function.R

# plumber.R

#* Echo back the input
#* @get /
function() {
  message("---- hello world triggered ----")
  list(msg = paste0("Hello, World!"))
}


#* Echo back the input
#* @get /iris
function() {
  message("---- call iris data ----")
  list(data = janitor::clean_names(head(iris)))
}

#* Echo back the input
#* @param msg The message to echo
#* @get /echo
function(msg="") {
  message("---- echo ----")
  list(msg = paste0("The message is: '", msg, "'"))
}

#* Return the sum of two numbers
#* @param a The first number to add
#* @param b The second number to add
#* @post /sum
function(a, b) {
  message("---- sum two number ----")
  as.numeric(a) + as.numeric(b)
}

#* Growth Calculation
#* #* @serializer unboxedJSON
#* @post /growth-calculation
function(req) {
  
  # data, 
  # init_size = 0.01, 
  # max_doc = 120, 
  # method = "gm"
  
  message("---- call growth function ----")

  data_from_user <- req$body
  
  data <- data_from_user$data
  init_size <- data_from_user$init_size
  max_doc <- data_from_user$max_doc
  method <- data_from_user$model_method
  
  # set maximum doc
  doc_vec <- 1:max_doc

  if (method == "gm") {
    print("use gm")
    model_res <-nls(
      mbw ~ asymptotic_growth*(1-(1-(init_size/asymptotic_growth)^(1/allometric_scale))*exp(-growth_rate*doc))^allometric_scale,
      data = data,
      start = list(
        asymptotic_growth = 40,
        allometric_scale = 3,
        growth_rate = 0.015
      )
    )

    pred_res <- predict(
      model_res,
      list(
        doc = doc_vec
      )
    )

    tibble(
      doc = doc_vec,
      mbw = round(pred_res,3)
    ) -> growth_res
  } else if(method == "interp_abw") {
    min_doc <- min(data$doc)
    max_doc <- max(data$doc)

    growth_res <- data.frame(
      with(data,
           approx(doc, mbw, xout = seq(min_doc, max_doc, by = 1), method = "linear")
      )
    ) %>%
      rename(doc = x, mbw = y)
  } else {
    initial_weight <- init_size
    final_doc <- max_doc
    adg_data <- data

    abw_container <- list()

    for (indeks in 1:nrow(adg_data)) {
      if (indeks == 1) {
        abw_container[[indeks]] <- c(initial_weight, initial_weight + cumsum(rep(adg_data$adg[indeks], adg_data$doc[indeks + 1] - adg_data$doc[indeks] - 1)))
      } else if(indeks == nrow(adg_data)) {
        initial_weight <- max(abw_container[[indeks-1]])
        abw_container[[indeks]] <- c(initial_weight + cumsum(rep(adg_data$adg[indeks], final_doc - adg_data$doc[indeks] + 1)))
      } else {
        initial_weight <- max(abw_container[[indeks-1]])
        abw_container[[indeks]] <- c(initial_weight + cumsum(rep(adg_data$adg[indeks], adg_data$doc[indeks + 1] - adg_data$doc[indeks])))
      }
    }

    mbw_ <- unlist(abw_container)

    tibble(
      doc = 1:final_doc,
      mbw = mbw_
    ) -> growth_res
  }
  
  return(growth_res)
}


#* One Cycle Feeding Generator
#* @serializer unboxedJSON
#* @post /one-cycle-generator
function(req) {
  data_from_user <- req$body

  start_date <-  data_from_user$start_date
  maximum_doc <-  data_from_user$maximum_doc
  pond_setting <-  data_from_user$pond_setting %>% 
    janitor::clean_names()
  partial_harvest_setting <-  data_from_user$partial_harvest_setting %>% 
    janitor::clean_names()
  survival_calculation_method <-  data_from_user$survival_calculation_method
  target_survival <-  data_from_user$target_survival
  survival_model <-  data_from_user$survival_model
  fi_multiplier_setting <-  data_from_user$fi_multiplier_setting
  fr_type_setting <- data_from_user$fr_type_setting
  fr_coef_x <-  data_from_user$fr_coef_x
  fr_coef_y <-  data_from_user$fr_coef_y
  blind_feeding_start_feeding_setting <-  data_from_user$blind_feeding_start_feeding_setting
  blind_feeding_increment_setting <-  data_from_user$blind_feeding_increment_setting %>% 
    janitor::clean_names()
  growth_calculation_method <- data_from_user$growth_calculation_method
  initial_abw <-  data_from_user$initial_abw
  initial_growth_data <-  data_from_user$initial_growth_data %>% 
    janitor::clean_names()
  
