# This file is a part of Julia. License is MIT: http://julialang.org/license

module CHOLMOD

import Base: (*), convert, copy, eltype, get, getindex, show, size,
             IndexStyle, IndexLinear, IndexCartesian, ctranspose

import Base.LinAlg: (\), A_mul_Bc, A_mul_Bt, Ac_ldiv_B, Ac_mul_B, At_ldiv_B, At_mul_B,
                 cholfact, cholfact!, det, diag, ishermitian, isposdef,
                 issymmetric, ldltfact, ldltfact!, logdet

importall ..SparseArrays

export
    Dense,
    Factor,
    Sparse

import ..SparseArrays: AbstractSparseMatrix, SparseMatrixCSC, increment, indtype

#########
# Setup #
#########

include("cholmod_h.jl")

const CHOLMOD_MIN_VERSION = v"2.1.1"

### These offsets are defined in SuiteSparse_wrapper.c
const common_size = ccall((:jl_cholmod_common_size,:libsuitesparse_wrapper),Int,())

const cholmod_com_offsets = Array{Csize_t}(19)
ccall((:jl_cholmod_common_offsets, :libsuitesparse_wrapper),
    Void, (Ptr{Csize_t},), cholmod_com_offsets)

## macro to generate the name of the C function according to the integer type
macro cholmod_name(nm, typ)
    string("cholmod_", eval(typ) == SuiteSparse_long ? "l_" : "", nm)
end

function start(a::Vector{UInt8})
    @isok ccall((@cholmod_name("start", SuiteSparse_long), :libcholmod),
        Cint, (Ptr{UInt8},), a)
    return a
end

function finish(a::Vector{UInt8})
    @isok ccall((@cholmod_name("finish", SuiteSparse_long), :libcholmod),
        Cint, (Ptr{UInt8},), a)
    return a
end

function defaults(a::Vector{UInt8})
    @isok ccall((@cholmod_name("defaults", SuiteSparse_long), :libcholmod),
        Cint, (Ptr{UInt8},), a)
    return a
end

common() = commonStruct

const build_version_array = Array{Cint}(3)
ccall((:jl_cholmod_version, :libsuitesparse_wrapper), Cint, (Ptr{Cint},), build_version_array)
const build_version = VersionNumber(build_version_array...)

function __init__()
    try
        ### Check if the linked library is compatible with the Julia code
        if Libdl.dlsym_e(Libdl.dlopen("libcholmod"), :cholmod_version) != C_NULL
            current_version_array = Array{Cint}(3)
            ccall((:cholmod_version, :libcholmod), Cint, (Ptr{Cint},), current_version_array)
            current_version = VersionNumber(current_version_array...)
        else # CHOLMOD < 2.1.1 does not include cholmod_version()
            current_version = v"0.0.0"
        end


        if current_version < CHOLMOD_MIN_VERSION
            warn("""

                CHOLMOD version incompatibility

                Julia was compiled with CHOLMOD version $build_version. It is
                currently linked with a version older than
                $(CHOLMOD_MIN_VERSION). This might cause Julia to
                terminate when working with sparse matrix factorizations,
                e.g. solving systems of equations with \\.

                It is recommended that you use Julia with a recent version
                of CHOLMOD, or download the generic binaries
                from www.julialang.org, which ship with the correct
                versions of all dependencies.
            """)
        elseif build_version_array[1] != current_version_array[1]
            warn("""

                CHOLMOD version incompatibility

                Julia was compiled with CHOLMOD version $build_version. It is
                currently linked with version $current_version.
                This might cause Julia to terminate when working with
                sparse matrix factorizations, e.g. solving systems of
                equations with \\.

                It is recommended that you use Julia with the same major
                version of CHOLMOD as the one used during the build, or
                download the generic binaries from www.julialang.org,
                which ship with the correct versions of all dependencies.
            """)
        end

        intsize = Int(ccall((:jl_cholmod_sizeof_long,:libsuitesparse_wrapper),Csize_t,()))
        if intsize != 4length(IndexTypes)
            warn("""

                 CHOLMOD integer size incompatibility

                 Julia was compiled with a version of CHOLMOD that
                 supported $(32length(IndexTypes)) bit integers. It is
                 currently linked with version that supports $(8intsize)
                 integers. This might cause Julia to terminate when
                 working with sparse matrix factorizations, e.g. solving
                 systems of equations with \\.

                 This problem can be fixed by modifying the Julia build
                 configuration or by downloading the OS X or generic
                 Linux binary from www.julialang.org, which include
                 the correct versions of all dependencies.
             """)
        end

        ### Initiate CHOLMOD
        ### The common struct. Controls the type of factorization and keeps pointers
        ### to temporary memory.
        global const commonStruct = fill(0xff, common_size)

        global const common_supernodal =
            convert(Ptr{Cint}, pointer(commonStruct, cholmod_com_offsets[4] + 1))
        global const common_final_ll =
            convert(Ptr{Cint}, pointer(commonStruct, cholmod_com_offsets[7] + 1))
        global const common_print =
            convert(Ptr{Cint}, pointer(commonStruct, cholmod_com_offsets[13] + 1))
        global const common_itype =
            convert(Ptr{Cint}, pointer(commonStruct, cholmod_com_offsets[18] + 1))
        global const common_dtype =
            convert(Ptr{Cint}, pointer(commonStruct, cholmod_com_offsets[19] + 1))
        global const common_nmethods =
            convert(Ptr{Cint}, pointer(commonStruct, cholmod_com_offsets[15] + 1))
        global const common_postorder =
            convert(Ptr{Cint}, pointer(commonStruct, cholmod_com_offsets[17] + 1))

        start(commonStruct)              # initializes CHOLMOD
        set_print_level(commonStruct, 0) # no printing from CHOLMOD by default

        # Register gc tracked allocator if CHOLMOD is new enough
        if current_version >= v"3.0.0"
            cnfg = cglobal((:SuiteSparse_config, :libsuitesparseconfig), Ptr{Void})
            unsafe_store!(cnfg, cglobal(:jl_malloc, Ptr{Void}), 1)
            unsafe_store!(cnfg, cglobal(:jl_calloc, Ptr{Void}), 2)
            unsafe_store!(cnfg, cglobal(:jl_realloc, Ptr{Void}), 3)
            unsafe_store!(cnfg, cglobal(:jl_free, Ptr{Void}), 4)
        end

    catch ex
        Base.showerror_nostdio(ex,
            "WARNING: Error during initialization of module CHOLMOD")
    end
end

function set_print_level(cm::Array{UInt8}, lev::Integer)
    global common_print
    unsafe_store!(common_print, lev)
end

####################
# Type definitions #
####################

abstract type SuiteSparseStruct end

# The three core data types for CHOLMOD: Dense, Sparse and Factor.
# CHOLMOD manages the memory, so the Julia versions only wrap a
# pointer to a struct.  Therefore finalizers should be registered each
# time a pointer is returned from CHOLMOD.

# Dense
struct C_Dense{T<:VTypes} <: SuiteSparseStruct
    nrow::Csize_t
    ncol::Csize_t
    nzmax::Csize_t
    d::Csize_t
    x::Ptr{T}
    z::Ptr{Void}
    xtype::Cint
    dtype::Cint
end

mutable struct Dense{T<:VTypes} <: DenseMatrix{T}
    p::Ptr{C_Dense{T}}
end

# Sparse
struct C_Sparse{Tv<:VTypes} <: SuiteSparseStruct
    nrow::Csize_t
    ncol::Csize_t
    nzmax::Csize_t
    p::Ptr{SuiteSparse_long}
    i::Ptr{SuiteSparse_long}
    nz::Ptr{SuiteSparse_long}
    x::Ptr{Tv}
    z::Ptr{Void}
    stype::Cint
    itype::Cint
    xtype::Cint
    dtype::Cint
    sorted::Cint
    packed::Cint
end

# Corresponds to the exact definition of cholmod_sparse_struct in the library.
# Useful when reading matrices of unknown type from files as in
# cholmod_read_sparse
struct C_SparseVoid <: SuiteSparseStruct
    nrow::Csize_t
    ncol::Csize_t
    nzmax::Csize_t
    p::Ptr{Void}
    i::Ptr{Void}
    nz::Ptr{Void}
    x::Ptr{Void}
    z::Ptr{Void}
    stype::Cint
    itype::Cint
    xtype::Cint
    dtype::Cint
    sorted::Cint
    packed::Cint
end

mutable struct Sparse{Tv<:VTypes} <: AbstractSparseMatrix{Tv,SuiteSparse_long}
    p::Ptr{C_Sparse{Tv}}
    function Sparse{Tv}(p::Ptr{C_Sparse{Tv}}) where Tv<:VTypes
        if p == C_NULL
            throw(ArgumentError("sparse matrix construction failed for " *
                "unknown reasons. Please submit a bug report."))
        end
        new(p)
    end
end
Sparse(p::Ptr{C_Sparse{Tv}}) where Tv<:VTypes = Sparse{Tv}(p)

