In noaa-garfo/discaRd: Cochran bycatch estimation

scal_trips = non_gf_dat %>% 
    filter(substr(ACTIVITY_CODE_1,1,3) == 'SES')

# the scallop_area code has been added upstream to oracle table import

# %>%
#   mutate(PROGRAM = substr(ACTIVITY_CODE_1, 9, 10)) %>% 
#   mutate( SCALLOP_AREA = case_when(PROGRAM == 'OP' ~ 'OPEN' 
#        , PROGRAM == 'NS' ~ 'NLS'
#        , PROGRAM == 'NN' ~ 'NLSN'
#        , PROGRAM == 'NH' ~ 'NLSS'  # includes the NLS south Deep
#        , PROGRAM == 'NW' ~ 'NLSW'
#        , PROGRAM == '1S' ~ 'CAI'
#        , PROGRAM == '2S' ~ 'CAII'
#        , PROGRAM %in% c('MA', 'ET', 'EF', 'HC', 'DM') ~ 'MAA'
#      )
# ) %>% 
#   mutate(SCALLOP_AREA = case_when(substr(ACTIVITY_CODE_1,1,3) == 'SES' ~ dplyr::coalesce(SCALLOP_AREA, 'OPEN'))
#                , ~SCALLOP_AREA) 

# scal_trips$ACCESSAREA[scal_trips$SCALLOP_AREA == 'OPEN'] = 'OPEN'


stratvars_scalgf = c('SPECIES_STOCK'
              ,'CAMS_GEAR_GROUP'
                            , 'MESHGROUP'
                          , 'TRIPCATEGORY'
                          , 'ACCESSAREA'
                            , 'SCALLOP_AREA')

scal_gf_species = species[species$SPECIES_ITIS %in% c('172909', '172746'),]

for(i in 1:length(scal_gf_species$SPECIES_ITIS)){

t1 = Sys.time() 

print(paste0('Running ', scal_gf_species$COMNAME[i]))   

# species_nespp3 = species$NESPP3[i]  
species_itis = scal_gf_species$SPECIES_ITIS[i] 
#--------------------------------------------------------------------------#
# Support table import by species

# GEAR TABLE
CAMS_GEAR_STRATA = tbl(bcon, sql('  select * from MAPS.CAMS_GEARCODE_STRATA')) %>% 
    collect() %>% 
  dplyr::rename(GEARCODE = VTR_GEAR_CODE) %>% 
    filter(SPECIES_ITIS == '079718') %>%  # scallop strata needed here.. or go with above code
  dplyr::select(-NESPP3, -SPECIES_ITIS)

# Stat areas table  
# unique stat areas for stock ID if needed
STOCK_AREAS = tbl(bcon, sql('select * from MAPS.CAMS_STATAREA_STOCK')) %>%
    filter(SPECIES_ITIS == species_itis) %>%
    collect() %>% 
  group_by(AREA_NAME, SPECIES_ITIS) %>% 
  distinct(STAT_AREA) %>%
  mutate(AREA = as.character(STAT_AREA)
         , SPECIES_STOCK = AREA_NAME) %>% 
  ungroup() 
# %>% 
#   dplyr::select(SPECIES_STOCK, AREA)

# Mortality table
CAMS_DISCARD_MORTALITY_STOCK = tbl(bcon, sql("select * from MAPS.CAMS_DISCARD_MORTALITY_STOCK"))  %>%
  collect() %>%
  mutate(SPECIES_STOCK = AREA_NAME
         , GEARCODE = CAMS_GEAR_GROUP
             , CAMS_GEAR_GROUP = as.character(CAMS_GEAR_GROUP)) %>%
  select(-AREA_NAME) %>%
  # mutate(CAREA = as.character(STAT_AREA)) %>% 
  # filter(NESPP3 == species_nespp3) %>% 
    filter(SPECIES_ITIS == species_itis) %>%
  dplyr::select(-SPECIES_ITIS) %>% 
  dplyr::rename(DISC_MORT_RATIO = Discard_Mortality_Ratio)

#--------------------------------------------------------------------------------#
# haddock example trips with full strata either in year_t or year _t-1
#--------------------------------------------------------------------------------#

