R/sisesat-auxiliar.R
In imarpe/imarpe: Tools for graphics and summary reports for IMARPE
Defines functions
input_report
indicators_trajectory
indicators_spatial_week
iTemporalSpatial
type_holdCapacity
effort_cpue_week
extrae_data
estima_semana
# auxiliar exported functions
estima_semana <- function(inicio.temp, fin.temp, ...){
require(lubridate)
diasTemporada = seq.Date(as.Date(inicio_temp, format = "%d/%m/%Y"), as.Date(fin_temp, format = "%d/%m/%Y"),by = "day")
dias = weekdays(diasTemporada)
temporada = data.frame(diasTemporada = diasTemporada, dias = dias)
nSem = c(rep(0, min(which(dias == "lunes"))-1), sort(rep(1:length(dias[dias=="lunes"]),7)))
temporada$semana <- nSem[1:length(dias)]
return(temporada)
}
rangeWeek <-function (data, fecha, semana)
{
monthE <- c("Ene", "Feb", "Mar", "Abr", "May", "Jun", "Jul",
"Ago", "Set", "Oct", "Nov", "Dic")
rFecha <- tapply(data[[fecha]], data[[semana]], range)
labelSem <- NULL
for (i in 1:length(rFecha)) {
mi = as.numeric(substring(rFecha[[i]][1], 6, 7))
mf = as.numeric(substring(rFecha[[i]][2], 6, 7))
if (mi %in% mf) {
rangeSem <- paste0(paste(substring(rFecha[[i]][1],
9, 10), substring(rFecha[[i]][2], 9, 10), sep = "-"),
monthE[mi])
}
else {
rangeSem <- paste(paste0(substring(rFecha[[i]][1],
9, 10), monthE[mi]), paste0(substring(rFecha[[i]][2],
9, 10), monthE[mf]), sep = "-")
}
labelSem = rbind(labelSem, rangeSem)
}
labelSem <- as.vector(labelSem)
return(labelSem)
}
# FUNCIONES PARA OBTENER INDICADORES ESPACIO TEMPORALES DEL ESFUERZO --------
##data = Pred_Emi_Todas
# esta funcion deberia ser input
extrae_data <- function(data, temp, date = "date.GMT", region = "NC", ...){
require(fenix)
data$Fecha <- as.Date(substring(data[[date]], 1,10), format = "%Y-%m-%d")
data$Temporada <- DateTemp(data$Fecha) # asignar temporadas
data <- data[!is.na(data$Temporada),]
# data <- data[!data$Temporada %in% c("1999_II"),]
# solo nos quedamos con las emisiones con cala
data0 = data[data$Temporada == temp,]
data = data[data$Temporada == temp & data$Calas == 1,]
# calculamos la distancia a costa
data$dc = as.numeric(estima.dc(data$Lon, data$Lat))# calculamos distancia a costa
# removemos viajes que no pertenecen a nc
Omit_Cod.Viaje.VMS <- unique(data[data$Lat < -16, "Cod.Viaje.VMS"])
data <- data[!data$Cod.Viaje.VMS %in% Omit_Cod.Viaje.VMS,]
# calculamos la etiqueta para el area de pesca
data$area = as.numeric(area.pesca(dc = as.numeric(data$dc), lat = data$Lat, grado = 1/12))# etiquetamos las areas de pesca
data$semana = NA
print(data$Fecha[1])
for(i in sort(unique(data$Fecha))){
print(i)
data[data$Fecha == i, "semana"] = out.semana[out.semana$diasTemporada == i, "semana"]
}
# #outdata <- list()
# outdata$data <- data
# outdata$data0 <- data0
return(list(data = data, data0 = data0))
}
## INPUT: data cpue (sisesat y seguimiento por viaje)
effort_cpue_week <- function(data = data, inicio_temp, fin_temp, ...){
out_report <- list()
data$num.dia = as.numeric(as.character(strftime(data$Fecha.Fin, format = "%j")))
out.semana = imarpe:::estima_semana(inicio.temp = inicio_temp, fin.temp = fin_temp)
data$Fecha = as.Date(substring(data$Fecha.Ini,1,10), format = "%Y-%m-%d")
data$semana = NA
for(i in sort(unique(data$Fecha))){
data[data$Fecha == i, "semana"] = out.semana[out.semana$diasTemporada == i, "semana"]
}
data$cpueDv = data$CPUE
data$cpueNcala = data$ANCHOVETA/data$Num.Calas
data$dia = as.numeric(as.character(strftime(data$Fecha.Fin, format = "%d")))
data$mes = as.numeric(as.character(strftime(data$Fecha.Fin, format = "%m")))
data$year = as.numeric(as.character(strftime(data$Fecha.Fin, format = "%Y")))
labelSEMANA = rangeWeek(data = out.semana, semana = "semana", fecha = "diasTemporada")
semana = sort(as.numeric(unique(data$semana) + 1))
Dur.viaje <- fun_mean_IC(data$Dur.Viaje, data$semana)
Num.Calas <- fun_mean_IC(data$Num.Calas, data$semana)
cpueDv <- fun_mean_IC(data$cpueDv, data$semana)
cpueNcala <- fun_mean_IC(data$cpueNcala, data$semana)
CB_mean <- fun_mean_IC(data$CB, data$semana)
semana <- sort(as.numeric(unique(data$semana) + 1))
Dur.viaje_ind <- fun_mean_IC(data$Dur.Viaje[data$TIPO == "IND"], data$semana[data$TIPO == "IND"])
Num.Calas_ind <- fun_mean_IC(data$Num.Calas[data$TIPO == "IND"], data$semana[data$TIPO == "IND"])
cpueDv_ind <- fun_mean_IC(data$cpueDv[data$TIPO == "IND"], data$semana[data$TIPO == "IND"])
cpueNcala_ind <- fun_mean_IC(data$cpueNcala[data$TIPO == "IND"], data$semana[data$TIPO == "IND"])
CB_mean_ind <- fun_mean_IC(data$CB[data$TIPO == "IND"], data$semana[data$TIPO == "IND"])
semana_ind <- sort(as.numeric(unique(data$semana[data$TIPO == "IND"]) + 1))
Dur.viaje_indmad <- fun_mean_IC(data$Dur.Viaje[data$TIPO == "IND MAD"], data$semana[data$TIPO == "IND MAD"])
Num.Calas_indmad <- fun_mean_IC(data$Num.Calas[data$TIPO == "IND MAD"], data$semana[data$TIPO == "IND MAD"])
cpueDv_indmad <- fun_mean_IC(data$cpueDv[data$TIPO == "IND MAD"], data$semana[data$TIPO == "IND MAD"])
cpueNcala_indmad <- fun_mean_IC(data$cpueNcala[data$TIPO == "IND MAD"], data$semana[data$TIPO == "IND MAD"])
CB_mean_indmad <- fun_mean_IC(data$CB[data$TIPO == "IND MAD"], data$semana[data$TIPO == "IND MAD"])
semana_indmad <- sort(as.numeric(unique(data$semana[data$TIPO == "IND MAD"]) + 1))
