discaRd: Cochran bycatch estimation

Documented in discard_herring_diagnostic

#' discard_herring: Calculate discards for January fishing year for herring only
#'
#' @param con ROracle connection to Oracle (e.g. MAPS)
#' @param species dataframe with species info
#' @param FY Fishing Year
#' @param herr_dat Data frame of trips built from CAMS_OBS_CATCH and control script routine
#' @param save_dir Directory to save (and load saved) results
#'
#' @return nothing currently, writes out to fst files (add oracle?)
#' @export
#'
#' @examples
#'
discard_herring_diagnostic <- function(con
                            , species = species
                            , FY = fy
                            , herr_dat = herr_dat
                            , CAMS_GEAR_STRATA = CAMS_GEAR_STRATA
                            , STOCK_AREAS =  STOCK_AREAS
                            , CAMS_DISCARD_MORTALITY_STOCK =  CAMS_DISCARD_MORTALITY_STOCK
                            , return_table = T
                            , return_summary = F
                            # , save_dir = file.path(getOption("maps.discardsPath"), "herring")
) {


  # if(!dir.exists(save_dir)) {
  #   dir.create(save_dir, recursive = TRUE)
  #   system(paste("chmod 770 -R", save_dir))
  # }

  FY_TYPE = species$RUN_ID[1]

  # Stratification variables ----

  stratvars = c('FY'
                , 'FY_TYPE'
                , 'HERR_TARG' # target vs non target herring
                ,'AREA_HERR' # herring management area
                ,'CAMS_GEAR_GROUP')# gear



  # single species, no loop needed ----

  i = 1

  # for(i in 1:length(species$ITIS_TSN)){

    t1 = Sys.time()

    print(paste0('Running ', species$ITIS_NAME[i], " for Fishing Year ", FY))


    species_itis <- as.character(species$ITIS_TSN[i])
    species_itis_srce = as.character(as.numeric(species$ITIS_TSN[i]))

    # add OBS_DISCARD column. Previously, this was done within the run_discard() step. 2/2/23 BG ----

    herr_dat = herr_dat %>%
      mutate(OBS_DISCARD = case_when(SPECIES_ITIS == species_itis ~ DISCARD_PRORATE
                                     , TRUE ~ 0))

    # Support table import by species ----

    # GEAR TABLE
    CAMS_GEAR_STRATA = CAMS_GEAR_STRATA
    # tbl(con, sql('  select * from CFG_GEARCODE_STRATA')) %>%
    #   collect() %>%
    #   dplyr::rename(GEARCODE = SECGEAR_MAPPED) %>%
    #   filter(ITIS_TSN == species_itis) %>%
    #   dplyr::select(-NESPP3, -ITIS_TSN) #%>%
    # #mutate_all(function(x) ifelse(str_detect(x, '^0_'), 'other', x))


    # Stat areas table
    # unique stat areas for stock ID if needed
    STOCK_AREAS =  STOCK_AREAS
    # tbl(con, sql('select * from CFG_STATAREA_STOCK')) %>%
    #   filter(ITIS_TSN == species_itis) %>%
    #   collect() %>%
    #   group_by(AREA_NAME, ITIS_TSN) %>%
    #   distinct(AREA) %>%
    #   mutate(AREA = as.character(AREA)
    #          , SPECIES_STOCK = AREA_NAME) %>%
    #   ungroup()
    #
    # Mortality table
    CAMS_DISCARD_MORTALITY_STOCK =  CAMS_DISCARD_MORTALITY_STOCK
    # tbl(con, sql("select * from CFG_DISCARD_MORTALITY_STOCK"))  %>%
    #   collect() %>%
    #   mutate(SPECIES_STOCK = AREA_NAME
    #          , GEARCODE = CAMS_GEAR_GROUP
    #          , CAMS_GEAR_GROUP = as.character(CAMS_GEAR_GROUP)) %>%
    #   select(-AREA_NAME) %>%
    #   filter(ITIS_TSN == species_itis) %>%
    #   dplyr::select(-ITIS_TSN)
    #
    # Observer codes to be removed
    OBS_REMOVE = tbl(con, sql("select * from CAMS_GARFO.CFG_OBSERVER_CODES"))  %>%
      collect() %>%
      filter(ITIS_TSN == species_itis) %>%
      distinct(OBS_CODES)