# Factor

if build_version >= v"2.1.0" # CHOLMOD version 2.1.0 or later
    struct C_Factor{Tv<:VTypes} <: SuiteSparseStruct
        n::Csize_t
        minor::Csize_t
        Perm::Ptr{SuiteSparse_long}
        ColCount::Ptr{SuiteSparse_long}
        IPerm::Ptr{SuiteSparse_long}        # this pointer was added in verison 2.1.0
        nzmax::Csize_t
        p::Ptr{SuiteSparse_long}
        i::Ptr{SuiteSparse_long}
        x::Ptr{Tv}
        z::Ptr{Void}
        nz::Ptr{SuiteSparse_long}
        next::Ptr{SuiteSparse_long}
        prev::Ptr{SuiteSparse_long}
        nsuper::Csize_t
        ssize::Csize_t
        xsize::Csize_t
        maxcsize::Csize_t
        maxesize::Csize_t
        super::Ptr{SuiteSparse_long}
        pi::Ptr{SuiteSparse_long}
        px::Ptr{SuiteSparse_long}
        s::Ptr{SuiteSparse_long}
        ordering::Cint
        is_ll::Cint
        is_super::Cint
        is_monotonic::Cint
        itype::Cint
        xtype::Cint
        dtype::Cint
    end
else
    struct C_Factor{Tv<:VTypes} <: SuiteSparseStruct
        n::Csize_t
        minor::Csize_t
        Perm::Ptr{SuiteSparse_long}
        ColCount::Ptr{SuiteSparse_long}
        nzmax::Csize_t
        p::Ptr{SuiteSparse_long}
        i::Ptr{SuiteSparse_long}
        x::Ptr{Tv}
        z::Ptr{Void}
        nz::Ptr{SuiteSparse_long}
        next::Ptr{SuiteSparse_long}
        prev::Ptr{SuiteSparse_long}
        nsuper::Csize_t
        ssize::Csize_t
        xsize::Csize_t
        maxcsize::Csize_t
        maxesize::Csize_t
        super::Ptr{SuiteSparse_long}
        pi::Ptr{SuiteSparse_long}
        px::Ptr{SuiteSparse_long}
        s::Ptr{SuiteSparse_long}
        ordering::Cint
        is_ll::Cint
        is_super::Cint
        is_monotonic::Cint
        itype::Cint
        xtype::Cint
        dtype::Cint
    end
end

mutable struct Factor{Tv} <: Factorization{Tv}
    p::Ptr{C_Factor{Tv}}
    function Factor{Tv}(p::Ptr{C_Factor{Tv}}) where Tv
        if p == C_NULL
            throw(ArgumentError("factorization construction failed for " *
                "unknown reasons. Please submit a bug report."))
        end
        new(p)
    end
end
Factor(p::Ptr{C_Factor{Tv}}) where Tv<:VTypes = Factor{Tv}(p)

# Define get similar to get(Nullable) to check pointers. All pointer loads
# should be wrapped in get to make sure that SuiteSparse is not called with
# a C_NULL pointer which could cause a segfault. Pointers are set to null
# when serialized so this can happen when mutiple processes are in use.
function get{T<:SuiteSparseStruct}(p::Ptr{T})
    if p == C_NULL
        throw(ArgumentError("pointer to the $T object is null. This can " *
            "happen if the object has been serialized."))
    else
        return p
    end
end

# FactorComponent, for encoding particular factors from a factorization
mutable struct FactorComponent{Tv,S} <: AbstractMatrix{Tv}
    F::Factor{Tv}

    function FactorComponent{Tv,S}(F::Factor{Tv}) where {Tv,S}
        s = unsafe_load(get(F.p))
        if s.is_ll != 0
            if !(S == :L || S == :U || S == :PtL || S == :UP)
                throw(CHOLMODException(string(S, " not supported for sparse ",
                    "LLt matrices; try :L, :U, :PtL, or :UP")))
            end
        elseif !(S == :L || S == :U || S == :PtL || S == :UP ||
                S == :D || S == :LD || S == :DU || S == :PtLD || S == :DUP)
            throw(CHOLMODException(string(S, " not supported for sparse LDLt ",
                "matrices; try :L, :U, :PtL, :UP, :D, :LD, :DU, :PtLD, or :DUP")))
        end
        new(F)
    end
end
function FactorComponent(F::Factor{Tv}, sym::Symbol) where Tv
    FactorComponent{Tv,sym}(F)
end

Factor(FC::FactorComponent) = Factor(FC.F)

#################
# Thin wrappers #
#################

# Dense wrappers
## Note! Integer type defaults to Cint, but this is actually not necessary, but
## making this a choice would require another type parameter in the Dense type

### cholmod_core_h ###
function allocate_dense(nrow::Integer, ncol::Integer, d::Integer, ::Type{Float64})
    d = Dense(ccall((:cholmod_l_allocate_dense, :libcholmod), Ptr{C_Dense{Float64}},
        (Csize_t, Csize_t, Csize_t, Cint, Ptr{Void}),
        nrow, ncol, d, REAL, common()))
    finalizer(d, free!)
    d
end
function allocate_dense(nrow::Integer, ncol::Integer, d::Integer, ::Type{Complex{Float64}})
    d = Dense(ccall((:cholmod_l_allocate_dense, :libcholmod), Ptr{C_Dense{Complex{Float64}}},
        (Csize_t, Csize_t, Csize_t, Cint, Ptr{Void}),
        nrow, ncol, d, COMPLEX, common()))
    finalizer(d, free!)
    d
end

free_dense!{T}(p::Ptr{C_Dense{T}}) = ccall((:cholmod_l_free_dense, :libcholmod),
    Cint, (Ref{Ptr{C_Dense{T}}}, Ptr{Void}), p, common())

function zeros{T<:VTypes}(m::Integer, n::Integer, ::Type{T})
    d = Dense(ccall((:cholmod_l_zeros, :libcholmod), Ptr{C_Dense{T}},
        (Csize_t, Csize_t, Cint, Ptr{UInt8}),
         m, n, xtyp(T), common()))
    finalizer(d, free!)
    d
end
zeros(m::Integer, n::Integer) = zeros(m, n, Float64)

function ones{T<:VTypes}(m::Integer, n::Integer, ::Type{T})
    d = Dense(ccall((:cholmod_l_ones, :libcholmod), Ptr{C_Dense{T}},
        (Csize_t, Csize_t, Cint, Ptr{UInt8}),
         m, n, xtyp(T), common()))
    finalizer(d, free!)
    d
end
ones(m::Integer, n::Integer) = ones(m, n, Float64)

function eye{T<:VTypes}(m::Integer, n::Integer, ::Type{T})
    d = Dense(ccall((:cholmod_l_eye, :libcholmod), Ptr{C_Dense{T}},
        (Csize_t, Csize_t, Cint, Ptr{UInt8}),
         m, n, xtyp(T), common()))
    finalizer(d, free!)
    d
end
eye(m::Integer, n::Integer) = eye(m, n, Float64)
eye(n::Integer) = eye(n, n, Float64)

function copy_dense{Tv<:VTypes}(A::Dense{Tv})
    d = Dense(ccall((:cholmod_l_copy_dense, :libcholmod), Ptr{C_Dense{Tv}},
        (Ptr{C_Dense{Tv}}, Ptr{UInt8}),
         get(A.p), common()))
    finalizer(d, free!)
    d
end

function sort!{Tv<:VTypes}(S::Sparse{Tv})
    @isok ccall((:cholmod_l_sort, :libcholmod), SuiteSparse_long,
        (Ptr{C_Sparse{Tv}}, Ptr{UInt8}),
         get(S.p), common())
    return S
end

