## 6: 1st diary day Monday June 2005 not in paid work
+## occup urban badcase sex
+## 1: clerical/office support could not be created good case Man
+## 2: sales/services/art support/clean could not be created good case Woman
+## 3: construct, asmble/repair, transpt could not be created good case Man
+## 4: sales/services/art support/clean could not be created good case Woman
+## 5: construct, asmble/repair, transpt could not be created good case Man
+## 6: construct, asmble/repair, transpt could not be created good case Man
+## hhtype income propwt
+## 1: 1 person household lowest 25% 1.3073545
+## 2: 1 person household lowest 25% 0.9952479
+## 3: 1 person household middle 50% 0.6583855
+## 4: Married/cohabiting couple + others lowest 25% 0.9510369
+## 5: Married/cohabiting couple + others middle 50% 1.0930131
+## 6: Other household types could not be created 1.9905120
+## i.ba_survey i.ba_pid age ba_age_cohort ba_age_r ba_nchild
+## 1: 2005 21033 37 (1.96e+03,1.97e+03] (35,45] 0
+## 2: 2005 21034 79 (1.92e+03,1.93e+03] (75,85] 0
+## 3: 2005 21035 80 (1.92e+03,1.93e+03] (75,85] 0
+## 4: 2005 21036 46 (1.95e+03,1.96e+03] (45,55] 1
+## 5: 2005 21037 47 (1.95e+03,1.96e+03] (45,55] 0
+## 6: 2005 21038 19 (1.98e+03,1.99e+03] (16,25] 1
+## ba_npeople
+## 1: 1
+## 2: 1
+## 3: 1
+## 4: 4
+## 5: 3
+## 6: 5+</code></pre>
+<pre><code>##
+## 1985 2005 <NA>
+## 903600 125136 3405</code></pre>
+<pre><code>##
+## 1985 2005 <NA>
+## 903600 125136 0</code></pre>
+<pre><code>## [1] "Recode to 'at a home' vs 'not at a home'"</code></pre>
+<pre><code>## Any_laundry_nothome
+## Any_laundry_home 0 1
+## 0 1014861 109
+## 1 13760 6</code></pre>
+<pre><code>## [1] "create types of laundry here so become part of survey design object"</code></pre>
+<pre><code>## [1] "Weekday early morning 06:00-09:00"</code></pre>
+<pre><code>## r_dow
+## r_hour Friday Monday Thursday Tuesday Wednesday
+## 6 12 13 13 12 5
+## 7 43 68 70 55 50
+## 8 106 154 123 123 94</code></pre>
+<pre><code>## [1] "Weekday morning 09:00-12:00"</code></pre>
+<pre><code>## r_dow
+## r_hour Friday Monday Thursday Tuesday Wednesday
+## 9 146 268 156 211 174
+## 10 171 280 213 217 217
+## 11 136 234 147 174 168</code></pre>
+<pre><code>## [1] "Weekday evening peak 18:00-21:00"</code></pre>
+<pre><code>## r_dow
+## r_hour Friday Monday Thursday Tuesday Wednesday
+## 18 98 123 101 97 94
+## 19 100 134 117 111 90
+## 20 83 162 124 107 115</code></pre>
+<pre><code>## [1] "Sunday morning 09:00-12:00"</code></pre>
+<pre><code>## r_dow
+## r_hour Sunday
+## 9 159
+## 10 243
+## 11 238</code></pre>
+<pre><code>## [1] "Other"</code></pre>
+<pre><code>## r_dow
+## r_hour Friday Monday Saturday Sunday Thursday Tuesday Wednesday
+## 0 4 4 7 4 3 3 5
+## 1 0 1 2 0 0 0 0
+## 2 1 0 0 0 0 0 0
+## 5 2 0 0 0 4 2 4
+## 6 0 0 7 6 0 0 0
+## 7 0 0 20 13 0 0 0
+## 8 0 0 93 68 0 0 0
+## 9 0 0 177 0 0 0 0
+## 10 0 0 256 0 0 0 0
+## 11 0 0 208 0 0 0 0
+## 12 101 145 136 197 117 121 99
+## 13 99 159 113 155 120 128 131
+## 14 122 215 114 154 147 158 157
+## 15 96 157 118 166 126 147 119
+## 16 88 127 103 165 123 133 112
+## 17 67 78 83 109 61 70 72
+## 18 0 0 67 143 0 0 0
+## 19 0 0 76 131 0 0 0
+## 20 0 0 67 108 0 0 0
+## 21 72 111 48 62 79 74 91
+## 22 46 43 24 51 30 51 50
+## 23 14 9 15 13 10 14 16</code></pre>
+<pre><code>## [1] "Set survey data"</code></pre>
+<pre><code>## N_laundry_p 0 1 2 3
+## ba_survey sex
+## 1985 Man 401562.51212 1540.82666 1511.61154 2716.63159
+## Woman 492799.58642 1842.90609 1897.28811 3057.06516
+## 2005 Man 57753.36741 146.73099 121.74276 334.35365
+## Woman 64015.33611 179.27798 98.40996 451.94026</code></pre>
+<pre><code>## Any_laundry_p 0 1
+## ba_survey
+## 1985 894362.099 12566.329
+## 2005 121768.704 1332.456</code></pre>
+<pre><code>## Any_laundry_ph 0 1
+## ba_survey
+## 1985 894547.098 12381.330
+## 2005 121905.662 1195.497</code></pre>
+<pre><code>## Any_laundry_photh 0 1
+## ba_survey
+## 1985 906836.0839 92.3438
+## 2005 123101.1591 0.0000</code></pre>
+<pre><code>## Any_laundry_psh 0 1
+## ba_survey
+## 1985 906883.14313 45.28458
+## 2005 123101.15912 0.00000</code></pre>
+<pre><code>## Any_laundry_poth 0 1
+## ba_survey
+## 1985 906891.39666 37.03104
+## 2005 123066.25706 34.90206</code></pre>
+<pre><code>## Any_laundry_home 0 1
+## ba_survey
+## 1985 894457.981 12470.447
+## 2005 121905.662 1195.497</code></pre>
+<pre><code>## Any_laundry_nothome 0 1
+## ba_survey
+## 1985 906846.11209 82.31562
+## 2005 123066.25706 34.90206</code></pre>
+<pre><code>## Any_laundry_p 0 1
+## ba_survey sex
+## 1985 Man 401562.5121 5769.0698
+## Woman 492799.5864 6797.2594
+## 2005 Man 57753.3674 602.8274
+## Woman 64015.3361 729.6282</code></pre>
+<pre><code>## ba_survey sex Any_laundry_p se
+## 1985.Man 1985 Man 0.01416308 0.0003752841
+## 2005.Man 2005 Man 0.01033014 0.0007438422
+## 1985.Woman 1985 Woman 0.01360549 0.0003142107
+## 2005.Woman 2005 Woman 0.01126927 0.0008500319</code></pre>
+<pre><code>## A B
+## sexMan 0.460605995 0.009544019
+## sexWoman 0.539394005 0.009544019</code></pre>
+<pre><code>## A B
+## sexMan 0.45021585 0.02965884
+## sexWoman 0.54978415 0.02965884</code></pre>
+<pre><code>## Any_laundry_home 0 1
+## ba_survey sex
+## 1985 Man 401587.6193 5743.9626
+## Woman 492870.3615 6726.4843
+## 2005 Man 57817.9630 538.2318
+## Woman 64087.6989 657.2654</code></pre>
+<pre><code>## Any_laundry_nothome 0 1
+## ba_survey sex
+## 1985 Man 407311.11628 20.46565
+## Woman 499534.99581 61.84997
+## 2005 Man 58339.72499 16.46982
+## Woman 64726.53207 18.43225</code></pre>
+<pre><code>## r_dow ba_survey Any_laundry_home se
+## Friday.1985 Friday 1985 0.010865766 0.0005378271
+## Monday.1985 Monday 1985 0.017711811 0.0007584997
+## Saturday.1985 Saturday 1985 0.012607221 0.0005939453
+## Sunday.1985 Sunday 1985 0.015019823 0.0006972744
+## Thursday.1985 Thursday 1985 0.012867461 0.0006046819
+## Tuesday.1985 Tuesday 1985 0.013982255 0.0006303033
+## Wednesday.1985 Wednesday 1985 0.013261458 0.0006007659
+## Friday.2005 Friday 2005 0.012043510 0.0021621787
+## Monday.2005 Monday 2005 0.010206423 0.0014793119
+## Saturday.2005 Saturday 2005 0.006371568 0.0012558053
+## Sunday.2005 Sunday 2005 0.013427341 0.0016461126
+## Thursday.2005 Thursday 2005 0.007598394 0.0009421517
+## Tuesday.2005 Tuesday 2005 0.010437059 0.0014434330
+## Wednesday.2005 Wednesday 2005 0.007927659 0.0012847600</code></pre>
+<pre><code>## ba_survey
+## r_dow 1985 2005
+## Friday 1410.61966 163.11676
+## Monday 2297.16559 186.56677
+## Saturday 1624.93405 87.58688
+## Sunday 1937.75346 250.47835
+## Thursday 1672.77956 158.76879
+## Tuesday 1810.65085 199.10762
+## Wednesday 1716.54372 149.87204</code></pre>
+<pre><code>## [1] "Feedback: Any laundry by day of the week: 1985"</code></pre>
+<pre><code>## mean SE
+## r_dowFriday 0.11312 0.0057
+## r_dowMonday 0.18421 0.0078
+## r_dowSaturday 0.13030 0.0063
+## r_dowSunday 0.15539 0.0072
+## r_dowThursday 0.13414 0.0064
+## r_dowTuesday 0.14520 0.0066
+## r_dowWednesday 0.13765 0.0064</code></pre>
+<pre><code>## 2.5 % 97.5 %
+## r_dowFriday 0.1018914 0.1243426
+## r_dowMonday 0.1689999 0.1994177
+## r_dowSaturday 0.1180454 0.1425602
+## r_dowSunday 0.1413386 0.1694367
+## r_dowThursday 0.1216119 0.1466671
+## r_dowTuesday 0.1321790 0.1582117
+## r_dowWednesday 0.1251639 0.1501340</code></pre>
+<pre><code>## [1] "Feedback: Any laundry by day of the week: 2005"</code></pre>
+<pre><code>## mean SE
+## r_dowFriday 0.136443 0.0239
+## r_dowMonday 0.156058 0.0222
+## r_dowSaturday 0.073264 0.0146
+## r_dowSunday 0.209518 0.0248
+## r_dowThursday 0.132806 0.0171
+## r_dowTuesday 0.166548 0.0226
+## r_dowWednesday 0.125364 0.0199</code></pre>
+<pre><code>## 2.5 % 97.5 %
+## r_dowFriday 0.08963304 0.1832522
+## r_dowMonday 0.11259840 0.1995174
+## r_dowSaturday 0.04467508 0.1018529
+## r_dowSunday 0.16086621 0.2581701
+## r_dowThursday 0.09930392 0.1663074
+## r_dowTuesday 0.12219560 0.2109003
+## r_dowWednesday 0.08627837 0.1644492</code></pre>
+<pre><code>## [1] "Feedback: Any laundry by day of the week/gender: 1985"</code></pre>
+<pre><code>## sex r_dowFriday r_dowMonday r_dowSaturday r_dowSunday
+## Man Man 0.1055920 0.1821372 0.1357020 0.1695936
+## Woman Woman 0.1195429 0.1859777 0.1256922 0.1432567
+## r_dowThursday r_dowTuesday r_dowWednesday se.r_dowFriday
+## Man 0.1272811 0.1418606 0.1378335 0.008412166
+## Woman 0.1399961 0.1480430 0.1374913 0.007814887
+## se.r_dowMonday se.r_dowSaturday se.r_dowSunday se.r_dowThursday
+## Man 0.01198236 0.009668095 0.011367669 0.009525777
+## Woman 0.01011515 0.008138816 0.009051124 0.008614564
+## se.r_dowTuesday se.r_dowWednesday
+## Man 0.009719074 0.009392583
+## Woman 0.009090003 0.008668376</code></pre>
+<pre><code>## 2.5 % 97.5 %
+## Man:r_dowFriday 0.08910442 0.1220795
+## Woman:r_dowFriday 0.10422600 0.1348598
+## Man:r_dowMonday 0.15865219 0.2056222
+## Woman:r_dowMonday 0.16615242 0.2058031
+## Man:r_dowSaturday 0.11675290 0.1546511
+## Woman:r_dowSaturday 0.10974043 0.1416440
+## Man:r_dowSunday 0.14731339 0.1918738
+## Woman:r_dowSunday 0.12551685 0.1609966
+## Man:r_dowThursday 0.10861091 0.1459513
+## Woman:r_dowThursday 0.12311189 0.1568804
+## Man:r_dowTuesday 0.12281159 0.1609097
+## Woman:r_dowTuesday 0.13022689 0.1658590
+## Man:r_dowWednesday 0.11942438 0.1562426
+## Woman:r_dowWednesday 0.12050162 0.1544810</code></pre>
+<pre><code>## [1] "Feedback: Any laundry by day of the week/gender: 2005"</code></pre>
+<pre><code>## sex r_dowFriday r_dowMonday r_dowSaturday r_dowSunday
+## Man Man 0.1453014 0.1804492 0.03721916 0.2015636
+## Woman Woman 0.1291882 0.1360840 0.10278092 0.2160321
+## r_dowThursday r_dowTuesday r_dowWednesday se.r_dowFriday
+## Man 0.17908932 0.1452861 0.1110913 0.03343892
+## Woman 0.09490417 0.1839592 0.1370515 0.03372592
+## se.r_dowMonday se.r_dowSaturday se.r_dowSunday se.r_dowThursday
+## Man 0.03381096 0.01509652 0.03255327 0.02936094
+## Woman 0.02906129 0.02342899 0.03641104 0.01878978
+## se.r_dowTuesday se.r_dowWednesday
+## Man 0.02719683 0.02290164
+## Woman 0.03445224 0.03092716</code></pre>
+<pre><code>## 2.5 % 97.5 %
+## Man:r_dowFriday 0.079762279 0.21084044
+## Woman:r_dowFriday 0.063086625 0.19528982
+## Man:r_dowMonday 0.114180901 0.24671745
+## Woman:r_dowMonday 0.079124887 0.19304305
+## Man:r_dowSaturday 0.007630535 0.06680779
+## Woman:r_dowSaturday 0.056860938 0.14870090
+## Man:r_dowSunday 0.137760360 0.26536684
+## Woman:r_dowSunday 0.144667736 0.28739641
+## Man:r_dowThursday 0.121542927 0.23663570
+## Woman:r_dowThursday 0.058076882 0.13173146
+## Man:r_dowTuesday 0.091981316 0.19859092
+## Woman:r_dowTuesday 0.116434027 0.25148434
+## Man:r_dowWednesday 0.066204872 0.15597767
+## Woman:r_dowWednesday 0.076435336 0.19766759</code></pre>
+<table>
+<thead>
+<tr class="header">
+<th align="left"></th>
+<th align="left">empstat</th>
+<th align="right">ba_survey</th>
+<th align="right">se</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">full-time</td>
+<td align="left">full-time</td>
+<td align="right">789134291</td>
+<td align="right">7373041</td>
+</tr>
+<tr class="even">
+<td align="left">not in paid work</td>
+<td align="left">not in paid work</td>
+<td align="right">772129046</td>
+<td align="right">7093351</td>
+</tr>
+<tr class="odd">
+<td align="left">part-time</td>
+<td align="left">part-time</td>
+<td align="right">434979443</td>
+<td align="right">5963395</td>
+</tr>
+<tr class="even">
+<td align="left">unknown job hours</td>
+<td align="left">unknown job hours</td>
+<td align="right">50827974</td>
+<td align="right">2306812</td>
+</tr>
+</tbody>
+</table>
+<table>
+<thead>
+<tr class="header">
+<th align="left"></th>
+<th align="left">empstat</th>
+<th align="left">r_dow</th>
+<th align="right">Any_laundry_home</th>
+<th align="right">se</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">full-time.Friday</td>
+<td align="left">full-time</td>
+<td align="left">Friday</td>
+<td align="right">0.0097277</td>
+<td align="right">0.0014502</td>
+</tr>
+<tr class="even">
+<td align="left">not in paid work.Friday</td>
+<td align="left">not in paid work</td>
+<td align="left">Friday</td>
+<td align="right">0.0115259</td>
+<td align="right">0.0010299</td>
+</tr>
+<tr class="odd">
+<td align="left">part-time.Friday</td>
+<td align="left">part-time</td>
+<td align="left">Friday</td>
+<td align="right">0.0117574</td>
+<td align="right">0.0013751</td>
+</tr>
+<tr class="even">
+<td align="left">unknown job hours.Friday</td>
+<td align="left">unknown job hours</td>
+<td align="left">Friday</td>
+<td align="right">0.0143683</td>
+<td align="right">0.0078934</td>
+</tr>
+<tr class="odd">
+<td align="left">full-time.Monday</td>
+<td align="left">full-time</td>
+<td align="left">Monday</td>
+<td align="right">0.0163043</td>
+<td align="right">0.0021178</td>
+</tr>
+<tr class="even">
+<td align="left">not in paid work.Monday</td>
+<td align="left">not in paid work</td>
+<td align="left">Monday</td>
+<td align="right">0.0194740</td>
+<td align="right">0.0014878</td>
+</tr>
+<tr class="odd">
+<td align="left">part-time.Monday</td>
+<td align="left">part-time</td>
+<td align="left">Monday</td>
+<td align="right">0.0146389</td>
+<td align="right">0.0014712</td>
+</tr>
+<tr class="even">
+<td align="left">unknown job hours.Monday</td>
+<td align="left">unknown job hours</td>
+<td align="left">Monday</td>
+<td align="right">0.0208089</td>
+<td align="right">0.0071219</td>
+</tr>
+<tr class="odd">
+<td align="left">full-time.Saturday</td>
+<td align="left">full-time</td>
+<td align="left">Saturday</td>
+<td align="right">0.0101133</td>
+<td align="right">0.0015787</td>
+</tr>
+<tr class="even">
+<td align="left">not in paid work.Saturday</td>
+<td align="left">not in paid work</td>
+<td align="left">Saturday</td>
+<td align="right">0.0120256</td>
+<td align="right">0.0010891</td>
+</tr>
+<tr class="odd">
+<td align="left">part-time.Saturday</td>
+<td align="left">part-time</td>
+<td align="left">Saturday</td>
+<td align="right">0.0129356</td>
+<td align="right">0.0014742</td>
+</tr>
+<tr class="even">
+<td align="left">unknown job hours.Saturday</td>
+<td align="left">unknown job hours</td>
+<td align="left">Saturday</td>
+<td align="right">0.0182949</td>
+<td align="right">0.0059958</td>
+</tr>
+<tr class="odd">
+<td align="left">full-time.Sunday</td>
+<td align="left">full-time</td>
+<td align="left">Sunday</td>
+<td align="right">0.0108672</td>
+<td align="right">0.0014745</td>
+</tr>
+<tr class="even">
+<td align="left">not in paid work.Sunday</td>
+<td align="left">not in paid work</td>
+<td align="left">Sunday</td>
+<td align="right">0.0139046</td>
+<td align="right">0.0012393</td>
+</tr>
+<tr class="odd">
+<td align="left">part-time.Sunday</td>
+<td align="left">part-time</td>
+<td align="left">Sunday</td>
+<td align="right">0.0154109</td>
+<td align="right">0.0017824</td>
+</tr>
+<tr class="even">
+<td align="left">unknown job hours.Sunday</td>
+<td align="left">unknown job hours</td>
+<td align="left">Sunday</td>
+<td align="right">0.0069965</td>
+<td align="right">0.0038260</td>
+</tr>
+<tr class="odd">
+<td align="left">full-time.Thursday</td>
+<td align="left">full-time</td>
+<td align="left">Thursday</td>
+<td align="right">0.0111536</td>
+<td align="right">0.0015468</td>
+</tr>
+<tr class="even">
+<td align="left">not in paid work.Thursday</td>
+<td align="left">not in paid work</td>
+<td align="left">Thursday</td>
+<td align="right">0.0126719</td>
+<td align="right">0.0011539</td>
+</tr>
+<tr class="odd">
+<td align="left">part-time.Thursday</td>
+<td align="left">part-time</td>
+<td align="left">Thursday</td>
+<td align="right">0.0147599</td>
+<td align="right">0.0015171</td>
+</tr>
+<tr class="even">
+<td align="left">unknown job hours.Thursday</td>
+<td align="left">unknown job hours</td>
+<td align="left">Thursday</td>
+<td align="right">0.0191084</td>
+<td align="right">0.0076637</td>
+</tr>
+<tr class="odd">
+<td align="left">full-time.Tuesday</td>
+<td align="left">full-time</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0129177</td>
+<td align="right">0.0017908</td>
+</tr>
+<tr class="even">
+<td align="left">not in paid work.Tuesday</td>
+<td align="left">not in paid work</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0146600</td>
+<td align="right">0.0013045</td>
+</tr>
+<tr class="odd">
+<td align="left">part-time.Tuesday</td>
+<td align="left">part-time</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0126611</td>
+<td align="right">0.0014005</td>
+</tr>
+<tr class="even">
+<td align="left">unknown job hours.Tuesday</td>
+<td align="left">unknown job hours</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0235239</td>
+<td align="right">0.0085679</td>
+</tr>
+<tr class="odd">
+<td align="left">full-time.Wednesday</td>
+<td align="left">full-time</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0121377</td>
+<td align="right">0.0017207</td>
+</tr>
+<tr class="even">
+<td align="left">not in paid work.Wednesday</td>
+<td align="left">not in paid work</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0139568</td>
+<td align="right">0.0012700</td>
+</tr>
+<tr class="odd">
+<td align="left">part-time.Wednesday</td>
+<td align="left">part-time</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0119973</td>
+<td align="right">0.0013507</td>
+</tr>
+<tr class="even">
+<td align="left">unknown job hours.Wednesday</td>
+<td align="left">unknown job hours</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0153026</td>
+<td align="right">0.0053168</td>
+</tr>
+</tbody>
+</table>
+<pre><code>## [1] "Feedback: Any laundry by day of the week/women/labour market status: 1985"</code></pre>
+<table>
+<thead>
+<tr class="header">
+<th align="left"></th>
+<th align="left">empstat</th>
+<th align="right">r_dowFriday</th>
+<th align="right">r_dowMonday</th>
+<th align="right">r_dowSaturday</th>
+<th align="right">r_dowSunday</th>
+<th align="right">r_dowThursday</th>
+<th align="right">r_dowTuesday</th>
+<th align="right">r_dowWednesday</th>
+<th align="right">se.r_dowFriday</th>
+<th align="right">se.r_dowMonday</th>
+<th align="right">se.r_dowSaturday</th>
+<th align="right">se.r_dowSunday</th>
+<th align="right">se.r_dowThursday</th>
+<th align="right">se.r_dowTuesday</th>
+<th align="right">se.r_dowWednesday</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">full-time</td>
+<td align="left">full-time</td>
+<td align="right">0.1179904</td>
+<td align="right">0.1985796</td>
+<td align="right">0.1176123</td>
+<td align="right">0.1216512</td>
+<td align="right">0.1363593</td>
+<td align="right">0.1593790</td>
+<td align="right">0.1484283</td>
+<td align="right">0.0178056</td>
+<td align="right">0.0246750</td>
+<td align="right">0.0184813</td>
+<td align="right">0.0171994</td>
+<td align="right">0.0191377</td>
+<td align="right">0.0217594</td>
+<td align="right">0.0208917</td>
+</tr>
+<tr class="even">
+<td align="left">not in paid work</td>
+<td align="left">not in paid work</td>
+<td align="right">0.1186923</td>
+<td align="right">0.2017162</td>
+<td align="right">0.1240708</td>
+<td align="right">0.1458790</td>
+<td align="right">0.1286404</td>
+<td align="right">0.1464133</td>
+<td align="right">0.1345879</td>
+<td align="right">0.0109782</td>
+<td align="right">0.0152157</td>
+<td align="right">0.0115232</td>
+<td align="right">0.0130310</td>
+<td align="right">0.0119272</td>
+<td align="right">0.0131036</td>
+<td align="right">0.0124293</td>
+</tr>
+<tr class="odd">
+<td align="left">part-time</td>
+<td align="left">part-time</td>
+<td align="right">0.1224041</td>
+<td align="right">0.1546634</td>
+<td align="right">0.1297886</td>
+<td align="right">0.1568272</td>
+<td align="right">0.1602732</td>
+<td align="right">0.1407495</td>
+<td align="right">0.1352940</td>
+<td align="right">0.0145667</td>
+<td align="right">0.0159786</td>
+<td align="right">0.0150606</td>
+<td align="right">0.0178133</td>
+<td align="right">0.0166367</td>
+<td align="right">0.0156954</td>
+<td align="right">0.0153613</td>
+</tr>
+<tr class="even">
+<td align="left">unknown job hours</td>
+<td align="left">unknown job hours</td>
+<td align="right">0.1105719</td>
+<td align="right">0.1671910</td>
+<td align="right">0.1726642</td>
+<td align="right">0.0625853</td>
+<td align="right">0.1414491</td>
+<td align="right">0.1990554</td>
+<td align="right">0.1464830</td>
+<td align="right">0.0604227</td>
+<td align="right">0.0619587</td>
+<td align="right">0.0603682</td>
+<td align="right">0.0354488</td>
+<td align="right">0.0603233</td>
+<td align="right">0.0737225</td>
+<td align="right">0.0552000</td>
+</tr>
+</tbody>
+</table>
+<pre><code>## [1] "Feedback: Any laundry by day of the week/women/labour market status: 1985"</code></pre>
+<table>
+<thead>
+<tr class="header">
+<th align="left"></th>
+<th align="left">empstat</th>
+<th align="right">r_dowFriday</th>
+<th align="right">r_dowMonday</th>
+<th align="right">r_dowSaturday</th>
+<th align="right">r_dowSunday</th>
+<th align="right">r_dowThursday</th>
+<th align="right">r_dowTuesday</th>
+<th align="right">r_dowWednesday</th>
+<th align="right">se.r_dowFriday</th>
+<th align="right">se.r_dowMonday</th>
+<th align="right">se.r_dowSaturday</th>
+<th align="right">se.r_dowSunday</th>
+<th align="right">se.r_dowThursday</th>
+<th align="right">se.r_dowTuesday</th>
+<th align="right">se.r_dowWednesday</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">full-time</td>
+<td align="left">full-time</td>
+<td align="right">0.2240102</td>
+<td align="right">0.0630661</td>
+<td align="right">0.0859171</td>
+<td align="right">0.2447814</td>
+<td align="right">0.1197949</td>
+<td align="right">0.1765103</td>
+<td align="right">0.0859201</td>
+<td align="right">0.0799600</td>
+<td align="right">0.0275682</td>
+<td align="right">0.0343156</td>
+<td align="right">0.0671075</td>
+<td align="right">0.0411371</td>
+<td align="right">0.0638993</td>
+<td align="right">0.0626110</td>
+</tr>
+<tr class="even">
+<td align="left">not in paid work</td>
+<td align="left">not in paid work</td>
+<td align="right">0.0533881</td>
+<td align="right">0.1715071</td>
+<td align="right">0.1001408</td>
+<td align="right">0.2633564</td>
+<td align="right">0.0708324</td>
+<td align="right">0.1814197</td>
+<td align="right">0.1593556</td>
+<td align="right">0.0258744</td>
+<td align="right">0.0463328</td>
+<td align="right">0.0320004</td>
+<td align="right">0.0602603</td>
+<td align="right">0.0220217</td>
+<td align="right">0.0510785</td>
+<td align="right">0.0458664</td>
+</tr>
+<tr class="odd">
+<td align="left">part-time</td>
+<td align="left">part-time</td>
+<td align="right">0.1240366</td>
+<td align="right">0.1801231</td>
+<td align="right">0.1318491</td>
+<td align="right">0.0916947</td>
+<td align="right">0.1008414</td>
+<td align="right">0.1991970</td>
+<td align="right">0.1722580</td>
+<td align="right">0.0502480</td>
+<td align="right">0.0760273</td>
+<td align="right">0.0622046</td>
+<td align="right">0.0444446</td>
+<td align="right">0.0347197</td>
+<td align="right">0.0659764</td>
+<td align="right">0.0506492</td>
+</tr>
+</tbody>
+</table>
+<table>
+<thead>
+<tr class="header">
+<th align="left"></th>
+<th align="left">st_halfhour</th>
+<th align="right">ba_survey</th>
+<th align="right">Any_laundry_home</th>
+<th align="right">se</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">00:00.1985</td>
+<td align="left">00:00</td>
+<td align="right">1985</td>
+<td align="right">0.0010556</td>
+<td align="right">0.0002491</td>
+</tr>
+<tr class="even">
+<td align="left">00:30.1985</td>
+<td align="left">00:30</td>
+<td align="right">1985</td>
+<td align="right">0.0005721</td>
+<td align="right">0.0001813</td>
+</tr>
+<tr class="odd">
+<td align="left">01:00.1985</td>
+<td align="left">01:00</td>
+<td align="right">1985</td>
+<td align="right">0.0001816</td>
+<td align="right">0.0001048</td>
+</tr>
+<tr class="even">
+<td align="left">01:30.1985</td>
+<td align="left">01:30</td>
+<td align="right">1985</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">02:00.1985</td>
+<td align="left">02:00</td>
+<td align="right">1985</td>
+<td align="right">0.0000567</td>
+<td align="right">0.0000567</td>
+</tr>
+<tr class="even">
+<td align="left">02:30.1985</td>
+<td align="left">02:30</td>
+<td align="right">1985</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">03:00.1985</td>
+<td align="left">03:00</td>
+<td align="right">1985</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">03:30.1985</td>
+<td align="left">03:30</td>
+<td align="right">1985</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">04:00.1985</td>
+<td align="left">04:00</td>
+<td align="right">1985</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">04:30.1985</td>
+<td align="left">04:30</td>
+<td align="right">1985</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">05:00.1985</td>
+<td align="left">05:00</td>
+<td align="right">1985</td>
+<td align="right">0.0001084</td>
+<td align="right">0.0000767</td>
+</tr>
+<tr class="even">
+<td align="left">05:30.1985</td>
+<td align="left">05:30</td>
+<td align="right">1985</td>
+<td align="right">0.0003492</td>
+<td align="right">0.0001432</td>
+</tr>
+<tr class="odd">
+<td align="left">06:00.1985</td>
+<td align="left">06:00</td>
+<td align="right">1985</td>
+<td align="right">0.0009972</td>
+<td align="right">0.0002422</td>
+</tr>
+<tr class="even">
+<td align="left">06:30.1985</td>
+<td align="left">06:30</td>
+<td align="right">1985</td>
+<td align="right">0.0021780</td>
+<td align="right">0.0003543</td>
+</tr>
+<tr class="odd">
+<td align="left">07:00.1985</td>
+<td align="left">07:00</td>
+<td align="right">1985</td>
+<td align="right">0.0052767</td>
+<td align="right">0.0005480</td>
+</tr>
+<tr class="even">
+<td align="left">07:30.1985</td>
+<td align="left">07:30</td>
+<td align="right">1985</td>
+<td align="right">0.0074772</td>
+<td align="right">0.0006465</td>
+</tr>
+<tr class="odd">
+<td align="left">08:00.1985</td>
+<td align="left">08:00</td>
+<td align="right">1985</td>
+<td align="right">0.0137881</td>
+<td align="right">0.0008801</td>
+</tr>
+<tr class="even">
+<td align="left">08:30.1985</td>
+<td align="left">08:30</td>
+<td align="right">1985</td>
+<td align="right">0.0202534</td>
+<td align="right">0.0010663</td>
+</tr>
+<tr class="odd">
+<td align="left">09:00.1985</td>
+<td align="left">09:00</td>
+<td align="right">1985</td>
+<td align="right">0.0261384</td>
+<td align="right">0.0012092</td>
+</tr>
+<tr class="even">
+<td align="left">09:30.1985</td>
+<td align="left">09:30</td>
+<td align="right">1985</td>
+<td align="right">0.0346053</td>
+<td align="right">0.0013847</td>
+</tr>
+<tr class="odd">
+<td align="left">10:00.1985</td>
+<td align="left">10:00</td>
+<td align="right">1985</td>
+<td align="right">0.0371609</td>
+<td align="right">0.0014307</td>
+</tr>
+<tr class="even">
+<td align="left">10:30.1985</td>
+<td align="left">10:30</td>
+<td align="right">1985</td>
+<td align="right">0.0383388</td>
+<td align="right">0.0014500</td>
+</tr>
+<tr class="odd">
+<td align="left">11:00.1985</td>
+<td align="left">11:00</td>
+<td align="right">1985</td>
+<td align="right">0.0322811</td>
+<td align="right">0.0013371</td>
+</tr>
+<tr class="even">
+<td align="left">11:30.1985</td>
+<td align="left">11:30</td>
+<td align="right">1985</td>
+<td align="right">0.0310342</td>
+<td align="right">0.0013132</td>
+</tr>
+<tr class="odd">
+<td align="left">12:00.1985</td>
+<td align="left">12:00</td>
+<td align="right">1985</td>
+<td align="right">0.0222697</td>
+<td align="right">0.0011172</td>
+</tr>
+<tr class="even">
+<td align="left">12:30.1985</td>
+<td align="left">12:30</td>
+<td align="right">1985</td>
+<td align="right">0.0220213</td>
+<td align="right">0.0011134</td>
+</tr>
+<tr class="odd">
+<td align="left">13:00.1985</td>
+<td align="left">13:00</td>
+<td align="right">1985</td>
+<td align="right">0.0191961</td>
+<td align="right">0.0010389</td>
+</tr>
+<tr class="even">
+<td align="left">13:30.1985</td>
+<td align="left">13:30</td>
+<td align="right">1985</td>
+<td align="right">0.0244418</td>
+<td align="right">0.0011707</td>
+</tr>
+<tr class="odd">
+<td align="left">14:00.1985</td>
+<td align="left">14:00</td>
+<td align="right">1985</td>
+<td align="right">0.0271532</td>
+<td align="right">0.0012306</td>
+</tr>
+<tr class="even">
+<td align="left">14:30.1985</td>
+<td align="left">14:30</td>
+<td align="right">1985</td>
+<td align="right">0.0253324</td>
+<td align="right">0.0011907</td>
+</tr>
+<tr class="odd">
+<td align="left">15:00.1985</td>
+<td align="left">15:00</td>
+<td align="right">1985</td>
+<td align="right">0.0226200</td>
+<td align="right">0.0011250</td>
+</tr>
+<tr class="even">
+<td align="left">15:30.1985</td>
+<td align="left">15:30</td>
+<td align="right">1985</td>
+<td align="right">0.0231902</td>
+<td align="right">0.0011389</td>
+</tr>
+<tr class="odd">
+<td align="left">16:00.1985</td>
+<td align="left">16:00</td>
+<td align="right">1985</td>
+<td align="right">0.0217943</td>
+<td align="right">0.0011069</td>
+</tr>
+<tr class="even">
+<td align="left">16:30.1985</td>
+<td align="left">16:30</td>
+<td align="right">1985</td>
+<td align="right">0.0198137</td>
+<td align="right">0.0010567</td>
+</tr>
+<tr class="odd">
+<td align="left">17:00.1985</td>
+<td align="left">17:00</td>
+<td align="right">1985</td>
+<td align="right">0.0120270</td>
+<td align="right">0.0008239</td>
+</tr>
+<tr class="even">
+<td align="left">17:30.1985</td>
+<td align="left">17:30</td>
+<td align="right">1985</td>
+<td align="right">0.0133019</td>
+<td align="right">0.0008650</td>
+</tr>
+<tr class="odd">
+<td align="left">18:00.1985</td>
+<td align="left">18:00</td>
+<td align="right">1985</td>
+<td align="right">0.0152872</td>
+<td align="right">0.0009250</td>
+</tr>
+<tr class="even">
+<td align="left">18:30.1985</td>
+<td align="left">18:30</td>
+<td align="right">1985</td>
+<td align="right">0.0191294</td>
+<td align="right">0.0010358</td>
+</tr>
+<tr class="odd">
+<td align="left">19:00.1985</td>
+<td align="left">19:00</td>
+<td align="right">1985</td>
+<td align="right">0.0190732</td>
+<td align="right">0.0010400</td>
+</tr>
+<tr class="even">
+<td align="left">19:30.1985</td>
+<td align="left">19:30</td>
+<td align="right">1985</td>
+<td align="right">0.0180119</td>
+<td align="right">0.0010104</td>
+</tr>
+<tr class="odd">
+<td align="left">20:00.1985</td>
+<td align="left">20:00</td>
+<td align="right">1985</td>
+<td align="right">0.0189935</td>
+<td align="right">0.0010328</td>
+</tr>
+<tr class="even">
+<td align="left">20:30.1985</td>
+<td align="left">20:30</td>
+<td align="right">1985</td>
+<td align="right">0.0185356</td>
+<td align="right">0.0010190</td>
+</tr>
+<tr class="odd">
+<td align="left">21:00.1985</td>
+<td align="left">21:00</td>
+<td align="right">1985</td>
+<td align="right">0.0144381</td>
+<td align="right">0.0009011</td>
+</tr>
+<tr class="even">
+<td align="left">21:30.1985</td>
+<td align="left">21:30</td>
+<td align="right">1985</td>
+<td align="right">0.0123343</td>
+<td align="right">0.0008312</td>
+</tr>
+<tr class="odd">
+<td align="left">22:00.1985</td>
+<td align="left">22:00</td>
+<td align="right">1985</td>
+<td align="right">0.0091150</td>
+<td align="right">0.0007132</td>
+</tr>
+<tr class="even">
+<td align="left">22:30.1985</td>
+<td align="left">22:30</td>
+<td align="right">1985</td>
+<td align="right">0.0055409</td>
+<td align="right">0.0005603</td>
+</tr>
+<tr class="odd">
+<td align="left">23:00.1985</td>
+<td align="left">23:00</td>
+<td align="right">1985</td>
+<td align="right">0.0030516</td>
+<td align="right">0.0004164</td>
+</tr>
+<tr class="even">
+<td align="left">23:30.1985</td>
+<td align="left">23:30</td>
+<td align="right">1985</td>
+<td align="right">0.0014843</td>
+<td align="right">0.0002922</td>
+</tr>
+<tr class="odd">
+<td align="left">00:00.2005</td>
+<td align="left">00:00</td>
+<td align="right">2005</td>
+<td align="right">0.0004289</td>
+<td align="right">0.0004288</td>
+</tr>
+<tr class="even">
+<td align="left">00:30.2005</td>
+<td align="left">00:30</td>
+<td align="right">2005</td>
+<td align="right">0.0004289</td>
+<td align="right">0.0004288</td>
+</tr>
+<tr class="odd">
+<td align="left">01:00.2005</td>
+<td align="left">01:00</td>
+<td align="right">2005</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">01:30.2005</td>
+<td align="left">01:30</td>
+<td align="right">2005</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">02:00.2005</td>
+<td align="left">02:00</td>
+<td align="right">2005</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">02:30.2005</td>
+<td align="left">02:30</td>
+<td align="right">2005</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">03:00.2005</td>
+<td align="left">03:00</td>
+<td align="right">2005</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">03:30.2005</td>
+<td align="left">03:30</td>
+<td align="right">2005</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">04:00.2005</td>
+<td align="left">04:00</td>
+<td align="right">2005</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">04:30.2005</td>
+<td align="left">04:30</td>
+<td align="right">2005</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">05:00.2005</td>
+<td align="left">05:00</td>
+<td align="right">2005</td>
+<td align="right">0.0005382</td>
+<td align="right">0.0005380</td>
+</tr>
+<tr class="even">
+<td align="left">05:30.2005</td>
+<td align="left">05:30</td>
+<td align="right">2005</td>
+<td align="right">0.0012199</td>
+<td align="right">0.0007245</td>
+</tr>
+<tr class="odd">
+<td align="left">06:00.2005</td>
+<td align="left">06:00</td>
+<td align="right">2005</td>
+<td align="right">0.0015144</td>
+<td align="right">0.0007819</td>
+</tr>
+<tr class="even">
+<td align="left">06:30.2005</td>
+<td align="left">06:30</td>
+<td align="right">2005</td>
+<td align="right">0.0043039</td>
+<td align="right">0.0015482</td>
+</tr>
+<tr class="odd">
+<td align="left">07:00.2005</td>
+<td align="left">07:00</td>
+<td align="right">2005</td>
+<td align="right">0.0103922</td>
+<td align="right">0.0021535</td>
+</tr>
+<tr class="even">
+<td align="left">07:30.2005</td>
+<td align="left">07:30</td>
+<td align="right">2005</td>
+<td align="right">0.0161986</td>
+<td align="right">0.0026600</td>
+</tr>
+<tr class="odd">
+<td align="left">08:00.2005</td>
+<td align="left">08:00</td>
+<td align="right">2005</td>
+<td align="right">0.0187639</td>
+<td align="right">0.0028371</td>
+</tr>
+<tr class="even">
+<td align="left">08:30.2005</td>
+<td align="left">08:30</td>
+<td align="right">2005</td>
+<td align="right">0.0239451</td>
+<td align="right">0.0031630</td>
+</tr>
+<tr class="odd">
+<td align="left">09:00.2005</td>
+<td align="left">09:00</td>
+<td align="right">2005</td>
+<td align="right">0.0282875</td>
+<td align="right">0.0033952</td>
+</tr>
+<tr class="even">
+<td align="left">09:30.2005</td>
+<td align="left">09:30</td>
+<td align="right">2005</td>
+<td align="right">0.0250066</td>
+<td align="right">0.0031481</td>
+</tr>
+<tr class="odd">
+<td align="left">10:00.2005</td>
+<td align="left">10:00</td>
+<td align="right">2005</td>
+<td align="right">0.0303511</td>
+<td align="right">0.0034738</td>
+</tr>
+<tr class="even">
+<td align="left">10:30.2005</td>
+<td align="left">10:30</td>
+<td align="right">2005</td>
+<td align="right">0.0220304</td>
+<td align="right">0.0029144</td>
+</tr>
+<tr class="odd">
+<td align="left">11:00.2005</td>
+<td align="left">11:00</td>
+<td align="right">2005</td>
+<td align="right">0.0167036</td>
+<td align="right">0.0025672</td>
+</tr>
+<tr class="even">
+<td align="left">11:30.2005</td>
+<td align="left">11:30</td>
+<td align="right">2005</td>
+<td align="right">0.0146088</td>
+<td align="right">0.0025741</td>
+</tr>
+<tr class="odd">
+<td align="left">12:00.2005</td>
+<td align="left">12:00</td>
+<td align="right">2005</td>
+<td align="right">0.0120755</td>
+<td align="right">0.0022353</td>
+</tr>
+<tr class="even">
+<td align="left">12:30.2005</td>
+<td align="left">12:30</td>
+<td align="right">2005</td>
+<td align="right">0.0113019</td>
+<td align="right">0.0022600</td>
+</tr>
+<tr class="odd">
+<td align="left">13:00.2005</td>
+<td align="left">13:00</td>
+<td align="right">2005</td>
+<td align="right">0.0142415</td>
+<td align="right">0.0024589</td>
+</tr>
+<tr class="even">
+<td align="left">13:30.2005</td>
+<td align="left">13:30</td>
+<td align="right">2005</td>
+<td align="right">0.0170105</td>
+<td align="right">0.0026592</td>
+</tr>
+<tr class="odd">
+<td align="left">14:00.2005</td>
+<td align="left">14:00</td>
+<td align="right">2005</td>
+<td align="right">0.0143518</td>
+<td align="right">0.0023896</td>
+</tr>
+<tr class="even">
+<td align="left">14:30.2005</td>
+<td align="left">14:30</td>
+<td align="right">2005</td>
+<td align="right">0.0121984</td>
+<td align="right">0.0021457</td>
+</tr>
+<tr class="odd">
+<td align="left">15:00.2005</td>
+<td align="left">15:00</td>
+<td align="right">2005</td>
+<td align="right">0.0125262</td>
+<td align="right">0.0022281</td>
+</tr>
+<tr class="even">
+<td align="left">15:30.2005</td>
+<td align="left">15:30</td>
+<td align="right">2005</td>
+<td align="right">0.0145229</td>
+<td align="right">0.0024785</td>
+</tr>
+<tr class="odd">
+<td align="left">16:00.2005</td>
+<td align="left">16:00</td>
+<td align="right">2005</td>
+<td align="right">0.0147880</td>
+<td align="right">0.0027606</td>
+</tr>
+<tr class="even">
+<td align="left">16:30.2005</td>
+<td align="left">16:30</td>
+<td align="right">2005</td>
+<td align="right">0.0130933</td>
+<td align="right">0.0024381</td>
+</tr>
+<tr class="odd">
+<td align="left">17:00.2005</td>
+<td align="left">17:00</td>
+<td align="right">2005</td>
+<td align="right">0.0104090</td>
+<td align="right">0.0021998</td>
+</tr>
+<tr class="even">
+<td align="left">17:30.2005</td>
+<td align="left">17:30</td>
+<td align="right">2005</td>
+<td align="right">0.0083849</td>
+<td align="right">0.0019649</td>
+</tr>
+<tr class="odd">
+<td align="left">18:00.2005</td>
+<td align="left">18:00</td>
+<td align="right">2005</td>
+<td align="right">0.0104222</td>
+<td align="right">0.0022736</td>
+</tr>
+<tr class="even">
+<td align="left">18:30.2005</td>
+<td align="left">18:30</td>
+<td align="right">2005</td>
+<td align="right">0.0117916</td>
+<td align="right">0.0023978</td>
+</tr>
+<tr class="odd">
+<td align="left">19:00.2005</td>
+<td align="left">19:00</td>
+<td align="right">2005</td>
+<td align="right">0.0149155</td>
+<td align="right">0.0026367</td>
+</tr>
+<tr class="even">
+<td align="left">19:30.2005</td>
+<td align="left">19:30</td>
+<td align="right">2005</td>
+<td align="right">0.0116972</td>
+<td align="right">0.0023677</td>
+</tr>
+<tr class="odd">
+<td align="left">20:00.2005</td>
+<td align="left">20:00</td>
+<td align="right">2005</td>
+<td align="right">0.0132548</td>
+<td align="right">0.0023569</td>
+</tr>
+<tr class="even">
+<td align="left">20:30.2005</td>
+<td align="left">20:30</td>
+<td align="right">2005</td>
+<td align="right">0.0115057</td>
+<td align="right">0.0021052</td>
+</tr>
+<tr class="odd">
+<td align="left">21:00.2005</td>
+<td align="left">21:00</td>
+<td align="right">2005</td>
+<td align="right">0.0076453</td>
+<td align="right">0.0017000</td>
+</tr>
+<tr class="even">
+<td align="left">21:30.2005</td>
+<td align="left">21:30</td>
+<td align="right">2005</td>
+<td align="right">0.0056360</td>
+<td align="right">0.0014326</td>
+</tr>
+<tr class="odd">
+<td align="left">22:00.2005</td>
+<td align="left">22:00</td>
+<td align="right">2005</td>
+<td align="right">0.0045470</td>
+<td align="right">0.0012778</td>
+</tr>
+<tr class="even">
+<td align="left">22:30.2005</td>
+<td align="left">22:30</td>
+<td align="right">2005</td>
+<td align="right">0.0023425</td>
+<td align="right">0.0009693</td>
+</tr>
+<tr class="odd">
+<td align="left">23:00.2005</td>
+<td align="left">23:00</td>
+<td align="right">2005</td>
+<td align="right">0.0022741</td>
+<td align="right">0.0009411</td>
+</tr>
+<tr class="even">
+<td align="left">23:30.2005</td>
+<td align="left">23:30</td>
+<td align="right">2005</td>
+<td align="right">0.0004944</td>
+<td align="right">0.0004943</td>
+</tr>
+</tbody>
+</table>
+<table>
+<thead>
+<tr class="header">
+<th align="left"></th>
+<th align="left">st_halfhour</th>
+<th align="left">r_dow</th>
+<th align="right">Any_laundry_home</th>
+<th align="right">se</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">00:00.Friday</td>
+<td align="left">00:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0003901</td>
+<td align="right">0.0003901</td>
+</tr>
+<tr class="even">
+<td align="left">00:30.Friday</td>
+<td align="left">00:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0003901</td>
+<td align="right">0.0003901</td>
+</tr>
+<tr class="odd">
+<td align="left">01:00.Friday</td>
+<td align="left">01:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">01:30.Friday</td>
+<td align="left">01:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">02:00.Friday</td>
+<td align="left">02:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0003963</td>
+<td align="right">0.0003963</td>
+</tr>
+<tr class="even">
+<td align="left">02:30.Friday</td>
+<td align="left">02:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">03:00.Friday</td>
+<td align="left">03:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">03:30.Friday</td>
+<td align="left">03:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">04:00.Friday</td>
+<td align="left">04:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">04:30.Friday</td>
+<td align="left">04:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">05:00.Friday</td>
+<td align="left">05:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0003712</td>
+<td align="right">0.0003711</td>
+</tr>
+<tr class="even">
+<td align="left">05:30.Friday</td>
+<td align="left">05:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0004278</td>
+<td align="right">0.0004277</td>
+</tr>
+<tr class="odd">
+<td align="left">06:00.Friday</td>
+<td align="left">06:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0012271</td>
+<td align="right">0.0007102</td>
+</tr>
+<tr class="even">
+<td align="left">06:30.Friday</td>
+<td align="left">06:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0028209</td>
+<td align="right">0.0010662</td>
+</tr>
+<tr class="odd">
+<td align="left">07:00.Friday</td>
+<td align="left">07:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0057709</td>
+<td align="right">0.0014910</td>
+</tr>
+<tr class="even">
+<td align="left">07:30.Friday</td>
+<td align="left">07:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0050491</td>
+<td align="right">0.0014024</td>
+</tr>
+<tr class="odd">
+<td align="left">08:00.Friday</td>
+<td align="left">08:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0136752</td>
+<td align="right">0.0023368</td>
+</tr>
+<tr class="even">
+<td align="left">08:30.Friday</td>
+<td align="left">08:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0189521</td>
+<td align="right">0.0027182</td>
+</tr>
+<tr class="odd">
+<td align="left">09:00.Friday</td>
+<td align="left">09:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0196416</td>
+<td align="right">0.0027592</td>
+</tr>
+<tr class="even">
+<td align="left">09:30.Friday</td>
+<td align="left">09:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0264381</td>
+<td align="right">0.0031963</td>
+</tr>
+<tr class="odd">
+<td align="left">10:00.Friday</td>
+<td align="left">10:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0297190</td>
+<td align="right">0.0033927</td>
+</tr>
+<tr class="even">
+<td align="left">10:30.Friday</td>
+<td align="left">10:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0230931</td>
+<td align="right">0.0029824</td>
+</tr>
+<tr class="odd">
+<td align="left">11:00.Friday</td>
+<td align="left">11:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0203808</td>
+<td align="right">0.0028327</td>
+</tr>
+<tr class="even">
+<td align="left">11:30.Friday</td>
+<td align="left">11:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0263012</td>
+<td align="right">0.0031824</td>
+</tr>
+<tr class="odd">
+<td align="left">12:00.Friday</td>
+<td align="left">12:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0183270</td>
+<td align="right">0.0026590</td>
+</tr>
+<tr class="even">
+<td align="left">12:30.Friday</td>
+<td align="left">12:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0153362</td>
+<td align="right">0.0024453</td>
+</tr>
+<tr class="odd">
+<td align="left">13:00.Friday</td>
+<td align="left">13:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0133169</td>
+<td align="right">0.0022760</td>
+</tr>
+<tr class="even">
+<td align="left">13:30.Friday</td>
+<td align="left">13:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0188765</td>
+<td align="right">0.0027088</td>
+</tr>
+<tr class="odd">
+<td align="left">14:00.Friday</td>
+<td align="left">14:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0207969</td>
+<td align="right">0.0028606</td>
+</tr>
+<tr class="even">
+<td align="left">14:30.Friday</td>
+<td align="left">14:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0184069</td>
+<td align="right">0.0026940</td>
+</tr>
+<tr class="odd">
+<td align="left">15:00.Friday</td>
+<td align="left">15:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0158436</td>
+<td align="right">0.0024908</td>
+</tr>
+<tr class="even">
+<td align="left">15:30.Friday</td>
+<td align="left">15:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0155307</td>
+<td align="right">0.0024744</td>
+</tr>
+<tr class="odd">
+<td align="left">16:00.Friday</td>
+<td align="left">16:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0139625</td>
+<td align="right">0.0023499</td>
+</tr>
+<tr class="even">
+<td align="left">16:30.Friday</td>
+<td align="left">16:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0167389</td>
+<td align="right">0.0025674</td>
+</tr>
+<tr class="odd">
+<td align="left">17:00.Friday</td>
+<td align="left">17:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0129159</td>
+<td align="right">0.0022758</td>
+</tr>
+<tr class="even">
+<td align="left">17:30.Friday</td>
+<td align="left">17:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0109492</td>
+<td align="right">0.0020681</td>
+</tr>
+<tr class="odd">
+<td align="left">18:00.Friday</td>
+<td align="left">18:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0148877</td>
+<td align="right">0.0023762</td>
+</tr>
+<tr class="even">
+<td align="left">18:30.Friday</td>
+<td align="left">18:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0181360</td>
+<td align="right">0.0026323</td>
+</tr>
+<tr class="odd">
+<td align="left">19:00.Friday</td>
+<td align="left">19:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0162720</td>
+<td align="right">0.0025288</td>
+</tr>
+<tr class="even">
+<td align="left">19:30.Friday</td>
+<td align="left">19:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0179092</td>
+<td align="right">0.0026536</td>
+</tr>
+<tr class="odd">
+<td align="left">20:00.Friday</td>
+<td align="left">20:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0125907</td>
+<td align="right">0.0022203</td>
+</tr>
+<tr class="even">
+<td align="left">20:30.Friday</td>
+<td align="left">20:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0133482</td>
+<td align="right">0.0022826</td>
+</tr>
+<tr class="odd">
+<td align="left">21:00.Friday</td>
+<td align="left">21:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0114288</td>
+<td align="right">0.0021175</td>
+</tr>
+<tr class="even">
+<td align="left">21:30.Friday</td>
+<td align="left">21:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0108226</td>
+<td align="right">0.0020406</td>
+</tr>
+<tr class="odd">
+<td align="left">22:00.Friday</td>
+<td align="left">22:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0096640</td>
+<td align="right">0.0019324</td>
+</tr>
+<tr class="even">
+<td align="left">22:30.Friday</td>
+<td align="left">22:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0057181</td>
+<td align="right">0.0014781</td>
+</tr>
+<tr class="odd">
+<td align="left">23:00.Friday</td>
+<td align="left">23:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0027772</td>
+<td align="right">0.0010539</td>
+</tr>
+<tr class="even">
+<td align="left">23:30.Friday</td>
+<td align="left">23:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0019566</td>
+<td align="right">0.0008776</td>
+</tr>
+<tr class="odd">
+<td align="left">00:00.Monday</td>
+<td align="left">00:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0008133</td>
+<td align="right">0.0005749</td>
+</tr>
+<tr class="even">
+<td align="left">00:30.Monday</td>
+<td align="left">00:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0007668</td>
+<td align="right">0.0005427</td>
+</tr>
+<tr class="odd">
+<td align="left">01:00.Monday</td>
+<td align="left">01:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0004258</td>
+<td align="right">0.0004257</td>
+</tr>
+<tr class="even">
+<td align="left">01:30.Monday</td>
+<td align="left">01:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">02:00.Monday</td>
+<td align="left">02:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">02:30.Monday</td>
+<td align="left">02:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">03:00.Monday</td>
+<td align="left">03:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">03:30.Monday</td>
+<td align="left">03:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">04:00.Monday</td>
+<td align="left">04:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">04:30.Monday</td>
+<td align="left">04:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">05:00.Monday</td>
+<td align="left">05:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">05:30.Monday</td>
+<td align="left">05:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">06:00.Monday</td>
+<td align="left">06:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0004608</td>
+<td align="right">0.0004606</td>
+</tr>
+<tr class="even">
+<td align="left">06:30.Monday</td>
+<td align="left">06:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0032522</td>
+<td align="right">0.0011505</td>
+</tr>
+<tr class="odd">
+<td align="left">07:00.Monday</td>
+<td align="left">07:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0084495</td>
+<td align="right">0.0018425</td>
+</tr>
+<tr class="even">
+<td align="left">07:30.Monday</td>
+<td align="left">07:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0098686</td>
+<td align="right">0.0019712</td>
+</tr>
+<tr class="odd">
+<td align="left">08:00.Monday</td>
+<td align="left">08:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0154802</td>
+<td align="right">0.0024384</td>
+</tr>
+<tr class="even">
+<td align="left">08:30.Monday</td>
+<td align="left">08:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0275616</td>
+<td align="right">0.0032629</td>
+</tr>
+<tr class="odd">
+<td align="left">09:00.Monday</td>
+<td align="left">09:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0418493</td>
+<td align="right">0.0040108</td>
+</tr>
+<tr class="even">
+<td align="left">09:30.Monday</td>
+<td align="left">09:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0484774</td>
+<td align="right">0.0042968</td>
+</tr>
+<tr class="odd">
+<td align="left">10:00.Monday</td>
+<td align="left">10:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0483581</td>
+<td align="right">0.0042848</td>
+</tr>
+<tr class="even">
+<td align="left">10:30.Monday</td>
+<td align="left">10:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0450101</td>
+<td align="right">0.0041339</td>
+</tr>
+<tr class="odd">
+<td align="left">11:00.Monday</td>
+<td align="left">11:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0434075</td>
+<td align="right">0.0040796</td>
+</tr>
+<tr class="even">
+<td align="left">11:30.Monday</td>
+<td align="left">11:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0373854</td>
+<td align="right">0.0037960</td>
+</tr>
+<tr class="odd">
+<td align="left">12:00.Monday</td>
+<td align="left">12:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0238394</td>
+<td align="right">0.0030518</td>
+</tr>
+<tr class="even">
+<td align="left">12:30.Monday</td>
+<td align="left">12:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0244933</td>
+<td align="right">0.0031069</td>
+</tr>
+<tr class="odd">
+<td align="left">13:00.Monday</td>
+<td align="left">13:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0261203</td>
+<td align="right">0.0031830</td>
+</tr>
+<tr class="even">
+<td align="left">13:30.Monday</td>
+<td align="left">13:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0321368</td>
+<td align="right">0.0035435</td>
+</tr>
+<tr class="odd">
+<td align="left">14:00.Monday</td>
+<td align="left">14:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0377973</td>
+<td align="right">0.0038167</td>
+</tr>
+<tr class="even">
+<td align="left">14:30.Monday</td>
+<td align="left">14:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0386287</td>
+<td align="right">0.0038590</td>
+</tr>
+<tr class="odd">
+<td align="left">15:00.Monday</td>
+<td align="left">15:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0299300</td>
+<td align="right">0.0034149</td>
+</tr>
+<tr class="even">
+<td align="left">15:30.Monday</td>
+<td align="left">15:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0282619</td>
+<td align="right">0.0033177</td>
+</tr>
+<tr class="odd">
+<td align="left">16:00.Monday</td>
+<td align="left">16:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0263313</td>
+<td align="right">0.0032080</td>
+</tr>
+<tr class="even">
+<td align="left">16:30.Monday</td>
+<td align="left">16:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0213524</td>
+<td align="right">0.0029105</td>
+</tr>
+<tr class="odd">
+<td align="left">17:00.Monday</td>
+<td align="left">17:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0115651</td>
+<td align="right">0.0021404</td>
+</tr>
+<tr class="even">
+<td align="left">17:30.Monday</td>
+<td align="left">17:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0145834</td>
+<td align="right">0.0023892</td>
+</tr>
+<tr class="odd">
+<td align="left">18:00.Monday</td>
+<td align="left">18:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0187620</td>
+<td align="right">0.0026937</td>
+</tr>
+<tr class="even">
+<td align="left">18:30.Monday</td>
+<td align="left">18:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0221181</td>
+<td align="right">0.0029324</td>
+</tr>
+<tr class="odd">
+<td align="left">19:00.Monday</td>
+<td align="left">19:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0248732</td>
+<td align="right">0.0031048</td>
+</tr>
+<tr class="even">
+<td align="left">19:30.Monday</td>
+<td align="left">19:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0214987</td>
+<td align="right">0.0029060</td>
+</tr>
+<tr class="odd">
+<td align="left">20:00.Monday</td>
+<td align="left">20:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0290735</td>
+<td align="right">0.0033862</td>
+</tr>
+<tr class="even">
+<td align="left">20:30.Monday</td>
+<td align="left">20:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0290144</td>
+<td align="right">0.0033583</td>
+</tr>
+<tr class="odd">
+<td align="left">21:00.Monday</td>
+<td align="left">21:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0211871</td>
+<td align="right">0.0028646</td>
+</tr>
+<tr class="even">
+<td align="left">21:30.Monday</td>
+<td align="left">21:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0191816</td>
+<td align="right">0.0027237</td>
+</tr>
+<tr class="odd">
+<td align="left">22:00.Monday</td>
+<td align="left">22:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0083826</td>
+<td align="right">0.0017859</td>
+</tr>
+<tr class="even">
+<td align="left">22:30.Monday</td>
+<td align="left">22:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0062310</td>
+<td align="right">0.0015590</td>
+</tr>
+<tr class="odd">
+<td align="left">23:00.Monday</td>
+<td align="left">23:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0024715</td>
+<td align="right">0.0010104</td>
+</tr>
+<tr class="even">
+<td align="left">23:30.Monday</td>
+<td align="left">23:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0007671</td>
+<td align="right">0.0005454</td>
+</tr>
+<tr class="odd">
+<td align="left">00:00.Saturday</td>
+<td align="left">00:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0016283</td>
+<td align="right">0.0008153</td>
+</tr>
+<tr class="even">
+<td align="left">00:30.Saturday</td>
+<td align="left">00:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0012065</td>
+<td align="right">0.0006978</td>
+</tr>
+<tr class="odd">
+<td align="left">01:00.Saturday</td>
+<td align="left">01:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0008492</td>
+<td align="right">0.0006003</td>
+</tr>
+<tr class="even">
+<td align="left">01:30.Saturday</td>
+<td align="left">01:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">02:00.Saturday</td>
+<td align="left">02:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">02:30.Saturday</td>
+<td align="left">02:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">03:00.Saturday</td>
+<td align="left">03:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">03:30.Saturday</td>
+<td align="left">03:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">04:00.Saturday</td>
+<td align="left">04:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">04:30.Saturday</td>
+<td align="left">04:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">05:00.Saturday</td>
+<td align="left">05:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">05:30.Saturday</td>
+<td align="left">05:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">06:00.Saturday</td>
+<td align="left">06:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0008344</td>
+<td align="right">0.0005900</td>
+</tr>
+<tr class="even">
+<td align="left">06:30.Saturday</td>
+<td align="left">06:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0020597</td>
+<td align="right">0.0009216</td>
+</tr>
+<tr class="odd">
+<td align="left">07:00.Saturday</td>
+<td align="left">07:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0021065</td>
+<td align="right">0.0009417</td>
+</tr>
+<tr class="even">
+<td align="left">07:30.Saturday</td>
+<td align="left">07:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0037099</td>
+<td align="right">0.0012375</td>
+</tr>
+<tr class="odd">
+<td align="left">08:00.Saturday</td>
+<td align="left">08:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0124805</td>
+<td align="right">0.0022358</td>
+</tr>
+<tr class="even">
+<td align="left">08:30.Saturday</td>
+<td align="left">08:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0197570</td>
+<td align="right">0.0028038</td>
+</tr>
+<tr class="odd">
+<td align="left">09:00.Saturday</td>
+<td align="left">09:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0244138</td>
+<td align="right">0.0031214</td>
+</tr>
+<tr class="even">
+<td align="left">09:30.Saturday</td>
+<td align="left">09:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0359478</td>
+<td align="right">0.0037532</td>
+</tr>
+<tr class="odd">
+<td align="left">10:00.Saturday</td>
+<td align="left">10:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0387591</td>
+<td align="right">0.0038714</td>
+</tr>
+<tr class="even">
+<td align="left">10:30.Saturday</td>
+<td align="left">10:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0501596</td>
+<td align="right">0.0043687</td>
+</tr>
+<tr class="odd">
+<td align="left">11:00.Saturday</td>
+<td align="left">11:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0375079</td>
+<td align="right">0.0038086</td>
+</tr>
+<tr class="even">
+<td align="left">11:30.Saturday</td>
+<td align="left">11:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0329629</td>
+<td align="right">0.0035907</td>
+</tr>
+<tr class="odd">
+<td align="left">12:00.Saturday</td>
+<td align="left">12:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0229262</td>
+<td align="right">0.0030109</td>
+</tr>
+<tr class="even">
+<td align="left">12:30.Saturday</td>
+<td align="left">12:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0219463</td>
+<td align="right">0.0029612</td>
+</tr>
+<tr class="odd">
+<td align="left">13:00.Saturday</td>
+<td align="left">13:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0170481</td>
+<td align="right">0.0026152</td>
+</tr>
+<tr class="even">
+<td align="left">13:30.Saturday</td>
+<td align="left">13:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0229396</td>
+<td align="right">0.0030144</td>
+</tr>
+<tr class="odd">
+<td align="left">14:00.Saturday</td>
+<td align="left">14:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0242461</td>
+<td align="right">0.0030785</td>
+</tr>
+<tr class="even">
+<td align="left">14:30.Saturday</td>
+<td align="left">14:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0188707</td>
+<td align="right">0.0027322</td>
+</tr>
+<tr class="odd">
+<td align="left">15:00.Saturday</td>
+<td align="left">15:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0225764</td>
+<td align="right">0.0029679</td>
+</tr>
+<tr class="even">
+<td align="left">15:30.Saturday</td>
+<td align="left">15:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0218942</td>
+<td align="right">0.0029326</td>
+</tr>
+<tr class="odd">
+<td align="left">16:00.Saturday</td>
+<td align="left">16:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0190385</td>
+<td align="right">0.0027323</td>
+</tr>
+<tr class="even">
+<td align="left">16:30.Saturday</td>
+<td align="left">16:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0166619</td>
+<td align="right">0.0025574</td>
+</tr>
+<tr class="odd">
+<td align="left">17:00.Saturday</td>
+<td align="left">17:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0140586</td>
+<td align="right">0.0023346</td>
+</tr>
+<tr class="even">