    #--------------------------------------------------------------------------------#
    # make tables ----
    ddat_focal <- herr_dat %>%
      filter(YEAR == FY) %>%   ## time element is here!!
      filter(AREA %in% STOCK_AREAS$AREA) %>%
      mutate(FY_TYPE = FY_TYPE
             , FY = FY) %>%
      mutate(LIVE_POUNDS = SUBTRIP_KALL
             ,SEADAYS = 0
             # , NESPP3 = NESPP3_FINAL
      ) %>%
      left_join(., y = STOCK_AREAS, by = 'AREA') %>%
      left_join(., y = CAMS_GEAR_STRATA, by = 'GEARCODE') %>%
      left_join(., y = CAMS_DISCARD_MORTALITY_STOCK
                , by = c('SPECIES_STOCK', 'CAMS_GEAR_GROUP')
      ) %>%
      dplyr::select(-GEARCODE.y, -NESPP3.y) %>%
      dplyr::rename(COMMON_NAME= 'COMMON_NAME.x',SPECIES_ITIS = 'SPECIES_ITIS', NESPP3 = 'NESPP3.x',
                    GEARCODE = 'GEARCODE.x') %>%
      relocate('COMMON_NAME','SPECIES_ITIS','NESPP3','SPECIES_STOCK','CAMS_GEAR_GROUP','DISC_MORT_RATIO') %>%
      assign_strata(., stratvars = stratvars)

    # DATE RANGE FOR PREVIOUS YEAR ----

    dr_prev = get_date_range(FY-1, FY_TYPE)
    end_date_prev = dr_prev[2]
    start_date_prev = dr_prev[1]


    ddat_prev <- herr_dat %>%
      filter(YEAR == FY-1) %>%   ## time element is here!!
      filter(AREA %in% STOCK_AREAS$AREA) %>%
      mutate(FY_TYPE = FY_TYPE
             , FY = FY) %>%
      mutate(LIVE_POUNDS = SUBTRIP_KALL
             ,SEADAYS = 0
             # , NESPP3 = NESPP3_FINAL
      ) %>%
      left_join(., y = STOCK_AREAS, by = 'AREA') %>%
      left_join(., y = CAMS_GEAR_STRATA, by = 'GEARCODE') %>%
      left_join(., y = CAMS_DISCARD_MORTALITY_STOCK
                , by = c('SPECIES_STOCK', 'CAMS_GEAR_GROUP')
      ) %>%
      dplyr::select(-NESPP3.y, -GEARCODE.y) %>%
      dplyr::rename(COMMON_NAME= 'COMMON_NAME.x',SPECIES_ITIS = 'SPECIES_ITIS', NESPP3 = 'NESPP3.x',
                    GEARCODE = 'GEARCODE.x') %>%
      relocate('COMMON_NAME','SPECIES_ITIS','NESPP3','SPECIES_STOCK','CAMS_GEAR_GROUP','DISC_MORT_RATIO')%>%
      assign_strata(., stratvars = stratvars)



    # need to slice the first record for each observed trip.. these trips are multi rowed while unobs trips are single row..
    ddat_focal_cy <- summarise_single_discard_row(data = ddat_focal, itis_tsn = species_itis)

    # and join to the unobserved trips ----

    ddat_focal_cy = ddat_focal_cy %>%
      union_all(ddat_focal %>%
                  filter(is.na(LINK1)))

    # if using the combined catch/obs table, which seems necessary for groundfish.. need to roll your own table to use with run_discard function
    # DO NOT NEED TO FILTER SPECIES HERE. NEED TO RETAIN ALL TRIPS. THE MAKE_BDAT_FOCAL.R FUNCTION TAKES CARE OF THIS.

    bdat_cy = ddat_focal %>%
      filter(!is.na(LINK1)) %>%
      filter(FISHDISP != '090') %>%
      filter(LINK3_OBS == 1) %>%
      filter(SOURCE != 'ASM') %>%
      filter(substr(LINK1, 1,3) %!in% OBS_REMOVE$OBS_CODES)

    if(nrow(bdat_cy) > 0 ) {
      bdat_cy <- bdat_cy %>%
        mutate(DISCARD_PRORATE = DISCARD
               , OBS_AREA = AREA
               , OBS_HAUL_KALL_TRIP = OBS_KALL
               , PRORATE = 1)
    }