### cholmod_matrixops.h ###
function norm_dense{Tv<:VTypes}(D::Dense{Tv}, p::Integer)
    s = unsafe_load(get(D.p))
    if p == 2
        if s.ncol > 1
            throw(ArgumentError("2 norm only supported when matrix has one column"))
        end
    elseif p != 0 && p != 1
        throw(ArgumentError("second argument must be either 0 (Inf norm), 1, or 2"))
    end
    ccall((:cholmod_l_norm_dense, :libcholmod), Cdouble,
        (Ptr{C_Dense{Tv}}, Cint, Ptr{UInt8}),
          get(D.p), p, common())
end

### cholmod_check.h ###
function check_dense{T<:VTypes}(A::Dense{T})
    ccall((:cholmod_l_check_dense, :libcholmod), Cint,
          (Ptr{C_Dense{T}}, Ptr{UInt8}),
          A.p, common())!=0
end

# Non-Dense wrappers
### cholmod_core.h ###
function allocate_sparse(nrow::Integer, ncol::Integer, nzmax::Integer,
        sorted::Bool, packed::Bool, stype::Integer, ::Type{Float64})
    s = Sparse(ccall((@cholmod_name("allocate_sparse", SuiteSparse_long), :libcholmod),
            Ptr{C_Sparse{Float64}},
                (Csize_t, Csize_t, Csize_t, Cint,
                 Cint, Cint, Cint, Ptr{Void}),
                nrow, ncol, nzmax, sorted,
                packed, stype, REAL, common()))
    finalizer(s, free!)
    s
end
function allocate_sparse(nrow::Integer, ncol::Integer, nzmax::Integer,
        sorted::Bool, packed::Bool, stype::Integer, ::Type{Complex{Float64}})
    s = Sparse(ccall((@cholmod_name("allocate_sparse", SuiteSparse_long), :libcholmod),
            Ptr{C_Sparse{Complex{Float64}}},
                (Csize_t, Csize_t, Csize_t, Cint,
                 Cint, Cint, Cint, Ptr{Void}),
                nrow, ncol, nzmax, sorted,
                packed, stype, COMPLEX, common()))
    finalizer(s, free!)
    s
end
function free_sparse!{Tv<:VTypes}(ptr::Ptr{C_Sparse{Tv}})
    @isok ccall((@cholmod_name("free_sparse", SuiteSparse_long), :libcholmod), Cint,
            (Ptr{Ptr{C_Sparse{Tv}}}, Ptr{UInt8}),
                &ptr, common())
end

function free_sparse!(ptr::Ptr{C_SparseVoid})
    @isok ccall((@cholmod_name("free_sparse", SuiteSparse_long), :libcholmod), Cint,
            (Ptr{Ptr{C_SparseVoid}}, Ptr{UInt8}),
                &ptr, common())
end

function free_factor!{Tv<:VTypes}(ptr::Ptr{C_Factor{Tv}})
    # Warning! Important that finalizer doesn't modify the global Common struct.
    @isok ccall((@cholmod_name("free_factor", SuiteSparse_long), :libcholmod), Cint,
            (Ptr{Ptr{C_Factor{Tv}}}, Ptr{Void}),
                &ptr, common())
end

function aat{Tv<:VRealTypes}(A::Sparse{Tv}, fset::Vector{SuiteSparse_long}, mode::Integer)
    s = Sparse(ccall((@cholmod_name("aat", SuiteSparse_long), :libcholmod),
        Ptr{C_Sparse{Tv}},
            (Ptr{C_Sparse{Tv}}, Ptr{SuiteSparse_long}, Csize_t, Cint, Ptr{UInt8}),
                get(A.p), fset, length(fset), mode, common()))
    finalizer(s, free!)
    s
end

function sparse_to_dense{Tv<:VTypes}(A::Sparse{Tv})
    d = Dense(ccall((@cholmod_name("sparse_to_dense", SuiteSparse_long),:libcholmod),
        Ptr{C_Dense{Tv}},
            (Ptr{C_Sparse{Tv}}, Ptr{UInt8}),
                get(A.p), common()))
    finalizer(d, free!)
    d
end
function dense_to_sparse{Tv<:VTypes}(D::Dense{Tv}, ::Type{SuiteSparse_long})
    s = Sparse(ccall((@cholmod_name("dense_to_sparse", SuiteSparse_long),:libcholmod),
        Ptr{C_Sparse{Tv}},
            (Ptr{C_Dense{Tv}}, Cint, Ptr{UInt8}),
                get(D.p), true, common()))
    finalizer(s, free!)
    s
end

function factor_to_sparse!{Tv<:VTypes}(F::Factor{Tv})
    ss = unsafe_load(F.p)
    ss.xtype > PATTERN || throw(CHOLMODException("only numeric factors are supported"))
    s = Sparse(ccall((@cholmod_name("factor_to_sparse", SuiteSparse_long),:libcholmod),
        Ptr{C_Sparse{Tv}},
            (Ptr{C_Factor{Tv}}, Ptr{UInt8}),
                get(F.p), common()))
    finalizer(s, free!)
    s
end

function change_factor!{Tv<:VTypes}(::Type{Float64}, to_ll::Bool,
        to_super::Bool, to_packed::Bool, to_monotonic::Bool, F::Factor{Tv})
    @isok ccall((@cholmod_name("change_factor", SuiteSparse_long),:libcholmod), Cint,
            (Cint, Cint, Cint, Cint, Cint, Ptr{C_Factor{Tv}}, Ptr{UInt8}),
                REAL, to_ll, to_super, to_packed, to_monotonic, get(F.p), common())
    Factor{Float64}(F.p)
end

function change_factor!{Tv<:VTypes}(::Type{Complex{Float64}}, to_ll::Bool,
        to_super::Bool, to_packed::Bool, to_monotonic::Bool, F::Factor{Tv})
    @isok ccall((@cholmod_name("change_factor", SuiteSparse_long),:libcholmod), Cint,
            (Cint, Cint, Cint, Cint, Cint, Ptr{C_Factor{Tv}}, Ptr{UInt8}),
                COMPLEX, to_ll, to_super, to_packed, to_monotonic, get(F.p), common())
    Factor{Complex{Float64}}(F.p)
end

function check_sparse{Tv<:VTypes}(A::Sparse{Tv})
    ccall((@cholmod_name("check_sparse", SuiteSparse_long),:libcholmod), Cint,
          (Ptr{C_Sparse{Tv}}, Ptr{UInt8}),
          get(A.p), common())!=0
end

function check_factor{Tv<:VTypes}(F::Factor{Tv})
    ccall((@cholmod_name("check_factor", SuiteSparse_long),:libcholmod), Cint,
          (Ptr{C_Factor{Tv}}, Ptr{UInt8}),
          get(F.p), common())!=0
end

function nnz{Tv<:VTypes}(A::Sparse{Tv})
    ccall((@cholmod_name("nnz", SuiteSparse_long),:libcholmod), Int,
            (Ptr{C_Sparse{Tv}}, Ptr{UInt8}),
                get(A.p), common())
end

function speye{Tv<:VTypes}(m::Integer, n::Integer, ::Type{Tv})
    s = Sparse(ccall((@cholmod_name("speye", SuiteSparse_long), :libcholmod),
        Ptr{C_Sparse{Tv}},
            (Csize_t, Csize_t, Cint, Ptr{UInt8}),
                m, n, xtyp(Tv), common()))
    finalizer(s, free!)
    s
end

function spzeros{Tv<:VTypes}(m::Integer, n::Integer, nzmax::Integer, ::Type{Tv})
    s = Sparse(ccall((@cholmod_name("spzeros", SuiteSparse_long), :libcholmod),
        Ptr{C_Sparse{Tv}},
            (Csize_t, Csize_t, Csize_t, Cint, Ptr{UInt8}),
             m, n, nzmax, xtyp(Tv), common()))
    finalizer(s, free!)
    s
end

function transpose_{Tv<:VTypes}(A::Sparse{Tv}, values::Integer)
    s = Sparse(ccall((@cholmod_name("transpose", SuiteSparse_long),:libcholmod),
        Ptr{C_Sparse{Tv}},
            (Ptr{C_Sparse{Tv}}, Cint, Ptr{UInt8}),
                get(A.p), values, common()))
    finalizer(s, free!)
    s
end

function copy_factor{Tv<:VTypes}(F::Factor{Tv})
    f = Factor(ccall((@cholmod_name("copy_factor", SuiteSparse_long),:libcholmod),
        Ptr{C_Factor{Tv}},
            (Ptr{C_Factor{Tv}}, Ptr{UInt8}),
                get(F.p), common()))
    finalizer(f, free!)
    f
end
function copy_sparse{Tv<:VTypes}(A::Sparse{Tv})
    s = Sparse(ccall((@cholmod_name("copy_sparse", SuiteSparse_long),:libcholmod),
        Ptr{C_Sparse{Tv}},
            (Ptr{C_Sparse{Tv}}, Ptr{UInt8}),
                get(A.p), common()))
    finalizer(s, free!)
    s
end
function copy{Tv<:VRealTypes}(A::Sparse{Tv}, stype::Integer, mode::Integer)
    s = Sparse(ccall((@cholmod_name("copy", SuiteSparse_long),:libcholmod),
        Ptr{C_Sparse{Tv}},
            (Ptr{C_Sparse{Tv}}, Cint, Cint, Ptr{UInt8}),
                get(A.p), stype, mode, common()))
    finalizer(s, free!)
    s
end