+<td align="left">17:30.Saturday</td>
+<td align="left">17:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0139002</td>
+<td align="right">0.0023409</td>
+</tr>
+<tr class="odd">
+<td align="left">18:00.Saturday</td>
+<td align="left">18:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0109572</td>
+<td align="right">0.0021024</td>
+</tr>
+<tr class="even">
+<td align="left">18:30.Saturday</td>
+<td align="left">18:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0112983</td>
+<td align="right">0.0021292</td>
+</tr>
+<tr class="odd">
+<td align="left">19:00.Saturday</td>
+<td align="left">19:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0147115</td>
+<td align="right">0.0024420</td>
+</tr>
+<tr class="even">
+<td align="left">19:30.Saturday</td>
+<td align="left">19:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0118358</td>
+<td align="right">0.0021922</td>
+</tr>
+<tr class="odd">
+<td align="left">20:00.Saturday</td>
+<td align="left">20:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0124971</td>
+<td align="right">0.0022366</td>
+</tr>
+<tr class="even">
+<td align="left">20:30.Saturday</td>
+<td align="left">20:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0107063</td>
+<td align="right">0.0020566</td>
+</tr>
+<tr class="odd">
+<td align="left">21:00.Saturday</td>
+<td align="left">21:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0082614</td>
+<td align="right">0.0018005</td>
+</tr>
+<tr class="even">
+<td align="left">21:30.Saturday</td>
+<td align="left">21:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0089101</td>
+<td align="right">0.0018960</td>
+</tr>
+<tr class="odd">
+<td align="left">22:00.Saturday</td>
+<td align="left">22:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0064642</td>
+<td align="right">0.0016153</td>
+</tr>
+<tr class="even">
+<td align="left">22:30.Saturday</td>
+<td align="left">22:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0029249</td>
+<td align="right">0.0011079</td>
+</tr>
+<tr class="odd">
+<td align="left">23:00.Saturday</td>
+<td align="left">23:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0036366</td>
+<td align="right">0.0012136</td>
+</tr>
+<tr class="even">
+<td align="left">23:30.Saturday</td>
+<td align="left">23:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0024535</td>
+<td align="right">0.0010025</td>
+</tr>
+<tr class="odd">
+<td align="left">00:00.Sunday</td>
+<td align="left">00:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0008473</td>
+<td align="right">0.0005991</td>
+</tr>
+<tr class="even">
+<td align="left">00:30.Sunday</td>
+<td align="left">00:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0007739</td>
+<td align="right">0.0005481</td>
+</tr>
+<tr class="odd">
+<td align="left">01:00.Sunday</td>
+<td align="left">01:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">01:30.Sunday</td>
+<td align="left">01:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">02:00.Sunday</td>
+<td align="left">02:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">02:30.Sunday</td>
+<td align="left">02:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">03:00.Sunday</td>
+<td align="left">03:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">03:30.Sunday</td>
+<td align="left">03:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">04:00.Sunday</td>
+<td align="left">04:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">04:30.Sunday</td>
+<td align="left">04:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">05:00.Sunday</td>
+<td align="left">05:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">05:30.Sunday</td>
+<td align="left">05:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">06:00.Sunday</td>
+<td align="left">06:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0012605</td>
+<td align="right">0.0007274</td>
+</tr>
+<tr class="even">
+<td align="left">06:30.Sunday</td>
+<td align="left">06:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0011475</td>
+<td align="right">0.0006651</td>
+</tr>
+<tr class="odd">
+<td align="left">07:00.Sunday</td>
+<td align="left">07:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0007562</td>
+<td align="right">0.0005355</td>
+</tr>
+<tr class="even">
+<td align="left">07:30.Sunday</td>
+<td align="left">07:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0032101</td>
+<td align="right">0.0011384</td>
+</tr>
+<tr class="odd">
+<td align="left">08:00.Sunday</td>
+<td align="left">08:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0068598</td>
+<td align="right">0.0016168</td>
+</tr>
+<tr class="even">
+<td align="left">08:30.Sunday</td>
+<td align="left">08:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0129800</td>
+<td align="right">0.0022535</td>
+</tr>
+<tr class="odd">
+<td align="left">09:00.Sunday</td>
+<td align="left">09:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0208784</td>
+<td align="right">0.0028751</td>
+</tr>
+<tr class="even">
+<td align="left">09:30.Sunday</td>
+<td align="left">09:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0278524</td>
+<td align="right">0.0032943</td>
+</tr>
+<tr class="odd">
+<td align="left">10:00.Sunday</td>
+<td align="left">10:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0346985</td>
+<td align="right">0.0036679</td>
+</tr>
+<tr class="even">
+<td align="left">10:30.Sunday</td>
+<td align="left">10:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0406372</td>
+<td align="right">0.0039368</td>
+</tr>
+<tr class="odd">
+<td align="left">11:00.Sunday</td>
+<td align="left">11:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0350324</td>
+<td align="right">0.0037023</td>
+</tr>
+<tr class="even">
+<td align="left">11:30.Sunday</td>
+<td align="left">11:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0450061</td>
+<td align="right">0.0041703</td>
+</tr>
+<tr class="odd">
+<td align="left">12:00.Sunday</td>
+<td align="left">12:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0363888</td>
+<td align="right">0.0037750</td>
+</tr>
+<tr class="even">
+<td align="left">12:30.Sunday</td>
+<td align="left">12:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0329210</td>
+<td align="right">0.0035872</td>
+</tr>
+<tr class="odd">
+<td align="left">13:00.Sunday</td>
+<td align="left">13:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0255788</td>
+<td align="right">0.0031663</td>
+</tr>
+<tr class="even">
+<td align="left">13:30.Sunday</td>
+<td align="left">13:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0244238</td>
+<td align="right">0.0030981</td>
+</tr>
+<tr class="odd">
+<td align="left">14:00.Sunday</td>
+<td align="left">14:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0240375</td>
+<td align="right">0.0030528</td>
+</tr>
+<tr class="even">
+<td align="left">14:30.Sunday</td>
+<td align="left">14:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0248046</td>
+<td align="right">0.0031210</td>
+</tr>
+<tr class="odd">
+<td align="left">15:00.Sunday</td>
+<td align="left">15:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0243976</td>
+<td align="right">0.0030975</td>
+</tr>
+<tr class="even">
+<td align="left">15:30.Sunday</td>
+<td align="left">15:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0284220</td>
+<td align="right">0.0033382</td>
+</tr>
+<tr class="odd">
+<td align="left">16:00.Sunday</td>
+<td align="left">16:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0264053</td>
+<td align="right">0.0032409</td>
+</tr>
+<tr class="even">
+<td align="left">16:30.Sunday</td>
+<td align="left">16:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0264804</td>
+<td align="right">0.0032496</td>
+</tr>
+<tr class="odd">
+<td align="left">17:00.Sunday</td>
+<td align="left">17:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0176915</td>
+<td align="right">0.0026503</td>
+</tr>
+<tr class="even">
+<td align="left">17:30.Sunday</td>
+<td align="left">17:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0186955</td>
+<td align="right">0.0027355</td>
+</tr>
+<tr class="odd">
+<td align="left">18:00.Sunday</td>
+<td align="left">18:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0226005</td>
+<td align="right">0.0029949</td>
+</tr>
+<tr class="even">
+<td align="left">18:30.Sunday</td>
+<td align="left">18:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0276144</td>
+<td align="right">0.0033114</td>
+</tr>
+<tr class="odd">
+<td align="left">19:00.Sunday</td>
+<td align="left">19:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0260492</td>
+<td align="right">0.0032230</td>
+</tr>
+<tr class="even">
+<td align="left">19:30.Sunday</td>
+<td align="left">19:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0213771</td>
+<td align="right">0.0029118</td>
+</tr>
+<tr class="odd">
+<td align="left">20:00.Sunday</td>
+<td align="left">20:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0186157</td>
+<td align="right">0.0026966</td>
+</tr>
+<tr class="even">
+<td align="left">20:30.Sunday</td>
+<td align="left">20:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0167842</td>
+<td align="right">0.0025743</td>
+</tr>
+<tr class="odd">
+<td align="left">21:00.Sunday</td>
+<td align="left">21:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0102985</td>
+<td align="right">0.0020166</td>
+</tr>
+<tr class="even">
+<td align="left">21:30.Sunday</td>
+<td align="left">21:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0102994</td>
+<td align="right">0.0020165</td>
+</tr>
+<tr class="odd">
+<td align="left">22:00.Sunday</td>
+<td align="left">22:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0107437</td>
+<td align="right">0.0020650</td>
+</tr>
+<tr class="even">
+<td align="left">22:30.Sunday</td>
+<td align="left">22:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0068068</td>
+<td align="right">0.0016511</td>
+</tr>
+<tr class="odd">
+<td align="left">23:00.Sunday</td>
+<td align="left">23:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0028992</td>
+<td align="right">0.0010279</td>
+</tr>
+<tr class="even">
+<td align="left">23:30.Sunday</td>
+<td align="left">23:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0014970</td>
+<td align="right">0.0007509</td>
+</tr>
+<tr class="odd">
+<td align="left">00:00.Thursday</td>
+<td align="left">00:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0008468</td>
+<td align="right">0.0005988</td>
+</tr>
+<tr class="even">
+<td align="left">00:30.Thursday</td>
+<td align="left">00:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0004347</td>
+<td align="right">0.0004346</td>
+</tr>
+<tr class="odd">
+<td align="left">01:00.Thursday</td>
+<td align="left">01:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">01:30.Thursday</td>
+<td align="left">01:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">02:00.Thursday</td>
+<td align="left">02:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">02:30.Thursday</td>
+<td align="left">02:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">03:00.Thursday</td>
+<td align="left">03:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">03:30.Thursday</td>
+<td align="left">03:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">04:00.Thursday</td>
+<td align="left">04:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">04:30.Thursday</td>
+<td align="left">04:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">05:00.Thursday</td>
+<td align="left">05:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">05:30.Thursday</td>
+<td align="left">05:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0011943</td>
+<td align="right">0.0006940</td>
+</tr>
+<tr class="odd">
+<td align="left">06:00.Thursday</td>
+<td align="left">06:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0019919</td>
+<td align="right">0.0008913</td>
+</tr>
+<tr class="even">
+<td align="left">06:30.Thursday</td>
+<td align="left">06:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0018997</td>
+<td align="right">0.0008508</td>
+</tr>
+<tr class="odd">
+<td align="left">07:00.Thursday</td>
+<td align="left">07:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0076987</td>
+<td align="right">0.0017212</td>
+</tr>
+<tr class="even">
+<td align="left">07:30.Thursday</td>
+<td align="left">07:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0104279</td>
+<td align="right">0.0020049</td>
+</tr>
+<tr class="odd">
+<td align="left">08:00.Thursday</td>
+<td align="left">08:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0180155</td>
+<td align="right">0.0026735</td>
+</tr>
+<tr class="even">
+<td align="left">08:30.Thursday</td>
+<td align="left">08:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0213863</td>
+<td align="right">0.0029140</td>
+</tr>
+<tr class="odd">
+<td align="left">09:00.Thursday</td>
+<td align="left">09:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0197452</td>
+<td align="right">0.0027734</td>
+</tr>
+<tr class="even">
+<td align="left">09:30.Thursday</td>
+<td align="left">09:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0319658</td>
+<td align="right">0.0035258</td>
+</tr>
+<tr class="odd">
+<td align="left">10:00.Thursday</td>
+<td align="left">10:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0364103</td>
+<td align="right">0.0037379</td>
+</tr>
+<tr class="even">
+<td align="left">10:30.Thursday</td>
+<td align="left">10:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0354102</td>
+<td align="right">0.0036979</td>
+</tr>
+<tr class="odd">
+<td align="left">11:00.Thursday</td>
+<td align="left">11:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0278652</td>
+<td align="right">0.0032742</td>
+</tr>
+<tr class="even">
+<td align="left">11:30.Thursday</td>
+<td align="left">11:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0221087</td>
+<td align="right">0.0029593</td>
+</tr>
+<tr class="odd">
+<td align="left">12:00.Thursday</td>
+<td align="left">12:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0177031</td>
+<td align="right">0.0026508</td>
+</tr>
+<tr class="even">
+<td align="left">12:30.Thursday</td>
+<td align="left">12:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0209848</td>
+<td align="right">0.0028600</td>
+</tr>
+<tr class="odd">
+<td align="left">13:00.Thursday</td>
+<td align="left">13:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0174340</td>
+<td align="right">0.0026122</td>
+</tr>
+<tr class="even">
+<td align="left">13:30.Thursday</td>
+<td align="left">13:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0201856</td>
+<td align="right">0.0028053</td>
+</tr>
+<tr class="odd">
+<td align="left">14:00.Thursday</td>
+<td align="left">14:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0245708</td>
+<td align="right">0.0030938</td>
+</tr>
+<tr class="even">
+<td align="left">14:30.Thursday</td>
+<td align="left">14:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0217070</td>
+<td align="right">0.0029083</td>
+</tr>
+<tr class="odd">
+<td align="left">15:00.Thursday</td>
+<td align="left">15:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0204246</td>
+<td align="right">0.0028164</td>
+</tr>
+<tr class="even">
+<td align="left">15:30.Thursday</td>
+<td align="left">15:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0224539</td>
+<td align="right">0.0029533</td>
+</tr>
+<tr class="odd">
+<td align="left">16:00.Thursday</td>
+<td align="left">16:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0236558</td>
+<td align="right">0.0030328</td>
+</tr>
+<tr class="even">
+<td align="left">16:30.Thursday</td>
+<td align="left">16:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0179555</td>
+<td align="right">0.0026381</td>
+</tr>
+<tr class="odd">
+<td align="left">17:00.Thursday</td>
+<td align="left">17:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0072596</td>
+<td align="right">0.0016693</td>
+</tr>
+<tr class="even">
+<td align="left">17:30.Thursday</td>
+<td align="left">17:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0118715</td>
+<td align="right">0.0021297</td>
+</tr>
+<tr class="odd">
+<td align="left">18:00.Thursday</td>
+<td align="left">18:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0136105</td>
+<td align="right">0.0022944</td>
+</tr>
+<tr class="even">
+<td align="left">18:30.Thursday</td>
+<td align="left">18:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0194210</td>
+<td align="right">0.0027572</td>
+</tr>
+<tr class="odd">
+<td align="left">19:00.Thursday</td>
+<td align="left">19:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0199375</td>
+<td align="right">0.0028008</td>
+</tr>
+<tr class="even">
+<td align="left">19:30.Thursday</td>
+<td align="left">19:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0189277</td>
+<td align="right">0.0027434</td>
+</tr>
+<tr class="odd">
+<td align="left">20:00.Thursday</td>
+<td align="left">20:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0239789</td>
+<td align="right">0.0030447</td>
+</tr>
+<tr class="even">
+<td align="left">20:30.Thursday</td>
+<td align="left">20:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0186605</td>
+<td align="right">0.0027058</td>
+</tr>
+<tr class="odd">
+<td align="left">21:00.Thursday</td>
+<td align="left">21:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0157042</td>
+<td align="right">0.0025033</td>
+</tr>
+<tr class="even">
+<td align="left">21:30.Thursday</td>
+<td align="left">21:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0106970</td>
+<td align="right">0.0020555</td>
+</tr>
+<tr class="odd">
+<td align="left">22:00.Thursday</td>
+<td align="left">22:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0072075</td>
+<td align="right">0.0016530</td>
+</tr>
+<tr class="even">
+<td align="left">22:30.Thursday</td>
+<td align="left">22:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0024017</td>
+<td align="right">0.0009819</td>
+</tr>
+<tr class="odd">
+<td align="left">23:00.Thursday</td>
+<td align="left">23:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0023223</td>
+<td align="right">0.0009498</td>
+</tr>
+<tr class="even">
+<td align="left">23:30.Thursday</td>
+<td align="left">23:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0011620</td>
+<td align="right">0.0006722</td>
+</tr>
+<tr class="odd">
+<td align="left">00:00.Tuesday</td>
+<td align="left">00:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0011632</td>
+<td align="right">0.0006717</td>
+</tr>
+<tr class="even">
+<td align="left">00:30.Tuesday</td>
+<td align="left">00:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">01:00.Tuesday</td>
+<td align="left">01:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">01:30.Tuesday</td>
+<td align="left">01:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">02:00.Tuesday</td>
+<td align="left">02:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">02:30.Tuesday</td>
+<td align="left">02:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">03:00.Tuesday</td>
+<td align="left">03:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">03:30.Tuesday</td>
+<td align="left">03:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">04:00.Tuesday</td>
+<td align="left">04:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">04:30.Tuesday</td>
+<td align="left">04:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">05:00.Tuesday</td>
+<td align="left">05:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">05:30.Tuesday</td>
+<td align="left">05:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">06:00.Tuesday</td>
+<td align="left">06:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0004047</td>
+<td align="right">0.0004046</td>
+</tr>
+<tr class="even">
+<td align="left">06:30.Tuesday</td>
+<td align="left">06:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0027227</td>
+<td align="right">0.0010345</td>
+</tr>
+<tr class="odd">
+<td align="left">07:00.Tuesday</td>
+<td align="left">07:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0065418</td>
+<td align="right">0.0016378</td>
+</tr>
+<tr class="even">
+<td align="left">07:30.Tuesday</td>
+<td align="left">07:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0094940</td>
+<td align="right">0.0019382</td>
+</tr>
+<tr class="odd">
+<td align="left">08:00.Tuesday</td>
+<td align="left">08:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0173656</td>
+<td align="right">0.0026056</td>
+</tr>
+<tr class="even">
+<td align="left">08:30.Tuesday</td>
+<td align="left">08:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0234043</td>
+<td align="right">0.0030457</td>
+</tr>
+<tr class="odd">
+<td align="left">09:00.Tuesday</td>
+<td align="left">09:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0305405</td>
+<td align="right">0.0034604</td>
+</tr>
+<tr class="even">
+<td align="left">09:30.Tuesday</td>
+<td align="left">09:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0367231</td>
+<td align="right">0.0037719</td>
+</tr>
+<tr class="odd">
+<td align="left">10:00.Tuesday</td>
+<td align="left">10:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0336252</td>
+<td align="right">0.0035998</td>
+</tr>
+<tr class="even">
+<td align="left">10:30.Tuesday</td>
+<td align="left">10:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0349350</td>
+<td align="right">0.0036537</td>
+</tr>
+<tr class="odd">
+<td align="left">11:00.Tuesday</td>
+<td align="left">11:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0293146</td>
+<td align="right">0.0033470</td>
+</tr>
+<tr class="even">
+<td align="left">11:30.Tuesday</td>
+<td align="left">11:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0273602</td>
+<td align="right">0.0032391</td>
+</tr>
+<tr class="odd">
+<td align="left">12:00.Tuesday</td>
+<td align="left">12:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0202424</td>
+<td align="right">0.0027889</td>
+</tr>
+<tr class="even">
+<td align="left">12:30.Tuesday</td>
+<td align="left">12:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0206631</td>
+<td align="right">0.0028471</td>
+</tr>
+<tr class="odd">
+<td align="left">13:00.Tuesday</td>
+<td align="left">13:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0164942</td>
+<td align="right">0.0025642</td>
+</tr>
+<tr class="even">
+<td align="left">13:30.Tuesday</td>
+<td align="left">13:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0265493</td>
+<td align="right">0.0032353</td>
+</tr>
+<tr class="odd">
+<td align="left">14:00.Tuesday</td>
+<td align="left">14:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0296586</td>
+<td align="right">0.0034068</td>
+</tr>
+<tr class="even">
+<td align="left">14:30.Tuesday</td>
+<td align="left">14:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0263515</td>
+<td align="right">0.0032095</td>
+</tr>
+<tr class="odd">
+<td align="left">15:00.Tuesday</td>
+<td align="left">15:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0252749</td>
+<td align="right">0.0031539</td>
+</tr>
+<tr class="even">
+<td align="left">15:30.Tuesday</td>
+<td align="left">15:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0258691</td>
+<td align="right">0.0031780</td>
+</tr>
+<tr class="odd">
+<td align="left">16:00.Tuesday</td>
+<td align="left">16:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0250524</td>
+<td align="right">0.0031522</td>
+</tr>
+<tr class="even">
+<td align="left">16:30.Tuesday</td>
+<td align="left">16:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0228410</td>
+<td align="right">0.0030011</td>
+</tr>
+<tr class="odd">
+<td align="left">17:00.Tuesday</td>
+<td align="left">17:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0092998</td>
+<td align="right">0.0019361</td>
+</tr>
+<tr class="even">
+<td align="left">17:30.Tuesday</td>
+<td align="left">17:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0128449</td>
+<td align="right">0.0022634</td>
+</tr>
+<tr class="odd">
+<td align="left">18:00.Tuesday</td>
+<td align="left">18:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0138945</td>
+<td align="right">0.0023405</td>
+</tr>
+<tr class="even">
+<td align="left">18:30.Tuesday</td>
+<td align="left">18:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0174506</td>
+<td align="right">0.0026182</td>
+</tr>
+<tr class="odd">
+<td align="left">19:00.Tuesday</td>
+<td align="left">19:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0179921</td>
+<td align="right">0.0026957</td>
+</tr>
+<tr class="even">
+<td align="left">19:30.Tuesday</td>
+<td align="left">19:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0185667</td>
+<td align="right">0.0027472</td>
+</tr>
+<tr class="odd">
+<td align="left">20:00.Tuesday</td>
+<td align="left">20:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0166979</td>
+<td align="right">0.0025940</td>
+</tr>
+<tr class="even">
+<td align="left">20:30.Tuesday</td>
+<td align="left">20:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0209572</td>
+<td align="right">0.0028860</td>
+</tr>
+<tr class="odd">
+<td align="left">21:00.Tuesday</td>
+<td align="left">21:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0164042</td>
+<td align="right">0.0025520</td>
+</tr>
+<tr class="even">
+<td align="left">21:30.Tuesday</td>
+<td align="left">21:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0121794</td>
+<td align="right">0.0021816</td>
+</tr>
+<tr class="odd">
+<td align="left">22:00.Tuesday</td>
+<td align="left">22:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0106101</td>
+<td align="right">0.0020364</td>
+</tr>
+<tr class="even">
+<td align="left">22:30.Tuesday</td>
+<td align="left">22:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0074836</td>
+<td align="right">0.0017163</td>
+</tr>
+<tr class="odd">
+<td align="left">23:00.Tuesday</td>
+<td align="left">23:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0032517</td>
+<td align="right">0.0011515</td>
+</tr>
+<tr class="even">
+<td align="left">23:30.Tuesday</td>
+<td align="left">23:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0009242</td>
+<td align="right">0.0006532</td>
+</tr>
+<tr class="odd">
+<td align="left">00:00.Wednesday</td>
+<td align="left">00:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0017059</td>
+<td align="right">0.0008536</td>
+</tr>
+<tr class="even">
+<td align="left">00:30.Wednesday</td>
+<td align="left">00:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0004360</td>
+<td align="right">0.0004359</td>
+</tr>
+<tr class="odd">
+<td align="left">01:00.Wednesday</td>
+<td align="left">01:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">01:30.Wednesday</td>
+<td align="left">01:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">02:00.Wednesday</td>
+<td align="left">02:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">02:30.Wednesday</td>
+<td align="left">02:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">03:00.Wednesday</td>
+<td align="left">03:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">03:30.Wednesday</td>
+<td align="left">03:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">04:00.Wednesday</td>
+<td align="left">04:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">04:30.Wednesday</td>
+<td align="left">04:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">05:00.Wednesday</td>
+<td align="left">05:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0003874</td>
+<td align="right">0.0003873</td>
+</tr>
+<tr class="even">
+<td align="left">05:30.Wednesday</td>
+<td align="left">05:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0008184</td>
+<td align="right">0.0005793</td>
+</tr>
+<tr class="odd">
+<td align="left">06:00.Wednesday</td>
+<td align="left">06:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0007962</td>
+<td align="right">0.0005647</td>
+</tr>
+<tr class="even">
+<td align="left">06:30.Wednesday</td>
+<td align="left">06:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0013411</td>
+<td align="right">0.0007747</td>
+</tr>
+<tr class="odd">
+<td align="left">07:00.Wednesday</td>
+<td align="left">07:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0055860</td>
+<td align="right">0.0014962</td>
+</tr>
+<tr class="even">
+<td align="left">07:30.Wednesday</td>
+<td align="left">07:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0105585</td>
+<td align="right">0.0019952</td>
+</tr>
+<tr class="odd">
+<td align="left">08:00.Wednesday</td>
+<td align="left">08:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0126192</td>
+<td align="right">0.0022250</td>
+</tr>
+<tr class="even">
+<td align="left">08:30.Wednesday</td>
+<td align="left">08:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0177218</td>
+<td align="right">0.0026293</td>
+</tr>
+<tr class="odd">
+<td align="left">09:00.Wednesday</td>
+<td align="left">09:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0259123</td>
+<td align="right">0.0031819</td>
+</tr>
+<tr class="even">
+<td align="left">09:30.Wednesday</td>
+<td align="left">09:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0348525</td>
+<td align="right">0.0036829</td>
+</tr>
+<tr class="odd">
+<td align="left">10:00.Wednesday</td>
+<td align="left">10:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0385703</td>
+<td align="right">0.0038711</td>
+</tr>
+<tr class="even">
+<td align="left">10:30.Wednesday</td>
+<td align="left">10:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0392206</td>
+<td align="right">0.0039139</td>
+</tr>
+<tr class="odd">
+<td align="left">11:00.Wednesday</td>
+<td align="left">11:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0325124</td>
+<td align="right">0.0035652</td>
+</tr>
+<tr class="even">
+<td align="left">11:30.Wednesday</td>
+<td align="left">11:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0261493</td>
+<td align="right">0.0032100</td>
+</tr>
+<tr class="odd">
+<td align="left">12:00.Wednesday</td>
+<td align="left">12:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0164774</td>
+<td align="right">0.0025593</td>
+</tr>
+<tr class="even">
+<td align="left">12:30.Wednesday</td>
+<td align="left">12:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0178117</td>
+<td align="right">0.0026680</td>
+</tr>
+<tr class="odd">
+<td align="left">13:00.Wednesday</td>
+<td align="left">13:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0183765</td>
+<td align="right">0.0026933</td>
+</tr>
+<tr class="even">
+<td align="left">13:30.Wednesday</td>
+<td align="left">13:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0259931</td>
+<td align="right">0.0031936</td>
+</tr>
+<tr class="odd">
+<td align="left">14:00.Wednesday</td>
+<td align="left">14:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0289639</td>
+<td align="right">0.0033770</td>
+</tr>
+<tr class="even">
+<td align="left">14:30.Wednesday</td>
+<td align="left">14:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0285402</td>
+<td align="right">0.0033502</td>
+</tr>
+<tr class="odd">
+<td align="left">15:00.Wednesday</td>
+<td align="left">15:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0199043</td>
+<td align="right">0.0027966</td>
+</tr>
+<tr class="even">
+<td align="left">15:30.Wednesday</td>
+<td align="left">15:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0199029</td>
+<td align="right">0.0027952</td>
+</tr>
+<tr class="odd">
+<td align="left">16:00.Wednesday</td>
+<td align="left">16:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0181022</td>
+<td align="right">0.0026550</td>
+</tr>
+<tr class="even">
+<td align="left">16:30.Wednesday</td>
+<td align="left">16:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0166576</td>
+<td align="right">0.0025569</td>
+</tr>
+<tr class="odd">
+<td align="left">17:00.Wednesday</td>
+<td align="left">17:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0114209</td>
+<td align="right">0.0021159</td>
+</tr>
+<tr class="even">
+<td align="left">17:30.Wednesday</td>
+<td align="left">17:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0102762</td>
+<td align="right">0.0020104</td>
+</tr>
+<tr class="odd">
+<td align="left">18:00.Wednesday</td>
+<td align="left">18:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0122735</td>
+<td align="right">0.0021986</td>
+</tr>
+<tr class="even">
+<td align="left">18:30.Wednesday</td>
+<td align="left">18:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0178214</td>
+<td align="right">0.0026430</td>
+</tr>
+<tr class="odd">
+<td align="left">19:00.Wednesday</td>
+<td align="left">19:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0136438</td>
+<td align="right">0.0023284</td>
+</tr>
+<tr class="even">
+<td align="left">19:30.Wednesday</td>
+<td align="left">19:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0159270</td>
+<td align="right">0.0024734</td>
+</tr>
+<tr class="odd">
+<td align="left">20:00.Wednesday</td>
+<td align="left">20:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0194519</td>
+<td align="right">0.0027338</td>
+</tr>
+<tr class="even">
+<td align="left">20:30.Wednesday</td>
+<td align="left">20:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0202401</td>
+<td align="right">0.0027873</td>
+</tr>
+<tr class="odd">
+<td align="left">21:00.Wednesday</td>
+<td align="left">21:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0177501</td>
+<td align="right">0.0026328</td>
+</tr>
+<tr class="even">
+<td align="left">21:30.Wednesday</td>
+<td align="left">21:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0142354</td>
+<td align="right">0.0023635</td>
+</tr>
+<tr class="odd">
+<td align="left">22:00.Wednesday</td>
+<td align="left">22:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0107275</td>
+<td align="right">0.0020592</td>
+</tr>
+<tr class="even">
+<td align="left">22:30.Wednesday</td>
+<td align="left">22:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0072182</td>
+<td align="right">0.0017016</td>
+</tr>
+<tr class="odd">
+<td align="left">23:00.Wednesday</td>
+<td align="left">23:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0040105</td>
+<td align="right">0.0012692</td>
+</tr>
+<tr class="even">
+<td align="left">23:30.Wednesday</td>
+<td align="left">23:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0016356</td>
+<td align="right">0.0008201</td>
+</tr>
+</tbody>
+</table>
+<table>
+<thead>
+<tr class="header">
+<th align="left"></th>
+<th align="left">st_halfhour</th>
+<th align="left">r_dow</th>
+<th align="right">Any_laundry_home</th>
+<th align="right">se</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">00:00.Friday</td>
+<td align="left">00:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0038984</td>
+<td align="right">0.0038905</td>
+</tr>
+<tr class="even">
+<td align="left">00:30.Friday</td>
+<td align="left">00:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0038984</td>
+<td align="right">0.0038905</td>
+</tr>
+<tr class="odd">
+<td align="left">01:00.Friday</td>
+<td align="left">01:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">01:30.Friday</td>
+<td align="left">01:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">02:00.Friday</td>
+<td align="left">02:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">02:30.Friday</td>
+<td align="left">02:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">03:00.Friday</td>
+<td align="left">03:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">03:30.Friday</td>
+<td align="left">03:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">04:00.Friday</td>
+<td align="left">04:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">04:30.Friday</td>
+<td align="left">04:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">05:00.Friday</td>
+<td align="left">05:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">05:30.Friday</td>
+<td align="left">05:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">06:00.Friday</td>
+<td align="left">06:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0026770</td>
+<td align="right">0.0026749</td>
+</tr>
+<tr class="even">
+<td align="left">06:30.Friday</td>
+<td align="left">06:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0026770</td>
+<td align="right">0.0026749</td>
+</tr>
+<tr class="odd">
+<td align="left">07:00.Friday</td>
+<td align="left">07:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0170177</td>
+<td align="right">0.0077478</td>
+</tr>
+<tr class="even">
+<td align="left">07:30.Friday</td>
+<td align="left">07:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0242379</td>
+<td align="right">0.0092930</td>
+</tr>
+<tr class="odd">
+<td align="left">08:00.Friday</td>
+<td align="left">08:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0268128</td>
+<td align="right">0.0095394</td>
+</tr>
+<tr class="even">
+<td align="left">08:30.Friday</td>
+<td align="left">08:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0267058</td>
+<td align="right">0.0101583</td>
+</tr>
+<tr class="odd">
+<td align="left">09:00.Friday</td>
+<td align="left">09:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0253632</td>
+<td align="right">0.0097334</td>
+</tr>
+<tr class="even">
+<td align="left">09:30.Friday</td>
+<td align="left">09:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0315013</td>
+<td align="right">0.0106625</td>
+</tr>
+<tr class="odd">
+<td align="left">10:00.Friday</td>
+<td align="left">10:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0359375</td>
+<td align="right">0.0114386</td>
+</tr>
+<tr class="even">
+<td align="left">10:30.Friday</td>
+<td align="left">10:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0342891</td>
+<td align="right">0.0114405</td>
+</tr>
+<tr class="odd">
+<td align="left">11:00.Friday</td>
+<td align="left">11:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0218640</td>
+<td align="right">0.0091293</td>
+</tr>
+<tr class="even">
+<td align="left">11:30.Friday</td>
+<td align="left">11:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0157292</td>
+<td align="right">0.0072380</td>
+</tr>
+<tr class="odd">
+<td align="left">12:00.Friday</td>
+<td align="left">12:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0129413</td>
+<td align="right">0.0065281</td>
+</tr>
+<tr class="even">
+<td align="left">12:30.Friday</td>
+<td align="left">12:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0073232</td>
+<td align="right">0.0053240</td>
+</tr>
+<tr class="odd">
+<td align="left">13:00.Friday</td>
+<td align="left">13:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0094221</td>
+<td align="right">0.0057152</td>
+</tr>
+<tr class="even">
+<td align="left">13:30.Friday</td>
+<td align="left">13:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0178800</td>
+<td align="right">0.0082194</td>
+</tr>
+<tr class="odd">
+<td align="left">14:00.Friday</td>
+<td align="left">14:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0244474</td>
+<td align="right">0.0094421</td>
+</tr>
+<tr class="even">
+<td align="left">14:30.Friday</td>
+<td align="left">14:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0167299</td>
+<td align="right">0.0069318</td>
+</tr>
+<tr class="odd">
+<td align="left">15:00.Friday</td>
+<td align="left">15:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0140319</td>
+<td align="right">0.0063902</td>
+</tr>
+<tr class="even">
+<td align="left">15:30.Friday</td>
+<td align="left">15:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0159001</td>
+<td align="right">0.0071352</td>
+</tr>
+<tr class="odd">
+<td align="left">16:00.Friday</td>
+<td align="left">16:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0213837</td>
+<td align="right">0.0110187</td>
+</tr>
+<tr class="even">
+<td align="left">16:30.Friday</td>
+<td align="left">16:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0065331</td>
+<td align="right">0.0046715</td>
+</tr>
+<tr class="odd">
+<td align="left">17:00.Friday</td>
+<td align="left">17:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0181322</td>
+<td align="right">0.0105596</td>
+</tr>
+<tr class="even">
+<td align="left">17:30.Friday</td>
+<td align="left">17:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0126491</td>
+<td align="right">0.0098732</td>
+</tr>
+<tr class="odd">
+<td align="left">18:00.Friday</td>
+<td align="left">18:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0136120</td>
+<td align="right">0.0102127</td>
+</tr>
+<tr class="even">
+<td align="left">18:30.Friday</td>
+<td align="left">18:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0166877</td>
+<td align="right">0.0106364</td>
+</tr>
+<tr class="odd">
+<td align="left">19:00.Friday</td>
+<td align="left">19:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0163883</td>
+<td align="right">0.0105279</td>
+</tr>
+<tr class="even">
+<td align="left">19:30.Friday</td>
+<td align="left">19:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0094389</td>
+<td align="right">0.0093673</td>
+</tr>
+<tr class="odd">
+<td align="left">20:00.Friday</td>
+<td align="left">20:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0119430</td>
+<td align="right">0.0069458</td>
+</tr>
+<tr class="even">
+<td align="left">20:30.Friday</td>
+<td align="left">20:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0163535</td>
+<td align="right">0.0073656</td>
+</tr>
+<tr class="odd">
+<td align="left">21:00.Friday</td>
+<td align="left">21:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0167281</td>
+<td align="right">0.0075331</td>
+</tr>
+<tr class="even">
+<td align="left">21:30.Friday</td>
+<td align="left">21:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0152585</td>
+<td align="right">0.0075713</td>
+</tr>
+<tr class="odd">
+<td align="left">22:00.Friday</td>
+<td align="left">22:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0038984</td>
+<td align="right">0.0038905</td>
+</tr>
+<tr class="even">
+<td align="left">22:30.Friday</td>
+<td align="left">22:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0038984</td>
+<td align="right">0.0038905</td>
+</tr>
+<tr class="odd">
+<td align="left">23:00.Friday</td>
+<td align="left">23:00</td>
+<td align="left">Friday</td>
+<td align="right">0.0038984</td>
+<td align="right">0.0038905</td>
+</tr>
+<tr class="even">
+<td align="left">23:30.Friday</td>
+<td align="left">23:30</td>
+<td align="left">Friday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">00:00.Monday</td>
+<td align="left">00:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">00:30.Monday</td>
+<td align="left">00:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">01:00.Monday</td>
+<td align="left">01:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">01:30.Monday</td>
+<td align="left">01:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">02:00.Monday</td>
+<td align="left">02:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">02:30.Monday</td>
+<td align="left">02:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">03:00.Monday</td>
+<td align="left">03:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">03:30.Monday</td>
+<td align="left">03:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">04:00.Monday</td>
+<td align="left">04:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">04:30.Monday</td>
+<td align="left">04:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">05:00.Monday</td>
+<td align="left">05:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">05:30.Monday</td>
+<td align="left">05:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">06:00.Monday</td>
+<td align="left">06:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0025772</td>
+<td align="right">0.0025744</td>
+</tr>
+<tr class="even">
+<td align="left">06:30.Monday</td>
+<td align="left">06:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0084764</td>
+<td align="right">0.0049191</td>
+</tr>
+<tr class="odd">
+<td align="left">07:00.Monday</td>
+<td align="left">07:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0128566</td>
+<td align="right">0.0058477</td>
+</tr>
+<tr class="even">
+<td align="left">07:30.Monday</td>
+<td align="left">07:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0304536</td>
+<td align="right">0.0092724</td>
+</tr>
+<tr class="odd">
+<td align="left">08:00.Monday</td>
+<td align="left">08:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0372646</td>
+<td align="right">0.0105226</td>
+</tr>
+<tr class="even">
+<td align="left">08:30.Monday</td>
+<td align="left">08:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0361415</td>
+<td align="right">0.0105416</td>
+</tr>
+<tr class="odd">
+<td align="left">09:00.Monday</td>
+<td align="left">09:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0333648</td>
+<td align="right">0.0092779</td>
+</tr>
+<tr class="even">
+<td align="left">09:30.Monday</td>
+<td align="left">09:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0250119</td>
+<td align="right">0.0084171</td>
+</tr>
+<tr class="odd">
+<td align="left">10:00.Monday</td>
+<td align="left">10:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0286481</td>
+<td align="right">0.0087626</td>
+</tr>
+<tr class="even">
+<td align="left">10:30.Monday</td>
+<td align="left">10:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0214633</td>
+<td align="right">0.0077000</td>
+</tr>
+<tr class="odd">
+<td align="left">11:00.Monday</td>
+<td align="left">11:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0216576</td>
+<td align="right">0.0076848</td>
+</tr>
+<tr class="even">
+<td align="left">11:30.Monday</td>
+<td align="left">11:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0170215</td>
+<td align="right">0.0065341</td>
+</tr>
+<tr class="odd">
+<td align="left">12:00.Monday</td>
+<td align="left">12:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0159740</td>
+<td align="right">0.0065412</td>
+</tr>
+<tr class="even">
+<td align="left">12:30.Monday</td>
+<td align="left">12:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0112036</td>
+<td align="right">0.0056444</td>
+</tr>
+<tr class="odd">
+<td align="left">13:00.Monday</td>
+<td align="left">13:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0060717</td>
+<td align="right">0.0043284</td>
+</tr>
+<tr class="even">
+<td align="left">13:30.Monday</td>
+<td align="left">13:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0083280</td>
+<td align="right">0.0049194</td>
+</tr>
+<tr class="odd">
+<td align="left">14:00.Monday</td>
+<td align="left">14:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0137849</td>
+<td align="right">0.0058541</td>
+</tr>
+<tr class="even">
+<td align="left">14:30.Monday</td>
+<td align="left">14:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0137849</td>
+<td align="right">0.0058541</td>
+</tr>
+<tr class="odd">
+<td align="left">15:00.Monday</td>
+<td align="left">15:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0067633</td>
+<td align="right">0.0039973</td>
+</tr>
+<tr class="even">
+<td align="left">15:30.Monday</td>
+<td align="left">15:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0067633</td>
+<td align="right">0.0039973</td>
+</tr>
+<tr class="odd">
+<td align="left">16:00.Monday</td>
+<td align="left">16:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0117024</td>
+<td align="right">0.0079286</td>
+</tr>
+<tr class="even">
+<td align="left">16:30.Monday</td>
+<td align="left">16:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0097672</td>
+<td align="right">0.0076597</td>
+</tr>
+<tr class="odd">
+<td align="left">17:00.Monday</td>
+<td align="left">17:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0050275</td>
+<td align="right">0.0035488</td>
+</tr>
+<tr class="even">
+<td align="left">17:30.Monday</td>
+<td align="left">17:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0073960</td>
+<td align="right">0.0043126</td>
+</tr>
+<tr class="odd">
+<td align="left">18:00.Monday</td>
+<td align="left">18:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0115815</td>
+<td align="right">0.0063117</td>
+</tr>
+<tr class="even">
+<td align="left">18:30.Monday</td>
+<td align="left">18:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0107879</td>
+<td align="right">0.0064776</td>
+</tr>
+<tr class="odd">
+<td align="left">19:00.Monday</td>
+<td align="left">19:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0160005</td>
+<td align="right">0.0075062</td>
+</tr>
+<tr class="even">
+<td align="left">19:30.Monday</td>
+<td align="left">19:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0154671</td>
+<td align="right">0.0073514</td>
+</tr>
+<tr class="odd">
+<td align="left">20:00.Monday</td>
+<td align="left">20:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0146169</td>
+<td align="right">0.0060750</td>
+</tr>
+<tr class="even">
+<td align="left">20:30.Monday</td>
+<td align="left">20:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0122765</td>
+<td align="right">0.0056461</td>
+</tr>
+<tr class="odd">
+<td align="left">21:00.Monday</td>
+<td align="left">21:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0034208</td>
+<td align="right">0.0024221</td>
+</tr>
+<tr class="even">
+<td align="left">21:30.Monday</td>
+<td align="left">21:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0055495</td>
+<td align="right">0.0032217</td>
+</tr>
+<tr class="odd">
+<td align="left">22:00.Monday</td>
+<td align="left">22:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0064419</td>
+<td align="right">0.0037149</td>
+</tr>
+<tr class="even">
+<td align="left">22:30.Monday</td>
+<td align="left">22:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0022617</td>
+<td align="right">0.0022600</td>
+</tr>
+<tr class="odd">
+<td align="left">23:00.Monday</td>
+<td align="left">23:00</td>
+<td align="left">Monday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">23:30.Monday</td>
+<td align="left">23:30</td>
+<td align="left">Monday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">00:00.Saturday</td>
+<td align="left">00:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">00:30.Saturday</td>
+<td align="left">00:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">01:00.Saturday</td>
+<td align="left">01:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">01:30.Saturday</td>
+<td align="left">01:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">02:00.Saturday</td>
+<td align="left">02:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">02:30.Saturday</td>
+<td align="left">02:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">03:00.Saturday</td>
+<td align="left">03:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">03:30.Saturday</td>
+<td align="left">03:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">04:00.Saturday</td>
+<td align="left">04:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">04:30.Saturday</td>
+<td align="left">04:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">05:00.Saturday</td>
+<td align="left">05:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">05:30.Saturday</td>
+<td align="left">05:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">06:00.Saturday</td>
+<td align="left">06:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">06:30.Saturday</td>
+<td align="left">06:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">07:00.Saturday</td>
+<td align="left">07:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0020800</td>
+<td align="right">0.0020796</td>
+</tr>
+<tr class="even">
+<td align="left">07:30.Saturday</td>
+<td align="left">07:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0119694</td>
+<td align="right">0.0061040</td>
+</tr>
+<tr class="odd">
+<td align="left">08:00.Saturday</td>
+<td align="left">08:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0116605</td>
+<td align="right">0.0059290</td>
+</tr>
+<tr class="even">
+<td align="left">08:30.Saturday</td>
+<td align="left">08:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0190270</td>
+<td align="right">0.0081409</td>
+</tr>
+<tr class="odd">
+<td align="left">09:00.Saturday</td>
+<td align="left">09:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0259417</td>
+<td align="right">0.0095114</td>
+</tr>
+<tr class="even">
+<td align="left">09:30.Saturday</td>
+<td align="left">09:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0333173</td>
+<td align="right">0.0109988</td>
+</tr>
+<tr class="odd">
+<td align="left">10:00.Saturday</td>
+<td align="left">10:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0259460</td>
+<td align="right">0.0093380</td>
+</tr>
+<tr class="even">
+<td align="left">10:30.Saturday</td>
+<td align="left">10:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0154620</td>
+<td align="right">0.0063145</td>
+</tr>
+<tr class="odd">
+<td align="left">11:00.Saturday</td>
+<td align="left">11:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0124675</td>
+<td align="right">0.0055724</td>
+</tr>
+<tr class="even">
+<td align="left">11:30.Saturday</td>
+<td align="left">11:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0051512</td>
+<td align="right">0.0036448</td>
+</tr>
+<tr class="odd">
+<td align="left">12:00.Saturday</td>
+<td align="left">12:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0105112</td>
+<td align="right">0.0067676</td>
+</tr>
+<tr class="even">
+<td align="left">12:30.Saturday</td>
+<td align="left">12:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0188715</td>
+<td align="right">0.0096156</td>
+</tr>
+<tr class="odd">
+<td align="left">13:00.Saturday</td>
+<td align="left">13:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0105063</td>
+<td align="right">0.0066532</td>
+</tr>
+<tr class="even">
+<td align="left">13:30.Saturday</td>
+<td align="left">13:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0020800</td>
+<td align="right">0.0020796</td>
+</tr>
+<tr class="odd">
+<td align="left">14:00.Saturday</td>
+<td align="left">14:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0036136</td>
+<td align="right">0.0036073</td>
+</tr>
+<tr class="even">
+<td align="left">14:30.Saturday</td>
+<td align="left">14:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0059125</td>
+<td align="right">0.0042729</td>
+</tr>
+<tr class="odd">
+<td align="left">15:00.Saturday</td>
+<td align="left">15:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0036136</td>
+<td align="right">0.0036073</td>
+</tr>
+<tr class="even">
+<td align="left">15:30.Saturday</td>
+<td align="left">15:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0114240</td>
+<td align="right">0.0065741</td>
+</tr>
+<tr class="odd">
+<td align="left">16:00.Saturday</td>
+<td align="left">16:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0079475</td>
+<td align="right">0.0056048</td>
+</tr>
+<tr class="even">
+<td align="left">16:30.Saturday</td>
+<td align="left">16:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0097744</td>
+<td align="right">0.0057138</td>
+</tr>
+<tr class="odd">
+<td align="left">17:00.Saturday</td>
+<td align="left">17:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0057436</td>
+<td align="right">0.0041049</td>
+</tr>
+<tr class="even">
+<td align="left">17:30.Saturday</td>
+<td align="left">17:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0081214</td>
+<td align="right">0.0047387</td>
+</tr>
+<tr class="odd">
+<td align="left">18:00.Saturday</td>
+<td align="left">18:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0083488</td>
+<td align="right">0.0048567</td>
+</tr>
+<tr class="even">
+<td align="left">18:30.Saturday</td>
+<td align="left">18:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0034279</td>
+<td align="right">0.0034227</td>
+</tr>
+<tr class="odd">
+<td align="left">19:00.Saturday</td>
+<td align="left">19:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0072689</td>
+<td align="right">0.0051300</td>
+</tr>
+<tr class="even">
+<td align="left">19:30.Saturday</td>
+<td align="left">19:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0072689</td>
+<td align="right">0.0051300</td>
+</tr>
+<tr class="odd">
+<td align="left">20:00.Saturday</td>
+<td align="left">20:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0072689</td>
+<td align="right">0.0051300</td>
+</tr>
+<tr class="even">
+<td align="left">20:30.Saturday</td>
+<td align="left">20:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0072689</td>
+<td align="right">0.0051300</td>
+</tr>
+<tr class="odd">
+<td align="left">21:00.Saturday</td>
+<td align="left">21:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0038410</td>
+<td align="right">0.0038334</td>
+</tr>
+<tr class="even">
+<td align="left">21:30.Saturday</td>
+<td align="left">21:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">22:00.Saturday</td>
+<td align="left">22:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">22:30.Saturday</td>
+<td align="left">22:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">23:00.Saturday</td>
+<td align="left">23:00</td>
+<td align="left">Saturday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">23:30.Saturday</td>
+<td align="left">23:30</td>
+<td align="left">Saturday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">00:00.Sunday</td>
+<td align="left">00:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">00:30.Sunday</td>
+<td align="left">00:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">01:00.Sunday</td>
+<td align="left">01:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">01:30.Sunday</td>
+<td align="left">01:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">02:00.Sunday</td>
+<td align="left">02:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">02:30.Sunday</td>
+<td align="left">02:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">03:00.Sunday</td>
+<td align="left">03:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">03:30.Sunday</td>
+<td align="left">03:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">04:00.Sunday</td>
+<td align="left">04:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">04:30.Sunday</td>
+<td align="left">04:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">05:00.Sunday</td>
+<td align="left">05:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">05:30.Sunday</td>
+<td align="left">05:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">06:00.Sunday</td>
+<td align="left">06:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">06:30.Sunday</td>
+<td align="left">06:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">07:00.Sunday</td>
+<td align="left">07:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0017522</td>
+<td align="right">0.0017516</td>
+</tr>
+<tr class="even">
+<td align="left">07:30.Sunday</td>
+<td align="left">07:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0058872</td>
+<td align="right">0.0041663</td>
+</tr>
+<tr class="odd">
+<td align="left">08:00.Sunday</td>
+<td align="left">08:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0142934</td>
+<td align="right">0.0063965</td>
+</tr>
+<tr class="even">
+<td align="left">08:30.Sunday</td>
+<td align="left">08:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0259706</td>
+<td align="right">0.0082367</td>
+</tr>
+<tr class="odd">
+<td align="left">09:00.Sunday</td>
+<td align="left">09:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0355208</td>
+<td align="right">0.0091828</td>
+</tr>
+<tr class="even">
+<td align="left">09:30.Sunday</td>
+<td align="left">09:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0311270</td>
+<td align="right">0.0092836</td>
+</tr>
+<tr class="odd">
+<td align="left">10:00.Sunday</td>
+<td align="left">10:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0538965</td>
+<td align="right">0.0118618</td>
+</tr>
+<tr class="even">
+<td align="left">10:30.Sunday</td>
+<td align="left">10:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0379432</td>
+<td align="right">0.0098284</td>
+</tr>
+<tr class="odd">
+<td align="left">11:00.Sunday</td>
+<td align="left">11:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0309188</td>
+<td align="right">0.0089866</td>
+</tr>
+<tr class="even">
+<td align="left">11:30.Sunday</td>
+<td align="left">11:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0253200</td>
+<td align="right">0.0079866</td>
+</tr>
+<tr class="odd">
+<td align="left">12:00.Sunday</td>
+<td align="left">12:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0187598</td>
+<td align="right">0.0066967</td>
+</tr>
+<tr class="even">
+<td align="left">12:30.Sunday</td>
+<td align="left">12:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0202072</td>
+<td align="right">0.0080422</td>
+</tr>
+<tr class="odd">
+<td align="left">13:00.Sunday</td>
+<td align="left">13:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0247415</td>
+<td align="right">0.0085821</td>
+</tr>
+<tr class="even">
+<td align="left">13:30.Sunday</td>
+<td align="left">13:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0343488</td>
+<td align="right">0.0098328</td>
+</tr>
+<tr class="odd">
+<td align="left">14:00.Sunday</td>
+<td align="left">14:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0384619</td>
+<td align="right">0.0098068</td>
+</tr>
+<tr class="even">
+<td align="left">14:30.Sunday</td>
+<td align="left">14:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0152902</td>
+<td align="right">0.0058437</td>
+</tr>
+<tr class="odd">
+<td align="left">15:00.Sunday</td>
+<td align="left">15:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0235043</td>
+<td align="right">0.0075190</td>
+</tr>
+<tr class="even">
+<td align="left">15:30.Sunday</td>
+<td align="left">15:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0316686</td>
+<td align="right">0.0092213</td>
+</tr>
+<tr class="odd">
+<td align="left">16:00.Sunday</td>
+<td align="left">16:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0350214</td>
+<td align="right">0.0102298</td>
+</tr>
+<tr class="even">
+<td align="left">16:30.Sunday</td>
+<td align="left">16:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0204069</td>
+<td align="right">0.0072664</td>
+</tr>
+<tr class="odd">
+<td align="left">17:00.Sunday</td>
