CalFishTrack

Central Valley Enhanced

Acoustic Tagging Project

Spring Pulse Flow Chinook salmon Survival Study

2022-2023 Season (PROVISIONAL DATA)

Telemetry Study Template for this study can be found here

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1. Project Status

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Study is complete, all tags are no longer active as of 2023-08-31. All times in Pacific Standard Time.

Study began on 2023-04-18 09:05:00, see tagging details below:

Release	First_release_time	Last_release_time	Number_fish_released	Release_location	Release_rkm	Mean_length	Mean_weight
Before Pulse	2023-04-18 09:05:00	2023-04-19 10:05:00	251	RBDD_Rel	461.579	84.1	7.5
First Pulse	2023-04-24 10:10:00	2023-04-25 09:45:00	250	RBDD_Rel	461.579	84.8	6.9
Second Pulse - Week 1	2023-05-02 09:07:00	2023-05-03 09:07:00	250	RBDD_Rel	461.579	86.2	7.5
Second Pulse - Week 2	2023-05-09 09:05:00	2023-05-10 08:25:00	250	RBDD_Rel	461.579	86.6	7.2
Second Pulse - Week 3	2023-05-16 08:26:00	2023-05-17 08:50:00	250	RBDD_Rel	461.579	88.5	7.5

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2. Real-time Fish Detections

---

library(leaflet)
library(maps)
library(htmlwidgets)
library(leaflet.extras)
library(dplyr)
library(dbplyr)
library(DBI)
library(odbc)
library(data.table)

# Create connection with cloud database
con <- dbConnect(odbc(),
                Driver = "SQL Server",
                Server = "calfishtrack-server.database.windows.net",
                Database = "realtime_detections",
                UID = "realtime_user",
                PWD = "Pass@123",
                Port = 1433)

try(setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = "")))

## THIS CODE CHUNK WILL NOT WORK IF USING ONLY ERDDAP DATA, REQUIRES ACCESS TO LOCAL FILES
if (nrow(detects_study[is.na(detects_study$DateTime_PST)==F,]) == 0){
   cat("No detections yet")

   # Use dbplyr to load realtime_locs and qryHexCodes sql table
   gen_locs <- tbl(con, "realtime_locs") %>% collect()
   # gen_locs <- read.csv("realtime_locs.csv", stringsAsFactors = F) %>% filter(is.na(stop))

   leaflet(data = gen_locs[is.na(gen_locs$stop),]) %>%
       # setView(-72.14600, 43.82977, zoom = 8) %>%
       addProviderTiles("Esri.WorldStreetMap", group = "Map") %>%
       addProviderTiles("Esri.WorldImagery", group = "Satellite") %>% 
       addProviderTiles("Esri.WorldShadedRelief", group = "Relief") %>%
       # Marker data are from the sites data frame. We need the ~ symbols
       # to indicate the columns of the data frame.
       addMarkers(~longitude, ~latitude, label = ~general_location, group = "Receiver Sites", popup = ~location) %>% 
       # addAwesomeMarkers(~lon_dd, ~lat_dd, label = ~locality, group = "Sites", icon=icons) %>%
       addScaleBar(position = "bottomleft") %>%
          addLayersControl(
          baseGroups = c("Street Map", "Satellite", "Relief"),
          overlayGroups = c("Receiver Sites"),
          options = layersControlOptions(collapsed = FALSE)) %>%
          addSearchFeatures(targetGroups = c("Receiver Sites"))
} else {

   # Use dbplyr to load realtime_locs and qryHexCodes sql table
   gen_locs <- tbl(con, "realtime_locs") %>% collect()
   # gen_locs <- read.csv("realtime_locs.csv", stringsAsFactors = F)

   endtime <- min(as.Date(format(Sys.time(), "%Y-%m-%d")),
                  max(as.Date(detects_study$release_time)+(as.numeric(detects_study$tag_life)*1.5)))

   beacon_by_day <- fread("beacon_by_day.csv", stringsAsFactors = F) %>%
      mutate(day = as.Date(day)) %>%
      # Subset to only look at data for the correct beacon for that day
      filter(TagCode == beacon)  %>% 
      # Only keep beacon by day for days since fish were released
      filter(day >= as.Date(min(study_tagcodes$release_time)) & day <= endtime) %>%
      dplyr::left_join(., gen_locs[,c("location", "general_location","rkm")], by = "location")

   arrivals_per_day <- detects_study %>%
      group_by(general_location, TagCode) %>%
      summarise(DateTime_PST = min(DateTime_PST, na.rm = T)) %>%
      arrange(TagCode, general_location) %>%
      mutate(day = as.Date(DateTime_PST, "%Y-%m-%d", tz = "Etc/GMT+8")) %>%
      group_by(day, general_location) %>%
      summarise(New_arrivals = length(TagCode)) %>%
      na.omit() %>%
      mutate(day = as.Date(day)) %>%
      dplyr::left_join(unique(beacon_by_day[,c("general_location", "day", "rkm")]), ., 
                       by = c("general_location", "day")) %>%
      arrange(general_location, day) %>%
      mutate(day = as.factor(day)) %>%
      filter(general_location != "Bench_test") %>% # Remove bench test
      filter(!(is.na(general_location))) # Remove NA locations

   ## Remove sites that were not operation the whole time
   #### FOR THE SEASONAL SURVIVAL PAGE, KEEP ALL SITES SINCE PEOPLE WANT TO SEE DETECTIONS OF LATER FISH AT NEWLY 
   #### DEPLOYED SPOTS
   gen_locs_days_in_oper <- arrivals_per_day %>%
      group_by(general_location) %>%
      summarise(days_in_oper = length(day))
   #gen_locs_days_in_oper <- gen_locs_days_in_oper[gen_locs_days_in_oper$days_in_oper ==
   #                                               max(gen_locs_days_in_oper$days_in_oper),]
   arrivals_per_day_in_oper <- arrivals_per_day %>%
      filter(general_location %in% gen_locs_days_in_oper$general_location)

   fish_per_site <- arrivals_per_day_in_oper %>%
      group_by(general_location) %>%
      summarise(fish_count = sum(New_arrivals, na.rm=T))

   gen_locs_mean_coords <- gen_locs %>%
      filter(is.na(stop) & general_location %in% fish_per_site$general_location) %>%
      group_by(general_location) %>%
      summarise(latitude = mean(latitude), # estimate mean lat and lons for each genloc
                longitude = mean(longitude))

   fish_per_site <- merge(fish_per_site, gen_locs_mean_coords)

   if(!is.na(release_stats$Release_lat[1])){
     leaflet(data = fish_per_site) %>%
       addProviderTiles("Esri.WorldStreetMap", group = "Street Map") %>%
       addProviderTiles("Esri.WorldImagery", group = "Satellite") %>%
       addProviderTiles("Esri.WorldShadedRelief", group = "Relief") %>%
       # Marker data are from the sites data frame. We need the ~ symbols
       # to indicate the columns of the data frame.
       addPulseMarkers(lng = fish_per_site$longitude, lat = fish_per_site$latitude, label = ~fish_count, 
                       labelOptions = labelOptions(noHide = T, textsize = "15px"), group = "Receiver Sites",
                       popup = ~general_location, icon = makePulseIcon(heartbeat = 1.3)) %>%
       addCircleMarkers(data = release_stats, ~Release_lon, ~Release_lat, label = ~Number_fish_released, stroke = F, color = "blue", fillOpacity = 1, 
                        group = "Release Sites", popup = ~Release_location, labelOptions = labelOptions(noHide = T, textsize = "15px")) %>%
       addScaleBar(position = "bottomleft") %>%
       addLegend("bottomright", labels = c("Receivers", "Release locations"), colors = c("red","blue")) %>%
       addLayersControl(baseGroups = c("Street Map", "Satellite", "Relief"), options = layersControlOptions(collapsed = FALSE))
   } else {
     leaflet(data = fish_per_site) %>%
       addProviderTiles("Esri.NatGeoWorldMap", group = "Street Map") %>%
       addProviderTiles("Esri.WorldImagery", group = "Satellite") %>%
       addProviderTiles("Esri.WorldShadedRelief", group = "Relief") %>%
       # Marker data are from the sites data frame. We need the ~ symbols
       # to indicate the columns of the data frame.
       addPulseMarkers(lng = fish_per_site$longitude, lat = fish_per_site$latitude, label = ~fish_count, 
                       labelOptions = labelOptions(noHide = T, textsize = "15px"), group = "Receiver Sites",
                       popup = ~general_location, icon = makePulseIcon(heartbeat = 1.3)) %>%
       addScaleBar(position = "bottomleft") %>%
       addLayersControl(baseGroups = c("Street Map", "Satellite", "Relief"),
                        options = layersControlOptions(collapsed = FALSE))
   }
}