### cholmod_check.h ###
function print_sparse{Tv<:VTypes}(A::Sparse{Tv}, name::String)
    isascii(name) || error("non-ASCII name: $name")
    cm = common()
    set_print_level(cm, 3)
    @isok ccall((@cholmod_name("print_sparse", SuiteSparse_long),:libcholmod), Cint,
            (Ptr{C_Sparse{Tv}}, Ptr{UInt8}, Ptr{UInt8}),
                 get(A.p), name, cm)
    nothing
end
function print_factor{Tv<:VTypes}(F::Factor{Tv}, name::String)
    cm = common()
    set_print_level(cm, 3)
    @isok ccall((@cholmod_name("print_factor", SuiteSparse_long),:libcholmod), Cint,
            (Ptr{C_Factor{Tv}}, Ptr{UInt8}, Ptr{UInt8}),
                get(F.p), name, cm)
    nothing
end

### cholmod_matrixops.h ###
function ssmult{Tv<:VRealTypes}(A::Sparse{Tv}, B::Sparse{Tv}, stype::Integer,
        values::Bool, sorted::Bool)
    lA = unsafe_load(get(A.p))
    lB = unsafe_load(get(B.p))
    if lA.ncol != lB.nrow
        throw(DimensionMismatch("inner matrix dimensions do not fit"))
    end
    s = Sparse(ccall((@cholmod_name("ssmult", SuiteSparse_long),:libcholmod),
        Ptr{C_Sparse{Tv}},
            (Ptr{C_Sparse{Tv}}, Ptr{C_Sparse{Tv}}, Cint, Cint,
                Cint, Ptr{UInt8}),
             get(A.p), get(B.p), stype, values,
                sorted, common()))
    finalizer(s, free!)
    s
end

function norm_sparse{Tv<:VTypes}(A::Sparse{Tv}, norm::Integer)
    if norm != 0 && norm != 1
        throw(ArgumentError("norm argument must be either 0 or 1"))
    end
    ccall((@cholmod_name("norm_sparse", SuiteSparse_long), :libcholmod), Cdouble,
            (Ptr{C_Sparse{Tv}}, Cint, Ptr{UInt8}),
                get(A.p), norm, common())
end

function horzcat{Tv<:VRealTypes}(A::Sparse{Tv}, B::Sparse{Tv}, values::Bool)
    s = Sparse(ccall((@cholmod_name("horzcat", SuiteSparse_long), :libcholmod),
        Ptr{C_Sparse{Tv}},
            (Ptr{C_Sparse{Tv}}, Ptr{C_Sparse{Tv}}, Cint, Ptr{UInt8}),
             get(A.p), get(B.p), values, common()))
    finalizer(s, free!)
    s
end

function scale!{Tv<:VRealTypes}(S::Dense{Tv}, scale::Integer, A::Sparse{Tv})
    sS = unsafe_load(get(S.p))
    sA = unsafe_load(get(A.p))
    if sS.ncol != 1 && sS.nrow != 1
        throw(DimensionMismatch("first argument must be a vector"))
    end
    if scale == SCALAR && sS.nrow != 1
        throw(DimensionMismatch("scaling argument must have length one"))
    elseif scale == ROW && sS.nrow*sS.ncol != sA.nrow
        throw(DimensionMismatch("scaling vector has length $(sS.nrow*sS.ncol), " *
            "but matrix has $(sA.nrow) rows."))
    elseif scale == COL && sS.nrow*sS.ncol != sA.ncol
        throw(DimensionMismatch("scaling vector has length $(sS.nrow*sS.ncol), " *
            "but matrix has $(sA.ncol) columns"))
    elseif scale == SYM
        if sA.nrow != sA.ncol
            throw(DimensionMismatch("matrix must be square"))
        elseif sS.nrow*sS.ncol != sA.nrow
            throw(DimensionMismatch("scaling vector has length $(sS.nrow*sS.ncol), " *
                "but matrix has $(sA.ncol) columns and rows"))
        end
    end

    sA = unsafe_load(get(A.p))
    @isok ccall((@cholmod_name("scale",SuiteSparse_long),:libcholmod), Cint,
            (Ptr{C_Dense{Tv}}, Cint, Ptr{C_Sparse{Tv}}, Ptr{UInt8}),
                get(S.p), scale, get(A.p), common())
    A
end

function sdmult!{Tv<:VTypes}(A::Sparse{Tv}, transpose::Bool,
        α::Number, β::Number, X::Dense{Tv}, Y::Dense{Tv})
    m, n = size(A)
    nc = transpose ? m : n
    nr = transpose ? n : m
    if nc != size(X, 1)
        throw(DimensionMismatch("incompatible dimensions, $nc and $(size(X,1))"))
    end
    @isok ccall((@cholmod_name("sdmult", SuiteSparse_long),:libcholmod), Cint,
            (Ptr{C_Sparse{Tv}}, Cint,
             Ref{Complex128}, Ref{Complex128},
             Ptr{C_Dense{Tv}}, Ptr{C_Dense{Tv}}, Ptr{UInt8}),
                get(A.p), transpose, α, β, get(X.p), get(Y.p), common())
    Y
end

function vertcat{Tv<:VRealTypes}(A::Sparse{Tv}, B::Sparse{Tv}, values::Bool)
    s = Sparse(ccall((@cholmod_name("vertcat", SuiteSparse_long), :libcholmod),
            Ptr{C_Sparse{Tv}},
            (Ptr{C_Sparse{Tv}}, Ptr{C_Sparse{Tv}}, Cint, Ptr{UInt8}),
                get(A.p), get(B.p), values, common()))
    finalizer(s, free!)
    s
end

function symmetry{Tv<:VTypes}(A::Sparse{Tv}, option::Integer)
    xmatched = Array{SuiteSparse_long}(1)
    pmatched = Array{SuiteSparse_long}(1)
    nzoffdiag = Array{SuiteSparse_long}(1)
    nzdiag = Array{SuiteSparse_long}(1)
    rv = ccall((@cholmod_name("symmetry", SuiteSparse_long), :libcholmod), Cint,
            (Ptr{C_Sparse{Tv}}, Cint, Ptr{SuiteSparse_long}, Ptr{SuiteSparse_long},
                Ptr{SuiteSparse_long}, Ptr{SuiteSparse_long}, Ptr{UInt8}),
                    get(A.p), option, xmatched, pmatched,
                        nzoffdiag, nzdiag, common())
    rv, xmatched[1], pmatched[1], nzoffdiag[1], nzdiag[1]
end