    # set up trips table for previous year ----
    ddat_prev_cy <- summarise_single_discard_row(data = ddat_prev, itis_tsn = species_itis)

    ddat_prev_cy = ddat_prev_cy %>%
      union_all(ddat_prev %>%
                  filter(is.na(LINK1))) #%>%

    # previous year observer data needed..
    bdat_prev_cy = ddat_prev %>%
      filter(!is.na(LINK1)) %>%
      filter(FISHDISP != '090') %>%
      filter(LINK3_OBS == 1) %>%
      filter(SOURCE != 'ASM') %>%
      filter(substr(LINK1, 1,3) %!in% OBS_REMOVE$OBS_CODES) %>%
      mutate(DISCARD_PRORATE = DISCARD
             , OBS_AREA = AREA
             , OBS_HAUL_KALL_TRIP = OBS_KALL
             , PRORATE = 1)

    # Run the discaRd functions on previous year ----
    d_prev = run_discard(bdat = bdat_prev_cy
                         , ddat = ddat_prev_cy
                         , c_o_tab = ddat_prev
                         # , year = 2018
                         # , species_nespp3 = species_nespp3
                         , species_itis = species_itis
                         , stratvars = stratvars
                         , aidx = c(1:length(stratvars))
    )

    # Run the discaRd functions on current year ----
    if(nrow(bdat_cy) > 0) {
      d_focal = run_discard(bdat = bdat_cy
                            , ddat = ddat_focal_cy
                            , c_o_tab = ddat_focal
                            # , year = 2019
                            # , species_nespp3 = '081' # haddock...
                            # , species_nespp3 = species_nespp3  #'081' #cod...
                            , species_itis = species_itis
                            , stratvars = stratvars
                            , aidx = c(1:length(stratvars))  # this makes sure this isn't used..
      )

      dest_strata_f = d_focal$allest$C %>% summarise(STRATA = STRATA
                                                     , N = N
                                                     , n = n
                                                     , orate = round(n/N, 2)
                                                     , drate = RE_mean
                                                     , KALL = K, disc_est = round(D)
                                                     , CV = round(RE_rse, 2)
      )

    }

    # summarize each result for convenience ----
    dest_strata_p = d_prev$allest$C %>% summarise(STRATA = STRATA
                                                  , N = N
                                                  , n = n
                                                  , orate = round(n/N, 2)
                                                  , drate = RE_mean
                                                  , KALL = K, disc_est = round(D)
                                                  , CV = round(RE_rse, 2)
    )

    # substitute transition rates where needed ----
    if(exists("dest_strata_f")) {
      trans_rate_df = dest_strata_f %>%
        left_join(., dest_strata_p, by = 'STRATA')

      trans_rate_df <- trans_rate_df %>%
        mutate(STRATA = STRATA
               , n_obs_trips_f = n.x
               , n_obs_trips_p = n.y
               , in_season_rate = drate.x
               , previous_season_rate = drate.y
        ) %>%
        mutate(n_obs_trips_p = coalesce(n_obs_trips_p, 0)) %>%
        mutate(trans_rate = get.trans.rate(l_observed_trips = n_obs_trips_f
                                           , l_assumed_rate = previous_season_rate
                                           , l_inseason_rate = in_season_rate
        )
        ) %>%
        dplyr::select(STRATA
                      , n_obs_trips_f
                      , n_obs_trips_p
                      , in_season_rate
                      , previous_season_rate
                      , trans_rate
                      , CV_f = CV.x
        )
    } else {
      trans_rate_df = dest_strata_p

      trans_rate_df <- trans_rate_df %>%
        mutate(STRATA = STRATA
               , n_obs_trips_f = 0L
               , n_obs_trips_p = n
               , in_season_rate = NA_real_
               , previous_season_rate = drate
        ) %>%
        mutate(n_obs_trips_p = coalesce(n_obs_trips_p, 0)) %>%
        mutate(trans_rate = get.trans.rate(l_observed_trips = n_obs_trips_f
                                           , l_assumed_rate = previous_season_rate
                                           , l_inseason_rate = in_season_rate
        ),
        CV_f = NA_real_
        ) %>%
        dplyr::select(STRATA
                      , n_obs_trips_f
                      , n_obs_trips_p
                      , in_season_rate
                      , previous_season_rate
                      , trans_rate
                      , CV_f
        )
    }