2.1 Map of unique fish detections at operational realtime detection locations

try(setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = "")))

if (nrow(detects_study[is.na(detects_study$DateTime_PST)==F,]) > 0){

   detects_study <- detects_study[order(detects_study$TagCode, detects_study$DateTime_PST),]
   ## Now estimate the time in hours between the previous and next detection, for each detection. 
   detects_study$prev_genloc <- shift(detects_study$general_location, fill = NA, type = "lag")
   #detects_study$prev_genloc <- shift(detects_study$General_Location, fill = NA, type = "lag")
   ## Now make NA the time diff values when it's between 2 different tagcodes or genlocs
   detects_study[which(detects_study$TagCode != shift(detects_study$TagCode, fill = NA, type = "lag")), "prev_genloc"] <- NA
   detects_study[which(detects_study$general_location != detects_study$prev_genloc), "prev_genloc"] <- NA
   detects_study$mov_score <- 0
   detects_study[is.na(detects_study$prev_genloc), "mov_score"] <- 1
   detects_study$mov_counter <- cumsum(detects_study$mov_score)

   detects_summary <- aggregate(list(first_detect = detects_study$DateTime_PST), by = list(TagCode = detects_study$TagCode, length = detects_study$length, release_time = detects_study$release_time, mov_counter = detects_study$mov_counter ,general_location = detects_study$general_location, river_km = detects_study$river_km, release_rkm = detects_study$release_rkm), min)

   detects_summary <- detects_summary[is.na(detects_summary$first_detect) == F,]
   releases <- aggregate(list(first_detect = detects_summary$release_time), by = list(TagCode = detects_summary$TagCode, length = detects_summary$length, release_time = detects_summary$release_time, release_rkm = detects_summary$release_rkm), min)
   releases$river_km <- releases$release_rkm
   releases$mov_counter <- NA
   releases$general_location <- NA

   detects_summary <- rbindlist(list(detects_summary, releases), use.names = T)
   detects_summary <- detects_summary[order(detects_summary$TagCode, detects_summary$first_detect),]

   starttime <- as.Date(min(detects_study$release_time), "Etc/GMT+8")
   ## Endtime should be either now, or end of predicted tag life, whichever comes first
   endtime <- min(as.Date(format(Sys.time(), "%Y-%m-%d"))+1, max(as.Date(detects_study$release_time)+(as.numeric(detects_study$tag_life))))
   #par(mar=c(6, 5, 2, 5) + 0.1)

   plot_ly(detects_summary, width = 900, height = 600, dynamicTicks = TRUE) %>%
      add_lines(x = ~first_detect, y = ~river_km, color = ~TagCode) %>%
      add_markers(x = ~first_detect, y = ~river_km, color = ~TagCode, showlegend = F) %>%
      layout(showlegend = T, 
         xaxis = list(title = "<b> Date <b>", mirror=T,ticks="outside",showline=T, range=c(starttime,endtime)),
         yaxis = list(title = "<b> Kilometers upstream of the Golden Gate <b>", mirror=T,ticks="outside",showline=T, range=c(max(detects_study$Rel_rkm)+10, min(gen_locs[is.na(gen_locs$stop),"rkm"])-10)),
         legend = list(title=list(text='<b> Tag Code </b>')),
         margin=list(l = 50,r = 100,b = 50,t = 50)
   )

}else{
   plot(1:2, type = "n", xlab = "",xaxt = "n", yaxt = "n", ylab = "Kilometers upstream of the Golden Gate")
   text(1.5,1.5, labels = "NO DETECTIONS YET", cex = 2)
}

2.2 Waterfall Detection Plot

_______________________________________________________________________________________________________

library(tidyr)

try(setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = "")))

detects_3 <- detects_study %>% filter(general_location == "Blw_Salt_RT")

if(nrow(detects_3) == 0){
   plot(1:2, type = "n", xlab = "",xaxt = "n", yaxt = "n", ylab = "Number of fish arrivals per day")
   text(1.5,1.5, labels = "NO DETECTIONS YET", cex = 2)
} else {
  detects_3 <- detects_3 %>%
    dplyr::left_join(., detects_3 %>%
                        group_by(TagCode) %>% 
                        summarise(first_detect = min(DateTime_PST))) %>%
                        mutate(Day = as.Date(as.Date(first_detect, "Etc/GMT+8")))

  starttime <- as.Date(min(detects_3$release_time), "Etc/GMT+8")

  # Endtime should be either now, or end of predicted tag life, whichever comes first
  endtime <- min(as.Date(format(Sys.time(), "%Y-%m-%d")),
                 max(as.Date(detects_study$release_time)+(as.numeric(detects_study$tag_life))))

  daterange <- data.frame(Day = seq.Date(from = starttime, to = endtime, by = "day"))

  rels            <- unique(study_tagcodes$Release)
  rel_num         <- length(rels)
  rels_no_detects <- as.character(rels[!(rels %in% unique(detects_3$Release))])

  tagcount1 <- detects_3 %>%
               group_by(Day, Release) %>%
               summarise(unique_tags = length(unique(TagCode))) %>%
               spread(Release, unique_tags)

  daterange1 <- merge(daterange, tagcount1, all.x=T)
  daterange1[is.na(daterange1)] <- 0

  if(length(rels_no_detects)>0){
    for(i in rels_no_detects){
      daterange1 <- cbind(daterange1, x=NA)
      names(daterange1)[names(daterange1) == "x"] <- paste(i)
    }
  }

  # Download flow data
  flow_day <- readNWISuv(siteNumbers = "11377100", parameterCd="00060", startDate = starttime, 
                         endDate = endtime+1) %>%
                  mutate(Day = as.Date(format(dateTime, "%Y-%m-%d"))) %>%
                  group_by(Day) %>%
                  summarise(parameter_value = mean(X_00060_00000))

  ## reorder columns in alphabetical order so its coloring in barplots is consistent
  daterange2 <- daterange1[,order(colnames(daterange1))] %>%
                dplyr::left_join(., flow_day, by = "Day")
  rownames(daterange2) <- daterange2$Day
  daterange2$Date      <- daterange2$Day
  daterange2$Day       <- NULL
  daterange2_flow      <- daterange2 %>% select(Date, parameter_value)
  daterange3           <- melt(daterange2[,!(names(daterange2) %in% c("parameter_value"))], 
                               id.vars = "Date", variable.name = ".")
  daterange3$.         <- factor(daterange3$., levels = sort(unique(daterange3$.), decreasing = T))

  par(mar=c(6, 5, 2, 5) + 0.1)
  ay <- list(
    overlaying = "y",
    nticks = 5,
    color = "#947FFF",
    side = "right",
    title = "Flow (cfs) at Bend Bridge",
    automargin = TRUE
  )

  plot_ly(daterange3, width = 900, height = 600, dynamicTicks = TRUE) %>%
          add_bars(x = ~Date, y = ~value, color = ~.) %>%
          add_annotations(text="Release (click on legend items to isolate)", xref="paper", yref="paper",
                          x=0.01, xanchor="left",
                          y=1.056, yanchor="top",    # Same y as legend below
                          legendtitle=TRUE, showarrow=FALSE ) %>%
          add_lines(x=~daterange2_flow$Date, 
                    y=~daterange2_flow$parameter_value, 
                    line = list(color = alpha("#947FFF", alpha = 0.5)), yaxis="y2", showlegend=FALSE, 
                    inherit=FALSE) %>%
          layout(yaxis2 = ay,showlegend = T, 
          barmode = "stack",
          xaxis = list(title = "Date", mirror=T,ticks="outside",showline=T), 
          yaxis = list(title = "Number of fish arrivals per day", mirror=T,ticks="outside",showline=T),
          legend = list(orientation = "h",x = 0.34, y = 1.066),
          margin=list(l = 50,r = 100,b = 50,t = 50))

}

2.3 Detections at Salt Creek versus Sacramento River flows at Bend Bridge for duration of tag life

_______________________________________________________________________________________________________

library(tidyr)

try(setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = "")))

detects_4 <- detects_study %>% filter(general_location == "MeridianBr")

if(nrow(detects_4) == 0){
   plot(1:2, type = "n", xlab = "",xaxt = "n", yaxt = "n", ylab = "Number of fish arrivals per day")
   text(1.5,1.5, labels = "NO DETECTIONS YET", cex = 2)
} else {
  detects_4 <- detects_4 %>%
    dplyr::left_join(., detects_4 %>%
                        group_by(TagCode) %>% 
                        summarise(first_detect = min(DateTime_PST))) %>%
                        mutate(Day = as.Date(as.Date(first_detect, "Etc/GMT+8")))

  starttime <- as.Date(min(detects_4$release_time), "Etc/GMT+8")

  # Endtime should be either now, or end of predicted tag life, whichever comes first
  endtime <- min(as.Date(format(Sys.time(), "%Y-%m-%d")),
                 max(as.Date(detects_study$release_time)+(as.numeric(detects_study$tag_life))))

  daterange <- data.frame(Day = seq.Date(from = starttime, to = endtime, by = "day"))

  rels            <- unique(study_tagcodes$Release)
  rel_num         <- length(rels)
  rels_no_detects <- as.character(rels[!(rels %in% unique(detects_4$Release))])

  tagcount1 <- detects_4 %>%
               group_by(Day, Release) %>%
               summarise(unique_tags = length(unique(TagCode))) %>%
               spread(Release, unique_tags)

  daterange1 <- merge(daterange, tagcount1, all.x=T)
  daterange1[is.na(daterange1)] <- 0

  if(length(rels_no_detects)>0){
    for(i in rels_no_detects){
      daterange1 <- cbind(daterange1, x=NA)
      names(daterange1)[names(daterange1) == "x"] <- paste(i)
    }
  }

  # Download flow data
  flow_day <- readNWISuv(siteNumbers = "11390500", parameterCd="00060", startDate = starttime, 
                         endDate = endtime+1) %>%
                  mutate(Day = as.Date(format(dateTime, "%Y-%m-%d"))) %>%
                  group_by(Day) %>%
                  summarise(parameter_value = mean(X_00060_00000))