tableReporte = data.frame(semana = semana, Dur.viaje = Dur.viaje$x, Num.Calas = Num.Calas$x, cpueDv = cpueDv$x, cpueNcala = cpueNcala$x, CB_mean = CB_mean$x)
sdtableReporte = data.frame(semana = semana, Dur.viaje = Dur.viaje$sterr, Num.Calas = Num.Calas$sterr, cpueDv = cpueDv$sterr, cpueNcala = cpueNcala$sterr, CB_mean = CB_mean$sterr)
tableReporte_ind = data.frame(semana = semana_ind, Dur.viaje = Dur.viaje_ind$x, Num.Calas = Num.Calas_ind$x, cpueDv = cpueDv_ind$x, cpueNcala = cpueNcala_ind$x, CB_mean_ind = CB_mean_ind$x)
sdtableReporte_ind = data.frame(semana = semana_ind, Dur.viaje = Dur.viaje_ind$sterr, Num.Calas = Num.Calas_ind$sterr, cpueDv = cpueDv_ind$sterr, cpueNcala = cpueNcala_ind$sterr, CB_mean_ind = CB_mean_indmad$sterr)
tableReporte_indmad = data.frame(semana = semana_indmad, Dur.viaje = Dur.viaje_indmad$x, Num.Calas = Num.Calas_indmad$x, cpueDv = cpueDv_indmad$x, cpueNcala = cpueNcala_indmad$x, CB_mean_indmad = CB_mean_indmad$x)
sdtableReporte_indmad = data.frame(semana = semana_indmad, Dur.viaje = Dur.viaje_indmad$sterr, Num.Calas = Num.Calas_indmad$sterr, cpueDv = cpueDv_indmad$sterr, cpueNcala = cpueNcala_indmad$sterr, CB_mean_indmad = CB_mean_indmad$sterr)
out_report$effort_total <- tableReporte
out_report$effort_ind <- tableReporte_ind
out_report$effort_indmad <- tableReporte_indmad
out_report$sdt_effort_total <- sdtableReporte
out_report$sdt_effort_ind <- sdtableReporte_ind
out_report$sdt_effort_indmad <- sdtableReporte_indmad
return(out_report)
}
# ------------------------------------------------------------------------
## INPUT: data sisesat por calas
# es necesario agrerar las variable tipo de embarcacion y capacidad de bodega
# necesitamos el listado de embarcaciones de cada temporada o año
# data: Sisesat_Point_Pesca_NC
#lista_barcos <- read.csv("F:/reporte/lista_barcos_2016.csv") # para la temporada de 2016
# data <- data_cala$data
#
# data$Cod.Barco
# list_vessel$COD
type_holdCapacity <- function(data, list_vessel, ...){
out <- lapply(split(data, data$Cod.Barco, drop = TRUE),function(x){
xlen <- dim(table(list_vessel$COD == x$Cod.Barco[1]))
len <- length(x$Cod.Barco)
if(xlen == 2){
iTIPO <- list_vessel[list_vessel$COD == x$Cod.Barco[1], "TIPO"]
iCB <- list_vessel[list_vessel$COD == x$Cod.Barco[1], "CB"]
tipo <- rep(iTIPO, len)
cb <- rep(iCB, len)
}else{
tipo <- rep(NA, len)
cb <- rep(NA, len)
}
Cod.Barco <- x$Cod.Barco
Cod.Viaje.VMS <- x$Cod.Viaje.VMS
date.GMT <- x$date.GMT
Dur.Viaje <- x$Dur.Viaje
Puerto.0.Mar.1 <- x$Puerto.0.Mar.1
Vel.Cal <- x$Vel.Cal
Calas <- x$Calas
Lon <- x$Lon
Lat <- x$Lat
Fecha <- x$Fecha
Temporada <- x$Temporada
dc <- x$dc
area <- x$area
semana <- x$semana
cbind.data.frame(Cod.Barco, Cod.Viaje.VMS, date.GMT, Dur.Viaje,
Puerto.0.Mar.1, Vel.Cal, Calas, Lon, Lat, Fecha,
Temporada, dc, area, semana, tipo, cb)
})
out <- do.call(rbind.data.frame,out); row.names(out) <- NULL
# barco que no disponen de la variable tipo o cb son quitados del analisis
omit_vessel <- unique(out[is.na(out$tipo), "Cod.Barco"])
out <- out[!out$Cod.Barco %in% omit_vessel,]
return(out)
}
# ------------------------------------------------------------------------
# ------------------------------------------------------------------------
#x = data0[data0$semana == 1,]
iTemporalSpatial <- function(data, ...){
require(ineq)
require(sp)
index <- lapply(split(data, data$semana, drop = TRUE),function(x){
area_pesca <- length(unique(x$area))*25
dc_mean <- mean(x$dc, na.rm = T)
dc_lower <- quantile(x$dc, probs = 0.3, na.rm = T)
dc_upper <- quantile(x$dc, probs = 0.7, na.rm = T)
Lat_mean <- mean(x$Lat, na.rm = T)
Lat_lower <- quantile(x$Lat, probs = 0.3, na.rm = T)
Lat_upper <- quantile(x$Lat, probs = 0.7, na.rm = T)
# indicadores de autocorrelación espacial
vector <- sort(as.numeric(table(x$area)),decreasing = TRUE)
Igini <- Gini(vector, corr = TRUE)
Irs <- RS(vector, na.rm = TRUE)
Itkinson <- Atkinson(vector, parameter = 0.5, na.rm = TRUE)
Itheil <- Theil(vector, parameter = 0, na.rm = TRUE)
Ikolm <- Kolm(vector, parameter = 1, na.rm = TRUE)
Ivar.coeff <- var.coeff(vector, square = FALSE, na.rm = TRUE)
Ientropy <- entropy(vector, parameter = 0.5, na.rm = TRUE)
semana <- x$semana[1] + 1
cbind.data.frame(semana, area_pesca, dc_mean, dc_lower, dc_upper,
Lat_mean, Lat_lower, Lat_upper,
Igini, Irs, Itkinson, Itheil, Ikolm, Ivar.coeff, Ientropy)
})
index <- do.call(rbind.data.frame,index); row.names(index) <- NULL
return(index)
}
#
indicators_spatial_week <- function(data, list_vessel, inicio_temp, fin_temp, space.perfil = -5, ...){
data0 = type_holdCapacity(data, list_vessel) # agrega tipo y capacidad de bodega
data_ind <- data0[data0$tipo == "IND",]
data_indmad <- data0[data0$tipo == "IND MAD",]
list_data <- list(3)
list_data[[1]] = data0
list_data[[2]] = data_ind
list_data[[3]] = data_indmad
require(ineq)
out <- list(3)
for(i in 1:3){
out[[i]] <- iTemporalSpatial(data = list_data[[i]])
}
out_report <- list()
out_report$spatial_indicators_total <- out[[1]]
out_report$spatial_indicators_ind <- out[[2]]
out_report$spatial_indicators_indmad <- out[[3]]
out_report$fishing_points <- list_data[[1]]
return(out_report)
}
# ------------------------------------------------------------------------
# ------------------------------------------------------------------------
indicators_trajectory <- function(data, list_vessel, inicio_temp, fin_temp, ...){
