minor comments, lintr, styler

Author: Rafnuss

R/geopressure_map_preprocess.R

@@ -188,7 +188,7 @@ geopressure_map_preprocess <- function(tag, compute_known = FALSE) {
 
     assertthat::assert_that(all(!is.na(pgi_reg$value)))
 
-    return(pgi_reg)
+    pgi_reg
   })
 
   # Combine into a single data.frame

R/geopressuretemplate_graph.R

@@ -85,7 +85,7 @@ geopressuretemplate_graph <- function(
         "i" = "Error while defining the movement model.{.var graph} is return.",
         ">" = "Debug line by line by opening {.code edit(geopressuretemplate_graph)}"
       ))
-      return(graph)
+      graph
     }
   )
 
@@ -137,7 +137,7 @@ geopressuretemplate_graph <- function(
         "x" = "Error while computing the outputs. {.var graph} is returned.",
         ">" = "Debug line by line by opening {.code edit(geopressuretemplate_graph)}"
       ))
-      return(graph)
+      graph
     }
   )
 

R/graph_assert.R

@@ -51,5 +51,5 @@ graph_status <- function(graph) {
   if (length(graph$s) > 0) {
     status <- append(status, "full")
   }
-  return(status)
+  status
 }

R/graph_create.R

@@ -175,7 +175,7 @@ graph_create <- function(tag,
     thr_prob <- ls[id_prob_percentile + 1]
 
     # return matrix if the values are above the threshold
-    return(l >= thr_prob)
+    l >= thr_prob
   })
 
   # Check that there are still values
@@ -371,6 +371,9 @@ graph_create <- function(tag,
     likelihood = likelihood
   )
 
+  # Check graph validity
+  # all(graph$s[!(graph$s  %in% graph$equipment)] %in% graph$t)
+
   return(graph)
 }
 

R/graph_most_likely.R

@@ -58,20 +58,22 @@ graph_most_likely <- function(graph, quiet = FALSE) {
   # number of nodes in the 3d grid
   n <- prod(graph$sz)
 
-  # Compute the matrix TO
+  # Compute the matrix TO (transition * observation)
   if (!quiet) {
     cli::cli_progress_step("Compute movement model")
   }
   trans_obs <- graph_transition(graph) * graph$obs[graph$t]
 
-  # Initiate the matrix providing for each node of the graph, the source id (index of the node)
-  # with the most likely path to get there.
+  # Initiate the sparse 1D matrix providing for each node of the graph, the source id (index of the
+  # node in the 3D grid) with the cumulative max probability to get there.
+  # Start with prob=1 at the equipment site (log = 0)
   path_s <- Matrix::sparseMatrix(
     rep(1, length(graph$equipment)),
     graph$equipment,
     x = 0, dims = c(1, n)
   )
-  # Initiate the same matrix providing the total probability of the current path so far
+  # Initiate the same matrix providing the cumulative total probability of the current path so far
+  # Not sure why x is differently specify, should be the same value for both path_s and path_max
   path_max <- Matrix::sparseMatrix(
     rep(1, length(graph$equipment)),
     graph$equipment,
@@ -89,9 +91,10 @@ graph_most_likely <- function(graph, quiet = FALSE) {
     stap = arrayInd(graph$s, graph$sz)[, 3]
   )
 
-  # Split this data.fram by stationary period (of the source)
+  # Split this data.frame by stationary period (of the source)
   node_stap <- split(node, node$stap)
 
+  # Compute number of nodes per stap
   n_edge <- sapply(node_stap, nrow)
 
   if (!quiet) {
@@ -109,12 +112,14 @@ graph_most_likely <- function(graph, quiet = FALSE) {
   }
 
   for (i_s in seq_len(length(node_stap))) {
+    # Select all nodes of the current stap
     node_i_s <- node_stap[[i_s]]
 
-    # compute the probability of all possible transition
+    # Compute the (cum) log probability of all possible transitions
     node_i_s$p <- path_max[node_i_s$s] + node_i_s$to
 
-    # Find the value of the maximum possible transition for each target node
+    # Find the value of the maximum possible transition for each target node and store it into
+    # path_max
     max_v <- sapply(split(node_i_s$p, node_i_s$t), max)
     max_t <- as.numeric(names(max_v))
     path_max[max_t] <- max_v

R/graph_transition.R

@@ -43,5 +43,5 @@ graph_transition <- function(graph) {
     ))
   }
 
-  return(transition)
+  transition
 }

R/light2mat.R

@@ -58,9 +58,9 @@ light2mat <- function(light, twl_offset = 0) {
 
     if (length(candidates) > 0) {
       closest_idx <- candidates[which.min(abs(light_date_num[candidates] - t))]
-      return(light_value[closest_idx])
+      light_value[closest_idx]
     } else {
-      return(NA)
+      NA
     }
   }
 

R/map_create.R

@@ -139,13 +139,13 @@ dim.map <- function(x) {
   # Compute value
   z$data <- mapply(\(p, l) {
     if (is.null(p) && is.null(l)) {
-      return(NULL)
+      NULL
     } else if (is.null(p)) {
-      return(l)
+      l
     } else if (is.null(l)) {
-      return(p)
+      p
     } else {
-      return(p * l)
+      p * l
     }
   }, x$data, y$data, SIMPLIFY = FALSE)
 
@@ -157,5 +157,5 @@ dim.map <- function(x) {
 
   z$type <- glue::glue("{x$type} x {y$type}")
 
-  return(z)
+  z
 }

R/map_expand.R

@@ -77,5 +77,5 @@ map_expand <- function(extent, scale) {
     lon = lon,
     dim = dim
   )
-  return(grid)
+  grid
 }

R/path2edge.R

@@ -129,5 +129,5 @@ path2edge <- function(path, tag_graph) {
 
   attr(edge, "type") <- attr(path, "type")
 
-  return(edge)
+  edge
 }