    trans_rate_df = trans_rate_df %>%
      mutate(
        final_rate = case_when(
          is.na(in_season_rate) ~ trans_rate,
          (in_season_rate != trans_rate & !is.na(trans_rate)) ~ trans_rate
        )
      )

    trans_rate_df$final_rate = coalesce(trans_rate_df$final_rate, trans_rate_df$in_season_rate)

    trans_rate_df_full = trans_rate_df

    # join calculated rates to trips table ----

    if(exists("d_focal")) {
      full_strata_table = trans_rate_df_full %>%
        # right_join(., y = d_focal$res, by = 'STRATA') %>%
        right_join(., y = ddat_focal_cy, by = 'STRATA') %>%
        as_tibble() %>%
        mutate(SPECIES_ITIS_EVAL = species_itis
               , COMNAME_EVAL = species$ITIS_NAME[i]
               , FISHING_YEAR = FY
               , FY_TYPE = FY_TYPE) %>%
        dplyr::rename(FULL_STRATA = STRATA)
    } else {
      full_strata_table = trans_rate_df_full %>%
        # right_join(., y = d_prev$res, by = 'STRATA') %>%
        right_join(., y = ddat_focal_cy, by = 'STRATA') %>%
        as_tibble() %>%
        mutate(SPECIES_ITIS_EVAL = species_itis
               , COMNAME_EVAL = species$ITIS_NAME[i]
               , FISHING_YEAR = FY
               , FY_TYPE = FY_TYPE) %>%
        dplyr::rename(FULL_STRATA = STRATA)
    }

    #
    # Target/non-target and gear stratification: second pass ----
    #


    stratvars_assumed = c("FY"
                          , "FY_TYPE"
                          , "HERR_TARG"
                          , "CAMS_GEAR_GROUP") #AWA
    #, "MESH_CAT")


    ### All tables in previous run can be re-used with diff stratification

    # Run the discaRd functions on previous year
    d_prev_pass2 = run_discard(bdat = bdat_prev_cy
                               , ddat = ddat_prev_cy
                               , c_o_tab = ddat_prev
                               # , year = 2018
                               # , species_nespp3 = species_nespp3
                               , species_itis = species_itis
                               , stratvars = stratvars_assumed
                               # , aidx = c(1:length(stratvars_assumed))  # this makes sure this isn't used..
                               , aidx = c(1)  # this creates an unstratified broad stock rate
    )


    # Run the discaRd functions on current year
    if(nrow(bdat_cy) > 0) {
      d_focal_pass2 = run_discard(bdat = bdat_cy
                                  , ddat = ddat_focal_cy
                                  , c_o_tab = ddat_focal
                                  # , year = 2019
                                  # , species_nespp3 = '081' # haddock...
                                  # , species_nespp3 = species_nespp3  #'081' #cod...
                                  , species_itis = species_itis
                                  , stratvars = stratvars_assumed
                                  # , aidx = c(1:length(stratvars_assumed))  # this makes sure this isn't used..
                                  , aidx = c(1)  # this creates an unstratified broad stock rate
      )

      dest_strata_f_pass2 = d_focal_pass2$allest$C %>% summarise(STRATA = STRATA
                                                                 , N = N
                                                                 , n = n
                                                                 , orate = round(n/N, 2)
                                                                 , drate = RE_mean
                                                                 , KALL = K, disc_est = round(D)
                                                                 , CV = round(RE_rse, 2)
      )

    }

    # summarize each result for convenience
    dest_strata_p_pass2 = d_prev_pass2$allest$C %>% summarise(STRATA = STRATA
                                                              , N = N
                                                              , n = n
                                                              , orate = round(n/N, 2)
                                                              , drate = RE_mean
                                                              , KALL = K, disc_est = round(D)
                                                              , CV = round(RE_rse, 2)
    )