+<td align="left">17:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0133188</td>
+<td align="right">0.0066222</td>
+</tr>
+<tr class="even">
+<td align="left">17:30.Sunday</td>
+<td align="left">17:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0052372</td>
+<td align="right">0.0038641</td>
+</tr>
+<tr class="odd">
+<td align="left">18:00.Sunday</td>
+<td align="left">18:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0105661</td>
+<td align="right">0.0054807</td>
+</tr>
+<tr class="even">
+<td align="left">18:30.Sunday</td>
+<td align="left">18:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0059875</td>
+<td align="right">0.0042638</td>
+</tr>
+<tr class="odd">
+<td align="left">19:00.Sunday</td>
+<td align="left">19:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0139238</td>
+<td align="right">0.0067357</td>
+</tr>
+<tr class="even">
+<td align="left">19:30.Sunday</td>
+<td align="left">19:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0123865</td>
+<td align="right">0.0061241</td>
+</tr>
+<tr class="odd">
+<td align="left">20:00.Sunday</td>
+<td align="left">20:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0188551</td>
+<td align="right">0.0074522</td>
+</tr>
+<tr class="even">
+<td align="left">20:30.Sunday</td>
+<td align="left">20:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0157974</td>
+<td align="right">0.0066759</td>
+</tr>
+<tr class="odd">
+<td align="left">21:00.Sunday</td>
+<td align="left">21:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0122935</td>
+<td align="right">0.0061597</td>
+</tr>
+<tr class="even">
+<td align="left">21:30.Sunday</td>
+<td align="left">21:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0043588</td>
+<td align="right">0.0031019</td>
+</tr>
+<tr class="odd">
+<td align="left">22:00.Sunday</td>
+<td align="left">22:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0043588</td>
+<td align="right">0.0031019</td>
+</tr>
+<tr class="even">
+<td align="left">22:30.Sunday</td>
+<td align="left">22:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">23:00.Sunday</td>
+<td align="left">23:00</td>
+<td align="left">Sunday</td>
+<td align="right">0.0023884</td>
+<td align="right">0.0023860</td>
+</tr>
+<tr class="even">
+<td align="left">23:30.Sunday</td>
+<td align="left">23:30</td>
+<td align="left">Sunday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">00:00.Thursday</td>
+<td align="left">00:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">00:30.Thursday</td>
+<td align="left">00:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">01:00.Thursday</td>
+<td align="left">01:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">01:30.Thursday</td>
+<td align="left">01:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">02:00.Thursday</td>
+<td align="left">02:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">02:30.Thursday</td>
+<td align="left">02:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">03:00.Thursday</td>
+<td align="left">03:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">03:30.Thursday</td>
+<td align="left">03:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">04:00.Thursday</td>
+<td align="left">04:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">04:30.Thursday</td>
+<td align="left">04:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">05:00.Thursday</td>
+<td align="left">05:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">05:30.Thursday</td>
+<td align="left">05:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0017615</td>
+<td align="right">0.0017606</td>
+</tr>
+<tr class="odd">
+<td align="left">06:00.Thursday</td>
+<td align="left">06:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0017615</td>
+<td align="right">0.0017606</td>
+</tr>
+<tr class="even">
+<td align="left">06:30.Thursday</td>
+<td align="left">06:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0042723</td>
+<td align="right">0.0030616</td>
+</tr>
+<tr class="odd">
+<td align="left">07:00.Thursday</td>
+<td align="left">07:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0144892</td>
+<td align="right">0.0059183</td>
+</tr>
+<tr class="even">
+<td align="left">07:30.Thursday</td>
+<td align="left">07:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0181486</td>
+<td align="right">0.0064627</td>
+</tr>
+<tr class="odd">
+<td align="left">08:00.Thursday</td>
+<td align="left">08:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0150695</td>
+<td align="right">0.0057093</td>
+</tr>
+<tr class="even">
+<td align="left">08:30.Thursday</td>
+<td align="left">08:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0130474</td>
+<td align="right">0.0054379</td>
+</tr>
+<tr class="odd">
+<td align="left">09:00.Thursday</td>
+<td align="left">09:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0144778</td>
+<td align="right">0.0060546</td>
+</tr>
+<tr class="even">
+<td align="left">09:30.Thursday</td>
+<td align="left">09:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0103292</td>
+<td align="right">0.0047090</td>
+</tr>
+<tr class="odd">
+<td align="left">10:00.Thursday</td>
+<td align="left">10:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0114390</td>
+<td align="right">0.0052532</td>
+</tr>
+<tr class="even">
+<td align="left">10:30.Thursday</td>
+<td align="left">10:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0037632</td>
+<td align="right">0.0026634</td>
+</tr>
+<tr class="odd">
+<td align="left">11:00.Thursday</td>
+<td align="left">11:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0073763</td>
+<td align="right">0.0044661</td>
+</tr>
+<tr class="even">
+<td align="left">11:30.Thursday</td>
+<td align="left">11:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0039276</td>
+<td align="right">0.0028243</td>
+</tr>
+<tr class="odd">
+<td align="left">12:00.Thursday</td>
+<td align="left">12:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0055342</td>
+<td align="right">0.0032469</td>
+</tr>
+<tr class="even">
+<td align="left">12:30.Thursday</td>
+<td align="left">12:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0106057</td>
+<td align="right">0.0049100</td>
+</tr>
+<tr class="odd">
+<td align="left">13:00.Thursday</td>
+<td align="left">13:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0215173</td>
+<td align="right">0.0073213</td>
+</tr>
+<tr class="even">
+<td align="left">13:30.Thursday</td>
+<td align="left">13:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0137892</td>
+<td align="right">0.0057665</td>
+</tr>
+<tr class="odd">
+<td align="left">14:00.Thursday</td>
+<td align="left">14:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0100902</td>
+<td align="right">0.0045491</td>
+</tr>
+<tr class="even">
+<td align="left">14:30.Thursday</td>
+<td align="left">14:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0081116</td>
+<td align="right">0.0041029</td>
+</tr>
+<tr class="odd">
+<td align="left">15:00.Thursday</td>
+<td align="left">15:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0140320</td>
+<td align="right">0.0059088</td>
+</tr>
+<tr class="even">
+<td align="left">15:30.Thursday</td>
+<td align="left">15:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0107233</td>
+<td align="right">0.0054381</td>
+</tr>
+<tr class="odd">
+<td align="left">16:00.Thursday</td>
+<td align="left">16:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0086119</td>
+<td align="right">0.0050214</td>
+</tr>
+<tr class="even">
+<td align="left">16:30.Thursday</td>
+<td align="left">16:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0158073</td>
+<td align="right">0.0065324</td>
+</tr>
+<tr class="odd">
+<td align="left">17:00.Thursday</td>
+<td align="left">17:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0082402</td>
+<td align="right">0.0047446</td>
+</tr>
+<tr class="even">
+<td align="left">17:30.Thursday</td>
+<td align="left">17:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0057025</td>
+<td align="right">0.0040204</td>
+</tr>
+<tr class="odd">
+<td align="left">18:00.Thursday</td>
+<td align="left">18:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0127536</td>
+<td align="right">0.0057537</td>
+</tr>
+<tr class="even">
+<td align="left">18:30.Thursday</td>
+<td align="left">18:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0138595</td>
+<td align="right">0.0057318</td>
+</tr>
+<tr class="odd">
+<td align="left">19:00.Thursday</td>
+<td align="left">19:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0220689</td>
+<td align="right">0.0070922</td>
+</tr>
+<tr class="even">
+<td align="left">19:30.Thursday</td>
+<td align="left">19:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0102572</td>
+<td align="right">0.0045872</td>
+</tr>
+<tr class="odd">
+<td align="left">20:00.Thursday</td>
+<td align="left">20:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0110372</td>
+<td align="right">0.0049213</td>
+</tr>
+<tr class="even">
+<td align="left">20:30.Thursday</td>
+<td align="left">20:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0121501</td>
+<td align="right">0.0049883</td>
+</tr>
+<tr class="odd">
+<td align="left">21:00.Thursday</td>
+<td align="left">21:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0080370</td>
+<td align="right">0.0040263</td>
+</tr>
+<tr class="even">
+<td align="left">21:30.Thursday</td>
+<td align="left">21:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0104456</td>
+<td align="right">0.0047686</td>
+</tr>
+<tr class="odd">
+<td align="left">22:00.Thursday</td>
+<td align="left">22:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0072626</td>
+<td align="right">0.0036492</td>
+</tr>
+<tr class="even">
+<td align="left">22:30.Thursday</td>
+<td align="left">22:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0021114</td>
+<td align="right">0.0021097</td>
+</tr>
+<tr class="odd">
+<td align="left">23:00.Thursday</td>
+<td align="left">23:00</td>
+<td align="left">Thursday</td>
+<td align="right">0.0021114</td>
+<td align="right">0.0021097</td>
+</tr>
+<tr class="even">
+<td align="left">23:30.Thursday</td>
+<td align="left">23:30</td>
+<td align="left">Thursday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">00:00.Tuesday</td>
+<td align="left">00:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">00:30.Tuesday</td>
+<td align="left">00:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">01:00.Tuesday</td>
+<td align="left">01:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">01:30.Tuesday</td>
+<td align="left">01:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">02:00.Tuesday</td>
+<td align="left">02:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">02:30.Tuesday</td>
+<td align="left">02:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">03:00.Tuesday</td>
+<td align="left">03:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">03:30.Tuesday</td>
+<td align="left">03:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">04:00.Tuesday</td>
+<td align="left">04:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">04:30.Tuesday</td>
+<td align="left">04:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">05:00.Tuesday</td>
+<td align="left">05:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0034730</td>
+<td align="right">0.0034659</td>
+</tr>
+<tr class="even">
+<td align="left">05:30.Tuesday</td>
+<td align="left">05:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0034730</td>
+<td align="right">0.0034659</td>
+</tr>
+<tr class="odd">
+<td align="left">06:00.Tuesday</td>
+<td align="left">06:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0034730</td>
+<td align="right">0.0034659</td>
+</tr>
+<tr class="even">
+<td align="left">06:30.Tuesday</td>
+<td align="left">06:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0130702</td>
+<td align="right">0.0078660</td>
+</tr>
+<tr class="odd">
+<td align="left">07:00.Tuesday</td>
+<td align="left">07:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0190107</td>
+<td align="right">0.0085239</td>
+</tr>
+<tr class="even">
+<td align="left">07:30.Tuesday</td>
+<td align="left">07:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0183694</td>
+<td align="right">0.0083424</td>
+</tr>
+<tr class="odd">
+<td align="left">08:00.Tuesday</td>
+<td align="left">08:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0196054</td>
+<td align="right">0.0082976</td>
+</tr>
+<tr class="even">
+<td align="left">08:30.Tuesday</td>
+<td align="left">08:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0295522</td>
+<td align="right">0.0089120</td>
+</tr>
+<tr class="odd">
+<td align="left">09:00.Tuesday</td>
+<td align="left">09:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0460630</td>
+<td align="right">0.0113542</td>
+</tr>
+<tr class="even">
+<td align="left">09:30.Tuesday</td>
+<td align="left">09:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0409067</td>
+<td align="right">0.0095814</td>
+</tr>
+<tr class="odd">
+<td align="left">10:00.Tuesday</td>
+<td align="left">10:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0341666</td>
+<td align="right">0.0088631</td>
+</tr>
+<tr class="even">
+<td align="left">10:30.Tuesday</td>
+<td align="left">10:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0264463</td>
+<td align="right">0.0077323</td>
+</tr>
+<tr class="odd">
+<td align="left">11:00.Tuesday</td>
+<td align="left">11:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0137620</td>
+<td align="right">0.0056337</td>
+</tr>
+<tr class="even">
+<td align="left">11:30.Tuesday</td>
+<td align="left">11:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0247419</td>
+<td align="right">0.0104107</td>
+</tr>
+<tr class="odd">
+<td align="left">12:00.Tuesday</td>
+<td align="left">12:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0050139</td>
+<td align="right">0.0035413</td>
+</tr>
+<tr class="even">
+<td align="left">12:30.Tuesday</td>
+<td align="left">12:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0023929</td>
+<td align="right">0.0023906</td>
+</tr>
+<tr class="odd">
+<td align="left">13:00.Tuesday</td>
+<td align="left">13:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0101491</td>
+<td align="right">0.0051405</td>
+</tr>
+<tr class="even">
+<td align="left">13:30.Tuesday</td>
+<td align="left">13:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0244274</td>
+<td align="right">0.0081159</td>
+</tr>
+<tr class="odd">
+<td align="left">14:00.Tuesday</td>
+<td align="left">14:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0073072</td>
+<td align="right">0.0054399</td>
+</tr>
+<tr class="even">
+<td align="left">14:30.Tuesday</td>
+<td align="left">14:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0142718</td>
+<td align="right">0.0068462</td>
+</tr>
+<tr class="odd">
+<td align="left">15:00.Tuesday</td>
+<td align="left">15:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0113797</td>
+<td align="right">0.0061541</td>
+</tr>
+<tr class="even">
+<td align="left">15:30.Tuesday</td>
+<td align="left">15:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0128505</td>
+<td align="right">0.0067048</td>
+</tr>
+<tr class="odd">
+<td align="left">16:00.Tuesday</td>
+<td align="left">16:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0023471</td>
+<td align="right">0.0023450</td>
+</tr>
+<tr class="even">
+<td align="left">16:30.Tuesday</td>
+<td align="left">16:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0040605</td>
+<td align="right">0.0029023</td>
+</tr>
+<tr class="odd">
+<td align="left">17:00.Tuesday</td>
+<td align="left">17:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0126881</td>
+<td align="right">0.0052100</td>
+</tr>
+<tr class="even">
+<td align="left">17:30.Tuesday</td>
+<td align="left">17:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0108402</td>
+<td align="right">0.0049204</td>
+</tr>
+<tr class="odd">
+<td align="left">18:00.Tuesday</td>
+<td align="left">18:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0067786</td>
+<td align="right">0.0039761</td>
+</tr>
+<tr class="even">
+<td align="left">18:30.Tuesday</td>
+<td align="left">18:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0153082</td>
+<td align="right">0.0059055</td>
+</tr>
+<tr class="odd">
+<td align="left">19:00.Tuesday</td>
+<td align="left">19:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0181895</td>
+<td align="right">0.0065229</td>
+</tr>
+<tr class="even">
+<td align="left">19:30.Tuesday</td>
+<td align="left">19:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0105055</td>
+<td align="right">0.0052428</td>
+</tr>
+<tr class="odd">
+<td align="left">20:00.Tuesday</td>
+<td align="left">20:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0093384</td>
+<td align="right">0.0055313</td>
+</tr>
+<tr class="even">
+<td align="left">20:30.Tuesday</td>
+<td align="left">20:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0067036</td>
+<td align="right">0.0039235</td>
+</tr>
+<tr class="odd">
+<td align="left">21:00.Tuesday</td>
+<td align="left">21:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0017134</td>
+<td align="right">0.0017129</td>
+</tr>
+<tr class="even">
+<td align="left">21:30.Tuesday</td>
+<td align="left">21:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">22:00.Tuesday</td>
+<td align="left">22:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0030595</td>
+<td align="right">0.0030546</td>
+</tr>
+<tr class="even">
+<td align="left">22:30.Tuesday</td>
+<td align="left">22:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0050912</td>
+<td align="right">0.0037048</td>
+</tr>
+<tr class="odd">
+<td align="left">23:00.Tuesday</td>
+<td align="left">23:00</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0072583</td>
+<td align="right">0.0042861</td>
+</tr>
+<tr class="even">
+<td align="left">23:30.Tuesday</td>
+<td align="left">23:30</td>
+<td align="left">Tuesday</td>
+<td align="right">0.0031906</td>
+<td align="right">0.0031850</td>
+</tr>
+<tr class="odd">
+<td align="left">00:00.Wednesday</td>
+<td align="left">00:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">00:30.Wednesday</td>
+<td align="left">00:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">01:00.Wednesday</td>
+<td align="left">01:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">01:30.Wednesday</td>
+<td align="left">01:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">02:00.Wednesday</td>
+<td align="left">02:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">02:30.Wednesday</td>
+<td align="left">02:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">03:00.Wednesday</td>
+<td align="left">03:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">03:30.Wednesday</td>
+<td align="left">03:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">04:00.Wednesday</td>
+<td align="left">04:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">04:30.Wednesday</td>
+<td align="left">04:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">05:00.Wednesday</td>
+<td align="left">05:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">05:30.Wednesday</td>
+<td align="left">05:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0024919</td>
+<td align="right">0.0024892</td>
+</tr>
+<tr class="odd">
+<td align="left">06:00.Wednesday</td>
+<td align="left">06:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">06:30.Wednesday</td>
+<td align="left">06:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="left">07:00.Wednesday</td>
+<td align="left">07:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0046071</td>
+<td align="right">0.0032560</td>
+</tr>
+<tr class="even">
+<td align="left">07:30.Wednesday</td>
+<td align="left">07:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0055597</td>
+<td align="right">0.0039316</td>
+</tr>
+<tr class="odd">
+<td align="left">08:00.Wednesday</td>
+<td align="left">08:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0079192</td>
+<td align="right">0.0045676</td>
+</tr>
+<tr class="even">
+<td align="left">08:30.Wednesday</td>
+<td align="left">08:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0181388</td>
+<td align="right">0.0069202</td>
+</tr>
+<tr class="odd">
+<td align="left">09:00.Wednesday</td>
+<td align="left">09:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0173675</td>
+<td align="right">0.0071956</td>
+</tr>
+<tr class="even">
+<td align="left">09:30.Wednesday</td>
+<td align="left">09:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0084436</td>
+<td align="right">0.0050234</td>
+</tr>
+<tr class="odd">
+<td align="left">10:00.Wednesday</td>
+<td align="left">10:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0250183</td>
+<td align="right">0.0085511</td>
+</tr>
+<tr class="even">
+<td align="left">10:30.Wednesday</td>
+<td align="left">10:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0186048</td>
+<td align="right">0.0071112</td>
+</tr>
+<tr class="odd">
+<td align="left">11:00.Wednesday</td>
+<td align="left">11:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0105477</td>
+<td align="right">0.0053480</td>
+</tr>
+<tr class="even">
+<td align="left">11:30.Wednesday</td>
+<td align="left">11:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0093616</td>
+<td align="right">0.0047747</td>
+</tr>
+<tr class="odd">
+<td align="left">12:00.Wednesday</td>
+<td align="left">12:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0165830</td>
+<td align="right">0.0074769</td>
+</tr>
+<tr class="even">
+<td align="left">12:30.Wednesday</td>
+<td align="left">12:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0097153</td>
+<td align="right">0.0049087</td>
+</tr>
+<tr class="odd">
+<td align="left">13:00.Wednesday</td>
+<td align="left">13:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0140369</td>
+<td align="right">0.0058019</td>
+</tr>
+<tr class="even">
+<td align="left">13:30.Wednesday</td>
+<td align="left">13:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0146071</td>
+<td align="right">0.0060394</td>
+</tr>
+<tr class="odd">
+<td align="left">14:00.Wednesday</td>
+<td align="left">14:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0035040</td>
+<td align="right">0.0024760</td>
+</tr>
+<tr class="even">
+<td align="left">14:30.Wednesday</td>
+<td align="left">14:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0113623</td>
+<td align="right">0.0052079</td>
+</tr>
+<tr class="odd">
+<td align="left">15:00.Wednesday</td>
+<td align="left">15:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0121603</td>
+<td align="right">0.0055670</td>
+</tr>
+<tr class="even">
+<td align="left">15:30.Wednesday</td>
+<td align="left">15:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0122613</td>
+<td align="right">0.0055470</td>
+</tr>
+<tr class="odd">
+<td align="left">16:00.Wednesday</td>
+<td align="left">16:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0174355</td>
+<td align="right">0.0066828</td>
+</tr>
+<tr class="even">
+<td align="left">16:30.Wednesday</td>
+<td align="left">16:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0223214</td>
+<td align="right">0.0074871</td>
+</tr>
+<tr class="odd">
+<td align="left">17:00.Wednesday</td>
+<td align="left">17:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0106974</td>
+<td align="right">0.0053599</td>
+</tr>
+<tr class="even">
+<td align="left">17:30.Wednesday</td>
+<td align="left">17:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0100705</td>
+<td align="right">0.0050175</td>
+</tr>
+<tr class="odd">
+<td align="left">18:00.Wednesday</td>
+<td align="left">18:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0094814</td>
+<td align="right">0.0054613</td>
+</tr>
+<tr class="even">
+<td align="left">18:30.Wednesday</td>
+<td align="left">18:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0152287</td>
+<td align="right">0.0068106</td>
+</tr>
+<tr class="odd">
+<td align="left">19:00.Wednesday</td>
+<td align="left">19:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0081398</td>
+<td align="right">0.0046839</td>
+</tr>
+<tr class="even">
+<td align="left">19:30.Wednesday</td>
+<td align="left">19:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0150043</td>
+<td align="right">0.0061582</td>
+</tr>
+<tr class="odd">
+<td align="left">20:00.Wednesday</td>
+<td align="left">20:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0181072</td>
+<td align="right">0.0068740</td>
+</tr>
+<tr class="even">
+<td align="left">20:30.Wednesday</td>
+<td align="left">20:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0102669</td>
+<td align="right">0.0051630</td>
+</tr>
+<tr class="odd">
+<td align="left">21:00.Wednesday</td>
+<td align="left">21:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0089552</td>
+<td align="right">0.0044695</td>
+</tr>
+<tr class="even">
+<td align="left">21:30.Wednesday</td>
+<td align="left">21:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0045554</td>
+<td align="right">0.0032181</td>
+</tr>
+<tr class="odd">
+<td align="left">22:00.Wednesday</td>
+<td align="left">22:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0051710</td>
+<td align="right">0.0036606</td>
+</tr>
+<tr class="even">
+<td align="left">22:30.Wednesday</td>
+<td align="left">22:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0028025</td>
+<td align="right">0.0027986</td>
+</tr>
+<tr class="odd">
+<td align="left">23:00.Wednesday</td>
+<td align="left">23:00</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="left">23:30.Wednesday</td>
+<td align="left">23:30</td>
+<td align="left">Wednesday</td>
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+</tbody>
+</table>
+<table>
+<thead>
+<tr class="header">
+<th align="right">mean</th>
+<th align="right">SE</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="right">0.0015993</td>
+<td align="right">0.0003765</td>
+</tr>
+<tr class="even">
+<td align="right">0.0008668</td>
+<td align="right">0.0002739</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0002751</td>
+<td align="right">0.0001588</td>
+</tr>
+<tr class="even">
+<td align="right">0.0000860</td>
+<td align="right">0.0000860</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0001643</td>
+<td align="right">0.0001162</td>
+</tr>
+<tr class="even">
+<td align="right">0.0005291</td>
+<td align="right">0.0002168</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0015108</td>
+<td align="right">0.0003668</td>
+</tr>
+<tr class="even">
+<td align="right">0.0033000</td>
+<td align="right">0.0005367</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0079948</td>
+<td align="right">0.0008263</td>
+</tr>
+<tr class="even">
+<td align="right">0.0113290</td>
+<td align="right">0.0009657</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0208908</td>
+<td align="right">0.0013023</td>
+</tr>
+<tr class="even">
+<td align="right">0.0306866</td>
+<td align="right">0.0015489</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0396030</td>
+<td align="right">0.0017206</td>
+</tr>
+<tr class="even">
+<td align="right">0.0524314</td>
+<td align="right">0.0018897</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0563036</td>
+<td align="right">0.0019350</td>
+</tr>
+<tr class="even">
+<td align="right">0.0580883</td>
+<td align="right">0.0019709</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0489101</td>
+<td align="right">0.0018278</td>
+</tr>
+<tr class="even">
+<td align="right">0.0470209</td>
+<td align="right">0.0017919</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0337415</td>
+<td align="right">0.0015829</td>
+</tr>
+<tr class="even">
+<td align="right">0.0333651</td>
+<td align="right">0.0015621</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0290846</td>
+<td align="right">0.0014742</td>
+</tr>
+<tr class="even">
+<td align="right">0.0370324</td>
+<td align="right">0.0016381</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0411405</td>
+<td align="right">0.0017131</td>
+</tr>
+<tr class="even">
+<td align="right">0.0383819</td>
+<td align="right">0.0016440</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0342722</td>
+<td align="right">0.0015682</td>
+</tr>
+<tr class="even">
+<td align="right">0.0351362</td>
+<td align="right">0.0015841</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0330212</td>
+<td align="right">0.0015609</td>
+</tr>
+<tr class="even">
+<td align="right">0.0300203</td>
+<td align="right">0.0015198</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0182224</td>
+<td align="right">0.0012086</td>
+</tr>
+<tr class="even">
+<td align="right">0.0201542</td>
+<td align="right">0.0012545</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0231621</td>
+<td align="right">0.0013451</td>
+</tr>
+<tr class="even">
+<td align="right">0.0289836</td>
+<td align="right">0.0014898</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0288983</td>
+<td align="right">0.0015035</td>
+</tr>
+<tr class="even">
+<td align="right">0.0272903</td>
+<td align="right">0.0014590</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0287776</td>
+<td align="right">0.0014878</td>
+</tr>
+<tr class="even">
+<td align="right">0.0280839</td>
+<td align="right">0.0014707</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0218756</td>
+<td align="right">0.0013106</td>
+</tr>
+<tr class="even">
+<td align="right">0.0186880</td>
+<td align="right">0.0012136</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0138104</td>
+<td align="right">0.0010518</td>
+</tr>
+<tr class="even">
+<td align="right">0.0083951</td>
+<td align="right">0.0008367</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0046236</td>
+<td align="right">0.0006255</td>
+</tr>
+<tr class="even">
+<td align="right">0.0022489</td>
+<td align="right">0.0004412</td>
+</tr>
+</tbody>
+</table>
+<table>
+<thead>
+<tr class="header">
+<th align="right">mean</th>
+<th align="right">SE</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="right">0.0009201</td>
+<td align="right">0.0009193</td>
+</tr>
+<tr class="even">
+<td align="right">0.0009201</td>
+<td align="right">0.0009193</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0011546</td>
+<td align="right">0.0011460</td>
+</tr>
+<tr class="even">
+<td align="right">0.0026169</td>
+<td align="right">0.0015420</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0032488</td>
+<td align="right">0.0016590</td>
+</tr>
+<tr class="even">
+<td align="right">0.0092327</td>
+<td align="right">0.0032788</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0222936</td>
+<td align="right">0.0045358</td>
+</tr>
+<tr class="even">
+<td align="right">0.0347495</td>
+<td align="right">0.0054670</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0402526</td>
+<td align="right">0.0056700</td>
+</tr>
+<tr class="even">
+<td align="right">0.0513676</td>
+<td align="right">0.0061640</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0606829</td>
+<td align="right">0.0066371</td>
+</tr>
+<tr class="even">
+<td align="right">0.0536446</td>
+<td align="right">0.0059849</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0651099</td>
+<td align="right">0.0066689</td>
+</tr>
+<tr class="even">
+<td align="right">0.0472602</td>
+<td align="right">0.0058133</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0358330</td>
+<td align="right">0.0051317</td>
+</tr>
+<tr class="even">
+<td align="right">0.0313392</td>
+<td align="right">0.0053081</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0259045</td>
+<td align="right">0.0045836</td>
+</tr>
+<tr class="even">
+<td align="right">0.0242450</td>
+<td align="right">0.0045934</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0305512</td>
+<td align="right">0.0049648</td>
+</tr>
+<tr class="even">
+<td align="right">0.0364914</td>
+<td align="right">0.0052980</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0307879</td>
+<td align="right">0.0049019</td>
+</tr>
+<tr class="even">
+<td align="right">0.0261682</td>
+<td align="right">0.0043371</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0268714</td>
+<td align="right">0.0044923</td>
+</tr>
+<tr class="even">
+<td align="right">0.0311549</td>
+<td align="right">0.0050126</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0317236</td>
+<td align="right">0.0054097</td>
+</tr>
+<tr class="even">
+<td align="right">0.0280881</td>
+<td align="right">0.0050430</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0223295</td>
+<td align="right">0.0044558</td>
+</tr>
+<tr class="even">
+<td align="right">0.0179874</td>
+<td align="right">0.0040419</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0223579</td>
+<td align="right">0.0046056</td>
+</tr>
+<tr class="even">
+<td align="right">0.0252955</td>
+<td align="right">0.0047897</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0319971</td>
+<td align="right">0.0052083</td>
+</tr>
+<tr class="even">
+<td align="right">0.0250932</td>
+<td align="right">0.0046728</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0284345</td>
+<td align="right">0.0049481</td>
+</tr>
+<tr class="even">
+<td align="right">0.0246823</td>
+<td align="right">0.0043784</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0164008</td>
+<td align="right">0.0035563</td>
+</tr>
+<tr class="even">
+<td align="right">0.0120904</td>
+<td align="right">0.0030307</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0097543</td>
+<td align="right">0.0027016</td>
+</tr>
+<tr class="even">
+<td align="right">0.0050252</td>
+<td align="right">0.0020696</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0048784</td>
+<td align="right">0.0020121</td>
+</tr>
+<tr class="even">
+<td align="right">0.0010607</td>
+<td align="right">0.0010593</td>
+</tr>
+</tbody>
+</table>
+<table>
+<thead>
+<tr class="header">
+<th align="right">mean</th>
+<th align="right">SE</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="right">0.0000846</td>
+<td align="right">0.0000846</td>
+</tr>
+<tr class="even">
+<td align="right">0.0000846</td>
+<td align="right">0.0000846</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="right">0.0000860</td>
+<td align="right">0.0000860</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0000805</td>
+<td align="right">0.0000805</td>
+</tr>
+<tr class="even">
+<td align="right">0.0000928</td>
+<td align="right">0.0000928</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0002661</td>
+<td align="right">0.0001541</td>
+</tr>
+<tr class="even">
+<td align="right">0.0006118</td>
+<td align="right">0.0002316</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0012516</td>
+<td align="right">0.0003242</td>
+</tr>
+<tr class="even">
+<td align="right">0.0010951</td>
+<td align="right">0.0003045</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0029659</td>
+<td align="right">0.0005091</td>
+</tr>
+<tr class="even">
+<td align="right">0.0041104</td>
+<td align="right">0.0005926</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0042599</td>
+<td align="right">0.0006010</td>
+</tr>
+<tr class="even">
+<td align="right">0.0057340</td>
+<td align="right">0.0006946</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0064456</td>
+<td align="right">0.0007404</td>
+</tr>
+<tr class="even">
+<td align="right">0.0050085</td>
+<td align="right">0.0006479</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0044202</td>
+<td align="right">0.0006143</td>
+</tr>
+<tr class="even">
+<td align="right">0.0057043</td>
+<td align="right">0.0006912</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0039748</td>
+<td align="right">0.0005785</td>
+</tr>
+<tr class="even">
+<td align="right">0.0033262</td>
+<td align="right">0.0005308</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0028882</td>
+<td align="right">0.0004934</td>
+</tr>
+<tr class="even">
+<td align="right">0.0040940</td>
+<td align="right">0.0005885</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0045105</td>
+<td align="right">0.0006224</td>
+</tr>
+<tr class="even">
+<td align="right">0.0039922</td>
+<td align="right">0.0005851</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0034362</td>
+<td align="right">0.0005409</td>
+</tr>
+<tr class="even">
+<td align="right">0.0033683</td>
+<td align="right">0.0005377</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0030282</td>
+<td align="right">0.0005106</td>
+</tr>
+<tr class="even">
+<td align="right">0.0036304</td>
+<td align="right">0.0005582</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0028012</td>
+<td align="right">0.0004954</td>
+</tr>
+<tr class="even">
+<td align="right">0.0023747</td>
+<td align="right">0.0004491</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0032289</td>
+<td align="right">0.0005170</td>
+</tr>
+<tr class="even">
+<td align="right">0.0039334</td>
+<td align="right">0.0005729</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0035291</td>
+<td align="right">0.0005506</td>
+</tr>
+<tr class="even">
+<td align="right">0.0038842</td>
+<td align="right">0.0005775</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0027307</td>
+<td align="right">0.0004826</td>
+</tr>
+<tr class="even">
+<td align="right">0.0028950</td>
+<td align="right">0.0004959</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0024787</td>
+<td align="right">0.0004599</td>
+</tr>
+<tr class="even">
+<td align="right">0.0023472</td>
+<td align="right">0.0004431</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0020960</td>
+<td align="right">0.0004196</td>
+</tr>
+<tr class="even">
+<td align="right">0.0012402</td>
+<td align="right">0.0003207</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0006023</td>
+<td align="right">0.0002285</td>
+</tr>
+<tr class="even">
+<td align="right">0.0004243</td>
+<td align="right">0.0001904</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0001762</td>
+<td align="right">0.0001244</td>
+</tr>
+<tr class="even">
+<td align="right">0.0001661</td>
+<td align="right">0.0001174</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0000923</td>
+<td align="right">0.0000923</td>
+</tr>
+<tr class="even">
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0000998</td>
+<td align="right">0.0000998</td>
+</tr>
+<tr class="even">
+<td align="right">0.0007047</td>
+<td align="right">0.0002496</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0018308</td>
+<td align="right">0.0003996</td>
+</tr>
+<tr class="even">
+<td align="right">0.0021383</td>
+<td align="right">0.0004275</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0033542</td>
+<td align="right">0.0005291</td>
+</tr>
+<tr class="even">
+<td align="right">0.0059719</td>
+<td align="right">0.0007080</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0090676</td>
+<td align="right">0.0008674</td>
+</tr>
+<tr class="even">
+<td align="right">0.0105038</td>
+<td align="right">0.0009258</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0104779</td>
+<td align="right">0.0009216</td>
+</tr>
+<tr class="even">
+<td align="right">0.0097525</td>
+<td align="right">0.0008937</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0094053</td>
+<td align="right">0.0008822</td>
+</tr>
+<tr class="even">
+<td align="right">0.0081004</td>
+<td align="right">0.0008191</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0051654</td>
+<td align="right">0.0006586</td>
+</tr>
+<tr class="even">
+<td align="right">0.0053070</td>
+<td align="right">0.0006697</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0056596</td>
+<td align="right">0.0006867</td>
+</tr>
+<tr class="even">
+<td align="right">0.0069632</td>
+<td align="right">0.0007647</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0081897</td>
+<td align="right">0.0008243</td>
+</tr>
+<tr class="even">
+<td align="right">0.0083698</td>
+<td align="right">0.0008322</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0064850</td>
+<td align="right">0.0007360</td>
+</tr>
+<tr class="even">
+<td align="right">0.0061236</td>
+<td align="right">0.0007140</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0057053</td>
+<td align="right">0.0006918</td>
+</tr>
+<tr class="even">
+<td align="right">0.0046265</td>
+<td align="right">0.0006298</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0025059</td>
+<td align="right">0.0004630</td>
+</tr>
+<tr class="even">
+<td align="right">0.0031598</td>
+<td align="right">0.0005160</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0040652</td>
+<td align="right">0.0005839</td>
+</tr>
+<tr class="even">
+<td align="right">0.0047924</td>
+<td align="right">0.0006352</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0053894</td>
+<td align="right">0.0006730</td>
+</tr>
+<tr class="even">
+<td align="right">0.0046582</td>
+<td align="right">0.0006303</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0062995</td>
+<td align="right">0.0007347</td>
+</tr>
+<tr class="even">
+<td align="right">0.0062867</td>
+<td align="right">0.0007298</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0045907</td>
+<td align="right">0.0006206</td>
+</tr>
+<tr class="even">
+<td align="right">0.0041562</td>
+<td align="right">0.0005903</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0018163</td>
+<td align="right">0.0003860</td>
+</tr>
+<tr class="even">
+<td align="right">0.0013501</td>
+<td align="right">0.0003375</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0005355</td>
+<td align="right">0.0002188</td>
+</tr>
+<tr class="even">
+<td align="right">0.0001662</td>
+<td align="right">0.0001182</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0003506</td>
+<td align="right">0.0001756</td>
+</tr>
+<tr class="even">
+<td align="right">0.0002598</td>
+<td align="right">0.0001502</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0001828</td>
+<td align="right">0.0001293</td>
+</tr>
+<tr class="even">
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0001797</td>
+<td align="right">0.0001270</td>
+</tr>
+<tr class="even">
+<td align="right">0.0004435</td>
+<td align="right">0.0001985</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0004536</td>
+<td align="right">0.0002029</td>
+</tr>
+<tr class="even">
+<td align="right">0.0007988</td>
+<td align="right">0.0002667</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0026873</td>
+<td align="right">0.0004830</td>
+</tr>
+<tr class="even">
+<td align="right">0.0042542</td>
+<td align="right">0.0006060</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0052569</td>
+<td align="right">0.0006748</td>
+</tr>
+<tr class="even">
+<td align="right">0.0077404</td>
+<td align="right">0.0008130</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0083458</td>
+<td align="right">0.0008390</td>
+</tr>
+<tr class="even">
+<td align="right">0.0108006</td>
+<td align="right">0.0009492</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0080764</td>
+<td align="right">0.0008239</td>
+</tr>
+<tr class="even">
+<td align="right">0.0070977</td>
+<td align="right">0.0007756</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0049366</td>
+<td align="right">0.0006504</td>
+</tr>
+<tr class="even">
+<td align="right">0.0047256</td>
+<td align="right">0.0006392</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0036709</td>
+<td align="right">0.0005638</td>
+</tr>
+<tr class="even">
+<td align="right">0.0049395</td>
+<td align="right">0.0006506</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0052208</td>
+<td align="right">0.0006646</td>
+</tr>
+<tr class="even">
+<td align="right">0.0040633</td>
+<td align="right">0.0005873</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0048613</td>
+<td align="right">0.0006385</td>
+</tr>
+<tr class="even">
+<td align="right">0.0047144</td>
+<td align="right">0.0006307</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0040994</td>
+<td align="right">0.0005888</td>
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+<tr class="even">
+<td align="right">0.0035877</td>
+<td align="right">0.0005521</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0030272</td>
+<td align="right">0.0005029</td>
+</tr>
+<tr class="even">
+<td align="right">0.0029930</td>
+<td align="right">0.0005048</td>
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+<tr class="odd">
+<td align="right">0.0023594</td>
+<td align="right">0.0004537</td>
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+<tr class="even">
+<td align="right">0.0024328</td>
+<td align="right">0.0004587</td>
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+<tr class="odd">
+<td align="right">0.0031677</td>
+<td align="right">0.0005274</td>
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+<td align="right">0.0025485</td>
+<td align="right">0.0004733</td>
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+<td align="right">0.0026909</td>
+<td align="right">0.0004826</td>
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+<td align="right">0.0023053</td>
+<td align="right">0.0004432</td>
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+<td align="right">0.0017789</td>
+<td align="right">0.0003877</td>
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+<tr class="even">
+<td align="right">0.0019186</td>
+<td align="right">0.0004081</td>
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+<tr class="odd">
+<td align="right">0.0013919</td>
+<td align="right">0.0003474</td>
+</tr>
+<tr class="even">
+<td align="right">0.0006298</td>
+<td align="right">0.0002384</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0007830</td>
+<td align="right">0.0002613</td>
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+<tr class="even">
+<td align="right">0.0005283</td>
+<td align="right">0.0002157</td>
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+<tr class="odd">
+<td align="right">0.0001834</td>
+<td align="right">0.0001297</td>
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+<tr class="even">
+<td align="right">0.0001675</td>
+<td align="right">0.0001186</td>
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+<td align="right">0.0000000</td>
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+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0002729</td>
+<td align="right">0.0001575</td>
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+<tr class="even">
+<td align="right">0.0002484</td>
+<td align="right">0.0001441</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0001637</td>
+<td align="right">0.0001159</td>
+</tr>
+<tr class="even">
+<td align="right">0.0006949</td>
+<td align="right">0.0002463</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0014851</td>
+<td align="right">0.0003502</td>
+</tr>
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+<td align="right">0.0028100</td>
+<td align="right">0.0004882</td>
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+<td align="right">0.0045199</td>
+<td align="right">0.0006247</td>
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+<td align="right">0.0060296</td>
+<td align="right">0.0007138</td>
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+<td align="right">0.0075117</td>
+<td align="right">0.0007952</td>
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+<td align="right">0.0087974</td>
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+<td align="right">0.0075840</td>
+<td align="right">0.0008005</td>
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+<td align="right">0.0097432</td>
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+<td align="right">0.0008184</td>
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+<td align="right">0.0071269</td>
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+<td align="right">0.0055375</td>
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+<td align="right">0.0052874</td>
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+<td align="right">0.0006678</td>
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+<td align="right">0.0057326</td>
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+<td align="right">0.0048927</td>
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+<td align="right">0.0059781</td>
+<td align="right">0.0007156</td>
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+<td align="right">0.0056393</td>
+<td align="right">0.0006981</td>
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+<td align="right">0.0046278</td>
+<td align="right">0.0006288</td>
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+<td align="right">0.0040300</td>
+<td align="right">0.0005821</td>
+</tr>
+<tr class="even">
+<td align="right">0.0036335</td>
+<td align="right">0.0005563</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0022295</td>
+<td align="right">0.0004349</td>
+</tr>
+<tr class="even">
+<td align="right">0.0022297</td>
+<td align="right">0.0004351</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0023259</td>
+<td align="right">0.0004475</td>
+</tr>
+<tr class="even">
+<td align="right">0.0014736</td>
+<td align="right">0.0003579</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0006276</td>
+<td align="right">0.0002227</td>
+</tr>
+<tr class="even">
+<td align="right">0.0003241</td>
+<td align="right">0.0001626</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0001839</td>
+<td align="right">0.0001301</td>
+</tr>
+<tr class="even">
+<td align="right">0.0000944</td>
+<td align="right">0.0000944</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="right">0.0002594</td>
+<td align="right">0.0001508</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0004326</td>
+<td align="right">0.0001938</td>
+</tr>
+<tr class="even">
+<td align="right">0.0004126</td>
+<td align="right">0.0001849</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0016720</td>
+<td align="right">0.0003748</td>
+</tr>
+<tr class="even">
+<td align="right">0.0022647</td>
+<td align="right">0.0004360</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0039126</td>
+<td align="right">0.0005820</td>
+</tr>
+<tr class="even">
+<td align="right">0.0046447</td>
+<td align="right">0.0006353</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0042883</td>
+<td align="right">0.0006038</td>
+</tr>
+<tr class="even">
+<td align="right">0.0069424</td>
+<td align="right">0.0007670</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0079076</td>
+<td align="right">0.0008137</td>
+</tr>
+<tr class="even">
+<td align="right">0.0076904</td>
+<td align="right">0.0008065</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0060518</td>
+<td align="right">0.0007129</td>
+</tr>
+<tr class="even">
+<td align="right">0.0048016</td>
+<td align="right">0.0006430</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0038448</td>
+<td align="right">0.0005767</td>
+</tr>
+<tr class="even">
+<td align="right">0.0045575</td>
+<td align="right">0.0006220</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0037863</td>
+<td align="right">0.0005675</td>
+</tr>
+<tr class="even">
+<td align="right">0.0043839</td>
+<td align="right">0.0006091</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0053363</td>
+<td align="right">0.0006717</td>
+</tr>
+<tr class="even">
+<td align="right">0.0047144</td>
+<td align="right">0.0006311</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0044358</td>
+<td align="right">0.0006116</td>
+</tr>
+<tr class="even">
+<td align="right">0.0048766</td>
+<td align="right">0.0006416</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0051376</td>
+<td align="right">0.0006606</td>
+</tr>
+<tr class="even">
+<td align="right">0.0038996</td>
+<td align="right">0.0005747</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0015766</td>
+<td align="right">0.0003630</td>
+</tr>
+<tr class="even">
+<td align="right">0.0025783</td>
+<td align="right">0.0004623</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0029559</td>
+<td align="right">0.0004981</td>
+</tr>
+<tr class="even">
+<td align="right">0.0042179</td>
+<td align="right">0.0005997</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0043300</td>
+<td align="right">0.0006094</td>
+</tr>
+<tr class="even">
+<td align="right">0.0041107</td>
+<td align="right">0.0005956</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0052078</td>
+<td align="right">0.0006634</td>
+</tr>
+<tr class="even">
+<td align="right">0.0040527</td>
+<td align="right">0.0005889</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0034107</td>
+<td align="right">0.0005450</td>
+</tr>
+<tr class="even">
+<td align="right">0.0023232</td>
+<td align="right">0.0004471</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0015653</td>
+<td align="right">0.0003596</td>
+</tr>
+<tr class="even">
+<td align="right">0.0005216</td>
+<td align="right">0.0002133</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0005044</td>
+<td align="right">0.0002063</td>
+</tr>
+<tr class="even">
+<td align="right">0.0002524</td>
+<td align="right">0.0001460</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0002517</td>
+<td align="right">0.0001453</td>
+</tr>
+<tr class="even">
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0000875</td>
+<td align="right">0.0000876</td>
+</tr>
+<tr class="even">
+<td align="right">0.0005890</td>
+<td align="right">0.0002241</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0014152</td>
+<td align="right">0.0003551</td>
+</tr>
+<tr class="even">
+<td align="right">0.0020539</td>
+<td align="right">0.0004198</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0037569</td>
+<td align="right">0.0005649</td>
+</tr>
+<tr class="even">
+<td align="right">0.0050633</td>
+<td align="right">0.0006618</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0066071</td>
+<td align="right">0.0007525</td>
+</tr>
+<tr class="even">
+<td align="right">0.0079446</td>
+<td align="right">0.0008187</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0072744</td>
+<td align="right">0.0007796</td>
+</tr>
+<tr class="even">
+<td align="right">0.0075578</td>
+<td align="right">0.0007920</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0063419</td>
+<td align="right">0.0007241</td>
+</tr>
+<tr class="even">
+<td align="right">0.0059191</td>
+<td align="right">0.0007003</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0043792</td>
+<td align="right">0.0006036</td>
+</tr>
+<tr class="even">
+<td align="right">0.0044702</td>
+<td align="right">0.0006153</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0035683</td>
+<td align="right">0.0005549</td>
+</tr>
+<tr class="even">
+<td align="right">0.0057436</td>
+<td align="right">0.0007001</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0064163</td>
+<td align="right">0.0007368</td>
+</tr>
+<tr class="even">
+<td align="right">0.0057008</td>
+<td align="right">0.0006936</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0054679</td>
+<td align="right">0.0006829</td>
+</tr>
+<tr class="even">
+<td align="right">0.0055965</td>
+<td align="right">0.0006886</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0054198</td>
+<td align="right">0.0006829</td>
+</tr>
+<tr class="even">
+<td align="right">0.0049414</td>
+<td align="right">0.0006514</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0020119</td>
+<td align="right">0.0004196</td>
+</tr>
+<tr class="even">
+<td align="right">0.0027789</td>
+<td align="right">0.0004906</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0030059</td>
+<td align="right">0.0005071</td>
+</tr>
+<tr class="even">
+<td align="right">0.0037752</td>
+<td align="right">0.0005675</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0038924</td>
+<td align="right">0.0005843</td>
+</tr>
+<tr class="even">
+<td align="right">0.0040167</td>
+<td align="right">0.0005961</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0036124</td>
+<td align="right">0.0005621</td>
+</tr>
+<tr class="even">
+<td align="right">0.0045339</td>
+<td align="right">0.0006258</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0035489</td>
+<td align="right">0.0005534</td>
+</tr>
+<tr class="even">
+<td align="right">0.0026349</td>
+<td align="right">0.0004723</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0022954</td>
+<td align="right">0.0004410</td>
+</tr>
+<tr class="even">
+<td align="right">0.0016190</td>
+<td align="right">0.0003717</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0007035</td>
+<td align="right">0.0002493</td>
+</tr>
+<tr class="even">
+<td align="right">0.0001999</td>
+<td align="right">0.0001414</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0003689</td>
+<td align="right">0.0001848</td>
+</tr>
+<tr class="even">
+<td align="right">0.0000943</td>
+<td align="right">0.0000943</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0000838</td>
+<td align="right">0.0000838</td>
+</tr>
+<tr class="even">
+<td align="right">0.0001770</td>
+<td align="right">0.0001253</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0001722</td>
+<td align="right">0.0001221</td>
+</tr>
+<tr class="even">
+<td align="right">0.0002900</td>
+<td align="right">0.0001676</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0012079</td>
+<td align="right">0.0003243</td>
+</tr>
+<tr class="even">
+<td align="right">0.0022832</td>
+<td align="right">0.0004330</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0027288</td>
+<td align="right">0.0004832</td>
+</tr>
+<tr class="even">
+<td align="right">0.0038322</td>
+<td align="right">0.0005698</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0056033</td>
+<td align="right">0.0006909</td>
+</tr>
+<tr class="even">
+<td align="right">0.0075366</td>
+<td align="right">0.0008006</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0083405</td>
+<td align="right">0.0008403</td>
+</tr>
+<tr class="even">
+<td align="right">0.0084811</td>
+<td align="right">0.0008500</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0070306</td>
+<td align="right">0.0007734</td>
+</tr>
+<tr class="even">
+<td align="right">0.0056546</td>
+<td align="right">0.0006949</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0035631</td>
+<td align="right">0.0005534</td>
+</tr>
+<tr class="even">
+<td align="right">0.0038516</td>
+<td align="right">0.0005767</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0039738</td>
+<td align="right">0.0005827</td>
+</tr>
+<tr class="even">
+<td align="right">0.0056208</td>
+<td align="right">0.0006917</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0062632</td>
+<td align="right">0.0007332</td>
+</tr>
+<tr class="even">
+<td align="right">0.0061716</td>
+<td align="right">0.0007268</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0043042</td>
+<td align="right">0.0006053</td>
+</tr>
+<tr class="even">
+<td align="right">0.0043039</td>
+<td align="right">0.0006043</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0039145</td>
+<td align="right">0.0005740</td>
+</tr>
+<tr class="even">
+<td align="right">0.0036021</td>
+<td align="right">0.0005551</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0024697</td>
+<td align="right">0.0004582</td>
+</tr>
+<tr class="even">
+<td align="right">0.0022222</td>
+<td align="right">0.0004350</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0026540</td>
+<td align="right">0.0004764</td>
+</tr>
+<tr class="even">
+<td align="right">0.0038537</td>
+<td align="right">0.0005737</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0029504</td>
+<td align="right">0.0005048</td>
+</tr>
+<tr class="even">
+<td align="right">0.0034441</td>
+<td align="right">0.0005361</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0042063</td>
+<td align="right">0.0005925</td>
+</tr>
+<tr class="even">
+<td align="right">0.0043768</td>
+<td align="right">0.0006040</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0038383</td>
+<td align="right">0.0005706</td>
+</tr>
+<tr class="even">
+<td align="right">0.0030783</td>
+<td align="right">0.0005118</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0023197</td>
+<td align="right">0.0004452</td>
+</tr>
+<tr class="even">
+<td align="right">0.0015609</td>
+<td align="right">0.0003683</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0008672</td>
+<td align="right">0.0002745</td>
+</tr>
+<tr class="even">
+<td align="right">0.0003537</td>
+<td align="right">0.0001774</td>
+</tr>
+</tbody>
+</table>
+<table>
+<thead>
+<tr class="header">
+<th align="right">mean</th>
+<th align="right">SE</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="right">0.0009201</td>
+<td align="right">0.0009193</td>
+</tr>
+<tr class="even">
+<td align="right">0.0009201</td>
+<td align="right">0.0009193</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0006318</td>
+<td align="right">0.0006302</td>
+</tr>
+<tr class="even">
+<td align="right">0.0006318</td>
+<td align="right">0.0006302</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0040166</td>
+<td align="right">0.0018369</td>
+</tr>
+<tr class="even">
+<td align="right">0.0057207</td>
+<td align="right">0.0021865</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0063285</td>
+<td align="right">0.0022487</td>
+</tr>
+<tr class="even">
+<td align="right">0.0063032</td>
+<td align="right">0.0023851</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0059863</td>
+<td align="right">0.0022839</td>
+</tr>
+<tr class="even">
+<td align="right">0.0074350</td>
+<td align="right">0.0025037</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0084821</td>
+<td align="right">0.0026871</td>
+</tr>
+<tr class="even">
+<td align="right">0.0080931</td>
+<td align="right">0.0026868</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0051604</td>
+<td align="right">0.0021352</td>
+</tr>
+<tr class="even">
+<td align="right">0.0037125</td>
+<td align="right">0.0016992</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0030545</td>
+<td align="right">0.0015436</td>
+</tr>
+<tr class="even">
+<td align="right">0.0017284</td>
+<td align="right">0.0012571</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0022238</td>
+<td align="right">0.0013471</td>
+</tr>
+<tr class="even">
+<td align="right">0.0042201</td>
+<td align="right">0.0019472</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0057702</td>
+<td align="right">0.0022389</td>
+</tr>
+<tr class="even">
+<td align="right">0.0039486</td>
+<td align="right">0.0016309</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0033119</td>