# cholmod_cholesky.h
# For analyze, analyze_p, and factorize_p!, the Common argument must be
# supplied in order to control if the factorization is LLt or LDLt
function analyze{Tv<:VTypes}(A::Sparse{Tv}, cmmn::Vector{UInt8})
    f = Factor(ccall((@cholmod_name("analyze", SuiteSparse_long),:libcholmod),
        Ptr{C_Factor{Tv}},
            (Ptr{C_Sparse{Tv}}, Ptr{UInt8}),
                get(A.p), cmmn))
    finalizer(f, free!)
    f
end
function analyze_p{Tv<:VTypes}(A::Sparse{Tv}, perm::Vector{SuiteSparse_long},
    cmmn::Vector{UInt8})
    length(perm) != size(A,1) && throw(BoundsError())
    f = Factor(ccall((@cholmod_name("analyze_p", SuiteSparse_long),:libcholmod),
            Ptr{C_Factor{Tv}},
            (Ptr{C_Sparse{Tv}}, Ptr{SuiteSparse_long}, Ptr{SuiteSparse_long},
                Csize_t, Ptr{UInt8}),
                get(A.p), perm, C_NULL, 0, cmmn))
    finalizer(f, free!)
    f
end
function factorize!{Tv<:VTypes}(A::Sparse{Tv}, F::Factor{Tv}, cmmn::Vector{UInt8})
    @isok ccall((@cholmod_name("factorize", SuiteSparse_long),:libcholmod), Cint,
        (Ptr{C_Sparse{Tv}}, Ptr{C_Factor{Tv}}, Ptr{UInt8}),
            get(A.p), get(F.p), cmmn)
    F
end
function factorize_p!{Tv<:VTypes}(A::Sparse{Tv}, β::Real, F::Factor{Tv}, cmmn::Vector{UInt8})
    # note that β is passed as a complex number (double beta[2]),
    # but the CHOLMOD manual says that only beta[0] (real part) is used
    @isok ccall((@cholmod_name("factorize_p", SuiteSparse_long),:libcholmod), Cint,
        (Ptr{C_Sparse{Tv}}, Ref{Complex128}, Ptr{SuiteSparse_long}, Csize_t,
         Ptr{C_Factor{Tv}}, Ptr{UInt8}),
            get(A.p), β, C_NULL, 0, get(F.p), cmmn)
    F
end

function solve{Tv<:VTypes}(sys::Integer, F::Factor{Tv}, B::Dense{Tv})
    if size(F,1) != size(B,1)
        throw(DimensionMismatch("LHS and RHS should have the same number of rows. " *
            "LHS has $(size(F,1)) rows, but RHS has $(size(B,1)) rows."))
    end
    d = Dense(ccall((@cholmod_name("solve", SuiteSparse_long),:libcholmod), Ptr{C_Dense{Tv}},
            (Cint, Ptr{C_Factor{Tv}}, Ptr{C_Dense{Tv}}, Ptr{UInt8}),
                sys, get(F.p), get(B.p), common()))
    finalizer(d, free!)
    d
end

function spsolve{Tv<:VTypes}(sys::Integer, F::Factor{Tv}, B::Sparse{Tv})
    if size(F,1) != size(B,1)
        throw(DimensionMismatch("LHS and RHS should have the same number of rows. " *
            "LHS has $(size(F,1)) rows, but RHS has $(size(B,1)) rows."))
    end
    s = Sparse(ccall((@cholmod_name("spsolve", SuiteSparse_long),:libcholmod),
        Ptr{C_Sparse{Tv}},
            (Cint, Ptr{C_Factor{Tv}}, Ptr{C_Sparse{Tv}}, Ptr{UInt8}),
                sys, get(F.p), get(B.p), common()))
    finalizer(s, free!)
    s
end

# Autodetects the types
function read_sparse(file::Libc.FILE, ::Type{SuiteSparse_long})
    ptr = ccall((@cholmod_name("read_sparse", SuiteSparse_long), :libcholmod),
        Ptr{C_SparseVoid},
            (Ptr{Void}, Ptr{UInt8}),
                file.ptr, common())
    if ptr == C_NULL
        throw(ArgumentError("sparse matrix construction failed. Check that input file is valid."))
    end
    s = Sparse(ptr)
    finalizer(s, free!)
    s
end

function read_sparse(file::IO, T)
    cfile = Libc.FILE(file)
    try return read_sparse(cfile, T)
    finally close(cfile)
    end
end

function get_perm(F::Factor)
    s = unsafe_load(get(F.p))
    p = unsafe_wrap(Array, s.Perm, s.n, false)
    p+1
end
get_perm(FC::FactorComponent) = get_perm(Factor(FC))

#########################
# High level interfaces #
#########################

# Convertion/construction
function convert{T<:VTypes}(::Type{Dense{T}}, A::StridedVecOrMat)
    d = allocate_dense(size(A, 1), size(A, 2), stride(A, 2), T)
    s = unsafe_load(d.p)
    for i in eachindex(A)
        unsafe_store!(s.x, A[i], i)
    end
    d
end
function convert(::Type{Dense}, A::StridedVecOrMat)
    T = promote_type(eltype(A), Float64)
    return convert(Dense{T}, A)
end
convert(::Type{Dense}, A::Sparse) = sparse_to_dense(A)

# This constructior assumes zero based colptr and rowval
function (::Type{Sparse}){Tv<:VTypes}(m::Integer, n::Integer,
        colptr0::Vector{SuiteSparse_long}, rowval0::Vector{SuiteSparse_long},
        nzval::Vector{Tv}, stype)
    # checks
    ## length of input
    if length(colptr0) <= n
        throw(ArgumentError("length of colptr0 must be at least n + 1 = $(n + 1) but was $(length(colptr0))"))
    end
    if colptr0[n + 1] > length(rowval0)
        throw(ArgumentError("length of rowval0 is $(length(rowval0)) but value of colptr0 requires length to be at least $(colptr0[n + 1])"))
    end
    if colptr0[n + 1] > length(nzval)
        throw(ArgumentError("length of nzval is $(length(nzval)) but value of colptr0 requires length to be at least $(colptr0[n + 1])"))
    end
    ## columns are sorted
    iss = true
    for i = 2:length(colptr0)
        if !issorted(view(rowval0, colptr0[i - 1] + 1:colptr0[i]))
            iss = false
            break
        end
    end

    o = allocate_sparse(m, n, colptr0[n + 1], iss, true, stype, Tv)
    s = unsafe_load(o.p)

    unsafe_copy!(s.p, pointer(colptr0), n + 1)
    unsafe_copy!(s.i, pointer(rowval0), colptr0[n + 1])
    unsafe_copy!(s.x, pointer(nzval) , colptr0[n + 1])

    @isok check_sparse(o)

    return o
end

function (::Type{Sparse})(m::Integer, n::Integer,
        colptr0::Vector{SuiteSparse_long},
        rowval0::Vector{SuiteSparse_long},
        nzval::Vector{<:VTypes})
    o = Sparse(m, n, colptr0, rowval0, nzval, 0)

    # sort indices
    sort!(o)

    # check if array is symmetric and change stype if it is
    if ishermitian(o)
        change_stype!(o, -1)
    end
    o
end

function (::Type{Sparse}){Tv<:VTypes}(A::SparseMatrixCSC{Tv,SuiteSparse_long}, stype::Integer)
    ## Check length of input. This should never fail but see #20024
    if length(A.colptr) <= A.n
        throw(ArgumentError("length of colptr must be at least size(A,2) + 1 = $(A.n + 1) but was $(length(A.colptr))"))
    end
    if nnz(A) > length(A.rowval)
        throw(ArgumentError("length of rowval is $(length(A.rowval)) but value of colptr requires length to be at least $(nnz(A))"))
    end
    if nnz(A) > length(A.nzval)
        throw(ArgumentError("length of nzval is $(length(A.nzval)) but value of colptr requires length to be at least $(nnz(A))"))
    end

    o = allocate_sparse(A.m, A.n, nnz(A), true, true, stype, Tv)
    s = unsafe_load(o.p)
    for i = 1:(A.n + 1)
        unsafe_store!(s.p, A.colptr[i] - 1, i)
    end
    for i = 1:nnz(A)
        unsafe_store!(s.i, A.rowval[i] - 1, i)
    end
    unsafe_copy!(s.x, pointer(A.nzval), nnz(A))

    @isok check_sparse(o)

    return o
end

# convert SparseVectors into CHOLMOD Sparse types through a mx1 CSC matrix
convert(::Type{Sparse}, A::SparseVector{<:VTypes,SuiteSparse_long}) =
    convert(Sparse, convert(SparseMatrixCSC, A))
function convert(::Type{Sparse}, A::SparseMatrixCSC{<:VTypes,<:ITypes})
    o = Sparse(A, 0)
    # check if array is symmetric and change stype if it is
    if ishermitian(o)
        change_stype!(o, -1)
    end
    o
end
convert(::Type{Sparse}, A::SparseMatrixCSC{Complex{Float32},<:ITypes}) =
    convert(Sparse, convert(SparseMatrixCSC{Complex{Float64},SuiteSparse_long}, A))
convert(::Type{Sparse}, A::Symmetric{Float64,SparseMatrixCSC{Float64,SuiteSparse_long}}) =
    Sparse(A.data, A.uplo == 'L' ? -1 : 1)
convert{Tv<:VTypes}(::Type{Sparse}, A::Hermitian{Tv,SparseMatrixCSC{Tv,SuiteSparse_long}}) =
    Sparse(A.data, A.uplo == 'L' ? -1 : 1)
function convert{Ti<:ITypes}(::Type{Sparse},
    A::Union{SparseMatrixCSC{BigFloat,Ti},
             Symmetric{BigFloat,SparseMatrixCSC{BigFloat,Ti}},
             Hermitian{Complex{BigFloat},SparseMatrixCSC{Complex{BigFloat},Ti}}},
    args...)
    throw(MethodError(convert, (Sparse, A)))
end
function convert{T,Ti<:ITypes}(::Type{Sparse},
    A::Union{SparseMatrixCSC{T,Ti},
             Symmetric{T,SparseMatrixCSC{T,Ti}},
             Hermitian{T,SparseMatrixCSC{T,Ti}}},
    args...)
    return Sparse(convert(AbstractMatrix{promote_type(Float64, T)}, A), args...)
end