  message("---- generate growth scenario table ----")
  growth_scenario_table <- growth_function_training(
    data = initial_growth_data,
    init_size = initial_abw,
    max_doc = maximum_doc,
    method = growth_calculation_method
  )
  
  message("---- generate feeding for one cycle ----")
  result <- all_feeding_generator_function_v2(
    date_data = as.Date(start_date),
    max_doc = maximum_doc,
    shrimp_stock_data = pond_setting,
    harvest_setting = partial_harvest_setting,
    survival_calculation_method = survival_calculation_method,
    target_survival = target_survival,
    survival_model = survival_model,
    blind_feeding_day_1_setting = blind_feeding_start_feeding_setting,
    blind_feeding_day_2_setting = blind_feeding_increment_setting,
    fi_multiplier = fi_multiplier_setting,
    fr_calculation_method = fr_type_setting,
    fr_coef_x = fr_coef_x,
    fr_coef_y = fr_coef_y,
    growth_scenario_table = growth_scenario_table
  )
  
  message("---- check result ----")

  ### blind feed table
  result$blind_feed_table -> blind_feed_table

  ### demand feeding table
  result$demand_feeding_table -> demand_feeding_table

  ### survival table
  result$survival_table -> survival_table

  ### stocking data
  pond_setting -> stocking_data

  ### partial harvest setting
  partial_harvest_setting

  ### feeding plan generation
  message("---- blind feeding generation ----")
  blind_feed_table %>%
    pivot_wider(names_from = type, values_from = total_feed) %>%
    mutate(
      feed_per_day_fr = blind_feeding,
      feed_per_day_indeks = blind_feeding
    ) %>%
    select(-blind_feeding) -> blind_feed_table

  message("---- demand feeding generation ----")
  blind_feed_table %>%
    bind_rows(
      demand_feeding_table %>%
        select(doc, pond_code, feed_per_day_indeks, feed_per_day_fr)
    ) %>%
    mutate(feed_type = ifelse(doc <= 30, "blind_feeding", "demand_feeding")) %>%
    left_join(growth_scenario_table) %>%
    left_join(survival_table) %>%
    left_join(stocking_data) %>%
    arrange(pond_code, doc) %>%
    group_by(pond_code) %>%
    mutate(cumulative_harvested = cumsum(harvested)) %>%
    ungroup() %>%
    mutate(biomass = mbw * population_left/1000) %>%
    mutate(biomass_total = biomass + (cumulative_harvested*mbw)/1000) %>%
    group_by(pond_code) %>%
    mutate(
      cumulative_feed_indeks = cumsum(feed_per_day_indeks),
      cumulative_feed_fr = cumsum(feed_per_day_fr),
    ) %>%
    ungroup() %>%
    mutate(
      fcr_indeks = cumulative_feed_indeks/biomass_total,
      fcr_fr = cumulative_feed_fr/biomass_total
    ) %>%
    mutate(
      data_type = "planning"
    ) -> result

  # variable setting
  ## datetime information
  datetime_info <- lubridate::now(tzone = "UTC")
  
  tibble(
    pond_code = as.character(),
    doc = as.numeric(),
    feed_index = as.numeric()
  ) -> feed_indeks_table
  
  for (pond_code in pond_setting$pond_code) {
    
    feed_indeks_table <- feed_indeks_table %>% 
      bind_rows(
        fi_generator(
          total_feed_day_1 = blind_feeding_start_feeding_setting,
          blind_feeding_rules_table = blind_feeding_increment_setting
        ) %>% 
          mutate(
            pond_code = pond_code
          )
      )
  }
  
  feed_indeks_table <- feed_indeks_table %>% 
    bind_rows(
      demand_feeding_table %>% 
        select(pond_code, doc, indeks_plan) %>% 
        rename(feed_index = indeks_plan)
    ) %>% 
    arrange(pond_code, doc)
  
  ## feeding_strategy_planning_datatable
  message("---- Save Feeding Plan for One Cycle ----")
  result %>%
    mutate(
      created_date = datetime_info
    ) %>%
    group_by(pond_code) %>% 
    mutate(adg = mbw-lag(mbw)) %>% 
    ungroup() -> result
  
  if (fr_type_setting == "FR Type 1") {
    result <- result %>% 
      mutate(
        fr_plan = 10^(fr_coef_x-(fr_coef_y*log10(mbw)))
      )
  } else if(fr_calculation_method == "FR Type 2") {
    result <- result %>% 
      mutate(
        fr_plan = 10^(fr_coef_x-(fr_coef_y*log10(mbw))) * 100
      )
  } else {
    result <- result %>% 
      mutate(
        fr_plan = 10^(fr_coef_x-(fr_coef_y*log10(mbw)))
      )
  }
  