out_report <- list()
# x = data[data$Cod.Viaje.VMS == "12977-10",]
require(dplyr)
indicators <- lapply(split(data, data$Cod.Viaje.VMS, drop = TRUE),function(x){
if(dim(x)[1] > 10){
tipo <- list_vessel[list_vessel$COD == x$Cod.Barco[1], "TIPO"]
cb <- list_vessel[list_vessel$COD == x$Cod.Barco[1], "CB"]
# print(x$Cod.Viaje.VMS[1])
Fecha.ini <- x$date.GMT[1]
Fecha.fin <- x$date.GMT[length(x$date.GMT)]
Fecha <- x$Fecha[1]
temp <- x$Temporada[1]
if(sum(x$Calas) > 1){
xcala = x[x$Calas == 1,]
distCalas <- NULL
for(i in 1:(nrow(xcala)-1)){
dist <- distXY(xcala[i,"Lon"], xcala[i,"Lat"], xcala[i+1,"Lon"], xcala[i+1,"Lat"])
distCalas <- c(distCalas, dist)
}
dist_calas <- sum(distCalas)
Lon_zarpe <- rep(x$Lon[1], nrow(xcala))
Lon_arribo <- rep(x$Lon[length(x$Lat)], nrow(xcala))
Lat_zarpe <- rep(x$Lat[1], nrow(xcala))
Lat_arribo <- rep(x$Lat[length(x$Lat)], nrow(xcala))
Cod.Viaje.VMS <- rep(x$Cod.Viaje.VMS[1], nrow(xcala))
Lon <- xcala$Lon
Lat <- xcala$Lat
hora_zarpe_1cala <- as.numeric(difftime(x$date.GMT[x$Calas == 1][1],x$date.GMT[1], units = "hours"))
}
if(sum(x$Calas) == 1){
dist_calas <- NA
Lon_zarpe <- x$Lon[1]
Lon_arribo <- x$Lon[length(x$Lat)]
Lat_zarpe <- x$Lat[1]
Lat_arribo <- x$Lat[length(x$Lat)]
Cod.Viaje.VMS <- x$Cod.Viaje.VMS[1]
Lon <- x$Lon[x$Calas == 1]
Lat <- x$Lat[x$Calas == 1]
hora_zarpe_1cala <- as.numeric(difftime(x$date.GMT[x$Calas == 1][1],x$date.GMT[1], units = "hours"))
}
if(sum(x$Calas) ==0){
dist_calas <- NA
Lon_zarpe <- x$Lon[1]
Lon_arribo <- x$Lon[length(x$Lat)]
Lat_zarpe <- x$Lat[1]
Lat_arribo <- x$Lat[length(x$Lat)]
Cod.Viaje.VMS <- x$Cod.Viaje.VMS[1]
Lon <- NA
Lat <- NA
hora_zarpe_1cala <- NA
}
require(SISESATools)
#SISESATools::puertos
Puerto_zarpe <- as.character(puertos[which.min(distAB(Lon_zarpe[1],Lat_zarpe[1], puertos$longitud, puertos$latitud)),"puerto2"])
Puerto_arribo <- as.character(puertos[which.min(distAB(Lon_arribo[1],Lat_arribo[1], puertos$longitud, puertos$latitud)),"puerto2"])
Puerto_zarpe <- rep(Puerto_zarpe, length(temp))
Puerto_arribo <- rep(Puerto_arribo, length(temp))
# distancia entre emisiones
x$distancia_emision <- NA
x$distancia_emision[2:(length(x[,1]))] <- distORTODROMICA(x$Lon[1:(length(x[,1])-1)],x$Lat[1:(length(x[,1])-1)],x$Lon[2:length(x[,1])],x$Lat[2:length(x[,1])])
# rumbo
x$rumbo <- NA
x$rumbo <- calcularRumbo(x$Lon,x$Lat)#$vectorRUMBO
rumbo_inicial <- x$rumbo[1+1]
rumbo_final <- x$rumbo[length(x$rumbo)-1]
# angulo
x$angle <- NA
x$angle[2:(length(x$angle)-1)] <- angle(x$Lon, x$Lat)
angulo_inicial <- x$angle[1+1]
angulo_final <- x$angle[length(x$angle)-1]
#
sinuosidad1 <- Sinuosidad1(x$angle,x$distancia_emision)
sinuosidad2 <- Sinuosidad2(x$angle,x$distancia_emision)
sinuosidad3 <- Sinuosidad3(x$angle)
if(length(cb) == 0){
cb <- NA
tipo <- NA
}
cbind.data.frame(Fecha.ini, Fecha.fin, Fecha, temp, Cod.Viaje.VMS,Puerto_zarpe, Puerto_arribo, Lon_zarpe,
Lon_arribo, Lat_zarpe, Lat_arribo, Lon, Lat, dist_calas,
angulo_inicial, angulo_final, rumbo_inicial, rumbo_final,
sinuosidad1 = sinuosidad1, sinuosidad2 = sinuosidad2, sinuosidad3 = sinuosidad3,
tipo = tipo, cb = cb)
}
})
indicators <- do.call(rbind.data.frame,indicators); row.names(indicators) <- NULL
out.semana <- imarpe:::estima_semana(inicio.temp = inicio_temp, fin.temp = fin_temp)
indicators$semana = NA
for(i in sort(unique(indicators$Fecha))){
indicators[indicators$Fecha == i, "semana"] = out.semana[out.semana$diasTemporada == i, "semana"]
}
# space = (0:(length(unique(indicators$semana))-1))*space.perfil
#
# indicators00 = indicators
# for(i in 1:length(unique(indicators00$semana))){
# indicators00[indicators00$semana == rev(sort(unique(indicators00$semana)))[i],"Lon"] = indicators00[indicators00$semana == rev(sort(unique(indicators00$semana)))[i],"Lon"] + space[i]
# }
# total
semana <- sort(as.numeric(unique(indicators$semana) + 1))
dist_calas <- fun_mean_IC(indicators$dist_calas, indicators$semana)
angulo_inicial <- fun_mean_IC(indicators$angulo_inicial, indicators$semana)
angulo_final <- fun_mean_IC(indicators$angulo_final, indicators$semana)
rumbo_inicial <- fun_mean_IC(indicators$rumbo_inicial, indicators$semana)
rumbo_final <- fun_mean_IC(indicators$rumbo_final, indicators$semana)
sinuosidad_1 <- fun_mean_IC(indicators$sinuosidad1, indicators$semana)
sinuosidad_2 <- fun_mean_IC(indicators$sinuosidad2, indicators$semana)
sinuosidad_3 <- fun_mean_IC(indicators$sinuosidad3, indicators$semana)
# industrial
semana_ind <- sort(as.numeric(unique(indicators$semana[indicators$tipo == "IND"]) + 1))
dist_calas_ind <- fun_mean_IC(indicators$dist_calas[indicators$tipo == "IND"], indicators$semana[indicators$tipo == "IND"])
angulo_inicial_ind <- fun_mean_IC(indicators$angulo_inicial[indicators$tipo == "IND"], indicators$semana[indicators$tipo == "IND"])
angulo_final_ind <- fun_mean_IC(indicators$angulo_final[indicators$tipo == "IND"], indicators$semana[indicators$tipo == "IND"])
rumbo_inicial_ind <- fun_mean_IC(indicators$rumbo_inicial[indicators$tipo == "IND"], indicators$semana[indicators$tipo == "IND"])
rumbo_final_ind <- fun_mean_IC(indicators$rumbo_final[indicators$tipo == "IND"], indicators$semana[indicators$tipo == "IND"])
sinuosidad_1_ind <- fun_mean_IC(indicators$sinuosidad1[indicators$tipo == "IND"], indicators$semana[indicators$tipo == "IND"])