  ## reorder columns in alphabetical order so its coloring in barplots is consistent
  daterange2 <- daterange1[,order(colnames(daterange1))] %>%
                dplyr::left_join(., flow_day, by = "Day")
  rownames(daterange2) <- daterange2$Day
  daterange2$Date      <- daterange2$Day
  daterange2$Day       <- NULL
  daterange2_flow      <- daterange2 %>% select(Date, parameter_value)
  daterange3           <- melt(daterange2[,!(names(daterange2) %in% c("parameter_value"))], 
                               id.vars = "Date", variable.name = ".")
  daterange3$.         <- factor(daterange3$., levels = sort(unique(daterange3$.), decreasing = T))

  par(mar=c(6, 5, 2, 5) + 0.1)
  ay <- list(
    overlaying = "y",
    nticks = 5,
    color = "#947FFF",
    side = "right",
    title = "Flow (cfs) at Wilkins Slough",
    automargin = TRUE
  )

  plot_ly(daterange3, width = 900, height = 600, dynamicTicks = TRUE) %>%
          add_bars(x = ~Date, y = ~value, color = ~.) %>%
          add_annotations(text="Release (click on legend items to isolate)", xref="paper", yref="paper",
                          x=0.01, xanchor="left",
                          y=1.056, yanchor="top",    # Same y as legend below
                          legendtitle=TRUE, showarrow=FALSE ) %>%
          add_lines(x=~daterange2_flow$Date, 
                    y=~daterange2_flow$parameter_value, 
                    line = list(color = alpha("#947FFF", alpha = 0.5)), yaxis="y2", showlegend=FALSE, 
                    inherit=FALSE) %>%
          layout(yaxis2 = ay,showlegend = T, 
          barmode = "stack",
          xaxis = list(title = "Date", mirror=T,ticks="outside",showline=T), 
          yaxis = list(title = "Number of fish arrivals per day", mirror=T,ticks="outside",showline=T),
          legend = list(orientation = "h",x = 0.34, y = 1.066),
          margin=list(l = 50,r = 100,b = 50,t = 50))

}

2.4 Detections at Meridian Bridge versus Sacramento River flows at Wilkins Slough for duration of tag life

_______________________________________________________________________________________________________

library(tidyr)

try(setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = "")))

detects_5 <- detects_study %>% filter(general_location == "TowerBridge")

if(nrow(detects_5) == 0){
   plot(1:2, type = "n", xlab = "",xaxt = "n", yaxt = "n", ylab = "Number of fish arrivals per day")
   text(1.5,1.5, labels = "NO DETECTIONS YET", cex = 2)
} else {
  detects_5 <- detects_5 %>%
    dplyr::left_join(., detects_5 %>%
                        group_by(TagCode) %>% 
                        summarise(first_detect = min(DateTime_PST))) %>%
                        mutate(Day = as.Date(as.Date(first_detect, "Etc/GMT+8")))

  starttime <- as.Date(min(detects_5$release_time), "Etc/GMT+8")

  # Endtime should be either now, or end of predicted tag life, whichever comes first
  endtime <- min(as.Date(format(Sys.time(), "%Y-%m-%d")),
                 max(as.Date(detects_study$release_time)+(as.numeric(detects_study$tag_life))))

  daterange <- data.frame(Day = seq.Date(from = starttime, to = endtime, by = "day"))

  rels            <- unique(study_tagcodes$Release)
  rel_num         <- length(rels)
  rels_no_detects <- as.character(rels[!(rels %in% unique(detects_5$Release))])

  tagcount1 <- detects_5 %>%
               group_by(Day, Release) %>%
               summarise(unique_tags = length(unique(TagCode))) %>%
               spread(Release, unique_tags)

  daterange1 <- merge(daterange, tagcount1, all.x=T)
  daterange1[is.na(daterange1)] <- 0

  if(length(rels_no_detects)>0){
    for(i in rels_no_detects){
      daterange1 <- cbind(daterange1, x=NA)
      names(daterange1)[names(daterange1) == "x"] <- paste(i)
    }
  }

  # Download flow data
  flow_day <- readNWISuv(siteNumbers = "11425500", parameterCd="00060", startDate = starttime, 
                         endDate = endtime+1) %>%
                  mutate(Day = as.Date(format(dateTime, "%Y-%m-%d"))) %>%
                  group_by(Day) %>%
                  summarise(parameter_value = mean(X_00060_00000))

  ## reorder columns in alphabetical order so its coloring in barplots is consistent
  daterange2 <- daterange1[,order(colnames(daterange1))] %>%
                dplyr::left_join(., flow_day, by = "Day")
  rownames(daterange2) <- daterange2$Day
  daterange2$Date      <- daterange2$Day
  daterange2$Day       <- NULL
  daterange2_flow      <- daterange2 %>% select(Date, parameter_value)
  daterange3           <- melt(daterange2[,!(names(daterange2) %in% c("parameter_value"))], 
                               id.vars = "Date", variable.name = ".")
  daterange3$.         <- factor(daterange3$., levels = sort(unique(daterange3$.), decreasing = T))

  par(mar=c(6, 5, 2, 5) + 0.1)
  ay <- list(
    overlaying = "y",
    nticks = 5,
    color = "#947FFF",
    side = "right",
    title = "Flow (cfs) at Verona",
    automargin = TRUE
  )

  plot_ly(daterange3, width = 900, height = 600, dynamicTicks = TRUE) %>%
          add_bars(x = ~Date, y = ~value, color = ~.) %>%
          add_annotations(text="Release (click on legend items to isolate)", xref="paper", yref="paper",
                          x=0.01, xanchor="left",
                          y=1.056, yanchor="top",    # Same y as legend below
                          legendtitle=TRUE, showarrow=FALSE ) %>%
          add_lines(x=~daterange2_flow$Date, 
                    y=~daterange2_flow$parameter_value, 
                    line = list(color = alpha("#947FFF", alpha = 0.5)), yaxis="y2", showlegend=FALSE, 
                    inherit=FALSE) %>%
          layout(yaxis2 = ay,showlegend = T, 
          barmode = "stack",
          xaxis = list(title = "Date", mirror=T,ticks="outside",showline=T), 
          yaxis = list(title = "Number of fish arrivals per day", mirror=T,ticks="outside",showline=T),
          legend = list(orientation = "h",x = 0.34, y = 1.066),
          margin=list(l = 50,r = 100,b = 50,t = 50))

}

2.5 Detections at Tower Bridge (downtown Sacramento) versus Sacramento River flows at Verona for duration of tag life

_______________________________________________________________________________________________________

library(tidyr)

try(setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = "")))

detects_6 <- detects_study %>% filter(general_location == "Benicia_west" | general_location == "Benicia_east")

if(nrow(detects_6) == 0){
   plot(1:2, type = "n", xlab = "",xaxt = "n", yaxt = "n", ylab = "Number of fish arrivals per day")
   text(1.5,1.5, labels = "NO DETECTIONS YET", cex = 2)
} else {
  detects_6 <- detects_6 %>%
    dplyr::left_join(., detects_6 %>%
                        group_by(TagCode) %>% 
                        summarise(first_detect = min(DateTime_PST))) %>%
                        mutate(Day = as.Date(as.Date(first_detect, "Etc/GMT+8")))

  starttime <- as.Date(min(detects_6$release_time), "Etc/GMT+8")

  # Endtime should be either now, or end of predicted tag life, whichever comes first
  endtime <- min(as.Date(format(Sys.time(), "%Y-%m-%d")),
                 max(as.Date(detects_study$release_time)+(as.numeric(detects_study$tag_life))))

  daterange <- data.frame(Day = seq.Date(from = starttime, to = endtime, by = "day"))

  rels            <- unique(study_tagcodes$Release)
  rel_num         <- length(rels)
  rels_no_detects <- as.character(rels[!(rels %in% unique(detects_6$Release))])

  tagcount1 <- detects_6 %>%
               group_by(Day, Release) %>%
               summarise(unique_tags = length(unique(TagCode))) %>%
               spread(Release, unique_tags)

  daterange1 <- merge(daterange, tagcount1, all.x=T)
  daterange1[is.na(daterange1)] <- 0

  if(length(rels_no_detects)>0){
    for(i in rels_no_detects){
      daterange1 <- cbind(daterange1, x=NA)
      names(daterange1)[names(daterange1) == "x"] <- paste(i)
    }
  }

  ## reorder columns in alphabetical order so its coloring in barplots is consistent
  daterange1 <- daterange1[,order(colnames(daterange1))]
  daterange2 <- daterange1
  rownames(daterange2) <- daterange2$Day
  daterange2$Day <- NULL

  par(mar=c(6, 5, 2, 5) + 0.1)

  daterange2$Date <- as.Date(row.names(daterange2))
  daterange3      <- melt(daterange2, id.vars = "Date", variable.name = ".", )
  daterange3$.    <- factor(daterange3$., levels = sort(unique(daterange3$.), decreasing = T))

  plot_ly(daterange3, width = 900, height = 600, dynamicTicks = TRUE) %>%
    add_bars(x = ~Date, y = ~value, color = ~.) %>%
    add_annotations( text="Release (click on legend items to isolate)", xref="paper", yref="paper",
                     x=0.01, xanchor="left",
                     y=1.056, yanchor="top",    # Same y as legend below
                     legendtitle=TRUE, showarrow=FALSE ) %>%
    layout(showlegend = T, 
           barmode = "stack",
           xaxis = list(title = "Date", mirror=T,ticks="outside",showline=T), 
           yaxis = list(title = "Number of fish arrivals per day", mirror=T,ticks="outside",showline=T),
           legend = list(orientation = "h",x = 0.34, y = 1.066),
           margin=list(l = 50,r = 100,b = 50,t = 50))
}