+<td align="right">0.0015081</td>
+</tr>
+<tr class="even">
+<td align="right">0.0037528</td>
+<td align="right">0.0016867</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0050471</td>
+<td align="right">0.0025724</td>
+</tr>
+<tr class="even">
+<td align="right">0.0015420</td>
+<td align="right">0.0011052</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0042796</td>
+<td align="right">0.0024693</td>
+</tr>
+<tr class="even">
+<td align="right">0.0029855</td>
+<td align="right">0.0023113</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0032127</td>
+<td align="right">0.0023949</td>
+</tr>
+<tr class="even">
+<td align="right">0.0039387</td>
+<td align="right">0.0024932</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0038680</td>
+<td align="right">0.0024659</td>
+</tr>
+<tr class="even">
+<td align="right">0.0022278</td>
+<td align="right">0.0021970</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0028188</td>
+<td align="right">0.0016505</td>
+</tr>
+<tr class="even">
+<td align="right">0.0038598</td>
+<td align="right">0.0017428</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0039482</td>
+<td align="right">0.0017818</td>
+</tr>
+<tr class="even">
+<td align="right">0.0036014</td>
+<td align="right">0.0017930</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0009201</td>
+<td align="right">0.0009167</td>
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+<tr class="even">
+<td align="right">0.0098245</td>
+<td align="right">0.0029206</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0153134</td>
+<td align="right">0.0037770</td>
+</tr>
+<tr class="even">
+<td align="right">0.0135992</td>
+<td align="right">0.0031570</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0113585</td>
+<td align="right">0.0029260</td>
+</tr>
+<tr class="even">
+<td align="right">0.0087920</td>
+<td align="right">0.0025506</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0045751</td>
+<td align="right">0.0018603</td>
+</tr>
+<tr class="even">
+<td align="right">0.0082253</td>
+<td align="right">0.0034984</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0016668</td>
+<td align="right">0.0011709</td>
+</tr>
+<tr class="even">
+<td align="right">0.0007955</td>
+<td align="right">0.0007945</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0033740</td>
+<td align="right">0.0017068</td>
+</tr>
+<tr class="even">
+<td align="right">0.0081208</td>
+<td align="right">0.0026752</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0024292</td>
+<td align="right">0.0018018</td>
+</tr>
+<tr class="even">
+<td align="right">0.0047446</td>
+<td align="right">0.0022724</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0037831</td>
+<td align="right">0.0020343</td>
+</tr>
+<tr class="even">
+<td align="right">0.0042721</td>
+<td align="right">0.0022209</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0007803</td>
+<td align="right">0.0007793</td>
+</tr>
+<tr class="even">
+<td align="right">0.0013499</td>
+<td align="right">0.0009648</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0042181</td>
+<td align="right">0.0017355</td>
+</tr>
+<tr class="even">
+<td align="right">0.0036038</td>
+<td align="right">0.0016403</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0022535</td>
+<td align="right">0.0013218</td>
+</tr>
+<tr class="even">
+<td align="right">0.0050891</td>
+<td align="right">0.0019686</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0060470</td>
+<td align="right">0.0021860</td>
+</tr>
+<tr class="even">
+<td align="right">0.0034925</td>
+<td align="right">0.0017521</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0031045</td>
+<td align="right">0.0018468</td>
+</tr>
+<tr class="even">
+<td align="right">0.0022286</td>
+<td align="right">0.0013102</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0005696</td>
+<td align="right">0.0005700</td>
+</tr>
+<tr class="even">
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0010171</td>
+<td align="right">0.0010169</td>
+</tr>
+<tr class="even">
+<td align="right">0.0016925</td>
+<td align="right">0.0012332</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0024130</td>
+<td align="right">0.0014273</td>
+</tr>
+<tr class="even">
+<td align="right">0.0010607</td>
+<td align="right">0.0010593</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="right">0.0008210</td>
+<td align="right">0.0008218</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0015178</td>
+<td align="right">0.0010755</td>
+</tr>
+<tr class="even">
+<td align="right">0.0018316</td>
+<td align="right">0.0012972</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0026090</td>
+<td align="right">0.0015029</td>
+</tr>
+<tr class="even">
+<td align="right">0.0059758</td>
+<td align="right">0.0022899</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0057217</td>
+<td align="right">0.0023698</td>
+</tr>
+<tr class="even">
+<td align="right">0.0027817</td>
+<td align="right">0.0016581</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0082422</td>
+<td align="right">0.0028482</td>
+</tr>
+<tr class="even">
+<td align="right">0.0061293</td>
+<td align="right">0.0023547</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0034749</td>
+<td align="right">0.0017630</td>
+</tr>
+<tr class="even">
+<td align="right">0.0030841</td>
+<td align="right">0.0015753</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0054632</td>
+<td align="right">0.0024691</td>
+</tr>
+<tr class="even">
+<td align="right">0.0032007</td>
+<td align="right">0.0016093</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0046244</td>
+<td align="right">0.0019029</td>
+</tr>
+<tr class="even">
+<td align="right">0.0048123</td>
+<td align="right">0.0020015</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0011544</td>
+<td align="right">0.0008160</td>
+</tr>
+<tr class="even">
+<td align="right">0.0037433</td>
+<td align="right">0.0016951</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0040062</td>
+<td align="right">0.0018113</td>
+</tr>
+<tr class="even">
+<td align="right">0.0040395</td>
+<td align="right">0.0018130</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0057441</td>
+<td align="right">0.0021869</td>
+</tr>
+<tr class="even">
+<td align="right">0.0073537</td>
+<td align="right">0.0024591</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0035242</td>
+<td align="right">0.0017526</td>
+</tr>
+<tr class="even">
+<td align="right">0.0033177</td>
+<td align="right">0.0016586</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0031236</td>
+<td align="right">0.0017901</td>
+</tr>
+<tr class="even">
+<td align="right">0.0050171</td>
+<td align="right">0.0022189</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0026816</td>
+<td align="right">0.0015406</td>
+</tr>
+<tr class="even">
+<td align="right">0.0049431</td>
+<td align="right">0.0020133</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0059654</td>
+<td align="right">0.0022547</td>
+</tr>
+<tr class="even">
+<td align="right">0.0033824</td>
+<td align="right">0.0016940</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0029503</td>
+<td align="right">0.0014623</td>
+</tr>
+<tr class="even">
+<td align="right">0.0015008</td>
+<td align="right">0.0010499</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0017036</td>
+<td align="right">0.0011971</td>
+</tr>
+<tr class="even">
+<td align="right">0.0009233</td>
+<td align="right">0.0009233</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+<tr class="even">
+<td align="right">0.0000000</td>
+<td align="right">0.0000000</td>
+</tr>
+</tbody>
+</table>
+<pre><code>## r_dow S M T W T F S
+## st_halfhour
+## 00:00 mean 8.461652e-05 8.461652e-05 0.000000e+00 8.595842e-05 8.049951e-05 9.278030e-05 2.661310e-04
+## SE 4.094002e-03 4.510494e-03 3.992159e-03 3.436225e-03 3.368346e-03 3.028240e-03 3.630390e-03
+## 00:30 mean 6.118147e-04 1.251603e-03 1.095065e-03 2.965924e-03 4.110392e-03 4.259927e-03 5.733988e-03
+## SE 2.801245e-03 2.374706e-03 3.228891e-03 3.933391e-03 3.529126e-03 3.884200e-03 2.730718e-03
+## 01:00 mean 6.445552e-03 5.008504e-03 4.420248e-03 5.704297e-03 3.974824e-03 3.326164e-03 2.888210e-03
+## SE 2.895014e-03 2.478719e-03 2.347239e-03 2.095954e-03 1.240167e-03 6.023241e-04 4.243458e-04</code></pre>
+<pre><code>## [1] "Feedback: Weekday early morning laundry (06:00 – 09:00) out of all laundry"</code></pre>
+<table>
+<caption>Weekday early morning laundry (06:00 – 09:00) out of all laundry</caption>
+<thead>
+<tr class="header">
+<th align="left"></th>
+<th align="left">ba_survey</th>
+<th align="right">weekday_earlyam</th>
+<th align="right">se</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">1985</td>
+<td align="left">1985</td>
+<td align="right">0.0612199</td>
+<td align="right">0.0027494</td>
+</tr>
+<tr class="even">
+<td align="left">2005</td>
+<td align="left">2005</td>
+<td align="right">0.1348555</td>
+<td align="right">0.0145266</td>
+</tr>
+</tbody>
+</table>
+<pre><code>## [1] "Feedback: Weekday morning laundry out of all laundry"</code></pre>
+<table>
+<caption>Weekday morning laundry out of all laundry</caption>
+<thead>
+<tr class="header">
+<th align="left"></th>
+<th align="left">ba_survey</th>
+<th align="right">weekday_am</th>
+<th align="right">se</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">1985</td>
+<td align="left">1985</td>
+<td align="right">0.2108538</td>
+<td align="right">0.0057673</td>
+</tr>
+<tr class="even">
+<td align="left">2005</td>
+<td align="left">2005</td>
+<td align="right">0.1957309</td>
+<td align="right">0.0191291</td>
+</tr>
+</tbody>
+</table>
+<pre><code>## [1] "Feedback: Weekday evening peak laundry out of all laundry"</code></pre>
+<table>
+<caption>Weekday evening peak laundry out of all laundry</caption>
+<thead>
+<tr class="header">
+<th align="left"></th>
+<th align="left">ba_survey</th>
+<th align="right">weekday_pm</th>
+<th align="right">se</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">1985</td>
+<td align="left">1985</td>
+<td align="right">0.1208896</td>
+<td align="right">0.0045649</td>
+</tr>
+<tr class="even">
+<td align="left">2005</td>
+<td align="left">2005</td>
+<td align="right">0.1228752</td>
+<td align="right">0.0161665</td>
+</tr>
+</tbody>
+</table>
+<pre><code>## [1] "Feedback: Sunday morning laundry out of all laundry"</code></pre>
+<table>
+<caption>Sunday morning laundry out of all laundry</caption>
+<thead>
+<tr class="header">
+<th align="left"></th>
+<th align="left">ba_survey</th>
+<th align="right">sunday_am</th>
+<th align="right">se</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">1985</td>
+<td align="left">1985</td>
+<td align="right">0.0441858</td>
+<td align="right">0.0029138</td>
+</tr>
+<tr class="even">
+<td align="left">2005</td>
+<td align="left">2005</td>
+<td align="right">0.0698033</td>
+<td align="right">0.0116076</td>
+</tr>
+</tbody>
+</table>
+<pre><code>## 2.5 % 97.5 %
+## 1985 0.03847499 0.04989669
+## 2005 0.04705275 0.09255377</code></pre>
+<pre><code>## [1] "Feedback: Other laundry out of all laundry"</code></pre>
+<table>
+<caption>Other laundry out of all laundry</caption>
+<thead>
+<tr class="header">
+<th align="left"></th>
+<th align="left">ba_survey</th>
+<th align="right">other</th>
+<th align="right">se</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">1985</td>
+<td align="left">1985</td>
+<td align="right">0.5628508</td>
+<td align="right">0.0069440</td>
+</tr>
+<tr class="even">
+<td align="left">2005</td>
+<td align="left">2005</td>
+<td align="right">0.4767352</td>
+<td align="right">0.0238243</td>
+</tr>
+</tbody>
+</table>
+<pre><code>## [1] "% within empstat who do sunday_am"</code></pre>
+<pre><code>## empstat sunday_am se
+## full-time full-time 0.07804306 0.01920174
+## not in paid work not in paid work 0.06989777 0.01804090
+## part-time part-time 0.04599771 0.01855852</code></pre>
+<pre><code>## [1] "% within empstat who do weekday early am"</code></pre>
+<pre><code>## empstat weekday_earlyam se
+## full-time full-time 0.1155997 0.01824415
+## not in paid work not in paid work 0.1584702 0.02712452
+## part-time part-time 0.1365675 0.03517791</code></pre>
+<pre><code>## [1] "% within empstat who do weekday am"</code></pre>
+<pre><code>## empstat weekday_am se
+## full-time full-time 0.2230604 0.03161854
+## not in paid work not in paid work 0.1745649 0.02764323
+## part-time part-time 0.1653634 0.03930053</code></pre>
+<pre><code>## [1] "% within empstat who do weekday pm"</code></pre>
+<pre><code>## empstat weekday_pm se
+## full-time full-time 0.1309584 0.02501823
+## not in paid work not in paid work 0.1224830 0.02370082
+## part-time part-time 0.1006188 0.04237536</code></pre>
+<pre><code>## [1] "% within empstat who do other"</code></pre>
+<pre><code>## empstat other se
+## full-time full-time 0.4523385 0.03593228
+## not in paid work not in paid work 0.4745841 0.03621508
+## part-time part-time 0.5514526 0.06317038</code></pre>
+<pre><code>## [1] "summarise the laundry types by diarypid for 1985 to see how many different kinds of laundry"</code></pre>
+<pre><code>## [1] "Summarise the laundry types by diarypid for 2005 to see how many different kinds of laundry"</code></pre>
+<pre><code>## [1] "Check laundry by sex & hh type - any change in proportions done by men in couples?"</code></pre>
+<table>
+<caption>Check laundry by sex & hh type - any change in proportions done by men in couples?</caption>
+<thead>
+<tr class="header">
+<th align="left"></th>
+<th align="right">Man</th>
+<th align="right">Woman</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">1 person household</td>
+<td align="right">117</td>
+<td align="right">229</td>
+</tr>
+<tr class="even">
+<td align="left">Married/cohabiting couple + others</td>
+<td align="right">1178</td>
+<td align="right">1306</td>
+</tr>
+<tr class="odd">
+<td align="left">Married/cohabiting couple alone</td>
+<td align="right">462</td>
+<td align="right">522</td>
+</tr>
+<tr class="even">
+<td align="left">Other household types</td>
+<td align="right">177</td>
+<td align="right">325</td>
+</tr>
+</tbody>
+</table>
+<table>
+<thead>
+<tr class="header">
+<th align="left"></th>
+<th align="right">Man</th>
+<th align="right">Woman</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">1 person household</td>
+<td align="right">64</td>
+<td align="right">74</td>
+</tr>
+<tr class="even">
+<td align="left">Married/cohabiting couple + others</td>
+<td align="right">65</td>
+<td align="right">67</td>
+</tr>
+<tr class="odd">
+<td align="left">Married/cohabiting couple alone</td>
+<td align="right">79</td>
+<td align="right">73</td>
+</tr>
+<tr class="even">
+<td align="left">Other household types</td>
+<td align="right">13</td>
+<td align="right">39</td>
+</tr>
+</tbody>
+</table>
+<pre><code>##
+## full-time not in paid work part-time unknown job hours
+## 1032 1188 383 4
+## <NA>
+## 0</code></pre>
+<pre><code>##
+## full-time not in paid work part-time unknown job hours
+## full-time 1032 0 0 0
+## notInPaidWork 0 1188 0 0
+## part-time 0 0 383 0
+## unknownHours 0 0 0 4
+## <NA> 0 0 0 0
+##
+## <NA>
+## full-time 0
+## notInPaidWork 0
+## part-time 0
+## unknownHours 0
+## <NA> 0</code></pre>
+<pre><code>##
+## 0 1 2 3 4 5 8 10 11
+## Autumn 0 0 0 0 0 0 14 4157 0
+## Spring 0 0 3123 5932 1274 0 0 0 0
+## Summer 0 0 0 0 0 56 0 0 0
+## Winter 3372 683 0 0 0 0 0 0 214</code></pre>
+<table>
+<caption>Check season flag</caption>
+<thead>
+<tr class="header">
+<th align="left"></th>
+<th align="right">2</th>
+<th align="right">5</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">Spring</td>
+<td align="right">1470</td>
+<td align="right">0</td>
+</tr>
+<tr class="even">
+<td align="left">Summer</td>
+<td align="right">0</td>
+<td align="right">1137</td>
+</tr>
+</tbody>
+</table>
+<table>
+<caption>combinations by employment type 1985</caption>
+<thead>
+<tr class="header">
+<th align="left"></th>
+<th align="right">full-time</th>
+<th align="right">not in paid work</th>
+<th align="right">part-time</th>
+<th align="right">unknown job hours</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">0</td>
+<td align="right">6093</td>
+<td align="right">6379</td>
+<td align="right">3638</td>
+<td align="right">460</td>
+</tr>
+<tr class="even">
+<td align="left">1</td>
+<td align="right">750</td>
+<td align="right">802</td>
+<td align="right">495</td>
+<td align="right">56</td>
+</tr>
+<tr class="odd">
+<td align="left">2</td>
+<td align="right">154</td>
+<td align="right">143</td>
+<td align="right">104</td>
+<td align="right">9</td>
+</tr>
+<tr class="even">
+<td align="left">3</td>
+<td align="right">13</td>
+<td align="right">13</td>
+<td align="right">3</td>
+<td align="right">0</td>
+</tr>
+</tbody>
+</table>
+<table>
+<caption>combinations by employment type 2005</caption>
+<thead>
+<tr class="header">
+<th align="left"></th>
+<th align="right">full-time</th>
+<th align="right">not in paid work</th>
+<th align="right">part-time</th>
+<th align="right">unknown job hours</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">0</td>
+<td align="right">902</td>
+<td align="right">1060</td>
+<td align="right">338</td>
+<td align="right">4</td>
+</tr>
+<tr class="even">
+<td align="left">1</td>
+<td align="right">112</td>
+<td align="right">107</td>
+<td align="right">40</td>
+<td align="right">0</td>
+</tr>
+<tr class="odd">
+<td align="left">2</td>
+<td align="right">18</td>
+<td align="right">20</td>
+<td align="right">5</td>
+<td align="right">0</td>
+</tr>
+<tr class="even">
+<td align="left">3</td>
+<td align="right">0</td>
+<td align="right">1</td>
+<td align="right">0</td>
+<td align="right">0</td>
+</tr>
+</tbody>
+</table>
+<table>
+<caption>Get number of people reporting any laundry by employment status (to give denominator 1985)</caption>
+<thead>
+<tr class="header">
+<th align="left"></th>
+<th align="right">full-time</th>
+<th align="right">not in paid work</th>
+<th align="right">part-time</th>
+<th align="right">unknown job hours</th>
+<th align="right">NA</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">0</td>
+<td align="right">5458</td>
+<td align="right">5682</td>
+<td align="right">3267</td>
+<td align="right">389</td>
+<td align="right">0</td>
+</tr>
+<tr class="even">
+<td align="left">1</td>
+<td align="right">1552</td>
+<td align="right">1655</td>
+<td align="right">973</td>
+<td align="right">136</td>
+<td align="right">0</td>
+</tr>
+<tr class="odd">
+<td align="left">NA</td>
+<td align="right">0</td>
+<td align="right">0</td>
+<td align="right">0</td>
+<td align="right">0</td>
+<td align="right">0</td>
+</tr>
+</tbody>
+</table>
+<table>
+<caption>Get number of people reporting any laundry by employment status (to give denominator 2005)</caption>
+<thead>
+<tr class="header">
+<th align="left"></th>
+<th align="right">full-time</th>
+<th align="right">not in paid work</th>
+<th align="right">part-time</th>
+<th align="right">unknown job hours</th>
+<th align="right">NA</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">0</td>
+<td align="right">837</td>
+<td align="right">987</td>
+<td align="right">305</td>
+<td align="right">4</td>
+<td align="right">0</td>
+</tr>
+<tr class="even">
+<td align="left">1</td>
+<td align="right">195</td>
+<td align="right">201</td>
+<td align="right">78</td>
+<td align="right">0</td>
+<td align="right">0</td>
+</tr>
+<tr class="odd">
+<td align="left">NA</td>
+<td align="right">0</td>
+<td align="right">0</td>
+<td align="right">0</td>
+<td align="right">0</td>
+<td align="right">0</td>
+</tr>
+</tbody>
+</table>
+<table>
+<caption>combinations by age - 1985</caption>
+<thead>
+<tr class="header">
+<th align="left"></th>
+<th align="right">(16,25]</th>
+<th align="right">(25,35]</th>
+<th align="right">(35,45]</th>
+<th align="right">(45,55]</th>
+<th align="right">(55,65]</th>
+<th align="right">(65,75]</th>
+<th align="right">(75,85]</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">0</td>
+<td align="right">2815</td>
+<td align="right">4187</td>
+<td align="right">3364</td>
+<td align="right">2807</td>
+<td align="right">2030</td>
+<td align="right">1001</td>
+<td align="right">366</td>
+</tr>
+<tr class="even">
+<td align="left">1</td>
+<td align="right">337</td>
+<td align="right">513</td>
+<td align="right">435</td>
+<td align="right">396</td>
+<td align="right">254</td>
+<td align="right">130</td>
+<td align="right">38</td>
+</tr>
+<tr class="odd">
+<td align="left">2</td>
+<td align="right">60</td>
+<td align="right">86</td>
+<td align="right">92</td>
+<td align="right">86</td>
+<td align="right">54</td>
+<td align="right">25</td>
+<td align="right">7</td>
+</tr>
+<tr class="even">
+<td align="left">3</td>
+<td align="right">4</td>
+<td align="right">7</td>
+<td align="right">4</td>
+<td align="right">8</td>
+<td align="right">3</td>
+<td align="right">2</td>
+<td align="right">1</td>
+</tr>
+</tbody>
+</table>
+<table>
+<caption>combinations by age - 2005</caption>
+<thead>
+<tr class="header">
+<th align="left"></th>
+<th align="right">(16,25]</th>
+<th align="right">(25,35]</th>
+<th align="right">(35,45]</th>
+<th align="right">(45,55]</th>
+<th align="right">(55,65]</th>
+<th align="right">(65,75]</th>
+<th align="right">(75,85]</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">0</td>
+<td align="right">181</td>
+<td align="right">377</td>
+<td align="right">426</td>
+<td align="right">367</td>
+<td align="right">384</td>
+<td align="right">304</td>
+<td align="right">265</td>
+</tr>
+<tr class="even">
+<td align="left">1</td>
+<td align="right">20</td>
+<td align="right">42</td>
+<td align="right">51</td>
+<td align="right">45</td>
+<td align="right">45</td>
+<td align="right">37</td>
+<td align="right">19</td>
+</tr>
+<tr class="odd">
+<td align="left">2</td>
+<td align="right">3</td>
+<td align="right">5</td>
+<td align="right">6</td>
+<td align="right">8</td>
+<td align="right">11</td>
+<td align="right">5</td>
+<td align="right">5</td>
+</tr>
+<tr class="even">
+<td align="left">3</td>
+<td align="right">0</td>
+<td align="right">0</td>
+<td align="right">0</td>
+<td align="right">0</td>
+<td align="right">1</td>
+<td align="right">0</td>
+<td align="right">0</td>
+</tr>
+</tbody>
+</table>
+<table>
+<thead>
+<tr class="header">
+<th align="left"></th>
+<th align="right">(16,25]</th>
+<th align="right">(25,35]</th>
+<th align="right">(35,45]</th>
+<th align="right">(45,55]</th>
+<th align="right">(55,65]</th>
+<th align="right">(65,75]</th>
+<th align="right">(75,85]</th>
+<th align="right">NA</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">0</td>
+<td align="right">2541</td>
+<td align="right">3753</td>
+<td align="right">3004</td>
+<td align="right">2480</td>
+<td align="right">1795</td>
+<td align="right">894</td>
+<td align="right">329</td>
+<td align="right">0</td>
+</tr>
+<tr class="even">
+<td align="left">1</td>
+<td align="right">675</td>
+<td align="right">1040</td>
+<td align="right">891</td>
+<td align="right">817</td>
+<td align="right">546</td>
+<td align="right">264</td>
+<td align="right">83</td>
+<td align="right">0</td>
+</tr>
+<tr class="odd">
+<td align="left">NA</td>
+<td align="right">0</td>
+<td align="right">0</td>
+<td align="right">0</td>
+<td align="right">0</td>
+<td align="right">0</td>
+<td align="right">0</td>
+<td align="right">0</td>
+<td align="right">0</td>
+</tr>
+</tbody>
+</table>
+<table>
+<thead>
+<tr class="header">
+<th align="left"></th>
+<th align="right">(16,25]</th>
+<th align="right">(25,35]</th>
+<th align="right">(35,45]</th>
+<th align="right">(45,55]</th>
+<th align="right">(55,65]</th>
+<th align="right">(65,75]</th>
+<th align="right">(75,85]</th>
+<th align="right">NA</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">0</td>
+<td align="right">170</td>
+<td align="right">348</td>
+<td align="right">397</td>
+<td align="right">338</td>
+<td align="right">353</td>
+<td align="right">277</td>
+<td align="right">250</td>
+<td align="right">0</td>
+</tr>
+<tr class="even">
+<td align="left">1</td>
+<td align="right">34</td>
+<td align="right">76</td>
+<td align="right">86</td>
+<td align="right">82</td>
+<td align="right">88</td>
+<td align="right">69</td>
+<td align="right">39</td>
+<td align="right">0</td>
+</tr>
+<tr class="odd">
+<td align="left">NA</td>
+<td align="right">0</td>
+<td align="right">0</td>
+<td align="right">0</td>
+<td align="right">0</td>
+<td align="right">0</td>
+<td align="right">0</td>
+<td align="right">0</td>
+<td align="right">0</td>
+</tr>
+</tbody>
+</table>
+<pre><code>## [1] "# <-- New model --> #"
+## [1] "Running logit model: ~"
+## [2] "Running logit model: Sunday_amd"
+## [3] "Running logit model: empstat + ba_age_r + ba_nchild"
+## [1] ""
+## [1] "# Model results"
+## term estimate std.error
+## (Intercept) (Intercept) -2.25106043 0.6626785
+## empstatnot in paid work empstatnot in paid work 0.07246519 0.4443794
+## empstatpart-time empstatpart-time 0.19530905 0.4716650
+## ba_age_r(25,35] ba_age_r(25,35] 0.27089076 0.7256474
+## ba_age_r(35,45] ba_age_r(35,45] 0.74329719 0.6912685
+## ba_age_r(45,55] ba_age_r(45,55] -0.49464112 0.7752806
+## ba_age_r(55,65] ba_age_r(55,65] -0.13008162 0.7380416
+## ba_age_r(65,75] ba_age_r(65,75] -0.18396754 0.8089391
+## ba_age_r(75,85] ba_age_r(75,85] -0.30962498 0.9199216
+## ba_nchild1 ba_nchild1 -0.04619857 0.5248261
+## ba_nchild2 ba_nchild2 -0.85050265 0.6145155
+## ba_nchild3+ ba_nchild3+ -0.39676938 0.8215772
+## statistic p.value 2.5 % 97.5 %
+## (Intercept) -3.39691200 0.0006815088 -3.7639820 -1.0883440
+## empstatnot in paid work 0.16307054 0.8704628949 -0.8203800 0.9343213
+## empstatpart-time 0.41408425 0.6788124055 -0.7746941 1.0959084
+## ba_age_r(25,35] 0.37330908 0.7089184203 -1.0742774 1.8629130
+## ba_age_r(35,45] 1.07526549 0.2822558835 -0.5048537 2.2902368
+## ba_age_r(45,55] -0.63801556 0.5234635508 -1.9885472 1.1605981
+## ba_age_r(55,65] -0.17625242 0.8600956370 -1.5018070 1.4814926
+## ba_age_r(65,75] -0.22741828 0.8200985059 -1.7185889 1.5380336
+## ba_age_r(75,85] -0.33657757 0.7364353634 -2.1704187 1.5634109
+## ba_nchild1 -0.08802643 0.9298556702 -1.1519717 0.9337535
+## ba_nchild2 -1.38402147 0.1663518809 -2.1880908 0.2735906
+## ba_nchild3+ -0.48293620 0.6291410392 -2.3337972 1.0396889
+## [1] "# Model summary"
+## null.deviance df.null logLik AIC BIC deviance df.residual
+## 1 301.9801 473 -147.6618 319.3237 369.2582 295.3237 462
+## *Pseudo R^2 for logistic regression
+## -> Hosmer and Lemeshow R^2 0.022
+## -> Cox and Snell R^2 0.014
+## -> Nagelkerke R^2 0.03
+## [1] ""
+## [1] "# Diagnostics: independence of errors"
+## lag Autocorrelation D-W Statistic p-value
+## 1 0.01472566 1.969515 0.724
+## Alternative hypothesis: rho != 0
+## [1] "# Diagnostics: collinearity (vif)"
+## GVIF Df GVIF^(1/(2*Df))
+## empstat 1.771673 2 1.153708
+## ba_age_r 2.360494 6 1.074196
+## ba_nchild 1.498813 3 1.069772
+## [1] "# Diagnostics: collinearity (tolerance)"
+## GVIF Df GVIF^(1/(2*Df))
+## empstat 0.5644383 0.5000000 0.8667705
+## ba_age_r 0.4236401 0.1666667 0.9309287
+## ba_nchild 0.6671946 0.3333333 0.9347786</code></pre>
+<pre><code>## [1] "# <-- New model --> #"
+## [1] "Running logit model: ~"
+## [2] "Running logit model: Sunday_amd"
+## [3] "Running logit model: empstat + ba_age_r + ba_nchild + ba_season"
+## [1] ""
+## [1] "# Model results"
+## term estimate std.error
+## (Intercept) (Intercept) -2.38412124 0.6805659
+## empstatnot in paid work empstatnot in paid work 0.07569039 0.4448707
+## empstatpart-time empstatpart-time 0.18734471 0.4714993
+## ba_age_r(25,35] ba_age_r(25,35] 0.27902683 0.7251250
+## ba_age_r(35,45] ba_age_r(35,45] 0.74599140 0.6900865
+## ba_age_r(45,55] ba_age_r(45,55] -0.50654472 0.7747391
+## ba_age_r(55,65] ba_age_r(55,65] -0.12684848 0.7388972
+## ba_age_r(65,75] ba_age_r(65,75] -0.20852085 0.8124010
+## ba_age_r(75,85] ba_age_r(75,85] -0.32579145 0.9221720
+## ba_nchild1 ba_nchild1 -0.06494171 0.5259901
+## ba_nchild2 ba_nchild2 -0.87154445 0.6149872
+## ba_nchild3+ ba_nchild3+ -0.33668148 0.8235682
+## ba_seasonSummer ba_seasonSummer 0.26973420 0.3169871
+## statistic p.value 2.5 % 97.5 %
+## (Intercept) -3.5031453 0.0004597985 -3.9224060 -1.1782015
+## empstatnot in paid work 0.1701402 0.8648998788 -0.8184432 0.9382740
+## empstatpart-time 0.3973382 0.6911180547 -0.7823939 1.0875456
+## ba_age_r(25,35] 0.3847983 0.7003868846 -1.0654174 1.8700050
+## ba_age_r(35,45] 1.0810114 0.2796920373 -0.4999787 2.2909249
+## ba_age_r(45,55] -0.6538262 0.5132237875 -1.9996363 1.1476382
+## ba_age_r(55,65] -0.1716727 0.8636948479 -1.5004011 1.4860238
+## ba_age_r(65,75] -0.2566723 0.7974317285 -1.7502570 1.5191441
+## ba_age_r(75,85] -0.3532871 0.7238732247 -2.1904471 1.5513886
+## ba_nchild1 -0.1234657 0.9017383664 -1.1732017 0.9169511
+## ba_nchild2 -1.4171749 0.1564317876 -2.2097758 0.2537567
+## ba_nchild3+ -0.4088083 0.6826803804 -2.2763818 1.1047165
+## ba_seasonSummer 0.8509310 0.3948076567 -0.3500234 0.8995334
+## [1] "# Model summary"
+## null.deviance df.null logLik AIC BIC deviance df.residual
+## 1 301.9801 473 -147.298 320.596 374.6917 294.596 461
+## *Pseudo R^2 for logistic regression
+## -> Hosmer and Lemeshow R^2 0.024
+## -> Cox and Snell R^2 0.015
+## -> Nagelkerke R^2 0.033
+## [1] ""
+## [1] "# Diagnostics: independence of errors"
+## lag Autocorrelation D-W Statistic p-value
+## 1 0.01323541 1.972475 0.674
+## Alternative hypothesis: rho != 0
+## [1] "# Diagnostics: collinearity (vif)"
+## GVIF Df GVIF^(1/(2*Df))
+## empstat 1.774903 2 1.154233
+## ba_age_r 2.368047 6 1.074482
+## ba_nchild 1.512619 3 1.071408
+## ba_season 1.020097 1 1.009999
+## [1] "# Diagnostics: collinearity (tolerance)"
+## GVIF Df GVIF^(1/(2*Df))
+## empstat 0.5634110 0.5000000 0.8663758
+## ba_age_r 0.4222889 0.1666667 0.9306809
+## ba_nchild 0.6611049 0.3333333 0.9333511
+## ba_season 0.9802986 1.0000000 0.9901003</code></pre>
+<pre><code>## [1] "# <-- New model --> #"
+## [1] "Running logit model: ~"
+## [2] "Running logit model: WeekdayEarly_amd"
+## [3] "Running logit model: empstat + ba_age_r + ba_nchild"
+## [1] ""
+## [1] "# Model results"
+## term estimate std.error
+## (Intercept) (Intercept) -1.59090022 0.4788073
+## empstatnot in paid work empstatnot in paid work 0.33705088 0.2968983
+## empstatpart-time empstatpart-time -0.06797079 0.3401498
+## ba_age_r(25,35] ba_age_r(25,35] 0.12183484 0.5167092
+## ba_age_r(35,45] ba_age_r(35,45] 0.09057922 0.5073915
+## ba_age_r(45,55] ba_age_r(45,55] 0.24343652 0.5201143
+## ba_age_r(55,65] ba_age_r(55,65] 0.62129148 0.5165602
+## ba_age_r(65,75] ba_age_r(65,75] -0.08302318 0.5750000
+## ba_age_r(75,85] ba_age_r(75,85] -0.25686325 0.6473666
+## ba_nchild1 ba_nchild1 -0.08999451 0.4282902
+## ba_nchild2 ba_nchild2 0.57880559 0.3882608
+## ba_nchild3+ ba_nchild3+ 0.42954649 0.5878169
+## statistic p.value 2.5 % 97.5 %
+## (Intercept) -3.3226313 0.0008917269 -2.6016972 -0.7031387
+## empstatnot in paid work 1.1352402 0.2562746740 -0.2483619 0.9183591
+## empstatpart-time -0.1998260 0.8416166394 -0.7535738 0.5859509
+## ba_age_r(25,35] 0.2357899 0.8135956672 -0.8635682 1.1858968
+## ba_age_r(35,45] 0.1785194 0.8583150820 -0.8740293 1.1390156
+## ba_age_r(45,55] 0.4680443 0.6397529052 -0.7450359 1.3165790
+## ba_age_r(55,65] 1.2027474 0.2290740885 -0.3537119 1.6923568
+## ba_age_r(65,75] -0.1443881 0.8851939747 -1.1881757 1.0868580
+## ba_age_r(75,85] -0.3967818 0.6915284081 -1.5379263 1.0293719
+## ba_nchild1 -0.2101251 0.8335700611 -0.9766954 0.7189866
+## ba_nchild2 1.4907650 0.1360232157 -0.1917540 1.3372626
+## ba_nchild3+ 0.7307487 0.4649326394 -0.8072768 1.5384047
+## [1] "# Model summary"
+## null.deviance df.null logLik AIC BIC deviance df.residual
+## 1 513.5908 473 -252.3306 528.6612 578.5957 504.6612 462
+## *Pseudo R^2 for logistic regression
+## -> Hosmer and Lemeshow R^2 0.017
+## -> Cox and Snell R^2 0.019
+## -> Nagelkerke R^2 0.028
+## [1] ""
+## [1] "# Diagnostics: independence of errors"
+## lag Autocorrelation D-W Statistic p-value
+## 1 0.02731998 1.939465 0.488
+## Alternative hypothesis: rho != 0
+## [1] "# Diagnostics: collinearity (vif)"
+## GVIF Df GVIF^(1/(2*Df))
+## empstat 1.609933 2 1.126424
+## ba_age_r 2.524795 6 1.080237
+## ba_nchild 1.729993 3 1.095656
+## [1] "# Diagnostics: collinearity (tolerance)"
+## GVIF Df GVIF^(1/(2*Df))
+## empstat 0.6211439 0.5000000 0.8877651
+## ba_age_r 0.3960717 0.1666667 0.9257232
+## ba_nchild 0.5780369 0.3333333 0.9126955</code></pre>
+<pre><code>## [1] "# <-- New model --> #"
+## [1] "Running logit model: ~"
+## [2] "Running logit model: Weekday_amd"
+## [3] "Running logit model: empstat + ba_age_r + ba_nchild"
+## [1] ""
+## [1] "# Model results"
+## term estimate std.error
+## (Intercept) (Intercept) -0.47153375 0.4209208
+## empstatnot in paid work empstatnot in paid work -0.66351274 0.3284628
+## empstatpart-time empstatpart-time -0.41958118 0.3386955
+## ba_age_r(25,35] ba_age_r(25,35] -0.77133255 0.4750686
+## ba_age_r(35,45] ba_age_r(35,45] -0.61933109 0.4616950
+## ba_age_r(45,55] ba_age_r(45,55] -0.56788309 0.4694252
+## ba_age_r(55,65] ba_age_r(55,65] -0.64183217 0.4886030
+## ba_age_r(65,75] ba_age_r(65,75] 0.14522591 0.5243426
+## ba_age_r(75,85] ba_age_r(75,85] 0.31759362 0.5745195
+## ba_nchild1 ba_nchild1 -0.07815546 0.4112193
+## ba_nchild2 ba_nchild2 0.43645699 0.3843440
+## ba_nchild3+ ba_nchild3+ -0.30661730 0.6808297
+## statistic p.value 2.5 % 97.5 %
+## (Intercept) -1.1202433 0.26261009 -1.3263236 0.33836969
+## empstatnot in paid work -2.0200544 0.04337775 -1.3246801 -0.03257784
+## empstatpart-time -1.2388153 0.21541391 -1.1046658 0.22892615
+## ba_age_r(25,35] -1.6236234 0.10445621 -1.7035084 0.17215124
+## ba_age_r(35,45] -1.3414291 0.17978118 -1.5209674 0.30187884
+## ba_age_r(45,55] -1.2097414 0.22637814 -1.4836746 0.36913057
+## ba_age_r(55,65] -1.3136068 0.18897856 -1.5960304 0.33201414
+## ba_age_r(65,75] 0.2769676 0.78180499 -0.8712554 1.19481144
+## ba_age_r(75,85] 0.5527987 0.58040125 -0.8061612 1.45809636
+## ba_nchild1 -0.1900578 0.84926380 -0.9249853 0.70101902
+## ba_nchild2 1.1355894 0.25612845 -0.3288492 1.18509012
+## ba_nchild3+ -0.4503583 0.65245213 -1.8369630 0.91969882
+## [1] "# Model summary"
+## null.deviance df.null logLik AIC BIC deviance df.residual
+## 1 520.665 473 -254.9798 533.9595 583.894 509.9595 462
+## *Pseudo R^2 for logistic regression
+## -> Hosmer and Lemeshow R^2 0.021
+## -> Cox and Snell R^2 0.022
+## -> Nagelkerke R^2 0.034
+## [1] ""
+## [1] "# Diagnostics: independence of errors"
+## lag Autocorrelation D-W Statistic p-value
+## 1 0.04875728 1.893013 0.202
+## Alternative hypothesis: rho != 0
+## [1] "# Diagnostics: collinearity (vif)"
+## GVIF Df GVIF^(1/(2*Df))
+## empstat 2.030107 2 1.193658
+## ba_age_r 2.858071 6 1.091456
+## ba_nchild 1.581850 3 1.079429
+## [1] "# Diagnostics: collinearity (tolerance)"
+## GVIF Df GVIF^(1/(2*Df))
+## empstat 0.4925848 0.5000000 0.8377612
+## ba_age_r 0.3498863 0.1666667 0.9162076
+## ba_nchild 0.6321711 0.3333333 0.9264154</code></pre>
+<pre><code>## [1] "# <-- New model --> #"
+## [1] "Running logit model: ~"
+## [2] "Running logit model: Weekday_pmd"
+## [3] "Running logit model: empstat + ba_age_r + ba_nchild"
+## [1] ""
+## [1] "# Model results"
+## term estimate std.error
+## (Intercept) (Intercept) -1.610307347 0.5421485
+## empstatnot in paid work empstatnot in paid work 0.096866852 0.3347478
+## empstatpart-time empstatpart-time -0.707022008 0.4355114
+## ba_age_r(25,35] ba_age_r(25,35] -0.127072651 0.6022041
+## ba_age_r(35,45] ba_age_r(35,45] -0.507283327 0.6099009
+## ba_age_r(45,55] ba_age_r(45,55] 0.546103516 0.5774865
+## ba_age_r(55,65] ba_age_r(55,65] 0.415731774 0.5902036
+## ba_age_r(65,75] ba_age_r(65,75] -0.465465943 0.6783961
+## ba_age_r(75,85] ba_age_r(75,85] 0.009440469 0.7045892
+## ba_nchild1 ba_nchild1 -0.980651054 0.6390646
+## ba_nchild2 ba_nchild2 0.323016053 0.4683272
+## ba_nchild3+ ba_nchild3+ 0.991064404 0.6119478
+## statistic p.value 2.5 % 97.5 %
+## (Intercept) -2.97023323 0.002975737 -2.7839177 -0.6214222
+## empstatnot in paid work 0.28937260 0.772296258 -0.5676561 0.7492618
+## empstatpart-time -1.62342953 0.104497617 -1.6212012 0.1050555
+## ba_age_r(25,35] -0.21101260 0.832877440 -1.2736698 1.1297847
+## ba_age_r(35,45] -0.83174713 0.405551693 -1.6769086 0.7589981
+## ba_age_r(45,55] 0.94565591 0.344324118 -0.5298530 1.7716354
+## ba_age_r(55,65] 0.70438699 0.481191804 -0.6854154 1.6646477
+## ba_age_r(65,75] -0.68612711 0.492632956 -1.7751687 0.9242429
+## ba_age_r(75,85] 0.01339854 0.989309830 -1.3600261 1.4429049
+## ba_nchild1 -1.53451010 0.124904210 -2.4543147 0.1374739
+## ba_nchild2 0.68972299 0.490368402 -0.6285273 1.2256043
+## ba_nchild3+ 1.61952455 0.105334450 -0.2791610 2.1632835
+## [1] "# Model summary"
+## null.deviance df.null logLik AIC BIC deviance df.residual
+## 1 427.132 473 -204.5332 433.0663 483.0008 409.0663 462
+## *Pseudo R^2 for logistic regression
+## -> Hosmer and Lemeshow R^2 0.042
+## -> Cox and Snell R^2 0.037
+## -> Nagelkerke R^2 0.063
+## [1] ""
+## [1] "# Diagnostics: independence of errors"
+## lag Autocorrelation D-W Statistic p-value
+## 1 0.1097293 1.778341 0.018
+## Alternative hypothesis: rho != 0
+## [1] "# Diagnostics: collinearity (vif)"
+## GVIF Df GVIF^(1/(2*Df))
+## empstat 1.641619 2 1.131926
+## ba_age_r 2.496787 6 1.079233
+## ba_nchild 1.640211 3 1.085967
+## [1] "# Diagnostics: collinearity (tolerance)"
+## GVIF Df GVIF^(1/(2*Df))
+## empstat 0.6091549 0.5000000 0.8834499
+## ba_age_r 0.4005148 0.1666667 0.9265842
+## ba_nchild 0.6096775 0.3333333 0.9208383</code></pre>
+<pre><code>## [1] "# <-- New model --> #"
+## [1] "Running linear model: ~"
+## [2] "Running linear model: laundry_sum"
+## [3] "Running linear model: empstat + ba_age_r + ba_nchild"
+## [1] "# Model results"
+## term estimate std.error
+## (Intercept) (Intercept) 0.80422583 0.11971777
+## empstatnot in paid work empstatnot in paid work -0.02405294 0.08119904
+## empstatpart-time empstatpart-time -0.14734215 0.08765235
+## ba_age_r(25,35] ba_age_r(25,35] -0.11301074 0.13088358
+## ba_age_r(35,45] ba_age_r(35,45] -0.08452151 0.12885828
+## ba_age_r(45,55] ba_age_r(45,55] -0.02692084 0.13107222
+## ba_age_r(55,65] ba_age_r(55,65] 0.03742763 0.13309842
+## ba_age_r(65,75] ba_age_r(65,75] -0.08829267 0.14545467
+## ba_age_r(75,85] ba_age_r(75,85] -0.03342188 0.16099247
+## ba_nchild1 ba_nchild1 -0.11586368 0.10521064
+## ba_nchild2 ba_nchild2 0.14831185 0.10531327
+## ba_nchild3+ ba_nchild3+ 0.14152604 0.16078729
+## statistic p.value 2.5 % 97.5 %
+## (Intercept) 6.7176812 5.443781e-11 0.56896700 1.03948467
+## empstatnot in paid work -0.2962220 7.671936e-01 -0.18361815 0.13551226
+## empstatpart-time -1.6809835 9.344198e-02 -0.31958883 0.02490453
+## ba_age_r(25,35] -0.8634448 3.883410e-01 -0.37021164 0.14419015
+## ba_age_r(35,45] -0.6559261 5.121982e-01 -0.33774246 0.16869945
+## ba_age_r(45,55] -0.2053894 8.373584e-01 -0.28449243 0.23065075
+## ba_age_r(55,65] 0.2812027 7.786808e-01 -0.22412567 0.29898094
+## ba_age_r(65,75] -0.6070116 5.441414e-01 -0.37412738 0.19754205
+## ba_age_r(75,85] -0.2075990 8.356335e-01 -0.34979013 0.28294637
+## ba_nchild1 -1.1012543 2.713594e-01 -0.32261437 0.09088702
+## ba_nchild2 1.4082921 1.597172e-01 -0.05864053 0.35526424
+## ba_nchild3+ 0.8802067 3.792050e-01 -0.17443899 0.45749108
+## r.squared adj.r.squared sigma statistic p.value df logLik
+## 1 0.02038023 -0.002944048 0.62548 0.8737775 0.5662077 12 -444.0818
+## AIC BIC deviance df.residual
+## 1 914.1635 968.2592 180.746 462
+## [1] "# Diagnostics: independence of errors"
+## lag Autocorrelation D-W Statistic p-value
+## 1 -0.03270472 2.058046 0.462
+## Alternative hypothesis: rho != 0
+## [1] "# Diagnostics: collinearity (vif)"
+## GVIF Df GVIF^(1/(2*Df))
+## empstat 1.761297 2 1.152015
+## ba_age_r 2.552027 6 1.081203
+## ba_nchild 1.606628 3 1.082229
+## [1] "# Diagnostics: collinearity (tolerance)"
+## GVIF Df GVIF^(1/(2*Df))
+## empstat 0.5677635 0.5000000 0.8680443
+## ba_age_r 0.3918454 0.1666667 0.9248960
+## ba_nchild 0.6224217 0.3333333 0.9240187</code></pre>
+<pre><code>## [1] "% in work in 1985"</code></pre>
+<pre><code>## sex empstatfull-time empstatnot in paid work empstatpart-time
+## Man Man 0.6728953 0.1735609 0.1162203
+## Woman Woman 0.2192669 0.4153912 0.3509855
+## empstatunknown job hours se.empstatfull-time
+## Man 0.03732346 0.005954984
+## Woman 0.01435640 0.004497221
+## se.empstatnot in paid work se.empstatpart-time
+## Man 0.004829776 0.004007465
+## Woman 0.005441926 0.005278946
+## se.empstatunknown job hours
+## Man 0.002441382
+## Woman 0.001301152</code></pre>
+<pre><code>## [1] "% in work in 2005"</code></pre>
+<pre><code>## sex empstatfull-time empstatnot in paid work empstatpart-time
+## Man Man 0.8059294 0.1255392 0.0634378
+## Woman Woman 0.4112487 0.2871118 0.3002517
+## empstatunknown job hours se.empstatfull-time
+## Man 0.005093638 0.01514926
+## Woman 0.001387849 0.01690216
+## se.empstatnot in paid work se.empstatpart-time
+## Man 0.01255103 0.009400211
+## Woman 0.01539688 0.015558580
+## se.empstatunknown job hours
+## Man 0.003451856
+## Woman 0.001387000</code></pre>
+<pre><code>## [1] "% people who report laundry in 1985"</code></pre>
+<pre><code>## mean SE
+## Any_laundry_homed 0.2237 0.0031</code></pre>
+<pre><code>## [1] "% people who report laundry in 2005"</code></pre>
+<pre><code>## mean SE
+## Any_laundry_homed 0.18156 0.008</code></pre>
+<pre><code>## [1] "Number of people reporting different number of laundry habits"</code></pre>
+<pre><code>## laundry_sum
+## 0 1 2 3
+## 2264.303512 256.275619 42.916280 1.112071</code></pre>
+<pre><code>## [1] "1985: N of respondents who reported laundry of given type"</code></pre>
+<pre><code>## total SE
+## Sunday_amd 305.83 18.142
+## WeekdayEarly_amd 581.85 24.708
+## Weekday_amd 1278.56 36.003
+## Weekday_pmd 812.29 28.954
+## Otherd 2871.44 51.689
+## Any_laundry_homed 4290.52 60.576</code></pre>
+<pre><code>## [1] "2005: N of respondents who reported laundry of given type"</code></pre>
+<pre><code>## total SE
+## Sunday_amd 44.171 6.7883
+## WeekdayEarly_amd 108.140 10.7480
+## Weekday_amd 113.794 11.1856
+## Weekday_pmd 79.339 9.2491
+## Otherd 256.672 15.9396
+## Any_laundry_homed 465.626 20.7243</code></pre>
+<pre><code>## [1] "Feedback: Done analysing sampled file using survey methods"</code></pre>
+<p>Finished</p>
+</div>
+</div>
+
+
+
+
+</div>
+
+<script>
+
+// add bootstrap table styles to pandoc tables
+$(document).ready(function () {
+ $('tr.header').parent('thead').parent('table').addClass('table table-condensed');
+});
+
+</script>
+
+<!-- dynamically load mathjax for compatibility with self-contained -->
+<script>
+ (function () {
+ var script = document.createElement("script");
+ script.type = "text/javascript";
+ script.src = "https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
+ document.getElementsByTagName("head")[0].appendChild(script);
+ })();
+</script>
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+</html>
diff --git a/Theme-1/laundryPaper/DEMAND-BA-MTUS-W6-Laundry-Change-Over-Time-v2.0-adult-analysis.md b/Theme-1/laundryPaper/DEMAND-BA-MTUS-W6-Laundry-Change-Over-Time-v2.0-adult-analysis.md
new file mode 100644
index 0000000000000000000000000000000000000000..940040a03c2de9d30af911ed33e7d588acaf98d2
--- /dev/null
+++ b/Theme-1/laundryPaper/DEMAND-BA-MTUS-W6-Laundry-Change-Over-Time-v2.0-adult-analysis.md
@@ -0,0 +1,3410 @@
+# Laundry, Energy & Time (ER&SS submission)
+Ben Anderson (b.anderson@soton.ac.uk/@dataknut) [Energy & Climate Change, Faculty of Engineering & Environment, University of Southampton]
+Last run at: `r Sys.time()`
+
+
+
+
+# Data analysis for 'Laundry' paper:
+
+Use MTUS World 6 time-use data (UK subset) to examine:
+ * distributions of laundry in 1985 & 2005
+ * changing laundry practice
+ * data source: www.timeuse.org/mtus
+ * data already in long format (but episodes) processed using DEMAND-BA-MTUS-W6-Laundry-Change-Over-Time-v2.0-adult-data-processing.R
+
+Uses FES/EFS/LCFS to examine:
+ * historical ownership of washing machines/tumble dryers
+ * Data source: http://discover.ukdataservice.ac.uk/series/?sn=200016
+
+Uses SPRG water practices survey:
+ * reported laundry practices
+ * data source: http://www.sprg.ac.uk/projects-fellowships/patterns-of-water
+
+This work was funded by RCUK through the End User Energy Demand Centres Programme via the
+"DEMAND: Dynamics of Energy, Mobility and Demand" Centre (www.demand.ac.uk, gow.epsrc.ac.uk/NGBOViewGrant.aspx?GrantRef=EP/K011723/1)
+
+> This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License (http://choosealicense.com/licenses/gpl-2.0/), or (at your option) any later version.
+
+> This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
+
+
+# MTUS codes of interest:
+
+1983/4/7: Main/Sec21 Laundry, ironing, clothing repair
+ * 0701 Wash clothes, hang out / bring in washing
+ * 0702 Iron clothes
+ * 0801 Repair, upkeep of clothes
+so may over-estimate laundry
+
+2005: Main/Sec21 Laundry, ironing, clothing repair
+ * Pact=7 (washing clothes)
+
+
+
+
+```
+## [1] "Feedback: Loading ~/Data/Family Expenditure Survey/1985/stata8//hchars.dta"
+```
+
+```
+## [1] "Feedback: Loading ~/Data/Expenditure and Food Survey/2004-2005/stata/dvhh.dta"
+```
+
+
+```
+## [1] "Loading via gunzip -c ~/Data/MTUS/World_6/processed/MTUSW6UKsurveyCore_DT.csv.gz"
+```
+
+```
+## [1] "Feedback: Done loading TU survey data"
+```
+
+
+```
+## [1] "Loading via gunzip -c ~/Data/MTUS/World_6/processed/MTUSW6UKdiaryEps_DT.csv.gz"
+```
+
+```
+##
+Read 60.8% of 1364047 rows
+Read 1364047 rows and 17 (of 17) columns from 0.299 GB file in 00:00:03
+```
+
+```
+## [1] "Feedback: Done loading TU episodes data"
+```
+
+
+```
+## [1] "Loading via gunzip -c ~/Data/MTUS/World_6/processed/MTUSW6UKdiarySampled_DT.csv.gz"
+```
+
+```
+##
+Read 0.0% of 8164512 rows
+Read 9.8% of 8164512 rows
+Read 20.3% of 8164512 rows
+Read 30.1% of 8164512 rows
+Read 38.6% of 8164512 rows
+Read 46.5% of 8164512 rows
+Read 56.1% of 8164512 rows
+Read 65.4% of 8164512 rows
+Read 74.6% of 8164512 rows
+Read 81.9% of 8164512 rows
+Read 89.8% of 8164512 rows
+Read 98.1% of 8164512 rows
+Read 8164512 rows and 21 (of 21) columns from 1.412 GB file in 00:00:17
+```
+
+```
+## [1] "Feedback: Done loading TU sampled data"
+```
+
+```
+## [1] "Loading via gunzip -c ~/Data/MTUS/World_6/processed/MTUSW6UK_halfhours_laundry_DT.csv.gz"
+```
+
+```
+##
+Read 63.2% of 2721504 rows
+Read 2721504 rows and 20 (of 20) columns from 0.190 GB file in 00:00:03
+```
+
+```
+## [1] "Feedback: Done loading TU sampled laundry data aggregated to half hour level"
+```
+
+# Analysing loaded data
+
+## EFS/FES data
+
+```
+##
+## not recorded washing machine
+## 0.1699943 0.8300057
+```
+
+```
+## [1] "Feedback: Loading ~/Data/Family Expenditure Survey/1985/stata8//hchars.dta"
+```
+
+```
+##
+## not recorded washing machine
+## 0.1699943 0.8300057
+```
+
+```
+##
+## washing machine no washing machine present
+## 0.95013239 0.04986761
+```
+
+```
+##
+## tumble drier tumble drier not present
+## 0.5854663 0.4145337
+```
+
+## MTUS episodes
+
+```
+## [1] "Feedback: Writing results to ~/Dropbox/Work/DraftPapers/The Time and Timing of Demand - Laundry/results/"
+```
+
+
+
+Table: n episodes in total per year
+
+ba_survey N
+---------- -------
+1974 301586
+1985 474096
+1995 28165
+2000 464164
+2005 96036
+
+```
+## ba_survey
+## laundry_p 1974 1985 1995 2000 2005
+## 0 300350 466018 27647 454161 94660
+## 1 1236 8078 518 10003 1376
+```
+
+```
+## ba_survey
+## laundry_s 1974 1985 1995 2000 2005
+## 0 300936 471850 28165 462685 95898
+## 1 650 2246 0 1479 138
+```
+
+```
+## ba_survey
+## laundry_all 1974 1985 1995 2000 2005
+## 0 299700 463889 27647 452745 94522
+## 1 1886 10207 518 11419 1514
+```
+
+```
+## [1] "Feedback: # n episodes by duration - to show how recording period varies things"
+```
+
+```
+## ba_survey
+## time 1974 1985 1995 2000 2005
+## 5 0 5833 0 0 0
+## 10 29987 6853 0 186507 20482
+## 15 0 197190 5129 0 0
+## 20 17382 2673 0 74010 14714
+## 25 0 612 0 0 0
+## 30 146905 81790 7060 52123 17602
+## 35 0 285 0 0 0
+## 40 223 1023 0 29261 4769
+## 45 0 43099 1721 0 0
+## 50 4557 599 0 19905 3442
+## 55 0 124 0 0 0
+## 60 30764 27370 3857 16775 7463
+## 65 0 65 0 0 0
+## 70 20 246 0 9689 2002
+## 75 0 16343 577 0 0
+## 80 1845 227 0 6852 1422
+## 85 0 27 0 0 0
+## 90 11858 11943 1178 6088 2915
+## 95 0 17 0 0 0
+## 100 18 114 0 4333 1013
+## 105 0 9370 416 0 0
+## 110 1024 74 0 4138 963
+## 115 0 1 0 0 0
+## 120 7881 8490 1406 4479 2684
+## 125 0 3 0 0 0
+## 130 10 55 0 2893 711
+## 135 0 6413 319 0 0
+## 140 748 34 0 2422 624
+## 150 6754 5342 693 2754 1556
+## 155 0 1 0 0 0
+## 160 15 30 0 2054 525
+## 165 0 5456 244 0 0
+## 170 819 22 0 2087 558
+## 175 0 3 0 0 0
+## 180 7354 4919 963 3877 1826
+## 190 27 8 0 1702 517
+## 195 0 4256 191 0 0
+## 200 924 7 0 1732 399
+## 210 7437 3824 606 2274 1332
+## 220 21 4 0 1649 345
+## 225 0 4103 211 0 0
+## 230 811 1 0 1878 356
+## 240 7255 4178 815 2910 1339
+## 250 6 3 0 1750 373
+## 255 0 3372 156 0 0
+## 260 578 2 0 1570 313
+## 270 5926 2852 511 1999 979
+## 280 5 1 0 1466 273
+## 285 0 3105 150 0 0
+## 290 357 0 0 1483 346
+## 300 3931 3005 636 2437 1150
+## 310 2 1 0 1357 249
+## 315 0 2138 119 0 0
+## 320 193 1 0 1025 225
+## 330 1887 1549 308 1276 646
+## 340 1 1 0 756 159
+## 345 0 1230 78 0 0
+## 350 152 0 0 633 177
+## 360 986 1065 340 1741 598
+## 370 2 0 0 850 78
+## 375 0 598 42 0 0
+## 380 90 0 0 320 62
+## 390 494 343 88 399 178
+## 400 1 0 0 229 38
+## 405 0 276 21 0 0
+## 410 60 0 0 206 50
+## 420 323 226 84 325 154
+## 430 5 0 0 176 31
+## 435 0 149 12 0 0
+## 440 49 0 0 99 19
+## 450 240 119 37 146 49
+## 460 0 0 0 111 18
+## 465 0 94 13 0 0
+## 470 43 0 0 91 14
+## 480 386 194 40 175 74
+## 490 1 0 0 127 15
+## 495 0 103 10 0 0
+## 500 46 0 0 71 17
+## 510 372 77 21 97 35
+## 520 0 0 0 78 13
+## 525 0 83 6 0 0
+## 530 56 0 0 65 6
+## 540 258 88 21 110 36
+## 550 0 0 0 81 6
+## 555 0 70 8 0 0
+## 560 49 0 0 55 11
+## 570 116 45 12 41 19
+## 580 0 0 0 39 5
+## 585 0 31 6 0 0
+## 590 16 0 0 34 7
+## 600 83 36 23 60 14
+## 610 0 0 0 38 7
+## 615 0 28 0 0 0
+## 620 14 0 0 21 4
+## 630 52 18 5 28 8
+## 640 0 0 0 19 1
+## 645 0 17 3 0 0
+## 650 24 0 0 17 0
+## 660 36 18 5 21 3
+## 670 0 0 0 18 1
+## 675 0 17 1 0 0
+## 680 8 0 0 9 2
+## 690 26 9 5 9 3
+## 700 0 0 0 7 1
+## 705 0 9 0 0 0
+## 710 8 0 0 10 0
+## 720 25 9 6 27 4
+## 730 0 0 0 9 1
+## 735 0 9 1 0 0
+## 740 3 0 0 7 3
+## 750 2 8 1 5 1
+## 760 0 0 0 3 0
+## 765 0 4 2 0 0
+## 770 1 0 0 3 0
+## 780 11 5 1 11 0
+## 790 0 0 0 4 0
+## 795 0 1 0 0 0
+## 800 0 0 0 5 0
+## 810 2 5 2 0 1
+## 820 0 0 0 3 0
+## 825 0 2 0 0 0
+## 830 0 0 0 2 0
+## 840 5 1 0 3 0
+## 850 0 0 0 1 0
+## 855 0 2 0 0 0
+## 860 2 0 0 2 0
+## 870 3 3 1 1 0
+## 880 0 0 0 1 0
+## 885 0 2 0 0 0
+## 890 1 0 0 2 0
+## 900 0 4 1 1 0
+## 915 0 2 0 0 0
+## 930 2 2 1 1 0
+## 945 0 3 0 0 0
+## 960 2 10 1 1 0
+## 975 0 7 0 0 0
+## 980 1 0 0 0 0
+## 990 0 3 0 0 0
+## 1000 0 0 0 1 0
+## 1005 0 2 0 0 0
+## 1010 0 0 0 1 0
+## 1020 2 4 0 0 0
+## 1050 1 2 0 1 0
+## 1065 0 1 0 0 0
+## 1070 0 0 0 1 0
+## 1080 2 1 1 1 0
+## 1095 0 1 0 0 0
+## 1125 0 1 0 0 0
+## 1140 0 1 0 0 0
+## 1170 0 1 0 0 0
+```
+
+```
+## [1] "Feedback: # n episodes of laundry as a primary act by duration (to show how recording period varies things)"
+```
+
+```
+## ba_survey
+## time 1974 1985 1995 2000 2005
+## 5 0 73 0 0 0
+## 10 0 81 0 5347 324
+## 15 0 3865 91 0 0
+## 20 51 27 0 1815 209
+## 25 0 4 0 0 0
+## 30 859 1512 152 961 283
+## 35 0 3 0 0 0
+## 40 0 11 0 550 79
+## 45 0 893 39 0 0
+## 50 14 4 0 415 67
+## 55 0 0 0 0 0
+## 60 173 659 124 332 175
+## 65 0 0 0 0 0
+## 70 0 4 0 180 41
+## 75 0 371 15 0 0
+## 80 4 1 0 127 24
+## 85 0 0 0 0 0
+## 90 68 213 22 84 64
+## 95 0 0 0 0 0
+## 100 0 0 0 58 13
+## 105 0 144 3 0 0
+## 110 0 0 0 40 16
+## 115 0 0 0 0 0
+## 120 33 85 37 31 30
+## 125 0 0 0 0 0
+## 130 0 0 0 19 3
+## 135 0 52 3 0 0
+## 140 1 1 0 12 6
+## 150 12 24 8 9 14
+## 155 0 0 0 0 0
+## 160 0 0 0 8 3
+## 165 0 16 1 0 0
+## 170 1 0 0 3 5
+## 175 0 0 0 0 0
+## 180 6 14 8 5 8
+## 190 0 0 0 2 1
+## 195 0 7 2 0 0
+## 200 0 0 0 0 0
+## 210 5 4 8 1 7
+## 220 0 0 0 2 0
+## 225 0 4 0 0 0
+## 230 0 0 0 0 1
+## 240 3 3 4 0 1
+## 250 0 0 0 1 0
+## 255 0 2 0 0 0
+## 260 0 0 0 0 0
+## 270 2 0 1 1 1
+## 280 0 0 0 0 0
+## 285 0 0 0 0 0
+## 290 0 0 0 0 0
+## 300 4 1 0 0 0
+## 310 0 0 0 0 0
+## 315 0 0 0 0 0
+## 320 0 0 0 0 0
+## 330 0 0 0 0 0
+## 340 0 0 0 0 0
+## 345 0 0 0 0 0
+## 350 0 0 0 0 0
+## 360 0 0 0 0 0
+## 370 0 0 0 0 0
+## 375 0 0 0 0 0
+## 380 0 0 0 0 0
+## 390 0 0 0 0 0
+## 400 0 0 0 0 0
+## 405 0 0 0 0 0
+## 410 0 0 0 0 0
+## 420 0 0 0 0 0
+## 430 0 0 0 0 0
+## 435 0 0 0 0 0
+## 440 0 0 0 0 0
+## 450 0 0 0 0 1
+## 460 0 0 0 0 0
+## 465 0 0 0 0 0
+## 470 0 0 0 0 0
+## 480 0 0 0 0 0
+## 490 0 0 0 0 0
+## 495 0 0 0 0 0
+## 500 0 0 0 0 0
+## 510 0 0 0 0 0
+## 520 0 0 0 0 0
+## 525 0 0 0 0 0
+## 530 0 0 0 0 0
+## 540 0 0 0 0 0
+## 550 0 0 0 0 0
+## 555 0 0 0 0 0
+## 560 0 0 0 0 0
+## 570 0 0 0 0 0
+## 580 0 0 0 0 0
+## 585 0 0 0 0 0
+## 590 0 0 0 0 0
+## 600 0 0 0 0 0
+## 610 0 0 0 0 0
+## 615 0 0 0 0 0
+## 620 0 0 0 0 0
+## 630 0 0 0 0 0
+## 640 0 0 0 0 0
+## 645 0 0 0 0 0
+## 650 0 0 0 0 0
+## 660 0 0 0 0 0
+## 670 0 0 0 0 0
+## 675 0 0 0 0 0
+## 680 0 0 0 0 0
+## 690 0 0 0 0 0
+## 700 0 0 0 0 0
+## 705 0 0 0 0 0
+## 710 0 0 0 0 0
+## 720 0 0 0 0 0
+## 730 0 0 0 0 0
+## 735 0 0 0 0 0
+## 740 0 0 0 0 0
+## 750 0 0 0 0 0
+## 760 0 0 0 0 0
+## 765 0 0 0 0 0
+## 770 0 0 0 0 0
+## 780 0 0 0 0 0
+## 790 0 0 0 0 0
+## 795 0 0 0 0 0
+## 800 0 0 0 0 0
+## 810 0 0 0 0 0
+## 820 0 0 0 0 0
+## 825 0 0 0 0 0
+## 830 0 0 0 0 0
+## 840 0 0 0 0 0
+## 850 0 0 0 0 0
+## 855 0 0 0 0 0
+## 860 0 0 0 0 0
+## 870 0 0 0 0 0
+## 880 0 0 0 0 0
+## 885 0 0 0 0 0
+## 890 0 0 0 0 0
+## 900 0 0 0 0 0
+## 915 0 0 0 0 0
+## 930 0 0 0 0 0
+## 945 0 0 0 0 0
+## 960 0 0 0 0 0
+## 975 0 0 0 0 0
+## 980 0 0 0 0 0
+## 990 0 0 0 0 0
+## 1000 0 0 0 0 0
+## 1005 0 0 0 0 0
+## 1010 0 0 0 0 0
+## 1020 0 0 0 0 0
+## 1050 0 0 0 0 0
+## 1065 0 0 0 0 0
+## 1070 0 0 0 0 0
+## 1080 0 0 0 0 0
+## 1095 0 0 0 0 0
+## 1125 0 0 0 0 0
+## 1140 0 0 0 0 0
+## 1170 0 0 0 0 0
+```
+
+```
+## [1] "% episodes that are laundry_all in 1985 = 2.15293948904863"
+```
+
+```
+## [1] "% episodes that are laundry_all in 2005 = 1.57649214877754"
+```
+
+```
+## [1] "Feedback: Plotting 1985 all episodes start"
+```
+
+<!-- -->
+
+```
+## [1] "Feedback: Plotting 1985 laundry episodes start"
+```
+
+<!-- -->
+
+```
+## [1] "Feedback: Plotting 2005 all episodes start"
+```
+
+<!-- -->
+
+```
+## [1] "Feedback: Plotting 2005 laundry episodes start"
+```
+
+<!-- --><!-- -->
+
+```
+## [1] "Feedback: Saving episodes results into: ~/Dropbox/Work/DraftPapers/The Time and Timing of Demand - Laundry/results/"
+```
+
+
+
+Table: Main acts before laundry in 1985
+
+V1 N
+---------------------------------- ------
+laundry, ironing, clothing repair 16.07
+cleaning 10.72
+meals or snacks in other places 10.66
+set table, wash/put away dishes 10.32
+food preparation, cooking 8.60
+wash, dress, care for self 7.02
+
+
+
+Table: Main acts after laundry in 1985
+
+V1 N
+---------------------------------- ------
+laundry, ironing, clothing repair 16.07
+cleaning 10.72
+meals or snacks in other places 10.66
+set table, wash/put away dishes 10.32
+food preparation, cooking 8.60
+wash, dress, care for self 7.02
+
+
+
+Table: Main acts after laundry in 2005
+
+V1 N
+------------------------------------ ------
+meals or snacks in other places 20.64
+cleaning 13.23
+food preparation, cooking 12.14
+wash, dress, care for self 8.07
+laundry, ironing, clothing repair 6.69
+watch TV, video, DVD, streamed film 5.89
+
+
+
+Table: Main acts after laundry in 2005
+
+V1 N
+------------------------------------ ------
+food preparation, cooking 16.42
+cleaning 12.35
+watch TV, video, DVD, streamed film 10.17
+meals or snacks in other places 7.49
+laundry, ironing, clothing repair 6.69
+wash, dress, care for self 6.69
+
+
+
+Table: Main acts after early weekday morning laundry in 2005
+
+V1 N
+---------------------------------- ------
+cleaning 21.78
+food preparation, cooking 12.38
+wash, dress, care for self 7.92
+laundry, ironing, clothing repair 7.43
+travel to/from work 7.43
+meals or snacks in other places 6.93
+
+
+
+Table: Main acts 2 after early weekday morning laundry in 2005
+
+V1 N
+---------------------------------- ------
+food preparation, cooking 10.89
+meals or snacks in other places 9.90
+laundry, ironing, clothing repair 9.41
+paid work-main job (not at home) 8.42
+cleaning 7.43
+other travel 5.94
+
+
+
+Table: Main acts 3 after early weekday morning laundry in 2005
+
+V1 N
+--------------------------------- ------
+shop, person/hhld care travel 10.40
+food preparation, cooking 9.41
+meals or snacks in other places 8.91
+other travel 8.91
+purchase goods 8.42
+paid work-main job (not at home) 7.43
+
+
+
+Table: Main acts before evening peak weekday laundry in 2005
+
+V1 N
+------------------------------------ ------
+meals or snacks in other places 32.16
+food preparation, cooking 11.70
+cleaning 8.77
+physical, medical child care 7.02
+watch TV, video, DVD, streamed film 7.02
+other travel 5.85
+
+
+
+Table: Main acts before evening peak weekday laundry in 2005
+
+V1 N
+------------------------------------ ------
+watch TV, video, DVD, streamed film 25.15
+food preparation, cooking 15.79
+meals or snacks in other places 9.36
+cleaning 7.02
+wash, dress, care for self 6.43
+laundry, ironing, clothing repair 5.85
+
+```
+## [1] "Feedback: Done analysing episode file"
+```
+
+
+## Switch to survey analysis of half hour sampled data
+
+```
+## [1] "Feedback: Join survey to derived half hour data"
+```
+
+```
+## ba_survey ba_diarypid diarypid pid ba_pid r_month r_dow r_hour
+## 1: 1985 14899 316349 193929 2609 10 Friday 0
+## 2: 1985 14899 316349 193929 2609 10 Friday 0
+## 3: 1985 14899 316349 193929 2609 10 Friday 1
+## 4: 1985 14899 316349 193929 2609 10 Friday 1
+## 5: 1985 14899 316349 193929 2609 10 Friday 2
+## 6: 1985 14899 316349 193929 2609 10 Friday 2
+## st_halfhour N_obs N_laundry_p N_laundry_ph N_laundry_photh
+## 1: 00:00 3 0 0 0
+## 2: 00:30 3 0 0 0
+## 3: 01:00 3 0 0 0
+## 4: 01:30 3 0 0 0
+## 5: 02:00 3 0 0 0
+## 6: 02:30 3 0 0 0
+## N_laundry_psh N_laundry_poth Any_laundry_p Any_laundry_ph
+## 1: 0 0 0 0
+## 2: 0 0 0 0
+## 3: 0 0 0 0
+## 4: 0 0 0 0
+## 5: 0 0 0 0
+## 6: 0 0 0 0
+## Any_laundry_photh Any_laundry_psh Any_laundry_poth countrya
+## 1: 0 0 0 United Kingdom
+## 2: 0 0 0 United Kingdom
+## 3: 0 0 0 United Kingdom
+## 4: 0 0 0 United Kingdom
+## 5: 0 0 0 United Kingdom
+## 6: 0 0 0 United Kingdom
+## survey swave msamp hldid persid id
+## 1: 1983 not longitudinal study/case 1 sample 1 4.940656e-324 1
+## 2: 1983 not longitudinal study/case 1 sample 1 4.940656e-324 1
+## 3: 1983 not longitudinal study/case 1 sample 1 4.940656e-324 1
+## 4: 1983 not longitudinal study/case 1 sample 1 4.940656e-324 1
+## 5: 1983 not longitudinal study/case 1 sample 1 4.940656e-324 1
+## 6: 1983 not longitudinal study/case 1 sample 1 4.940656e-324 1
+## diary mtus_day mtus_month mtus_year empstat occup
+## 1: 1st diary day Wednesday November 1983 part-time missing
+## 2: 1st diary day Wednesday November 1983 part-time missing
+## 3: 1st diary day Wednesday November 1983 part-time missing
+## 4: 1st diary day Wednesday November 1983 part-time missing
+## 5: 1st diary day Wednesday November 1983 part-time missing
+## 6: 1st diary day Wednesday November 1983 part-time missing
+## urban badcase sex hhtype income
+## 1: urban/suburban good case Woman Married/cohabiting couple alone missing
+## 2: urban/suburban good case Woman Married/cohabiting couple alone missing
+## 3: urban/suburban good case Woman Married/cohabiting couple alone missing
+## 4: urban/suburban good case Woman Married/cohabiting couple alone missing
+## 5: urban/suburban good case Woman Married/cohabiting couple alone missing
+## 6: urban/suburban good case Woman Married/cohabiting couple alone missing
+## propwt i.ba_survey i.ba_pid age ba_age_cohort ba_age_r ba_nchild
+## 1: 1.07194 1985 2599 26 (1.95e+03,1.96e+03] (25,35] 0
+## 2: 1.07194 1985 2599 26 (1.95e+03,1.96e+03] (25,35] 0
+## 3: 1.07194 1985 2599 26 (1.95e+03,1.96e+03] (25,35] 0
+## 4: 1.07194 1985 2599 26 (1.95e+03,1.96e+03] (25,35] 0
+## 5: 1.07194 1985 2599 26 (1.95e+03,1.96e+03] (25,35] 0
+## 6: 1.07194 1985 2599 26 (1.95e+03,1.96e+03] (25,35] 0
+## ba_npeople
+## 1: 2
+## 2: 2
+## 3: 2
+## 4: 2
+## 5: 2
+## 6: 2
+```
+
+```
+## ba_survey ba_diarypid diarypid pid ba_pid r_month r_dow r_hour
+## 1: NA 58940 NA NA NA NA NA NA
+## 2: NA 58941 NA NA NA NA NA NA
+## 3: NA 58942 NA NA NA NA NA NA
+## 4: NA 58943 NA NA NA NA NA NA
+## 5: NA 58944 NA NA NA NA NA NA
+## 6: NA 58945 NA NA NA NA NA NA
+## st_halfhour N_obs N_laundry_p N_laundry_ph N_laundry_photh
+## 1: NA NA NA NA NA
+## 2: NA NA NA NA NA
+## 3: NA NA NA NA NA
+## 4: NA NA NA NA NA
+## 5: NA NA NA NA NA
+## 6: NA NA NA NA NA
+## N_laundry_psh N_laundry_poth Any_laundry_p Any_laundry_ph
+## 1: NA NA NA NA
+## 2: NA NA NA NA
+## 3: NA NA NA NA
+## 4: NA NA NA NA
+## 5: NA NA NA NA
+## 6: NA NA NA NA
+## Any_laundry_photh Any_laundry_psh Any_laundry_poth countrya
+## 1: NA NA NA United Kingdom
+## 2: NA NA NA United Kingdom
+## 3: NA NA NA United Kingdom
+## 4: NA NA NA United Kingdom
+## 5: NA NA NA United Kingdom
+## 6: NA NA NA United Kingdom
+## survey swave msamp hldid persid id
+## 1: 2005 not longitudinal study/case 1 sample 0 9.906327e-312 1
+## 2: 2005 not longitudinal study/case 1 sample 0 9.906327e-312 1
+## 3: 2005 not longitudinal study/case 1 sample 0 9.906327e-312 1
+## 4: 2005 not longitudinal study/case 1 sample 0 9.906327e-312 1
+## 5: 2005 not longitudinal study/case 1 sample 0 9.906327e-312 1
+## 6: 2005 not longitudinal study/case 1 sample 0 9.906327e-312 1
+## diary mtus_day mtus_month mtus_year empstat
+## 1: 1st diary day Saturday June 2005 not in paid work
+## 2: 1st diary day Saturday June 2005 not in paid work
+## 3: 1st diary day Tuesday June 2005 not in paid work
+## 4: 1st diary day Tuesday June 2005 part-time
+## 5: 1st diary day Tuesday June 2005 full-time
