From 9fdda3115106eed2f0bdc750a944d51dc3874ef3 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Max=20L=C3=BCbke?= <mluebke@gfz-potsdam.de>
Date: Tue, 19 Mar 2024 09:47:40 +0000
Subject: [PATCH] Refactor pqc_to_grid function to use matrix instead of
 data.table

---
 R_lib/init_r_lib.R | 82 +++++++++-------------------------------------
 1 file changed, 15 insertions(+), 67 deletions(-)

diff --git a/R_lib/init_r_lib.R b/R_lib/init_r_lib.R
index 8879ac703..672e372cc 100644
--- a/R_lib/init_r_lib.R
+++ b/R_lib/init_r_lib.R
@@ -1,82 +1,30 @@
-input <- readRDS("/home/max/Storage/poet/build/apps/out.rds")
-
-grid_def <- matrix(c(2, 3), nrow = 2, ncol = 5)
-
-library(data.table)
-
 pqc_to_grid <- function(pqc_in, grid) {
-    # Convert the input DataFrame to a data.table
-    dt <- data.table(pqc_in)
+    # Convert the input DataFrame to a matrix
+    dt <- as.matrix(pqc_in)
 
     # Flatten the matrix into a vector
     id_vector <- as.vector(t(grid))
 
-    # Initialize an empty data.table to store the results
-    result_dt <- data.table()
+    # Initialize an empty matrix to store the results
+    result_mat <- matrix(nrow = 0, ncol = ncol(dt))
 
     # Iterate over each ID in the vector
     for (id_mat in id_vector) {
-        # Find the matching row in the data.table
-        matching_dt <- dt[dt$id == id_mat]
+        # Find the matching row in the matrix
+        matching_row <- dt[dt[, "ID"] == id_mat, ]
 
-        # Append the matching data.table row to the result data.table
-        result_dt <- rbind(result_dt, matching_dt)
+        # Append the matching row to the result matrix
+        result_mat <- rbind(result_mat, matching_row)
     }
 
+    # Convert the result matrix to a data frame
+    res_df <- as.data.frame(result_mat)
+
     # Remove all columns which only contain NaN
-    # result_dt <- result_dt[, colSums(is.na(result_dt)) != nrow(result_dt)]
+    res_df <- res_df[, colSums(is.na(res_df)) != nrow(res_df)]
 
-    res_df <- as.data.frame(result_dt)
+    # Remove row names
+    rownames(res_df) <- NULL
 
-    return(res_df[, colSums(is.na(res_df)) != nrow(res_df)])
+    return(res_df)
 }
-
-pqc_init <- pqc_to_grid(input, grid_def)
-test <- pqc_init
-
-modify_module_sizes <- function(mod_sizes, pqc_mat, init_grid) {
-    # Find all unique IDs in init_grid
-    unique_ids <- unique(init_grid$id)
-
-    # remove rows from pqc_mat that are not in init_grid
-    pqc_mat <- as.data.frame(pqc_mat)
-    pqc_mat <- pqc_mat[pqc_mat$id %in% unique_ids, ]
-
-    # Find the column indices where all rows are NaN
-    na_cols <- which(sapply(pqc_mat, function(x) all(is.na(x))))
-    # na_cols <- which(colSums(is.nan(pqc_mat)) == nrow(pqc_mat))
-
-    # Build cumsum over mod_sizes
-    cum_mod_sizes <- cumsum(mod_sizes)
-
-    # Find the indices where the value of na_cols is equal to the value of cum_mod_sizes
-    idx <- which(cum_mod_sizes %in% na_cols)
-
-    # Set the value of mod_sizes to 0 at the indices found in the previous step
-    mod_sizes[idx] <- 0
-
-    return(mod_sizes)
-}
-
-# mod_sizes <- c(7, 0, 4, 2, 0)
-
-# unique_ids <- unique(as.vector(pqc_init$id))
-
-# tmp <- as.data.frame(input)
-# pqc_test <- tmp[tmp$id %in% unique_ids, ]
-# na_cols <- which(colSums(is.na(pqc_test)) == nrow(pqc_test))
-# cum_mod_sizes <- cumsum(mod_sizes)
-
-# # Get the indices of the columns of cum_mod_sizes where the value of the column is equal to the value of na_cols
-# idx <- which(cum_mod_sizes %in% na_cols)
-# mod_sizes[idx] <- 0
-
-# idx <- which(na_cols == cum_mod_sizes)
-
-
-
-# idx <- which(na_cols[1] >= cum_mod_sizes)
-
-# mod_sizes <- modify_module_sizes(mod_sizes, pqc_init, pqc_init)
-
-# remove column with all NA