2.6 Detections at Benicia Bridge for duration of tag life

---

3. Survival and Routing Probability

---

try(setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = "")))

detects_tower <- detects_study %>% filter(general_location == "TowerBridge")

if(nrow(detects_tower) == 0){
  WR.surv <- data.frame("Release"=NA, "Survival (%)"="NO DETECTIONS YET", "SE"=NA, "95% lower C.I."=NA,
                        "95% upper C.I."=NA, "Detection efficiency (%)"=NA)
  colnames(WR.surv) <- c("Release", "Survival (%)", "SE", "95% lower C.I.",
                         "95% upper C.I.", "Detection efficiency (%)")
  print(kable(WR.surv, row.names = F, "html", caption = "3.1 Minimum survival to Tower Bridge (using CJS
              survival model). If Yolo Bypass Weirs are overtopping during migration, fish may have taken
              that route, and therefore this is a minimum estimate of survival") %>%
    kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"),
                  full_width = F, position = "left"))

} else {

  study_count <- nrow(study_tagcodes)

  # Only do survival to Sac for now
  surv <- detects_study %>% filter(river_km > 168 & river_km < 175)

  # calculate mean and SD travel time
  travel <- aggregate(list(first_detect = surv$DateTime_PST), by = list(Release = surv$Release, TagCode = surv$TagCode, RelDT = surv$RelDT), min)
  travel$days <- as.numeric(difftime(travel$first_detect, travel$RelDT, units = "days"))

  travel_final <- aggregate(list(mean_travel_time = travel$days), by = list(Release = travel$Release), mean)
  travel_final <- merge(travel_final, aggregate(list(sd_travel_time = travel$days), by = list(Release = travel$Release), sd))
  travel_final <- merge(travel_final, aggregate(list(n = travel$days), by = list(Release = travel$Release), length))
  travel_final <- rbind(travel_final, data.frame(Release = "ALL", mean_travel_time = mean(travel$days), sd_travel_time = sd(travel$days),n = nrow(travel)))

  # Create inp for survival estimation
  inp <- as.data.frame(reshape2::dcast(surv, TagCode ~ river_km, fun.aggregate = length))

  # Sort columns by river km in descending order
  gen_loc_sites <- ncol(inp)-1 # Count number of genlocs
  if(gen_loc_sites < 2){
    WR.surv <- data.frame("Release"=NA, "Survival (%)"="NOT ENOUGH DETECTIONS", "SE"=NA, "95% lower C.I."=NA,
                          "95% upper C.I."=NA, "Detection efficiency (%)"=NA)
    colnames(WR.surv) <- c("Release", "Survival (%)", "SE", "95% lower C.I.", "95% upper C.I.",
                           "Detection efficiency (%)")
    print(kable(WR.surv, row.names = F, "html", caption = "3.1 Minimum survival to Tower Bridge (using CJS
                survival model). If Yolo Bypass Weirs are overtopping during migration, fish may
                have taken that route, and therefore this is a minimum estimate of survival") %>%
          kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), 
                        full_width = F,position = "left"))
  } else {
    inp <- inp[,c(1,order(names(inp[,2:(gen_loc_sites+1)]), decreasing = T)+1)] %>%
            dplyr::left_join(study_tagcodes, ., by = "TagCode")

    inp2 <- inp[,(ncol(inp)-gen_loc_sites+1):ncol(inp)] %>%
            replace(is.na(.), 0) %>%
            replace(., . > 0, 1)

    inp          <- cbind(inp, inp2)
    groups       <- as.character(sort(unique(inp$Release)))
    surv$Release <- factor(surv$Release, levels = groups)
    inp[,groups] <- 0

    for (i in groups) {
      inp[as.character(inp$Release) == i, i] <- 1
    }

    inp$inp_final <- paste("1",apply(inp2, 1, paste, collapse=""),sep="")

    if(length(groups) > 1){
      # make sure factor levels have a release that has detections first. if first release in factor order
      # has zero detectins, model goes haywire
      inp.df <- data.frame(ch = as.character(inp$inp_final), freq = 1,
      rel = factor(inp$Release, levels = names(sort(table(surv$Release),decreasing = T))),
                   stringsAsFactors = F)

      WR.process <- process.data(inp.df, model="CJS", begin.time=1, groups = "rel")

      WR.ddl <- make.design.data(WR.process)

      WR.mark.all <- mark(WR.process, WR.ddl,
                          model.parameters=list(Phi=list(formula=~time),p=list(formula=~time)),
                          silent = T, output = F)

      WR.mark.rel <- mark(WR.process, WR.ddl,
                          model.parameters=list(Phi=list(formula=~time*rel),p=list(formula=~time)),
                          silent = T, output = F)

      WR.surv <- round(WR.mark.all$results$real[1,c("estimate", "se", "lcl", "ucl")] * 100,1)
      WR.surv <- rbind(WR.surv, round(WR.mark.rel$results$real[seq(from=1,to=length(groups)*2,by = 2),
                       c("estimate", "se", "lcl", "ucl")] * 100,1))
      WR.surv$Detection_efficiency <- NA
      WR.surv[1,"Detection_efficiency"] <- round(WR.mark.all$results$real[gen_loc_sites+1,"estimate"] * 100,1)
      WR.surv <- cbind(c("ALL", names(sort(table(surv$Release),decreasing = T))), WR.surv)
    }
    if(length(intersect(colnames(inp),groups)) < 2){
      inp$inp_final <- paste("1",apply(inp2, 1, paste, collapse=""), " ", 1,sep = "")
      write.table(inp$inp_final,"WRinp.inp",row.names = F, col.names = F, quote = F)
      WRinp <- convert.inp("WRinp.inp")
      WR.process <- process.data(WRinp, model="CJS", begin.time=1)

      WR.ddl <- make.design.data(WR.process)

      WR.mark.all <- mark(WR.process, WR.ddl,
                          model.parameters=list(Phi=list(formula=~time),p=list(formula=~time)),
                          silent = T, output = F)

      WR.mark.rel <- mark(WR.process, WR.ddl,
                          model.parameters=list(Phi=list(formula=~time),p=list(formula=~time)),
                          silent = T, output = F)

      WR.surv <- round(WR.mark.all$results$real[1,c("estimate", "se", "lcl", "ucl")] * 100,1)
      WR.surv <- rbind(WR.surv, round(WR.mark.rel$results$real[seq(from=1,to=length(groups)*2,by = 2),
                                                               c("estimate", "se", "lcl", "ucl")] * 100,1))
      WR.surv$Detection_efficiency <- NA
      WR.surv[1,"Detection_efficiency"] <- round(WR.mark.all$results$real[gen_loc_sites+1,"estimate"] * 100,1)
      WR.surv <- cbind(c("ALL", groups), WR.surv)
    }

    colnames(WR.surv)[1] <- "Release"
    WR.surv <- merge(WR.surv, travel_final, by = "Release", all.x = T)
    WR.surv$mean_travel_time <- round(WR.surv$mean_travel_time,1)
    WR.surv$sd_travel_time <- round(WR.surv$sd_travel_time,1)
    colnames(WR.surv) <- c("Release", "Survival (%)", "SE", "95% lower C.I.", 
                           "95% upper C.I.", "Detection efficiency (%)", "Mean time to Tower (days)", "SD of time to Tower (days)","Count")


  WR.surv <- WR.surv %>% arrange(., Release)
  print(kable(WR.surv, row.names = F, "html", caption = "3.1 Minimum survival to Tower Bridge (using CJS
        survival model), and travel time. If Yolo Bypass Weirs are overtopping during migration, fish may have taken 
        that route, and therefore this is a minimum estimate of survival") %>%
          kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), 
                        full_width = F, position = "left"))
  }
}

3.1 Minimum survival to Tower Bridge (using CJS survival model), and travel time. If Yolo Bypass Weirs are overtopping during migration, fish may have taken that route, and therefore this is a minimum estimate of survival

Release	Survival (%)	SE	95% lower C.I.	95% upper C.I.	Detection efficiency (%)	Mean time to Tower (days)	SD of time to Tower (days)	Count
ALL	41.3	1.4	38.6	44.1	88.2	6.2	4.5	510
Before Pulse	53.8	3.2	47.5	59.9	NA	8.5	2.8	134
First Pulse	25.1	2.8	20.0	30.9	NA	5.5	6.0	62
Second Pulse - Week 1	34.5	3.0	28.8	40.7	NA	7.0	6.1	85
Second Pulse - Week 2	48.6	3.2	42.3	55.0	NA	4.8	3.8	119
Second Pulse - Week 3	44.8	3.2	38.6	51.1	NA	4.6	2.7	110

try(setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = "")))

try(Delta <- read.csv("Delta_surv.csv", stringsAsFactors = F))

if(nrow(detects_study[is.na(detects_study$DateTime_PST) == F,]) == 0){
    WR.surv1 <- data.frame("Measure"=NA, "Estimate"="NO DETECTIONS YET", "SE"=NA, "95% lower C.I."=NA, "95% upper C.I."=NA)
    colnames(WR.surv1) <- c("Measure", "Estimate", "SE", "95% lower C.I.", "95% upper C.I.")
    print(kable(WR.surv1, row.names = F, "html", caption = "3.2 Minimum through-Delta survival: City of Sacramento to Benicia (using CJS survival model)") %>%
            kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), full_width = F, position = "left"))