+## 6: 1st diary day Monday June 2005 not in paid work
+## occup urban badcase sex
+## 1: clerical/office support could not be created good case Man
+## 2: sales/services/art support/clean could not be created good case Woman
+## 3: construct, asmble/repair, transpt could not be created good case Man
+## 4: sales/services/art support/clean could not be created good case Woman
+## 5: construct, asmble/repair, transpt could not be created good case Man
+## 6: construct, asmble/repair, transpt could not be created good case Man
+## hhtype income propwt
+## 1: 1 person household lowest 25% 1.3073545
+## 2: 1 person household lowest 25% 0.9952479
+## 3: 1 person household middle 50% 0.6583855
+## 4: Married/cohabiting couple + others lowest 25% 0.9510369
+## 5: Married/cohabiting couple + others middle 50% 1.0930131
+## 6: Other household types could not be created 1.9905120
+## i.ba_survey i.ba_pid age ba_age_cohort ba_age_r ba_nchild
+## 1: 2005 21033 37 (1.96e+03,1.97e+03] (35,45] 0
+## 2: 2005 21034 79 (1.92e+03,1.93e+03] (75,85] 0
+## 3: 2005 21035 80 (1.92e+03,1.93e+03] (75,85] 0
+## 4: 2005 21036 46 (1.95e+03,1.96e+03] (45,55] 1
+## 5: 2005 21037 47 (1.95e+03,1.96e+03] (45,55] 0
+## 6: 2005 21038 19 (1.98e+03,1.99e+03] (16,25] 1
+## ba_npeople
+## 1: 1
+## 2: 1
+## 3: 1
+## 4: 4
+## 5: 3
+## 6: 5+
+```
+
+```
+##
+## 1985 2005 <NA>
+## 903600 125136 3405
+```
+
+```
+##
+## 1985 2005 <NA>
+## 903600 125136 0
+```
+
+```
+## [1] "Recode to 'at a home' vs 'not at a home'"
+```
+
+```
+## Any_laundry_nothome
+## Any_laundry_home 0 1
+## 0 1014861 109
+## 1 13760 6
+```
+
+```
+## [1] "create types of laundry here so become part of survey design object"
+```
+
+```
+## [1] "Weekday early morning 06:00-09:00"
+```
+
+```
+## r_dow
+## r_hour Friday Monday Thursday Tuesday Wednesday
+## 6 12 13 13 12 5
+## 7 43 68 70 55 50
+## 8 106 154 123 123 94
+```
+
+```
+## [1] "Weekday morning 09:00-12:00"
+```
+
+```
+## r_dow
+## r_hour Friday Monday Thursday Tuesday Wednesday
+## 9 146 268 156 211 174
+## 10 171 280 213 217 217
+## 11 136 234 147 174 168
+```
+
+```
+## [1] "Weekday evening peak 18:00-21:00"
+```
+
+```
+## r_dow
+## r_hour Friday Monday Thursday Tuesday Wednesday
+## 18 98 123 101 97 94
+## 19 100 134 117 111 90
+## 20 83 162 124 107 115
+```
+
+```
+## [1] "Sunday morning 09:00-12:00"
+```
+
+```
+## r_dow
+## r_hour Sunday
+## 9 159
+## 10 243
+## 11 238
+```
+
+```
+## [1] "Other"
+```
+
+```
+## r_dow
+## r_hour Friday Monday Saturday Sunday Thursday Tuesday Wednesday
+## 0 4 4 7 4 3 3 5
+## 1 0 1 2 0 0 0 0
+## 2 1 0 0 0 0 0 0
+## 5 2 0 0 0 4 2 4
+## 6 0 0 7 6 0 0 0
+## 7 0 0 20 13 0 0 0
+## 8 0 0 93 68 0 0 0
+## 9 0 0 177 0 0 0 0
+## 10 0 0 256 0 0 0 0
+## 11 0 0 208 0 0 0 0
+## 12 101 145 136 197 117 121 99
+## 13 99 159 113 155 120 128 131
+## 14 122 215 114 154 147 158 157
+## 15 96 157 118 166 126 147 119
+## 16 88 127 103 165 123 133 112
+## 17 67 78 83 109 61 70 72
+## 18 0 0 67 143 0 0 0
+## 19 0 0 76 131 0 0 0
+## 20 0 0 67 108 0 0 0
+## 21 72 111 48 62 79 74 91
+## 22 46 43 24 51 30 51 50
+## 23 14 9 15 13 10 14 16
+```
+
+```
+## [1] "Set survey data"
+```
+
+```
+## N_laundry_p 0 1 2 3
+## ba_survey sex
+## 1985 Man 401562.51212 1540.82666 1511.61154 2716.63159
+## Woman 492799.58642 1842.90609 1897.28811 3057.06516
+## 2005 Man 57753.36741 146.73099 121.74276 334.35365
+## Woman 64015.33611 179.27798 98.40996 451.94026
+```
+
+```
+## Any_laundry_p 0 1
+## ba_survey
+## 1985 894362.099 12566.329
+## 2005 121768.704 1332.456
+```
+
+```
+## Any_laundry_ph 0 1
+## ba_survey
+## 1985 894547.098 12381.330
+## 2005 121905.662 1195.497
+```
+
+```
+## Any_laundry_photh 0 1
+## ba_survey
+## 1985 906836.0839 92.3438
+## 2005 123101.1591 0.0000
+```
+
+```
+## Any_laundry_psh 0 1
+## ba_survey
+## 1985 906883.14313 45.28458
+## 2005 123101.15912 0.00000
+```
+
+```
+## Any_laundry_poth 0 1
+## ba_survey
+## 1985 906891.39666 37.03104
+## 2005 123066.25706 34.90206
+```
+
+```
+## Any_laundry_home 0 1
+## ba_survey
+## 1985 894457.981 12470.447
+## 2005 121905.662 1195.497
+```
+
+```
+## Any_laundry_nothome 0 1
+## ba_survey
+## 1985 906846.11209 82.31562
+## 2005 123066.25706 34.90206
+```
+
+```
+## Any_laundry_p 0 1
+## ba_survey sex
+## 1985 Man 401562.5121 5769.0698
+## Woman 492799.5864 6797.2594
+## 2005 Man 57753.3674 602.8274
+## Woman 64015.3361 729.6282
+```
+
+```
+## ba_survey sex Any_laundry_p se
+## 1985.Man 1985 Man 0.01416308 0.0003752841
+## 2005.Man 2005 Man 0.01033014 0.0007438422
+## 1985.Woman 1985 Woman 0.01360549 0.0003142107
+## 2005.Woman 2005 Woman 0.01126927 0.0008500319
+```
+
+```
+## A B
+## sexMan 0.460605995 0.009544019
+## sexWoman 0.539394005 0.009544019
+```
+
+```
+## A B
+## sexMan 0.45021585 0.02965884
+## sexWoman 0.54978415 0.02965884
+```
+
+```
+## Any_laundry_home 0 1
+## ba_survey sex
+## 1985 Man 401587.6193 5743.9626
+## Woman 492870.3615 6726.4843
+## 2005 Man 57817.9630 538.2318
+## Woman 64087.6989 657.2654
+```
+
+```
+## Any_laundry_nothome 0 1
+## ba_survey sex
+## 1985 Man 407311.11628 20.46565
+## Woman 499534.99581 61.84997
+## 2005 Man 58339.72499 16.46982
+## Woman 64726.53207 18.43225
+```
+
+```
+## r_dow ba_survey Any_laundry_home se
+## Friday.1985 Friday 1985 0.010865766 0.0005378271
+## Monday.1985 Monday 1985 0.017711811 0.0007584997
+## Saturday.1985 Saturday 1985 0.012607221 0.0005939453
+## Sunday.1985 Sunday 1985 0.015019823 0.0006972744
+## Thursday.1985 Thursday 1985 0.012867461 0.0006046819
+## Tuesday.1985 Tuesday 1985 0.013982255 0.0006303033
+## Wednesday.1985 Wednesday 1985 0.013261458 0.0006007659
+## Friday.2005 Friday 2005 0.012043510 0.0021621787
+## Monday.2005 Monday 2005 0.010206423 0.0014793119
+## Saturday.2005 Saturday 2005 0.006371568 0.0012558053
+## Sunday.2005 Sunday 2005 0.013427341 0.0016461126
+## Thursday.2005 Thursday 2005 0.007598394 0.0009421517
+## Tuesday.2005 Tuesday 2005 0.010437059 0.0014434330
+## Wednesday.2005 Wednesday 2005 0.007927659 0.0012847600
+```
+
+```
+## ba_survey
+## r_dow 1985 2005
+## Friday 1410.61966 163.11676
+## Monday 2297.16559 186.56677
+## Saturday 1624.93405 87.58688
+## Sunday 1937.75346 250.47835
+## Thursday 1672.77956 158.76879
+## Tuesday 1810.65085 199.10762
+## Wednesday 1716.54372 149.87204
+```
+
+```
+## [1] "Feedback: Any laundry by day of the week: 1985"
+```
+
+```
+## mean SE
+## r_dowFriday 0.11312 0.0057
+## r_dowMonday 0.18421 0.0078
+## r_dowSaturday 0.13030 0.0063
+## r_dowSunday 0.15539 0.0072
+## r_dowThursday 0.13414 0.0064
+## r_dowTuesday 0.14520 0.0066
+## r_dowWednesday 0.13765 0.0064
+```
+
+```
+## 2.5 % 97.5 %
+## r_dowFriday 0.1018914 0.1243426
+## r_dowMonday 0.1689999 0.1994177
+## r_dowSaturday 0.1180454 0.1425602
+## r_dowSunday 0.1413386 0.1694367
+## r_dowThursday 0.1216119 0.1466671
+## r_dowTuesday 0.1321790 0.1582117
+## r_dowWednesday 0.1251639 0.1501340
+```
+
+```
+## [1] "Feedback: Any laundry by day of the week: 2005"
+```
+
+```
+## mean SE
+## r_dowFriday 0.136443 0.0239
+## r_dowMonday 0.156058 0.0222
+## r_dowSaturday 0.073264 0.0146
+## r_dowSunday 0.209518 0.0248
+## r_dowThursday 0.132806 0.0171
+## r_dowTuesday 0.166548 0.0226
+## r_dowWednesday 0.125364 0.0199
+```
+
+```
+## 2.5 % 97.5 %
+## r_dowFriday 0.08963304 0.1832522
+## r_dowMonday 0.11259840 0.1995174
+## r_dowSaturday 0.04467508 0.1018529
+## r_dowSunday 0.16086621 0.2581701
+## r_dowThursday 0.09930392 0.1663074
+## r_dowTuesday 0.12219560 0.2109003
+## r_dowWednesday 0.08627837 0.1644492
+```
+
+```
+## [1] "Feedback: Any laundry by day of the week/gender: 1985"
+```
+
+```
+## sex r_dowFriday r_dowMonday r_dowSaturday r_dowSunday
+## Man Man 0.1055920 0.1821372 0.1357020 0.1695936
+## Woman Woman 0.1195429 0.1859777 0.1256922 0.1432567
+## r_dowThursday r_dowTuesday r_dowWednesday se.r_dowFriday
+## Man 0.1272811 0.1418606 0.1378335 0.008412166
+## Woman 0.1399961 0.1480430 0.1374913 0.007814887
+## se.r_dowMonday se.r_dowSaturday se.r_dowSunday se.r_dowThursday
+## Man 0.01198236 0.009668095 0.011367669 0.009525777
+## Woman 0.01011515 0.008138816 0.009051124 0.008614564
+## se.r_dowTuesday se.r_dowWednesday
+## Man 0.009719074 0.009392583
+## Woman 0.009090003 0.008668376
+```
+
+```
+## 2.5 % 97.5 %
+## Man:r_dowFriday 0.08910442 0.1220795
+## Woman:r_dowFriday 0.10422600 0.1348598
+## Man:r_dowMonday 0.15865219 0.2056222
+## Woman:r_dowMonday 0.16615242 0.2058031
+## Man:r_dowSaturday 0.11675290 0.1546511
+## Woman:r_dowSaturday 0.10974043 0.1416440
+## Man:r_dowSunday 0.14731339 0.1918738
+## Woman:r_dowSunday 0.12551685 0.1609966
+## Man:r_dowThursday 0.10861091 0.1459513
+## Woman:r_dowThursday 0.12311189 0.1568804
+## Man:r_dowTuesday 0.12281159 0.1609097
+## Woman:r_dowTuesday 0.13022689 0.1658590
+## Man:r_dowWednesday 0.11942438 0.1562426
+## Woman:r_dowWednesday 0.12050162 0.1544810
+```
+
+```
+## [1] "Feedback: Any laundry by day of the week/gender: 2005"
+```
+
+```
+## sex r_dowFriday r_dowMonday r_dowSaturday r_dowSunday
+## Man Man 0.1453014 0.1804492 0.03721916 0.2015636
+## Woman Woman 0.1291882 0.1360840 0.10278092 0.2160321
+## r_dowThursday r_dowTuesday r_dowWednesday se.r_dowFriday
+## Man 0.17908932 0.1452861 0.1110913 0.03343892
+## Woman 0.09490417 0.1839592 0.1370515 0.03372592
+## se.r_dowMonday se.r_dowSaturday se.r_dowSunday se.r_dowThursday
+## Man 0.03381096 0.01509652 0.03255327 0.02936094
+## Woman 0.02906129 0.02342899 0.03641104 0.01878978
+## se.r_dowTuesday se.r_dowWednesday
+## Man 0.02719683 0.02290164
+## Woman 0.03445224 0.03092716
+```
+
+```
+## 2.5 % 97.5 %
+## Man:r_dowFriday 0.079762279 0.21084044
+## Woman:r_dowFriday 0.063086625 0.19528982
+## Man:r_dowMonday 0.114180901 0.24671745
+## Woman:r_dowMonday 0.079124887 0.19304305
+## Man:r_dowSaturday 0.007630535 0.06680779
+## Woman:r_dowSaturday 0.056860938 0.14870090
+## Man:r_dowSunday 0.137760360 0.26536684
+## Woman:r_dowSunday 0.144667736 0.28739641
+## Man:r_dowThursday 0.121542927 0.23663570
+## Woman:r_dowThursday 0.058076882 0.13173146
+## Man:r_dowTuesday 0.091981316 0.19859092
+## Woman:r_dowTuesday 0.116434027 0.25148434
+## Man:r_dowWednesday 0.066204872 0.15597767
+## Woman:r_dowWednesday 0.076435336 0.19766759
+```
+
+ empstat ba_survey se
+------------------ ------------------ ---------- --------
+full-time full-time 789134291 7373041
+not in paid work not in paid work 772129046 7093351
+part-time part-time 434979443 5963395
+unknown job hours unknown job hours 50827974 2306812
+
+ empstat r_dow Any_laundry_home se
+---------------------------- ------------------ ---------- ----------------- ----------
+full-time.Friday full-time Friday 0.0097277 0.0014502
+not in paid work.Friday not in paid work Friday 0.0115259 0.0010299
+part-time.Friday part-time Friday 0.0117574 0.0013751
+unknown job hours.Friday unknown job hours Friday 0.0143683 0.0078934
+full-time.Monday full-time Monday 0.0163043 0.0021178
+not in paid work.Monday not in paid work Monday 0.0194740 0.0014878
+part-time.Monday part-time Monday 0.0146389 0.0014712
+unknown job hours.Monday unknown job hours Monday 0.0208089 0.0071219
+full-time.Saturday full-time Saturday 0.0101133 0.0015787
+not in paid work.Saturday not in paid work Saturday 0.0120256 0.0010891
+part-time.Saturday part-time Saturday 0.0129356 0.0014742
+unknown job hours.Saturday unknown job hours Saturday 0.0182949 0.0059958
+full-time.Sunday full-time Sunday 0.0108672 0.0014745
+not in paid work.Sunday not in paid work Sunday 0.0139046 0.0012393
+part-time.Sunday part-time Sunday 0.0154109 0.0017824
+unknown job hours.Sunday unknown job hours Sunday 0.0069965 0.0038260
+full-time.Thursday full-time Thursday 0.0111536 0.0015468
+not in paid work.Thursday not in paid work Thursday 0.0126719 0.0011539
+part-time.Thursday part-time Thursday 0.0147599 0.0015171
+unknown job hours.Thursday unknown job hours Thursday 0.0191084 0.0076637
+full-time.Tuesday full-time Tuesday 0.0129177 0.0017908
+not in paid work.Tuesday not in paid work Tuesday 0.0146600 0.0013045
+part-time.Tuesday part-time Tuesday 0.0126611 0.0014005
+unknown job hours.Tuesday unknown job hours Tuesday 0.0235239 0.0085679
+full-time.Wednesday full-time Wednesday 0.0121377 0.0017207
+not in paid work.Wednesday not in paid work Wednesday 0.0139568 0.0012700
+part-time.Wednesday part-time Wednesday 0.0119973 0.0013507
+unknown job hours.Wednesday unknown job hours Wednesday 0.0153026 0.0053168
+
+```
+## [1] "Feedback: Any laundry by day of the week/women/labour market status: 1985"
+```
+
+ empstat r_dowFriday r_dowMonday r_dowSaturday r_dowSunday r_dowThursday r_dowTuesday r_dowWednesday se.r_dowFriday se.r_dowMonday se.r_dowSaturday se.r_dowSunday se.r_dowThursday se.r_dowTuesday se.r_dowWednesday
+------------------ ------------------ ------------ ------------ -------------- ------------ -------------- ------------- --------------- --------------- --------------- ----------------- --------------- ----------------- ---------------- ------------------
+full-time full-time 0.1179904 0.1985796 0.1176123 0.1216512 0.1363593 0.1593790 0.1484283 0.0178056 0.0246750 0.0184813 0.0171994 0.0191377 0.0217594 0.0208917
+not in paid work not in paid work 0.1186923 0.2017162 0.1240708 0.1458790 0.1286404 0.1464133 0.1345879 0.0109782 0.0152157 0.0115232 0.0130310 0.0119272 0.0131036 0.0124293
+part-time part-time 0.1224041 0.1546634 0.1297886 0.1568272 0.1602732 0.1407495 0.1352940 0.0145667 0.0159786 0.0150606 0.0178133 0.0166367 0.0156954 0.0153613
+unknown job hours unknown job hours 0.1105719 0.1671910 0.1726642 0.0625853 0.1414491 0.1990554 0.1464830 0.0604227 0.0619587 0.0603682 0.0354488 0.0603233 0.0737225 0.0552000
+
+```
+## [1] "Feedback: Any laundry by day of the week/women/labour market status: 1985"
+```
+
+ empstat r_dowFriday r_dowMonday r_dowSaturday r_dowSunday r_dowThursday r_dowTuesday r_dowWednesday se.r_dowFriday se.r_dowMonday se.r_dowSaturday se.r_dowSunday se.r_dowThursday se.r_dowTuesday se.r_dowWednesday
+----------------- ----------------- ------------ ------------ -------------- ------------ -------------- ------------- --------------- --------------- --------------- ----------------- --------------- ----------------- ---------------- ------------------
+full-time full-time 0.2240102 0.0630661 0.0859171 0.2447814 0.1197949 0.1765103 0.0859201 0.0799600 0.0275682 0.0343156 0.0671075 0.0411371 0.0638993 0.0626110
+not in paid work not in paid work 0.0533881 0.1715071 0.1001408 0.2633564 0.0708324 0.1814197 0.1593556 0.0258744 0.0463328 0.0320004 0.0602603 0.0220217 0.0510785 0.0458664
+part-time part-time 0.1240366 0.1801231 0.1318491 0.0916947 0.1008414 0.1991970 0.1722580 0.0502480 0.0760273 0.0622046 0.0444446 0.0347197 0.0659764 0.0506492
+
+ st_halfhour ba_survey Any_laundry_home se
+----------- ------------ ---------- ----------------- ----------
+00:00.1985 00:00 1985 0.0010556 0.0002491
+00:30.1985 00:30 1985 0.0005721 0.0001813
+01:00.1985 01:00 1985 0.0001816 0.0001048
+01:30.1985 01:30 1985 0.0000000 0.0000000
+02:00.1985 02:00 1985 0.0000567 0.0000567
+02:30.1985 02:30 1985 0.0000000 0.0000000
+03:00.1985 03:00 1985 0.0000000 0.0000000
+03:30.1985 03:30 1985 0.0000000 0.0000000
+04:00.1985 04:00 1985 0.0000000 0.0000000
+04:30.1985 04:30 1985 0.0000000 0.0000000
+05:00.1985 05:00 1985 0.0001084 0.0000767
+05:30.1985 05:30 1985 0.0003492 0.0001432
+06:00.1985 06:00 1985 0.0009972 0.0002422
+06:30.1985 06:30 1985 0.0021780 0.0003543
+07:00.1985 07:00 1985 0.0052767 0.0005480
+07:30.1985 07:30 1985 0.0074772 0.0006465
+08:00.1985 08:00 1985 0.0137881 0.0008801
+08:30.1985 08:30 1985 0.0202534 0.0010663
+09:00.1985 09:00 1985 0.0261384 0.0012092
+09:30.1985 09:30 1985 0.0346053 0.0013847
+10:00.1985 10:00 1985 0.0371609 0.0014307
+10:30.1985 10:30 1985 0.0383388 0.0014500
+11:00.1985 11:00 1985 0.0322811 0.0013371
+11:30.1985 11:30 1985 0.0310342 0.0013132
+12:00.1985 12:00 1985 0.0222697 0.0011172
+12:30.1985 12:30 1985 0.0220213 0.0011134
+13:00.1985 13:00 1985 0.0191961 0.0010389
+13:30.1985 13:30 1985 0.0244418 0.0011707
+14:00.1985 14:00 1985 0.0271532 0.0012306
+14:30.1985 14:30 1985 0.0253324 0.0011907
+15:00.1985 15:00 1985 0.0226200 0.0011250
+15:30.1985 15:30 1985 0.0231902 0.0011389
+16:00.1985 16:00 1985 0.0217943 0.0011069
+16:30.1985 16:30 1985 0.0198137 0.0010567
+17:00.1985 17:00 1985 0.0120270 0.0008239
+17:30.1985 17:30 1985 0.0133019 0.0008650
+18:00.1985 18:00 1985 0.0152872 0.0009250
+18:30.1985 18:30 1985 0.0191294 0.0010358
+19:00.1985 19:00 1985 0.0190732 0.0010400
+19:30.1985 19:30 1985 0.0180119 0.0010104
+20:00.1985 20:00 1985 0.0189935 0.0010328
+20:30.1985 20:30 1985 0.0185356 0.0010190
+21:00.1985 21:00 1985 0.0144381 0.0009011
+21:30.1985 21:30 1985 0.0123343 0.0008312
+22:00.1985 22:00 1985 0.0091150 0.0007132
+22:30.1985 22:30 1985 0.0055409 0.0005603
+23:00.1985 23:00 1985 0.0030516 0.0004164
+23:30.1985 23:30 1985 0.0014843 0.0002922
+00:00.2005 00:00 2005 0.0004289 0.0004288
+00:30.2005 00:30 2005 0.0004289 0.0004288
+01:00.2005 01:00 2005 0.0000000 0.0000000
+01:30.2005 01:30 2005 0.0000000 0.0000000
+02:00.2005 02:00 2005 0.0000000 0.0000000
+02:30.2005 02:30 2005 0.0000000 0.0000000
+03:00.2005 03:00 2005 0.0000000 0.0000000
+03:30.2005 03:30 2005 0.0000000 0.0000000
+04:00.2005 04:00 2005 0.0000000 0.0000000
+04:30.2005 04:30 2005 0.0000000 0.0000000
+05:00.2005 05:00 2005 0.0005382 0.0005380
+05:30.2005 05:30 2005 0.0012199 0.0007245
+06:00.2005 06:00 2005 0.0015144 0.0007819
+06:30.2005 06:30 2005 0.0043039 0.0015482
+07:00.2005 07:00 2005 0.0103922 0.0021535
+07:30.2005 07:30 2005 0.0161986 0.0026600
+08:00.2005 08:00 2005 0.0187639 0.0028371
+08:30.2005 08:30 2005 0.0239451 0.0031630
+09:00.2005 09:00 2005 0.0282875 0.0033952
+09:30.2005 09:30 2005 0.0250066 0.0031481
+10:00.2005 10:00 2005 0.0303511 0.0034738
+10:30.2005 10:30 2005 0.0220304 0.0029144
+11:00.2005 11:00 2005 0.0167036 0.0025672
+11:30.2005 11:30 2005 0.0146088 0.0025741
+12:00.2005 12:00 2005 0.0120755 0.0022353
+12:30.2005 12:30 2005 0.0113019 0.0022600
+13:00.2005 13:00 2005 0.0142415 0.0024589
+13:30.2005 13:30 2005 0.0170105 0.0026592
+14:00.2005 14:00 2005 0.0143518 0.0023896
+14:30.2005 14:30 2005 0.0121984 0.0021457
+15:00.2005 15:00 2005 0.0125262 0.0022281
+15:30.2005 15:30 2005 0.0145229 0.0024785
+16:00.2005 16:00 2005 0.0147880 0.0027606
+16:30.2005 16:30 2005 0.0130933 0.0024381
+17:00.2005 17:00 2005 0.0104090 0.0021998
+17:30.2005 17:30 2005 0.0083849 0.0019649
+18:00.2005 18:00 2005 0.0104222 0.0022736
+18:30.2005 18:30 2005 0.0117916 0.0023978
+19:00.2005 19:00 2005 0.0149155 0.0026367
+19:30.2005 19:30 2005 0.0116972 0.0023677
+20:00.2005 20:00 2005 0.0132548 0.0023569
+20:30.2005 20:30 2005 0.0115057 0.0021052
+21:00.2005 21:00 2005 0.0076453 0.0017000
+21:30.2005 21:30 2005 0.0056360 0.0014326
+22:00.2005 22:00 2005 0.0045470 0.0012778
+22:30.2005 22:30 2005 0.0023425 0.0009693
+23:00.2005 23:00 2005 0.0022741 0.0009411
+23:30.2005 23:30 2005 0.0004944 0.0004943
+
+ st_halfhour r_dow Any_laundry_home se
+---------------- ------------ ---------- ----------------- ----------
+00:00.Friday 00:00 Friday 0.0003901 0.0003901
+00:30.Friday 00:30 Friday 0.0003901 0.0003901
+01:00.Friday 01:00 Friday 0.0000000 0.0000000
+01:30.Friday 01:30 Friday 0.0000000 0.0000000
+02:00.Friday 02:00 Friday 0.0003963 0.0003963
+02:30.Friday 02:30 Friday 0.0000000 0.0000000
+03:00.Friday 03:00 Friday 0.0000000 0.0000000
+03:30.Friday 03:30 Friday 0.0000000 0.0000000
+04:00.Friday 04:00 Friday 0.0000000 0.0000000
+04:30.Friday 04:30 Friday 0.0000000 0.0000000
+05:00.Friday 05:00 Friday 0.0003712 0.0003711
+05:30.Friday 05:30 Friday 0.0004278 0.0004277
+06:00.Friday 06:00 Friday 0.0012271 0.0007102
+06:30.Friday 06:30 Friday 0.0028209 0.0010662
+07:00.Friday 07:00 Friday 0.0057709 0.0014910
+07:30.Friday 07:30 Friday 0.0050491 0.0014024
+08:00.Friday 08:00 Friday 0.0136752 0.0023368
+08:30.Friday 08:30 Friday 0.0189521 0.0027182
+09:00.Friday 09:00 Friday 0.0196416 0.0027592
+09:30.Friday 09:30 Friday 0.0264381 0.0031963
+10:00.Friday 10:00 Friday 0.0297190 0.0033927
+10:30.Friday 10:30 Friday 0.0230931 0.0029824
+11:00.Friday 11:00 Friday 0.0203808 0.0028327
+11:30.Friday 11:30 Friday 0.0263012 0.0031824
+12:00.Friday 12:00 Friday 0.0183270 0.0026590
+12:30.Friday 12:30 Friday 0.0153362 0.0024453
+13:00.Friday 13:00 Friday 0.0133169 0.0022760
+13:30.Friday 13:30 Friday 0.0188765 0.0027088
+14:00.Friday 14:00 Friday 0.0207969 0.0028606
+14:30.Friday 14:30 Friday 0.0184069 0.0026940
+15:00.Friday 15:00 Friday 0.0158436 0.0024908
+15:30.Friday 15:30 Friday 0.0155307 0.0024744
+16:00.Friday 16:00 Friday 0.0139625 0.0023499
+16:30.Friday 16:30 Friday 0.0167389 0.0025674
+17:00.Friday 17:00 Friday 0.0129159 0.0022758
+17:30.Friday 17:30 Friday 0.0109492 0.0020681
+18:00.Friday 18:00 Friday 0.0148877 0.0023762
+18:30.Friday 18:30 Friday 0.0181360 0.0026323
+19:00.Friday 19:00 Friday 0.0162720 0.0025288
+19:30.Friday 19:30 Friday 0.0179092 0.0026536
+20:00.Friday 20:00 Friday 0.0125907 0.0022203
+20:30.Friday 20:30 Friday 0.0133482 0.0022826
+21:00.Friday 21:00 Friday 0.0114288 0.0021175
+21:30.Friday 21:30 Friday 0.0108226 0.0020406
+22:00.Friday 22:00 Friday 0.0096640 0.0019324
+22:30.Friday 22:30 Friday 0.0057181 0.0014781
+23:00.Friday 23:00 Friday 0.0027772 0.0010539
+23:30.Friday 23:30 Friday 0.0019566 0.0008776
+00:00.Monday 00:00 Monday 0.0008133 0.0005749
+00:30.Monday 00:30 Monday 0.0007668 0.0005427
+01:00.Monday 01:00 Monday 0.0004258 0.0004257
+01:30.Monday 01:30 Monday 0.0000000 0.0000000
+02:00.Monday 02:00 Monday 0.0000000 0.0000000
+02:30.Monday 02:30 Monday 0.0000000 0.0000000
+03:00.Monday 03:00 Monday 0.0000000 0.0000000
+03:30.Monday 03:30 Monday 0.0000000 0.0000000
+04:00.Monday 04:00 Monday 0.0000000 0.0000000
+04:30.Monday 04:30 Monday 0.0000000 0.0000000
+05:00.Monday 05:00 Monday 0.0000000 0.0000000
+05:30.Monday 05:30 Monday 0.0000000 0.0000000
+06:00.Monday 06:00 Monday 0.0004608 0.0004606
+06:30.Monday 06:30 Monday 0.0032522 0.0011505
+07:00.Monday 07:00 Monday 0.0084495 0.0018425
+07:30.Monday 07:30 Monday 0.0098686 0.0019712
+08:00.Monday 08:00 Monday 0.0154802 0.0024384
+08:30.Monday 08:30 Monday 0.0275616 0.0032629
+09:00.Monday 09:00 Monday 0.0418493 0.0040108
+09:30.Monday 09:30 Monday 0.0484774 0.0042968
+10:00.Monday 10:00 Monday 0.0483581 0.0042848
+10:30.Monday 10:30 Monday 0.0450101 0.0041339
+11:00.Monday 11:00 Monday 0.0434075 0.0040796
+11:30.Monday 11:30 Monday 0.0373854 0.0037960
+12:00.Monday 12:00 Monday 0.0238394 0.0030518
+12:30.Monday 12:30 Monday 0.0244933 0.0031069
+13:00.Monday 13:00 Monday 0.0261203 0.0031830
+13:30.Monday 13:30 Monday 0.0321368 0.0035435
+14:00.Monday 14:00 Monday 0.0377973 0.0038167
+14:30.Monday 14:30 Monday 0.0386287 0.0038590
+15:00.Monday 15:00 Monday 0.0299300 0.0034149
+15:30.Monday 15:30 Monday 0.0282619 0.0033177
+16:00.Monday 16:00 Monday 0.0263313 0.0032080
+16:30.Monday 16:30 Monday 0.0213524 0.0029105
+17:00.Monday 17:00 Monday 0.0115651 0.0021404
+17:30.Monday 17:30 Monday 0.0145834 0.0023892
+18:00.Monday 18:00 Monday 0.0187620 0.0026937
+18:30.Monday 18:30 Monday 0.0221181 0.0029324
+19:00.Monday 19:00 Monday 0.0248732 0.0031048
+19:30.Monday 19:30 Monday 0.0214987 0.0029060
+20:00.Monday 20:00 Monday 0.0290735 0.0033862
+20:30.Monday 20:30 Monday 0.0290144 0.0033583
+21:00.Monday 21:00 Monday 0.0211871 0.0028646
+21:30.Monday 21:30 Monday 0.0191816 0.0027237
+22:00.Monday 22:00 Monday 0.0083826 0.0017859
+22:30.Monday 22:30 Monday 0.0062310 0.0015590
+23:00.Monday 23:00 Monday 0.0024715 0.0010104
+23:30.Monday 23:30 Monday 0.0007671 0.0005454
+00:00.Saturday 00:00 Saturday 0.0016283 0.0008153
+00:30.Saturday 00:30 Saturday 0.0012065 0.0006978
+01:00.Saturday 01:00 Saturday 0.0008492 0.0006003
+01:30.Saturday 01:30 Saturday 0.0000000 0.0000000
+02:00.Saturday 02:00 Saturday 0.0000000 0.0000000
+02:30.Saturday 02:30 Saturday 0.0000000 0.0000000
+03:00.Saturday 03:00 Saturday 0.0000000 0.0000000
+03:30.Saturday 03:30 Saturday 0.0000000 0.0000000
+04:00.Saturday 04:00 Saturday 0.0000000 0.0000000
+04:30.Saturday 04:30 Saturday 0.0000000 0.0000000
+05:00.Saturday 05:00 Saturday 0.0000000 0.0000000
+05:30.Saturday 05:30 Saturday 0.0000000 0.0000000
+06:00.Saturday 06:00 Saturday 0.0008344 0.0005900
+06:30.Saturday 06:30 Saturday 0.0020597 0.0009216
+07:00.Saturday 07:00 Saturday 0.0021065 0.0009417
+07:30.Saturday 07:30 Saturday 0.0037099 0.0012375
+08:00.Saturday 08:00 Saturday 0.0124805 0.0022358
+08:30.Saturday 08:30 Saturday 0.0197570 0.0028038
+09:00.Saturday 09:00 Saturday 0.0244138 0.0031214
+09:30.Saturday 09:30 Saturday 0.0359478 0.0037532
+10:00.Saturday 10:00 Saturday 0.0387591 0.0038714
+10:30.Saturday 10:30 Saturday 0.0501596 0.0043687
+11:00.Saturday 11:00 Saturday 0.0375079 0.0038086
+11:30.Saturday 11:30 Saturday 0.0329629 0.0035907
+12:00.Saturday 12:00 Saturday 0.0229262 0.0030109
+12:30.Saturday 12:30 Saturday 0.0219463 0.0029612
+13:00.Saturday 13:00 Saturday 0.0170481 0.0026152
+13:30.Saturday 13:30 Saturday 0.0229396 0.0030144
+14:00.Saturday 14:00 Saturday 0.0242461 0.0030785
+14:30.Saturday 14:30 Saturday 0.0188707 0.0027322
+15:00.Saturday 15:00 Saturday 0.0225764 0.0029679
+15:30.Saturday 15:30 Saturday 0.0218942 0.0029326
+16:00.Saturday 16:00 Saturday 0.0190385 0.0027323
+16:30.Saturday 16:30 Saturday 0.0166619 0.0025574
+17:00.Saturday 17:00 Saturday 0.0140586 0.0023346
+17:30.Saturday 17:30 Saturday 0.0139002 0.0023409
+18:00.Saturday 18:00 Saturday 0.0109572 0.0021024
+18:30.Saturday 18:30 Saturday 0.0112983 0.0021292
+19:00.Saturday 19:00 Saturday 0.0147115 0.0024420
+19:30.Saturday 19:30 Saturday 0.0118358 0.0021922
+20:00.Saturday 20:00 Saturday 0.0124971 0.0022366
+20:30.Saturday 20:30 Saturday 0.0107063 0.0020566
+21:00.Saturday 21:00 Saturday 0.0082614 0.0018005
+21:30.Saturday 21:30 Saturday 0.0089101 0.0018960
+22:00.Saturday 22:00 Saturday 0.0064642 0.0016153
+22:30.Saturday 22:30 Saturday 0.0029249 0.0011079
+23:00.Saturday 23:00 Saturday 0.0036366 0.0012136
+23:30.Saturday 23:30 Saturday 0.0024535 0.0010025
+00:00.Sunday 00:00 Sunday 0.0008473 0.0005991
+00:30.Sunday 00:30 Sunday 0.0007739 0.0005481
+01:00.Sunday 01:00 Sunday 0.0000000 0.0000000
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+06:30.Thursday 06:30 Thursday 0.0042723 0.0030616
+07:00.Thursday 07:00 Thursday 0.0144892 0.0059183
+07:30.Thursday 07:30 Thursday 0.0181486 0.0064627
+08:00.Thursday 08:00 Thursday 0.0150695 0.0057093
+08:30.Thursday 08:30 Thursday 0.0130474 0.0054379
+09:00.Thursday 09:00 Thursday 0.0144778 0.0060546
+09:30.Thursday 09:30 Thursday 0.0103292 0.0047090
+10:00.Thursday 10:00 Thursday 0.0114390 0.0052532
+10:30.Thursday 10:30 Thursday 0.0037632 0.0026634
+11:00.Thursday 11:00 Thursday 0.0073763 0.0044661
+11:30.Thursday 11:30 Thursday 0.0039276 0.0028243
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+12:30.Thursday 12:30 Thursday 0.0106057 0.0049100
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+13:30.Thursday 13:30 Thursday 0.0137892 0.0057665
+14:00.Thursday 14:00 Thursday 0.0100902 0.0045491
+14:30.Thursday 14:30 Thursday 0.0081116 0.0041029
+15:00.Thursday 15:00 Thursday 0.0140320 0.0059088
+15:30.Thursday 15:30 Thursday 0.0107233 0.0054381
+16:00.Thursday 16:00 Thursday 0.0086119 0.0050214
+16:30.Thursday 16:30 Thursday 0.0158073 0.0065324
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+17:30.Thursday 17:30 Thursday 0.0057025 0.0040204
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+18:30.Thursday 18:30 Thursday 0.0138595 0.0057318
+19:00.Thursday 19:00 Thursday 0.0220689 0.0070922
+19:30.Thursday 19:30 Thursday 0.0102572 0.0045872
+20:00.Thursday 20:00 Thursday 0.0110372 0.0049213
+20:30.Thursday 20:30 Thursday 0.0121501 0.0049883
+21:00.Thursday 21:00 Thursday 0.0080370 0.0040263
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+22:00.Thursday 22:00 Thursday 0.0072626 0.0036492
+22:30.Thursday 22:30 Thursday 0.0021114 0.0021097
+23:00.Thursday 23:00 Thursday 0.0021114 0.0021097
+23:30.Thursday 23:30 Thursday 0.0000000 0.0000000
+00:00.Tuesday 00:00 Tuesday 0.0000000 0.0000000
+00:30.Tuesday 00:30 Tuesday 0.0000000 0.0000000
+01:00.Tuesday 01:00 Tuesday 0.0000000 0.0000000
+01:30.Tuesday 01:30 Tuesday 0.0000000 0.0000000
+02:00.Tuesday 02:00 Tuesday 0.0000000 0.0000000
+02:30.Tuesday 02:30 Tuesday 0.0000000 0.0000000
+03:00.Tuesday 03:00 Tuesday 0.0000000 0.0000000
+03:30.Tuesday 03:30 Tuesday 0.0000000 0.0000000
+04:00.Tuesday 04:00 Tuesday 0.0000000 0.0000000
+04:30.Tuesday 04:30 Tuesday 0.0000000 0.0000000
+05:00.Tuesday 05:00 Tuesday 0.0034730 0.0034659
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+08:30.Tuesday 08:30 Tuesday 0.0295522 0.0089120
+09:00.Tuesday 09:00 Tuesday 0.0460630 0.0113542
+09:30.Tuesday 09:30 Tuesday 0.0409067 0.0095814
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+15:00.Tuesday 15:00 Tuesday 0.0113797 0.0061541
+15:30.Tuesday 15:30 Tuesday 0.0128505 0.0067048
+16:00.Tuesday 16:00 Tuesday 0.0023471 0.0023450
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+17:30.Tuesday 17:30 Tuesday 0.0108402 0.0049204
+18:00.Tuesday 18:00 Tuesday 0.0067786 0.0039761
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+20:00.Tuesday 20:00 Tuesday 0.0093384 0.0055313
+20:30.Tuesday 20:30 Tuesday 0.0067036 0.0039235
+21:00.Tuesday 21:00 Tuesday 0.0017134 0.0017129
+21:30.Tuesday 21:30 Tuesday 0.0000000 0.0000000
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+22:30.Tuesday 22:30 Tuesday 0.0050912 0.0037048
+23:00.Tuesday 23:00 Tuesday 0.0072583 0.0042861
+23:30.Tuesday 23:30 Tuesday 0.0031906 0.0031850
+00:00.Wednesday 00:00 Wednesday 0.0000000 0.0000000
+00:30.Wednesday 00:30 Wednesday 0.0000000 0.0000000
+01:00.Wednesday 01:00 Wednesday 0.0000000 0.0000000
+01:30.Wednesday 01:30 Wednesday 0.0000000 0.0000000
+02:00.Wednesday 02:00 Wednesday 0.0000000 0.0000000
+02:30.Wednesday 02:30 Wednesday 0.0000000 0.0000000
+03:00.Wednesday 03:00 Wednesday 0.0000000 0.0000000
+03:30.Wednesday 03:30 Wednesday 0.0000000 0.0000000
+04:00.Wednesday 04:00 Wednesday 0.0000000 0.0000000
+04:30.Wednesday 04:30 Wednesday 0.0000000 0.0000000
+05:00.Wednesday 05:00 Wednesday 0.0000000 0.0000000
+05:30.Wednesday 05:30 Wednesday 0.0024919 0.0024892
+06:00.Wednesday 06:00 Wednesday 0.0000000 0.0000000
+06:30.Wednesday 06:30 Wednesday 0.0000000 0.0000000
+07:00.Wednesday 07:00 Wednesday 0.0046071 0.0032560
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+09:30.Wednesday 09:30 Wednesday 0.0084436 0.0050234
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+11:30.Wednesday 11:30 Wednesday 0.0093616 0.0047747
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+12:30.Wednesday 12:30 Wednesday 0.0097153 0.0049087
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+13:30.Wednesday 13:30 Wednesday 0.0146071 0.0060394
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+15:00.Wednesday 15:00 Wednesday 0.0121603 0.0055670
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+16:30.Wednesday 16:30 Wednesday 0.0223214 0.0074871
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+17:30.Wednesday 17:30 Wednesday 0.0100705 0.0050175
+18:00.Wednesday 18:00 Wednesday 0.0094814 0.0054613
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+19:30.Wednesday 19:30 Wednesday 0.0150043 0.0061582
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+22:00.Wednesday 22:00 Wednesday 0.0051710 0.0036606
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+23:00.Wednesday 23:00 Wednesday 0.0000000 0.0000000
+23:30.Wednesday 23:30 Wednesday 0.0000000 0.0000000
+
+ mean SE
+---------- ----------
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+
+ mean SE
+---------- ----------
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+
+ mean SE
+---------- ----------
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+
+ mean SE
+---------- ----------
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+ 0.0050969 0.0023778
+ 0.0049270 0.0023284
+ 0.0046561 0.0019400
+ 0.0039106 0.0018014
+ 0.0010897 0.0007713
+ 0.0017678 0.0010280
+ 0.0020520 0.0011871
+ 0.0007205 0.0007197
+ 0.0000000 0.0000000
+ 0.0000000 0.0000000
+ 0.0000000 0.0000000
+ 0.0000000 0.0000000
+ 0.0000000 0.0000000
+ 0.0000000 0.0000000
+ 0.0000000 0.0000000
+ 0.0000000 0.0000000
+ 0.0004983 0.0004970
+ 0.0028673 0.0014678
+ 0.0027933 0.0014226
+ 0.0045580 0.0019612
+ 0.0062144 0.0022908
+ 0.0079813 0.0026514
+ 0.0062155 0.0022467
+ 0.0037040 0.0015206
+ 0.0029866 0.0013387
+ 0.0012340 0.0008731
+ 0.0025180 0.0016246
+ 0.0045207 0.0023145
+ 0.0025168 0.0016004
+ 0.0004983 0.0004970
+ 0.0008656 0.0008643
+ 0.0014164 0.0010250
+ 0.0008656 0.0008643
+ 0.0027367 0.0015816
+ 0.0019038 0.0013482
+ 0.0023415 0.0013728
+ 0.0013759 0.0009830
+ 0.0019455 0.0011343
+ 0.0020000 0.0011663
+ 0.0008212 0.0008166
+ 0.0017413 0.0012232
+ 0.0017413 0.0012232
+ 0.0017413 0.0012232
+ 0.0017413 0.0012232
+ 0.0009201 0.0009176
+ 0.0000000 0.0000000
+ 0.0000000 0.0000000
+ 0.0000000 0.0000000
+ 0.0000000 0.0000000
+ 0.0000000 0.0000000
+ 0.0000000 0.0000000
+ 0.0000000 0.0000000
+ 0.0000000 0.0000000
+ 0.0000000 0.0000000
+ 0.0000000 0.0000000
+ 0.0000000 0.0000000
+ 0.0005696 0.0005697
+ 0.0019138 0.0013552
+ 0.0046465 0.0020873
+ 0.0084425 0.0027115
+ 0.0115471 0.0029948
+ 0.0101188 0.0029675
+ 0.0175207 0.0037861
+ 0.0123346 0.0031953
+ 0.0100511 0.0029221
+ 0.0082310 0.0025960
+ 0.0060985 0.0021711
+ 0.0065689 0.0025872
+ 0.0080430 0.0027430
+ 0.0111661 0.0031392
+ 0.0125032 0.0031885
+ 0.0049705 0.0018805
+ 0.0076408 0.0024249
+ 0.0102948 0.0029804
+ 0.0113848 0.0032579
+ 0.0066339 0.0023475
+ 0.0043297 0.0021528
+ 0.0017025 0.0012540
+ 0.0034348 0.0017804
+ 0.0019464 0.0013895
+ 0.0045264 0.0021701
+ 0.0040266 0.0019690
+ 0.0061294 0.0024468
+ 0.0051354 0.0021755
+ 0.0039964 0.0020062
+ 0.0014170 0.0010096
+ 0.0014170 0.0010085
+ 0.0000000 0.0000000
+ 0.0007764 0.0007773
+ 0.0000000 0.0000000
+ 0.0000000 0.0000000
+ 0.0000000 0.0000000
+ 0.0000000 0.0000000
+ 0.0006414 0.0006413
+ 0.0006414 0.0006413
+ 0.0015557 0.0011168
+ 0.0052759 0.0021785
+ 0.0066084 0.0023807
+ 0.0054872 0.0020946
+ 0.0047509 0.0019799
+ 0.0052718 0.0022128
+ 0.0037612 0.0017174
+ 0.0041652 0.0019225
+ 0.0013703 0.0009719
+ 0.0026859 0.0016347
+ 0.0014301 0.0010297
+ 0.0020151 0.0011843
+ 0.0038618 0.0017891
+ 0.0078350 0.0026845
+ 0.0050210 0.0021051
+ 0.0036741 0.0016612
+ 0.0029536 0.0014976
+ 0.0051094 0.0021583
+ 0.0039046 0.0019822
+ 0.0031358 0.0018287
+ 0.0057559 0.0023808
+ 0.0030005 0.0017285
+ 0.0020764 0.0014630
+ 0.0046439 0.0021056
+ 0.0050466 0.0020875
+ 0.0080359 0.0025904
+ 0.0037349 0.0016723
+ 0.0040190 0.0017954
+ 0.0044242 0.0018167
+ 0.0029265 0.0014650
+ 0.0038035 0.0017442
+ 0.0026445 0.0013313
+ 0.0007688 0.0007691
+ 0.0007688 0.0007691
+ 0.0000000 0.0000000
+ 0.0000000 0.0000000
+ 0.0000000 0.0000000
+ 0.0011546 0.0011460
+ 0.0011546 0.0011460
+ 0.0011546 0.0011460
+ 0.0043451 0.0026090
+ 0.0063200 0.0028333
+ 0.0061068 0.0027596
+ 0.0065177 0.0027431
+ 0.0098245 0.0029206
+ 0.0153134 0.0037770
+ 0.0135992 0.0031570
+ 0.0113585 0.0029260
+ 0.0087920 0.0025506
+ 0.0045751 0.0018603
+ 0.0082253 0.0034984
+ 0.0016668 0.0011709
+ 0.0007955 0.0007945
+ 0.0033740 0.0017068
+ 0.0081208 0.0026752
+ 0.0024292 0.0018018
+ 0.0047446 0.0022724
+ 0.0037831 0.0020343
+ 0.0042721 0.0022209
+ 0.0007803 0.0007793
+ 0.0013499 0.0009648
+ 0.0042181 0.0017355
+ 0.0036038 0.0016403
+ 0.0022535 0.0013218
+ 0.0050891 0.0019686
+ 0.0060470 0.0021860
+ 0.0034925 0.0017521
+ 0.0031045 0.0018468
+ 0.0022286 0.0013102
+ 0.0005696 0.0005700
+ 0.0000000 0.0000000
+ 0.0010171 0.0010169
+ 0.0016925 0.0012332
+ 0.0024130 0.0014273
+ 0.0010607 0.0010593
+ 0.0000000 0.0000000
+ 0.0000000 0.0000000
+ 0.0000000 0.0000000
+ 0.0008210 0.0008218
+ 0.0000000 0.0000000
+ 0.0000000 0.0000000
+ 0.0015178 0.0010755
+ 0.0018316 0.0012972
+ 0.0026090 0.0015029
+ 0.0059758 0.0022899
+ 0.0057217 0.0023698
+ 0.0027817 0.0016581
+ 0.0082422 0.0028482
+ 0.0061293 0.0023547
+ 0.0034749 0.0017630
+ 0.0030841 0.0015753
+ 0.0054632 0.0024691
+ 0.0032007 0.0016093
+ 0.0046244 0.0019029
+ 0.0048123 0.0020015
+ 0.0011544 0.0008160
+ 0.0037433 0.0016951
+ 0.0040062 0.0018113
+ 0.0040395 0.0018130
+ 0.0057441 0.0021869
+ 0.0073537 0.0024591
+ 0.0035242 0.0017526
+ 0.0033177 0.0016586
+ 0.0031236 0.0017901
+ 0.0050171 0.0022189
+ 0.0026816 0.0015406
+ 0.0049431 0.0020133
+ 0.0059654 0.0022547
+ 0.0033824 0.0016940
+ 0.0029503 0.0014623
+ 0.0015008 0.0010499
+ 0.0017036 0.0011971
+ 0.0009233 0.0009233
+ 0.0000000 0.0000000
+ 0.0000000 0.0000000
+
+```
+## r_dow S M T W T F S
+## st_halfhour
+## 00:00 mean 8.461652e-05 8.461652e-05 0.000000e+00 8.595842e-05 8.049951e-05 9.278030e-05 2.661310e-04
+## SE 4.094002e-03 4.510494e-03 3.992159e-03 3.436225e-03 3.368346e-03 3.028240e-03 3.630390e-03
+## 00:30 mean 6.118147e-04 1.251603e-03 1.095065e-03 2.965924e-03 4.110392e-03 4.259927e-03 5.733988e-03
+## SE 2.801245e-03 2.374706e-03 3.228891e-03 3.933391e-03 3.529126e-03 3.884200e-03 2.730718e-03
+## 01:00 mean 6.445552e-03 5.008504e-03 4.420248e-03 5.704297e-03 3.974824e-03 3.326164e-03 2.888210e-03
+## SE 2.895014e-03 2.478719e-03 2.347239e-03 2.095954e-03 1.240167e-03 6.023241e-04 4.243458e-04
+```
+
+```
+## [1] "Feedback: Weekday early morning laundry (06:00 – 09:00) out of all laundry"
+```
+
+
+
+Table: Weekday early morning laundry (06:00 – 09:00) out of all laundry
+
+ ba_survey weekday_earlyam se
+----- ---------- ---------------- ----------
+1985 1985 0.0612199 0.0027494
+2005 2005 0.1348555 0.0145266
+
+```
+## [1] "Feedback: Weekday morning laundry out of all laundry"
+```
+
+
+
+Table: Weekday morning laundry out of all laundry
+
+ ba_survey weekday_am se
+----- ---------- ----------- ----------
+1985 1985 0.2108538 0.0057673
+2005 2005 0.1957309 0.0191291
+
+```
+## [1] "Feedback: Weekday evening peak laundry out of all laundry"
+```
+
+
+
+Table: Weekday evening peak laundry out of all laundry
+
+ ba_survey weekday_pm se
+----- ---------- ----------- ----------
+1985 1985 0.1208896 0.0045649
+2005 2005 0.1228752 0.0161665
+
+```
+## [1] "Feedback: Sunday morning laundry out of all laundry"
+```
+
+
+
+Table: Sunday morning laundry out of all laundry
+
+ ba_survey sunday_am se
+----- ---------- ---------- ----------
+1985 1985 0.0441858 0.0029138
+2005 2005 0.0698033 0.0116076
+
+```
+## 2.5 % 97.5 %
+## 1985 0.03847499 0.04989669
+## 2005 0.04705275 0.09255377
+```
+
+```
+## [1] "Feedback: Other laundry out of all laundry"
+```
+
+
+
+Table: Other laundry out of all laundry
+
+ ba_survey other se
+----- ---------- ---------- ----------
+1985 1985 0.5628508 0.0069440
+2005 2005 0.4767352 0.0238243
+
+```
+## [1] "% within empstat who do sunday_am"
+```
+
+```
+## empstat sunday_am se
+## full-time full-time 0.07804306 0.01920174
+## not in paid work not in paid work 0.06989777 0.01804090
+## part-time part-time 0.04599771 0.01855852
+```
+
+```
+## [1] "% within empstat who do weekday early am"
+```
+
+```
+## empstat weekday_earlyam se
+## full-time full-time 0.1155997 0.01824415
+## not in paid work not in paid work 0.1584702 0.02712452
+## part-time part-time 0.1365675 0.03517791
+```
+
+```
+## [1] "% within empstat who do weekday am"
+```
+
+```
+## empstat weekday_am se
+## full-time full-time 0.2230604 0.03161854
+## not in paid work not in paid work 0.1745649 0.02764323
+## part-time part-time 0.1653634 0.03930053
+```
+
+```
+## [1] "% within empstat who do weekday pm"
+```
+
+```
+## empstat weekday_pm se
+## full-time full-time 0.1309584 0.02501823
+## not in paid work not in paid work 0.1224830 0.02370082
+## part-time part-time 0.1006188 0.04237536
+```
+
+```
+## [1] "% within empstat who do other"
+```
+
+```
+## empstat other se
+## full-time full-time 0.4523385 0.03593228
+## not in paid work not in paid work 0.4745841 0.03621508
+## part-time part-time 0.5514526 0.06317038
+```
+
+```
+## [1] "summarise the laundry types by diarypid for 1985 to see how many different kinds of laundry"
+```
+
+```
+## [1] "Summarise the laundry types by diarypid for 2005 to see how many different kinds of laundry"
+```
+
+```
+## [1] "Check laundry by sex & hh type - any change in proportions done by men in couples?"
+```
+
+
+
+Table: Check laundry by sex & hh type - any change in proportions done by men in couples?
+
+ Man Woman
+----------------------------------- ----- ------
+1 person household 117 229
+Married/cohabiting couple + others 1178 1306
+Married/cohabiting couple alone 462 522
+Other household types 177 325
+
+ Man Woman
+----------------------------------- ---- ------
+1 person household 64 74
+Married/cohabiting couple + others 65 67
+Married/cohabiting couple alone 79 73
+Other household types 13 39
+
+```
+##
+## full-time not in paid work part-time unknown job hours
+## 1032 1188 383 4
+## <NA>
+## 0
+```
+
+```
+##
+## full-time not in paid work part-time unknown job hours
+## full-time 1032 0 0 0
+## notInPaidWork 0 1188 0 0
+## part-time 0 0 383 0
+## unknownHours 0 0 0 4
+## <NA> 0 0 0 0
+##
+## <NA>
+## full-time 0
+## notInPaidWork 0
+## part-time 0
+## unknownHours 0
+## <NA> 0
+```
+
+```
+##
+## 0 1 2 3 4 5 8 10 11
+## Autumn 0 0 0 0 0 0 14 4157 0
+## Spring 0 0 3123 5932 1274 0 0 0 0
+## Summer 0 0 0 0 0 56 0 0 0
+## Winter 3372 683 0 0 0 0 0 0 214
+```
+
+
+
+Table: Check season flag
+
+ 2 5
+------- ----- -----
+Spring 1470 0
+Summer 0 1137
+
+
+
+Table: combinations by employment type 1985
+
+ full-time not in paid work part-time unknown job hours
+--- ---------- ----------------- ---------- ------------------
+0 6093 6379 3638 460
+1 750 802 495 56
+2 154 143 104 9
+3 13 13 3 0
+
+
+
+Table: combinations by employment type 2005
+
+ full-time not in paid work part-time unknown job hours
+--- ---------- ----------------- ---------- ------------------
+0 902 1060 338 4
+1 112 107 40 0
+2 18 20 5 0
+3 0 1 0 0
+
+
+
+Table: Get number of people reporting any laundry by employment status (to give denominator 1985)
+
+ full-time not in paid work part-time unknown job hours NA
+--- ---------- ----------------- ---------- ------------------ ---
+0 5458 5682 3267 389 0
+1 1552 1655 973 136 0
+NA 0 0 0 0 0
+
+
+
+Table: Get number of people reporting any laundry by employment status (to give denominator 2005)
+
+ full-time not in paid work part-time unknown job hours NA
+--- ---------- ----------------- ---------- ------------------ ---
+0 837 987 305 4 0
+1 195 201 78 0 0
+NA 0 0 0 0 0
+
+
+
+Table: combinations by age - 1985
+
+ (16,25] (25,35] (35,45] (45,55] (55,65] (65,75] (75,85]
+--- -------- -------- -------- -------- -------- -------- --------
+0 2815 4187 3364 2807 2030 1001 366
+1 337 513 435 396 254 130 38
+2 60 86 92 86 54 25 7
+3 4 7 4 8 3 2 1
+
+
+
+Table: combinations by age - 2005
+
+ (16,25] (25,35] (35,45] (45,55] (55,65] (65,75] (75,85]
+--- -------- -------- -------- -------- -------- -------- --------
+0 181 377 426 367 384 304 265
+1 20 42 51 45 45 37 19
+2 3 5 6 8 11 5 5
+3 0 0 0 0 1 0 0
+
+ (16,25] (25,35] (35,45] (45,55] (55,65] (65,75] (75,85] NA
+--- -------- -------- -------- -------- -------- -------- -------- ---
+0 2541 3753 3004 2480 1795 894 329 0
+1 675 1040 891 817 546 264 83 0
+NA 0 0 0 0 0 0 0 0
+
+ (16,25] (25,35] (35,45] (45,55] (55,65] (65,75] (75,85] NA
+--- -------- -------- -------- -------- -------- -------- -------- ---
+0 170 348 397 338 353 277 250 0
+1 34 76 86 82 88 69 39 0
+NA 0 0 0 0 0 0 0 0
+
+```
+## [1] "# <-- New model --> #"
+## [1] "Running logit model: ~"
+## [2] "Running logit model: Sunday_amd"
+## [3] "Running logit model: empstat + ba_age_r + ba_nchild"
+## [1] ""
+## [1] "# Model results"
+## term estimate std.error
+## (Intercept) (Intercept) -2.25106043 0.6626785
+## empstatnot in paid work empstatnot in paid work 0.07246519 0.4443794
+## empstatpart-time empstatpart-time 0.19530905 0.4716650
+## ba_age_r(25,35] ba_age_r(25,35] 0.27089076 0.7256474
+## ba_age_r(35,45] ba_age_r(35,45] 0.74329719 0.6912685
+## ba_age_r(45,55] ba_age_r(45,55] -0.49464112 0.7752806
+## ba_age_r(55,65] ba_age_r(55,65] -0.13008162 0.7380416
+## ba_age_r(65,75] ba_age_r(65,75] -0.18396754 0.8089391
+## ba_age_r(75,85] ba_age_r(75,85] -0.30962498 0.9199216
+## ba_nchild1 ba_nchild1 -0.04619857 0.5248261
+## ba_nchild2 ba_nchild2 -0.85050265 0.6145155
+## ba_nchild3+ ba_nchild3+ -0.39676938 0.8215772
+## statistic p.value 2.5 % 97.5 %
+## (Intercept) -3.39691200 0.0006815088 -3.7639820 -1.0883440
+## empstatnot in paid work 0.16307054 0.8704628949 -0.8203800 0.9343213
+## empstatpart-time 0.41408425 0.6788124055 -0.7746941 1.0959084
+## ba_age_r(25,35] 0.37330908 0.7089184203 -1.0742774 1.8629130
+## ba_age_r(35,45] 1.07526549 0.2822558835 -0.5048537 2.2902368
+## ba_age_r(45,55] -0.63801556 0.5234635508 -1.9885472 1.1605981
+## ba_age_r(55,65] -0.17625242 0.8600956370 -1.5018070 1.4814926
+## ba_age_r(65,75] -0.22741828 0.8200985059 -1.7185889 1.5380336
+## ba_age_r(75,85] -0.33657757 0.7364353634 -2.1704187 1.5634109
+## ba_nchild1 -0.08802643 0.9298556702 -1.1519717 0.9337535
+## ba_nchild2 -1.38402147 0.1663518809 -2.1880908 0.2735906
+## ba_nchild3+ -0.48293620 0.6291410392 -2.3337972 1.0396889
+## [1] "# Model summary"
+## null.deviance df.null logLik AIC BIC deviance df.residual
+## 1 301.9801 473 -147.6618 319.3237 369.2582 295.3237 462
+## *Pseudo R^2 for logistic regression
+## -> Hosmer and Lemeshow R^2 0.022
+## -> Cox and Snell R^2 0.014
+## -> Nagelkerke R^2 0.03
+## [1] ""
+## [1] "# Diagnostics: independence of errors"
+## lag Autocorrelation D-W Statistic p-value
+## 1 0.01472566 1.969515 0.724
+## Alternative hypothesis: rho != 0
+## [1] "# Diagnostics: collinearity (vif)"
+## GVIF Df GVIF^(1/(2*Df))
+## empstat 1.771673 2 1.153708
+## ba_age_r 2.360494 6 1.074196
+## ba_nchild 1.498813 3 1.069772
+## [1] "# Diagnostics: collinearity (tolerance)"
+## GVIF Df GVIF^(1/(2*Df))
+## empstat 0.5644383 0.5000000 0.8667705
+## ba_age_r 0.4236401 0.1666667 0.9309287
+## ba_nchild 0.6671946 0.3333333 0.9347786
+```
+
+```
+## [1] "# <-- New model --> #"
+## [1] "Running logit model: ~"
+## [2] "Running logit model: Sunday_amd"
+## [3] "Running logit model: empstat + ba_age_r + ba_nchild + ba_season"
+## [1] ""
+## [1] "# Model results"
+## term estimate std.error
+## (Intercept) (Intercept) -2.38412124 0.6805659
+## empstatnot in paid work empstatnot in paid work 0.07569039 0.4448707
+## empstatpart-time empstatpart-time 0.18734471 0.4714993
+## ba_age_r(25,35] ba_age_r(25,35] 0.27902683 0.7251250
+## ba_age_r(35,45] ba_age_r(35,45] 0.74599140 0.6900865
+## ba_age_r(45,55] ba_age_r(45,55] -0.50654472 0.7747391
+## ba_age_r(55,65] ba_age_r(55,65] -0.12684848 0.7388972
+## ba_age_r(65,75] ba_age_r(65,75] -0.20852085 0.8124010
+## ba_age_r(75,85] ba_age_r(75,85] -0.32579145 0.9221720
+## ba_nchild1 ba_nchild1 -0.06494171 0.5259901
+## ba_nchild2 ba_nchild2 -0.87154445 0.6149872
+## ba_nchild3+ ba_nchild3+ -0.33668148 0.8235682
+## ba_seasonSummer ba_seasonSummer 0.26973420 0.3169871
+## statistic p.value 2.5 % 97.5 %
+## (Intercept) -3.5031453 0.0004597985 -3.9224060 -1.1782015
+## empstatnot in paid work 0.1701402 0.8648998788 -0.8184432 0.9382740
+## empstatpart-time 0.3973382 0.6911180547 -0.7823939 1.0875456
+## ba_age_r(25,35] 0.3847983 0.7003868846 -1.0654174 1.8700050
+## ba_age_r(35,45] 1.0810114 0.2796920373 -0.4999787 2.2909249
+## ba_age_r(45,55] -0.6538262 0.5132237875 -1.9996363 1.1476382
+## ba_age_r(55,65] -0.1716727 0.8636948479 -1.5004011 1.4860238
+## ba_age_r(65,75] -0.2566723 0.7974317285 -1.7502570 1.5191441
+## ba_age_r(75,85] -0.3532871 0.7238732247 -2.1904471 1.5513886
+## ba_nchild1 -0.1234657 0.9017383664 -1.1732017 0.9169511
+## ba_nchild2 -1.4171749 0.1564317876 -2.2097758 0.2537567
+## ba_nchild3+ -0.4088083 0.6826803804 -2.2763818 1.1047165
+## ba_seasonSummer 0.8509310 0.3948076567 -0.3500234 0.8995334
+## [1] "# Model summary"
+## null.deviance df.null logLik AIC BIC deviance df.residual
+## 1 301.9801 473 -147.298 320.596 374.6917 294.596 461
+## *Pseudo R^2 for logistic regression
+## -> Hosmer and Lemeshow R^2 0.024
+## -> Cox and Snell R^2 0.015
+## -> Nagelkerke R^2 0.033
+## [1] ""
+## [1] "# Diagnostics: independence of errors"
+## lag Autocorrelation D-W Statistic p-value
+## 1 0.01323541 1.972475 0.674
+## Alternative hypothesis: rho != 0
+## [1] "# Diagnostics: collinearity (vif)"
+## GVIF Df GVIF^(1/(2*Df))
+## empstat 1.774903 2 1.154233
+## ba_age_r 2.368047 6 1.074482
+## ba_nchild 1.512619 3 1.071408
+## ba_season 1.020097 1 1.009999
+## [1] "# Diagnostics: collinearity (tolerance)"
+## GVIF Df GVIF^(1/(2*Df))
+## empstat 0.5634110 0.5000000 0.8663758
+## ba_age_r 0.4222889 0.1666667 0.9306809
+## ba_nchild 0.6611049 0.3333333 0.9333511
+## ba_season 0.9802986 1.0000000 0.9901003
+```
+
+```
+## [1] "# <-- New model --> #"
+## [1] "Running logit model: ~"
+## [2] "Running logit model: WeekdayEarly_amd"
+## [3] "Running logit model: empstat + ba_age_r + ba_nchild"
+## [1] ""
+## [1] "# Model results"
+## term estimate std.error
+## (Intercept) (Intercept) -1.59090022 0.4788073
+## empstatnot in paid work empstatnot in paid work 0.33705088 0.2968983
+## empstatpart-time empstatpart-time -0.06797079 0.3401498
+## ba_age_r(25,35] ba_age_r(25,35] 0.12183484 0.5167092
+## ba_age_r(35,45] ba_age_r(35,45] 0.09057922 0.5073915
+## ba_age_r(45,55] ba_age_r(45,55] 0.24343652 0.5201143
+## ba_age_r(55,65] ba_age_r(55,65] 0.62129148 0.5165602
+## ba_age_r(65,75] ba_age_r(65,75] -0.08302318 0.5750000
+## ba_age_r(75,85] ba_age_r(75,85] -0.25686325 0.6473666
+## ba_nchild1 ba_nchild1 -0.08999451 0.4282902
+## ba_nchild2 ba_nchild2 0.57880559 0.3882608
+## ba_nchild3+ ba_nchild3+ 0.42954649 0.5878169
+## statistic p.value 2.5 % 97.5 %
+## (Intercept) -3.3226313 0.0008917269 -2.6016972 -0.7031387
+## empstatnot in paid work 1.1352402 0.2562746740 -0.2483619 0.9183591
+## empstatpart-time -0.1998260 0.8416166394 -0.7535738 0.5859509
+## ba_age_r(25,35] 0.2357899 0.8135956672 -0.8635682 1.1858968
+## ba_age_r(35,45] 0.1785194 0.8583150820 -0.8740293 1.1390156
+## ba_age_r(45,55] 0.4680443 0.6397529052 -0.7450359 1.3165790
+## ba_age_r(55,65] 1.2027474 0.2290740885 -0.3537119 1.6923568
+## ba_age_r(65,75] -0.1443881 0.8851939747 -1.1881757 1.0868580
+## ba_age_r(75,85] -0.3967818 0.6915284081 -1.5379263 1.0293719
+## ba_nchild1 -0.2101251 0.8335700611 -0.9766954 0.7189866
+## ba_nchild2 1.4907650 0.1360232157 -0.1917540 1.3372626
+## ba_nchild3+ 0.7307487 0.4649326394 -0.8072768 1.5384047
+## [1] "# Model summary"
+## null.deviance df.null logLik AIC BIC deviance df.residual
+## 1 513.5908 473 -252.3306 528.6612 578.5957 504.6612 462
+## *Pseudo R^2 for logistic regression
+## -> Hosmer and Lemeshow R^2 0.017
+## -> Cox and Snell R^2 0.019
+## -> Nagelkerke R^2 0.028
+## [1] ""
+## [1] "# Diagnostics: independence of errors"
+## lag Autocorrelation D-W Statistic p-value
+## 1 0.02731998 1.939465 0.488
+## Alternative hypothesis: rho != 0
+## [1] "# Diagnostics: collinearity (vif)"
+## GVIF Df GVIF^(1/(2*Df))
+## empstat 1.609933 2 1.126424
+## ba_age_r 2.524795 6 1.080237
+## ba_nchild 1.729993 3 1.095656
+## [1] "# Diagnostics: collinearity (tolerance)"
+## GVIF Df GVIF^(1/(2*Df))
+## empstat 0.6211439 0.5000000 0.8877651
+## ba_age_r 0.3960717 0.1666667 0.9257232
+## ba_nchild 0.5780369 0.3333333 0.9126955
+```
+
+```
+## [1] "# <-- New model --> #"
+## [1] "Running logit model: ~"
+## [2] "Running logit model: Weekday_amd"
+## [3] "Running logit model: empstat + ba_age_r + ba_nchild"
+## [1] ""
+## [1] "# Model results"
+## term estimate std.error
+## (Intercept) (Intercept) -0.47153375 0.4209208
+## empstatnot in paid work empstatnot in paid work -0.66351274 0.3284628
+## empstatpart-time empstatpart-time -0.41958118 0.3386955
+## ba_age_r(25,35] ba_age_r(25,35] -0.77133255 0.4750686
+## ba_age_r(35,45] ba_age_r(35,45] -0.61933109 0.4616950
+## ba_age_r(45,55] ba_age_r(45,55] -0.56788309 0.4694252
+## ba_age_r(55,65] ba_age_r(55,65] -0.64183217 0.4886030
+## ba_age_r(65,75] ba_age_r(65,75] 0.14522591 0.5243426
+## ba_age_r(75,85] ba_age_r(75,85] 0.31759362 0.5745195
+## ba_nchild1 ba_nchild1 -0.07815546 0.4112193
+## ba_nchild2 ba_nchild2 0.43645699 0.3843440
+## ba_nchild3+ ba_nchild3+ -0.30661730 0.6808297
+## statistic p.value 2.5 % 97.5 %
+## (Intercept) -1.1202433 0.26261009 -1.3263236 0.33836969
+## empstatnot in paid work -2.0200544 0.04337775 -1.3246801 -0.03257784
+## empstatpart-time -1.2388153 0.21541391 -1.1046658 0.22892615
+## ba_age_r(25,35] -1.6236234 0.10445621 -1.7035084 0.17215124
+## ba_age_r(35,45] -1.3414291 0.17978118 -1.5209674 0.30187884
+## ba_age_r(45,55] -1.2097414 0.22637814 -1.4836746 0.36913057
+## ba_age_r(55,65] -1.3136068 0.18897856 -1.5960304 0.33201414
+## ba_age_r(65,75] 0.2769676 0.78180499 -0.8712554 1.19481144
+## ba_age_r(75,85] 0.5527987 0.58040125 -0.8061612 1.45809636
+## ba_nchild1 -0.1900578 0.84926380 -0.9249853 0.70101902
+## ba_nchild2 1.1355894 0.25612845 -0.3288492 1.18509012
+## ba_nchild3+ -0.4503583 0.65245213 -1.8369630 0.91969882
+## [1] "# Model summary"
+## null.deviance df.null logLik AIC BIC deviance df.residual
+## 1 520.665 473 -254.9798 533.9595 583.894 509.9595 462
+## *Pseudo R^2 for logistic regression
+## -> Hosmer and Lemeshow R^2 0.021
+## -> Cox and Snell R^2 0.022
+## -> Nagelkerke R^2 0.034
+## [1] ""
+## [1] "# Diagnostics: independence of errors"
+## lag Autocorrelation D-W Statistic p-value
+## 1 0.04875728 1.893013 0.202
+## Alternative hypothesis: rho != 0
+## [1] "# Diagnostics: collinearity (vif)"
+## GVIF Df GVIF^(1/(2*Df))
+## empstat 2.030107 2 1.193658
+## ba_age_r 2.858071 6 1.091456
+## ba_nchild 1.581850 3 1.079429
+## [1] "# Diagnostics: collinearity (tolerance)"
+## GVIF Df GVIF^(1/(2*Df))
+## empstat 0.4925848 0.5000000 0.8377612
+## ba_age_r 0.3498863 0.1666667 0.9162076
+## ba_nchild 0.6321711 0.3333333 0.9264154
+```
+
+```
+## [1] "# <-- New model --> #"
+## [1] "Running logit model: ~"
+## [2] "Running logit model: Weekday_pmd"
+## [3] "Running logit model: empstat + ba_age_r + ba_nchild"
+## [1] ""
+## [1] "# Model results"
+## term estimate std.error
+## (Intercept) (Intercept) -1.610307347 0.5421485
+## empstatnot in paid work empstatnot in paid work 0.096866852 0.3347478
+## empstatpart-time empstatpart-time -0.707022008 0.4355114
+## ba_age_r(25,35] ba_age_r(25,35] -0.127072651 0.6022041
+## ba_age_r(35,45] ba_age_r(35,45] -0.507283327 0.6099009
+## ba_age_r(45,55] ba_age_r(45,55] 0.546103516 0.5774865
+## ba_age_r(55,65] ba_age_r(55,65] 0.415731774 0.5902036
+## ba_age_r(65,75] ba_age_r(65,75] -0.465465943 0.6783961
+## ba_age_r(75,85] ba_age_r(75,85] 0.009440469 0.7045892
+## ba_nchild1 ba_nchild1 -0.980651054 0.6390646
+## ba_nchild2 ba_nchild2 0.323016053 0.4683272
+## ba_nchild3+ ba_nchild3+ 0.991064404 0.6119478
+## statistic p.value 2.5 % 97.5 %
+## (Intercept) -2.97023323 0.002975737 -2.7839177 -0.6214222
+## empstatnot in paid work 0.28937260 0.772296258 -0.5676561 0.7492618
+## empstatpart-time -1.62342953 0.104497617 -1.6212012 0.1050555
+## ba_age_r(25,35] -0.21101260 0.832877440 -1.2736698 1.1297847
+## ba_age_r(35,45] -0.83174713 0.405551693 -1.6769086 0.7589981
+## ba_age_r(45,55] 0.94565591 0.344324118 -0.5298530 1.7716354
+## ba_age_r(55,65] 0.70438699 0.481191804 -0.6854154 1.6646477
+## ba_age_r(65,75] -0.68612711 0.492632956 -1.7751687 0.9242429
+## ba_age_r(75,85] 0.01339854 0.989309830 -1.3600261 1.4429049
+## ba_nchild1 -1.53451010 0.124904210 -2.4543147 0.1374739
+## ba_nchild2 0.68972299 0.490368402 -0.6285273 1.2256043
+## ba_nchild3+ 1.61952455 0.105334450 -0.2791610 2.1632835
+## [1] "# Model summary"
+## null.deviance df.null logLik AIC BIC deviance df.residual
+## 1 427.132 473 -204.5332 433.0663 483.0008 409.0663 462
+## *Pseudo R^2 for logistic regression
+## -> Hosmer and Lemeshow R^2 0.042
+## -> Cox and Snell R^2 0.037
+## -> Nagelkerke R^2 0.063
+## [1] ""
+## [1] "# Diagnostics: independence of errors"
+## lag Autocorrelation D-W Statistic p-value
+## 1 0.1097293 1.778341 0.018
+## Alternative hypothesis: rho != 0
+## [1] "# Diagnostics: collinearity (vif)"
+## GVIF Df GVIF^(1/(2*Df))
+## empstat 1.641619 2 1.131926
+## ba_age_r 2.496787 6 1.079233
+## ba_nchild 1.640211 3 1.085967
+## [1] "# Diagnostics: collinearity (tolerance)"
+## GVIF Df GVIF^(1/(2*Df))
+## empstat 0.6091549 0.5000000 0.8834499
+## ba_age_r 0.4005148 0.1666667 0.9265842
+## ba_nchild 0.6096775 0.3333333 0.9208383
+```
+
+```
+## [1] "# <-- New model --> #"
+## [1] "Running linear model: ~"
+## [2] "Running linear model: laundry_sum"
+## [3] "Running linear model: empstat + ba_age_r + ba_nchild"
+## [1] "# Model results"
+## term estimate std.error
+## (Intercept) (Intercept) 0.80422583 0.11971777
+## empstatnot in paid work empstatnot in paid work -0.02405294 0.08119904
+## empstatpart-time empstatpart-time -0.14734215 0.08765235
+## ba_age_r(25,35] ba_age_r(25,35] -0.11301074 0.13088358
+## ba_age_r(35,45] ba_age_r(35,45] -0.08452151 0.12885828
+## ba_age_r(45,55] ba_age_r(45,55] -0.02692084 0.13107222
+## ba_age_r(55,65] ba_age_r(55,65] 0.03742763 0.13309842
+## ba_age_r(65,75] ba_age_r(65,75] -0.08829267 0.14545467
+## ba_age_r(75,85] ba_age_r(75,85] -0.03342188 0.16099247
+## ba_nchild1 ba_nchild1 -0.11586368 0.10521064
+## ba_nchild2 ba_nchild2 0.14831185 0.10531327
+## ba_nchild3+ ba_nchild3+ 0.14152604 0.16078729
+## statistic p.value 2.5 % 97.5 %
+## (Intercept) 6.7176812 5.443781e-11 0.56896700 1.03948467
+## empstatnot in paid work -0.2962220 7.671936e-01 -0.18361815 0.13551226
+## empstatpart-time -1.6809835 9.344198e-02 -0.31958883 0.02490453
+## ba_age_r(25,35] -0.8634448 3.883410e-01 -0.37021164 0.14419015
+## ba_age_r(35,45] -0.6559261 5.121982e-01 -0.33774246 0.16869945
+## ba_age_r(45,55] -0.2053894 8.373584e-01 -0.28449243 0.23065075
+## ba_age_r(55,65] 0.2812027 7.786808e-01 -0.22412567 0.29898094
+## ba_age_r(65,75] -0.6070116 5.441414e-01 -0.37412738 0.19754205
+## ba_age_r(75,85] -0.2075990 8.356335e-01 -0.34979013 0.28294637
+## ba_nchild1 -1.1012543 2.713594e-01 -0.32261437 0.09088702
+## ba_nchild2 1.4082921 1.597172e-01 -0.05864053 0.35526424
+## ba_nchild3+ 0.8802067 3.792050e-01 -0.17443899 0.45749108
+## r.squared adj.r.squared sigma statistic p.value df logLik
+## 1 0.02038023 -0.002944048 0.62548 0.8737775 0.5662077 12 -444.0818
+## AIC BIC deviance df.residual
+## 1 914.1635 968.2592 180.746 462
+## [1] "# Diagnostics: independence of errors"
+## lag Autocorrelation D-W Statistic p-value
+## 1 -0.03270472 2.058046 0.462
+## Alternative hypothesis: rho != 0
+## [1] "# Diagnostics: collinearity (vif)"
+## GVIF Df GVIF^(1/(2*Df))
+## empstat 1.761297 2 1.152015
+## ba_age_r 2.552027 6 1.081203
+## ba_nchild 1.606628 3 1.082229
+## [1] "# Diagnostics: collinearity (tolerance)"
+## GVIF Df GVIF^(1/(2*Df))
+## empstat 0.5677635 0.5000000 0.8680443
+## ba_age_r 0.3918454 0.1666667 0.9248960
+## ba_nchild 0.6224217 0.3333333 0.9240187
+```
+
+```
+## [1] "% in work in 1985"
+```
+
+```
+## sex empstatfull-time empstatnot in paid work empstatpart-time
+## Man Man 0.6728953 0.1735609 0.1162203
+## Woman Woman 0.2192669 0.4153912 0.3509855
+## empstatunknown job hours se.empstatfull-time
+## Man 0.03732346 0.005954984
+## Woman 0.01435640 0.004497221
+## se.empstatnot in paid work se.empstatpart-time
+## Man 0.004829776 0.004007465
+## Woman 0.005441926 0.005278946
+## se.empstatunknown job hours
+## Man 0.002441382
+## Woman 0.001301152
+```
+
+```
+## [1] "% in work in 2005"
+```
+
+```
+## sex empstatfull-time empstatnot in paid work empstatpart-time
+## Man Man 0.8059294 0.1255392 0.0634378
+## Woman Woman 0.4112487 0.2871118 0.3002517
+## empstatunknown job hours se.empstatfull-time
+## Man 0.005093638 0.01514926
+## Woman 0.001387849 0.01690216
+## se.empstatnot in paid work se.empstatpart-time
+## Man 0.01255103 0.009400211
+## Woman 0.01539688 0.015558580
+## se.empstatunknown job hours
+## Man 0.003451856
+## Woman 0.001387000
+```
+
+```
+## [1] "% people who report laundry in 1985"
+```
+
+```
+## mean SE
+## Any_laundry_homed 0.2237 0.0031
+```
+
+```
+## [1] "% people who report laundry in 2005"
+```
+
+```
+## mean SE
+## Any_laundry_homed 0.18156 0.008
+```
+
+```
+## [1] "Number of people reporting different number of laundry habits"
+```
+
+```
+## laundry_sum
+## 0 1 2 3
+## 2264.303512 256.275619 42.916280 1.112071
+```
+
+```
+## [1] "1985: N of respondents who reported laundry of given type"
+```
+
+```
+## total SE
+## Sunday_amd 305.83 18.142
+## WeekdayEarly_amd 581.85 24.708
+## Weekday_amd 1278.56 36.003
+## Weekday_pmd 812.29 28.954
+## Otherd 2871.44 51.689
+## Any_laundry_homed 4290.52 60.576
+```
+
+```
+## [1] "2005: N of respondents who reported laundry of given type"
+```
+
+```
+## total SE
+## Sunday_amd 44.171 6.7883
+## WeekdayEarly_amd 108.140 10.7480
+## Weekday_amd 113.794 11.1856
+## Weekday_pmd 79.339 9.2491
+## Otherd 256.672 15.9396
+## Any_laundry_homed 465.626 20.7243
+```
+
+```
+## [1] "Feedback: Done analysing sampled file using survey methods"
+```
+
+Finished
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diff --git a/Theme-1/laundryPaper/DEMAND-BA-MTUS-W6-Laundry-Change-Over-Time-v2.0-adult-data-processing.R b/Theme-1/laundryPaper/DEMAND-BA-MTUS-W6-Laundry-Change-Over-Time-v2.0-adult-data-processing.R
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--- /dev/null
+++ b/Theme-1/laundryPaper/DEMAND-BA-MTUS-W6-Laundry-Change-Over-Time-v2.0-adult-data-processing.R
@@ -0,0 +1,791 @@
+# Begin header ###########################################
+# Data processing for 'Laundry' paper:
+
+# Use MTUS World 6 time-use data (UK subset) to examine:
+# - distributions of laundry in 1985 & 2005
+# - changing laundry practice
+# Data source: www.timeuse.org/mtus
+# data already in long format (but episodes)
+
+# This work was funded by RCUK through the End User Energy Demand Centres Programme via the
+# "DEMAND: Dynamics of Energy, Mobility and Demand" Centre (www.demand.ac.uk, gow.epsrc.ac.uk/NGBOViewGrant.aspx?GrantRef=EP/K011723/1)
+
+# Copyright (C) 2014 University of Southampton
+# Author: Ben Anderson (b.anderson@soton.ac.uk, @dataknut, https://github.com/dataknut)
+
+# This program is free software; you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation; either version 2 of the License
+# (http://choosealicense.com/licenses/gpl-2.0/), or
+# (at your option) any later version.
