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#### Triangular Sparse Matrices in compressed column-oriented format

setAs("dtCMatrix", "ltCMatrix",
      function(from) new("ltCMatrix", i = from@i, p = from@p,
             uplo = from@uplo, diag = from@diag,
                         x = as.logical(from@x),
             ## FIXME?: use from@factors smartly
             Dim = from@Dim, Dimnames = from@Dimnames))
setAs("dtCMatrix", "ntCMatrix", # just drop 'x' slot:
      function(from) new("ntCMatrix", i = from@i, p = from@p,
             uplo = from@uplo, diag = from@diag,
             ## FIXME?: use from@factors smartly
             Dim = from@Dim, Dimnames = from@Dimnames))


setAs("matrix", "dtCMatrix",
      function(from) as(as(from, "dtTMatrix"), "dtCMatrix"))

setAs("dtCMatrix", "dgCMatrix",
      function(from) {
          if (from@diag == "U")
              from <- .Call(Csparse_diagU2N, from)
          new("dgCMatrix",
              i = from@i, p = from@p, x = from@x,
              Dim = from@Dim, Dimnames = from@Dimnames)
      })

setAs("dtCMatrix", "dsCMatrix", function(from) as(from, "symmetricMatrix"))

setAs("dtCMatrix", "dgTMatrix",
      function(from) {
          if (from@diag == "U") from <- .Call(Csparse_diagU2N, from)
          ## ignore triangularity in conversion to TsparseMatrix
          .Call(Csparse_to_Tsparse, from, FALSE)
      })

## FIXME: make more efficient
## -----  and  as(., "triangularMatrix") is even worse via as_Sp()
setAs("dgCMatrix", "dtCMatrix", # to triangular, needed for triu,..
      function(from) as(as(as(from, "dgTMatrix"), "dtTMatrix"), "dtCMatrix"))

setAs("dtCMatrix", "dgeMatrix",
      function(from) as(as(from, "dgTMatrix"), "dgeMatrix"))

## These are all needed because cholmod doesn't support triangular:
## (see end of ./Csparse.R ), e.g. for triu()
setAs("dtCMatrix", "dtTMatrix",
      function(from) .Call(Csparse_to_Tsparse, from, TRUE))
##   {# and this is not elegant:
##           x <- as(from, "dgTMatrix")
##        if (from@diag == "U") { ## drop diagonal entries '1':
##            i <- x@i; j <- x@j
##            nonD <- i != j
##            xx <- x@x[nonD] ; i <- i[nonD] ; j <- j[nonD]
##        } else {
##            xx <- x@x; i <- x@i; j <- x@j
##        }
##        new("dtTMatrix", x = xx, i = i, j = j, Dim = x@Dim,
##            Dimnames = x@Dimnames, uplo = from@uplo, diag = from@diag)
##       })

## Now that we support triangular matrices use the inherited method.
## setAs("dtCMatrix", "TsparseMatrix", function(from) as(from, "dtTMatrix"))

setAs("dtCMatrix", "dtrMatrix",
      function(from) as(as(from, "dtTMatrix"), "dtrMatrix"))

setMethod("solve", signature(a = "dtCMatrix", b = "missing"),
      function(a, b, ...) {
          if (a@diag == "U") {
          if (a@uplo == "U")
              return(.Call(dtCMatrix_upper_solve, a))
          else
              return(t(.Call(dtCMatrix_upper_solve, t(a))))
          }
          .Call(dtCMatrix_solve, a)
      }, valueClass = "dtCMatrix")

setMethod("solve", signature(a = "dtCMatrix", b = "dgeMatrix"),
      function(a, b, ...) {
              if (a@diag == "U") a <- .Call(Csparse_diagU2N, a)
          .Call(dtCMatrix_matrix_solve, a, b, TRUE)
      }, valueClass = "dgeMatrix")

setMethod("solve", signature(a = "dtCMatrix", b = "matrix"),
      function(a, b, ...) {
              if (a@diag == "U") a <- .Call(Csparse_diagU2N, a)
          storage.mode(b) <- "double"
          .Call(dtCMatrix_matrix_solve, a, b, FALSE)
      }, valueClass = "dgeMatrix")

## Isn't this case handled by the method for (a = "Matrix', b =
## "numeric") in ./Matrix.R? Or is this method defined here for
## the as.double coercion?
setMethod("solve", signature(a = "dtCMatrix", b = "numeric"),
      function(a, b, ...) {
          if (a@diag == "U") a <- as(diagU2N(a), "dtCMatrix")
          .Call(dtCMatrix_matrix_solve, a, as.matrix(as.double(b)),
            FALSE)
      }, valueClass = "dgeMatrix")