# print(paste0("Getting in-season rates for ", species_itis, " ", FY))

# make tables
ddat_focal <- scal_trips %>% 
  filter(SCAL_YEAR == FY) %>%   ## time element is here!! NOTE THE SCAL YEAR>>>
  filter(AREA %in% STOCK_AREAS$AREA) %>% 
  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(-SPECIES_ITIS.y, -GEARCODE.y) %>% 
    dplyr::rename(SPECIES_ITIS = 'SPECIES_ITIS.x', GEARCODE = 'GEARCODE.x') %>% 
  relocate('COMMON_NAME','SPECIES_ITIS','NESPP3','SPECIES_STOCK','CAMS_GEAR_GROUP','DISC_MORT_RATIO')


ddat_prev <- scal_trips %>% 
  filter(SCAL_YEAR == FY-1) %>%   ## time element is here!! NOTE THE SCAL YEAR>>>
  filter(AREA %in% STOCK_AREAS$AREA) %>% 
  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(-SPECIES_ITIS.y, -GEARCODE.y) %>% 
    dplyr::rename(SPECIES_ITIS = 'SPECIES_ITIS.x', GEARCODE = 'GEARCODE.x') %>% 
  relocate('COMMON_NAME','SPECIES_ITIS','NESPP3','SPECIES_STOCK','CAMS_GEAR_GROUP','DISC_MORT_RATIO')



# need to slice the first record for each observed trip.. these trips are multi rowed while unobs trips are single row.. 
# need to select only discards for species evaluated. All OBS trips where nothing of that species was disacrded Must be zero!

ddat_focal_scal = ddat_focal %>% 
  filter(!is.na(LINK1)) %>% 
    mutate(SPECIES_EVAL_DISCARD = case_when(SPECIES_ITIS == species_itis ~ DISCARD
                                                                                    )) %>% 
    mutate(SPECIES_EVAL_DISCARD = coalesce(SPECIES_EVAL_DISCARD, 0)) %>% 
  group_by(LINK1, VTRSERNO) %>% 
    arrange(desc(SPECIES_EVAL_DISCARD)) %>% 
    slice(1) %>% 
  ungroup()

# and join to the unobserved trips

ddat_focal_scal = ddat_focal_scal %>% 
  union_all(ddat_focal %>% 
              filter(is.na(LINK1)) %>% 
               # group_by(VTRSERNO, CAMSID) %>% 
               # slice(1) %>% 
               ungroup()
            )


# 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_scal = ddat_focal %>% 
  filter(!is.na(LINK1)) %>% 
  mutate(DISCARD_PRORATE = DISCARD
         , OBS_AREA = AREA
         , OBS_HAUL_KALL_TRIP = OBS_KALL
         , PRORATE = 1)


# set up trips table for previous year
ddat_prev_scal = ddat_prev %>% 
  filter(!is.na(LINK1)) %>% 
    mutate(SPECIES_EVAL_DISCARD = case_when(SPECIES_ITIS == species_itis ~ DISCARD
                                                                                    )) %>% 
    mutate(SPECIES_EVAL_DISCARD = coalesce(SPECIES_EVAL_DISCARD, 0)) %>% 
  group_by(LINK1, VTRSERNO) %>% 
    arrange(desc(SPECIES_EVAL_DISCARD)) %>% 
    slice(1) %>% 
  ungroup()

ddat_prev_scal = ddat_prev_scal %>% 
  union_all(ddat_prev %>% 
                         filter(is.na(LINK1)) %>% 
               # group_by(VTRSERNO, CAMSID) %>% 
               # slice(1) %>% 
               ungroup()
                    )


# previous year observer data needed.. 
bdat_prev_scal = ddat_prev %>% 
  filter(!is.na(LINK1)) %>% 
  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_scal
                                             , ddat = ddat_prev_scal
                                             , c_o_tab = ddat_prev
                                           , species_itis = species_itis
                                             , stratvars = stratvars_scalgf
                                             # , aidx = c(1:length(stratvars))
                                           , aidx = c(1:2) # uses GEAR as assumed
                                             )


# Run the discaRd functions on current year
d_focal = run_discard(bdat = bdat_scal
                                             , ddat = ddat_focal_scal
                                             , c_o_tab = ddat_focal
                                             , species_itis = species_itis
                                             , stratvars = stratvars_scalgf
                                             # , aidx = c(1:length(stratvars))  # this makes sure this isn't used.. 
                                             , aidx = c(1:2) # uses GEAR as assumed
                                             )

# 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)
                                        )

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)
                                        )

# substitute transition rates where needed

trans_rate_df = dest_strata_f %>% 
  left_join(., dest_strata_p, 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
         )


trans_rate_df = trans_rate_df %>% 
  mutate(final_rate = case_when((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

 full_strata_table = trans_rate_df_full %>% 
   right_join(., y = d_focal$res, by = 'STRATA') %>% 
   as_tibble() %>% 
        mutate(SPECIES_ITIS_EVAL = species_itis
                 , COMNAME_EVAL = species$COMNAME[i]
                 , FISHING_YEAR = FY
                 , FY_TYPE = FY_TYPE) %>% 
       dplyr::rename(FULL_STRATA = STRATA) 

