Updated bycatch text and pm24.R

2024/runs/fixsel_bts/pm.prj

@@ -20,10 +20,10 @@ EBS_Pollock_2024
 # selectivity
  0.000181488 0.0583427 0.214017 0.519824 0.961364 1.05539 1.21717 1.61394 1.48198 1.36844 1.34579 1.29089 1.29089 1.29089 1.29089
 # natage
- 29377.7 7566.64 4424.96 2239.68 1188.1 1222.5 2491.5 352.087 104.913 56.7207 66.3959 59.5054 23.0356 5.62418 15.784
+ 29377.7 7566.63 4424.96 2239.68 1188.1 1222.5 2491.5 352.088 104.913 56.7208 66.396 59.5054 23.0355 5.62417 15.7839
 # Nrec
 46
 # rec
- 22984.9 57379.6 28788.1 34715 17584.1 51766.1 15329 35913.3 13342.3 7762.49 5528.46 11541.2 48025.1 23391.7 18959.2 39063.3 13731.8 10043.2 21546.4 29936.1 14868.3 16092.7 24498.1 33831.7 22697.3 14746.4 6773.38 4746.48 12066.6 25746.3 13475 49761.6 23374.9 14161.9 12013.6 42351.6 46782.3 19486.4 6935.84 6611.14 12991.8 65502.3 17634 10318.8 12351.6 17245.1 18611.7
+ 22984.9 57379.6 28788.1 34714.9 17584.1 51766.1 15329 35913.3 13342.3 7762.49 5528.46 11541.2 48025.1 23391.7 18959.2 39063.3 13731.8 10043.2 21546.4 29936.1 14868.3 16092.7 24498.1 33831.7 22697.3 14746.4 6773.38 4746.48 12066.6 25746.3 13475 49761.6 23374.9 14161.9 12013.6 42351.6 46782.3 19486.4 6935.85 6611.14 12991.8 65502.3 17634 10318.8 12351.6 17245.1 18611.7
 # SpawningBiomass
  1122.96 990.621 887.765 1015.69 1571.86 2247.75 3121.15 3297.53 3840.69 3797.41 3918.96 3780.11 3323.83 2847.22 2299.74 2138.5 2699.93 3006.85 3066.83 2903.41 2757.96 2331.08 2496.47 2478.59 2586.65 2459.34 2544.75 2863.39 2535.16 2337.55 1942.57 1512.81 1631.22 1681.31 2063.13 2507.15 2855.02 2806.27 2594.98 2797.64 3158.47 2936.94 2763.18 2026.16 2048.03 2713.04 2591.63