+
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+#
+# End header ###########################################
+
+# MTUS codes of interest: Main/Sec21 Laundry, ironing, clothing repair
+
+# Housekeeping ----
+# clear out all old objects etc to avoid confusion
+rm(list = ls())
+
+
+# set up some useful data paths
+tudpath <- "~/Documents/Work/Data/MTUS/World_6/processed/"
+
+# define laundry for time use data
+laundry <- "laundry, ironing, clothing repair"
+
+# Generic functions ----
+
+# load packages
+loadPackages <- function() {
+ # add packages
+ library(foreign) # as loading stata files
+ library(lattice) # fancy graphs
+ library(ggplot2) # fancy graphs II
+ library(data.table) # why use anything else?
+ library(survey) # weighted survey analysis
+ library(car) # regression diagnostics
+ library(gmodels) # nice crosstabs
+ library(fasttime) # VERY fast time string conversion to POSIXct
+ # but only IF the input string is in a fixed format - see http://rforge.net/doc/packages/fasttime/fastPOSIXct.html
+}
+
+# Feedback function - cos I can't be bothered to keep writing it out
+feedBack <- function(string) {
+ print(paste0("Feedback: ", string))
+}
+
+# Functions for loading & processing data
+# Each one exposes 1 or more processed/aggregated data tables and then saves them out for future use
+processMtusSurvey <- function() {
+ sfile <- paste0(tudpath, "MTUS-adult-aggregate-UK-only-wf.dta")
+ feedBack(paste0("Loading: ", sfile))
+ MTUSW6UKsurvey_DT <- as.data.table(read.dta(sfile))
+ setkey(MTUSW6UKsurvey_DT, diarypid)
+
+ # create a reduced survey frame with the few variables we need so the join
+ # does not break memory
+ # use global assignment so we can see the DT for later use
+ gMTUSW6UKsurveyCore_DT <<- MTUSW6UKsurvey_DT[, .(survey, countrya, swave, diarypid, pid, empstat, urban,
+ badcase, nchild, sex, age, hhtype, hhldsize, income, propwt)
+ ]
+ feedBack("Summary of gMTUSW6UKsurveyCore_DT")
+ print(summary(gMTUSW6UKsurveyCore_DT)) # force print from within function
+
+ feedBack("Making filter for years of interest")
+ gMTUSW6UKsurveyCore_DT$ba_survey <- ifelse(
+ gMTUSW6UKsurveyCore_DT$survey == 1974,
+ 1974, # if true
+ NA # if not
+ )
+
+ gMTUSW6UKsurveyCore_DT$ba_survey <- ifelse(
+ gMTUSW6UKsurveyCore_DT$survey == 1983 |
+ gMTUSW6UKsurveyCore_DT$survey == 1987 ,
+ 1985, # if true
+ gMTUSW6UKsurveyCore_DT$ba_survey # if not
+ )
+
+ gMTUSW6UKsurveyCore_DT$ba_survey <- ifelse(
+ gMTUSW6UKsurveyCore_DT$survey == 1995 ,
+ 1995, # if true
+ gMTUSW6UKsurveyCore_DT$ba_survey # if not
+ )
+
+ gMTUSW6UKsurveyCore_DT$ba_survey <- ifelse(
+ gMTUSW6UKsurveyCore_DT$survey == 2000 ,
+ 2000, # if true
+ gMTUSW6UKsurveyCore_DT$ba_survey # if not
+ )
+
+ gMTUSW6UKsurveyCore_DT$ba_survey <- ifelse(
+ gMTUSW6UKsurveyCore_DT$survey == 2005 ,
+ 2005, # if true
+ gMTUSW6UKsurveyCore_DT$ba_survey # if not
+ )
+ # save out the two working files for later use (saves re-running)
+ coreSurvey_DT <- paste0(tudpath, "gMTUSW6UKsurveyCore_DT.csv")
+ print(paste0("Saving processed file in: ", coreSurvey_DT))
+ write.csv(gMTUSW6UKsurveyCore_DT,
+ file = coreSurvey_DT, row.names = FALSE
+ )
+
+ dir <- getwd()
+ setwd(tudpath)
+ print("Now gzip the file")
+ system("gzip -f gMTUSW6UKsurveyCore_DT.csv &") # gzip & force over-write, shame can't do this directly as part of write
+ setwd(dir) # set back to working directory otherwise R will save .RData in an odd place
+
+ feedBack("Done loading, processing & saving TU survey data")
+} # works
+
+processMtusEpisodes <- function() {
+ # Load as STATA file
+ mtusfile <- paste0(tudpath, "MTUS-adult-episode-UK-only-wf.dta")
+ feedBack(paste0("Loading: ", mtusfile))
+ mtusEpsDT <- as.data.table(read.dta(mtusfile))
+ setkey(mtusEpsDT, diarypid)
+
+ feedBack("Fixing episode dates")
+ # The stata times are all loaded as POSIXct which is not all that helpful
+ # Need to create a proper start/end time but 2005 does not have a valid date so we are forced to impute them
+ # 2005 months:
+ # March: synthetic week = Sunday 6th -> Saturday 12th
+ mtusEpsDT$date_2005 <- ifelse(
+ mtusEpsDT$survey == 2005 & mtusEpsDT$mtus_month == "March"
+ & mtusEpsDT$s_dow == "Sunday", "06/03/2005", "na"
+ )
+ mtusEpsDT$date_2005 <- ifelse(
+ mtusEpsDT$survey == 2005 & mtusEpsDT$mtus_month == "March"
+ & mtusEpsDT$s_dow == "Monday", "07/03/2005", mtusEpsDT$date_2005
+ )
+ mtusEpsDT$date_2005 <- ifelse(
+ mtusEpsDT$survey == 2005 & mtusEpsDT$mtus_month == "March"
+ & mtusEpsDT$s_dow == "Tuesday", "08/03/2005", mtusEpsDT$date_2005
+ )
+ mtusEpsDT$date_2005 <- ifelse(
+ mtusEpsDT$survey == 2005 & mtusEpsDT$mtus_month == "March"
+ & mtusEpsDT$s_dow == "Wednesday", "09/03/2005", mtusEpsDT$date_2005
+ )
+ mtusEpsDT$date_2005 <- ifelse(
+ mtusEpsDT$survey == 2005 & mtusEpsDT$mtus_month == "March"
+ & mtusEpsDT$s_dow == "Thursday", "10/03/2005", mtusEpsDT$date_2005
+ )
+ mtusEpsDT$date_2005 <- ifelse(
+ mtusEpsDT$survey == 2005 & mtusEpsDT$mtus_month == "March"
+ & mtusEpsDT$s_dow == "Friday", "11/03/2005", mtusEpsDT$date_2005
+ )
+ mtusEpsDT$date_2005 <- ifelse(
+ mtusEpsDT$survey == 2005 & mtusEpsDT$mtus_month == "March"
+ & mtusEpsDT$s_dow == "Saturday", "12/03/2005", mtusEpsDT$date_2005
+ )
+
+ # June: 5th -> 11th
+ mtusEpsDT$date_2005 <- ifelse(
+ mtusEpsDT$survey == 2005 & mtusEpsDT$mtus_month == "June"
+ & mtusEpsDT$s_dow == "Sunday", "05/06/2005", mtusEpsDT$date_2005
+ )
+ mtusEpsDT$date_2005 <- ifelse(
+ mtusEpsDT$survey == 2005 & mtusEpsDT$mtus_month == "June"
+ & mtusEpsDT$s_dow == "Monday", "06/06/2005", mtusEpsDT$date_2005
+ )
+ mtusEpsDT$date_2005 <- ifelse(
+ mtusEpsDT$survey == 2005 & mtusEpsDT$mtus_month == "June"
+ & mtusEpsDT$s_dow == "Tuesday", "07/06/2005", mtusEpsDT$date_2005
+ )
+ mtusEpsDT$date_2005 <- ifelse(
+ mtusEpsDT$survey == 2005 & mtusEpsDT$mtus_month == "June"
+ & mtusEpsDT$s_dow == "Wednesday", "08/06/2005", mtusEpsDT$date_2005
+ )
+ mtusEpsDT$date_2005 <- ifelse(
+ mtusEpsDT$survey == 2005 & mtusEpsDT$mtus_month == "June"
+ & mtusEpsDT$s_dow == "Thursday", "09/06/2005", mtusEpsDT$date_2005
+ )
+ mtusEpsDT$date_2005 <- ifelse(
+ mtusEpsDT$survey == 2005 & mtusEpsDT$mtus_month == "June"
+ & mtusEpsDT$s_dow == "Friday", "10/06/2005", mtusEpsDT$date_2005
+ )
+ mtusEpsDT$date_2005 <- ifelse(
+ mtusEpsDT$survey == 2005 & mtusEpsDT$mtus_month == "June"
+ & mtusEpsDT$s_dow == "Saturday", "11/06/2005", mtusEpsDT$date_2005
+ )
+
+ # September: 4th -> 10th
+ mtusEpsDT$date_2005 <- ifelse(
+ mtusEpsDT$survey == 2005 & mtusEpsDT$mtus_month == "September"
+ & mtusEpsDT$s_dow == "Sunday", "04/09/2005", mtusEpsDT$date_2005
+ )
+ mtusEpsDT$date_2005 <- ifelse(
+ mtusEpsDT$survey == 2005 & mtusEpsDT$mtus_month == "September"
+ & mtusEpsDT$s_dow == "Monday", "05/09/2005", mtusEpsDT$date_2005
+ )
+ mtusEpsDT$date_2005 <- ifelse(
+ mtusEpsDT$survey == 2005 & mtusEpsDT$mtus_month == "September"
+ & mtusEpsDT$s_dow == "Tuesday", "06/09/2005", mtusEpsDT$date_2005
+ )
+ mtusEpsDT$date_2005 <- ifelse(
+ mtusEpsDT$survey == 2005 & mtusEpsDT$mtus_month == "September"
+ & mtusEpsDT$s_dow == "Wednesday", "07/09/2005", mtusEpsDT$date_2005
+ )
+ mtusEpsDT$date_2005 <- ifelse(
+ mtusEpsDT$survey == 2005 & mtusEpsDT$mtus_month == "September"
+ & mtusEpsDT$s_dow == "Thursday", "08/09/2005", mtusEpsDT$date_2005
+ )
+ mtusEpsDT$date_2005 <- ifelse(
+ mtusEpsDT$survey == 2005 & mtusEpsDT$mtus_month == "September"
+ & mtusEpsDT$s_dow == "Friday", "09/09/2005", mtusEpsDT$date_2005
+ )
+ mtusEpsDT$date_2005 <- ifelse(
+ mtusEpsDT$survey == 2005 & mtusEpsDT$mtus_month == "September"
+ & mtusEpsDT$s_dow == "Saturday", "10/09/2005", mtusEpsDT$date_2005
+ )
+
+ # November: 6th - 12th
+ mtusEpsDT$date_2005 <- ifelse(
+ mtusEpsDT$survey == 2005 & mtusEpsDT$mtus_month == "November"
+ & mtusEpsDT$s_dow == "Sunday", "06/11/2005", mtusEpsDT$date_2005
+ )
+ mtusEpsDT$date_2005 <- ifelse(
+ mtusEpsDT$survey == 2005 & mtusEpsDT$mtus_month == "November"
+ & mtusEpsDT$s_dow == "Monday", "07/11/2005", mtusEpsDT$date_2005
+ )
+ mtusEpsDT$date_2005 <- ifelse(
+ mtusEpsDT$survey == 2005 & mtusEpsDT$mtus_month == "November"
+ & mtusEpsDT$s_dow == "Tuesday", "08/11/2005", mtusEpsDT$date_2005
+ )
+ mtusEpsDT$date_2005 <- ifelse(
+ mtusEpsDT$survey == 2005 & mtusEpsDT$mtus_month == "November"
+ & mtusEpsDT$s_dow == "Wednesday", "09/11/2005", mtusEpsDT$date_2005
+ )
+ mtusEpsDT$date_2005 <- ifelse(
+ mtusEpsDT$survey == 2005 & mtusEpsDT$mtus_month == "November"
+ & mtusEpsDT$s_dow == "Thursday", "10/11/2005", mtusEpsDT$date_2005
+ )
+ mtusEpsDT$date_2005 <- ifelse(
+ mtusEpsDT$survey == 2005 & mtusEpsDT$mtus_month == "November"
+ & mtusEpsDT$s_dow == "Friday", "11/11/2005", mtusEpsDT$date_2005
+ )
+ mtusEpsDT$date_2005 <- ifelse(
+ mtusEpsDT$survey == 2005 & mtusEpsDT$mtus_month == "November"
+ & mtusEpsDT$s_dow == "Saturday", "12/11/2005", mtusEpsDT$date_2005
+ )
+
+ # 2005 dates as POSIX - can't use fasttime
+ mtusEpsDT$r_date_2005 <- as.POSIXct(mtusEpsDT$date_2005, tz = "",
+ "%d/%m/%Y"
+ )
+ # check
+ table(as.POSIXlt(mtusEpsDT$r_date_2005)$wday, mtusEpsDT$s_dow)
+
+ # others dates as POSIX
+ mtusEpsDT$r_date <- as.POSIXct(mtusEpsDT$s_date, tz = "",
+ "%Y-%m-%d"
+ )
+ # check
+ table(as.POSIXlt(mtusEpsDT$r_date)$wday, mtusEpsDT$s_dow)
+
+ # combine the dates - why does this generate a number and not a POSIX?
+ mtusEpsDT$r_date_n <- ifelse(
+ mtusEpsDT$survey == 2005,
+ mtusEpsDT$r_date_2005, # if 2005
+ mtusEpsDT$r_date # if not, date already set
+ )
+
+ # convert to POSIX
+ mtusEpsDT$r_datef <- as.POSIXct(mtusEpsDT$r_date_n, origin = "1970-01-01")
+ # check matches
+ table(as.POSIXlt(mtusEpsDT$r_datef)$wday,
+ mtusEpsDT$s_dow,
+ useNA = "ifany"
+ )
+ feedBack("Setting up corrected start and end timestamps")
+ # all diaries start at 04:00 on the date given
+ # POSIXct works in seconds
+ mtusEpsDT$r_epstart <- mtusEpsDT$r_datef + (3*60*60)
+ # now add the corrected minutes up to the start of the episode
+ mtusEpsDT$r_epstart <- mtusEpsDT$r_epstart + (mtusEpsDT$ba_startm*60)
+
+ # same for episode end
+ mtusEpsDT$r_epend <- mtusEpsDT$r_epstart + (mtusEpsDT$end*60)
+
+ # define year (started)
+ mtusEpsDT$r_year <- as.POSIXlt(mtusEpsDT$r_date)$year
+
+ # keep the variables we need
+ mtusEpsDT <- mtusEpsDT[, .(survey, swave, hldid, persid, id, diarypid, pid,
+ diary, survey, r_year, badcase, sex, age,
+ r_epstart, r_epend, time, epnum,
+ main, sec, inout, eloc, mtrav, child, sppart)
+ ]
+
+ # check the distribution of episodes by time of day and year of survey
+ table("Hour" = as.POSIXlt(mtusEpsDT$r_epstart)$hour,
+ "Year"= as.POSIXlt(mtusEpsDT$r_epstart)$year
+ )
+
+ # set a new year variable to be:
+ # 74 + 75 = drop
+ # 83 + 84 + 85 = 1985
+ # 95 = drop
+ # 100 + 101 = 2001 = drop
+ # 105 = 2005
+ feedBack("Making filter for years of interest")
+ mtusEpsDT$ba_survey <- ifelse(
+ mtusEpsDT$survey == 1974,
+ 1974, # if true
+ NA # if not
+ )
+
+ mtusEpsDT$ba_survey <- ifelse(
+ mtusEpsDT$survey == 1983 |
+ mtusEpsDT$survey == 1987 ,
+ 1985, # if true
+ mtusEpsDT$ba_survey # if not
+ )
+
+ mtusEpsDT$ba_survey <- ifelse(
+ mtusEpsDT$survey == 1995 ,
+ 1995, # if true
+ mtusEpsDT$ba_survey # if not
+ )
+
+ mtusEpsDT$ba_survey <- ifelse(
+ mtusEpsDT$survey == 2000 ,
+ 2000, # if true
+ mtusEpsDT$ba_survey # if not
+ )
+
+ mtusEpsDT$ba_survey <- ifelse(
+ mtusEpsDT$survey == 2005 ,
+ 2005, # if true
+ mtusEpsDT$ba_survey # if not
+ )
+
+ # check data for analysis in this paper (1985 -> 2005)
+ print(
+ table(mtusEpsDT$ba_survey,
+ mtusEpsDT$survey,
+ useNA = "ifany"
+ )
+ )
+
+ # add hour & half hour of the start of the episode
+ mtusEpsDT$st_hour <- as.POSIXlt(mtusEpsDT$r_epstart)$hour
+ mtusEpsDT$st_hour <- ifelse(mtusEpsDT$st_hour < 10 ,
+ paste0("0",mtusEpsDT$st_hour), # if true - add leading 0
+ mtusEpsDT$st_hour # if not
+ )
+ mtusEpsDT$st_mins <- as.POSIXlt(mtusEpsDT$r_epstart)$min
+ mtusEpsDT$st_hh <- ifelse(mtusEpsDT$st_mins < 30 ,
+ "00", # if true
+ "30" # if not
+ )
+ mtusEpsDT$st_halfhour <- paste0(mtusEpsDT$st_hour,
+ ":",
+ mtusEpsDT$st_hh)
+ # check
+ with(mtusEpsDT,
+ table(st_halfhour)
+ )
+
+ with(mtusEpsDT,
+ table(badcase,ba_survey)
+ )
+ feedBack("Keeping good cases")
+ # Keep only good cases
+ mtusEpsDT <- mtusEpsDT[badcase == "good case"]
+ #mtusEpsDT <- mtusEpsDT[ba_survey %in% c("1985","2005")] keep inly 1985 & 2005
+
+ # check
+ print(
+ with(mtusEpsDT,
+ table(badcase,ba_survey)
+ )
+ )
+ # only keep the vars we need - saves memory etc and use global assignment for re-use elsewhere
+ gMTUSW6UKdiaryEps_DT <<- mtusEpsDT[, .(ba_survey, diarypid, main, sec, time, st_halfhour)]
+ feedBack("Summary of gMTUSW6UKdiaryEps_DT:")
+ print(summary(gMTUSW6UKdiaryEps_DT))
+
+ # save out the working file for later use (saves re-running)
+ episodes_DT <- paste0(tudpath, "gMTUSW6UKdiaryEps_DT.csv")
+ print(paste0("Saving processed file in: ", episodes_DT))
+ write.csv(gMTUSW6UKdiaryEps_DT,
+ file = episodes_DT, row.names = FALSE
+ )
+
+ dir <- getwd()
+ setwd(tudpath)
+ print("Now gzip the file")
+ system("gzip -f gMTUSW6UKdiaryEps_DT.csv &") # gzip & force over-write, shame can't do this directly as part of write
+ setwd(dir) # set back to working directory otherwise R will save .RData in an odd place
+ feedBack("Finished loading & fixing episodes data")
+} # works
+
+processMtusSampled <- function() {
+
+ # This was created in STATA - will port to R at some point
+ sampledmtus <- paste0(tudpath, "MTUS-adult-episode-UK-only-wf-10min-samples-long-v1.0.dta")
+ feedBack(paste0("Loading: ", sampledmtus))
+ mtusSampDT <- as.data.table(read.dta(sampledmtus))
+ setkey(mtusSampDT, diarypid)
+
+ feedBack("Fixing sampled dates")
+ # re-run date & time re-formatting as before
+ feedBack("Fixing sampled dates: March 2005")
+ mtusSampDT$date_2005 <- ifelse(
+ mtusSampDT$survey == 2005 & mtusSampDT$mtus_month == "March"
+ & mtusSampDT$s_dow == "Sunday", "2005-March-06", "na"
+ )
+ mtusSampDT$date_2005 <- ifelse(
+ mtusSampDT$survey == 2005 & mtusSampDT$mtus_month == "March"
+ & mtusSampDT$s_dow == "Monday", "2005-March-07", mtusSampDT$date_2005
+ )
+ mtusSampDT$date_2005 <- ifelse(
+ mtusSampDT$survey == 2005 & mtusSampDT$mtus_month == "March"
+ & mtusSampDT$s_dow == "Tuesday", "2005-March-08", mtusSampDT$date_2005
+ )
+ mtusSampDT$date_2005 <- ifelse(
+ mtusSampDT$survey == 2005 & mtusSampDT$mtus_month == "March"
+ & mtusSampDT$s_dow == "Wednesday", "2005-March-09", mtusSampDT$date_2005
+ )
+ mtusSampDT$date_2005 <- ifelse(
+ mtusSampDT$survey == 2005 & mtusSampDT$mtus_month == "March"
+ & mtusSampDT$s_dow == "Thursday", "2005-March-10", mtusSampDT$date_2005
+ )
+ mtusSampDT$date_2005 <- ifelse(
+ mtusSampDT$survey == 2005 & mtusSampDT$mtus_month == "March"
+ & mtusSampDT$s_dow == "Friday", "2005-March-11", mtusSampDT$date_2005
+ )
+ mtusSampDT$date_2005 <- ifelse(
+ mtusSampDT$survey == 2005 & mtusSampDT$mtus_month == "March"
+ & mtusSampDT$s_dow == "Saturday", "2005-March-12", mtusSampDT$date_2005
+ )
+ feedBack("Fixing sampled dates: June 2005")
+ # June: 5th -> 11th
+ mtusSampDT$date_2005 <- ifelse(
+ mtusSampDT$survey == 2005 & mtusSampDT$mtus_month == "June"
+ & mtusSampDT$s_dow == "Sunday", "2005-June-05", mtusSampDT$date_2005
+ )
+ mtusSampDT$date_2005 <- ifelse(
+ mtusSampDT$survey == 2005 & mtusSampDT$mtus_month == "June"
+ & mtusSampDT$s_dow == "Monday", "2005-June-06", mtusSampDT$date_2005
+ )
+ mtusSampDT$date_2005 <- ifelse(
+ mtusSampDT$survey == 2005 & mtusSampDT$mtus_month == "June"
+ & mtusSampDT$s_dow == "Tuesday", "2005-June-07", mtusSampDT$date_2005
+ )
+ mtusSampDT$date_2005 <- ifelse(
+ mtusSampDT$survey == 2005 & mtusSampDT$mtus_month == "June"
+ & mtusSampDT$s_dow == "Wednesday", "2005-June-08", mtusSampDT$date_2005
+ )
+ mtusSampDT$date_2005 <- ifelse(
+ mtusSampDT$survey == 2005 & mtusSampDT$mtus_month == "June"
+ & mtusSampDT$s_dow == "Thursday", "2005-June-09", mtusSampDT$date_2005
+ )
+ mtusSampDT$date_2005 <- ifelse(
+ mtusSampDT$survey == 2005 & mtusSampDT$mtus_month == "June"
+ & mtusSampDT$s_dow == "Friday", "2005-June-10", mtusSampDT$date_2005
+ )
+ mtusSampDT$date_2005 <- ifelse(
+ mtusSampDT$survey == 2005 & mtusSampDT$mtus_month == "June"
+ & mtusSampDT$s_dow == "Saturday", "2005-June-11", mtusSampDT$date_2005
+ )
+ feedBack("Fixing sampled dates: September 2005")
+ # September: 4th -> 10th
+ mtusSampDT$date_2005 <- ifelse(
+ mtusSampDT$survey == 2005 & mtusSampDT$mtus_month == "September"
+ & mtusSampDT$s_dow == "Sunday", "2005-September-04", mtusSampDT$date_2005
+ )
+ mtusSampDT$date_2005 <- ifelse(
+ mtusSampDT$survey == 2005 & mtusSampDT$mtus_month == "September"
+ & mtusSampDT$s_dow == "Monday", "2005-September-05", mtusSampDT$date_2005
+ )
+ mtusSampDT$date_2005 <- ifelse(
+ mtusSampDT$survey == 2005 & mtusSampDT$mtus_month == "September"
+ & mtusSampDT$s_dow == "Tuesday", "2005-September-06", mtusSampDT$date_2005
+ )
+ mtusSampDT$date_2005 <- ifelse(
+ mtusSampDT$survey == 2005 & mtusSampDT$mtus_month == "September"
+ & mtusSampDT$s_dow == "Wednesday", "2005-September-07", mtusSampDT$date_2005
+ )
+ mtusSampDT$date_2005 <- ifelse(
+ mtusSampDT$survey == 2005 & mtusSampDT$mtus_month == "September"
+ & mtusSampDT$s_dow == "Thursday", "2005-September-08", mtusSampDT$date_2005
+ )
+ mtusSampDT$date_2005 <- ifelse(
+ mtusSampDT$survey == 2005 & mtusSampDT$mtus_month == "September"
+ & mtusSampDT$s_dow == "Friday", "2005-September-09", mtusSampDT$date_2005
+ )
+ mtusSampDT$date_2005 <- ifelse(
+ mtusSampDT$survey == 2005 & mtusSampDT$mtus_month == "September"
+ & mtusSampDT$s_dow == "Saturday", "2005-September-10", mtusSampDT$date_2005
+ )
+ feedBack("Fixing sampled dates: November 2005")
+ # November: 6th - 12th
+ mtusSampDT$date_2005 <- ifelse(
+ mtusSampDT$survey == 2005 & mtusSampDT$mtus_month == "November"
+ & mtusSampDT$s_dow == "Sunday", "2005-November-06", mtusSampDT$date_2005
+ )
+ mtusSampDT$date_2005 <- ifelse(
+ mtusSampDT$survey == 2005 & mtusSampDT$mtus_month == "November"
+ & mtusSampDT$s_dow == "Monday", "2005-November-07", mtusSampDT$date_2005
+ )
+ mtusSampDT$date_2005 <- ifelse(
+ mtusSampDT$survey == 2005 & mtusSampDT$mtus_month == "November"
+ & mtusSampDT$s_dow == "Tuesday", "2005-November-08", mtusSampDT$date_2005
+ )
+ mtusSampDT$date_2005 <- ifelse(
+ mtusSampDT$survey == 2005 & mtusSampDT$mtus_month == "November"
+ & mtusSampDT$s_dow == "Wednesday", "2005-November-09", mtusSampDT$date_2005
+ )
+ mtusSampDT$date_2005 <- ifelse(
+ mtusSampDT$survey == 2005 & mtusSampDT$mtus_month == "November"
+ & mtusSampDT$s_dow == "Thursday", "2005-November-10", mtusSampDT$date_2005
+ )
+ mtusSampDT$date_2005 <- ifelse(
+ mtusSampDT$survey == 2005 & mtusSampDT$mtus_month == "November"
+ & mtusSampDT$s_dow == "Friday", "2005-November-11", mtusSampDT$date_2005
+ )
+ mtusSampDT$date_2005 <- ifelse(
+ mtusSampDT$survey == 2005 & mtusSampDT$mtus_month == "November"
+ & mtusSampDT$s_dow == "Saturday", "2005-November-12", mtusSampDT$date_2005
+ )
+
+ feedBack("Set up character hours/mins/secs so POSIXct recognises them")
+ mtusSampDT$start_hour <- as.POSIXlt(mtusSampDT$s_starttime)$hour
+ mtusSampDT$start_hour <- ifelse(mtusSampDT$start_hour < 10,
+ paste0("0",mtusSampDT$start_hour), # < 10
+ mtusSampDT$start_hour # not < 10
+ )
+ mtusSampDT$start_min <- as.POSIXlt(mtusSampDT$s_starttime)$min
+ mtusSampDT$start_min <- ifelse(mtusSampDT$start_min < 10,
+ paste0("0",mtusSampDT$start_min), # < 10
+ mtusSampDT$start_min # not < 10
+ )
+ table(mtusSampDT$start_hour,mtusSampDT$start_min)
+ mtusSampDT$start_sec <- "00"
+
+ feedBack("Construct start times")
+ # if 2005 -> use 2005 dates & character times
+ mtusSampDT$start_2005 <- ifelse(mtusSampDT$survey == 2005,
+ paste0(mtusSampDT$date_2005," ",
+ mtusSampDT$start_hour, ":",
+ mtusSampDT$start_min, ":",
+ mtusSampDT$start_sec),
+ NA
+ )
+
+ head(mtusSampDT$start_2005)
+ tail(mtusSampDT$start_2005)
+
+ # construct other start times in almost the same way but using MTUS date
+ mtusSampDT$start_other <- ifelse(mtusSampDT$survey != 2005,
+ paste0(mtusSampDT$mtus_year,"-",
+ mtusSampDT$mtus_month,"-",
+ mtusSampDT$mtus_cday, " ",
+ mtusSampDT$start_hour, ":",
+ mtusSampDT$start_min, ":",
+ mtusSampDT$start_sec),
+ NA
+ )
+
+ head(mtusSampDT$start_other)
+ tail(mtusSampDT$start_other)
+
+ # combine the dates - why does this generate a number and not a POSIX?
+ mtusSampDT$r_start_temp <- ifelse(
+ mtusSampDT$survey == 2005,
+ mtusSampDT$start_2005, # if 2005
+ mtusSampDT$start_other # if not
+ )
+
+ head(mtusSampDT$r_start_temp)
+ tail(mtusSampDT$r_start_temp)
+
+ feedBack("Convert start time to POSIXct")
+ # mtus_month is full name and we set the 2005 date to the same form
+ mtusSampDT$r_start <- as.POSIXct(mtusSampDT$r_start_temp, tz = "",
+ "%Y-%B-%d %H:%M:%S"
+ )
+ # check
+ head(mtusSampDT$r_start)
+ tail(mtusSampDT$r_start)
+
+ # define month and day of the week (started)
+ mtusSampDT$r_month <- as.POSIXlt(mtusSampDT$r_start)$mon # 0 = Jan
+ mtusSampDT$r_dow <- as.POSIXlt(mtusSampDT$r_start)$wday # 0 = Sunday
+ # add labels to dow
+ mtusSampDT$r_dow <- factor(mtusSampDT$r_dow,
+ levels = c(0,1,2,3,4,5,6),
+ labels = c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday")
+ )
+
+ feedBack("Check days of the week")
+ print(
+ xtabs(~ mtusSampDT$r_dow + mtusSampDT$s_dow
+ )
+ )
+
+ # define hour & mins (started)
+ mtusSampDT$r_hour <- as.POSIXlt(mtusSampDT$r_start)$hour
+ mtusSampDT$r_min <- as.POSIXlt(mtusSampDT$r_start)$min
+
+ # set a new year variable to be:
+ # 74 + 75 = drop
+ # 83 + 84 + 85 = 1985
+ # 95 = drop
+ # 100 + 101 = 2001 = drop
+ # 105 = 2005
+ feedBack("Create survey year filter")
+ mtusSampDT$ba_survey <- ifelse(
+ mtusSampDT$survey == 1974,
+ 1974, # if true
+ NA # if not
+ )
+
+ mtusSampDT$ba_survey <- ifelse(
+ mtusSampDT$survey == 1983 |
+ mtusSampDT$survey == 1987 ,
+ 1985, # if true
+ mtusSampDT$ba_survey # if not
+ )
+
+ mtusSampDT$ba_survey <- ifelse(
+ mtusSampDT$survey == 1995 ,
+ 1995, # if true
+ mtusSampDT$ba_survey # if not
+ )
+
+ mtusSampDT$ba_survey <- ifelse(
+ mtusSampDT$survey == 2000 ,
+ 2000, # if true
+ mtusSampDT$ba_survey # if not
+ )
+
+ mtusSampDT$ba_survey <- ifelse(
+ mtusSampDT$survey == 2005 ,
+ 2005, # if true
+ mtusSampDT$ba_survey # if not
+ )
+
+ feedBack("Check data for this paper (1985 -> 2005)")
+ print(
+ table(mtusSampDT$ba_survey,
+ mtusSampDT$survey,
+ useNA = "ifany"
+ )
+ )
+
+ # add hour & half hour to each sampled record
+
+ mtusSampDT$r_hour <- as.POSIXlt(mtusSampDT$s_starttime)$hour
+ mtusSampDT$st_hour <- ifelse(mtusSampDT$r_hour < 10 ,
+ paste0("0",mtusSampDT$r_hour), # if true - add leading 0
+ mtusSampDT$r_hour # if not
+ )
+ mtusSampDT$r_mins <- as.POSIXlt(mtusSampDT$s_starttime)$min
+ mtusSampDT$st_hh <- ifelse(mtusSampDT$r_mins < 30 ,
+ "00", # if true
+ "30" # if not
+ )
+ mtusSampDT$st_halfhour <- paste0(mtusSampDT$st_hour,
+ ":",
+ mtusSampDT$st_hh)
+ # check
+ with(mtusSampDT,
+ table(st_halfhour)
+ )
+ # set laundry vars
+ feedBack("Set a laundry flag")
+ mtusSampDT$laundry_p <- ifelse(mtusSampDT$pact == laundry,
+ 1, # laundry as main act
+ 0)
+ mtusSampDT$laundry_ph <- ifelse(mtusSampDT$pact == laundry &
+ mtusSampDT$eloc == "at own home",
+ 1, # laundry as main act at own home
+ 0)
+ mtusSampDT$laundry_photh <- ifelse(mtusSampDT$pact == laundry &
+ mtusSampDT$eloc == "at another's home",
+ 1, # laundry as main act at another's home
+ 0)
+ mtusSampDT$laundry_psh <- ifelse(mtusSampDT$pact == laundry &
+ mtusSampDT$eloc == "at services or shops",
+ 1, # laundry as main act at service/shops
+ 0)
+ mtusSampDT$laundry_poth <- ifelse(mtusSampDT$pact == laundry &
+ mtusSampDT$eloc == "other locations",
+ 1, # laundry as main act at other locations
+ 0)
+ feedBack("Make global table keeping the variables we need")
+ gMTUSW6UKdiarySampled_DT <<- mtusSampDT[, .(hldid, diarypid, pid,
+ diary, ba_survey,
+ r_start, r_month, r_dow, r_hour, st_halfhour,
+ pact, sact, eloc, mtrav,
+ laundry_p, laundry_ph, laundry_photh, laundry_psh, laundry_poth)
+ ]
+ feedBack("Summary of gMTUSW6UKdiarySampled_DT:")
+ print(
+ summary(gMTUSW6UKdiarySampled_DT)
+ )
+
+ feedBack("Creating a derived data table to enable us to count laundry obs within half hours")
+ # First count the number of obs for all half hours - should be 3!
+ byList <- c("ba_survey", "diarypid", "r_month", "r_dow", "r_hour", "st_halfhour") # -> easy to repeat
+ MTUSW6UK_halfhours_DT <- gMTUSW6UKdiarySampled_DT[,
+ .(
+ N_obs = length(pact) # number of 10 min samples (should be 3!)
+ ),
+ by = byList
+ ]
+ # Now count the various forms of laundry we are interested in
+ MTUSW6UK_halfhours_DTl <- gMTUSW6UKdiarySampled_DT[,
+ .(
+ N_laundry_p = sum(laundry_p), # number of 10 min laundry obs (should be 1-3!)
+ N_laundry_ph = sum(laundry_ph), # number of 10 min laundry obs (should be 1-3!)
+ N_laundry_photh = sum(laundry_photh), # number of 10 min laundry obs (should be 1-3!)
+ N_laundry_psh = sum(laundry_psh), # number of 10 min laundry obs (should be 1-3!)
+ N_laundry_poth = sum(laundry_poth)
+ ),
+ by = byList
+ ]
+
+
+ # set an indicator if any laundry was recorded
+ MTUSW6UK_halfhours_DTl$Any_laundry_p <- ifelse(
+ MTUSW6UK_halfhours_DTl$N_laundry_p > 0, 1, 0 # set no to 0 for later ease of summary
+ )
+ MTUSW6UK_halfhours_DTl$Any_laundry_ph <- ifelse(
+ MTUSW6UK_halfhours_DTl$N_laundry_ph > 0, 1, 0 # set no to 0 for later ease of summary
+ )
+ MTUSW6UK_halfhours_DTl$Any_laundry_photh <- ifelse(
+ MTUSW6UK_halfhours_DTl$N_laundry_photh > 0, 1, 0 # set no to 0 for later ease of summary
+ )
+ MTUSW6UK_halfhours_DTl$Any_laundry_psh <- ifelse(
+ MTUSW6UK_halfhours_DTl$N_laundry_psh > 0, 1, 0 # set no to 0 for later ease of summary
+ )
+ MTUSW6UK_halfhours_DTl$Any_laundry_poth <- ifelse(
+ MTUSW6UK_halfhours_DTl$N_laundry_poth > 0, 1, 0 # set no to 0 for later ease of summary
+ )
+
+ feedBack("Creating global derived half hours table")
+ setkeyv(MTUSW6UK_halfhours_DT, byList)
+ setkeyv(MTUSW6UK_halfhours_DTl, byList)
+ gMTUSW6UK_halfhours_laundry_DT <<- MTUSW6UK_halfhours_DT[MTUSW6UK_halfhours_DTl]
+
+ # check
+ head(gMTUSW6UK_halfhours_laundry_DT[gMTUSW6UK_halfhours_laundry_DT$N_laundry_p == 1])
+ head(gMTUSW6UK_halfhours_laundry_DT[gMTUSW6UK_halfhours_laundry_DT$N_laundry_p == 3])
+
+ # save out the two working files for later use (saves re-running)
+ sampled_DT <- paste0(tudpath, "gMTUSW6UKdiarySampled_DT.csv")
+ print(paste0("Saving processed file in: ", sampled_DT))
+ write.csv(gMTUSW6UKdiarySampled_DT,
+ file = sampled_DT, row.names = FALSE
+ )
+ laundry_hh_DT <- paste0(tudpath, "gMTUSW6UK_halfhours_laundry_DT.csv")
+ print(paste0("Saving processed file in: ", laundry_hh_DT))
+ write.csv(gMTUSW6UK_halfhours_laundry_DT,
+ file = laundry_hh_DT, row.names = FALSE
+ )
+ dir <- getwd()
+ setwd(tudpath)
+ print("Now gzip those files via system")
+ system("gzip -f gMTUSW6UKdiarySampled_DT.csv &") # gzip & force over-write, shame can't do this directly as part of write
+ system("gzip -f gMTUSW6UK_halfhours_laundry_DT.csv &") # gzip & force over-write, shame can't do this directly as part of write
+ setwd(dir) # set back to working directory otherwise R will save .RData in an odd place
+ print("Done")
+} # works
+
+# Run to here to load functions
+
+# Controller ----
+loadPackages()
+processMtusSurvey()
+processMtusEpisodes()
+processMtusSampled()
\ No newline at end of file
diff --git a/Theme-1/laundryPaper/DEMAND-BA-MTUS-W6-Laundry-gender-over-time.R b/Theme-1/laundryPaper/DEMAND-BA-MTUS-W6-Laundry-gender-over-time.R
new file mode 100644
index 0000000000000000000000000000000000000000..46c8154be475081f535d5d7fdcb72dc6b80cec52
--- /dev/null
+++ b/Theme-1/laundryPaper/DEMAND-BA-MTUS-W6-Laundry-gender-over-time.R
@@ -0,0 +1,232 @@
+# Begin header ###########################################
+# Use MTUS World 6 time-use data (UK subset) to examine:
+# - distributions of laundry in 1985 -> 2005
+# - for discussions with @tulliajack re comparisons with Sweden
+
+# Data source: www.timeuse.org/mtus
+# data already in long format (but episodes) processed using
+# DEMAND-BA-MTUS-W6-Laundry-Change-Over-Time-v2.0-adult-data-processing.R
+
+# This work was funded by RCUK through the End User Energy Demand Centres Programme via the
+# "DEMAND: Dynamics of Energy, Mobility and Demand" Centre (www.demand.ac.uk, gow.epsrc.ac.uk/NGBOViewGrant.aspx?GrantRef=EP/K011723/1)
+
+# Copyright (C) 2014 University of Southampton
+# Author: Ben Anderson (b.anderson@soton.ac.uk, @dataknut, https://github.com/dataknut)
+
+# This program is free software; you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation; either version 2 of the License
+# (http://choosealicense.com/licenses/gpl-2.0/), or
+# (at your option) any later version.
+
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+#
+# End header ###########################################
+
+# MTUS codes of interest: Main/Sec21 Laundry, ironing, clothing repair
+# Defined as:
+
+# 1983/4/7:
+# <- 0701 Wash clothes, hang out / bring in washing
+# 0702 Iron clothes
+# 0801 Repair, upkeep of clothes
+# => may over-estimate laundry
+
+# 1995 14 Clothes
+# => may over-estimate laundry
+
+# 2000 3300 Unspecified making and care for textiles
+# 3310 Laundry
+# 3320 Ironing
+# 3390 Other specified making and care for textiles
+# => may over-estimate laundry
+
+# 2005 Pact=7 (washing clothes)
+
+
+# Housekeeping ----
+# clear out all old objects etc to avoid confusion
+rm(list = ls())
+
+# set up some useful data paths
+tudpath <- "~/Documents/Work/Data/MTUS/World_6/processed/" # presume processed data is already in here
+epsfile <- "gMTUSW6UKdiaryEps_DT.csv" # need to change to processed csv
+survfile <- "gMTUSW6UKsurveyCore_DT.csv.gz"
+
+rpath <- "~/Documents/Work/Projects/RCUK_DEMAND/Theme 1/results/MTUS/"
+
+# define laundry for time use data
+laundry <- "laundry, ironing, clothing repair"
+
+# Generic functions ----
+
+# load packages
+loadPackages <- function() {
+ # add packages
+ library(foreign) # as loading stata files
+ library(lattice) # fancy graphs
+ library(ggplot2) # fancy graphs II
+ library(data.table) # why use anything else?
+ library(survey) # weighted survey analysis
+ library(car) # regression diagnostics
+ library(gmodels) # nice crosstabs
+ library(broom) # turns stats objects into dataframes - useful for table output
+ library(fasttime) # VERY fast time string conversion to POSIXct
+ # but only IF the input string is in a fixed format - see http://rforge.net/doc/packages/fasttime/fastPOSIXct.html
+}
+
+# Feedback function - cos I can't be bothered to keep writing it out
+feedBack <- function(string) {
+ print(paste0("Feedback: ", string))
+}
+
+# Functions for loading pre-processed data
+
+loadCoreMtusSurvey <- function() {
+ cmd <- paste0("gunzip -c ", tudpath, survfile)
+ print(paste0("Loading via ", cmd))
+
+ # read in the gzipped file using gunzip to 'pipe' the file to fread
+ # this is a lot faster than non-piped gunzip then fread and you get feedback from fread
+ # Don't forget to globalise!
+ gMTUSW6UKsurveyCore_DT <<- fread(cmd,
+ stringsAsFactors = FALSE)
+ feedBack("Done loading TU survey data")
+} # works
+
+loadMtusEpisodes <- function() {
+ cmd <- paste0("gunzip -c ", tudpath, epsfile)
+ print(paste0("Loading via ", cmd))
+
+ # read in the gzipped file using gunzip to 'pipe' the file to fread
+ # this is a lot faster than non-piped gunzip then fread and you get feedback from fread
+ # Don't forget to globalise!
+ gMTUSW6UKdiaryEps_DT <<- fread(cmd,
+ stringsAsFactors = FALSE)
+ feedBack("Done loading TU survey data")
+}
+
+# Controller
+loadPackages()
+loadCoreMtusSurvey()
+loadMtusEpisodes()
+
+setkey(gMTUSW6UKdiaryEps_DT, diarypid)
+setkey(gMTUSW6UKsurveyCore_DT, diarypid)
+
+gMTUSW6UKdiaryEps_DT$laundry_p <- ifelse(gMTUSW6UKdiaryEps_DT$main == laundry,
+ 1, # laundry as main act
+ 0)
+gMTUSW6UKdiaryEps_DT$laundry_s <- ifelse(gMTUSW6UKdiaryEps_DT$sec == laundry,
+ 1, # laundry as main act
+ 0)
+gMTUSW6UKdiaryEps_DT$laundry_all <- ifelse(gMTUSW6UKdiaryEps_DT$main == laundry | gMTUSW6UKdiaryEps_DT$sec == laundry,
+ 1, # laundry as either act
+ 0)
+
+# merge keeping only good cases
+gMTUSW6UKdiaryEpsMerged_DT <- gMTUSW6UKdiaryEps_DT[gMTUSW6UKsurveyCore_DT[gMTUSW6UKsurveyCore_DT$badcase == "good case"]]
+
+# checks
+with(gMTUSW6UKdiaryEpsMerged_DT,
+ table(sex, useNA = c("always"))
+)
+with(gMTUSW6UKdiaryEpsMerged_DT,
+ table(badcase, useNA = c("always"))
+)
+with(gMTUSW6UKdiaryEpsMerged_DT,
+ summary(propwt)
+)
+
+svygMTUSW6UKdiaryEpsMerged_DT <- svydesign(ids = ~diarypid,
+ weight = ~propwt,
+ data = gMTUSW6UKdiaryEpsMerged_DT # all data
+) # does not produce a data table
+
+# mean duration of laundry episodes
+svyby(~time, # the data to summarise
+ ~ba_survey + sex, # the row * columns we want
+ svygMTUSW6UKdiaryEpsMerged_DT[
+ svygMTUSW6UKdiaryEpsMerged_DT$variables$laundry_all == 1], # the data in survey form
+ svymean # the function to use to summarise
+)
+
+# total duration of laundry episodes
+svyby(~time, # the data to summarise
+ ~ba_survey + sex, # the row * columns we want
+ svygMTUSW6UKdiaryEpsMerged_DT[
+ svygMTUSW6UKdiaryEpsMerged_DT$variables$laundry_all == 1], # the data in survey form
+ svytotal # the function to use to summarise
+)
+
+laundrySummaryByPersonDTp <- gMTUSW6UKdiaryEpsMerged_DT[laundry_p == 1,
+ .(
+ laundry_p_minutes = sum(time)
+ ),
+ by = .(
+ diarypid
+ )
+ ]
+setkey(laundrySummaryByPersonDTp, diarypid)
+laundrySummaryByPersonDTs <- gMTUSW6UKdiaryEpsMerged_DT[laundry_s == 1,
+ .(
+ laundry_s_minutes = sum(time)
+ ),
+ by = .(
+ diarypid
+ )
+ ]
+setkey(laundrySummaryByPersonDTs, diarypid)
+laundrySummaryByPersonDT <- merge(laundrySummaryByPersonDTp,
+ laundrySummaryByPersonDTs, all = TRUE) # keep all
+
+laundrySummaryByPersonDT$laundry_p_minutes <- ifelse(
+ is.na(laundrySummaryByPersonDT$laundry_p_minutes),
+ 0,
+ laundrySummaryByPersonDT$laundry_p_minutes
+ )
+laundrySummaryByPersonDT$laundry_s_minutes <- ifelse(
+ is.na(laundrySummaryByPersonDT$laundry_s_minutes),
+ 0,
+ laundrySummaryByPersonDT$laundry_s_minutes
+ )
+
+laundrySummaryByPersonDT$total_laundry <- laundrySummaryByPersonDT$laundry_p_minutes +
+ laundrySummaryByPersonDT$laundry_s_minutes
+
+# merge back to survey data
+laundrySummaryByPersonDT <- merge(laundrySummaryByPersonDT,
+ gMTUSW6UKsurveyCore_DT) # keep matches
+
+print("Set survey data")
+# tell survey that the diarypids are the ids (they repeat)
+svyLaundrySummaryByPersonDT <- svydesign(ids = ~diarypid,
+ weight = ~propwt,
+ data = laundrySummaryByPersonDT # laundry only
+ ) # does not produce a data table
+
+# reporting laundry by gender?
+# any laundry
+svytable(~ba_survey + sex , # the row * columns we want
+ svyLaundrySummaryByPersonDT # the data in survey form
+)
+
+# mean total time spent on laundry (primary)
+# XX not correct? XX
+svyby(~laundry_p_minutes, # the data to summarise
+ ~ba_survey + sex, # the row * columns we want
+ svyLaundrySummaryByPersonDT, # the data in survey form
+ svymean, # the function to use to summarise
+ na.rm = TRUE
+)
+
+# mean total time spent on laundry (primary)
+svyby(~laundry_s_minutes, # the data to summarise
+ ~ba_survey + sex, # the row * columns we want
+ svyLaundrySummaryByPersonDT, # the data in survey form
+ svymean, # the function to use to summarise
+ na.rm = TRUE
+)
diff --git a/Theme-1/laundryPaper/DEMAND-BA-ons-2005-laundry-data-exploration.R b/Theme-1/laundryPaper/DEMAND-BA-ons-2005-laundry-data-exploration.R
new file mode 100644
index 0000000000000000000000000000000000000000..3d4918ff47373d9107eb9886085b3b68bf561a41
--- /dev/null
+++ b/Theme-1/laundryPaper/DEMAND-BA-ons-2005-laundry-data-exploration.R
@@ -0,0 +1,262 @@
+# Header ###########################################
+# Time Use data analysis for 'Laundry' paper
+#
+# Use ONS UK Time Use Survey 2005 to examine:
+# - distributions of laundry in 2005
+#
+# Data source: http://discover.ukdataservice.ac.uk/catalogue/?sn=5592
+# Data already in long format (but 10 minute slots)
+#
+# This work was funded by RCUK through the End User Energy Demand Centres Programme via the
+# "DEMAND: Dynamics of Energy, Mobility and Demand" Centre (www.demand.ac.uk, gow.epsrc.ac.uk/NGBOViewGrant.aspx?GrantRef=EP/K011723/1)
+#
+# Copyright (C) 2014 University of Southampton
+#
+# Author: Ben Anderson (b.anderson@soton.ac.uk, @dataknut, https://github.com/dataknut)
+# [Energy & Climate Change, Faculty of Engineering & Environment, University of Southampton]
+#
+# The MIT License (MIT) applies - see https://github.com/dataknut/
+#
+# end header
+
+# To do: -----------------------------------------------------------------
+
+# Prelims -----------------------------------------------------------------
+
+# clear out all old objects etc to avoid confusion
+rm(list = ls())
+
+# set time
+starttime <- proc.time()
+
+# load required packages
+# foreign - not needed if we are loading csv files
+# NB: this will fail if you do not have internet access
+# on OS X at least it asks if you want to re-start R first, click no
+packagel <- c("ggplot2","plyr")
+install.packages(packagel)
+lapply(packagel, require, character.only = T)
+
+# path to data & results
+
+# where's the data?
+dpath <- "~/Documents/Work/Data/Social Science Datatsets/Time Use 2005/processed/"
+# where do you want the results to go?
+rpath <- "~/Documents/Work/Projects/RCUK-DEMAND/Theme 1/results/ONS TU 2005"
+
+# time axis defnition
+# can't get this to work!
+# halfhourlab <- "\"04:00\",\"06:00\", \"08:00\", \"10:00\", \"12:00\", \"14:00\", \"16:00\",\"18:00\",\"20:00\",\"22:00\""
+
+# Load long form data -----------------------------------------------------------------
+# Time use data in long form - this has data in 10 minute time 'slots'
+# It also has a few survey variables attached to each time use slot
+
+tu2005data <- read.csv(paste0(dpath,"UK-2005-TU-merged-long-reduced.csv"))
+
+# Now stop to check what's in it and make sure we understand the format!
+head(tu2005data)
+
+# check values of main acts (the things people reported doing) by location
+all_acts_by_location <- table("Main acts"= tu2005data$pact)
+# ouptput to a csv file so we can keep for reference (useful later)
+write.csv(all_acts_by_location, paste0(rpath,"all_acts_by_location-table.csv"), row.names=FALSE, na="")
+
+# check values of months variable (so we see that seasons are represented)
+table("Month"= tu2005data$t_month)
+
+# recode month so it is easier to interpret
+tu2005data$t_month[tu2005data$t_month == 2] <- "February"
+tu2005data$t_month[tu2005data$t_month == 6] <- "June"
+tu2005data$t_month[tu2005data$t_month == 9] <- "September"
+tu2005data$t_month[tu2005data$t_month == 11] <- "November"
+
+# set the order of the month factor
+tu2005data$t_month <- factor(tu2005data$t_month,
+ levels = c("February","June","September","November"))
+
+# re-check
+table("Month"= tu2005data$t_month)
+
+# check the days
+table("Days"= tu2005data$s_dow)
+
+# Out of order!
+# set the order of the dow factor
+tu2005data$s_dow <- factor(tu2005data$s_dow,
+ levels = c("Monday","Tuesday","Wednesday","Thursday","Friday","Saturday","Sunday"))
+
+# recheck
+table("Days"= tu2005data$s_dow)
+
+# Test: all acts -----------------------------------------------------------------
+# add a dummy variable we can count
+tu2005data$count <- 1
+# create a table which counts the occurences of 'pact' in each 10 minute slot
+all_acts <- ddply(tu2005data, c("s_starttime","pact"), summarise, count=sum(count))
+
+# draw an unintelligible line graph using the table
+all_acts_lplot <- ggplot(all_acts, aes(x=s_starttime, y=count, colour=pact, group=pact)) + geom_line()
+all_acts_lplot + xlab("Time of Day") + ylab("N reporting") +
+ labs(colour="Activity") +
+ scale_x_discrete(breaks=c("00:00","04:00","08:00","12:00","16:00","20:00","23:30"),
+ labels=c("00:00","04:00","08:00","12:00","16:00","20:00","23:30")) +
+ theme(axis.text.x = element_text(angle=90, hjust=1, vjust=1)) +
+ theme(legend.position="right")
+# save the plot
+ggsave(paste0(rpath,"all_acts_tod_lineplot.pdf"), width=12, height=8, unit="cm", dpi=300)
+
+# and an unintelligible stacked chart
+all_acts_stplot <- ggplot(all_acts, aes(x=s_starttime, y=count, fill=pact, group=pact)) + geom_area()
+all_acts_stplot + xlab("Time of Day") + ylab("N reporting") +
+ labs(fill="Activity") +
+ scale_x_discrete(breaks=c("00:00","04:00","08:00","12:00","16:00","20:00","23:30"),
+ labels=c("00:00","04:00","08:00","12:00","16:00","20:00","23:30")) +
+ theme(axis.text.x = element_text(angle=90, hjust=1, vjust=1)) +
+ theme(legend.position="right")
+# save the plot
+ggsave(paste0(rpath,"all_acts_tod_stackedplot.pdf"), width=12, height=8, unit="cm", dpi=300)
+
+# What we really want to do is to create a new column with primary acts where:
+# - all travel is collapsed to 1 code
+# - the '/' are removed to make graph saving easier later
+# Something like:
+tu2005data$pact_c <- as.character(tu2005data$pact)
+tu2005data$pact_nc <- gsub("/", " or ", tu2005data$pact_c)
+# find the travel
+tu2005data$pact_t <- grepl("travel", tu2005data$pact_c)
+
+tu2005data$sact_c <- as.character(tu2005data$sact)
+tu2005data$sact_nc <- gsub("/", " or ", tu2005data$sact_c)
+# find the travel
+tu2005data$sact_t <- grepl("travel", tu2005data$sact_c)
+
+# set travel
+tu2005data$pact_nc[tu2005data$pact_t == TRUE] <- "travel"
+tu2005data$sact_nc[tu2005data$sact_t == TRUE] <- "travel"
+
+# convert back to factors
+tu2005data$pact_nf <- as.factor(tu2005data$pact_nc)
+tu2005data$sact_nf <- as.factor(tu2005data$sact_nc)
+
+table(tu2005data$pact_nf)
+
+# now re-try the stacked chart - there are fewer categories (but still a lot!)