#-----------------------------------------------------------------------
# join full and assumed strata tables
#-----------------------------------------------------------------------

# BROAD_STOCK_RATE_TABLE = d_focal$res %>% 
#   group_by(SPECIES_STOCK) %>% 
#   dplyr::summarise(BROAD_STOCK_RATE = mean(ARATE, na.rm = T)) # mean rate is max rate.. they are all the same within STOCK, as they should be 

# get a broad stock rate across all gears (no stratification)
# NOTE: This is slightly different than what is used for GF trips. 
# For non-GF trips, the Assumed rate above is GEAR/MESH.

BROAD_STOCK_RATE_TABLE = make_assumed_rate(bdat_scal
                                                                                     , species_itis = species$SPECIES_ITIS[i]
                                                                                     , stratvars = stratvars_scalgf[1]) %>% 
    mutate(SPECIES_STOCK = STRATA
                 , STRAT_DESC = paste(stratvars_scalgf[1], sep = '-')) %>% 
    dplyr::rename('BROAD_STOCK_RATE' = 'dk') %>% 
    dplyr::select(-STRATA, -KALL, -BYCATCH)

print(paste0("Constructing output table for ", species_itis, " in SCALLOP YEAR ", FY)) 

joined_table = assign_strata(full_strata_table, stratvars_scalgf) %>% 
    # dplyr::select(-STRATA_ASSUMED) %>%  # not using this anymore here..
    # dplyr::rename(STRATA_ASSUMED = STRATA) %>% 
    # left_join(., y = trans_rate_df_pass2, by = c('STRATA_ASSUMED' = 'STRATA_a')) %>% 
    left_join(x =., y = BROAD_STOCK_RATE_TABLE, by = c('SPECIES_STOCK')) %>% 
    mutate(COAL_RATE = case_when(n_obs_trips_f >= 5 ~ trans_rate  # this is an in season rate
                                                             , n_obs_trips_f < 5 & n_obs_trips_p >=5 ~ trans_rate  # this is a final IN SEASON rate taking transition into account
                                                             , n_obs_trips_f < 5 & n_obs_trips_p < 5 ~ ARATE  # assumed rate
                                   )
    ) %>% 
    mutate(COAL_RATE = coalesce(COAL_RATE, BROAD_STOCK_RATE)) %>%
    mutate(SPECIES_ITIS_EVAL = species_itis
                 , COMNAME_EVAL = species$COMNAME[i]
                 , FISHING_YEAR = FY
                 , FY_TYPE = FY_TYPE) 

#-----------------------------------------------------------------------
# add discard source
#-----------------------------------------------------------------------


# >5 trips in season gets in season rate
# < 5 i nseason but >=5 past year gets transition
# < 5 and < 5 in season, but >= 5 sector rolled up rate (in season) gets get sector rolled up rate
# <5, <5,  and <5 gets broad stock rate

joined_table = joined_table %>% 
    mutate(DISCARD_SOURCE = case_when(!is.na(LINK1) ~ 'O'
                                                                        , 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 &
                                                                            !is.na(ARATE) ~ 'A'
                                                                        , is.na(LINK1) & n_obs_trips_f < 5 &
                                                                            n_obs_trips_p < 5 &
                                                                            is.na(ARATE) ~ 'B'
                                                                        # , is.na(LINK1) & 
                                                                        #   n_obs_trips_f < 5 & 
                                                                        #   n_obs_trips_p < 5 & ~ 'B'
                                                                            # n_obs_trips_f_a < 5 & 
                                                                            # n_obs_trips_p_a < 5 
                                                                        )  # this may be replaced with model estimate!
                 )

#-----------------------------------------------------------------------
# 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 == 'AT' ~ CV_f_a
                                                )  # , DISCARD_SOURCE == 'B' ~ NA
                 )


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

strata_scalgf = paste(stratvars_scalgf, collapse = ';')
strata_scalgf_a = paste(stratvars_scalgf[1:2], collapse = ';')
strata_scalgf_b = stratvars_scalgf[1]

joined_table = joined_table %>% 
    mutate(STRATA_USED = case_when(DISCARD_SOURCE == 'O' ~ ''
                                                , DISCARD_SOURCE == 'I' ~ strata_scalgf
                                                , DISCARD_SOURCE == 'T' ~ strata_scalgf
                                                , DISCARD_SOURCE == 'A' ~ strata_scalgf_a
                                                , DISCARD_SOURCE == 'B' ~ strata_scalgf_b
                                                ) 
                 )