# Useful when reading in files, but not type stable
function convert(::Type{Sparse}, p::Ptr{C_SparseVoid})
    if p == C_NULL
        throw(ArgumentError("sparse matrix construction failed for " *
            "unknown reasons. Please submit a bug report."))
    end

    s = unsafe_load(p)

    # Check integer type
    if s.itype == INT
        free_sparse!(p)
        throw(CHOLMODException("the value of itype was $s.itype. " *
            "Only integer type of $SuiteSparse_long is supported."))
    elseif s.itype == INTLONG
        free_sparse!(p)
        throw(CHOLMODException("the value of itype was $s.itype. This combination " *
            "of integer types shouldn't happen. Please submit a bug report."))
    elseif s.itype != LONG # must be s.itype == LONG
        free_sparse!(p)
        throw(CHOLMODException("illegal value of itype: $s.itype"))
    end

    # Check for double or single precision
    if s.dtype == DOUBLE
        Tv = Float64
    elseif s.dtype == SINGLE
        # Tv = Float32 # this should be supported at some point
        free_sparse!(p)
        throw(CHOLMODException("single precision not supported yet"))
    else
        free_sparse!(p)
        throw(CHOLMODException("illegal value of dtype: $s.dtype"))
    end

    # Check for real or complex
    if s.xtype == COMPLEX
        Tv = Complex{Tv}
    elseif s.xtype != REAL
        free_sparse!(p)
        throw(CHOLMODException("illegal value of xtype: $s.xtype"))
    end

    return Sparse(convert(Ptr{C_Sparse{Tv}}, p))
end

convert(::Type{Sparse}, A::Dense) = dense_to_sparse(A, SuiteSparse_long)
convert(::Type{Sparse}, L::Factor) = factor_to_sparse!(copy(L))
function (::Type{Sparse})(filename::String)
    open(filename) do f
        return read_sparse(f, SuiteSparse_long)
    end
end

## convertion back to base Julia types
function convert{T}(::Type{Matrix{T}}, D::Dense{T})
    s = unsafe_load(D.p)
    a = Array{T}(s.nrow, s.ncol)
    copy!(a, D)
end

Base.copy!(dest::Base.PermutedDimsArrays.PermutedDimsArray, src::Dense) = _copy!(dest, src) # ambig
Base.copy!{T<:VTypes}(dest::Dense{T}, D::Dense{T}) = _copy!(dest, D)
Base.copy!{T<:VTypes}(dest::AbstractArray{T}, D::Dense{T}) = _copy!(dest, D)
Base.copy!{T<:VTypes}(dest::AbstractArray{T,2}, D::Dense{T}) = _copy!(dest, D)
Base.copy!(dest::AbstractArray, D::Dense) = _copy!(dest, D)

function _copy!(dest::AbstractArray, D::Dense)
    s = unsafe_load(D.p)
    n = s.nrow*s.ncol
    n <= length(dest) || throw(BoundsError(dest, n))
    if s.d == s.nrow && isa(dest, Array)
        unsafe_copy!(pointer(dest), s.x, s.d*s.ncol)
    else
        k = 0
        for j = 1:s.ncol
            for i = 1:s.nrow
                dest[k+=1] = unsafe_load(s.x, i + (j - 1)*s.d)
            end
        end
    end
    dest
end
convert{T}(::Type{Matrix}, D::Dense{T}) = convert(Matrix{T}, D)
function convert{T}(::Type{Vector{T}}, D::Dense{T})
    if size(D, 2) > 1
        throw(DimensionMismatch("input must be a vector but had $(size(D, 2)) columns"))
    end
    copy!(Array{T}(size(D, 1)), D)
end
convert{T}(::Type{Vector}, D::Dense{T}) = convert(Vector{T}, D)

function convert{Tv}(::Type{SparseMatrixCSC{Tv,SuiteSparse_long}}, A::Sparse{Tv})
    s = unsafe_load(A.p)
    if s.stype != 0
        throw(ArgumentError("matrix has stype != 0. Convert to matrix " *
            "with stype == 0 before converting to SparseMatrixCSC"))
    end

    B = SparseMatrixCSC(s.nrow, s.ncol,
        increment(unsafe_wrap(Array, s.p, (s.ncol + 1,), false)),
        increment(unsafe_wrap(Array, s.i, (s.nzmax,), false)),
        copy(unsafe_wrap(Array, s.x, (s.nzmax,), false)))

    if s.sorted == 0
        return SparseArrays.sortSparseMatrixCSC!(B)
    else
        return B
    end
end
function convert(::Type{Symmetric{Float64,SparseMatrixCSC{Float64,SuiteSparse_long}}}, A::Sparse{Float64})
    s = unsafe_load(A.p)
    if !issymmetric(A)
        throw(ArgumentError("matrix is not symmetric"))
    end

    B = Symmetric(SparseMatrixCSC(s.nrow, s.ncol,
        increment(unsafe_wrap(Array, s.p, (s.ncol + 1,), false)),
        increment(unsafe_wrap(Array, s.i, (s.nzmax,), false)),
        copy(unsafe_wrap(Array, s.x, (s.nzmax,), false))), s.stype > 0 ? :U : :L)

    if s.sorted == 0
        return SparseArrays.sortSparseMatrixCSC!(B.data)
    else
        return B
    end
end
function convert{Tv<:VTypes}(::Type{Hermitian{Tv,SparseMatrixCSC{Tv,SuiteSparse_long}}}, A::Sparse{Tv})
    s = unsafe_load(A.p)
    if !ishermitian(A)
        throw(ArgumentError("matrix is not Hermitian"))
    end

    B = Hermitian(SparseMatrixCSC(s.nrow, s.ncol,
        increment(unsafe_wrap(Array, s.p, (s.ncol + 1,), false)),
        increment(unsafe_wrap(Array, s.i, (s.nzmax,), false)),
        copy(unsafe_wrap(Array, s.x, (s.nzmax,), false))), s.stype > 0 ? :U : :L)

    if s.sorted == 0
        return SparseArrays.sortSparseMatrixCSC!(B.data)
    else
        return B
    end
end
function sparse(A::Sparse{Float64}) # Notice! Cannot be type stable because of stype
    s = unsafe_load(A.p)
    if s.stype == 0
        return convert(SparseMatrixCSC{Float64,SuiteSparse_long}, A)
    end
    return convert(Symmetric{Float64,SparseMatrixCSC{Float64,SuiteSparse_long}}, A)
end
function sparse(A::Sparse{Complex{Float64}}) # Notice! Cannot be type stable because of stype
    s = unsafe_load(A.p)
    if s.stype == 0
        return convert(SparseMatrixCSC{Complex{Float64},SuiteSparse_long}, A)
    end
    return convert(Hermitian{Complex{Float64},SparseMatrixCSC{Complex{Float64},SuiteSparse_long}}, A)
end
function sparse(F::Factor)
    s = unsafe_load(F.p)
    if s.is_ll != 0
        L = Sparse(F)
        A = sparse(L*L')
    else
        LD = sparse(F[:LD])
        L, d = getLd!(LD)
        A = (L * Diagonal(d)) * L'
    end
    SparseArrays.sortSparseMatrixCSC!(A)
    p = get_perm(F)
    if p != [1:s.n;]
        pinv = Array{Int}(length(p))
        for k = 1:length(p)
            pinv[p[k]] = k
        end
        A = A[pinv,pinv]
    end
    A
end

sparse(D::Dense) = sparse(Sparse(D))

function sparse{Tv}(FC::FactorComponent{Tv,:L})
    F = Factor(FC)
    s = unsafe_load(F.p)
    if s.is_ll == 0
        throw(CHOLMODException("sparse: supported only for :LD on LDLt factorizations"))
    end
    sparse(Sparse(F))
end
sparse{Tv}(FC::FactorComponent{Tv,:LD}) = sparse(Sparse(Factor(FC)))