    # substitute transition rates where needed
    if(exists("dest_strata_f_pass2")) {
      trans_rate_df_pass2 = dest_strata_f_pass2 %>%
        left_join(., dest_strata_p_pass2, by = 'STRATA') %>%
        mutate(STRATA = STRATA
               , n_obs_trips_f = n.x
               , n_obs_trips_p = n.y
               , in_season_rate = drate.x
               , previous_season_rate = drate.y
        ) %>%
        mutate(n_obs_trips_p = coalesce(n_obs_trips_p, 0)) %>%
        mutate(trans_rate = get.trans.rate(l_observed_trips = n_obs_trips_f
                                           , l_assumed_rate = previous_season_rate
                                           , l_inseason_rate = in_season_rate
        )
        ) %>%
        dplyr::select(STRATA
                      , n_obs_trips_f
                      , n_obs_trips_p
                      , in_season_rate
                      , previous_season_rate
                      , trans_rate
                      , CV_f = CV.x
        )
    } else {
      trans_rate_df_pass2 = dest_strata_p_pass2 %>%
        mutate(STRATA = STRATA
               , n_obs_trips_f = 0L
               , n_obs_trips_p = n
               , in_season_rate = NA_real_
               , previous_season_rate = drate
        ) %>%
        mutate(n_obs_trips_p = coalesce(n_obs_trips_p, 0)) %>%
        mutate(trans_rate = get.trans.rate(l_observed_trips = n_obs_trips_f
                                           , l_assumed_rate = previous_season_rate
                                           , l_inseason_rate = in_season_rate
        ),
        CV_f = NA_real_
        ) %>%
        dplyr::select(STRATA
                      , n_obs_trips_f
                      , n_obs_trips_p
                      , in_season_rate
                      , previous_season_rate
                      , trans_rate
                      , CV_f)
    }


    trans_rate_df_pass2 = trans_rate_df_pass2 %>%
      mutate(
        final_rate = case_when(
          is.na(in_season_rate) ~ trans_rate,
          (in_season_rate != trans_rate & !is.na(trans_rate)) ~ trans_rate
        )
      )

    trans_rate_df_pass2$final_rate = coalesce(trans_rate_df_pass2$final_rate, trans_rate_df_pass2$in_season_rate)


    # get a table of broad stock rates using discaRd functions. Previously we used sector rollupresults (ARATE in pass2)
    # herring uses this as target vs non target level rate

    if(nrow(bdat_cy) > 0) {
      bdat_2yrs = bind_rows(bdat_prev_cy, bdat_cy)
    } else {
      bdat_2yrs = bdat_prev_cy
    }

    ddat_cy_2yr = bind_rows(ddat_prev_cy, ddat_focal_cy)
    ddat_2yr = bind_rows(ddat_prev, ddat_focal)

    # previous year plus current year broad stock rate
    gear_only_prev = run_discard(bdat = bdat_2yrs
                                 , ddat_focal = ddat_cy_2yr
                                 , c_o_tab = ddat_2yr
                                 , species_itis = species_itis
                                 , stratvars = stratvars[1:3]  # FY, FY_TYPE, AREA_TARG
    )


    # THIS SECTION IS REDUNDANT AS THE PREVIOUS SECTION ALREADY USES A 2 YEAR TIME WINDOW FOR BROAD STOCK
    # Run the discaRd functions on current year broad stock: third pass ----
    # if(nrow(bdat_cy) > 0) {
    # gear_only_current = run_discard(bdat = bdat_cy
    #													, ddat = ddat_focal_cy
    #													, c_o_tab = ddat_focal
    #													, species_itis = species_itis
    #													, stratvars = stratvars[1:3]  # FY, FY_TYPE, HERR_TARG
    #)
    #BROAD_STOCK_RATE_CUR <-  gear_only_current$allest$C$RE_mean
    #CV_b_cur <- round(gear_only_current$allest$C$RE_rse, 2)
    #} else {
    #  BROAD_STOCK_RATE_CUR <-  NA_real_
    #  CV_b_cur <- NA_real_
    #}

    #SPECIES_STOCK <-sub("_.*", "", gear_only$allest$C$STRATA)
    # HERR_TARG <- gear_only_prev$allest$C$STRATA

    BROAD_STOCK_RATE_TABLE = gear_only_prev$allest$C |>
      dplyr::select(STRATA, N, n, RE_mean, RE_rse) |>
      mutate(FY = as.numeric(sub("_.*", "", STRATA))
             , FY_TYPE = FY_TYPE) |>
      mutate(HERR_TARG = stringr::str_split(string = STRATA, pattern = '_') |>
               lapply(function(x) x[3]) |>
               unlist()
             , CV_b = round(RE_rse, 2)
      ) |>
      dplyr::rename(BROAD_STOCK_RATE = RE_mean
                    , n_B = n
                    , N_B = N) |>
      dplyr::select(FY
                    , FY_TYPE
                    , HERR_TARG
                    , BROAD_STOCK_RATE
                    , CV_b
                    , n_B
                    , N_B)