} else {
  test4 <- detects_study[detects_study$general_location %in% c("TowerBridge", "I80-50_Br", "Benicia_west", "Benicia_east"),]

  if(nrow(test4[test4$general_location =="Benicia_west",]) == 0 | nrow(test4[test4$general_location =="Benicia_east",]) == 0){
    WR.surv1 <- data.frame("Measure"=NA, "Estimate"="NOT ENOUGH DETECTIONS", "SE"=NA, "95% lower C.I."=NA, "95% upper C.I."=NA)
    colnames(WR.surv1) <- c("Measure", "Estimate", "SE", "95% lower C.I.", "95% upper C.I.")
    print(kable(WR.surv1, row.names = F, "html", caption = "3.2 Minimum through-Delta survival: City of Sacramento to Benicia (using CJS survival model)") %>%
            kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), full_width = F, position = "left"))

  } else {

  # calculate mean and SD travel time
  sac <- test4[test4$general_location %in% c("TowerBridge", "I80-50_Br"),]
  ben <- test4[test4$general_location %in% c("Benicia_west", "Benicia_east"),]
  travel_sac <- aggregate(list(first_detect_sac = sac$DateTime_PST), by = list(Release = sac$Release, TagCode = sac$TagCode), min)
  travel_ben <- aggregate(list(first_detect_ben = ben$DateTime_PST), by = list(Release = ben$Release, TagCode = ben$TagCode), min)
  travel <- merge(travel_sac, travel_ben, by = c("Release","TagCode"))
  travel$days <- as.numeric(difftime(travel$first_detect_ben, travel$first_detect_sac, units = "days"))

  travel_final <- aggregate(list(mean_travel_time = travel$days), by = list(Release = travel$Release), mean)
  travel_final <- merge(travel_final, aggregate(list(sd_travel_time = travel$days), by = list(Release = travel$Release), sd))
  travel_final <- merge(travel_final, aggregate(list(n = travel$days), by = list(Release = travel$Release), length))
  travel_final <- rbind(travel_final, data.frame(Release = "ALL", mean_travel_time = mean(travel$days), sd_travel_time = sd(travel$days), n = nrow(travel)))

  inp <- as.data.frame(reshape2::dcast(test4, TagCode ~ general_location, fun.aggregate = length))

  # add together detections at Tower and I80 to ensure good detection entering Delta
  if("I80-50_Br" %in% colnames(inp) & "TowerBridge" %in% colnames(inp)){
  inp$`I80-50_Br` <- inp$`I80-50_Br` + inp$TowerBridge

  } else if("TowerBridge" %in% colnames(inp)){
    inp$`I80-50_Br` <- inp$TowerBridge
  }

  # Sort columns by river km in descending order, this also removes TowerBridge, no longer needed
  inp <- inp[,c("TagCode","I80-50_Br", "Benicia_east", "Benicia_west")]

  # Count number of genlocs
  gen_loc_sites <- ncol(inp)-1

  inp <- inp[,c(1,order(names(inp[,2:(gen_loc_sites+1)]), decreasing = T)+1)]
  inp <- merge(study_tagcodes, inp, by = "TagCode", all.x = T)

  inp2 <- inp[,(ncol(inp)-gen_loc_sites+1):ncol(inp)]
  inp2[is.na(inp2)] <- 0
  inp2[inp2 > 0] <- 1

  inp <- cbind(inp, inp2)
  groups <- as.character(sort(unique(inp$Release)))
  groups_w_detects <- names(table(detects_study[which(detects_study$river_km < 53),"Release"]))
  inp[,groups] <- 0

  for(i in groups){
    inp[as.character(inp$Release) == i, i] <- 1
  }

  inp$inp_final <- paste("1",apply(inp2, 1, paste, collapse=""),sep="")

  if(length(groups) > 1){
    # make sure factor levels have a release that has detections first. if first release in factor order has zero #detectins, model goes haywire
    inp.df <- data.frame(ch = as.character(inp$inp_final), freq = 1, rel = inp$Release, stringsAsFactors = F)

    WR.process <- process.data(inp.df, model="CJS", begin.time=1) 

    WR.ddl <- make.design.data(WR.process)

    WR.mark.all <- mark(WR.process, WR.ddl, model.parameters=list(Phi=list(formula=~time),p=list(formula=~time)),
                        silent = T, output = F)

    inp.df <- inp.df[inp.df$rel %in% groups_w_detects,]
    inp.df$rel <- factor(inp.df$rel, levels = groups_w_detects)

    if(length(groups_w_detects) > 1){
      WR.process <- process.data(inp.df, model="CJS", begin.time=1, groups = "rel")

      WR.ddl <- make.design.data(WR.process)

      WR.mark.rel <- mark(WR.process, WR.ddl, model.parameters=list(Phi=list(formula=~time*rel),p=list(formula=~time)),
                          silent = T, output = F)

    } else {
      WR.process <- process.data(inp.df, model="CJS", begin.time=1) 

      WR.ddl <- make.design.data(WR.process)

      WR.mark.rel <- mark(WR.process, WR.ddl, model.parameters=list(Phi=list(formula=~time),p=list(formula=~time)),
                          silent = T, output = F)
    }

    WR.surv <- cbind(Release = "ALL",round(WR.mark.all$results$real[2,c("estimate", "se", "lcl", "ucl")] * 100,1))
    WR.surv.rel <- cbind(Release = groups_w_detects,
                         round(WR.mark.rel$results$real[seq(from=2,to=length(groups_w_detects)*3,by = 3),
                                                        c("estimate", "se", "lcl", "ucl")] * 100,1))
    WR.surv.rel <- merge(WR.surv.rel, data.frame(Release = groups), all.y = T)
    WR.surv.rel[is.na(WR.surv.rel$estimate),"estimate"] <- 0
    WR.surv <- rbind(WR.surv, WR.surv.rel)

  } else {
    inp.df <- data.frame(ch = as.character(inp$inp_final), freq = 1, stringsAsFactors = F)

    WR.process <- process.data(inp.df, model="CJS", begin.time=1) 

    WR.ddl <- make.design.data(WR.process)

    WR.mark.all <- mark(WR.process, WR.ddl, model.parameters=list(Phi=list(formula=~time),p=list(formula=~time)),
                        silent = T, output = F)
    WR.surv <- cbind(Release = c("ALL", groups),round(WR.mark.all$results$real[2,c("estimate", "se", "lcl", "ucl")] * 100,1))
  }

  WR.surv1 <- WR.surv

  colnames(WR.surv1)[1] <- "Release"
  WR.surv1 <- merge(WR.surv1, travel_final, by = "Release", all.x = T)
  WR.surv1$mean_travel_time <- round(WR.surv1$mean_travel_time,1)
  WR.surv1$sd_travel_time <- round(WR.surv1$sd_travel_time,1)
  colnames(WR.surv1) <- c("Release", "Survival (%)", "SE", "95% lower C.I.", 
                          "95% upper C.I.", "Mean Delta passage (days)", "SD of Delta Passage (days)","Count")
  #colnames(WR.surv1) <- c("Release Group", "Survival (%)", "SE", "95% lower C.I.", "95% upper C.I.")
  print(kable(WR.surv1, row.names = F, "html", caption = "3.2 Minimum through-Delta survival, and travel time: City of Sacramento to Benicia (using CJS survival model)") %>%
          kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), full_width = F, position = "left"))

  if(exists("Delta")==T & is.numeric(WR.surv1[1,2])){
    reltimes <- aggregate(list(RelDT = study_tagcodes$release_time), by = list(Release = study_tagcodes$Release), FUN = mean)
    reltimes <- rbind(reltimes, data.frame(Release = "ALL", RelDT = mean(study_tagcodes$release_time)))

    # Assign whether the results are tentative or final
    quality <- "tentative"
    if(endtime < as.Date(format(Sys.time(), "%Y-%m-%d"))){
      quality <- "final"}

      WR.surv <- merge(WR.surv, reltimes, by = "Release", all.x = T)

      WR.surv$RelDT <- as.POSIXct(WR.surv$RelDT, origin = "1970-01-01")

      Delta$RelDT <- as.POSIXct(Delta$RelDT)

      # remove old benicia record for this studyID
      Delta <- Delta[!Delta$StudyID %in% unique(detects_study$Study_ID),]
      Delta <- rbind(Delta, data.frame(WR.surv, StudyID = unique(detects_study$Study_ID), data_quality = quality))

      write.csv(Delta, "Delta_surv.csv", row.names = F, quote = F) 
    }
  }
}

3.2 Minimum through-Delta survival, and travel time: City of Sacramento to Benicia (using CJS survival model)

Release	Survival (%)	SE	95% lower C.I.	95% upper C.I.	Mean Delta passage (days)	SD of Delta Passage (days)	Count
ALL	69.1	2.0	64.9	72.9	3.4	1.3	352
Before Pulse	74.2	3.8	66.1	80.9	3.5	1.2	100
First Pulse	67.8	5.9	55.4	78.2	3.5	2.6	42
Second Pulse - Week 1	75.3	4.7	65.0	83.3	3.9	1.1	64
Second Pulse - Week 2	76.2	3.9	67.7	83.0	3.1	0.7	91
Second Pulse - Week 3	52.0	4.6	42.9	60.9	2.8	0.9	55

try(setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = "")))

try(benicia <- read.csv("benicia_surv.csv", stringsAsFactors = F))

detects_benicia <- detects_study[detects_study$general_location %in% c("Benicia_west", "Benicia_east"),]
endtime         <- min(as.Date(format(Sys.time(), "%Y-%m-%d")), max(as.Date(detects_study$release_time)+(as.numeric(detects_study$tag_life))))

if(nrow(detects_benicia) == 0){
  if(as.numeric(difftime(Sys.time(), min(detects_study$RelDT), units = "days"))>30){
    WR.surv <- data.frame("Release"="ALL", "estimate"=0, "se"=NA, "lcl"=NA, "ucl"=NA, "Detection_efficiency"=NA)