+all_acts_nf <- ddply(tu2005data, c("s_starttime","pact_nf"), summarise, count=sum(count))
+all_acts_stplotn <- ggplot(all_acts_nf, aes(x=s_starttime, y=count, fill=pact_nf, group=pact_nf)) + geom_area()
+all_acts_stplotn + xlab("Time of Day") + ylab("N reporting") +
+ labs(fill="Activity") +
+ scale_x_discrete(breaks=c("00:00","04:00","08:00","12:00","16:00","20:00","23:30"),
+ labels=c("00:00","04:00","08:00","12:00","16:00","20:00","23:30")) +
+ theme(axis.text.x = element_text(angle=90, hjust=1, vjust=1)) +
+ theme(legend.position="right")
+# well that worked slightly better but it's still fairly illegible, further grouping required!!
+# save the plot
+ggsave(paste0(rpath,"all_acts_nf_tod_stackedplot.pdf"), width=12, height=8, unit="cm", dpi=300)
+
+# Practices: Laundry -----------------------------------------------------------------
+# Interesting of itself but we also want to try to compare the results with the HES data
+# set our y axis label
+ylabt <- "laundry"
+tu2005data$laundry_all <- 0
+# we're interested in laundry at home (for now!)
+tu2005data$laundry_all[tu2005data$pact == "washing clothes" &
+ tu2005data$lact != "elsewhere" |
+ tu2005data$sact == "washing clothes" &
+ tu2005data$lact != "elsewhere"] <- 1
+
+# make the table
+laundry <- ddply(tu2005data, c("s_dow", "s_halfhour"), summarise,
+ n=sum(count),
+ pc=mean(laundry_all),
+ sd=sd(laundry_all))
+# CI for propn
+# +/- (1.96 ∗ sqrt(p∗(1−p)/n))
+laundry$se <- sqrt(laundry$pc*(1-laundry$pc)/laundry$n)
+laundry$ci <- 1.96 * laundry$se
+
+# plot it
+laundry_plot <- ggplot(laundry, aes(x=s_halfhour, y=pc, colour=s_dow, group=s_dow)) + geom_line()
+laundry_plot + xlab("Time of Day") +
+ ylab(paste("% reporting", ylabt)) +
+ labs(colour="Day of the week") +
+ scale_x_discrete(breaks=c("00:00","04:00","08:00","12:00","16:00","20:00","23:30"),
+ labels=c("00:00","04:00","08:00","12:00","16:00","20:00","23:30")) +
+ theme(axis.text.x = element_text(angle=90, hjust=1, vjust=1)) +
+ facet_wrap( ~ s_dow) +
+ geom_errorbar(aes(ymin=pc-ci, ymax=pc+ci), width=.2)
+# save the plot
+ggsave(paste0(rpath,"laundry_ci_tod_dow_plot.pdf"), width=12, height=8, unit="cm", dpi=300)
+
+# try a contour plot/heat map to make day of the week easier to see
+laundry_hmplot <- ggplot(laundry, aes(x=s_halfhour, y=s_dow, fill=pc))
+laundry_hmplot + geom_raster() + xlab("Time of Day") +
+ ylab("Day of week") +
+ labs(fill=paste("% reporting", ylabt)) +
+ theme(legend.position="bottom") +
+ scale_x_discrete(breaks=c("00:00","04:00","08:00","12:00","16:00","20:00","23:30"),
+ labels=c("00:00","04:00","08:00","12:00","16:00","20:00","23:30")) +
+ theme(axis.text.x = element_text(angle=90, hjust=1, vjust=1))
+# save the plot
+ggsave(paste0(rpath,"laundry_tod_dow_hmplot.pdf"), width=12, height=8, unit="cm", dpi=300)
+
+# now try by age group to analyse differences
+laundry_age <- ddply(tu2005data, c("agegrp", "s_halfhour"), summarise, pc=100*mean(laundry_all))
+laundry_plot <- ggplot(laundry_age, aes(x=s_halfhour, y=pc, colour=agegrp, group=agegrp)) + geom_line()
+laundry_plot + xlab("Time of Day") +
+ ylab(paste("% reporting", ylabt)) +
+ labs(colour="Age group") +
+ scale_x_discrete(breaks=c("00:00","04:00","08:00","12:00","16:00","20:00","23:30"),
+ labels=c("00:00","04:00","08:00","12:00","16:00","20:00","23:30")) +
+ theme(axis.text.x = element_text(angle=90, hjust=1, vjust=1))
+# save the plot
+ggsave(paste0(rpath,"laundry_tod_by_age_plot.pdf"), width=12, height=8, unit="cm", dpi=300)
+
+# working status
+laundry_wrk <- ddply(tu2005data, c("wrking", "s_halfhour", "s_dow"), summarise,
+ n=sum(count),
+ pc=mean(laundry_all),
+ sd=sd(laundry_all))
+# CI for propn
+# +/- (1.96 ∗ sqrt(p∗(1−p)/n))
+laundry_wrk$se <- sqrt(laundry$pc*(1-laundry$pc)/laundry$n)
+laundry_wrk$ci <- 1.96 * laundry$se
+
+laundry_plot <- ggplot(laundry_wrk, aes(x=s_halfhour, y=pc, colour=wrking, group=wrking)) + geom_line()
+laundry_plot + xlab("Time of Day") +
+ ylab(paste("% reporting", ylabt)) +
+ labs(colour="Working status") +
+ scale_x_discrete(breaks=c("00:00","04:00","08:00","12:00","16:00","20:00","23:30"),
+ labels=c("00:00","04:00","08:00","12:00","16:00","20:00","23:30")) +
+ theme(axis.text.x = element_text(angle=90, hjust=1, vjust=1)) +
+ geom_errorbar(aes(ymin=pc-ci, ymax=pc+ci), width=.2) +
+ facet_wrap( ~ s_dow) +
+ theme(legend.position=c(0.5,0.2))
+# save the plot
+ggsave(paste0(rpath,"laundry_tod_by_working_plot.pdf"), width=12, height=8, unit="cm", dpi=300)
+
+# To compare with the HES data on 'washing/drying' we need to create a table by weekend ('holiday') vs weekday
+# And it needs to have 10 minute time slots as the HES data is in 10 minute chunks
+tu2005data$weekend <- "Weekday"
+tu2005data$weekend[tu2005data$s_dow == "Saturday" | tu2005data$s_dow == "Sunday"] <- "Weekend"
+# check
+table(tu2005data$s_dow,tu2005data$weekend)
+
+laundry_hes <- ddply(tu2005data, c("weekend", "s_starttime"), summarise, pc=100*mean(laundry_all))
+laundry_hes_plot <- ggplot(laundry_hes, aes(x=s_starttime, y=pc, colour=weekend, group=weekend)) + geom_line()
+laundry_hes_plot + xlab("Time of Day") +
+ ylab(paste("% reporting", ylabt)) +
+ labs(colour="Weekday/Weekend") +
+ scale_x_discrete(breaks=c("00:00","04:00","08:00","12:00","16:00","20:00","23:30"),
+ labels=c("00:00","04:00","08:00","12:00","16:00","20:00","23:30")) +
+ theme(axis.text.x = element_text(angle=90, hjust=1, vjust=1))
+ggsave(paste0(rpath,"laundry_tod_hes_plot.pdf"), width=12, height=8, unit="cm", dpi=300)
+
+# to get the data on the same graph as the HES results we need to export the table we made
+# -> csv with blank cells where na
+# NB this is long form - we could switch it to wide form to make it easier
+write.csv(laundry_hes, paste0(rpath,"laundry_tod_hes_compare_data.csv"), row.names=FALSE, na="")
+
+print("Done!")
+# stop clock - how long did that take?
+proc.time() - starttime
\ No newline at end of file
diff --git a/Theme-1/laundryPaper/LICENSE b/Theme-1/laundryPaper/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..d6a93266f748d606b884f9434ff662fe80b9dc21
--- /dev/null
+++ b/Theme-1/laundryPaper/LICENSE
@@ -0,0 +1,340 @@
+GNU GENERAL PUBLIC LICENSE
+ Version 2, June 1991
+
+ Copyright (C) 1989, 1991 Free Software Foundation, Inc., <http://fsf.org/>
+ 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+ Preamble
+
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+the "copyright" line and a pointer to where the full notice is found.
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+ {description}
+ Copyright (C) {year} {fullname}
+
+ This program is free software; you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation; either version 2 of the License, or
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+
+ {signature of Ty Coon}, 1 April 1989
+ Ty Coon, President of Vice
+
+This General Public License does not permit incorporating your program into
+proprietary programs. If your program is a subroutine library, you may
+consider it more useful to permit linking proprietary applications with the
+library. If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.
+
diff --git a/Theme-1/laundryPaper/README.md b/Theme-1/laundryPaper/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..d8e5fe5f58176e635e8810093b8a8485a8c18980
--- /dev/null
+++ b/Theme-1/laundryPaper/README.md
@@ -0,0 +1,17 @@
+# DEMAND: Dynamics of Energy, Mobility and Demand
+
+Unless otherwise indicated this work was funded by RCUK through the End User Energy Demand Centres Programme via the "DEMAND: Dynamics of Energy, Mobility and Demand" Centre:
+ * http://www.demand.ac.uk
+ * http://gtr.rcuk.ac.uk/project/0B657D54-247D-4AD6-9858-64E411D3D06C
+
+# DEMAND_Laundry
+Analysis for a paper on the changing practices of laundry using UK Time-Use data 1985-2005.
+
+Paper: https://eprints.soton.ac.uk/400478/
+
+### Terms of Use
+GPL: V2 - http://choosealicense.com/licenses/gpl-2.0/
+
+See license file for details.
+
+[YMMV](http://en.wiktionary.org/wiki/YMMV)
diff --git a/Theme-1/laundryPaper/old_stata/DEMAND-BA-Laundry-Change-Over-Time-v2.0-adult.do b/Theme-1/laundryPaper/old_stata/DEMAND-BA-Laundry-Change-Over-Time-v2.0-adult.do
new file mode 100644
index 0000000000000000000000000000000000000000..bf347d2d872e01b0d503d63bbb545b2fb53ab9a6
--- /dev/null
+++ b/Theme-1/laundryPaper/old_stata/DEMAND-BA-Laundry-Change-Over-Time-v2.0-adult.do
@@ -0,0 +1,592 @@
+capture log close _all
+
+*******************************************
+* Script to use a number of datasets to examine:
+* - distributions of laundry in 1975 & 2005
+* - changing laundry practices
+
+* uses:
+* - MTUS World 6 time-use data (www.timeuse.org/mtus UK subset) - data already in long format (but episodes)
+* - FES 1985 & EFS 2005-6 to analyse uptake of washers/dryers
+* - SPRG water practices survey
+
+* This work was funded by RCUK through the End User Energy Demand Centres Programme via the
+* "DEMAND: Dynamics of Energy, Mobility and Demand" Centre (www.demand.ac.uk, gow.epsrc.ac.uk/NGBOViewGrant.aspx?GrantRef=EP/K011723/1)
+
+/*
+
+Copyright (C) 2014 University of Southampton
+
+Author: Ben Anderson (b.anderson@soton.ac.uk, @dataknut, https://github.com/dataknut)
+ [Energy & Climate Change, Faculty of Engineering & Environment, University of Southampton]
+
+This program is free software; you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation; either version 2 of the License
+(http://choosealicense.com/licenses/gpl-2.0/), or (at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+#YMMV - http://en.wiktionary.org/wiki/YMMV
+
+*/
+
+clear all
+
+* do package checks
+local dependencies "tabout labellist estout"
+foreach d of local dependencies {
+ # this will throw an error if not found
+ di "*******"
+ di "* Testing for presence of required user package: `d' "
+ di "* (use -> ssc install `d' <- if needed)"
+ which `d'
+}
+
+* change these to run this script on different PC
+* use globals so can re-run parts of the script
+
+global where "~/Documents/Work"
+global droot "$where/Data/Social Science Datasets"
+
+* LCFS/EFS
+global efspath "$droot/Expenditure and Food Survey/processed"
+
+* MTUS
+global mtuspath "$droot/MTUS/World 6/processed"
+
+* SPRG
+global sprgpath "$where/Projects/ESRC-SPRG/WP4-Micro_water/data/sprg_survey/data/safe/v6"
+
+* where to put results
+global rpath "$where/Papers and Conferences/The Time and Timing of Demand - Laundry/results"
+
+* version
+local version = "v2.0"
+* switches to using '1985' as base year
+* weights the final counts
+* which subgroup of mtus are we interested in?
+global mtusfilter "_all"
+
+*local version "v1.1-singles"
+*local filter "if hhtype == 1"
+* single person hhs only
+
+*local version "v1.1-all-hhs-sanity-check"
+*local filter "_all"
+* counts if 1 or more acts (sanity check)
+
+*local version = "v1.1-all-hhs"
+* local filter "_all"
+* adds in secondary acts
+
+* local version = "v1.0-main"
+* counts main acts only
+
+capture log close
+
+local mainlog "DEMAND-BA-MTUS-W6-Laundry-Change-Over-Time-`version'-adult"
+log using "$rpath/`mainlog'.smcl", replace name(main)
+
+* control what gets done
+local do_efs 1 // check FES/EFS/LCFS for appliance uptake rates
+local do_sprg 1 // check SPRG survey for washing practices in 2011
+local do_halfhour_episodes 1 // do duration analysis using episodes
+local do_halfhour_samples 1 // do half hour analysis using '10 minute' sampled file
+local do_sequences 0
+
+* make script run without waiting for user input
+set more off
+
+if `do_efs' {
+ log off main
+ log using "$rpath/`mainlog'_do_efs.smcl", replace name(do_efs)
+
+ **********
+ * FES data for washing machine & tumble dryer uptake levels in 1985
+ * to do
+
+ * EFS data for washing machine & tumble dryer uptake levels to 2005
+ use "$efspath/EFS-2005-2006-extract-BA.dta", clear
+ lookfor tumble weight
+ tab survey_year a167 [iw=weighta], row
+
+ * 2005 only
+ tab a167 c_nchild [iw=weighta] if survey_year == "2005", col
+ tab a167 c_nearners [iw=weighta] if survey_year == "2005", col
+ tab a167 c_empl [iw=weighta] if survey_year == "2005", col
+
+ log close do_efs
+ log on main
+}
+
+if `do_sprg' {
+ log off main
+ log using "$rpath/`mainlog'_do_sprg.smcl", replace name(do_sprg)
+
+ **********
+ * SPRG data on laundry practices
+ use "$sprgpath/8369-clt-050312-v6-wf-safe.dta", clear
+
+ desc q27*
+
+ rename q27_sum sum_q27
+
+ * 1 = yes, 2 = no
+ recode q27* (2=0)
+
+ * use mean to get % who said yes to each
+ su q27* [iw=weight_respondent2], sep(0)
+
+ * mean number of 'yes' responses
+ su sum_q27 [iw=weight_respondent2]
+
+ * distribution
+ tab sum_q27 [iw=weight_respondent2]
+
+ log close do_sprg
+ log on main
+}
+
+**********************************
+* codes of interest
+* 1985: Main/Sec21 Laundry, ironing, clothing repair <- 0701 Wash clothes, hang out / bring in washing;
+* 0702 Iron clothes;
+* 0801 Repair, upkeep of clothes
+
+
+* 2005: Main/Sec21 Laundry, ironing, clothing repair <- Pact=7 (washing clothes)
+
+* start with processing the aggregate (survey) data
+use "$mtuspath/MTUS-adult-aggregate-UK-only-wf.dta", clear
+
+* drop all bad cases
+keep if badcase == 0
+
+* set as survey data for descriptives
+svyset [iw=propwt]
+
+* create a bespoke survey which merges 1983 & 1987
+* has the advantage of providing all seasons for '1985'
+recode survey (1974=1974 "1974") (1983/1987=1985 "1985") (1995 = 1995 "1995") (2000=2000 "2000") (2005=2005 "2005"), gen(ba_survey)
+
+
+* this is minutes per day not episodes
+* check 18 (Cooking) & 20 (Cleaning) & 22 (maintain home/vehicle) against laundry
+* seems to under-report laundry in 1974, esp for women?
+svy: mean main18 main20 main21 main22, over(ba_survey sex)
+
+* keep whatever sample we define above
+keep $mtusfilter
+
+* keep only the vars we want to keep memory required low
+keep sex age main7 main21 hhtype empstat emp unemp student retired propwt survey ///
+ hhldsize famstat nchild *pid ba*
+
+* number of diary-days
+svy: tab survey ba_survey, obs
+
+if `do_halfhour_episodes' {
+ preserve
+ log using "$rpath/`mainlog'_do_halfhour_episodes.smcl", replace name(do_halfhour_episodes)
+ *************************
+ * merge in the episode data
+ * do analysis at episode level
+
+ merge 1:m diarypid using "$mtuspath/MTUS-adult-episode-UK-only-wf.dta", ///
+ gen(m_aggvars)
+
+ * won't match the dropped years & badcases
+ tab m_aggvars ba_survey
+
+ * keep the matched cases
+ keep if m_aggvars == 3
+
+ * number of episodes per day
+ svy: tab s_dow survey, obs col
+
+ * overall durations
+ gen duration = s_endtime - s_starttime
+ format duration %tcHH:MM
+ tab duration ba_survey [iw=propwt]
+
+ ***************
+ * Laundry
+
+ * define laundry
+ gen laundry_p = 0
+ lab var laundry_p "Main act = laundry (21)"
+ replace laundry_p = 1 if main == 21
+
+ gen laundry_s = 0
+ lab var laundry_s "Secondary act = laundry (21)"
+ replace laundry_s = 1 if sec == 21
+
+ gen laundry_all = 0
+ replace laundry_all = 1 if laundry_p == 1 | laundry_s == 1
+
+ * check % episodes which are laundry
+ * NB reporting frame shorter in 2005 (10 mins) so may be higher frequency (e.g. interruption in 10-20 mins coded)
+ * row %
+ tab ba_survey laundry_p [iw=propwt]
+ tab ba_survey laundry_s [iw=propwt]
+ * all
+ tab ba_survey laundry_all [iw=propwt]
+
+ * check duration of laundry
+ * before we do this merge together episodes that are contiguous (e.g. laundry (s) then laundry(p) -> 1 episode)
+ * same approach as for dinner
+ * calculate duration
+ gen laundry_duration = duration if laundry_all == 1
+ format laundry_duration %tcHH:MM
+ * count back & forward maxcount episodes within the same diary day to check if they are also laundry
+ * indicates something changed - primary/secondary act or location or who with etc
+ * add on duration if previous and subsequent episodes are also laundry and location is unchanged
+ * NB: if you make maxcount > 1 then you could have episodes of eating separated by a long episode of something else
+ local maxcount = 1
+
+ * This is vital - we have to have the episodes in diary & time order!
+ sort diarypid start
+
+ foreach n of numlist 1/`maxcount' {
+ local prev = `n' - 1
+ di "* Now = `n', previous = `prev'"
+ di "* Before"
+ su laundry_duration
+ bysort survey diarypid: replace laundry_duration = laundry_duration + duration[_n-`n'] if laundry_all == 1 & ///
+ laundry_all[_n-`n'] == 1 & eloc == eloc[_n-`n']
+ bysort survey diarypid: replace laundry_duration = laundry_duration + duration[_n+`n'] if laundry_all == 1 & ///
+ laundry_all[_n+`n'] == 1 & eloc == eloc[_n+`n']
+ di "* After"
+ su laundry_duration
+ }
+
+
+ * Means are probably not going to tell us much given the differences in recording frames
+ * Use table instead as durations are so 'rounded'
+ * even so have to allow for differences in recording frames
+ di "* Duration of merged laundry episodes (weighted)"
+ *table laundry_duration sex ba_survey [iw=propwt]
+ * not interested in gender differences here
+ tabout laundry_duration ba_survey [iw=propwt] ///
+ using "$rpath/MTUS_W6_laundry_duration_by_year_`version'.txt" , ///
+ replace ///
+ format(3)
+
+ log close do_halfhour_episodes
+ log on main
+ restore
+
+}
+
+if `do_halfhour_samples' {
+ *preserve
+ log off main
+ log using "$rpath/`mainlog'_do_halfhour_samples.smcl", replace name(do_halfhour_samples)
+
+ *************************
+ * sampled data
+ * this requires the 10 minute sampling process implemented in XXX to have been run over the MTUS first
+ * merge in the sampled data
+ * do analysis by collapsing 10 minute sampled data to half hours
+ merge 1:m diarypid using "$mtuspath/MTUS-adult-episode-UK-only-wf-10min-samples-long-v1.0.dta", ///
+ gen(m_aggvars)
+
+ * set up half-hour variable
+ gen ba_hourt = hh(s_starttime)
+ gen ba_minst = mm(s_starttime)
+
+ gen ba_hh = 0 if ba_minst < 30
+ replace ba_hh = 30 if ba_minst > 29
+ gen ba_sec = 0
+ * sets date to 1969!
+ gen s_halfhour = hms(ba_hourt, ba_hh, ba_sec)
+ lab var s_halfhour "Episode starts during the half hour following"
+ format s_halfhour %tcHH:MM
+
+ * define laundry
+ gen laundry_p = 0
+ lab var laundry_p "Main act = laundry (21)"
+ replace laundry_p = 1 if pact == 21
+
+ gen laundry_s = 0
+ lab var laundry_s "Secondary act = laundry (21)"
+ replace laundry_s = 1 if sact == 21
+
+ gen laundry_all = 0
+ replace laundry_all = 1 if laundry_p == 1 | laundry_s == 1
+
+ * this is the number of 10 minute samples by survey & day of the week
+ tab ba_survey s_dow [iw=propwt]
+
+ * check % samples which are laundry
+ * NB reporting frame longer in 1974 (30 mins) so may be higher frequency (e.g. interruption in 10-20 mins coded)
+ di "* main"
+ tab ba_survey laundry_p [iw=propwt]
+ di "* secondary"
+ tab ba_survey laundry_s [iw=propwt]
+ di "* all"
+ tab ba_survey laundry_all [iw=propwt]
+
+ * keep 1985 & 2005 only
+ keep if ba_survey == 1985 | ba_survey == 2005
+
+ * collapse to add up the sampled laundry by half hour
+ * use the byvars we're interested in (or could re-merge with aggregated file)
+ * use mean to keep the weight
+ collapse (sum) laundry_* (mean) propwt, by(diarypid pid ba_survey s_dow mtus_month mtus_year s_halfhour ///
+ ba_birth_cohort ba_age_r ba_nchild sex emp empstat nchild)
+ * because the different surveys have different reporting periods we need to just count at least 1 laundry in the half hour
+ lab val emp EMP
+ lab val empstat EMPSTAT
+ local acts "p s all"
+ foreach a of local acts {
+ gen any_laundry_`a' = 0
+ replace any_laundry_`a' = 1 if laundry_`a' > 0
+ }
+ * the number of half hour data points by survey & day
+ tab s_dow ba_survey [iw=propwt]
+
+ svyset [pw=propwt]
+ * now compare the 'any_laundry' indicator with 'all laundry' to see
+ * which is affected by variation in diary time slot
+ di "* test of 'any laundry' in half hour"
+ svy: mean any_laundry_*, over(ba_survey)
+ table ba_survey [pw=propwt], c(mean any_laundry_all)
+ table s_halfhour ba_survey [pw=propwt], c(mean any_laundry_all)
+
+ di "* test of how much laundry in half hour"
+ svy: mean laundry_*, over(ba_survey)
+ table ba_survey [pw=propwt], c(mean laundry_all)
+ table s_halfhour ba_survey [pw=propwt], c(mean laundry_all)
+
+ di "* tests suggest we should stick to any_laundry_all if we want to compare absolute rates over time"
+ di "* as it does not inflate values where diary slot duration > 10 minutes"
+
+ svyset [iw=propwt]
+ * the distribution of laundry by survey
+ * should give same results as above?
+ di "* primary"
+ svy: tab ba_survey any_laundry_p, row ci
+
+ di "* secondary"
+ svy: tab ba_survey any_laundry_p, row ci
+
+ di "* all"
+ svy: tab ba_survey any_laundry_all, row ci
+
+ * by gender for all laundry reported
+ svy: tab ba_survey sex if any_laundry_all == 1, ci row
+
+ * gender & age
+ svy: tab ba_age_r sex if any_laundry_all == 1 & ba_survey == "1985", ci row
+ svy: tab ba_age_r sex if any_laundry_all == 1 & ba_survey == "2005", ci row
+
+ * Separate days
+ table ba_survey day [iw=propwt], by(any_laundry_all)
+
+ * days by gender
+ table survey day sex [iw=propwt], by(any_laundry_all)
+
+ * laundry by employment status if female
+ table survey day empstat if sex == 2 & any_laundry_all == 1 [iw=propwt]
+
+ di "* Tables for all days"
+ * All years, all days
+ table s_halfhour survey any_laundry_all [iw=propwt]
+
+ * days by half hour
+ table s_halfhour survey day [iw=propwt], by(any_laundry_all)
+
+ * seasons
+ recode month (3 4 5 = 1 "Spring") (6 7 8 = 2 "Summer") (9 10 11 = 3 "Autumn") (12 1 2 = 4 "Winter"), gen(season)
+ * check
+ * tab month season
+ table s_halfhour survey season [iw=propwt], by(any_laundry_all)
+
+ * by half hour & employment status for women
+ table s_halfhour empstat survey if sex == 2 [iw=propwt], by(any_laundry_all)
+
+ *repeat by day for 2005
+ table s_halfhour empstat day if survey == 2005 & sex == 2 [iw=propwt], by(any_laundry_all)
+
+ * analysis by laundry type
+ * sunday morning
+
+ * set time variable so can select by time
+ xtset diarypid s_halfhour, delta(30 mins)
+ * only code for laundry within year
+ gen laundry_timing = 5 if any_laundry_all == 1 // other
+ replace laundry_timing = 1 if any_laundry_all == 1 & day == 1 & tin(08:00, 12:00) // sunday morning
+ replace laundry_timing = 2 if any_laundry_all == 1 & day > 1 & day < 6 & tin(09:00, 12:00) // weekday morning
+ replace laundry_timing = 3 if any_laundry_all == 1 & day > 1 & day < 6 & tin(17:00, 20:00) // weekday evening peak
+ replace laundry_timing = 4 if any_laundry_all == 1 & tin(00:00, 01:30) // night-time
+ replace laundry_timing = 4 if any_laundry_all == 1 & tin(22:30, 23:30) // night-time
+
+ tab laundry_timing, gen(laundry_timing_)
+
+ * check for missing
+ table s_halfhour laundry_timing any_laundry_all, mi
+
+ lab def laundry_timing 1 "Sunday morning 09:00-12:00" 2 "Weekday morning 09:00-12:00" 3 "Weekday evening peak 17:00-20:00" 4 "Night-time 22:30-01:30" 5 "Other"
+ lab val laundry_timing laundry_timing
+ tab laundry_timing survey [iw=propwt], col
+ svy:tab laundry_timing survey, col ci
+ table laundry_timing ba_age_r survey [iw=propwt], col
+ table laundry_timing empstat survey [iw=propwt], col
+ table laundry_timing ba_nchild survey [iw=propwt], col
+
+ * collapse to single person record
+ * remember 1974/5 = 1 week diary
+ collapse (sum) laundry_timing_* any_laundry_all (mean) propwt, by(pid survey ///
+ ba_birth_cohort ba_age_r ba_nchild sex emp empstat nchild)
+ recode any_laundry_all (1/max=1)
+ recode laundry_timing_1 (1/max=1)
+ recode laundry_timing_2 (1/max=1)
+ recode laundry_timing_3 (1/max=1)
+ recode laundry_timing_4 (1/max=1)
+ recode laundry_timing_5 (1/max=1)
+
+ *how many people are in multiple types?
+ egen nlaundry_types = rowtotal( laundry_timing_*)
+ svy: tab nlaundry_types survey, col
+
+ * what % of respondents in each?
+ svy: mean laundry_timing_*, over(survey)
+ * % of launderers
+ svy: mean laundry_timing_* if any_laundry_all == 1, over(survey)
+
+ foreach v of numlist 1/4 {
+ logit laundry_timing_`v' sex ib4.empstat i.ba_age_r i.ba_nchild if survey == 1974
+ est store laundry_timing_`v'_1974
+ logit laundry_timing_`v' sex ib4.empstat i.ba_age_r i.ba_nchild if survey == 2005
+ est store laundry_timing_`v'_2005
+ }
+ estout laundry_*_2005 using "$rpath/laundry_type_1974_regressions.txt", cells("b ci_l ci_u se _star") stats(N r2_p chi2 p ll) replace
+ estout laundry_*_2005 using "$rpath/laundry_type_2005_regressions.txt", cells("b ci_l ci_u se _star") stats(N r2_p chi2 p ll) replace
+
+}
+*restore
+
+*************************
+* sequences
+if `do_sequences' {
+ * back to the episodes
+ merge 1:m diarypid using "$mtuspath/MTUS-adult-episode-UK-only-wf.dta", ///
+ gen(m_aggvars)
+ * this won't have matched the dropped years & badcases
+ * tab m_aggvars survey
+
+ * keep the matched cases
+ keep if m_aggvars == 3
+
+ * define laundry
+ gen laundry_p = 0
+ lab var laundry_p "Main act = laundry (21)"
+ replace laundry_p = 1 if main == 21
+
+ gen laundry_s = 0
+ lab var laundry_s "Secondary act = laundry (21)"
+ replace laundry_s = 1 if sec == 21
+
+ gen laundry_all = 0
+ replace laundry_all = 1 if laundry_p == 1 | laundry_s == 1
+
+ * we can't use the lag notation and xtset as there are various time periods represented in the data
+ * and we would need to set up some fake (or real!) dates to attach the start times to.
+ * we could do this but we don't really need to.
+
+ * we want to use episodes not time slots (as we are ignoring duration here)
+
+ * This is vital - we have to have the episodes in diary & time order!
+ sort diarypid start
+
+ * we are NOT going to worry about sequential episodes which are both laundry_all as this will indicate
+ * that something changed - most likely a switch of laundry from primary to secondary activity (or vice versa)
+ * this may be of interest in itself
+
+ local acts "all"
+ foreach a of local acts {
+ * make sure we do this within diaries otherwise we might get a 'before' or 'after' belonging to a previous day (for multi day diaries)
+ * or to someone else (for 1 day diaries or the first day)!
+
+ qui: by diarypid: gen before_laundry_`a' = main[_n-1] if laundry_`a' == 1
+ qui: by diarypid: gen after_laundry_`a' = main[_n+1] if laundry_`a' == 1
+
+ lab val before_laundry_`a' after_laundry_`a' MAIN
+
+ qui: tabout before_laundry_`a' survey [iw=propwt] using "$rpath/before-laundry-by-survey.txt", replace
+ qui: tabout after_laundry_`a' survey [iw=propwt] using "$rpath/after-laundry-by-survey.txt", replace
+ }
+ tab laundry_all
+ * create a sequence variable (horrible kludge but hey, it works :-)
+ egen laundry_seq = concat(before_laundry_all laundry_all after_laundry_all) if laundry_all == 1 , punct("_")
+
+ * get frequencies of sequencies (this will be a very big table)
+ * the few which have missing (.) before laundry indicate nothing recorded before hand which seems a bit odd?
+
+ tab laundry_seq
+
+ preserve
+ * contract doesn't like iw - only allows fw (which need to be integers)
+ * so these will be unweighted
+ contract laundry_seq survey, nomiss
+ qui: tab laundry_seq
+
+ qui: return li
+ di "For laundry_seq after contract : N = " r(N) ", r = " r(r)
+
+ * reshape it to get the frequencies per survey into columns
+ qui: reshape wide _freq, i(laundry_seq) j(survey)
+ qui: return li
+
+ li in 1/5
+ outsheet using "$rpath/laundry-sequences-by-survey-wide.txt", replace
+ * totals
+ * the number of different sequences will probably vary by sample size - more potential variation
+ su _freq*, sep(0)
+ tabstat _freq*, s(n sum)
+ * top in 1974?
+ gsort - _freq1974
+ li in 1/10, sep(0)
+ * top in 2005?
+ gsort - _freq2005
+ li in 1/10, sep(0)
+
+
+ restore
+
+ /*
+ * try using the sqset commands
+
+ * tell it to look at sequences
+ sqset main diarypid s_starttime
+
+ * top 20 sequences
+ sqtab survey if before_laundry ! = 1 | after_laundry ! = 1, ranks(1/20)
+ */
+}
+
+* we're back to the main survey aggregate file here.
+* drop diary duplicates & do some basic stats
+
+duplicates drop pid, force
+
+* create working age variable
+gen ba_working_age = 0
+replace ba_working_age = 1 if age > 18 // OK, it should be 16 but...
+* women
+replace ba_working_age = 0 if age > 60 & sex == 2
+* men
+replace ba_working_age = 0 if age > 65 & sex == 1
+* check
+table ba_age_r ba_working_age sex
+
+* Propoprtion of women in work
+tab survey empstat [iw=propwt] if ba_working_age == 1 & sex == 2, row
+
+
+log close
diff --git a/Theme-1/laundryPaper/old_stata/DEMAND-BA-Laundry-Energy-Time-As-Submitted-v1.0.do b/Theme-1/laundryPaper/old_stata/DEMAND-BA-Laundry-Energy-Time-As-Submitted-v1.0.do
new file mode 100644
index 0000000000000000000000000000000000000000..d6da13468631f1e243c333fe4eeda524ee7eb048
--- /dev/null
+++ b/Theme-1/laundryPaper/old_stata/DEMAND-BA-Laundry-Energy-Time-As-Submitted-v1.0.do
@@ -0,0 +1,365 @@
+*******************************************
+* Script to use a number of datasets to examine:
+* - distributions of laundry in 1975 & 2005
+* - changing laundry practices
+
+* uses:
+* - MTUS World 6 time-use data (www.timeuse.org/mtus UK subset) - data already in long format (but episodes)
+* - EFS 2005-6 to analyse uptake of washers/dryers
+* - SPRG water practices survey
+
+* This work was funded by RCUK through the End User Energy Demand Centres Programme via the
+* "DEMAND: Dynamics of Energy, Mobility and Demand" Centre (www.demand.ac.uk, gow.epsrc.ac.uk/NGBOViewGrant.aspx?GrantRef=EP/K011723/1)
+
+/*
+
+Copyright (C) 2014 University of Southampton
+
+Author: Ben Anderson (b.anderson@soton.ac.uk, @dataknut, https://github.com/dataknut)
+ [Energy & Climate Change, Faculty of Engineering & Environment, University of Southampton]
+
+This program is free software; you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation; either version 2 of the License
+(http://choosealicense.com/licenses/gpl-2.0/), or (at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+#YMMV - http://en.wiktionary.org/wiki/YMMV
+
+*/
+
+clear all
+
+* change these to run this script on different PC
+* use globals so can re-run parts of the script
+
+global where "~/Documents/Work"
+global droot "$where/Data/Social Science Datatsets"
+
+* LCFS/EFS
+global efspath "$droot/Expenditure and Food Survey/processed"
+
+* MTUS
+global mtuspath "$droot/MTUS/World 6/processed"
+
+* SPRG
+global sprgpath "$where/Projects/ESRC-SPRG/WP4-Micro_water/data/sprg_survey/data/safe/v6"
+
+* where to put results
+global proot "$where/Projects/RCUK-DEMAND/Theme 1"
+global rpath "$proot/results/MTUS"
+
+* version
+global version = "v1.0-all-locs"
+* weights the final counts
+
+* which subgroup of mtus are we interested in?
+global mtusfilter "_all"
+
+capture log close
+
+log using "$rpath/DEMAND-BA-MTUS-W6-Laundry-Change-Over-Time-$version-adult.smcl", replace
+
+* control what gets done
+local do_halfhour_samples = 1
+
+* make script run without waiting for user input
+set more off
+
+**********
+* LCFS data for tumble dryer uptake levels to 2005
+use "$efspath/EFS-2005-2006-extract-BA.dta", clear
+lookfor tumble weight
+tab year a167 [iw=weighta], row
+
+* 2005 only
+tab a167 c_nchild [iw=weighta] if year == 2005, col
+tab a167 c_nearners [iw=weighta] if year == 2005, col
+tab a167 c_empl [iw=weighta] if year == 2005, col
+
+**********
+* SPRG data on laundry practices
+use "$sprgpath/8369-clt-050312-v6-wf-safe.dta", clear
+
+desc q27*
+
+rename q27_sum sum_q27
+
+* 1 = yes, 2 = no
+recode q27* (2=0)
+
+* use mean to get % who said yes to each
+su q27* [iw=weight_respondent2], sep(0)
+
+* mean number of 'yes' responses
+su sum_q27 [iw=weight_respondent2]
+
+* distribution
+tab sum_q27 [iw=weight_respondent2]
+
+**********************************
+* codes of interest
+* 1974: Main/Sec21 Laundry, ironing, clothing repair <- 50 Other essential domestic work (i.e. NOT preparing meals or routine housework)
+* so laundry in 1974 may be over-estimated
+* BUT 1975 is partly a 7 day diary - so more likely to detect laundry?
+
+* 2005: Main/Sec21 Laundry, ironing, clothing repair <- Pact=7 (washing clothes)
+
+* start with processing the aggregate (survey) data
+use "$mtuspath/MTUS-adult-aggregate-UK-only-wf.dta", clear
+
+* drop all bad cases
+keep if badcase == 0
+
+* set as survey data for descriptives
+svyset [iw=propwt]
+
+* keep only 1974 & 2005 for simplicity
+* keep if survey == 1974 | survey == 2005
+* no, let's keep them all for birth cohort analysis!
+
+* this is minutes per day not episodes
+* check 18 (Cooking) & 20 (Cleaning) & 22 (maintain home/vehicle) against laundry
+* seems to under-report laundry in 1974, esp for women?
+svy: mean main18 main20 main21 main22, over(survey sex)
+
+* keep whatever sample we define above
+keep $mtusfilter
+
+* number of diary days by hh type
+* svy: tab hhtype survey, col count
+
+* number of diary days by number of days covered
+* 1974 = 7 day dairy
+svy: tab id survey, col count
+
+
+* keep only the vars we want to keep memory required low
+keep sex age main7 main21 hhtype empstat emp unemp student retired propwt survey day month year ///
+ hhldsize famstat nchild *pid ba*
+
+* number of diary-days
+svy: tab survey, obs
+
+
+preserve
+*************************
+* sampled data
+* this requires the 10 minute sampling process implemented in
+* https://github.com/dataknut/MTUS/blob/master/process-MTUS-W6-convert-to-X-min-samples-v1.0-adult.do
+* to have been run over the MTUS first with X set to 10
+
+if `do_halfhour_samples' {
+ * merge in the sampled data
+ * do analysis by collapsing 10 minute sampled data to half hours
+ merge 1:m diarypid using "$mtuspath/MTUS-adult-episode-UK-only-wf-10min-samples-long-v1.0.dta", ///
+ gen(m_aggvars)
+
+ * set up half-hour variable
+ gen ba_hourt = hh(s_starttime)
+ gen ba_minst = mm(s_starttime)
+
+ gen ba_hh = 0 if ba_minst < 30
+ replace ba_hh = 30 if ba_minst > 29
+ gen ba_sec = 0
+ * sets date to 1969!
+ gen s_halfhour = hms(ba_hourt, ba_hh, ba_sec)
+ lab var s_halfhour "Episode starts during the half hour following"
+ format s_halfhour %tcHH:MM
+
+ * define laundry
+ gen laundry_p = 0
+ lab var laundry_p "Main act = laundry (21)"
+ replace laundry_p = 1 if pact == 21
+
+ gen laundry_s = 0
+ lab var laundry_s "Secondary act = laundry (21)"
+ replace laundry_s = 1 if sact == 21
+
+ gen laundry_all = 0
+ replace laundry_all = 1 if laundry_p == 1 | laundry_s == 1
+
+ lab var laundry_all "Any act = laundry (21)"
+
+ * done at home or elsewhere?
+ tab survey eloc if laundry_all == 1 [iw=propwt], mi
+
+ * a lot of 1974 done 'elsewhere'?
+
+ * this is the number of 10 minute samples by survey & day of the week
+ tab survey day [iw=propwt]
+
+ * check % of sampled X minute points which are laundry
+ * NB reporting frame longer in 1974 (30 mins) so may be higher frequency (e.g. interruption in 10-20 mins coded)
+ di "* main"
+ tab survey laundry_p [iw=propwt]
+ di "* secondary"
+ tab survey laundry_s [iw=propwt]
+ di "* all"
+ tab survey laundry_all [iw=propwt]
+
+ * which years could we use?
+ tab month survey [iw=propwt]
+
+ * 1974 = Feb, Mar & Aug,Sept -> has winter & summer
+ * 1984 = winter only
+ * 1987 = early summer only
+ * 1995 = May
+ * 2000 = all year
+ * 2005 = each season (March, June, Sept, Nov)
+
+ * keep 1974 & 2005 only
+ keep if survey == 1974 | survey == 2005
+
+ * check for duplicates
+ duplicates report diarypid ba_starttime
+ * none
+
+ duplicates report diarypid s_halfhour
+ * three -> because each s_halfhour value can stand for x:10 x:20 x:30
+ * collapse to add up the sampled laundry by half hour
+
+ * use the byvars we're interested in (or could re-merge with aggregated file)
+ collapse (sum) laundry_* (mean) propwt, by(diarypid pid survey day month year s_halfhour ///
+ ba_birth_cohort ba_age_r ba_nchild sex emp empstat nchild)
+ * because the different surveys have different reporting periods we need to just count at least 1 laundry in the half hour
+ lab val emp EMP
+ lab val empstat EMPSTAT
+ local acts "p s all"
+ foreach a of local acts {
+ gen any_laundry_`a' = 0
+ replace any_laundry_`a' = 1 if laundry_`a' > 0
+ }
+ * the number of half hour data points by survey & day
+ tab survey day [iw=propwt]
+
+ svyset [iw=propwt]
+ * the distribution of laundry by survey and location
+ di "* primary"
+ svy: tab survey if any_laundry_p == 1, col ci
+
+ di "* secondary"
+ svy: tab survey if any_laundry_s == 1, col ci
+
+ di "* all"
+ svy: tab survey if any_laundry_all == 1, col ci
+
+ * by gender for all laundry reported
+ svy: tab survey sex if any_laundry_all == 1, ci row
+
+ * gender & age
+ svy: tab ba_age_r sex if any_laundry_all == 1 & survey == 1974, ci row
+ svy: tab ba_age_r sex if any_laundry_all == 1 & survey == 2005, ci row
+
+ * Separate days
+ table survey day [iw=propwt], by(any_laundry_all)
+
+ * days by gender
+ table survey day sex [iw=propwt], by(any_laundry_all)
+
+ * laundry by employment status if female
+ table survey day empstat if sex == 2 & any_laundry_all == 1 [iw=propwt]
+
+ * set time variable so can select by time & also tables should look nicer
+ xtset diarypid s_halfhour, delta(30 mins) format(%tcHH:MM)
+
+ di "* Tables for all days"
+ * All years, all days
+ table s_halfhour survey any_laundry_all [iw=propwt]
+
+ * days by half hour
+ table s_halfhour survey day [iw=propwt], by(any_laundry_all)
+
+ * seasons
+ recode month (3 4 5 = 1 "Spring") (6 7 8 = 2 "Summer") (9 10 11 = 3 "Autumn") (12 1 2 = 4 "Winter"), gen(season)
+ * check
+ * tab month season
+ table s_halfhour survey season [iw=propwt], by(any_laundry_all)
+
+ * by half hour & employment status for women
+ table s_halfhour empstat survey if sex == 2 [iw=propwt], by(any_laundry_all)
+
+ *repeat by day for 2005
+ table s_halfhour empstat day if survey == 2005 & sex == 2 [iw=propwt], by(any_laundry_all)
+
+ * analysis by laundry type
+ * sunday morning
+
+ * only code for laundry within year
+ gen laundry_timing = 5 if any_laundry_all == 1 // other
+ replace laundry_timing = 1 if any_laundry_all == 1 & day == 1 & tin(08:00, 12:00) // sunday morning
+ replace laundry_timing = 2 if any_laundry_all == 1 & day > 1 & day < 6 & tin(09:00, 12:00) // weekday morning
+ replace laundry_timing = 3 if any_laundry_all == 1 & day > 1 & day < 6 & tin(17:00, 20:00) // weekday evening peak
+ replace laundry_timing = 4 if any_laundry_all == 1 & tin(00:00, 01:30) // night-time
+ replace laundry_timing = 4 if any_laundry_all == 1 & tin(22:30, 23:30) // night-time
+
+ tab laundry_timing, gen(laundry_timing_)
+
+ * check for missing
+ table s_halfhour laundry_timing any_laundry_all, mi
+
+ lab def laundry_timing 1 "Sunday morning 09:00-12:00" 2 "Weekday morning 09:00-12:00" 3 "Weekday evening peak 17:00-20:00" 4 "Night-time 22:30-01:30" 5 "Other"
+ lab val laundry_timing laundry_timing
+ tab laundry_timing survey [iw=propwt], col
+ svy:tab laundry_timing survey, col ci
+ table laundry_timing ba_age_r survey [iw=propwt], col
+ table laundry_timing empstat survey [iw=propwt], col
+ table laundry_timing ba_nchild survey [iw=propwt], col
+
+ * collapse to single person record
+ * remember 1974/5 = 1 week diary
+ collapse (sum) laundry_timing_* any_laundry_all (mean) propwt, by(pid survey ///
+ ba_birth_cohort ba_age_r ba_nchild sex emp empstat nchild)
+ recode any_laundry_all (1/max=1)
+ recode laundry_timing_1 (1/max=1)
+ recode laundry_timing_2 (1/max=1)
+ recode laundry_timing_3 (1/max=1)
+ recode laundry_timing_4 (1/max=1)
+ recode laundry_timing_5 (1/max=1)
+
+ *how many people are in multiple types?
+ egen nlaundry_types = rowtotal( laundry_timing_*)
+ svy: tab nlaundry_types survey, col
+
+ * what % of respondents in each?
+ svy: mean laundry_timing_*, over(survey)
+ * % of launderers
+ svy: mean laundry_timing_* if any_laundry_all == 1, over(survey)
+
+ foreach v of numlist 1/4 {
+ logit laundry_timing_`v' sex ib4.empstat i.ba_age_r i.ba_nchild if survey == 1974
+ est store laundry_timing_`v'_1974
+ logit laundry_timing_`v' sex ib4.empstat i.ba_age_r i.ba_nchild if survey == 2005
+ est store laundry_timing_`v'_2005
+ }
+ estout laundry_*_2005 using "$rpath/laundry_type_1974_regressions.txt", cells("b ci_l ci_u se _star") stats(N r2_p chi2 p ll) replace
+ estout laundry_*_2005 using "$rpath/laundry_type_2005_regressions.txt", cells("b ci_l ci_u se _star") stats(N r2_p chi2 p ll) replace
+
+}
+restore
+
+* we're back to the main survey aggregate file here.
+* drop diary duplicates & do some basic stats
+
+duplicates drop pid, force
+
+* create working age variable
+gen ba_working_age = 0
+replace ba_working_age = 1 if age > 18 // OK, it should be 16 but...
+* women
+replace ba_working_age = 0 if age > 60 & sex == 2
+* men
+replace ba_working_age = 0 if age > 65 & sex == 1
+* check
+table ba_age_r ba_working_age sex
+
+* Proportion of women in work
+tab survey empstat [iw=propwt] if ba_working_age == 1 & sex == 2, row
+
+di "Done!"
+
+log close
diff --git a/Theme-1/laundryPaper/old_stata/DEMAND-BA-Laundry-Energy-Time-As-Submitted-v1.1.do b/Theme-1/laundryPaper/old_stata/DEMAND-BA-Laundry-Energy-Time-As-Submitted-v1.1.do
new file mode 100644
index 0000000000000000000000000000000000000000..4c630f274d028b5890a76e0eea57f3367eed38e7
--- /dev/null
+++ b/Theme-1/laundryPaper/old_stata/DEMAND-BA-Laundry-Energy-Time-As-Submitted-v1.1.do
@@ -0,0 +1,433 @@
+*******************************************
+* Script to use a number of datasets to examine:
+* - distributions of laundry in 1975 & 2005
+* - changing laundry practices
+
+* uses:
+* - MTUS World 6 time-use data (www.timeuse.org/mtus UK subset) - data already in long format (but episodes)
+* - EFS 2005-6 to analyse uptake of washers/dryers
+* - SPRG water practices survey
+
+* This work was funded by RCUK through the End User Energy Demand Centres Programme via the
+* "DEMAND: Dynamics of Energy, Mobility and Demand" Centre (www.demand.ac.uk, gow.epsrc.ac.uk/NGBOViewGrant.aspx?GrantRef=EP/K011723/1)
+
+/*
+
+Copyright (C) 2014 University of Southampton
+
+Author: Ben Anderson (b.anderson@soton.ac.uk, @dataknut, https://github.com/dataknut)
+ [Energy & Climate Change, Faculty of Engineering & Environment, University of Southampton]
+
+This program is free software; you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation; either version 2 of the License
+(http://choosealicense.com/licenses/gpl-2.0/), or (at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+#YMMV - http://en.wiktionary.org/wiki/YMMV
+
+*/
+
+clear all
+
+* change these to run this script on different PC
+* use globals so can re-run parts of the script
+
+global where "~/Documents/Work"
+global droot "$where/Data/Social Science Datatsets"
+
+* LCFS/EFS
+global efspath "$droot/Expenditure and Food Survey/processed"
+
+* MTUS
+global mtuspath "$droot/MTUS/World 6/processed"
+
+* SPRG
+global sprgpath "$where/Projects/ESRC-SPRG/WP4-Micro_water/data/sprg_survey/data/safe/v6"
+
+* where to put results
+global proot "$where/Projects/RCUK-DEMAND/Theme 1"
+global rpath "$proot/results/MTUS"
+
+* version
+global version = "v1.1-at-home"
+* excludes any laundry "not at home or someone else's home" (eloc = 1 or 2)
+
+* global version = "v1.0-all-locs"
+* weights the final counts
+
+* which subgroup of mtus are we interested in?
+global mtusfilter "_all"
+
+capture log close
+
+log using "$rpath/DEMAND-BA-MTUS-W6-Laundry-Change-Over-Time-$version-adult.smcl", replace
+
+* control what gets done
+local do_halfhour_samples = 1
+
+* make script run without waiting for user input
+set more off
+
+**********************************
+**********************************
+* LCFS data for tumble dryer uptake levels to 2005
+use "$efspath/EFS-2005-2006-extract-BA.dta", clear
+lookfor tumble weight
+tab year a167 [iw=weighta], row
+
+* 2005 only
+tab a167 c_nchild [iw=weighta] if year == 2005, col
+tab a167 c_nearners [iw=weighta] if year == 2005, col
+tab a167 c_empl [iw=weighta] if year == 2005, col
+
+**********************************
+**********************************
+* SPRG data on laundry practices
+use "$sprgpath/8369-clt-050312-v6-wf-safe.dta", clear
+
+desc q27*
+
+rename q27_sum sum_q27
+
+* 1 = yes, 2 = no
+recode q27* (2=0)
+
+* use mean to get % who said yes to each
+su q27* [iw=weight_respondent2], sep(0)
+
+* mean number of 'yes' responses
+su sum_q27 [iw=weight_respondent2]
+
+* distribution
+tab sum_q27 [iw=weight_respondent2]
+
+**********************************
+**********************************
+* MTUS
+* codes of interest
+* 1974: Main/Sec21 Laundry, ironing, clothing repair <- 50 Other essential domestic work (i.e. NOT preparing meals or routine housework)
+* so laundry in 1974 may be over-estimated
+* BUT 1975 is partly a 7 day diary - so more likely to detect laundry?
+
+* 2005: Main/Sec21 Laundry, ironing, clothing repair <- Pact=7 (washing clothes)
+
+**********
+* start with processing the aggregate (survey) data
+use "$mtuspath/MTUS-adult-aggregate-UK-only-wf.dta", clear
+
+* drop all bad cases
+keep if badcase == 0
+
+* set as survey data for descriptives
+svyset [iw=propwt]
+
+* keep only 1974 & 2005 for simplicity
+* keep if survey == 1974 | survey == 2005
+* no, let's keep them all for birth cohort analysis!
+
+* this is minutes per day not episodes
+* check 18 (Cooking) & 20 (Cleaning) & 22 (maintain home/vehicle) against laundry
+* seems to under-report laundry in 1974, esp for women?
+svy: mean main18 main20 main21 main22, over(survey sex)
+
+* keep whatever sample we define above
+keep $mtusfilter
+
+* number of diary days by hh type
+* svy: tab hhtype survey, col count
+
+* number of diary days by number of days covered
+* 1974 = 7 day dairy
+svy: tab id survey, col count
+
+* change order of income variable
+recode income (-9=4)
+lab def INCOME 4 "Not known", add
+
+
+* keep only the vars we want to keep memory required low
+keep sex age main7 main21 hhtype empstat income emp unemp student retired propwt survey day month year ///
+ hhldsize famstat nchild *pid ba*
+
+* number of diary-days
+svy: tab survey, obs
+
+**********
+* use raw data to assess raw episodes
+preserve
+
+ use "$mtuspath/MTUS-adult-episode-UK-only-wf.dta", clear
+
+restore
+**********
+* switch to sampled data
+* this requires the 10 minute sampling process implemented in
+* https://github.com/dataknut/MTUS/blob/master/process-MTUS-W6-convert-to-X-min-samples-v1.0-adult.do
+* to have been run over the MTUS first with X set to 10
+
+if `do_halfhour_samples' {
+ * merge in the sampled data
+ * do analysis by collapsing 10 minute sampled data to half hours
+ merge 1:m diarypid using "$mtuspath/MTUS-adult-episode-UK-only-wf-10min-samples-long-v1.0.dta", ///
+ gen(m_aggvars)
+
+ * set up half-hour variable
+ gen ba_hourt = hh(s_starttime)
+ gen ba_minst = mm(s_starttime)
+
+ gen ba_hh = 0 if ba_minst < 30
+ replace ba_hh = 30 if ba_minst > 29
+ gen ba_sec = 0
+ * sets date to 1969!
+ gen s_halfhour = hms(ba_hourt, ba_hh, ba_sec)
+ lab var s_halfhour "Episode starts during the half hour following"
+ format s_halfhour %tcHH:MM
+
+ * define laundry
+ gen laundry_p = 0
+ lab var laundry_p "Main act = laundry (21)"
+ replace laundry_p = 1 if pact == 21
+
+ gen laundry_s = 0
+ lab var laundry_s "Secondary act = laundry (21)"
+ replace laundry_s = 1 if sact == 21
+
+ gen laundry_all = 0
+ replace laundry_all = 1 if laundry_p == 1 | laundry_s == 1
+ lab var laundry_all "Any act = laundry (21)"
+
+
+ * distribution of locations
+ svy: tab eloc survey, col
+
+ * laundry done at home or elsewhere?
+ svy: tab eloc survey if laundry_all == 1, col
+
+ * a lot of 1974 done at 'other locations'?
+ * test who does laundry at 'other locations'
+ gen laundry_all_other = 0
+ replace laundry_all_other = 1 if laundry_all == 1 & eloc == 9
+ logit laundry_all_other i.sex i.ba_age_r i.empstat i.income if survey == 1974, cluster(pid)
+
+ * can we work out where?
+ bysort survey: tab sact pact if eloc == 9 & laundry_all == 1
+ * a bit - for the most part there is no recorded secondary actitivy if main = laundry
+ bysort survey: tab mtrav if eloc == 9 & laundry_all == 1
+ * that doesn't help - all not travelling
+ * NB "someone else's home is not set for 1974/2005" - maybe these are the 'other' locations?
+
+ gen laundry_allh = 0
+ replace laundry_allh = 1 if laundry_all == 1 & (eloc == 1 | eloc == 2 ) // specifically at home or someone else's home (the latter not set in 1974/2005)
+ * we'll also assume that visiting/receiving friends whilst laundry is at someone's home - doesn't really matter whose for this paper
+ replace laundry_allh = 1 if laundry_all == 1 & (pact == 48 | sact == 48)
+ lab var laundry_allh "Any act = laundry (21) at someone's home"
+
+ gen laundry_allnh = 0
+ replace laundry_allnh = 1 if laundry_all == 1 & (eloc != 1 & eloc != 2) & (pact != 48 & sact != 48)
+ lab var laundry_allnh "Any act = laundry (21) not at home"
+
+ * this is the number of 10 minute samples by survey & day of the week
+ tab survey day [iw=propwt]
+
+ * check % of sampled X minute points which are laundry
+ * NB reporting frame longer in 1974 (30 mins) so may be higher frequency (e.g. interruption in 10-20 mins coded)
+ di "* all"
+ tab survey laundry_all [iw=propwt], row
+ tab survey laundry_allh [iw=propwt], row
+ tab survey laundry_allnh [iw=propwt], row
+
+ * which years could we use?
+ tab month survey [iw=propwt]
+
+ * 1974 = Feb, Mar & Aug,Sept -> has winter & summer
+ * 1984 = winter only
+ * 1987 = early summer only
+ * 1995 = May
+ * 2000 = all year
+ * 2005 = each season (March, June, Sept, Nov)
+
+ * keep 1974 & 2005 only
+ keep if survey == 1974 | survey == 2005
+
+ * collapse to add up the sampled laundry by half hour
+ * use the byvars we're interested in (or could re-merge with aggregated file)
+ collapse (sum) laundry_* (mean) propwt, by(diarypid pid survey day month year s_halfhour ///
+ ba_birth_cohort ba_age_r ba_nchild sex emp empstat nchild income)
+ * because the different surveys have different reporting periods we need to just count at least 1 laundry in the half hour
+ lab val emp EMP
+ lab val empstat EMPSTAT
+ local acts "all allh allnh"
+ foreach a of local acts {
+ gen any_laundry_`a' = 0
+ replace any_laundry_`a' = 1 if laundry_`a' > 0
+ lab var any_laundry_`a' "`a' $version"
+ }
+
+ * set the weight
+ svyset [iw=propwt]
+
+ * the number of half hour data points by survey & day
+ svy: tab survey day
+
+ * loop through locations
+ local acts "all allh allnh"
+ foreach a of local acts {
+ di "*****************************"
+ di "*****************************"
+
+ di "*****************************"
+ di "* 'at least 1 reported reported laundry instance' for: any_laundry_`a'"
+ svy: tab any_laundry_`a' survey , col ci
+
+ di "*******"
+ di "* Tables for: any_laundry_`a' = 1"
+ di "* Income"
+ svy: tab income survey if any_laundry_`a' == 1, col ci
+
+ di "*******"
+ di "* Sex"
+ svy: tab sex survey if any_laundry_`a' == 1, col ci
+ di "* Sex & age"
+ di "* 1974 (if any_laundry_`a' = 1)"
+ svy: tab ba_age_r sex if any_laundry_`a' == 1 & survey == 1974, ci row
+ di "* 2005 (if any_laundry_`a' = 1)"
+ svy: tab ba_age_r sex if any_laundry_`a' == 1 & survey == 2005, ci row
+
+ di "*******"
+ di "* Days by survey for any_laundry_`a' = 1 - Fig 2"
+ svy: tab day survey if any_laundry_`a' == 1, ci col
+
+ di "*******"
+ di "* Days by gender & survey for any_laundry_`a' =1 - used for Fig 3 "
+ table day sex survey if any_laundry_`a' == 1 [iw=propwt]
+
+ di "*******"
+ di "* Employment status by survey for any_laundry_`a' = 1"
+ table empstat survey if any_laundry_`a' == 1 [iw=propwt]
+
+ di "*******"
+ di "* Days by survey & employment status if female for any_laundry_`a' = 1 - used for Figs 4 & 5"
+ table day empstat survey if sex == 2 & any_laundry_`a' == 1 [iw=propwt]
+
+ }
+
+ * set time variable so can select by time
+ xtset diarypid s_halfhour, delta(30 mins) format(%tcHH:MM)
+
+ * time of day comparisons
+ local acts "all allh allnh"
+ foreach a of local acts {
+ di "*****************************"
+ di "* Tables for: any_laundry_`a'"
+ table s_halfhour survey any_laundry_`a' [iw=propwt]
+
+ di "* days by half hour for: any_laundry_`a'"
+ table s_halfhour survey day [iw=propwt], by(any_laundry_`a')
+
+ /* seasons - leave out for now (small N)
+ recode month (3 4 5 = 1 "Spring") (6 7 8 = 2 "Summer") (9 10 11 = 3 "Autumn") (12 1 2 = 4 "Winter"), gen(season)
+ * check
+ * tab month season
+ table s_halfhour survey season [iw=propwt], by(any_laundry_`a')
+ */
+
+ di "* by half hour & employment status for women for: any_laundry_`a'"
+ table s_halfhour empstat survey if sex == 2 [iw=propwt], by(any_laundry_`a')
+
+ di "*repeat by day for 2005 for: any_laundry_`a'"
+ table s_halfhour empstat day if survey == 2005 & sex == 2 [iw=propwt], by(any_laundry_`a')
+ }
+ local acts "all allh"
+ foreach a of local acts {
+ * analysis by laundry type
+ preserve
+ gen laundry_timing_`a' = 5 if any_laundry_`a' == 1 // other
+ replace laundry_timing_`a' = 1 if any_laundry_`a' == 1 & day == 1 & tin(08:00, 12:00) // sunday morning
+ replace laundry_timing_`a' = 2 if any_laundry_`a' == 1 & day > 1 & day < 6 & tin(09:00, 12:00) // weekday morning
+ replace laundry_timing_`a' = 3 if any_laundry_`a' == 1 & day > 1 & day < 6 & tin(17:00, 20:00) // weekday evening peak
+ replace laundry_timing_`a' = 4 if any_laundry_`a' == 1 & tin(00:00, 01:30) // night-time
+ replace laundry_timing_`a' = 4 if any_laundry_`a' == 1 & tin(22:30, 23:30) // night-time
+
+ tab laundry_timing_`a', gen(laundry_timing_`a')
+
+ * check for missing
+ table s_halfhour laundry_timing_`a' any_laundry_`a', mi
+
+ lab def laundry_timing 1 "Sunday morning 09:00-12:00" 2 "Weekday morning 09:00-12:00" 3 "Weekday evening peak 17:00-20:00" 4 "Night-time 22:30-01:30" 5 "Other"
+ lab val laundry_timing_`a' laundry_timing
+ tab laundry_timing_`a' survey [iw=propwt], col
+ svy:tab laundry_timing_`a' survey, col ci
+ table laundry_timing_`a' ba_age_r survey [iw=propwt], col
+ table laundry_timing_`a' empstat survey [iw=propwt], col
+ table laundry_timing_`a' ba_nchild survey [iw=propwt], col
+
+ * collapse to single person record
+ * note that this does not mean classifying 1 person to 1 'type' - a person can display multiple laundry types
+ * remember 1974/5 = 1 week diary
+
+ collapse (sum) laundry_timing_* any_laundry_all* (mean) propwt, by(pid survey ///
+ ba_birth_cohort ba_age_r ba_nchild sex emp empstat nchild)
+
+ recode any_laundry_all any_laundry_allh any_laundry_allnh (1/max=1)
+ recode laundry_timing_`a'1 (1/max=1)
+ recode laundry_timing_`a'2 (1/max=1)
+ recode laundry_timing_`a'3 (1/max=1)
+ recode laundry_timing_`a'4 (1/max=1)
+ recode laundry_timing_`a'5 (1/max=1)
+
+ *how many people are in multiple types?
+ egen nlaundry_types_`a' = rowtotal(laundry_timing_`a'*)
+ svy: tab nlaundry_types_`a' survey, col
+
+ * what % of respondents in each?
+ svy: mean laundry_timing_`a'*, over(survey)
+ * % of launderers
+ svy: mean laundry_timing_`a'* if any_laundry_all == 1, over(survey)
+
+ foreach v of numlist 1/4 {
+ logit laundry_timing_`a'`v' sex ib4.empstat i.ba_age_r i.ba_nchild if survey == 1974
+ est store laundry_timing_`a'`v'_1974
+ logit laundry_timing_`a'`v' sex ib4.empstat i.ba_age_r i.ba_nchild if survey == 2005
+ est store laundry_timing_`a'`v'_2005
+ }
+ estout laundry_timing_`a'*_2005 using "$rpath/laundry_type_`a'_1974_$version-regressions.txt", cells("b ci_l ci_u se _star") stats(N r2_p chi2 p ll) replace
+ estout laundry_timing_`a'*_2005 using "$rpath/laundry_type_`a'_2005_$version-regressions.txt", cells("b ci_l ci_u se _star") stats(N r2_p chi2 p ll) replace
+ restore
+ }
+}
+
+
+* go back to the main survey aggregate file
+use "$mtuspath/MTUS-adult-aggregate-UK-only-wf.dta", clear
+
+* drop all bad cases
+keep if badcase == 0
+
+* set as survey data for descriptives
+svyset [iw=propwt]
+
+* drop diary duplicates & do some basic stats
+
+duplicates drop pid, force
+
+* create working age variable
+gen ba_working_age = 0
+replace ba_working_age = 1 if age > 18 // OK, it should be 16 but...