sinuosidad_2_ind <- fun_mean_IC(indicators$sinuosidad2[indicators$tipo == "IND"], indicators$semana[indicators$tipo == "IND"])
sinuosidad_3_ind <- fun_mean_IC(indicators$sinuosidad3[indicators$tipo == "IND"], indicators$semana[indicators$tipo == "IND"])
# industrial de madera
semana_indmad <- sort(as.numeric(unique(indicators$semana[indicators$tipo == "IND MAD"]) + 1))
dist_calas_indmad <- fun_mean_IC(indicators$dist_calas[indicators$tipo == "IND MAD"], indicators$semana[indicators$tipo == "IND MAD"])
angulo_inicial_indmad <- fun_mean_IC(indicators$angulo_inicial[indicators$tipo == "IND MAD"], indicators$semana[indicators$tipo == "IND MAD"])
angulo_final_indmad <- fun_mean_IC(indicators$angulo_final[indicators$tipo == "IND MAD"], indicators$semana[indicators$tipo == "IND MAD"])
rumbo_inicial_indmad <- fun_mean_IC(indicators$rumbo_inicial[indicators$tipo == "IND MAD"], indicators$semana[indicators$tipo == "IND MAD"])
rumbo_final_indmad <- fun_mean_IC(indicators$rumbo_final[indicators$tipo == "IND MAD"], indicators$semana[indicators$tipo == "IND MAD"])
sinuosidad_1_indmad <- fun_mean_IC(indicators$sinuosidad1[indicators$tipo == "IND MAD"], indicators$semana[indicators$tipo == "IND MAD"])
sinuosidad_2_indmad <- fun_mean_IC(indicators$sinuosidad2[indicators$tipo == "IND MAD"], indicators$semana[indicators$tipo == "IND MAD"])
sinuosidad_3_indmad <- fun_mean_IC(indicators$sinuosidad3[indicators$tipo == "IND MAD"], indicators$semana[indicators$tipo == "IND MAD"])
tableResult <- data.frame(semana = semana, dist_calas = dist_calas$x, angulo_inicial = angulo_inicial$x, angulo_final = angulo_final$x, rumbo_inicial = rumbo_inicial$x, rumbo_final = rumbo_final$x, sinuosidad_1 = sinuosidad_1$x, sinuosidad_2 = sinuosidad_2$x, sinuosidad_3 = sinuosidad_3$x)
sdtableResult <- data.frame(semana = semana, dist_calas = dist_calas$sterr, angulo_inicial = angulo_inicial$sterr, angulo_final = angulo_final$sterr, rumbo_inicial = rumbo_inicial$sterr, rumbo_final = rumbo_final$sterr, sinuosidad_1 = sinuosidad_1$sterr, sinuosidad_2 = sinuosidad_2$sterr, sinuosidad_3 = sinuosidad_3$sterr)
tableResult_ind <- data.frame(semana = semana_ind, dist_calas = dist_calas_ind$x, angulo_inicial = angulo_inicial_ind$x, angulo_final = angulo_final_ind$x, rumbo_inicial = rumbo_inicial_ind$x, rumbo_final = rumbo_final_ind$x, sinuosidad_1 = sinuosidad_1_ind$x, sinuosidad_2 = sinuosidad_2_ind$x, sinuosidad_3 = sinuosidad_3_ind$x)
sdtableResult_ind <- data.frame(semana = semana_ind, dist_calas = dist_calas_ind$sterr, angulo_inicial = angulo_inicial_ind$sterr, angulo_final = angulo_final_ind$sterr, rumbo_inicial = rumbo_inicial_ind$sterr, rumbo_final = rumbo_final_ind$sterr, sinuosidad_1 = sinuosidad_1_ind$sterr, sinuosidad_2 = sinuosidad_2_ind$sterr, sinuosidad_3 = sinuosidad_3_ind$sterr)
tableResult_indmad <- data.frame(semana = semana_indmad, dist_calas = dist_calas_indmad$x, angulo_inicial = angulo_inicial_indmad$x, angulo_final = angulo_final_indmad$x, rumbo_inicial = rumbo_inicial_indmad$x, rumbo_final = rumbo_final_indmad$x, sinuosidad_1 = sinuosidad_1_indmad$x, sinuosidad_2 = sinuosidad_2_indmad$x, sinuosidad_3 = sinuosidad_3_indmad$x)
sdtableResult_indmad <- data.frame(semana = semana_indmad, dist_calas = dist_calas_indmad$sterr, angulo_inicial = angulo_inicial_indmad$sterr, angulo_final = angulo_final_indmad$sterr, rumbo_inicial = rumbo_inicial_indmad$sterr, rumbo_final = rumbo_final_indmad$sterr, sinuosidad_1 = sinuosidad_1_indmad$sterr, sinuosidad_2 = sinuosidad_2_indmad$sterr, sinuosidad_3 = sinuosidad_3_indmad$sterr)
out_report$index_trajectory_total <- tableResult
out_report$index_trajectory_total_ind <- tableResult_ind
out_report$index_trajectory_total_indmad <- tableResult_indmad
out_report$sd_index_trajectory_total <- sdtableResult
out_report$sd_index_trajectory_total_ind <- sdtableResult_ind
out_report$sd_index_trajectory_total_indmad <- sdtableResult_indmad
return(out_report)
}
# ------------------------------------------------------------------------
input_report <- function(object1, object2, object3){
out_report <- list()
out_report$effort_total <- out_report1$effort_total
out_report$effort_ind <- out_report1$effort_ind
out_report$effort_indmad <- out_report1$effort_indmad
out_report$sdt_effort_total <- out_report1$sdt_effort_total
out_report$sdt_effort_ind <- out_report1$sdt_effort_ind
out_report$sdt_effort_indmad <- out_report1$sdt_effort_indmad
out_report$spatial_indicators_total <- object2$spatial_indicators_total
out_report$spatial_indicators_ind <- object2$spatial_indicators_ind
out_report$spatial_indicators_indmad <- object2$spatial_indicators_indmad
out_report$fishing_points <- object2$fishing_points
out_report$index_trajectory_total <- object3$index_trajectory_total
out_report$index_trajectory_total_ind <- object3$index_trajectory_total_ind
out_report$index_trajectory_total_indmad <- object3$index_trajectory_total_indmad
out_report$sd_index_trajectory_total <- object3$sd_index_trajectory_total
out_report$sd_index_trajectory_total_ind <- object3$sd_index_trajectory_total_ind
out_report$sd_index_trajectory_total_indmad <- object3$sd_index_trajectory_total_indmad
return(out_report)
}
imarpe/imarpe documentation built on Aug. 8, 2020, 8:40 p.m.