    #
    # HERR_TARG = stringr::str_split(gear_only_prev$allest$C$STRATA, pattern = '_') %>%
    #   lapply(., function(x) x[3]) %>%
    #   unlist()
    #
    # #CAMS_GEAR_GROUP <- sub(".*?_", "", gear_only$allest$C$STRATA)
    #
    # # make broad stock rate table ----
    # FY_BST = as.numeric(sub("_.*", "", gear_only_prev$allest$C$STRATA))
    #
    # BROAD_STOCK_RATE <-  gear_only_prev$allest$C$RE_mean
    #
    # CV_b <- round(gear_only_prev$allest$C$RE_rse, 2)
    #
    # BROAD_STOCK_RATE_TABLE <- data.frame(FY = FY_BST, FY_TYPE = FY_TYPE, cbind(HERR_TARG, BROAD_STOCK_RATE, CV_b))

    # BROAD_STOCK_RATE_TABLE$BROAD_STOCK_RATE <- as.numeric(BROAD_STOCK_RATE_TABLE$BROAD_STOCK_RATE)
    # BROAD_STOCK_RATE_TABLE$CV_b <- as.numeric(BROAD_STOCK_RATE_TABLE$CV_b)

    names(trans_rate_df_pass2) = paste0(names(trans_rate_df_pass2), '_a')

    #
    # join full and assumed strata tables ----
    #
    # print(paste0("Constructing output table for ", species_itis, " ", FY))

    joined_table = assign_strata(full_strata_table, stratvars_assumed)

    if("STRATA_ASSUMED" %in% names(joined_table)) {
      joined_table = joined_table %>%
        dplyr::select(-STRATA_ASSUMED)   # not using this anymore here..
    }

    joined_table = joined_table %>%
      dplyr::rename(STRATA_ASSUMED = STRATA) %>%
      dplyr::mutate(HERR_TARG = as.character(HERR_TARG)) %>%
      left_join(., y = trans_rate_df_pass2, by = c('STRATA_ASSUMED' = 'STRATA_a')) %>%
      left_join(., y = BROAD_STOCK_RATE_TABLE, by = c('FY', 'FY_TYPE', 'HERR_TARG')) %>%
      mutate(COAL_RATE = case_when(n_obs_trips_f >= 5 ~ final_rate  # this is an in season rate (target,gear, HMA)
                                   , n_obs_trips_f < 5 &
                                     n_obs_trips_p >=5 ~ final_rate  # in season transition (target, gear, HMA)
                                   , n_obs_trips_f < 5 &
                                     n_obs_trips_p < 5 &
                                     n_obs_trips_p_a > 5 ~ trans_rate_a  #in season gear, HMA transition
      )
      ) %>%
      mutate(COAL_RATE = coalesce(COAL_RATE, BROAD_STOCK_RATE)) %>%
      mutate(SPECIES_ITIS_EVAL = species_itis
             , COMNAME_EVAL = species$ITIS_NAME[i]
             , FISHING_YEAR = FY
             , FY_TYPE = FY_TYPE)

    #
    # add discard source ----
    #

    # joined_table = joined_table %>%
    # 		mutate(DISCARD_SOURCE = case_when(!is.na(LINK1) & LINK3_OBS == 1 & OFFWATCH_LINK1 == 0 ~ 'O'  # observed with at least one obs haul and no offwatch hauls on trip
    # 																			, !is.na(LINK1) & LINK3_OBS == 1 & OFFWATCH_LINK1 == 1 ~ 'I'  # observed with at least one obs haul
    # 																			, !is.na(LINK1) & LINK3_OBS == 0 ~ 'I'  # observed but no obs hauls..
    # 																		, is.na(LINK1) &
    # 																			n_obs_trips_f >= 5 ~ 'I'
    # 																		# , is.na(LINK1) & COAL_RATE == previous_season_rate ~ 'P'
    # 																		, is.na(LINK1) &
    # 																			n_obs_trips_f < 5 &
    # 																			n_obs_trips_p >=5 ~ 'T'
    # 																		, is.na(LINK1) &
    # 																			n_obs_trips_f < 5 &
    # 																			n_obs_trips_p < 5 &
    # 																			n_obs_trips_f_a >= 5 ~ 'A'
    # 																		, is.na(LINK1) &
    # 																			n_obs_trips_f < 5 &
    # 																			n_obs_trips_p < 5 &
    # 																			n_obs_trips_f_a <= 5 &
    # 																			n_obs_trips_p_a >= 5 ~ 'G'
    # 																		, is.na(LINK1) &
    # 																			n_obs_trips_f < 5 &
    # 																			n_obs_trips_p < 5 &
    # 																			n_obs_trips_f_a < 5 &
    # 																			n_obs_trips_p_a < 5 ~ 'B'))

