Fix JuliaLang#4503

Address all issues discussed in JuliaLang#4547
Add test for spdiagm and update docs

base/sparse/sparsematrix.jl

@@ -1288,32 +1288,19 @@ function istril(A::SparseMatrixCSC)
     return true
 end
 
-function spdiagm{T}(v::Union(AbstractVector{T},AbstractMatrix{T}), k::Integer=0)
-    if isa(v, AbstractMatrix)
-        if (size(v,1) != 1 && size(v,2) != 1)
-            error("Input should be nx1 or 1xn")
-        end
-    end
-
-    nv = length(v)
-    nr = nc = nv + abs(k)
-
-    x = diagind(nr, nc, k)
-    I, J = ind2sub((nr,nc), x)
+# Create a sparse diagonal matrix by specifying multiple diagonals 
+# packed into a tuple, alongside their diagonal offsets and matrix shape
 
-    sparse(I,J,v,nr,nc)
-end
-
-## extend spdiagm by specifying multiple diagonals packed into a tuple alongside their diagonal offsets and matrix shape
-function spdiagm{T<:Integer}(B, d::Array{T, 1}, m::Integer, n::Integer)
+function spdiagm_int(B::Tuple, d::Tuple)
     ndiags = length(d)
+    if length(B) != ndiags; throw(ArgumentError); end
     ncoeffs = 0
     for vec in B
         ncoeffs += length(vec)
     end
-    I = Array(T, ncoeffs)
-    J = Array(T, ncoeffs)
-    V = Array(Float64, ncoeffs)
+    I = Array(Int, ncoeffs)
+    J = Array(Int, ncoeffs)
+    V = Array(eltype(B[1]), ncoeffs)
     id = 0
     i = 0
     for vec in B
@@ -1333,12 +1320,27 @@ function spdiagm{T<:Integer}(B, d::Array{T, 1}, m::Integer, n::Integer)
         range = 1+i:numel+i
         I[range] = row+1:row+numel
         J[range] = col+1:col+numel
-        V[range] = vec[1:numel]
+        copy!(sub(V, range), vec)
         i += numel
     end
-    return sparse(I, J, V, m, n)
+
+    return (I,J,V)
 end
 
+function spdiagm(B::Tuple, d::Tuple, m::Integer, n::Integer)
+    (I,J,V) = spdiagm_int(B, d)
+    return sparse(I,J,V,m,n)
+end
+
+function spdiagm(B::Tuple, d::Tuple)
+    (I,J,V) = spdiagm_int(B, d)
+    return sparse(I,J,V)
+end
+
+spdiagm(B::AbstractVector, d::Number, m, n) = spdiagm((B,), (d,), m, n)
+
+spdiagm(B::AbstractVector, d::Number) = spdiagm((B,), (d,))
+
 ## expand a colptr or rowptr into a dense index vector
 function expandptr{T<:Integer}(V::Vector{T})
     if V[1] != 1 error("expandptr: first index must be one") end

doc/stdlib/sparse.rst

@@ -51,9 +51,9 @@ Sparse matrices support much of the same set of operations as dense matrices. Th
 
    Create a sparse identity matrix of specified type of size ``m x m``. In case ``n`` is supplied, create a sparse identity matrix of size ``m x n``.
 
-.. function:: spdiagm(v)
+.. function:: spdiagm(B, d[, m, n])
 
-   Construct a sparse diagonal matrix and place "v" on the diagonal.
+   Construct a sparse diagonal matrix. ``B`` is a tuple of vectors containing the diagonals and ``d`` is a tuple containing the positions of the diagonals. In the case the input contains only one diagonaly, ``B`` can be a vector (instead of a tuple) and ``d`` can be the diagonal position (instead of a tuple). Optionally, ``m`` and ``n`` specify the size of the resulting sparse matrix.
 
 .. function:: sprand(m,n,density[,rng])
 

test/sparse.jl

@@ -76,6 +76,10 @@ end
 @test prod(se33, 1) == [0.0 0.0 0.0]
 @test prod(se33, 2) == [0.0 0.0 0.0]'
 
+# spdiagm
+@test full(spdiagm((ones(2), ones(2)), (0, -1), 3, 3)) ==  
+                       [1.0  0.0  0.0; 1.0  1.0  0.0;  0.0  1.0  0.0]
+
 # elimination tree
 ## upper triangle of the pattern test matrix from Figure 4.2 of
 ## "Direct Methods for Sparse Linear Systems" by Tim Davis, SIAM, 2006