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Defines functions input_report indicators_trajectory indicators_spatial_week iTemporalSpatial type_holdCapacity effort_cpue_week extrae_data estima_semana
# auxiliar exported functions
estima_semana <- function(inicio.temp, fin.temp, ...){
require(lubridate)
diasTemporada = seq.Date(as.Date(inicio_temp, format = "%d/%m/%Y"), as.Date(fin_temp, format = "%d/%m/%Y"),by = "day")
dias = weekdays(diasTemporada)
temporada = data.frame(diasTemporada = diasTemporada, dias = dias)
nSem = c(rep(0, min(which(dias == "lunes"))-1), sort(rep(1:length(dias[dias=="lunes"]),7)))
temporada$semana <- nSem[1:length(dias)]
return(temporada)
}
rangeWeek <-function (data, fecha, semana)
{
monthE <- c("Ene", "Feb", "Mar", "Abr", "May", "Jun", "Jul",
"Ago", "Set", "Oct", "Nov", "Dic")
rFecha <- tapply(data[[fecha]], data[[semana]], range)
labelSem <- NULL
for (i in 1:length(rFecha)) {
mi = as.numeric(substring(rFecha[[i]][1], 6, 7))
mf = as.numeric(substring(rFecha[[i]][2], 6, 7))
if (mi %in% mf) {
rangeSem <- paste0(paste(substring(rFecha[[i]][1],
9, 10), substring(rFecha[[i]][2], 9, 10), sep = "-"),
monthE[mi])
}
else {
rangeSem <- paste(paste0(substring(rFecha[[i]][1],
9, 10), monthE[mi]), paste0(substring(rFecha[[i]][2],
9, 10), monthE[mf]), sep = "-")
}
labelSem = rbind(labelSem, rangeSem)
}
labelSem <- as.vector(labelSem)
return(labelSem)
}
# FUNCIONES PARA OBTENER INDICADORES ESPACIO TEMPORALES DEL ESFUERZO --------
##data = Pred_Emi_Todas
# esta funcion deberia ser input
extrae_data <- function(data, temp, date = "date.GMT", region = "NC", ...){
require(fenix)
data$Fecha <- as.Date(substring(data[[date]], 1,10), format = "%Y-%m-%d")
data$Temporada <- DateTemp(data$Fecha) # asignar temporadas
data <- data[!is.na(data$Temporada),]
# data <- data[!data$Temporada %in% c("1999_II"),]
# solo nos quedamos con las emisiones con cala
data0 = data[data$Temporada == temp,]
data = data[data$Temporada == temp & data$Calas == 1,]
# calculamos la distancia a costa
data$dc = as.numeric(estima.dc(data$Lon, data$Lat))# calculamos distancia a costa
# removemos viajes que no pertenecen a nc
Omit_Cod.Viaje.VMS <- unique(data[data$Lat < -16, "Cod.Viaje.VMS"])
data <- data[!data$Cod.Viaje.VMS %in% Omit_Cod.Viaje.VMS,]
# calculamos la etiqueta para el area de pesca
data$area = as.numeric(area.pesca(dc = as.numeric(data$dc), lat = data$Lat, grado = 1/12))# etiquetamos las areas de pesca
data$semana = NA
print(data$Fecha[1])
for(i in sort(unique(data$Fecha))){
print(i)
data[data$Fecha == i, "semana"] = out.semana[out.semana$diasTemporada == i, "semana"]
}
# #outdata <- list()
# outdata$data <- data
# outdata$data0 <- data0
return(list(data = data, data0 = data0))
}
## INPUT: data cpue (sisesat y seguimiento por viaje)
effort_cpue_week <- function(data = data, inicio_temp, fin_temp, ...){
out_report <- list()
data$num.dia = as.numeric(as.character(strftime(data$Fecha.Fin, format = "%j")))
out.semana = imarpe:::estima_semana(inicio.temp = inicio_temp, fin.temp = fin_temp)
data$Fecha = as.Date(substring(data$Fecha.Ini,1,10), format = "%Y-%m-%d")
data$semana = NA
for(i in sort(unique(data$Fecha))){
data[data$Fecha == i, "semana"] = out.semana[out.semana$diasTemporada == i, "semana"]
}
data$cpueDv = data$CPUE
data$cpueNcala = data$ANCHOVETA/data$Num.Calas
data$dia = as.numeric(as.character(strftime(data$Fecha.Fin, format = "%d")))
data$mes = as.numeric(as.character(strftime(data$Fecha.Fin, format = "%m")))
data$year = as.numeric(as.character(strftime(data$Fecha.Fin, format = "%Y")))
labelSEMANA = rangeWeek(data = out.semana, semana = "semana", fecha = "diasTemporada")
semana = sort(as.numeric(unique(data$semana) + 1))
Dur.viaje <- fun_mean_IC(data$Dur.Viaje, data$semana)
Num.Calas <- fun_mean_IC(data$Num.Calas, data$semana)
cpueDv <- fun_mean_IC(data$cpueDv, data$semana)
cpueNcala <- fun_mean_IC(data$cpueNcala, data$semana)
CB_mean <- fun_mean_IC(data$CB, data$semana)
semana <- sort(as.numeric(unique(data$semana) + 1))
Dur.viaje_ind <- fun_mean_IC(data$Dur.Viaje[data$TIPO == "IND"], data$semana[data$TIPO == "IND"])
Num.Calas_ind <- fun_mean_IC(data$Num.Calas[data$TIPO == "IND"], data$semana[data$TIPO == "IND"])
cpueDv_ind <- fun_mean_IC(data$cpueDv[data$TIPO == "IND"], data$semana[data$TIPO == "IND"])
cpueNcala_ind <- fun_mean_IC(data$cpueNcala[data$TIPO == "IND"], data$semana[data$TIPO == "IND"])
CB_mean_ind <- fun_mean_IC(data$CB[data$TIPO == "IND"], data$semana[data$TIPO == "IND"])
semana_ind <- sort(as.numeric(unique(data$semana[data$TIPO == "IND"]) + 1))
Dur.viaje_indmad <- fun_mean_IC(data$Dur.Viaje[data$TIPO == "IND MAD"], data$semana[data$TIPO == "IND MAD"])
Num.Calas_indmad <- fun_mean_IC(data$Num.Calas[data$TIPO == "IND MAD"], data$semana[data$TIPO == "IND MAD"])
cpueDv_indmad <- fun_mean_IC(data$cpueDv[data$TIPO == "IND MAD"], data$semana[data$TIPO == "IND MAD"])
cpueNcala_indmad <- fun_mean_IC(data$cpueNcala[data$TIPO == "IND MAD"], data$semana[data$TIPO == "IND MAD"])
CB_mean_indmad <- fun_mean_IC(data$CB[data$TIPO == "IND MAD"], data$semana[data$TIPO == "IND MAD"])
semana_indmad <- sort(as.numeric(unique(data$semana[data$TIPO == "IND MAD"]) + 1))
tableReporte = data.frame(semana = semana, Dur.viaje = Dur.viaje$x, Num.Calas = Num.Calas$x, cpueDv = cpueDv$x, cpueNcala = cpueNcala$x, CB_mean = CB_mean$x)