+* women
+replace ba_working_age = 0 if age > 60 & sex == 2
+* men
+replace ba_working_age = 0 if age > 65 & sex == 1
+* check
+table ba_age_r ba_working_age sex
+
+* Proportion of women in work
+svy: tab survey empstat if ba_working_age == 1 & sex == 2, row
+
+di "Done!"
+
+log close
diff --git a/Theme-1/laundryPaper/old_stata/DEMAND-BA-Laundry-Energy-Time-As-Submitted-v1.2.do b/Theme-1/laundryPaper/old_stata/DEMAND-BA-Laundry-Energy-Time-As-Submitted-v1.2.do
new file mode 100644
index 0000000000000000000000000000000000000000..f5b06f4e43fdd2364cffa507c450e1676090a8ee
--- /dev/null
+++ b/Theme-1/laundryPaper/old_stata/DEMAND-BA-Laundry-Energy-Time-As-Submitted-v1.2.do
@@ -0,0 +1,482 @@
+*******************************************
+* Script to use a number of datasets to examine:
+* - distributions of laundry in 1975 & 2005
+* - changing laundry practices
+
+* uses:
+* - MTUS World 6 time-use data (www.timeuse.org/mtus UK subset) - data already in long format (but episodes)
+* - EFS 2005-6 to analyse uptake of washers/dryers
+* - SPRG water practices survey
+
+* This work was funded by RCUK through the End User Energy Demand Centres Programme via the
+* "DEMAND: Dynamics of Energy, Mobility and Demand" Centre (www.demand.ac.uk, gow.epsrc.ac.uk/NGBOViewGrant.aspx?GrantRef=EP/K011723/1)
+
+/*
+
+Copyright (C) 2014 University of Southampton
+
+Author: Ben Anderson (b.anderson@soton.ac.uk, @dataknut, https://github.com/dataknut)
+ [Energy & Climate Change, Faculty of Engineering & Environment, University of Southampton]
+
+This program is free software; you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation; either version 2 of the License
+(http://choosealicense.com/licenses/gpl-2.0/), or (at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+#YMMV - http://en.wiktionary.org/wiki/YMMV
+
+*/
+
+clear all
+
+* change these to run this script on different PC
+* use globals so can re-run parts of the script
+
+global where "~/Documents/Work"
+global droot "$where/Data/Social Science Datatsets"
+
+* LCFS/EFS
+global efspath "$droot/Expenditure and Food Survey/processed"
+
+* MTUS
+global mtuspath "$droot/MTUS/World 6/processed"
+
+* SPRG
+global sprgpath "$where/Projects/ESRC-SPRG/WP4-Micro_water/data/sprg_survey/data/safe/v6"
+
+* where to put results
+global proot "$where/Projects/RCUK-DEMAND/Theme 1"
+global rpath "$proot/results/MTUS"
+
+* version
+global version = "v1.2-at-home"
+* assumes "other location" = someone else's house (as it is not laundrette and "other person's house" is not defined for 1974 & 2005
+
+* global version = "v1.1-at-home"
+* excludes any laundry "not at home or someone else's home" (eloc = 1 or 2)
+
+* global version = "v1.0-all-locs"
+* weights the final counts
+
+* which subgroup of mtus are we interested in?
+global mtusfilter "_all"
+
+capture log close
+
+log using "$rpath/DEMAND-BA-MTUS-W6-Laundry-Change-Over-Time-$version-adult.smcl", replace
+
+* control what gets done
+local do_halfhour_samples = 1
+
+* make script run without waiting for user input
+set more off
+
+**********************************
+**********************************
+* LCFS data for tumble dryer uptake levels to 2005
+use "$efspath/EFS-2005-2006-extract-BA.dta", clear
+lookfor tumble weight
+tab year a167 [iw=weighta], row
+
+* 2005 only
+tab a167 c_nchild [iw=weighta] if year == 2005, col
+tab a167 c_nearners [iw=weighta] if year == 2005, col
+tab a167 c_empl [iw=weighta] if year == 2005, col
+
+**********************************
+**********************************
+* SPRG data on laundry practices
+use "$sprgpath/8369-clt-050312-v6-wf-safe.dta", clear
+
+desc q27*
+
+rename q27_sum sum_q27
+
+* 1 = yes, 2 = no
+recode q27* (2=0)
+
+* use mean to get % who said yes to each
+su q27* [iw=weight_respondent2], sep(0)
+
+* mean number of 'yes' responses
+su sum_q27 [iw=weight_respondent2]
+
+* distribution
+tab sum_q27 [iw=weight_respondent2]
+
+**********************************
+**********************************
+* MTUS
+* codes of interest
+* 1974: Main/Sec21 Laundry, ironing, clothing repair <- 50 Other essential domestic work (i.e. NOT preparing meals or routine housework)
+* so laundry in 1974 may be over-estimated
+* BUT 1975 is partly a 7 day diary - so more likely to detect laundry?
+
+* 2005: Main/Sec21 Laundry, ironing, clothing repair <- Pact=7 (washing clothes)
+
+**********
+* start with processing the aggregate (survey) data
+use "$mtuspath/MTUS-adult-aggregate-UK-only-wf.dta", clear
+
+* drop all bad cases
+keep if badcase == 0
+
+* set as survey data for descriptives
+svyset [iw=propwt]
+
+* keep only 1974 & 2005 for simplicity
+* keep if survey == 1974 | survey == 2005
+* no, let's keep them all for birth cohort analysis!
+
+* this is minutes per day not episodes
+* check 18 (Cooking) & 20 (Cleaning) & 22 (maintain home/vehicle) against laundry
+* seems to under-report laundry in 1974, esp for women?
+svy: mean main18 main20 main21 main22, over(survey sex)
+
+* keep whatever sample we define above
+keep $mtusfilter
+
+* number of diary days by hh type
+* svy: tab hhtype survey, col count
+
+* number of diary days by number of days covered
+* 1974 = 7 day dairy
+svy: tab id survey, col count
+
+* change order of income variable
+recode income (-9=4)
+lab def INCOME 4 "Not known", add
+
+
+* keep only the vars we want to keep memory required low
+keep sex age main7 main21 hhtype empstat income emp unemp student retired propwt survey day month year ///
+ hhldsize famstat nchild hhldsize *pid ba*
+
+* number of diary-days
+svy: tab survey, obs
+
+**********
+* use raw data to assess raw episodes
+preserve
+
+ use "$mtuspath/MTUS-adult-episode-UK-only-wf.dta", clear
+
+restore
+**********
+* switch to sampled data
+* this requires the 10 minute sampling process implemented in
+* https://github.com/dataknut/MTUS/blob/master/process-MTUS-W6-convert-to-X-min-samples-v1.0-adult.do
+* to have been run over the MTUS first with X set to 10
+
+if `do_halfhour_samples' {
+ * merge in the sampled data
+ * do analysis by collapsing 10 minute sampled data to half hours
+ merge 1:m diarypid using "$mtuspath/MTUS-adult-episode-UK-only-wf-10min-samples-long-v1.0.dta", ///
+ gen(m_aggvars)
+
+ * which years could we use?
+ * NB - we need full years or at least quarters/all seasons for laundry as it may well be seasonally variant.
+ tab month survey [iw=propwt]
+
+ * 1974 = Feb, Mar & Aug,Sept -> has winter & summer
+ * 1984 = winter only
+ * 1987 = early summer only
+ * 1995 = May
+ * 2000 = all year
+ * 2005 = each season (March, June, Sept, Nov)
+
+ * keep 1974 & 2005 only as problems with coverage in all others
+ * could pool 1984 & 1987 but 1995 useless (May!)
+ keep if survey == 1974 | survey == 2005
+
+ * set up half-hour variable
+ gen ba_hourt = hh(s_starttime)
+ gen ba_minst = mm(s_starttime)
+
+ gen ba_hh = 0 if ba_minst < 30
+ replace ba_hh = 30 if ba_minst > 29
+ gen ba_sec = 0
+ * sets date to 1969!
+ gen s_halfhour = hms(ba_hourt, ba_hh, ba_sec)
+ lab var s_halfhour "Episode starts during the half hour following"
+ format s_halfhour %tcHH:MM
+
+ * define laundry
+ gen laundry_p = 0
+ lab var laundry_p "Main act = laundry (21)"
+ replace laundry_p = 1 if pact == 21
+
+ gen laundry_s = 0
+ lab var laundry_s "Secondary act = laundry (21)"
+ replace laundry_s = 1 if sact == 21
+
+ gen laundry_all = 0
+ replace laundry_all = 1 if laundry_p == 1 | laundry_s == 1
+ lab var laundry_all "Any act = laundry (21)"
+
+ * distribution of locations
+ svy: tab eloc survey, count col
+
+ * laundry done at home or elsewhere?
+ svy: tab eloc survey, count col
+
+ * a lot of 1974 done at 'other locations'?
+
+ * can we work out where?
+ bysort survey: tab sact pact if eloc == 9 & laundry_all == 1
+ * a bit - for the most part there is no recorded secondary actitivy if main = laundry
+ bysort survey: tab mtrav if eloc == 9 & laundry_all == 1
+ * that doesn't help - all not travelling
+ * NB "someone else's home is not set for 1974/2005" - maybe these are the 'other' locations?
+
+ gen laundry_rh = 0
+ replace laundry_rh = 1 if laundry_all == 1 & (eloc == 1 | eloc == 2) // definitely at home
+
+ gen laundry_sh = 0
+ replace laundry_sh = 1 if laundry_all == 1 & (eloc == 5) // definitely at shops/services
+
+ gen laundry_oth = 0
+ replace laundry_oth = 1 if laundry_all == 1 & (eloc == 9) // definitely at other location
+
+ * set defined locations
+ gen laundry_h = 0
+ replace laundry_h = 1 if laundry_all == 1 & (eloc == 1 | eloc == 2 | eloc == 9) // specifically at home or someone else's home (the latter not set in 1974/2005)
+ * we'll also assume that visiting/receiving friends whilst laundry is at someone's home - doesn't really matter whose for this paper
+ replace laundry_h = 1 if laundry_all == 1 & (pact == 48 | sact == 48)
+ lab var laundry_h "Any act = laundry (21) at someone's home"
+
+ gen laundry_nh = 0
+ replace laundry_nh = 1 if laundry_all == 1 & (eloc != 1 & eloc != 2 & eloc != 9) & (pact != 48 & sact != 48)
+ lab var laundry_nh "Any act = laundry (21) not at home"
+
+ * this is the number of 10 minute samples by survey & day of the week
+ tab survey day [iw=propwt]
+
+ di "* check % of sampled X minute points which are laundry"
+ di "* NB reporting frame longer in 1974 (30 mins) so may be higher frequency (e.g. interruption in 10-20 mins coded)"
+ di "* all"
+ tab survey laundry_all [iw=propwt], row
+ di "* home ($version)"
+ tab survey laundry_h [iw=propwt], row
+ di "* not at home ($version)"
+ tab survey laundry_nh [iw=propwt], row
+
+ *********************
+ * collapse to add up the sampled laundry by half hour
+ * use the byvars we're interested in (or could re-merge with aggregated file)
+ * because the different surveys have different reporting periods we need to just count at least 1 laundry in the half hour
+
+ collapse (sum) laundry_* (mean) propwt, by(diarypid pid survey day month year s_halfhour ///
+ ba_birth_cohort ba_age_r ba_nchild sex emp empstat nchild income hhldsize)
+
+ lab val emp EMP
+ lab val empstat EMPSTAT
+
+ * set the weight
+ svyset [iw=propwt]
+
+ * the number of half hour data points by survey & day
+ svy: tab survey day
+
+ * do original locations
+ local acts "all rh sh oth"
+
+ foreach a of local acts {
+ di "* Adding up 'at least 1' & basic stats for laundry_`a' ($version)"
+ gen any_laundry_`a' = 0
+ replace any_laundry_`a' = 1 if laundry_`a' > 0
+ lab var any_laundry_`a' "`a' $version"
+ di "* overall prevalence of 'any laundry in a half hour' - any_laundry_`a' ($version)"
+ di "* counts (any_laundry_`a' $version)"
+ svy: tab any_laundry_`a' survey, count format(%9.2f)
+ di "* proportions (any_laundry_`a' $version)"
+ svy: tab any_laundry_`a' survey, col ci
+ di "* proportions by sex (any_laundry_`a' == 1 $version)"
+ svy: tab sex survey if any_laundry_`a' == 1, col ci
+ }
+ di "* test who does laundry at 'other locations' at half hour level"
+ logit any_laundry_oth i.sex i.ba_age_r i.ba_nchild hhldsize i.empstat i.income if survey == 1974, cluster(pid)
+
+ * now do the defined/derived locations
+ local acts "h nh"
+ foreach a of local acts {
+ di "* Adding up 'at least 1' & basic stats for laundry_`a' ($version)"
+ gen any_laundry_`a' = 0
+ replace any_laundry_`a' = 1 if laundry_`a' > 0
+ lab var any_laundry_`a' "`a' $version"
+ di "* overall prevalence of 'any laundry in a half hour' - any_laundry_`a' ($version)"
+ di "* counts (any_laundry_`a' $version)"
+ svy: tab any_laundry_`a' survey, count format(%9.2f)
+ di "* proportions (any_laundry_`a' $version)"
+ svy: tab any_laundry_`a' survey, col ci
+ di "* proportions by sex (any_laundry_`a' == 1 $version)"
+ svy: tab sex survey if any_laundry_`a' == 1, col ci
+ }
+
+
+
+
+ * loop through locations
+ local acts "all h nh"
+ foreach a of local acts {
+ di "*****************************"
+ di "*****************************"
+
+ di "*****************************"
+ di "* 'at least 1 reported reported laundry instance' for: any_laundry_`a'"
+ svy: tab any_laundry_`a' survey , col ci
+
+ di "*******"
+ di "* Tables for: any_laundry_`a' = 1"
+ di "* Income"
+ svy: tab income survey if any_laundry_`a' == 1, col ci
+
+ di "*******"
+ di "* Sex"
+ svy: tab sex survey if any_laundry_`a' == 1, col ci
+ di "* Sex & age"
+ di "* 1974 (if any_laundry_`a' = 1)"
+ svy: tab ba_age_r sex if any_laundry_`a' == 1 & survey == 1974, ci row
+ di "* 2005 (if any_laundry_`a' = 1)"
+ svy: tab ba_age_r sex if any_laundry_`a' == 1 & survey == 2005, ci row
+
+ di "*******"
+ di "* Days by survey for any_laundry_`a' = 1 - Fig 2"
+ svy: tab day survey if any_laundry_`a' == 1, ci col
+
+ di "*******"
+ di "* Days by gender & survey for any_laundry_`a' =1 - used for Fig 3 "
+ table day sex survey if any_laundry_`a' == 1 [iw=propwt]
+
+ di "*******"
+ di "* Employment status by survey for any_laundry_`a' = 1"
+ table empstat survey if any_laundry_`a' == 1 [iw=propwt]
+
+ di "*******"
+ di "* Days by survey & employment status if female for any_laundry_`a' = 1 - used for Figs 4 & 5"
+ bysort survey: table day empstat if sex == 2 & any_laundry_`a' == 1 [iw=propwt]
+
+ }
+
+ * set time variable so can select by time
+ xtset diarypid s_halfhour, delta(30 mins) format(%tcHH:MM)
+
+ * time of day comparisons
+ local acts "all h nh"
+ foreach a of local acts {
+ di "*****************************"
+ di "* Tables for: any_laundry_`a'"
+ table s_halfhour survey any_laundry_`a' [iw=propwt]
+
+ di "* days by half hour for: any_laundry_`a'"
+ table s_halfhour survey day [iw=propwt], by(any_laundry_`a')
+
+ /* seasons - leave out for now (small N)
+ recode month (3 4 5 = 1 "Spring") (6 7 8 = 2 "Summer") (9 10 11 = 3 "Autumn") (12 1 2 = 4 "Winter"), gen(season)
+ * check
+ * tab month season
+ table s_halfhour survey season [iw=propwt], by(any_laundry_`a')
+
+ * by employment status - not used (small n esp if just for women)
+ di "* by half hour & employment status for women for: any_laundry_`a'"
+ table s_halfhour empstat survey if sex == 2 [iw=propwt], by(any_laundry_`a')
+
+ di "*repeat by day for 2005 for: any_laundry_`a'"
+ table s_halfhour empstat day if survey == 2005 & sex == 2 [iw=propwt], by(any_laundry_`a')
+ */
+ }
+ local acts "h"
+ foreach a of local acts {
+ * analysis by laundry type
+ preserve
+ gen laundry_timing_`a' = 5 if any_laundry_`a' == 1 // other
+ replace laundry_timing_`a' = 1 if any_laundry_`a' == 1 & day == 1 & tin(08:00, 12:00) // sunday morning
+ replace laundry_timing_`a' = 2 if any_laundry_`a' == 1 & day > 1 & day < 6 & tin(09:00, 12:00) // weekday morning
+ replace laundry_timing_`a' = 3 if any_laundry_`a' == 1 & day > 1 & day < 6 & tin(17:00, 20:00) // weekday evening peak
+ replace laundry_timing_`a' = 4 if any_laundry_`a' == 1 & tin(00:00, 01:30) // night-time
+ replace laundry_timing_`a' = 4 if any_laundry_`a' == 1 & tin(22:30, 23:30) // night-time
+
+ tab laundry_timing_`a', gen(laundry_timing_`a')
+
+ * check for missing
+ table s_halfhour laundry_timing_`a' any_laundry_`a'
+ tab laundry_timing_`a' survey, mi
+
+ lab def laundry_timing 1 "Sunday morning 09:00-12:00" 2 "Weekday morning 09:00-12:00" 3 "Weekday evening peak 17:00-20:00" 4 "Night-time 22:30-01:30" 5 "Other"
+ lab val laundry_timing_`a' laundry_timing
+ tab laundry_timing_`a' survey [iw=propwt], col
+ svy:tab laundry_timing_`a' survey, col ci
+ *table laundry_timing_`a' ba_age_r survey [iw=propwt], col
+ table laundry_timing_`a' empstat survey [iw=propwt], col
+ *table laundry_timing_`a' ba_nchild survey [iw=propwt], col
+
+ di "* collapse to single person record"
+ * note that this does not mean classifying 1 person to 1 'type' - a person can display multiple laundry types
+ * remember 1974/5 = 1 week diary
+
+ collapse (sum) laundry_timing_* any_laundry_* (mean) propwt, by(pid survey ///
+ ba_birth_cohort ba_age_r ba_nchild sex emp empstat nchild hhldsize)
+
+ recode any_laundry_all any_laundry_h any_laundry_nh (1/max=1)
+ recode laundry_timing_`a'1 (1/max=1)
+ recode laundry_timing_`a'2 (1/max=1)
+ recode laundry_timing_`a'3 (1/max=1)
+ recode laundry_timing_`a'4 (1/max=1)
+ recode laundry_timing_`a'5 (1/max=1)
+
+ di "* how many people are in multiple types? (nlaundry_types_`a' $version)"
+ egen nlaundry_types_`a' = rowtotal(laundry_timing_`a'*)
+ svy: tab nlaundry_types_`a' survey, col
+
+ * what % of respondents in each?
+ svy: mean laundry_timing_`a'*, over(survey)
+ * % of launderers
+ svy: mean laundry_timing_`a'* if any_laundry_all == 1, over(survey)
+
+ foreach v of numlist 1/4 {
+ logit laundry_timing_`a'`v' sex ib4.empstat i.ba_age_r i.ba_nchild hhldsize if survey == 1974
+ est store laundry_timing_`a'`v'_1974
+ logit laundry_timing_`a'`v' sex ib4.empstat i.ba_age_r i.ba_nchild hhldsize if survey == 2005
+ est store laundry_timing_`a'`v'_2005
+ }
+ estout laundry_timing_`a'*_2005 using "$rpath/laundry_type_`a'_1974_$version-regressions.txt", cells("b ci_l ci_u se p _star") stats(N r2_p chi2 p ll) replace
+ estout laundry_timing_`a'*_2005 using "$rpath/laundry_type_`a'_2005_$version-regressions.txt", cells("b ci_l ci_u se p _star") stats(N r2_p chi2 p ll) replace
+ restore
+ }
+}
+
+
+* go back to the main survey aggregate file
+use "$mtuspath/MTUS-adult-aggregate-UK-only-wf.dta", clear
+
+* drop all bad cases
+keep if badcase == 0
+
+* set as survey data for descriptives
+svyset [iw=propwt]
+
+* drop diary duplicates & do some basic stats
+
+duplicates drop pid, force
+
+* create working age variable
+gen ba_working_age = 0
+replace ba_working_age = 1 if age > 18 // OK, it should be 16 but...
+* women
+replace ba_working_age = 0 if age > 60 & sex == 2
+* men
+replace ba_working_age = 0 if age > 65 & sex == 1
+* check
+table ba_age_r ba_working_age sex
+
+* Proportion of women in work
+svy: tab survey empstat if ba_working_age == 1 & sex == 2, row
+
+di "Done!"
+
+log close
diff --git a/Theme-1/laundryPaper/old_stata/DEMAND-BA-Laundry-Energy-Time-As-Submitted-v1.3.do b/Theme-1/laundryPaper/old_stata/DEMAND-BA-Laundry-Energy-Time-As-Submitted-v1.3.do
new file mode 100644
index 0000000000000000000000000000000000000000..d4d77f38e138ce041fc30c545dcd177c6736b981
--- /dev/null
+++ b/Theme-1/laundryPaper/old_stata/DEMAND-BA-Laundry-Energy-Time-As-Submitted-v1.3.do
@@ -0,0 +1,504 @@
+*******************************************
+* Script to use a number of datasets to examine:
+* - distributions of laundry in 1975 & 2005
+* - changing laundry practices
+
+* uses:
+* - MTUS World 6 time-use data (www.timeuse.org/mtus UK subset) - data already in long format (but episodes)
+* - EFS 2005-6 to analyse uptake of washers/dryers
+* - SPRG water practices survey
+
+* This work was funded by RCUK through the End User Energy Demand Centres Programme via the
+* "DEMAND: Dynamics of Energy, Mobility and Demand" Centre (www.demand.ac.uk, gow.epsrc.ac.uk/NGBOViewGrant.aspx?GrantRef=EP/K011723/1)
+
+/*
+
+Copyright (C) 2014 University of Southampton
+
+Author: Ben Anderson (b.anderson@soton.ac.uk, @dataknut, https://github.com/dataknut)
+ [Energy & Climate Change, Faculty of Engineering & Environment, University of Southampton]
+
+This program is free software; you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation; either version 2 of the License
+(http://choosealicense.com/licenses/gpl-2.0/), or (at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+#YMMV - http://en.wiktionary.org/wiki/YMMV
+
+*/
+
+clear all
+
+* change these to run this script on different PC
+* use globals so can re-run parts of the script
+
+global where "~/Documents/Work"
+global droot "$where/Data/Social Science Datatsets"
+
+* LCFS/EFS
+global efspath "$droot/Expenditure and Food Survey/processed"
+
+* MTUS
+global mtuspath "$droot/MTUS/World 6/processed"
+
+* SPRG
+global sprgpath "$where/Projects/ESRC-SPRG/WP4-Micro_water/data/sprg_survey/data/safe/v6"
+
+* where to put results
+global proot "$where/Projects/RCUK-DEMAND/Theme 1"
+global rpath "$proot/results/MTUS"
+
+* version
+global version = "v1.3"
+* tests use of 1985 due to definitional issues with 1974/5
+
+*global version = "v1.2-at-home"
+* assumes "other location" = someone else's house (as it is not laundrette and "other person's house" is not defined for 1974 & 2005
+
+* global version = "v1.1-at-home"
+* excludes any laundry "not at home or someone else's home" (eloc = 1 or 2)
+
+* global version = "v1.0-all-locs"
+* weights the final counts
+
+* which subgroup of mtus are we interested in?
+global mtusfilter "_all"
+
+capture log close
+
+log using "$rpath/DEMAND-BA-MTUS-W6-Laundry-Change-Over-Time-$version-adult.smcl", replace
+
+* control what gets done
+local do_halfhour_samples = 1
+
+* make script run without waiting for user input
+set more off
+
+**********************************
+**********************************
+* LCFS data for tumble dryer uptake levels to 2005
+use "$efspath/EFS-2005-2006-extract-BA.dta", clear
+lookfor tumble weight
+tab ba_year a167 [iw=weighta], row
+
+* 2005 only
+tab a167 c_nchild [iw=weighta] if ba_year == 2005, col
+tab a167 c_nearners [iw=weighta] if ba_year == 2005, col
+tab a167 c_empl [iw=weighta] if ba_year == 2005, col
+
+**********************************
+**********************************
+* SPRG data on laundry practices
+use "$sprgpath/8369-clt-050312-v6-wf-safe.dta", clear
+
+desc q27*
+
+rename q27_sum sum_q27
+
+* 1 = yes, 2 = no
+recode q27* (2=0)
+
+* use mean to get % who said yes to each
+su q27* [iw=weight_respondent2], sep(0)
+
+* mean number of 'yes' responses
+su sum_q27 [iw=weight_respondent2]
+
+* distribution
+tab sum_q27 [iw=weight_respondent2]
+
+**********************************
+**********************************
+* MTUS
+* codes of interest
+* 1974: Main/Sec21 Laundry, ironing, clothing repair <- 50 Other essential domestic work (i.e. NOT preparing meals or routine housework)
+* so laundry in 1974 may be under-estimated as laundry may have been considered 'routine housework' by respondents
+* BUT 1975 is partly a 7 day diary - so more likely to detect laundry?
+
+* 1983/4/7: Main/Sec21 Laundry, ironing, clothing repair
+* <- 0701 Wash clothes, hang out / bring in washing
+* 0702 Iron clothes
+* 0801 Repair, upkeep of clothes
+* so may over-estimate laundry
+
+* 2005: Main/Sec21 Laundry, ironing, clothing repair <- Pact=7 (washing clothes)
+
+**********
+* start with processing the aggregate (survey) data
+use "$mtuspath/MTUS-adult-aggregate-UK-only-wf.dta", clear
+
+* drop all bad cases
+keep if badcase == 0
+
+* set as survey data for descriptives
+svyset [iw=propwt]
+
+* keep only 1974 & 2005 for simplicity
+* keep if survey == 1974 | survey == 2005
+* no, let's keep them all for birth cohort analysis!
+
+* this is minutes per day not episodes
+* check 18 (Cooking) & 20 (Cleaning) & 22 (maintain home/vehicle) against laundry
+* seems to under-report laundry in 1974, esp for women?
+* not surprising given definitional issues - 'routine housework' is classified as 'cleaning' by MTUS for 1974, laundry probably was considered
+* 'routine' housework by female respondents but perhaps less so for men? :-)
+svy: mean main18 main20 main21 main22, over(survey sex)
+
+* keep whatever sample we define above
+keep $mtusfilter
+
+* number of diary days by hh type
+* svy: tab hhtype survey, col count
+
+* number of diary days by number of days covered
+* 1974 = 7 day dairy
+svy: tab id survey, col count
+
+* change order of income variable
+recode income (-9=4)
+lab def INCOME 4 "Not known", add
+
+
+* keep only the vars we want to keep memory required low
+keep sex age main7 main21 hhtype empstat income emp unemp student retired propwt survey mtus_* ///
+ hhldsize famstat nchild hhldsize *pid ba*
+
+* number of diary-days
+svy: tab survey, obs
+
+**********
+* use raw data to assess raw episodes
+preserve
+
+ use "$mtuspath/MTUS-adult-episode-UK-only-wf.dta", clear
+
+restore
+**********
+* switch to sampled data
+* this requires the 10 minute sampling process implemented in
+* https://github.com/dataknut/MTUS/blob/master/process-MTUS-W6-convert-to-X-min-samples-v1.0-adult.do
+* to have been run over the MTUS first with X set to 10
+
+if `do_halfhour_samples' {
+ * merge in the sampled data
+ * do analysis by collapsing 10 minute sampled data to half hours
+ merge 1:m diarypid using "$mtuspath/MTUS-adult-episode-UK-only-wf-10min-samples-long-v1.0.dta", ///
+ gen(m_aggvars)
+
+ * which years could we use?
+ * NB - we need full years or at least quarters/all seasons for laundry as it may well be seasonally variant.
+ tab mtus_month survey [iw=propwt]
+
+ * 1974 = Feb, Mar & Aug,Sept -> has winter & summer
+ * 1984 = winter only
+ * 1987 = early summer only
+ * 1995 = May
+ * 2000 = all year
+ * 2005 = each season (March, June, Sept, Nov)
+
+ * keep 1974 & 2005 only as problems with coverage in all others
+ * keep if survey == 1974 | survey == 2005
+
+ * create a bespoke survey which merges 1983 & 1987
+ * has the advantage of providing all seasons for '1985'
+ recode survey (1974=1974 "1974") (1983/1987=1985 "1985") (1995 = 1995 "1995") (2000=2000 "2000") (2005=2005 "2005"), gen(ba_survey)
+
+ * set up half-hour variable
+ gen ba_hourt = hh(s_starttime)
+ gen ba_minst = mm(s_starttime)
+
+ gen ba_hh = 0 if ba_minst < 30
+ replace ba_hh = 30 if ba_minst > 29
+ gen ba_sec = 0
+ * sets date to 1969!
+ gen s_halfhour = hms(ba_hourt, ba_hh, ba_sec)
+ lab var s_halfhour "Episode starts during the half hour following"
+ format s_halfhour %tcHH:MM
+
+ * define laundry
+ gen laundry_p = 0
+ lab var laundry_p "Main act = laundry (21)"
+ replace laundry_p = 1 if pact == 21
+
+ gen laundry_s = 0
+ lab var laundry_s "Secondary act = laundry (21)"
+ replace laundry_s = 1 if sact == 21
+
+ gen laundry_all = 0
+ replace laundry_all = 1 if laundry_p == 1 | laundry_s == 1
+ lab var laundry_all "Any act = laundry (21)"
+
+ * distribution of locations
+ tab eloc ba_survey, col
+
+ * laundry done at home or elsewhere?
+ tab eloc ba_survey if laundry_all == 1, col
+
+ * a lot of 1974 done at 'other locations'?
+ * can we work out where?
+ bysort ba_survey: tab sact pact if eloc == 9 & laundry_all == 1
+ * a bit - for the most part there is no recorded secondary actitivy if main = laundry
+ bysort ba_survey: tab mtrav if eloc == 9 & laundry_all == 1
+ * that doesn't help - all not travelling
+ * NB "someone else's home is not set for 1974/2005" - maybe these are the 'other' locations?
+
+ gen laundry_rh = 0
+ replace laundry_rh = 1 if laundry_all == 1 & (eloc == 1 | eloc == 2) // definitely at home
+
+ gen laundry_sh = 0
+ replace laundry_sh = 1 if laundry_all == 1 & (eloc == 5) // definitely at shops/services
+
+ gen laundry_oth = 0
+ replace laundry_oth = 1 if laundry_all == 1 & (eloc == 9) // definitely at other location
+
+ * set defined locations
+ gen laundry_h = 0
+ replace laundry_h = 1 if laundry_all == 1 & (eloc == 1 | eloc == 2 | eloc == 9) // specifically at home or someone else's home (the latter not set in 1974/2005)
+ * we'll also assume that visiting/receiving friends whilst laundry is at someone's home - doesn't really matter whose for this paper
+ replace laundry_h = 1 if laundry_all == 1 & (pact == 48 | sact == 48)
+ lab var laundry_h "Any act = laundry (21) at someone's home"
+
+ gen laundry_nh = 0
+ replace laundry_nh = 1 if laundry_all == 1 & (eloc != 1 & eloc != 2 & eloc != 9) & (pact != 48 & sact != 48)
+ lab var laundry_nh "Any act = laundry (21) not at home"
+
+ * this is the number of 10 minute samples by survey & day of the week
+ tab ba_survey s_dow [iw=propwt]
+
+ di "* check % of sampled X minute points which are laundry"
+ di "* all"
+ tab ba_survey laundry_all [iw=propwt], row
+ di "* home ($version)"
+ tab ba_survey laundry_h [iw=propwt], row
+ di "* not at home ($version)"
+ tab ba_survey laundry_nh [iw=propwt], row
+
+ *********************
+ di "* collapse to add up the sampled laundry by half hour"
+ * use the byvars we're interested in (or could re-merge with aggregated file)
+ * because the different surveys have different reporting periods we need to just count at least 1 laundry in the half hour
+ di "*****************************"
+ di "* from here on we have half-hour aggregations"
+
+* keep the years we are interested in to save memory & time
+ keep if ba_survey == 1985 | ba_survey == 2005
+
+ * be sure to use s_dow as it is the corrected day for 1984/7
+
+ collapse (sum) laundry_* (mean) propwt, by(diarypid pid ba_survey s_dow mtus_month mtus_month mtus_year s_halfhour ///
+ ba_birth_cohort ba_age_r ba_nchild sex emp empstat nchild income hhldsize)
+
+ lab val emp EMP
+ lab val empstat EMPSTAT
+
+ * set the weight
+ svyset [iw=propwt]
+
+ * the number of half hour data points by survey & day
+ svy: tab ba_survey s_dow
+
+ * add up 'at least 1 mention' per half-hour
+ * for original locations
+ local loc "all rh sh oth"
+
+ foreach l of local loc {
+ di "* Adding up 'at least 1' & basic stats for laundry_`a' ($version)"
+ gen any_laundry_`l' = 0
+ replace any_laundry_`l' = 1 if laundry_`l' > 0
+ lab var any_laundry_`l' "`l' $version"
+ }
+ di "* test who does laundry at 'other locations' at half hour level"
+ bysort ba_survey: logit any_laundry_oth i.sex i.ba_age_r i.ba_nchild hhldsize i.empstat i.income, cluster(pid)
+
+ * for derived locations
+ local dloc "h nh"
+ foreach l of local dloc {
+ di "* Adding up 'at least 1' for laundry_`l' ($version)"
+ gen any_laundry_`l' = 0
+ replace any_laundry_`l' = 1 if laundry_`l' > 0
+ lab var any_laundry_`l' "`l' $version"
+ }
+
+
+ * loop through all & derived locations
+ local acts "all h nh"
+ foreach a of local acts {
+ di "*****************************"
+ di "* any_laundry_`a' = at least 1 reported instance of laundry in the half hour"
+ di "*******"
+ di "* 'at least 1 reported reported laundry instance' for: any_laundry_`a'"
+ svy: tab any_laundry_`a' ba_survey , col ci
+
+ di "*******"
+ di "* Days by survey for any_laundry_`a' = 1 - Fig 2"
+ svy: tab s_dow ba_survey if any_laundry_`a' == 1, ci col
+
+ di "*******"
+ di "* Days by gender & survey for any_laundry_`a' =1 - used for Fig 3 "
+ bysort ba_survey: table s_dow sex any_laundry_`a' [iw=propwt]
+
+ di "*******"
+ di "* Days by survey & employment status if female for any_laundry_`a' = 1 - used for Figs 4 & 5"
+ bysort ba_survey: table s_dow empstat any_laundry_`a' if sex == 2 [iw=propwt]
+ }
+
+ * time of day comparisons
+ local acts "all h nh"
+ foreach a of local acts {
+ di "*****************************"
+ di "* Tables for: any_laundry_`a'"
+ table s_halfhour ba_survey any_laundry_`a' [iw=propwt]
+
+ di "* days by half hour for: any_laundry_`a'"
+ * use tabout here
+
+ local count = 0
+ * by day of week
+ local filemethod = "replace"
+ levelsof ba_survey, local(levels)
+ *di "* levels: `levels'"
+ local labels: value label ba_survey
+ *di "* labels: `labels'"
+ local heading "h1(any_laundry_`a')"
+ foreach l of local levels {
+ if `count' > 0 {
+ * we already made one pass so now append
+ local filemethod = "append"
+ *ocal heading = "h1(nil) h2(nil)"
+ }
+ local vlabel : label `labels' `l'
+ di "*-> Level: `l'"
+ * use mean to give per half-hour rates
+ qui: tabout s_halfhour s_dow if ba_survey == `l' ///
+ using "$rpath/MTUS_any_laundry_`a'_by_halfhour_per_year_day_mean_$version.txt", `filemethod' ///
+ h3("Year: `vlabel'") ///
+ cells(mean any_laundry_`a' se) ///
+ format(5) ///
+ svy sum
+ * use freq as can then summarise across all days to give relative freq
+ qui: tabout s_halfhour s_dow if any_laundry_`a' == 1 & ba_survey == `l' ///
+ using "$rpath/MTUS_any_laundry_`a'_by_halfhour_per_year_day_freq_$version.txt", `filemethod' ///
+ h3("Year: `vlabel'") ///
+ cells(freq) ///
+ format(5) ///
+ svy
+ local count = `count' + 1
+ }
+
+ /* seasons - leave out for now (small N)
+ recode mtus_month (3 4 5 = 1 "Spring") (6 7 8 = 2 "Summer") (9 10 11 = 3 "Autumn") (12 1 2 = 4 "Winter"), gen(season)
+ * check
+ * tab mtus_month season
+ table s_halfhour ba_survey season [iw=propwt], by(any_laundry_`a')
+
+ * by employment status - not used (small n esp if just for women)
+ di "* by half hour & employment status for women for: any_laundry_`a'"
+ table s_halfhour empstat ba_survey if sex == 2 [iw=propwt], by(any_laundry_`a')
+
+ di "*repeat by day for 2005 for: any_laundry_`a'"
+ table s_halfhour empstat s_dow if ba_survey == 2005 & sex == 2 [iw=propwt], by(any_laundry_`a')
+ */
+ }
+
+ * set time variable so can select by time
+ xtset diarypid s_halfhour, delta(30 mins) format(%tcHH:MM)
+
+ local acts "h"
+ foreach a of local acts {
+ * analysis by laundry type
+ preserve
+ gen laundry_timing_`a' = 5 if any_laundry_`a' == 1 // other
+ replace laundry_timing_`a' = 1 if any_laundry_`a' == 1 & s_dow == 0 & tin(08:00, 12:00) // sunday morning
+ replace laundry_timing_`a' = 2 if any_laundry_`a' == 1 & s_dow > 0 & s_dow < 6 & tin(09:00, 12:00) // weekday morning
+ replace laundry_timing_`a' = 3 if any_laundry_`a' == 1 & s_dow > 0 & s_dow < 6 & tin(17:00, 20:00) // weekday evening peak
+ replace laundry_timing_`a' = 4 if any_laundry_`a' == 1 & tin(00:00, 01:30) // night-time
+ replace laundry_timing_`a' = 4 if any_laundry_`a' == 1 & tin(22:30, 23:30) // night-time
+
+ tab laundry_timing_`a', gen(laundry_timing_`a')
+
+ * check for missing
+ table s_halfhour laundry_timing_`a' any_laundry_`a'
+ tab laundry_timing_`a' ba_survey, mi
+
+ lab def laundry_timing 1 "Sunday morning 09:00-12:00" 2 "Weekday morning 09:00-12:00" 3 "Weekday evening peak 17:00-20:00" 4 "Night-time 22:30-01:30" 5 "Other"
+ lab val laundry_timing_`a' laundry_timing
+ tab laundry_timing_`a' ba_survey [iw=propwt], col
+ svy:tab laundry_timing_`a' ba_survey, col ci
+ *table laundry_timing_`a' ba_age_r ba_survey [iw=propwt], col
+ table laundry_timing_`a' empstat ba_survey [iw=propwt], col
+ *table laundry_timing_`a' ba_nchild ba_survey [iw=propwt], col
+
+ di "* collapse to single person record"
+ * note that this does not mean classifying 1 person to 1 'type' - a person can display multiple laundry types
+
+ collapse (sum) laundry_timing_* any_laundry_* (mean) propwt, by(pid ba_survey ///
+ ba_birth_cohort ba_age_r ba_nchild sex emp empstat nchild hhldsize)
+
+ recode any_laundry_all any_laundry_h any_laundry_nh (1/max=1)
+ recode laundry_timing_`a'1 (1/max=1)
+ recode laundry_timing_`a'2 (1/max=1)
+ recode laundry_timing_`a'3 (1/max=1)
+ recode laundry_timing_`a'4 (1/max=1)
+ recode laundry_timing_`a'5 (1/max=1)
+
+ di "* how many people are in multiple types? (nlaundry_types_`a' $version)"
+ egen nlaundry_types_`a' = rowtotal(laundry_timing_`a'*)
+ svy: tab nlaundry_types_`a' ba_survey, col
+
+ * what % of respondents in each?
+ svy: mean laundry_timing_`a'*, over(ba_survey)
+ * % of launderers
+ svy: mean laundry_timing_`a'* if any_laundry_all == 1, over(ba_survey)
+
+ * test predictors of exhibiting different laundry practices
+ foreach v of numlist 1/4 {
+ logit laundry_timing_`a'`v' sex ib4.empstat i.ba_age_r i.ba_nchild hhldsize if ba_survey == 1985
+ est store laundry_timing_`a'`v'_1985
+ logit laundry_timing_`a'`v' sex ib4.empstat i.ba_age_r i.ba_nchild hhldsize if ba_survey == 2005
+ est store laundry_timing_`a'`v'_2005
+ }
+ estout laundry_timing_`a'*_1985 using "$rpath/laundry_type_`a'_1985_$version-regressions.txt", cells("b ci_l ci_u se p _star") stats(N r2_p chi2 p ll) replace
+ estout laundry_timing_`a'*_2005 using "$rpath/laundry_type_`a'_2005_$version-regressions.txt", cells("b ci_l ci_u se p _star") stats(N r2_p chi2 p ll) replace
+ restore
+ }
+}
+
+
+* go back to the main survey aggregate file
+use "$mtuspath/MTUS-adult-aggregate-UK-only-wf.dta", clear
+
+* create a bespoke survey which merges 1983 & 1987
+* has the advantage of providing all seasons for '1985'
+recode survey (1974=1974 "1974") (1983/1987=1985 "1985") (1995 = 1995 "1995") (2000=2000 "2000") (2005=2005 "2005"), gen(ba_survey)
+
+* drop all bad cases
+keep if badcase == 0
+
+* set as survey data for descriptives
+svyset [iw=propwt]
+
+* drop diary duplicates & do some basic stats
+
+duplicates drop pid, force
+
+* create working age variable
+gen ba_working_age = 0
+replace ba_working_age = 1 if age > 18 // OK, it should be 16 but...
+* women
+replace ba_working_age = 0 if age > 60 & sex == 2
+* men
+replace ba_working_age = 0 if age > 65 & sex == 1
+* check
+table ba_age_r ba_working_age sex
+
+* Proportion of women in work
+svy: tab ba_survey empstat if ba_working_age == 1 & sex == 2, row
+
+di "Done!"
+
+log close
diff --git a/Theme-1/laundryPaper/old_stata/DEMAND-BA-MTUS-W6-Laundry-Change-Over-Time-v1.0-adult.do b/Theme-1/laundryPaper/old_stata/DEMAND-BA-MTUS-W6-Laundry-Change-Over-Time-v1.0-adult.do
new file mode 100644
index 0000000000000000000000000000000000000000..35a2d091b48ce925cc5437436c4a60e3b28955cd
--- /dev/null
+++ b/Theme-1/laundryPaper/old_stata/DEMAND-BA-MTUS-W6-Laundry-Change-Over-Time-v1.0-adult.do
@@ -0,0 +1,564 @@
+*******************************************
+* Script to use MTUS World 6 time-use data (www.timeuse.org/mtus UK subset) to examine:
+* - distributions of laundry in 1975 & 2005
+* - changing laundry practices
+
+* data already in long format (but episodes)
+
+* also uses EFS 2005-6 to analyse uptake of washers/dryers
+
+* This work was funded by RCUK through the End User Energy Demand Centres Programme via the
+* "DEMAND: Dynamics of Energy, Mobility and Demand" Centre (www.demand.ac.uk, gow.epsrc.ac.uk/NGBOViewGrant.aspx?GrantRef=EP/K011723/1)
+
+/*
+
+Copyright (C) 2014 University of Southampton
+
+Author: Ben Anderson (b.anderson@soton.ac.uk, @dataknut, https://github.com/dataknut)
+ [Energy & Climate Change, Faculty of Engineering & Environment, University of Southampton]
+
+This program is free software; you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation; either version 2 of the License
+(http://choosealicense.com/licenses/gpl-2.0/), or (at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+#YMMV - http://en.wiktionary.org/wiki/YMMV
+
+*/
+
+clear all
+
+* change these to run this script on different PC
+local where "~/Documents/Work"
+local droot "`where'/Data/Social Science Datatsets"
+* LCFS/EFS
+local efspath "`droot'/Expenditure and Food Survey/processed"
+
+* location of time-use diary data
+local mtuspath "`droot'/MTUS/World 6/processed"
+local dfile "MTUS-adult-episode-UK-only"
+
+local proot "`where'/Projects/RCUK-DEMAND/Theme 1"
+local rpath "`proot'/results/MTUS"
+
+* version
+local version = "v1.2-all-hhs"
+local filter "_all"
+* weights the final counts
+
+*local version "v1.1-singles"
+*local filter "if hhtype == 1"
+* single person hhs only
+
+*local version "v1.1-all-hhs-sanity-check"
+*local filter "_all"
+* counts if 1 or more acts (sanity check)
+
+*local version = "v1.1-all-hhs"
+* local filter "_all"
+* adds in secondary acts
+
+* local version = "v1.0-main"
+* counts main acts only
+
+capture log close
+
+log using "`rpath'/DEMAND-BA-MTUS-W6-Laundry-Change-Over-Time-`version'-adult.smcl", replace
+
+local do_halfhour_episodes = 0
+local do_halfhour_samples = 1
+local do_sequences = 0
+
+* make script run without waiting for user input
+set more off
+
+**********
+* LCFS data for tumble dryer uptake levels to 2005
+use "`efspath'/EFS-2001-2010-extract-BA.dta", clear
+lookfor tumble weight
+tab year a167 [iw=weighta], row
+
+* 2005 only
+tab a167 c_nchild [iw=weighta] if year == 2005, col
+tab a167 c_nearners [iw=weighta] if year == 2005, col
+tab a167 c_empl [iw=weighta] if year == 2005, col
+
+
+**********************************
+* codes of interest
+* 1974: Main/Sec21 Laundry, ironing, clothing repair <- 50 Other essential domestic work (i.e. NOT preparing meals or routine housework)
+* so laundry in 1974 may be over-estimated
+* BUT 1975 is partly a 7 day diary - so more likely to detect laundry?
+
+* 2005: Main/Sec21 Laundry, ironing, clothing repair <- Pact=7 (washing clothes)
+
+* start with processing the aggregate (survey) data
+use "`mtuspath'/MTUS-adult-aggregate-UK-only-wf.dta", clear
+
+* drop all bad cases
+keep if badcase == 0
+
+* set as survey data for descriptives
+svyset [iw=propwt]
+
+* keep only 1974 & 2005 for simplicity
+* keep if survey == 1974 | survey == 2005
+* no, let's keep them all for birth cohort analysis!
+
+* this is minutes per day not episodes
+* check 18 (Cooking) & 20 (Cleaning) & 22 (maintain home/vehicle) against laundry
+* seems to under-report laundry in 1974, esp for women?
+svy: mean main18 main20 main21 main22, over(survey sex)
+
+* keep whatever sample we define above
+keep `filter'
+
+* number of diary days by hh type
+* svy: tab hhtype survey, col count
+
+* number of diary days by number of days covered
+* 1974 = 7 day dairy
+svy: tab id survey, col count
+
+/* already done in input file
+
+* hh size recode & test
+recode hhldsize (1=1) (2=2) (3=3) (4=4) (5/max=5), gen(ba_hhsize)
+lab var ba_hhsize "Recoded household size"
+lab def ba_hhsize 1 "1" 2 "2" 3 "3" 4 "4" 5 "5+"
+lab var ba_hhsize ba_hhsize
+
+* svy: tab ba_hhsize survey, col count
+
+* main7 & main8 = paid work
+gen ba_4hrspaidwork = 0
+* mark those who worked more than 4 hours that day
+replace ba_4hrspaidwork = 1 if main7 > 240
+
+* set up n child & n people variables
+recode nchild (0=0) (1=1) (2=2) (3/max=3), gen(ba_nchild)
+lab var ba_nchild "Recoded nchild"
+lab def ba_nchild 0 "0" 1 "1" 2 "2" 3 "3+"
+lab val ba_nchild ba_nchild
+recode hhldsize (0=0) (1=1) (2=2) (3=3) (4=4) (5/max=5), gen(ba_npeople)
+lab def ba_npeople 0 "0" 1 "1" 2 "2" 3 "3" 4 "4" 5 "5+"
+lab val ba_npeople ba_npeople
+
+* age categories
+egen ba_age_r = cut(age), at(16,24,34,44,54,64,74,84)
+lab var ba_age_r "Recoded age -> decades"
+tab ba_age_r
+
+* age cohorts
+gen ba_birthyear = year - age
+egen ba_birth_cohort = cut(ba_birthyear), at(1890,1900,1910,1920,1930,1940,1950,1960,1970,1980)
+tab ba_birth_cohort survey
+* NB - max age = 80 so older cohorts missing from 2005
+
+* weekday variable
+gen ba_weekday = 0
+replace ba_weekday = 1 if day > 1 & day < 7
+
+*/
+
+
+* keep only the vars we want to keep memory required low
+keep sex age main7 main21 hhtype empstat emp unemp student retired propwt survey day month year ///
+ hhldsize famstat nchild *pid ba*
+
+* number of diary-days
+svy: tab survey, obs
+
+preserve
+
+if `do_halfhour_episodes' {
+ *************************
+ * merge in the episode data
+ * egen diarypid = group(countrya survey swave msamp hldid persid day)
+ * egen pid = group(countrya survey swave msamp hldid persid)
+ merge 1:m diarypid using "`dpath'/MTUS-adult-episode-UK-only-wf.dta", ///
+ gen(m_aggvars)
+
+ * won't match the dropped years & badcases
+ tab m_aggvars survey
+
+ * keep the matched cases
+ keep if m_aggvars == 3
+
+ * number of episodes per day
+ svy: tab day survey, obs col
+
+ * overall durations
+ gen duration = s_endtime - s_starttime
+ format duration %tcHH:MM
+ tab duration survey [iw=propwt]
+
+ ***************
+ * Laundry
+
+ * define laundry
+ gen laundry_p = 0
+ lab var laundry_p "Main act = laundry (21)"
+ replace laundry_p = 1 if main == 21
+
+ gen laundry_s = 0
+ lab var laundry_s "Secondary act = laundry (21)"
+ replace laundry_s = 1 if sec == 21
+
+ gen laundry_all = 0
+ replace laundry_all = 1 if laundry_p == 1 | laundry_s == 1
+
+ * check % episodes which are laundry
+ * NB reporting frame shorter in 2005 (10 mins) so may be higher frequency (e.g. interruption in 10-20 mins coded)
+ * row %
+ tab survey laundry_p [iw=propwt]
+ tab survey laundry_s [iw=propwt]
+ * all
+ tab survey laundry_all [iw=propwt]
+
+ * check duration of laundry
+ * before we do this mere together episodes that are contiguous (e.g. laundry (s) then laundry(p) -> 1 episode)
+ * same approach as for dinner
+ * calculate duration
+ gen laundry_duration = duration if laundry_all == 1
+ format laundry_duration %tcHH:MM
+ * count back & forward maxcount episodes within the same diary day to check if they are also laundry
+ * indicates something changed - primary/secondary act or location or who with etc
+ * add on duration if previous and subsequent episodes are also laundry and location is unchanged
+ * NB: if you make maxcount > 1 then you could have episodes of eating separated by a long episode of something else
+ local maxcount = 1
+
+ * This is vital - we have to have the episodes in diary & time order!
+ sort diarypid start
+
+ foreach n of numlist 1/`maxcount' {
+ local prev = `n' - 1
+ di "* Now = `n', previous = `prev'"
+ di "* Before"
+ su laundry_duration
+ bysort survey diarypid: replace laundry_duration = laundry_duration + duration[_n-`n'] if laundry_all == 1 & ///
+ laundry_all[_n-`n'] == 1 & eloc == eloc[_n-`n']
+ bysort survey diarypid: replace laundry_duration = laundry_duration + duration[_n+`n'] if laundry_all == 1 & ///
+ laundry_all[_n+`n'] == 1 & eloc == eloc[_n+`n']
+ di "* After"
+ su laundry_duration
+ }
+
+
+ * Means are probably not going to tell us much given the differences in recording frames
+ * Use table instead as durations are so 'rounded'
+ * even so have to allow for differences in recording frames
+ table laundry_duration sex survey [iw=propwt]
+
+ * age cohort differences in incidence of laundry
+ table ba_birth_cohort laundry_all survey [iw=propwt]
+
+
+ * leave this empty to skip the (time consuming) aggregations & table outputs
+ local vars "21"
+ local l5 "meals_work"
+ local l6 "meals_oth"
+ local l18 "food_prep"
+ local l21 "laundry"
+ local l59 "tv_video"
+ local l60 "computer_games"
+ local l61 "computer_internet"
+
+ *************************
+ * Aggregation to half hours
+ * logic: the time use diaries rarely have the same duration of recorded time slot, 1974 = 30 mins, 2005 = 10 mins for example
+ * To make comparison easier we need to use the lowest common multiple - in this case 30 minutes
+ * We have already set up a variable (s_halfhour) which is the stata episode start-time converted into a stata half hour
+ * i.e. if s_starttime = 21:24 s_halfhour = 21:00, if s_starttime = 21:44 s_halfhour = 21:30 etc
+
+ * Note that where the diary has episodes shorter than 30 minutes we may get more than 1 episode of laundry
+ * reported per half hour
+ * Check
+ duplicates tag s_halfhour diarypid laundry_all, gen(laundry_dup_flag)
+ table laundry_dup_flag laundry_all survey
+
+ * We'd expect them all to occur in 2005 but they don't suggesting the 1974 -> MTUS conversion process has creates
+ * some episodes shorter than 30 minutes
+ * In any case we do need to watch out for situations where we sum the number of episodes per halfhour as
+ * we may have more episodes in 2005 due to the smaller recording time frame.
+
+ * Note that where the diary has episodes shorter than 30 minutes we may get more than 1 episode reported per half hour
+ * We will also miss longer episodes that started this half-hour and are continuing in the next half hour
+
+ * This will record all episodes that started within the half hour but it won't catch episodes that started before and
+ * are long-lasting. So it is good for looking at the distribution of episodes that are short, like laundry (mostly)
+ * It does NOT work for longer-lasting episodes like sleep or paid work
+ di "* Tables for all days"
+ * All years, all days
+ table s_halfhour survey laundry_all [iw=propwt]
+
+ * Separate days
+ table survey day [iw=propwt], by(laundry_all)
+
+ * days by half hour
+ table s_halfhour survey day [iw=propwt], by(laundry_all)
+}
+
+restore
+
+*preserve
+*************************
+* sampled data
+if `do_halfhour_samples' {
+ * merge in the sampled data
+ merge 1:m diarypid using "`dpath'/MTUS-adult-episode-UK-only-wf-10min-samples-long-v1.0.dta", ///
+ gen(m_aggvars)
+
+ * set up half-hour variable
+ gen ba_hourt = hh(s_starttime)
+ gen ba_minst = mm(s_starttime)
+
+ gen ba_hh = 0 if ba_minst < 30
+ replace ba_hh = 30 if ba_minst > 29
+ gen ba_sec = 0
+ * sets date to 1969!
+ gen s_halfhour = hms(ba_hourt, ba_hh, ba_sec)
+ lab var s_halfhour "Episode starts during the half hour following"
+ format s_halfhour %tcHH:MM
+
+ * define laundry
+ gen laundry_p = 0
+ lab var laundry_p "Main act = laundry (21)"
+ replace laundry_p = 1 if pact == 21
+
+ gen laundry_s = 0
+ lab var laundry_s "Secondary act = laundry (21)"
+ replace laundry_s = 1 if sact == 21
+
+ gen laundry_all = 0
+ replace laundry_all = 1 if laundry_p == 1 | laundry_s == 1
+
+ * this is the number of 10 minute samples by survey & day of the week
+ tab survey day [iw=propwt]
+
+ * check % samples which are laundry
+ * NB reporting frame longer in 1974 (30 mins) so may be higher frequency (e.g. interruption in 10-20 mins coded)
+ di "* main"
+ tab survey laundry_p [iw=propwt]
+ di "* secondary"
+ tab survey laundry_s [iw=propwt]
+ di "* all"
+ tab survey laundry_all [iw=propwt]
+
+ * keep 1974 & 2005 only
+ keep if survey == 1974 | survey == 2005
+
+ * collapse to add up the sampled laundry by half hour
+ * use the byvars we're interested in (or could re-merge with aggregated file)
+
+ collapse (sum) laundry_* (mean) propwt, by(diarypid pid survey day month year s_halfhour ///
+ ba_birth_cohort ba_age_r sex emp empstat nchild)
+ * because the different surveys have different reporting periods we need to just count at least 1 laundry in the half hour
+ lab val emp EMP
+ lab val empstat EMPSTAT
+ local acts "p s all"
+ foreach a of local acts {
+ gen any_laundry_`a' = 0
+ replace any_laundry_`a' = 1 if laundry_`a' > 0
+ }
+ * the number of half hour data points by survey & day
+ tab survey day [iw=propwt]
+
+ svyset [iw=propwt]
+ * the distribution of laundry by survey
+ di "* primary"
+ svy: tab survey any_laundry_p, row ci
+
+ di "* secondary"
+ svy: tab survey any_laundry_p, row ci
+
+ di "* all"
+ svy: tab survey any_laundry_all, row ci
+
+ * Separate days
+ table survey day [iw=propwt], by(any_laundry_all)
+
+ * days by gender
+ table survey day sex [iw=propwt], by(any_laundry_all)
+
+ * laundry by employment status if female
+ table survey day empstat if sex == 2 & any_laundry_all == 1 [iw=propwt]
+
+ di "* Tables for all days"
+ * All years, all days
+ table s_halfhour survey any_laundry_all [iw=propwt]
+
+ * days by half hour
+ table s_halfhour survey day [iw=propwt], by(any_laundry_all)
+
+ * seasons
+ recode month (3 4 5 = 1 "Spring") (6 7 8 = 2 "Summer") (9 10 11 = 3 "Autumn") (12 1 2 = 4 "Winter"), gen(season)
+ * check
+ * tab month season
+ table s_halfhour survey season [iw=propwt], by(any_laundry_all)
+
+ * by half hour & employment status for women
+ table s_halfhour empstat survey if sex == 2 [iw=propwt], by(any_laundry_all)
+
+ *repeat by day for 2005
+ table s_halfhour empstat day if survey == 2005 & sex == 2 [iw=propwt], by(any_laundry_all)
+
+ * analysis by laundry type
+ * sunday morning
+
+ * set time variable so can select by time
+ xtset diarypid s_halfhour, delta(30 mins)
+ * only code for laundry within year
+ gen laundry_timing = 5 if any_laundry_all == 1 // other
+ replace laundry_timing = 1 if any_laundry_all == 1 & day == 1 & tin(10:00, 12:00) // sunday morning
+ replace laundry_timing = 2 if any_laundry_all == 1 & day > 1 & day < 6 & tin(11:00, 15:00) // weekday mid-day
+ replace laundry_timing = 3 if any_laundry_all == 1 & tin(16:30, 20:30) // evening peak
+ replace laundry_timing = 4 if any_laundry_all == 1 & tin(00:00, 01:30) // night-time
+ replace laundry_timing = 4 if any_laundry_all == 1 & tin(22:30, 23:30) // night-time
+
+ tab laundry_timing, gen(laundry_timing_)
+
+ * check for missing
+ table s_halfhour laundry_timing any_laundry_all, mi
+
+ lab def laundry_timing 0 "No laundry" 1 "Sunday morning" 2 "Weekday mid-day" 3 "Evening peak" 4 "Night-time" 5 "Other"
+ lab val laundry_timing laundry_timing
+ tab laundry_timing survey [iw=propwt], col
+ table laundry_timing ba_age_r survey [iw=propwt]
+ table laundry_timing empstat survey [iw=propwt]
+ table laundry_timing nchild survey [iw=propwt]
+
+ * collapse to single person record
+ * remember 1974/5 = 1 week diary
+ collapse (sum) laundry_timing_* (mean) propwt, by(pid survey ///
+ ba_birth_cohort ba_age_r sex emp empstat nchild)
+ recode laundry_timing_1 (1/max=1)
+
+ stop
+}
+*restore
+
+*************************
+* sequences
+if `do_sequences' {
+ * back to the episodes
+ merge 1:m diarypid using "`dpath'/MTUS-adult-episode-UK-only-wf.dta", ///
+ gen(m_aggvars)
+ * this won't have matched the dropped years & badcases
+ * tab m_aggvars survey
+
+ * keep the matched cases
+ keep if m_aggvars == 3
+
+ * define laundry
+ gen laundry_p = 0
+ lab var laundry_p "Main act = laundry (21)"
+ replace laundry_p = 1 if main == 21
+
+ gen laundry_s = 0
+ lab var laundry_s "Secondary act = laundry (21)"
+ replace laundry_s = 1 if sec == 21
+
+ gen laundry_all = 0
+ replace laundry_all = 1 if laundry_p == 1 | laundry_s == 1
+
+ * we can't use the lag notation and xtset as there are various time periods represented in the data
+ * and we would need to set up some fake (or real!) dates to attach the start times to.
+ * we could do this but we don't really need to.
+
+ * we want to use episodes not time slots (as we are ignoring duration here)
+
+ * This is vital - we have to have the episodes in diary & time order!
+ sort diarypid start
+
+ * we are NOT going to worry about sequential episodes which are both laundry_all as this will indicate
+ * that something changed - most likely a switch of laundry from primary to secondary activity (or vice versa)
+ * this may be of interest in itself
+
+ local acts "all"
+ foreach a of local acts {
+ * make sure we do this within diaries otherwise we might get a 'before' or 'after' belonging to a previous day (for multi day diaries)
+ * or to someone else (for 1 day diaries or the first day)!
+
+ qui: by diarypid: gen before_laundry_`a' = main[_n-1] if laundry_`a' == 1
+ qui: by diarypid: gen after_laundry_`a' = main[_n+1] if laundry_`a' == 1
+
+ lab val before_laundry_`a' after_laundry_`a' MAIN
+
+ qui: tabout before_laundry_`a' survey [iw=propwt] using "`rpath'/before-laundry-by-survey.txt", replace
+ qui: tabout after_laundry_`a' survey [iw=propwt] using "`rpath'/after-laundry-by-survey.txt", replace
+ }
+ tab laundry_all
+ * create a sequence variable (horrible kludge but hey, it works :-)
+ egen laundry_seq = concat(before_laundry_all laundry_all after_laundry_all) if laundry_all == 1 , punct("_")
+
+ * get frequencies of sequencies (this will be a very big table)
+ * the few which have missing (.) before laundry indicate nothing recorded before hand which seems a bit odd?
+
+ tab laundry_seq
+
+ preserve
+ * contract doesn't like iw - only allows fw (which need to be integers)
+ * so these will be unweighted
+ contract laundry_seq survey, nomiss
+ qui: tab laundry_seq
+
+ qui: return li
+ di "For laundry_seq after contract : N = " r(N) ", r = " r(r)
+
+ * reshape it to get the frequencies per survey into columns
+ qui: reshape wide _freq, i(laundry_seq) j(survey)
+ qui: return li
+
+ li in 1/5
+ outsheet using "`rpath'/laundry-sequences-by-survey-wide.txt", replace
+ * totals
+ * the number of different sequences will probably vary by sample size - more potential variation
+ su _freq*, sep(0)
+ tabstat _freq*, s(n sum)
+ * top in 1974?
+ gsort - _freq1974
+ li in 1/10, sep(0)
+ * top in 2005?
+ gsort - _freq2005
+ li in 1/10, sep(0)
+
+
+ restore
+
+ /*
+ * try using the sqset commands
+
+ * tell it to look at sequences
+ sqset main diarypid s_starttime
+
+ * top 20 sequences
+ sqtab survey if before_laundry ! = 1 | after_laundry ! = 1, ranks(1/20)
+ */
+}
+
+* we're back to the main survey aggregate file here.
+* drop diary duplicates & do some basic stats
+
+duplicates drop pid, force
+
+* create working age variable
+gen ba_working_age = 0
+replace ba_working_age = 1 if age > 18 // OK, it should be 16 but...
+* women
+replace ba_working_age = 0 if age > 60 & sex == 2
+* men
+replace ba_working_age = 0 if age > 65 & sex == 1
+* check
+table ba_age_r ba_working_age sex
+
+* Propoprtion of women in work
+tab survey empstat [iw=propwt] if ba_working_age == 1 & sex == 2, row
+
+
+log close