2024/runs/fixsel_bts/pmsr.prj

@@ -30,12 +30,12 @@ EBS_Pollock
 # selectivity
  0.000181488 0.0583427 0.214017 0.519824 0.961364 1.05539 1.21717 1.61394 1.48198 1.36844 1.34579 1.29089 1.29089 1.29089 1.29089
 # natage
- 18611.7 7011.07 3171.64 1871.73 2112.24 4280.96 602.805 191.223 103.296 120.844 108.297 42.0465 10.2657 8.52994 20.2803
+ 18611.7 7011.07 3171.63 1871.73 2112.24 4280.96 602.805 191.223 103.296 120.844 108.297 42.0465 10.2657 8.52994 20.2803
 # natage
- 18611.7 7011.07 3171.64 1871.73 2112.24 4280.96 602.805 191.223 103.296 120.844 108.297 42.0465 10.2657 8.52994 20.2803
+ 18611.7 7011.07 3171.63 1871.73 2112.24 4280.96 602.805 191.223 103.296 120.844 108.297 42.0465 10.2657 8.52994 20.2803
 # Nrec
 47
 # rec
- 22984.9 57379.6 28788.1 34715 17584.1 51766.1 15329 35913.3 13342.3 7762.49 5528.46 11541.2 48025.1 23391.7 18959.2 39063.3 13731.8 10043.2 21546.4 29936.1 14868.3 16092.7 24498.1 33831.7 22697.3 14746.4 6773.38 4746.48 12066.6 25746.3 13475 49761.6 23374.9 14161.9 12013.6 42351.6 46782.3 19486.4 6935.84 6611.14 12991.8 65502.3 17634 10318.8 12351.6 17245.1 18611.7
+ 22984.9 57379.6 28788.1 34714.9 17584.1 51766.1 15329 35913.3 13342.3 7762.49 5528.46 11541.2 48025.1 23391.7 18959.2 39063.3 13731.8 10043.2 21546.4 29936.1 14868.3 16092.7 24498.1 33831.7 22697.3 14746.4 6773.38 4746.48 12066.6 25746.3 13475 49761.6 23374.9 14161.9 12013.6 42351.6 46782.3 19486.4 6935.85 6611.14 12991.8 65502.3 17634 10318.8 12351.6 17245.1 18611.7
 # SpawningBiomass
  1122.96 990.621 887.765 1015.69 1571.86 2247.75 3121.15 3297.53 3840.69 3797.41 3918.96 3780.11 3323.83 2847.22 2299.74 2138.5 2699.93 3006.85 3066.83 2903.41 2757.96 2331.08 2496.47 2478.59 2586.65 2459.34 2544.75 2863.39 2535.16 2337.55 1942.57 1512.81 1631.22 1681.31 2063.13 2507.15 2855.02 2806.27 2594.98 2797.64 3158.47 2936.94 2763.18 2026.16 2048.03 2713.04 2591.63

2024/runs/fixsel_bts/proj_df.csv

@@ -1,9 +1,9 @@
 Scenario, Year, SSB, Catch, R, SER
-1,2025,2280.29,1300,29377.7,0.179024
-1,2026,2133.74,1206.34,28568.6,0.168083
-1,2027,2199.72,1165.63,28014.4,0.146121
-1,2028,2492.75,1255.87,28278,0.140715
-1,2029,2772.53,1415.23,29180.2,0.146861
+1,2025,2427.57,10,29377.7,0.00138857
+1,2026,2765.47,11.6543,29015.4,0.00142926
+1,2027,3221.61,12.9589,29670.5,0.00132761
+1,2028,3842.79,14.8878,29875.7,0.00129652
+1,2029,4463.77,17.4947,29219.5,0.00135099
 2,2025,2393.95,325,29377.7,0.0450444
 2,2026,2605.91,359.283,28922.7,0.0453915
 2,2027,2938.92,385.087,29421.4,0.0414827
@@ -12,7 +12,7 @@ Scenario, Year, SSB, Catch, R, SER
 3,2025,2357.78,650,29377.7,0.0899065
 3,2026,2444.8,679.202,28816.9,0.0885654
 3,2027,2670.54,701.858,29060.9,0.0795808
-3,2028,3084.08,775.462,29534.7,0.0768643
+3,2028,3084.08,775.461,29534.7,0.0768643
 3,2029,3492.08,888.204,29885.4,0.0798408
 4,2025,2319.95,975,29377.7,0.134572
 4,2026,2287.37,961.085,28699.4,0.129476
@@ -27,15 +27,15 @@ Scenario, Year, SSB, Catch, R, SER
 6,2025,2238.61,1625,29377.7,0.223244
 6,2026,1984.03,1416.45,28422.5,0.204349
 6,2027,1994.67,1328.8,27325.5,0.174943
-6,2028,2250.99,1422.02,27378.8,0.168925
+6,2028,2250.99,1422.02,27378.7,0.168925
 6,2029,2484.98,1592.84,28466.8,0.177227
 7,2025,2194.66,1950,29377.7,0.26721
 7,2026,1838.37,1593.01,28258.7,0.238249
 7,2027,1808.1,1455.99,26525.9,0.201148
 7,2028,2038.59,1553.38,26342.7,0.195362
 7,2029,2233.52,1730,27591.6,0.206334
 8,2025,2098.83,2600,29377.7,0.354274
-8,2026,1559.83,1852.39,27865.2,0.298914
+8,2026,1559.83,1852.39,27865.2,0.298915
 8,2027,1484.98,1628.27,24602.3,0.247078
 8,2028,1684.71,1743.02,23988.7,0.245074
 8,2029,1811.37,1912.91,25532.3,0.262863