sdtableReporte = data.frame(semana = semana, Dur.viaje = Dur.viaje$sterr, Num.Calas = Num.Calas$sterr, cpueDv = cpueDv$sterr, cpueNcala = cpueNcala$sterr, CB_mean = CB_mean$sterr)
tableReporte_ind = data.frame(semana = semana_ind, Dur.viaje = Dur.viaje_ind$x, Num.Calas = Num.Calas_ind$x, cpueDv = cpueDv_ind$x, cpueNcala = cpueNcala_ind$x, CB_mean_ind = CB_mean_ind$x)
sdtableReporte_ind = data.frame(semana = semana_ind, Dur.viaje = Dur.viaje_ind$sterr, Num.Calas = Num.Calas_ind$sterr, cpueDv = cpueDv_ind$sterr, cpueNcala = cpueNcala_ind$sterr, CB_mean_ind = CB_mean_indmad$sterr)
tableReporte_indmad = data.frame(semana = semana_indmad, Dur.viaje = Dur.viaje_indmad$x, Num.Calas = Num.Calas_indmad$x, cpueDv = cpueDv_indmad$x, cpueNcala = cpueNcala_indmad$x, CB_mean_indmad = CB_mean_indmad$x)
sdtableReporte_indmad = data.frame(semana = semana_indmad, Dur.viaje = Dur.viaje_indmad$sterr, Num.Calas = Num.Calas_indmad$sterr, cpueDv = cpueDv_indmad$sterr, cpueNcala = cpueNcala_indmad$sterr, CB_mean_indmad = CB_mean_indmad$sterr)
out_report$effort_total <- tableReporte
out_report$effort_ind <- tableReporte_ind
out_report$effort_indmad <- tableReporte_indmad
out_report$sdt_effort_total <- sdtableReporte
out_report$sdt_effort_ind <- sdtableReporte_ind
out_report$sdt_effort_indmad <- sdtableReporte_indmad
return(out_report)
}
# ------------------------------------------------------------------------
## INPUT: data sisesat por calas
# es necesario agrerar las variable tipo de embarcacion y capacidad de bodega
# necesitamos el listado de embarcaciones de cada temporada o año
# data: Sisesat_Point_Pesca_NC
#lista_barcos <- read.csv("F:/reporte/lista_barcos_2016.csv") # para la temporada de 2016
# data <- data_cala$data
#
# data$Cod.Barco
# list_vessel$COD
type_holdCapacity <- function(data, list_vessel, ...){
out <- lapply(split(data, data$Cod.Barco, drop = TRUE),function(x){
xlen <- dim(table(list_vessel$COD == x$Cod.Barco[1]))
len <- length(x$Cod.Barco)
if(xlen == 2){
iTIPO <- list_vessel[list_vessel$COD == x$Cod.Barco[1], "TIPO"]
iCB <- list_vessel[list_vessel$COD == x$Cod.Barco[1], "CB"]
tipo <- rep(iTIPO, len)
cb <- rep(iCB, len)
}else{
tipo <- rep(NA, len)
cb <- rep(NA, len)
}
Cod.Barco <- x$Cod.Barco
Cod.Viaje.VMS <- x$Cod.Viaje.VMS
date.GMT <- x$date.GMT
Dur.Viaje <- x$Dur.Viaje
Puerto.0.Mar.1 <- x$Puerto.0.Mar.1
Vel.Cal <- x$Vel.Cal
Calas <- x$Calas
Lon <- x$Lon
Lat <- x$Lat
Fecha <- x$Fecha
Temporada <- x$Temporada
dc <- x$dc
area <- x$area
semana <- x$semana
cbind.data.frame(Cod.Barco, Cod.Viaje.VMS, date.GMT, Dur.Viaje,
Puerto.0.Mar.1, Vel.Cal, Calas, Lon, Lat, Fecha,
Temporada, dc, area, semana, tipo, cb)
})
out <- do.call(rbind.data.frame,out); row.names(out) <- NULL
# barco que no disponen de la variable tipo o cb son quitados del analisis
omit_vessel <- unique(out[is.na(out$tipo), "Cod.Barco"])
out <- out[!out$Cod.Barco %in% omit_vessel,]
return(out)
}
# ------------------------------------------------------------------------
# ------------------------------------------------------------------------
#x = data0[data0$semana == 1,]
iTemporalSpatial <- function(data, ...){
require(ineq)
require(sp)
index <- lapply(split(data, data$semana, drop = TRUE),function(x){
area_pesca <- length(unique(x$area))*25
dc_mean <- mean(x$dc, na.rm = T)
dc_lower <- quantile(x$dc, probs = 0.3, na.rm = T)
dc_upper <- quantile(x$dc, probs = 0.7, na.rm = T)
Lat_mean <- mean(x$Lat, na.rm = T)
Lat_lower <- quantile(x$Lat, probs = 0.3, na.rm = T)
Lat_upper <- quantile(x$Lat, probs = 0.7, na.rm = T)
# indicadores de autocorrelación espacial
vector <- sort(as.numeric(table(x$area)),decreasing = TRUE)
Igini <- Gini(vector, corr = TRUE)
Irs <- RS(vector, na.rm = TRUE)
Itkinson <- Atkinson(vector, parameter = 0.5, na.rm = TRUE)
Itheil <- Theil(vector, parameter = 0, na.rm = TRUE)
Ikolm <- Kolm(vector, parameter = 1, na.rm = TRUE)
Ivar.coeff <- var.coeff(vector, square = FALSE, na.rm = TRUE)
Ientropy <- entropy(vector, parameter = 0.5, na.rm = TRUE)
semana <- x$semana[1] + 1
cbind.data.frame(semana, area_pesca, dc_mean, dc_lower, dc_upper,
Lat_mean, Lat_lower, Lat_upper,
Igini, Irs, Itkinson, Itheil, Ikolm, Ivar.coeff, Ientropy)
})
index <- do.call(rbind.data.frame,index); row.names(index) <- NULL
return(index)
}
#
indicators_spatial_week <- function(data, list_vessel, inicio_temp, fin_temp, space.perfil = -5, ...){
data0 = type_holdCapacity(data, list_vessel) # agrega tipo y capacidad de bodega
data_ind <- data0[data0$tipo == "IND",]
data_indmad <- data0[data0$tipo == "IND MAD",]
list_data <- list(3)
list_data[[1]] = data0
list_data[[2]] = data_ind
list_data[[3]] = data_indmad
require(ineq)
out <- list(3)
for(i in 1:3){
out[[i]] <- iTemporalSpatial(data = list_data[[i]])
}
out_report <- list()
out_report$spatial_indicators_total <- out[[1]]
out_report$spatial_indicators_ind <- out[[2]]
out_report$spatial_indicators_indmad <- out[[3]]
out_report$fishing_points <- list_data[[1]]
return(out_report)
}
# ------------------------------------------------------------------------
# ------------------------------------------------------------------------
indicators_trajectory <- function(data, list_vessel, inicio_temp, fin_temp, ...){