  } else {
    WR.surv <- data.frame("Release"=NA, "estimate"="NO DETECTIONS YET", "se"=NA, "lcl"=NA, "ucl"=NA, "Detection_efficiency"=NA)
  }

  WR.surv1 <- WR.surv
  colnames(WR.surv1) <- c("Release Group", "Survival (%)", "SE", "95% lower C.I.", "95% upper C.I.", "Detection efficiency (%)")
  print(kable(WR.surv1, row.names = F, "html", caption = "3.3 Minimum survival to Benicia Bridge East Span (using CJS survival model)") %>%
          kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), full_width = F, position = "left"))

} else if(length(table(detects_benicia$general_location)) == 1){
  if(as.numeric(difftime(Sys.time(), min(detects_study$RelDT), units = "days"))>30){
    WR.surv <- data.frame("Release"="ALL", "estimate"=round(length(unique(detects_benicia$TagCode))/length(unique(detects_study$TagCode))*100,1),
                          "se"=NA, "lcl"=NA, "ucl"=NA, "Detection_efficiency"=NA)

  } else {
    WR.surv <- data.frame("Release" = NA, "estimate" = "NOT ENOUGH DETECTIONS", "se" = NA, "lcl" = NA, "ucl" = NA, "Detection_efficiency" = NA)
  }

  WR.surv1 <- WR.surv
  colnames(WR.surv1) <- c("Release Group", "Survival (%)", "SE", "95% lower C.I.", "95% upper C.I.", "Detection efficiency (%)")
  print(kable(WR.surv1, row.names = F, "html", caption = "3.3 Minimum survival to Benicia Bridge East Span (using CJS survival model)") %>%
         kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), full_width = F, position = "left"))

} else {
  # Only do survival to Benicia here
  test3 <- detects_study[which(detects_study$river_km < 53),]

  # calculate mean and SD travel time
  travel <- aggregate(list(first_detect = test3$DateTime_PST), by = list(Release = test3$Release, TagCode = test3$TagCode, RelDT = test3$RelDT), min)
  travel$days <- as.numeric(difftime(travel$first_detect, travel$RelDT, units = "days"))

  travel_final <- aggregate(list(mean_travel_time = travel$days), by = list(Release = travel$Release), mean)
  travel_final <- merge(travel_final, aggregate(list(sd_travel_time = travel$days), by = list(Release = travel$Release), sd))
  travel_final <- merge(travel_final, aggregate(list(n = travel$days), by = list(Release = travel$Release), length))
  travel_final <- rbind(travel_final, data.frame(Release = "ALL", mean_travel_time = mean(travel$days), sd_travel_time = sd(travel$days), n = nrow(travel)))

  # Create inp for survival estimation
  inp <- as.data.frame(reshape2::dcast(test3, TagCode ~ river_km, fun.aggregate = length))

  # Sort columns by river km in descending order
  # Count number of genlocs
  gen_loc_sites <- ncol(inp)-1

  inp  <- inp[,c(1,order(names(inp[,2:(gen_loc_sites+1)]), decreasing = T)+1)]
  inp  <- merge(study_tagcodes, inp, by = "TagCode", all.x = T)
  inp2 <- inp[,(ncol(inp)-gen_loc_sites+1):ncol(inp)]

  inp2[is.na(inp2)] <- 0
  inp2[inp2 > 0]    <- 1

  inp    <- cbind(inp, inp2)
  groups <- as.character(sort(unique(inp$Release)))
  groups_w_detects <- names(table(test3$Release))

  inp[,groups] <- 0

  for(i in groups){
    inp[as.character(inp$Release) == i, i] <- 1
  }

  inp$inp_final <- paste("1",apply(inp2, 1, paste, collapse=""),sep="")

  if(length(groups) > 1){
    # make sure factor levels have a release that has detections first. if first release in factor order has zero #detectins, model goes haywire
    inp.df <- data.frame(ch = as.character(inp$inp_final), freq = 1, rel = inp$Release, stringsAsFactors = F)

    WR.process <- process.data(inp.df, model="CJS", begin.time=1)

    WR.ddl <- make.design.data(WR.process)

    WR.mark.all <- mark(WR.process, WR.ddl, model.parameters=list(Phi=list(formula=~time),p=list(formula=~time)), silent = T, output = F)

    inp.df <- inp.df[inp.df$rel %in% groups_w_detects,]
    inp.df$rel <- factor(inp.df$rel, levels = groups_w_detects)

    if(length(groups_w_detects) > 1){
      WR.process <- process.data(inp.df, model="CJS", begin.time=1, groups = "rel")
      WR.ddl <- make.design.data(WR.process)
      WR.mark.rel <- mark(WR.process, WR.ddl, model.parameters=list(Phi=list(formula=~time*rel),p=list(formula=~time)), silent = T, output = F)

    } else {
      WR.process <- process.data(inp.df, model="CJS", begin.time=1)
      WR.ddl <- make.design.data(WR.process)
      WR.mark.rel <- mark(WR.process, WR.ddl, model.parameters=list(Phi=list(formula=~time),p=list(formula=~time)), silent = T, output = F)
    }

    WR.surv <- cbind(Release = "ALL",round(WR.mark.all$results$real[1,c("estimate", "se", "lcl", "ucl")] * 100,1))
    WR.surv.rel <- cbind(Release = groups_w_detects, round(WR.mark.rel$results$real[seq(from=1,to=length(groups_w_detects)*2,by = 2),
                                                                                    c("estimate", "se", "lcl", "ucl")] * 100,1))
    WR.surv.rel <- merge(WR.surv.rel, data.frame(Release = groups), all.y = T)
    WR.surv.rel[is.na(WR.surv.rel$estimate),"estimate"] <- 0
    WR.surv <- rbind(WR.surv, WR.surv.rel)

  } else {
    inp.df      <- data.frame(ch = as.character(inp$inp_final), freq = 1, stringsAsFactors = F)
    WR.process  <- process.data(inp.df, model="CJS", begin.time=1) 
    WR.ddl      <- make.design.data(WR.process)
    WR.mark.all <- mark(WR.process, WR.ddl, model.parameters=list(Phi=list(formula=~time),p=list(formula=~time)), silent = T, output = F)
    WR.surv     <- cbind(Release = c("ALL", groups),round(WR.mark.all$results$real[1,c("estimate", "se", "lcl", "ucl")] * 100,1))
  }

  WR.surv$Detection_efficiency <- NA
  WR.surv[1,"Detection_efficiency"] <- round(WR.mark.all$results$real[gen_loc_sites+1,"estimate"] * 100,1)
  WR.surv1 <- WR.surv

  colnames(WR.surv1)[1] <- "Release"
  WR.surv1 <- merge(WR.surv1, travel_final, by = "Release", all.x = T)
  WR.surv1$mean_travel_time <- round(WR.surv1$mean_travel_time,1)
  WR.surv1$sd_travel_time <- round(WR.surv1$sd_travel_time,1)
  colnames(WR.surv1) <- c("Release", "Survival (%)", "SE", "95% lower C.I.", 
                          "95% upper C.I.", "Detection efficiency (%)", "Mean time to Benicia (days)", "SD of time to Benicia (days)", "Count")
  #colnames(WR.surv1) <- c("Release Group", "Survival (%)", "SE", "95% lower C.I.", "95% upper C.I.", "Detection efficiency (%)")

  print(kable(WR.surv1, row.names = F, "html", caption = "3.3 Minimum survival to Benicia Bridge East Span (using CJS survival model), and travel time") %>%
          kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), full_width = F, position = "left"))
}

3.3 Minimum survival to Benicia Bridge East Span (using CJS survival model), and travel time

Release	Survival (%)	SE	95% lower C.I.	95% upper C.I.	Detection efficiency (%)	Mean time to Benicia (days)	SD of time to Benicia (days)	Count
ALL	29.4	1.3	26.9	31.9	99.1	9.2	3.7	367
Before Pulse	40.6	3.1	34.7	46.8	NA	11.8	1.8	102
First Pulse	17.6	2.4	13.4	22.8	NA	8.1	3.4	44
Second Pulse - Week 1	26.4	2.8	21.3	32.2	NA	10.3	4.8	66
Second Pulse - Week 2	37.2	3.1	31.4	43.4	NA	7.4	2.6	93
Second Pulse - Week 3	24.8	2.7	19.9	30.6	NA	7.2	3.5	62

if(exists("benicia")==T & is.numeric(WR.surv1[1,2])){
  # Find mean release time per release group, and ALL
  reltimes <- aggregate(list(RelDT = study_tagcodes$release_time), by = list(Release = study_tagcodes$Release), FUN = mean)
  reltimes <- rbind(reltimes, data.frame(Release = "ALL", RelDT = mean(study_tagcodes$release_time)))