# Calculate the offset into the stype field of the cholmod_sparse_struct and
# change the value
let offset = fieldoffset(C_Sparse{Float64}, findfirst(name -> name === :stype, fieldnames(C_Sparse{Float64})))
    global change_stype!
    function change_stype!(A::Sparse, i::Integer)
        unsafe_store!(convert(Ptr{Cint}, A.p), i, div(offset, 4) + 1)
        return A
    end
end

free!(A::Dense) = free_dense!(A.p)
free!(A::Sparse) = free_sparse!(A.p)
free!(F::Factor) = free_factor!(F.p)

eltype{T<:VTypes}(::Type{Dense{T}}) = T
eltype{T<:VTypes}(::Type{Factor{T}}) = T
eltype{T<:VTypes}(::Type{Sparse{T}}) = T

nnz(F::Factor) = nnz(Sparse(F))

function show(io::IO, F::Factor)
    println(io, typeof(F))
    showfactor(io, F)
end

function show(io::IO, FC::FactorComponent)
    println(io, typeof(FC))
    showfactor(io, Factor(FC))
end

function showfactor(io::IO, F::Factor)
    s = unsafe_load(get(F.p))
    @printf(io, "type: %12s\n", s.is_ll!=0 ? "LLt" : "LDLt")
    @printf(io, "method: %10s\n", s.is_super!=0 ? "supernodal" : "simplicial")
    @printf(io, "maxnnz: %10d\n", Int(s.nzmax))
    @printf(io, "nnz: %13d\n", nnz(F))
end

# getindex not defined for these, so don't use the normal array printer
show(io::IO, ::MIME"text/plain", FC::FactorComponent) = show(io, FC)
show(io::IO, ::MIME"text/plain", F::Factor) = show(io, F)

isvalid(A::Dense) = check_dense(A)
isvalid(A::Sparse) = check_sparse(A)
isvalid(A::Factor) = check_factor(A)

copy(A::Dense) = copy_dense(A)
copy(A::Sparse) = copy_sparse(A)
copy(A::Factor) = copy_factor(A)

function size(A::Union{Dense,Sparse})
    s = unsafe_load(get(A.p))
    return (Int(s.nrow), Int(s.ncol))
end
function size(F::Factor, i::Integer)
    if i < 1
        throw(ArgumentError("dimension must be positive"))
    end
    s = unsafe_load(get(F.p))
    if i <= 2
        return Int(s.n)
    end
    return 1
end
size(F::Factor) = (size(F, 1), size(F, 2))

IndexStyle(::Dense) = IndexLinear()

size(FC::FactorComponent, i::Integer) = size(FC.F, i)
size(FC::FactorComponent) = size(FC.F)

ctranspose{Tv}(FC::FactorComponent{Tv,:L}) = FactorComponent{Tv,:U}(FC.F)
ctranspose{Tv}(FC::FactorComponent{Tv,:U}) = FactorComponent{Tv,:L}(FC.F)
ctranspose{Tv}(FC::FactorComponent{Tv,:PtL}) = FactorComponent{Tv,:UP}(FC.F)
ctranspose{Tv}(FC::FactorComponent{Tv,:UP}) = FactorComponent{Tv,:PtL}(FC.F)
ctranspose{Tv}(FC::FactorComponent{Tv,:D}) = FC
ctranspose{Tv}(FC::FactorComponent{Tv,:LD}) = FactorComponent{Tv,:DU}(FC.F)
ctranspose{Tv}(FC::FactorComponent{Tv,:DU}) = FactorComponent{Tv,:LD}(FC.F)
ctranspose{Tv}(FC::FactorComponent{Tv,:PtLD}) = FactorComponent{Tv,:DUP}(FC.F)
ctranspose{Tv}(FC::FactorComponent{Tv,:DUP}) = FactorComponent{Tv,:PtLD}(FC.F)

function getindex(A::Dense, i::Integer)
    s = unsafe_load(get(A.p))
    0 < i <= s.nrow*s.ncol || throw(BoundsError())
    unsafe_load(s.x, i)
end

IndexStyle(::Sparse) = IndexCartesian()
function getindex{T}(A::Sparse{T}, i0::Integer, i1::Integer)
    s = unsafe_load(get(A.p))
    !(1 <= i0 <= s.nrow && 1 <= i1 <= s.ncol) && throw(BoundsError())
    s.stype < 0 && i0 < i1 && return conj(A[i1,i0])
    s.stype > 0 && i0 > i1 && return conj(A[i1,i0])

    r1 = Int(unsafe_load(s.p, i1) + 1)
    r2 = Int(unsafe_load(s.p, i1 + 1))
    (r1 > r2) && return zero(T)
    r1 = Int(searchsortedfirst(unsafe_wrap(Array, s.i, (s.nzmax,), false),
        i0 - 1, r1, r2, Base.Order.Forward))
    ((r1 > r2) || (unsafe_load(s.i, r1) + 1 != i0)) ? zero(T) : unsafe_load(s.x, r1)
end

function getindex(F::Factor, sym::Symbol)
    sym == :p && return get_perm(F)
    FactorComponent(F, sym)
end

function getLd!(S::SparseMatrixCSC)
    d = Array{eltype(S)}(size(S, 1))
    fill!(d, 0)
    col = 1
    for k = 1:nnz(S)
        while k >= S.colptr[col+1]
            col += 1
        end
        if S.rowval[k] == col
            d[col] = S.nzval[k]
            S.nzval[k] = 1
        end
    end
    S, d
end

## Multiplication
(*)(A::Sparse, B::Sparse) = ssmult(A, B, 0, true, true)
(*)(A::Sparse, B::Dense) = sdmult!(A, false, 1., 0., B, zeros(size(A, 1), size(B, 2)))
(*)(A::Sparse, B::VecOrMat) = (*)(A, Dense(B))

function A_mul_Bc{Tv<:VRealTypes}(A::Sparse{Tv}, B::Sparse{Tv})
    cm = common()

    if A !== B
        aa1 = transpose_(B, 2)
        ## result of ssmult will have stype==0, contain numerical values and be sorted
        return ssmult(A, aa1, 0, true, true)
    end

    ## The A*A' case is handled by cholmod_aat. This routine requires
    ## A->stype == 0 (storage of upper and lower parts). If neccesary
    ## the matrix A is first converted to stype == 0
    s = unsafe_load(A.p)
    if s.stype != 0
        aa1 = copy(A, 0, 1)
        return aat(aa1, SuiteSparse_long[0:s.ncol-1;], 1)
    else
        return aat(A, SuiteSparse_long[0:s.ncol-1;], 1)
    end
end

function Ac_mul_B(A::Sparse, B::Sparse)
    aa1 = transpose_(A, 2)
    if A === B
        return A_mul_Bc(aa1, aa1)
    end
    ## result of ssmult will have stype==0, contain numerical values and be sorted
    return ssmult(aa1, B, 0, true, true)
end

Ac_mul_B(A::Sparse, B::Dense) = sdmult!(A, true, 1., 0., B, zeros(size(A, 2), size(B, 2)))
Ac_mul_B(A::Sparse, B::VecOrMat) =  Ac_mul_B(A, Dense(B))


## Factorization methods

## Compute that symbolic factorization only
function fact_(A::Sparse{<:VTypes}, cm::Array{UInt8};
    perm::AbstractVector{SuiteSparse_long}=SuiteSparse_long[],
    postorder::Bool=true, userperm_only::Bool=true)

    sA = unsafe_load(get(A.p))
    sA.stype == 0 && throw(ArgumentError("sparse matrix is not symmetric/Hermitian"))

    if !postorder
        unsafe_store!(common_postorder, 0)
    end

    if isempty(perm)
        F = analyze(A, cm)
    else # user permutation provided
        if userperm_only # use perm even if it is worse than AMD
            unsafe_store!(common_nmethods, 1)
        end
        F = analyze_p(A, SuiteSparse_long[p-1 for p in perm], cm)
    end

    return F
end

function cholfact!{Tv}(F::Factor{Tv}, A::Sparse{Tv}; shift::Real=0.0)
    cm = common()

    # Makes it an LLt
    unsafe_store!(common_final_ll, 1)

    # Compute the numerical factorization
    factorize_p!(A, shift, F, cm)

    s = unsafe_load(get(F.p))
    s.minor < size(A, 1) && throw(Base.LinAlg.PosDefException(s.minor))
    return F
end

"""
    cholfact!(F::Factor, A; shift = 0.0) -> CHOLMOD.Factor

Compute the Cholesky (``LL'``) factorization of `A`, reusing the symbolic
factorization `F`. `A` must be a `SparseMatrixCSC`, `Symmetric{SparseMatrixCSC}`,
or `Hermitian{SparseMatrixCSC}`. Note that even if `A` doesn't
have the type tag, it must still be symmetric or Hermitian.