    # should likely replace the above with this to match other modules

    joined_table = joined_table %>%
      mutate(DISCARD_SOURCE = case_when(!is.na(LINK1) & LINK3_OBS == 1 & OFFWATCH_LINK1 == 0  ~ 'O'  # observed with at least one obs haul and no offwatch hauls on trip
                                        , !is.na(LINK1) & LINK3_OBS == 1 & OFFWATCH_LINK1 == 1 ~ 'I'  # observed with at least one obs haul
                                        , !is.na(LINK1) & LINK3_OBS == 0 ~ 'I'  # observed but no obs hauls..
                                        , is.na(LINK1) &
                                          n_obs_trips_f >= 5 ~ 'I'
                                        # , is.na(LINK1) & COAL_RATE == previous_season_rate ~ 'P'
                                        , is.na(LINK1) &
                                          n_obs_trips_f < 5 &
                                          n_obs_trips_p >=5 ~ 'T' # this only applies to in-season full strata
                                        , is.na(LINK1) &
                                          n_obs_trips_f < 5 &
                                          n_obs_trips_p < 5 &
                                          n_obs_trips_f_a >= 5 ~ 'A' # assumed rate for Herring: CAMS_GEAR_GROUP and HERR_TARG
                                        , is.na(LINK1) &
                                          n_obs_trips_f < 5 &
                                          n_obs_trips_p < 5 &
                                          n_obs_trips_p_a >= 5 ~ 'G' # Gear only, replaces broad stock for non-GF
                                        , is.na(LINK1) &
                                          n_obs_trips_f < 5 &
                                          n_obs_trips_p < 5 &
                                          n_obs_trips_f_a < 5 &
                                          n_obs_trips_p_a < 5 ~ 'G')) # Gear only, replaces broad stock for non-GF

    #
    # make sure CV type matches DISCARD SOURCE ----
    #

    # obs trips get 0, broad stock rate is NA



    joined_table = joined_table %>%
      mutate(CV = case_when(DISCARD_SOURCE == 'O' ~ 0
                            , DISCARD_SOURCE == 'I' ~ CV_f
                            , DISCARD_SOURCE == 'T' ~ CV_f
                            , DISCARD_SOURCE == 'A' ~ CV_f_a
                            , DISCARD_SOURCE == 'G' ~ CV_b
                            #	, DISCARD_SOURCE == 'NA' ~ 'NA'
      )  # , DISCARD_SOURCE == 'B' ~ NA
      )

    # Make note of the stratification variables used according to discard source ----

    stratvars_gear = c(#"SPECIES_STOCK", #AWA
      "FY", "FY_TYPE", "HERR_TARG")

    strata_f = paste(stratvars, collapse = ';')
    strata_a = paste(stratvars_assumed, collapse = ';')
    strata_b = paste(stratvars_gear, collapse = ';')

    joined_table = joined_table %>%
      mutate(STRATA_USED = case_when(DISCARD_SOURCE == 'O' & LINK3_OBS == 1 ~ ''
                                     , DISCARD_SOURCE == 'O' & LINK3_OBS == 0 ~ strata_f
                                     , DISCARD_SOURCE == 'I' ~ strata_f
                                     , DISCARD_SOURCE == 'T' ~ strata_f
                                     , DISCARD_SOURCE == 'A' ~ strata_a
                                     , DISCARD_SOURCE == 'G' ~ strata_b
                                     , TRUE ~ NA_character_
                                     #	, DISCARD_SOURCE == 'NA' ~ 'NA'
      )
      )


    #
    # get the discard for each trip using COAL_RATE}
    #

    # discard mort ratio tht are NA for odd gear types (e.g. cams gear 0) get a 1 mort ratio.
    # the KALLs should be small..