  # Assign whether the results are tentative or final
  quality <- "tentative"
  if(endtime < as.Date(format(Sys.time(), "%Y-%m-%d"))){
    quality <- "final"
  }

  WR.surv       <- merge(WR.surv, reltimes, by = "Release", all.x = T)
  WR.surv$RelDT <- as.POSIXct(WR.surv$RelDT, origin = "1970-01-01")
  benicia$RelDT <- as.POSIXct(benicia$RelDT)

  # remove old benicia record for this studyID
  benicia <- benicia[!benicia$StudyID == unique(detects_study$Study_ID),]
  benicia <- rbind(benicia, data.frame(WR.surv, StudyID = unique(detects_study$Study_ID), data_quality = quality))

  write.csv(benicia, "benicia_surv.csv", row.names = F, quote = F) 
}

---

4. Detections statistics at all realtime receivers

---

try(setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = "")))

if(nrow(detects_study[is.na(detects_study$DateTime_PST)==F,]) == 0){
  "No detections yet"

} else {
  arrivals <- detects_study %>%
              group_by(general_location, TagCode) %>%
              summarise(DateTime_PST = min(DateTime_PST)) %>%
              arrange(TagCode)

  tag_stats <- arrivals %>%
               group_by(general_location) %>%
               summarise(First_arrival = min(DateTime_PST),
                         Mean_arrival = mean(DateTime_PST),
                         Last_arrival = max(DateTime_PST),
                         Fish_count = length(unique(TagCode))) %>%
               mutate(Percent_arrived = round(Fish_count/nrow(study_tagcodes) * 100,2)) %>%
               dplyr::left_join(., unique(detects_study[,c("general_location", "river_km")])) %>%
               arrange(desc(river_km)) %>%
               mutate(First_arrival = format(First_arrival, tz = "Etc/GMT+8"),
                      Mean_arrival = format(Mean_arrival, tz = "Etc/GMT+8"),
                      Last_arrival = format(Last_arrival, tz = "Etc/GMT+8")) %>%
               na.omit()

  print(kable(tag_stats, row.names = F,
              caption = "4.1 Detections for all releases combined",
              "html") %>%
          kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), full_width = F, position = "left"))

  count <- 0

  for(j in sort(unique(study_tagcodes$Release))){

    if(nrow(detects_study[detects_study$Release == j,]) > 0){
      count <- count + 1
      arrivals1 <- detects_study %>%
                   filter(Release == j) %>%
                   group_by(general_location, TagCode) %>%
                   summarise(DateTime_PST = min(DateTime_PST)) %>%
                   arrange(TagCode)

      rel_count <- nrow(study_tagcodes[study_tagcodes$Release == j,])

      tag_stats1 <- arrivals1 %>%
                    group_by(general_location) %>%
                    summarise(First_arrival = min(DateTime_PST),
                              Mean_arrival = mean(DateTime_PST),
                              Last_arrival = max(DateTime_PST),
                              Fish_count = length(unique(TagCode))) %>%
                    mutate(Percent_arrived = round(Fish_count/rel_count * 100,2)) %>%
                    dplyr::left_join(., unique(detects_study[,c("general_location", "river_km")])) %>%
                    arrange(desc(river_km)) %>%
                    mutate(First_arrival = format(First_arrival, tz = "Etc/GMT+8"),
                           Mean_arrival = format(Mean_arrival, tz = "Etc/GMT+8"),
                           Last_arrival = format(Last_arrival, tz = "Etc/GMT+8")) %>%
                    na.omit()

      final_stats <- kable(tag_stats1, row.names = F,
            caption = paste("4.2.", count, " Detections for ", j, " release groups", sep = ""),
            "html")
      print(kable_styling(final_stats, bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), full_width = F, position = "left"))

    } else {
      cat("\n\n\\pagebreak\n")
      print(paste("No detections for",j,"release group yet", sep=" "), quote = F)
      cat("\n\n\\pagebreak\n")
    }
  }
}

4.1 Detections for all releases combined

general_location	First_arrival	Mean_arrival	Last_arrival	Fish_count	Percent_arrived	river_km
Blw_Salt_RT	2023-04-18 10:26:00	2023-05-01 11:11:38	2023-05-18 05:01:20	1017	81.29	457.000
MeridianBr	2023-04-20 16:57:28	2023-05-07 06:29:09	2023-06-01 19:55:48	610	48.76	290.848
TowerBridge	2023-04-22 11:39:42	2023-05-08 16:56:40	2023-06-10 08:38:08	456	36.45	172.000
I80-50_Br	2023-04-22 12:03:41	2023-05-08 15:06:55	2023-06-10 08:58:06	456	36.45	170.748
Sac_BlwGeorgiana	2023-04-24 15:54:43	2023-05-10 03:31:59	2023-06-10 20:22:23	313	25.02	119.058
Sac_BlwGeorgiana2	2023-04-24 16:08:25	2023-05-10 06:08:12	2023-06-10 20:35:34	320	25.58	118.398
Benicia_east	2023-04-26 15:16:35	2023-05-11 06:51:37	2023-06-15 03:19:25	364	29.10	52.240
Benicia_west	2023-04-27 09:50:56	2023-05-10 22:08:02	2023-06-15 03:25:47	340	27.18	52.040

4.2.1 Detections for Before Pulse release groups

general_location	First_arrival	Mean_arrival	Last_arrival	Fish_count	Percent_arrived	river_km
Blw_Salt_RT	2023-04-18 10:26:00	2023-04-19 07:51:27	2023-04-25 14:30:12	226	90.04	457.000
MeridianBr	2023-04-20 16:57:28	2023-04-25 14:45:33	2023-05-05 18:23:22	145	57.77	290.848
TowerBridge	2023-04-22 11:39:42	2023-04-27 08:02:27	2023-05-22 18:12:20	122	48.61	172.000
I80-50_Br	2023-04-22 12:03:41	2023-04-27 05:57:30	2023-05-02 02:16:20	127	50.60	170.748
Sac_BlwGeorgiana	2023-04-24 15:54:43	2023-04-28 01:28:24	2023-05-23 17:52:55	82	32.67	119.058
Sac_BlwGeorgiana2	2023-04-24 16:08:25	2023-04-28 01:40:31	2023-05-23 18:05:04	82	32.67	118.398
Benicia_east	2023-04-26 15:16:35	2023-04-30 16:03:51	2023-05-11 13:22:23	102	40.64	52.240
Benicia_west	2023-04-27 09:50:56	2023-04-30 16:21:28	2023-05-11 13:29:19	100	39.84	52.040

4.2.2 Detections for First Pulse release groups

general_location	First_arrival	Mean_arrival	Last_arrival	Fish_count	Percent_arrived	river_km
Blw_Salt_RT	2023-04-24 11:16:12	2023-04-25 01:45:38	2023-04-29 12:30:57	246	98.4	457.000
MeridianBr	2023-04-26 04:01:50	2023-04-28 12:10:07	2023-05-28 23:43:45	85	34.0	290.848
TowerBridge	2023-04-27 19:30:09	2023-04-30 09:34:28	2023-06-05 00:54:25	59	23.6	172.000
I80-50_Br	2023-04-27 19:51:43	2023-04-30 09:13:00	2023-06-05 01:18:05	57	22.8	170.748
Sac_BlwGeorgiana	2023-04-28 11:01:03	2023-05-01 15:11:19	2023-06-05 15:27:55	36	14.4	119.058
Sac_BlwGeorgiana2	2023-04-28 11:11:56	2023-05-01 14:08:32	2023-06-05 15:38:02	37	14.8	118.398
Benicia_east	2023-04-30 13:50:55	2023-05-02 20:55:28	2023-05-22 06:52:18	44	17.6	52.240
Benicia_west	2023-04-30 13:58:14	2023-05-02 21:07:04	2023-05-22 06:55:06	44	17.6	52.040

4.2.3 Detections for Second Pulse - Week 1 release groups

general_location	First_arrival	Mean_arrival	Last_arrival	Fish_count	Percent_arrived	river_km
Blw_Salt_RT	2023-05-02 10:00:47	2023-05-02 22:25:51	2023-05-03 14:18:35	228	91.2	457.000
MeridianBr	2023-05-03 22:52:07	2023-05-07 20:30:46	2023-05-31 03:53:52	109	43.6	290.848
TowerBridge	2023-05-05 14:51:21	2023-05-09 17:26:35	2023-05-31 18:30:26	74	29.6	172.000
I80-50_Br	2023-05-05 15:12:05	2023-05-09 16:55:47	2023-05-31 19:06:51	76	30.4	170.748
Sac_BlwGeorgiana	2023-05-06 06:47:16	2023-05-11 13:33:48	2023-06-01 07:53:48	49	19.6	119.058
Sac_BlwGeorgiana2	2023-05-06 06:58:30	2023-05-11 14:53:56	2023-06-01 08:01:07	52	20.8	118.398
Benicia_east	2023-05-09 09:22:04	2023-05-13 05:12:46	2023-06-02 17:16:24	64	25.6	52.240
Benicia_west	2023-05-09 07:03:39	2023-05-13 08:38:44	2023-06-02 17:23:34	58	23.2	52.040