#-----------------------------------------------------------------------
# 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 = case_when(!is.na(LINK1) ~ DISC_MORT_RATIO*OBS_DISCARD
                                                         , is.na(LINK1) ~ DISC_MORT_RATIO*COAL_RATE*LIVE_POUNDS)
                 )


 # saveRDS(joined_table, file = paste0('~/PROJECTS/discaRd/CAMS/MODULES/GROUNDFISH/OUTPUT/discard_est_', species_itis, '_non_gftrips.RDS'))

fst::write_fst(x = joined_table, path = paste0('~/PROJECTS/discaRd/CAMS/MODULES/APRIL/OUTPUT/discard_est_', species_itis, '_scal_trips_SCAL', FY,'.fst'))

t2 = Sys.time()

print(paste(species_itis, ' RAN IN ', round(difftime(t2, t1, units = "mins"),2), ' MINUTES',  sep = ''))

}

# for(j in 2018:2019){
t1 = Sys.time()

    GF_YEAR = FY

    for(i in 1:length(scal_gf_species$SPECIES_ITIS)){

        print(paste0('Adding scallop trip estimates of: ',  scal_gf_species$COMNAME[i], ' for Groundfish Year ', GF_YEAR))

        sp_itis = scal_gf_species$SPECIES_ITIS[i]

        # get only the non-gf trips for each species and fishing year   
        gf_file_dir = '~/PROJECTS/discaRd/CAMS/MODULES/GROUNDFISH/OUTPUT/'
        gf_files = list.files(gf_file_dir, pattern = paste0('discard_est_', sp_itis), full.names = T)
        gf_files = gf_files[grep(GF_YEAR, gf_files)]
        gf_files = gf_files[grep('non_gf', gf_files)]

        # get list all scallop trips bridging fishing years                                             
        scal_file_dir = '~/PROJECTS/discaRd/CAMS/MODULES/APRIL/OUTPUT/'
        scal_files = list.files(scal_file_dir, pattern = paste0('discard_est_', sp_itis), full.names = T)

        # read in files 
        res_scal = lapply(as.list(scal_files), function(x) fst::read_fst(x))
        res_gf = lapply(as.list(gf_files), function(x) fst::read_fst(x))

        # create standard output table structures for scallop trips
        outlist <- lapply(res_scal, function(x) { 
                x %>% 
                mutate(GF_STOCK_DEF = paste0(COMNAME_EVAL, '-', SPECIES_STOCK)) %>% 
                dplyr::select(-COMMON_NAME, -SPECIES_ITIS) %>% 
            dplyr::rename('STRATA_FULL' = 'FULL_STRATA'
                                        , 'CAMS_DISCARD_RATE' = 'COAL_RATE'
                                        , 'COMMON_NAME' = 'COMNAME_EVAL'
                                        , 'SPECIES_ITIS' = 'SPECIES_ITIS_EVAL'
                                        , 'ACTIVITY_CODE' = 'ACTIVITY_CODE_1'
                                        , 'N_OBS_TRIPS_F' = 'n_obs_trips_f'
                                        ) %>% 
            mutate(DATE_RUN = as.character(Sys.Date())
                         , FY = as.integer(FY)) %>%
            dplyr::select(
                DATE_RUN,
                FY,
                YEAR,
                MONTH,
                SPECIES_ITIS,
                COMMON_NAME,
                FY_TYPE,
                ACTIVITY_CODE,
                VTRSERNO,
                CAMSID,
                TRIP_TYPE,
                GF,
                AREA,
                LINK1,
                N_OBS_TRIPS_F,
                STRATA_USED,
                STRATA_FULL,
                STRATA_ASSUMED,
                DISCARD_SOURCE,
                OBS_DISCARD,
                SUBTRIP_KALL,
                BROAD_STOCK_RATE,
                CAMS_DISCARD_RATE,
                DISC_MORT_RATIO,
                DISCARD,
                CV,
                SPECIES_STOCK,
                CAMS_GEAR_GROUP,
                MESHGROUP,
                SECTID,
                EM,
                REDFISH_EXEMPTION,
                SNE_SMALLMESH_EXEMPTION,
                XLRG_GILLNET_EXEMPTION,
                TRIPCATEGORY,
                ACCESSAREA,
                SCALLOP_AREA
              # eval(strata_unique)
            )

        }
        )

        rm(res_scal)

        assign(paste0('outlist_df_scal'),  do.call(rbind, outlist))

        rm(outlist)