out_report <- list()
# x = data[data$Cod.Viaje.VMS == "12977-10",]
require(dplyr)
indicators <- lapply(split(data, data$Cod.Viaje.VMS, drop = TRUE),function(x){
if(dim(x)[1] > 10){
tipo <- list_vessel[list_vessel$COD == x$Cod.Barco[1], "TIPO"]
cb <- list_vessel[list_vessel$COD == x$Cod.Barco[1], "CB"]
# print(x$Cod.Viaje.VMS[1])
Fecha.ini <- x$date.GMT[1]
Fecha.fin <- x$date.GMT[length(x$date.GMT)]
Fecha <- x$Fecha[1]
temp <- x$Temporada[1]
if(sum(x$Calas) > 1){
xcala = x[x$Calas == 1,]
distCalas <- NULL
for(i in 1:(nrow(xcala)-1)){
dist <- distXY(xcala[i,"Lon"], xcala[i,"Lat"], xcala[i+1,"Lon"], xcala[i+1,"Lat"])
distCalas <- c(distCalas, dist)
}
dist_calas <- sum(distCalas)
Lon_zarpe <- rep(x$Lon[1], nrow(xcala))
Lon_arribo <- rep(x$Lon[length(x$Lat)], nrow(xcala))
Lat_zarpe <- rep(x$Lat[1], nrow(xcala))
Lat_arribo <- rep(x$Lat[length(x$Lat)], nrow(xcala))
Cod.Viaje.VMS <- rep(x$Cod.Viaje.VMS[1], nrow(xcala))
Lon <- xcala$Lon
Lat <- xcala$Lat
hora_zarpe_1cala <- as.numeric(difftime(x$date.GMT[x$Calas == 1][1],x$date.GMT[1], units = "hours"))
}
if(sum(x$Calas) == 1){
dist_calas <- NA
Lon_zarpe <- x$Lon[1]
Lon_arribo <- x$Lon[length(x$Lat)]
Lat_zarpe <- x$Lat[1]
Lat_arribo <- x$Lat[length(x$Lat)]
Cod.Viaje.VMS <- x$Cod.Viaje.VMS[1]
Lon <- x$Lon[x$Calas == 1]
Lat <- x$Lat[x$Calas == 1]
hora_zarpe_1cala <- as.numeric(difftime(x$date.GMT[x$Calas == 1][1],x$date.GMT[1], units = "hours"))
}
if(sum(x$Calas) ==0){
dist_calas <- NA
Lon_zarpe <- x$Lon[1]
Lon_arribo <- x$Lon[length(x$Lat)]
Lat_zarpe <- x$Lat[1]
Lat_arribo <- x$Lat[length(x$Lat)]
Cod.Viaje.VMS <- x$Cod.Viaje.VMS[1]
Lon <- NA
Lat <- NA
hora_zarpe_1cala <- NA
}
require(SISESATools)
#SISESATools::puertos
Puerto_zarpe <- as.character(puertos[which.min(distAB(Lon_zarpe[1],Lat_zarpe[1], puertos$longitud, puertos$latitud)),"puerto2"])
Puerto_arribo <- as.character(puertos[which.min(distAB(Lon_arribo[1],Lat_arribo[1], puertos$longitud, puertos$latitud)),"puerto2"])
Puerto_zarpe <- rep(Puerto_zarpe, length(temp))
Puerto_arribo <- rep(Puerto_arribo, length(temp))
# distancia entre emisiones
x$distancia_emision <- NA
x$distancia_emision[2:(length(x[,1]))] <- distORTODROMICA(x$Lon[1:(length(x[,1])-1)],x$Lat[1:(length(x[,1])-1)],x$Lon[2:length(x[,1])],x$Lat[2:length(x[,1])])
# rumbo
x$rumbo <- NA
x$rumbo <- calcularRumbo(x$Lon,x$Lat)#$vectorRUMBO
rumbo_inicial <- x$rumbo[1+1]
rumbo_final <- x$rumbo[length(x$rumbo)-1]
# angulo
x$angle <- NA
x$angle[2:(length(x$angle)-1)] <- angle(x$Lon, x$Lat)
angulo_inicial <- x$angle[1+1]
angulo_final <- x$angle[length(x$angle)-1]
#
sinuosidad1 <- Sinuosidad1(x$angle,x$distancia_emision)
sinuosidad2 <- Sinuosidad2(x$angle,x$distancia_emision)
sinuosidad3 <- Sinuosidad3(x$angle)
if(length(cb) == 0){
cb <- NA
tipo <- NA
}
cbind.data.frame(Fecha.ini, Fecha.fin, Fecha, temp, Cod.Viaje.VMS,Puerto_zarpe, Puerto_arribo, Lon_zarpe,
Lon_arribo, Lat_zarpe, Lat_arribo, Lon, Lat, dist_calas,
angulo_inicial, angulo_final, rumbo_inicial, rumbo_final,
sinuosidad1 = sinuosidad1, sinuosidad2 = sinuosidad2, sinuosidad3 = sinuosidad3,
tipo = tipo, cb = cb)
}
})
indicators <- do.call(rbind.data.frame,indicators); row.names(indicators) <- NULL
out.semana <- imarpe:::estima_semana(inicio.temp = inicio_temp, fin.temp = fin_temp)
indicators$semana = NA
for(i in sort(unique(indicators$Fecha))){
indicators[indicators$Fecha == i, "semana"] = out.semana[out.semana$diasTemporada == i, "semana"]
}
# space = (0:(length(unique(indicators$semana))-1))*space.perfil
#
# indicators00 = indicators
# for(i in 1:length(unique(indicators00$semana))){
# indicators00[indicators00$semana == rev(sort(unique(indicators00$semana)))[i],"Lon"] = indicators00[indicators00$semana == rev(sort(unique(indicators00$semana)))[i],"Lon"] + space[i]
# }
# total
semana <- sort(as.numeric(unique(indicators$semana) + 1))
dist_calas <- fun_mean_IC(indicators$dist_calas, indicators$semana)
angulo_inicial <- fun_mean_IC(indicators$angulo_inicial, indicators$semana)
angulo_final <- fun_mean_IC(indicators$angulo_final, indicators$semana)
rumbo_inicial <- fun_mean_IC(indicators$rumbo_inicial, indicators$semana)
rumbo_final <- fun_mean_IC(indicators$rumbo_final, indicators$semana)
sinuosidad_1 <- fun_mean_IC(indicators$sinuosidad1, indicators$semana)
sinuosidad_2 <- fun_mean_IC(indicators$sinuosidad2, indicators$semana)
sinuosidad_3 <- fun_mean_IC(indicators$sinuosidad3, indicators$semana)
# industrial
semana_ind <- sort(as.numeric(unique(indicators$semana[indicators$tipo == "IND"]) + 1))
dist_calas_ind <- fun_mean_IC(indicators$dist_calas[indicators$tipo == "IND"], indicators$semana[indicators$tipo == "IND"])
angulo_inicial_ind <- fun_mean_IC(indicators$angulo_inicial[indicators$tipo == "IND"], indicators$semana[indicators$tipo == "IND"])
angulo_final_ind <- fun_mean_IC(indicators$angulo_final[indicators$tipo == "IND"], indicators$semana[indicators$tipo == "IND"])
rumbo_inicial_ind <- fun_mean_IC(indicators$rumbo_inicial[indicators$tipo == "IND"], indicators$semana[indicators$tipo == "IND"])
rumbo_final_ind <- fun_mean_IC(indicators$rumbo_final[indicators$tipo == "IND"], indicators$semana[indicators$tipo == "IND"])
sinuosidad_1_ind <- fun_mean_IC(indicators$sinuosidad1[indicators$tipo == "IND"], indicators$semana[indicators$tipo == "IND"])