4.2.4 Detections for Second Pulse - Week 2 release groups

general_location	First_arrival	Mean_arrival	Last_arrival	Fish_count	Percent_arrived	river_km
Blw_Salt_RT	2023-05-09 10:06:23	2023-05-10 02:06:06	2023-05-10 12:31:40	135	54.0	457.000
MeridianBr	2023-05-10 19:28:48	2023-05-12 19:06:31	2023-06-01 19:55:48	140	56.0	290.848
TowerBridge	2023-05-12 05:57:01	2023-05-14 16:14:33	2023-06-06 05:25:36	113	45.2	172.000
I80-50_Br	2023-05-12 06:17:59	2023-05-14 16:55:33	2023-06-06 05:42:59	100	40.0	170.748
Sac_BlwGeorgiana	2023-05-12 22:50:27	2023-05-15 04:25:49	2023-06-06 17:39:31	75	30.0	119.058
Sac_BlwGeorgiana2	2023-05-12 23:09:15	2023-05-15 04:11:08	2023-06-06 17:50:27	76	30.4	118.398
Benicia_east	2023-05-15 05:12:28	2023-05-17 02:32:26	2023-05-27 12:18:23	92	36.8	52.240
Benicia_west	2023-05-15 05:15:45	2023-05-17 07:10:13	2023-06-02 07:17:26	88	35.2	52.040

4.2.5 Detections for Second Pulse - Week 3 release groups

general_location	First_arrival	Mean_arrival	Last_arrival	Fish_count	Percent_arrived	river_km
Blw_Salt_RT	2023-05-16 09:16:54	2023-05-16 22:43:59	2023-05-18 05:01:20	182	72.8	457.000
MeridianBr	2023-05-17 19:32:07	2023-05-19 11:10:02	2023-05-26 13:34:00	131	52.4	290.848
TowerBridge	2023-05-19 07:55:20	2023-05-21 12:20:54	2023-06-10 08:38:08	88	35.2	172.000
I80-50_Br	2023-05-19 08:10:06	2023-05-21 09:39:33	2023-06-10 08:58:06	96	38.4	170.748
Sac_BlwGeorgiana	2023-05-19 23:33:24	2023-05-22 02:56:55	2023-06-10 20:22:23	71	28.4	119.058
Sac_BlwGeorgiana2	2023-05-19 23:46:41	2023-05-22 05:50:21	2023-06-10 20:35:34	73	29.2	118.398
Benicia_east	2023-05-21 09:23:09	2023-05-24 02:14:06	2023-06-15 03:19:25	62	24.8	52.240
Benicia_west	2023-05-21 09:27:27	2023-05-24 10:19:20	2023-06-15 03:25:47	50	20.0	52.040

library(dplyr)
library(dbplyr)
library(DBI)
library(odbc)
library(data.table)

# Create connection with cloud database
con <- dbConnect(odbc(),
                Driver = "SQL Server",
                Server = "calfishtrack-server.database.windows.net",
                Database = "realtime_detections",
                UID = "realtime_user",
                PWD = "Pass@123",
                Port = 1433)

try(setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = "")))

# THIS CODE CHUNK WILL NOT WORK IF USING ONLY ERDDAP DATA, REQUIRES ACCESS TO LOCAL FILES
if(nrow(detects_study[is.na(detects_study$DateTime_PST)==F,]) == 0){
  "No detections yet"

} else {
  arrivals <- detects_study %>%
              group_by(general_location, TagCode) %>%
              summarise(DateTime_PST = min(DateTime_PST)) %>%
              mutate(day = as.Date(DateTime_PST, "%Y-%m-%d", tz = "Etc/GMT+8"))

  # Use dbplyr to load realtime_locs and qryHexCodes sql table
  gen_locs <- tbl(con, "realtime_locs") %>% collect()
  # gen_locs <- read.csv("realtime_locs.csv", stringsAsFactors = F)

  beacon_by_day <- fread("beacon_by_day.csv", stringsAsFactors = F) %>%
                   mutate(day = as.Date(day)) %>%
                   filter(TagCode == beacon) %>% # Now subset to only look at data for the correct beacon for that day
                   filter(day >= as.Date(min(study_tagcodes$release_time)) & 
                          day <= endtime) %>% # Now only keep beacon by day for days since fish were released
                   dplyr::left_join(., gen_locs[,c("location", "general_location","rkm")], by = "location")

  arrivals_per_day <- arrivals %>%
                      group_by(day, general_location) %>%
                      summarise(New_arrivals = length(TagCode)) %>%
                      arrange(general_location) %>% na.omit() %>%
                      mutate(day = as.Date(day)) %>%
                      dplyr::left_join(unique(beacon_by_day[,c("general_location", "day", "rkm")]),
                                       ., by = c("general_location", "day")) %>%
                      arrange(general_location, day) %>%
                      mutate(day = factor(day)) %>%
                      filter(general_location != "Bench_test") %>% # Remove bench test and other NA locations
                      filter(!(is.na(general_location))) %>%
                      arrange(desc(rkm)) %>% # Change order of data to plot decreasing river_km
                      mutate(general_location = factor(general_location, unique(general_location)))

  endtime <- min(as.Date(format(Sys.time(), "%Y-%m-%d")),
                 max(as.Date(detects_study$release_time)+(as.numeric(detects_study$tag_life)*1.5)))

  crosstab <- xtabs(formula = arrivals_per_day$New_arrivals ~ arrivals_per_day$day + arrivals_per_day$general_location,
                    addNA =T)
  crosstab[is.na(crosstab)] <- ""
  crosstab[crosstab==0] <- NA
  crosstab <- as.data.frame.matrix(crosstab)

  kable(crosstab, align = "c", caption = "4.3 Fish arrivals per day (\"NA\" means receivers were non-operational)") %>%
    kable_styling(c("striped", "condensed"), font_size = 11, full_width = F, position = "left", fixed_thead = TRUE) %>%
    column_spec(column = 1:ncol(crosstab),width_min = "50px",border_left = T, border_right = T) %>%
    column_spec(1, bold = T, width_min = "75px")%>%
    scroll_box(height = "700px")
}

4.3 Fish arrivals per day (“NA” means receivers were non-operational)

	Blw_Salt_RT	MeridianBr	TowerBridge	I80-50_Br	MiddleRiver	Clifton_Court_US_Radial_Gates	Holland_Cut_Quimby	CVP_Tank	CVP_Trash_Rack_1	Clifton_Court_Intake_Canal	Old_River_Quimby	Sac_BlwGeorgiana	Sac_BlwGeorgiana2	Benicia_east	Benicia_west
2023-04-18	93
2023-04-19	115
2023-04-20	10	1
2023-04-21	1	2
2023-04-22		9	1	1
2023-04-23	1	9	3	3
2023-04-24	115	35	8	8								1	1
2023-04-25	129	30	22	21								11	10
2023-04-26	6	43	28	25								12	13	1
2023-04-27	1	62	25	30								27	27	2	3
2023-04-28		19	46	48								25	25	13	13
2023-04-29	1	8	30	32								28	29	19	19
2023-04-30		5	10	10								7	7	32	31
2023-05-01		1	1	1								1	1	44	43
2023-05-02	115	2	1	1								1	1	18	18
2023-05-03	113	3	2	1										5	5
2023-05-04		21										1	1	7	7
2023-05-05		46	8	7										2	2
2023-05-06		9	22	22								7	7
2023-05-07		9	15	16								11	13
2023-05-08		4	9	8								11	11
2023-05-09	47	1	5	6								7	7	9	9
2023-05-10	88	17	4	3								2	2	15	14
2023-05-11		53		2										17	14
2023-05-12		46	37	32								1	1	4	4
2023-05-13		12	41	36								30	31	6	4
2023-05-14		8	17	14								28	28	3	4
2023-05-15		1	5	7								7	7	28	27
2023-05-16	87	3	6	5								6	6	37	36
2023-05-17	94	15	1	2								1	1	14	11
2023-05-18	1	53	3	3								3	3	8	7
2023-05-19		43	21	19								3	3	4	4
2023-05-20		9	42	42								29	27	4	4
2023-05-21		8	11	24								27	27	4	3
2023-05-22		2	8	7								3	5	21	17
2023-05-23		1	2	3								4	5	27	20
2023-05-24		5	2	2								2	4	7	7
2023-05-25		5	5	5								1	1	4	4
2023-05-26		1	1	1								3	3
2023-05-27			2	1								1	1	2	1
2023-05-28		6	3	1								1	1		1
2023-05-29		1										2	2	2	1
2023-05-30			2	1								3	3	1	1
2023-05-31		1	4	3								2	2	2	3
2023-06-01		1										1	1
2023-06-02														1	2
2023-06-03
2023-06-04
2023-06-05			1	1								1	1
2023-06-06			1	1								1	1
2023-06-07
2023-06-08
2023-06-09
2023-06-10			1	1								1	1
2023-06-11
2023-06-12
2023-06-13
2023-06-14
2023-06-15														1	1
2023-06-16
2023-06-17
2023-06-18
2023-06-19
2023-06-20
2023-06-21
2023-06-22
2023-06-23
2023-06-24
2023-06-25
2023-06-26
2023-06-27
2023-06-28
2023-06-29
2023-06-30
2023-07-01
2023-07-02
2023-07-03
2023-07-04
2023-07-05
2023-07-06
2023-07-07
2023-07-08
2023-07-09
2023-07-10
2023-07-11
2023-07-12
2023-07-13
2023-07-14
2023-07-15
2023-07-16
2023-07-17
2023-07-18
2023-07-19
2023-07-20
2023-07-21
2023-07-22
2023-07-23
2023-07-24
2023-07-25
2023-07-26
2023-07-27

rm(list = ls())
cleanup(ask = F)

For questions or comments, please contact cyril.michel@noaa.gov