R/pm24.R

@@ -34,7 +34,7 @@ dec_tab_ord <<- 1:8
 #)
 mod_names <- c(
   "Last year", # 1
-  "Model 23","Drop new BTS", "and ATS", "and AVO" , "Fix BTS Sel.")
+  "Model 23","Drop new BTS", "and ATS", "and AVO" )#, "Fix BTS Sel.")
 # BTS age compositions included through 2023
 # "Sept version",
 # "2023 AVO point",
@@ -46,7 +46,7 @@ mod_names <- c(
 # but with Hulsons BTS input sample sizes
 
 mod_dir <- c("lastyr",
-  "m23","m1", "m2", "m3", "fixsel_bts")
+  "m23","m1", "m2", "m3")#, "fixsel_bts")
 # mod_names <- c("MSY=1.2","Fmsy=F35%", "Base est.")
 # mod_dir <- c("condmn", "condF35", "m8")
 # WARNING,  commented out line will re-run all the models in the mod_dir directories within "runs"
@@ -58,7 +58,7 @@ mod_dir <- c("lastyr",
 modlst <- get_results(rundir = "2024/runs")
 # names(modlst)
 M <<- modlst[[thismod]]
-Fix <<- modlst[[6]]
+#Fix <<- modlst[[6]]
 #M <<- modlst[[3]];saveRDS(M, "~/m8.rds")
 
 #.MODELDIR <<- paste0("2023_runs/", mod_dir, "/")

ebswp.qmd

@@ -4058,7 +4058,8 @@ print(tab,
 #| output: asis
 #| echo: false
 # byc <- read_csv("../data/fishery/pollock_catch.csv")[,c(1,16,19,22,27,28)]
-if (do_plot) {
+# do_bycatch_data <- FALSE
+if (FALSE) {
   byc <- read.csv("doc/data/pollock_catch.csv")[, c(1, 6, 9, 4, 10, 11)]
 
   names(byc) <- c("Year", "target", "gear", "NMFS_area", "Catch", "wed")
@@ -4089,23 +4090,23 @@ if (do_plot) {
     group_by(Year, target) %>%
     summarise(Catch = sum(Catch), .groups = "drop")
   byc <- byc %>% pivot_wider(names_from = target, values_from = Catch)
+  byc$Total=rowSums(byc[,2:6],na.rm=TRUE)
   byc <- data.frame(byc)
   write_csv(byc, "doc/data/bycatch.csv")
 }
 byc <- read_csv("doc/data/bycatch.csv")
-byc$Total=rowSums(byc[,2:6],na.rm=TRUE)
 
 # byc <- read.csv("data/byc_of_pollock.csv",header=T)
 for (i in 2:length(byc[1, ])) {
   byc[, i] <- formatC(byc[, i], format = "d", digits = 0, big.mark = ",")
 }
 cap <- tabcap[38]
 lab <- tablab[38]
-tab <- xtable::xtable(byc, caption = cap, label = paste0("tbl:", tablab[38]), digits = 0, auto = TRUE, align = rep("r", (length(byc[1, ]) + 1)))
+tab <- xtable::xtable(byc, caption = cap, label = paste0("tbl:", tablab[38]), digits = 0, 
+       auto = TRUE, align = rep("r", (length(byc[1, ]) + 1)))
 print(tab,
-  caption.placement = "top", include.rownames = FALSE, rotate.colnames = FALSE, sanitize.text.function = function(x) {
-    x
-  },
+  caption.placement = "top", include.rownames = FALSE, rotate.colnames = FALSE, 
+  sanitize.text.function = function(x) { x },
   scalebox = .90, format.args = list(decimal.mark = "."), comment = FALSE
 )
 ```