    # joined_table = joined_table %>%
    # 	mutate(DISC_MORT_RATIO = coalesce(DISC_MORT_RATIO, 1)) %>%
    # 	mutate(DISCARD = ifelse(DISCARD_SOURCE == 'O', DISC_MORT_RATIO*OBS_DISCARD # observed with at least one obs haul
    # 														 , DISC_MORT_RATIO*COAL_RATE*LIVE_POUNDS) # all other cases
    #
    # 	)

    # get the final discard amount from estimation, observation and discard mortality ----

    joined_table = joined_table %>%
      mutate(DISC_MORT_RATIO = coalesce(DISC_MORT_RATIO, 1)) %>%
      mutate(DISCARD = ifelse(DISCARD_SOURCE == 'O', DISC_MORT_RATIO*OBS_DISCARD # observed with at least one obs haul
                              , DISC_MORT_RATIO*COAL_RATE*LIVE_POUNDS) # all other cases

      )

    ## AWA modify non-target purse seine so we are not estimating discards in HMA 2(set to zero), menhaden fishery estimates not supported
    joined_table = joined_table %>% mutate(ESTIMATE_DISCARDS = replace(ESTIMATE_DISCARDS, FULL_STRATA == '0_2_120',0)) %>%
      mutate(COAL_RATE = replace(COAL_RATE, FULL_STRATA == '0_2_120',0)) %>%
      mutate(DISCARD = replace(DISCARD, FULL_STRATA == '0_2_120',0))

    # force remove duplicates
    # add element for non-estimated discard gears
    joined_table <- joined_table |>
      dplyr::distinct()%>%
      mutate(DISCARD_SOURCE = case_when(ESTIMATE_DISCARDS == 0 & DISCARD_SOURCE != 'O' ~ 'N'
                                        ,TRUE ~ DISCARD_SOURCE)) %>%
      mutate(DISCARD = case_when(ESTIMATE_DISCARDS == 0 & DISCARD_SOURCE != 'O' ~ 0.0
                                 ,TRUE ~ DISCARD))%>%
      mutate(CV = case_when(ESTIMATE_DISCARDS == 0 & DISCARD_SOURCE != 'O' ~ NA_real_
                            ,TRUE ~ CV))


    # add N, n, and covariance ----
    # now these steps are separate functions
    joined_table <- joined_table |>
      add_nobs() |>
      make_strata_desc() |>
      get_covrow() |>
      mutate(covrow = case_when(DISCARD_SOURCE =='N' ~ NA_real_
                                , TRUE ~ covrow))

# output objects ----
    dest_obj = joined_table %>%
      group_by(FISHING_YEAR
               # , GF_YEAR
               # , SCAL_YEAR
               , GF
               , STRATA_USED
               , STRATA_USED_DESC
               , DISCARD_SOURCE
               , SPECIES_STOCK
               , HERR_TARG
               , AREA_HERR
               , CAMS_GEAR_GROUP
               , MESH_CAT
               , TRIPCATEGORY
               , ACCESSAREA
               , FED_OR_STATE
               ) %>%
      dplyr::summarise(rate = max(COAL_RATE, na.rm = T)
                       , n_obs = max(n_USED)
                       , n_unobs = max(N_USED) # max(N_USED-n_USED)
                       , n_total = n_distinct(CAMS_SUBTRIP)
                       # , rate_min = min(COAL_RATE, na.rm = T)
                       , KALL = round(sum(LIVE_POUNDS, na.rm = T))
                       , D = round(sum(DISCARD, na.rm = T), 2)
                       , CV = max(CV, na.rm = T)
      )

    if(return_table == T & return_summary == F){return(joined_table)}
    if (return_table == F & return_summary == T) {return(dest_obj)}
    if (return_table == T & return_summary == T) {return(list(trips_discard = joined_table, discard_summary = dest_obj))}
    if(return_table == F & return_summary == F) {(print("What did you do all that work for?"))}

    t2 = Sys.time()

    print(paste('RUNTIME: ', round(difftime(t2, t1, units = "mins"),2), ' MINUTES',  sep = ''))


    t2 = Sys.time()

    print(paste('RUNTIME: ', round(difftime(t2, t1, units = "mins"),2), ' MINUTES',  sep = ''))

  # }

}