sinuosidad_2_ind <- fun_mean_IC(indicators$sinuosidad2[indicators$tipo == "IND"], indicators$semana[indicators$tipo == "IND"])
sinuosidad_3_ind <- fun_mean_IC(indicators$sinuosidad3[indicators$tipo == "IND"], indicators$semana[indicators$tipo == "IND"])
# industrial de madera
semana_indmad <- sort(as.numeric(unique(indicators$semana[indicators$tipo == "IND MAD"]) + 1))
dist_calas_indmad <- fun_mean_IC(indicators$dist_calas[indicators$tipo == "IND MAD"], indicators$semana[indicators$tipo == "IND MAD"])
angulo_inicial_indmad <- fun_mean_IC(indicators$angulo_inicial[indicators$tipo == "IND MAD"], indicators$semana[indicators$tipo == "IND MAD"])
angulo_final_indmad <- fun_mean_IC(indicators$angulo_final[indicators$tipo == "IND MAD"], indicators$semana[indicators$tipo == "IND MAD"])
rumbo_inicial_indmad <- fun_mean_IC(indicators$rumbo_inicial[indicators$tipo == "IND MAD"], indicators$semana[indicators$tipo == "IND MAD"])
rumbo_final_indmad <- fun_mean_IC(indicators$rumbo_final[indicators$tipo == "IND MAD"], indicators$semana[indicators$tipo == "IND MAD"])
sinuosidad_1_indmad <- fun_mean_IC(indicators$sinuosidad1[indicators$tipo == "IND MAD"], indicators$semana[indicators$tipo == "IND MAD"])
sinuosidad_2_indmad <- fun_mean_IC(indicators$sinuosidad2[indicators$tipo == "IND MAD"], indicators$semana[indicators$tipo == "IND MAD"])
sinuosidad_3_indmad <- fun_mean_IC(indicators$sinuosidad3[indicators$tipo == "IND MAD"], indicators$semana[indicators$tipo == "IND MAD"])
tableResult <- data.frame(semana = semana, dist_calas = dist_calas$x, angulo_inicial = angulo_inicial$x, angulo_final = angulo_final$x, rumbo_inicial = rumbo_inicial$x, rumbo_final = rumbo_final$x, sinuosidad_1 = sinuosidad_1$x, sinuosidad_2 = sinuosidad_2$x, sinuosidad_3 = sinuosidad_3$x)
sdtableResult <- data.frame(semana = semana, dist_calas = dist_calas$sterr, angulo_inicial = angulo_inicial$sterr, angulo_final = angulo_final$sterr, rumbo_inicial = rumbo_inicial$sterr, rumbo_final = rumbo_final$sterr, sinuosidad_1 = sinuosidad_1$sterr, sinuosidad_2 = sinuosidad_2$sterr, sinuosidad_3 = sinuosidad_3$sterr)
tableResult_ind <- data.frame(semana = semana_ind, dist_calas = dist_calas_ind$x, angulo_inicial = angulo_inicial_ind$x, angulo_final = angulo_final_ind$x, rumbo_inicial = rumbo_inicial_ind$x, rumbo_final = rumbo_final_ind$x, sinuosidad_1 = sinuosidad_1_ind$x, sinuosidad_2 = sinuosidad_2_ind$x, sinuosidad_3 = sinuosidad_3_ind$x)
sdtableResult_ind <- data.frame(semana = semana_ind, dist_calas = dist_calas_ind$sterr, angulo_inicial = angulo_inicial_ind$sterr, angulo_final = angulo_final_ind$sterr, rumbo_inicial = rumbo_inicial_ind$sterr, rumbo_final = rumbo_final_ind$sterr, sinuosidad_1 = sinuosidad_1_ind$sterr, sinuosidad_2 = sinuosidad_2_ind$sterr, sinuosidad_3 = sinuosidad_3_ind$sterr)
tableResult_indmad <- data.frame(semana = semana_indmad, dist_calas = dist_calas_indmad$x, angulo_inicial = angulo_inicial_indmad$x, angulo_final = angulo_final_indmad$x, rumbo_inicial = rumbo_inicial_indmad$x, rumbo_final = rumbo_final_indmad$x, sinuosidad_1 = sinuosidad_1_indmad$x, sinuosidad_2 = sinuosidad_2_indmad$x, sinuosidad_3 = sinuosidad_3_indmad$x)
sdtableResult_indmad <- data.frame(semana = semana_indmad, dist_calas = dist_calas_indmad$sterr, angulo_inicial = angulo_inicial_indmad$sterr, angulo_final = angulo_final_indmad$sterr, rumbo_inicial = rumbo_inicial_indmad$sterr, rumbo_final = rumbo_final_indmad$sterr, sinuosidad_1 = sinuosidad_1_indmad$sterr, sinuosidad_2 = sinuosidad_2_indmad$sterr, sinuosidad_3 = sinuosidad_3_indmad$sterr)
out_report$index_trajectory_total <- tableResult
out_report$index_trajectory_total_ind <- tableResult_ind
out_report$index_trajectory_total_indmad <- tableResult_indmad
out_report$sd_index_trajectory_total <- sdtableResult
out_report$sd_index_trajectory_total_ind <- sdtableResult_ind
out_report$sd_index_trajectory_total_indmad <- sdtableResult_indmad
return(out_report)
}
# ------------------------------------------------------------------------
input_report <- function(object1, object2, object3){
out_report <- list()
out_report$effort_total <- out_report1$effort_total
out_report$effort_ind <- out_report1$effort_ind
out_report$effort_indmad <- out_report1$effort_indmad
out_report$sdt_effort_total <- out_report1$sdt_effort_total
out_report$sdt_effort_ind <- out_report1$sdt_effort_ind
out_report$sdt_effort_indmad <- out_report1$sdt_effort_indmad
out_report$spatial_indicators_total <- object2$spatial_indicators_total
out_report$spatial_indicators_ind <- object2$spatial_indicators_ind
out_report$spatial_indicators_indmad <- object2$spatial_indicators_indmad
out_report$fishing_points <- object2$fishing_points
out_report$index_trajectory_total <- object3$index_trajectory_total
out_report$index_trajectory_total_ind <- object3$index_trajectory_total_ind
out_report$index_trajectory_total_indmad <- object3$index_trajectory_total_indmad
out_report$sd_index_trajectory_total <- object3$sd_index_trajectory_total
out_report$sd_index_trajectory_total_ind <- object3$sd_index_trajectory_total_ind
out_report$sd_index_trajectory_total_indmad <- object3$sd_index_trajectory_total_indmad
return(out_report)
}
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