array_group -> list_to_matrix fixes

comparisons.scad

@@ -745,7 +745,7 @@ function group_sort(list, idx) =
 // Usage:
 //   groupings = group_data(groups, values);
 // Topics: Array Handling
-// See Also: zip(), zip_long(), array_group()
+// See Also: zip(), zip_long()
 // Description:
 //   Given a list of integer group numbers, and an equal-length list of values,
 //   returns a list of groups with the values sorted into the corresponding groups.

linalg.scad

@@ -321,7 +321,7 @@ function submatrix_set(M,A,m=0,n=0) =
 // Description:
 //   Constructs a matrix by horizontally "stacking" together compatible matrices or vectors.  Vectors are treated as columsn in the stack.
 //   This command is the inverse of `column`.  Note: strings given in vectors are broken apart into lists of characters.  Strings given
-//   in matrices are preserved as strings.  If you need to combine vectors of strings use array_group as shown below to convert the
+//   in matrices are preserved as strings.  If you need to combine vectors of strings use {{list_to_matrix}} as shown below to convert the
 //   vector into a column matrix.  Also note that vertical stacking can be done directly with concat.  
 // Arguments:
 //   M1 = If given with other arguments, the first matrix (or vector) to stack.  If given alone, a list of matrices/vectors to stack. 
@@ -344,7 +344,7 @@ function submatrix_set(M,A,m=0,n=0) =
 //   strmat = [["three","four"], ["five","six"]];
 //   e = hstack(strvec,strvec); // Returns [["o", "n", "e", "o", "n", "e"],
 //                              //          ["t", "w", "o", "t", "w", "o"]]
-//   f = hstack(array_group(strvec,1), array_group(strvec,1));
+//   f = hstack(list_to_matrix(strvec,1), list_to_matrix(strvec,1));
 //                              // Returns [["one", "one"],
 //                              //          ["two", "two"]]
 //   g = hstack(strmat,strmat); //  Returns: [["three", "four", "three", "four"],

math.scad

@@ -549,7 +549,7 @@ function gaussian_rands(N=1, mean=0, cov=1, seed=undef) =
         L = cholesky(cov)
     )
     assert(is_def(L), "Supplied covariance matrix is not positive definite")
-    move(mean,array_group(rdata,dim)*transpose(L));
+    move(mean,list_to_matrix(rdata,dim)*transpose(L));
 
 
 // Function: spherical_random_points()

paths.scad

@@ -795,7 +795,7 @@ function _path_cut_points(path, dists, closed=false, direction=false) =
              dir = _path_cuts_dir(path, cuts, closed),
              normals = _path_cuts_normals(path, cuts, dir, closed)
          )
-         hstack(cuts, array_group(dir,1), array_group(normals,1));
+         hstack(cuts, list_to_matrix(dir,1), list_to_matrix(normals,1));
 
 // Main recursive path cut function
 function _path_cut_points_recurse(path, dists, closed=false, pind=0, dtotal=0, dind=0, result=[]) =

tests/test_comparisons.scad

@@ -1,3 +1,5 @@
+include<../std.scad>
+
 module test_sort() {
     assert(sort([7,3,9,4,3,1,8]) == [1,3,3,4,7,8,9]);
     assert(sort([[4,0],[7],[3,9],20,[4],[3,1],[8]]) == [20,[3,1],[3,9],[4],[4,0],[7],[8]]);

vectors.scad

@@ -363,7 +363,7 @@ function furthest_point(pt, points) =
 // Example: A set of four queries to find points within 1 unit of the query.  The circles show the search region and all have radius 1.  
 //   $fn=32;
 //   k = 2000;
-//   points = array_group(rands(0,10,k*2,seed=13333),2);
+//   points = list_to_matrix(rands(0,10,k*2,seed=13333),2);
 //   queries = [for(i=[3,7],j=[3,7]) [i,j]];
 //   search_ind = vector_search(queries, points, 1);
 //   move_copies(points) circle(r=.08);
@@ -374,7 +374,7 @@ function furthest_point(pt, points) =
 // Example: when a series of search with different radius are needed, its is faster to pre-compute the tree
 //   $fn=32;
 //   k = 2000;
-//   points = array_group(rands(0,10,k*2),2,seed=13333);
+//   points = list_to_matrix(rands(0,10,k*2),2,seed=13333);
 //   queries1 = [for(i=[3,7]) [i,i]];
 //   queries2 = [for(i=[3,7]) [10-i,i]];
 //   r1 = 1;
@@ -517,7 +517,7 @@ function _bt_tree(points, ind, leafsize=25) =
 // Example:  Four queries to find the 15 nearest points.  The circles show the radius defined by the most distant query result.  Note they are different for each query.  
 //    $fn=32;
 //    k = 1000;
-//    points = array_group(rands(0,10,k*2,seed=13333),2);
+//    points = list_to_matrix(rands(0,10,k*2,seed=13333),2);
 //    tree = vector_search_tree(points);
 //    queries = [for(i=[3,7],j=[3,7]) [i,j]];
 //    search_ind = [for(q=queries) vector_nearest(q, 15, tree)];