  result <- result %>% 
    group_by(pond_code) %>% 
    mutate(adg = round(mbw-lag(mbw), 2)) %>% 
    ungroup() %>%
    mutate(
      carrying_capacity = biomass/pond_area,
      partial_biomass = population_left * harvest_percentage * mbw
    ) %>% 
    left_join(
      feed_indeks_table
    ) %>% 
    select(
      pond_code, doc, date, mbw, adg, fr_plan, sr_est,
      harvest_percentage, biomass, carrying_capacity, partial_biomass,
      biomass_total, population_left, feed_index, feed_per_day_fr, feed_per_day_indeks,
      cumulative_feed_fr, cumulative_feed_indeks, fcr_fr, fcr_indeks
    )

  return(result)
}

#* One Cycle Feeding Generator
#* @serializer unboxedJSON
#* @post /one-week-generator
function(req) {
  
  data_from_user <- req$body
  
  # Weekly pond setting ----
  result_data <- data_from_user$data %>% 
    janitor::clean_names()
  
  # date input ----
  weekly_feeding_date_input <- as.Date(data_from_user$date_input)
  
  # sr setting ----
  sr_method <- data_from_user$sr_method
  sr_model <- data_from_user$sr_model
  
  # fr setting ----
  fr_method <- data_from_user$fr_method
  fr_x_coef <- data_from_user$fr_x_coef
  fr_y_coef <- data_from_user$fr_y_coef
  
  # growth method ----
  growth_method <- data_from_user$growth_method
  
  feed_data_container <- tibble(
    pond_code = as.character(),
    doc_data = as.numeric(),
    mbw_data = as.numeric(),
    sr_data = as.numeric(),
    fi_data = as.numeric(),
    fr_data = as.numeric(),
    feed_fi = as.numeric(),
    feed_fr = as.numeric(),
    biomass_data = as.numeric(),
    population_data = as.numeric(),
    date_data = as.Date(character())
  )
  
  result_data %>% 
    select(pond_code, stocking_actual, fi_multiplier) -> ponds_setting
  
  for (i in 1:nrow(result_data)) {
    pond_data <- result_data[i,]
    
    stock_data <- ponds_setting %>%
      filter(pond_code == pond_data$pond) %>% 
      pull(stocking_actual)
    
    fi_mult <- ponds_setting %>%
      filter(pond_code == pond_data$pond) %>% 
      pull(fi_multiplier)
    
    doc_data <- cumsum(c(pond_data$doc, rep(1, 7)))[2:8]
    mbw_data <- cumsum(c(pond_data$mbw_sampling, rep(pond_data$adg_target, 7)))[2:8]
    fi_data <- rep(pond_data$adg_target* fi_mult, 7)
    
    if (fr_method == "FR Tipe 1") {
      fr_data <- 10^(fr_x_coef-(fr_y_coef*log10(mbw_data))) * 100
    } else if(fr_method == "FR Tipe 2") {
      fr_data <- 10^(fr_x_coef-(fr_y_coef*log10(mbw_data)))
    } else {
      fr_data <- 10^(fr_x_coef-(fr_y_coef*log10(mbw_data))) * 100
    }
    
    if (sr_method == "Nilai SR Konstan") {
      message("---- Use Constant SR Model ----")
      sr_est <- pond_data$sr_input
    } else if(sr_method == "Berdasarkan Target") {
      message("---- Use Target Based SR ----")
      survival_input <- pond_data$sr_input
      survival_target <- pond_data$sr_target
      sr_est <- seq(from = survival_input, to = survival_target, length.out = 7)
    } else {
      if(model == "Model Survival Logistik") {
        message("---- Use Survival Logistic Model ----")
        sr_est <- (100 - 2.86*log2(doc_data))/100
      }
    }
    
    feed_fi <- (stock_data * (sr_est) * doc_data * fi_data)/100000
    feed_fr <- (stock_data * (sr_est) * mbw_data * fr_data)/1000
    
    population_data <- stock_data * (sr_est)
    biomass <- population_data * mbw_data
    
    date_data <- seq(weekly_feeding_date_input, by = "day", length.out = 7)
    
    tibble(
      pond_code = pond_data$pond,
      doc_data = doc_data,
      mbw_data = mbw_data,
      sr_data = sr_est,
      fi_data = fi_data,
      fr_data = fr_data,
      feed_fi = feed_fi,
      feed_fr = feed_fr,
      fi_fr_ratio = feed_fi/feed_fr,
      biomass_data = biomass,
      population_data = population_data,
      date_data = date_data
    ) %>% 
      bind_rows(feed_data_container) -> feed_data_container
  }
  
  feed_data_container <- feed_data_container %>% 
    arrange(date_data, pond_code)
  
  return(feed_data_container)
}