        # now do the same for GF trips

        outlist <- lapply(res_gf, function(x) { 
                x %>% 
                mutate(GF_STOCK_DEF = paste0(COMNAME_EVAL, '-', SPECIES_STOCK)) %>% 
                dplyr::select(-COMMON_NAME, -SPECIES_ITIS) %>% 
            dplyr::rename('STRATA_FULL' = 'FULL_STRATA'
                                        , 'CAMS_DISCARD_RATE' = 'COAL_RATE'
                                        , 'COMMON_NAME' = 'COMNAME_EVAL'
                                        , 'SPECIES_ITIS' = 'SPECIES_ITIS_EVAL'
                                        , 'ACTIVITY_CODE' = 'ACTIVITY_CODE_1'
                                        , 'N_OBS_TRIPS_F' = 'n_obs_trips_f'
                                        ) %>% 
            mutate(DATE_RUN = as.character(Sys.Date())
                         , FY = as.integer(FY)) %>%
            dplyr::select(
                DATE_RUN,
                FY,
                YEAR,
                MONTH,
                SPECIES_ITIS,
                COMMON_NAME,
                FY_TYPE,
                ACTIVITY_CODE,
                VTRSERNO,
                CAMSID,
                TRIP_TYPE,
                GF,
                AREA,
                LINK1,
                N_OBS_TRIPS_F,
                STRATA_USED,
                STRATA_FULL,
                STRATA_ASSUMED,
                DISCARD_SOURCE,
                OBS_DISCARD,
                SUBTRIP_KALL,
                BROAD_STOCK_RATE,
                CAMS_DISCARD_RATE,
                DISC_MORT_RATIO,
                DISCARD,
                CV,
                SPECIES_STOCK,
                CAMS_GEAR_GROUP,
                MESHGROUP,
                SECTID,
                EM,
                REDFISH_EXEMPTION,
                SNE_SMALLMESH_EXEMPTION,
                XLRG_GILLNET_EXEMPTION,
                TRIPCATEGORY,
                ACCESSAREA
                # SCALLOP_AREA
              # eval(strata_unique)
            ) %>% 
                mutate(SCALLOP_AREA = '')

        }
        )

        rm(res_gf)

        assign(paste0('outlist_df_',sp_itis,'_',GF_YEAR),  do.call(rbind, outlist))

        rm(outlist)

        t1  = get(paste0('outlist_df_',sp_itis,'_',GF_YEAR))
        t2 = get(paste0('outlist_df_scal')) 

        # index scallop records present in groundfish year table 
        t2idx = t2$VTRSERNO %in% t1$VTRSERNO & t2$CAMSID %in% t1$CAMSID

        # index records in groundfish table to be removed
        t1idx = t1$VTRSERNO %in% t2$VTRSERNO & t1$CAMSID %in% t2$CAMSID

        # swap the scallop estimated trips into the groundfish records
        t1[t1idx,] = t2[t2idx,]


        # test against the scallop fy 19
        t2 %>% 
            filter(YEAR == 2019 & MONTH >= 4) %>% 
            bind_rows(t2 %>% 
            filter(YEAR == 2020 & MONTH < 4)) %>% 
            group_by(SPECIES_STOCK, ACCESSAREA, TRIP_TYPE) %>% 
            dplyr::summarise(round(sum(DISCARD, na.rm = T))) %>% 
          write.csv(paste0('~/PROJECTS/discaRd/CAMS/MODULES/APRIL/OUTPUT/', sp_itis,'_for_SCAL_YEAR_2019.csv'), row.names = F)

        write_fst(x = t1, path = gf_files)

        t2 = Sys.time()

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

        # look at the GF table with scallop trips swapped in. tHIS WILL BE LOWER SINCE THE FISHIGN YEARS BEGIN AT DIFFERNT MONTHS
        # t1 %>% 
        #   filter(YEAR == 2019 & MONTH >= 5) %>% 
        #   bind_rows(t1 %>% 
        #   filter(YEAR == 2020 & MONTH < 4)) %>% 
        #   filter(substr(ACTIVITY_CODE, 1,3) == 'SES') %>% 
        #   group_by(SPECIES_STOCK, ACCESSAREA, SPECIES_ITIS, TRIP_TYPE) %>% 
        #   dplyr::summarise(round(sum(DISCARD, na.rm = T)))
            # write.csv(paste0('~/PROJECTS/discaRd/CAMS/MODULES/APRIL/OUTPUT/', sp_itis,'_for_SCAL_YEAR_2019.csv'), row.names = F)


    }

# }