See also [`cholfact`](@ref).

!!! note
    This method uses the CHOLMOD library from SuiteSparse, which only supports
    doubles or complex doubles. Input matrices not of those element types will
    be converted to `SparseMatrixCSC{Float64}` or `SparseMatrixCSC{Complex128}`
    as appropriate.
"""
cholfact!{T<:Real}(F::Factor, A::Union{SparseMatrixCSC{T},
        SparseMatrixCSC{Complex{T}},
        Symmetric{T,SparseMatrixCSC{T,SuiteSparse_long}},
        Hermitian{Complex{T},SparseMatrixCSC{Complex{T},SuiteSparse_long}},
        Hermitian{T,SparseMatrixCSC{T,SuiteSparse_long}}};
    shift = 0.0) =
    cholfact!(F, Sparse(A); shift = shift)

function cholfact(A::Sparse; shift::Real=0.0,
    perm::AbstractVector{SuiteSparse_long}=SuiteSparse_long[])

    cm = defaults(common())
    set_print_level(cm, 0)

    # Compute the symbolic factorization
    F = fact_(A, cm; perm = perm)

    # Compute the numerical factorization
    cholfact!(F, A; shift = shift)

    s = unsafe_load(get(F.p))
    s.minor < size(A, 1) && throw(Base.LinAlg.PosDefException(s.minor))
    return F
end

"""
    cholfact(A; shift = 0.0, perm = Int[]) -> CHOLMOD.Factor

Compute the Cholesky factorization of a sparse positive definite matrix `A`.
`A` must be a `SparseMatrixCSC`, `Symmetric{SparseMatrixCSC}`, or
`Hermitian{SparseMatrixCSC}`. Note that even if `A` doesn't
have the type tag, it must still be symmetric or Hermitian.
A fill-reducing permutation is used.
`F = cholfact(A)` is most frequently used to solve systems of equations with `F\\b`,
but also the methods [`diag`](@ref), [`det`](@ref), and
[`logdet`](@ref) are defined for `F`.
You can also extract individual factors from `F`, using `F[:L]`.
However, since pivoting is on by default, the factorization is internally
represented as `A == P'*L*L'*P` with a permutation matrix `P`;
using just `L` without accounting for `P` will give incorrect answers.
To include the effects of permutation,
it's typically preferable to extract "combined" factors like `PtL = F[:PtL]`
(the equivalent of `P'*L`) and `LtP = F[:UP]` (the equivalent of `L'*P`).

Setting the optional `shift` keyword argument computes the factorization of
`A+shift*I` instead of `A`. If the `perm` argument is nonempty,
it should be a permutation of `1:size(A,1)` giving the ordering to use
(instead of CHOLMOD's default AMD ordering).

!!! note
    This method uses the CHOLMOD library from SuiteSparse, which only supports
    doubles or complex doubles. Input matrices not of those element types will
    be converted to `SparseMatrixCSC{Float64}` or `SparseMatrixCSC{Complex128}`
    as appropriate.

    Many other functions from CHOLMOD are wrapped but not exported from the
    `Base.SparseArrays.CHOLMOD` module.
"""
cholfact{T<:Real}(A::Union{SparseMatrixCSC{T}, SparseMatrixCSC{Complex{T}},
    Symmetric{T,SparseMatrixCSC{T,SuiteSparse_long}},
    Hermitian{Complex{T},SparseMatrixCSC{Complex{T},SuiteSparse_long}},
    Hermitian{T,SparseMatrixCSC{T,SuiteSparse_long}}};
    kws...) = cholfact(Sparse(A); kws...)


function ldltfact!{Tv}(F::Factor{Tv}, A::Sparse{Tv}; shift::Real=0.0)
    cm = common()

    # Compute the numerical factorization
    factorize_p!(A, shift, F, cm)

    s = unsafe_load(get(F.p))
    s.minor < size(A, 1) && throw(Base.LinAlg.ArgumentError("matrix has one or more zero pivots"))
    return F
end

"""
    ldltfact!(F::Factor, A; shift = 0.0) -> CHOLMOD.Factor

Compute the ``LDL'`` factorization of `A`, reusing the symbolic factorization `F`.
`A` must be a `SparseMatrixCSC`, `Symmetric{SparseMatrixCSC}`, or
`Hermitian{SparseMatrixCSC}`. Note that even if `A` doesn't
have the type tag, it must still be symmetric or Hermitian.

See also [`ldltfact`](@ref).

!!! note
    This method uses the CHOLMOD library from SuiteSparse, which only supports
    doubles or complex doubles. Input matrices not of those element types will
    be converted to `SparseMatrixCSC{Float64}` or `SparseMatrixCSC{Complex128}`
    as appropriate.
"""
ldltfact!{T<:Real}(F::Factor, A::Union{SparseMatrixCSC{T},
    SparseMatrixCSC{Complex{T}},
    Symmetric{T,SparseMatrixCSC{T,SuiteSparse_long}},
    Hermitian{Complex{T},SparseMatrixCSC{Complex{T},SuiteSparse_long}},
    Hermitian{T,SparseMatrixCSC{T,SuiteSparse_long}}};
    shift = 0.0) =
    ldltfact!(F, Sparse(A), shift = shift)

function ldltfact(A::Sparse; shift::Real=0.0,
    perm::AbstractVector{SuiteSparse_long}=SuiteSparse_long[])

    cm = defaults(common())
    set_print_level(cm, 0)

    # Makes it an LDLt
    unsafe_store!(common_final_ll, 0)
    # Really make sure it's an LDLt by avoiding supernodal factorization
    unsafe_store!(common_supernodal, 0)

    # Compute the symbolic factorization
    F = fact_(A, cm; perm = perm)

    # Compute the numerical factorization
    ldltfact!(F, A; shift = shift)

    s = unsafe_load(get(F.p))
    if s.minor < size(A, 1)
        throw(Base.LinAlg.ArgumentError("matrix has one or more zero pivots"))
    end
    return F
end

"""
    ldltfact(A; shift = 0.0, perm=Int[]) -> CHOLMOD.Factor

Compute the ``LDL'`` factorization of a sparse matrix `A`.
`A` must be a `SparseMatrixCSC`, `Symmetric{SparseMatrixCSC}`, or
`Hermitian{SparseMatrixCSC}`. Note that even if `A` doesn't
have the type tag, it must still be symmetric or Hermitian.
A fill-reducing permutation is used. `F = ldltfact(A)` is most frequently
used to solve systems of equations `A*x = b` with `F\\b`. The returned
factorization object `F` also supports the methods [`diag`](@ref),
[`det`](@ref), and [`logdet`](@ref).
You can extract individual factors from `F` using `F[:L]`.
However, since pivoting is on by default, the factorization is internally
represented as `A == P'*L*D*L'*P` with a permutation matrix `P`;
using just `L` without accounting for `P` will give incorrect answers.
To include the effects of permutation, it is typically preferable to extract
"combined" factors like `PtL = F[:PtL]` (the equivalent of
`P'*L`) and `LtP = F[:UP]` (the equivalent of `L'*P`).
The complete list of supported factors is `:L, :PtL, :D, :UP, :U, :LD, :DU, :PtLD, :DUP`.

Setting the optional `shift` keyword argument computes the factorization of
`A+shift*I` instead of `A`. If the `perm` argument is nonempty,
it should be a permutation of `1:size(A,1)` giving the ordering to use
(instead of CHOLMOD's default AMD ordering).

!!! note
    This method uses the CHOLMOD library from SuiteSparse, which only supports
    doubles or complex doubles. Input matrices not of those element types will
    be converted to `SparseMatrixCSC{Float64}` or `SparseMatrixCSC{Complex128}`
    as appropriate.

    Many other functions from CHOLMOD are wrapped but not exported from the
    `Base.SparseArrays.CHOLMOD` module.
"""
ldltfact{T<:Real}(A::Union{SparseMatrixCSC{T},SparseMatrixCSC{Complex{T}},
    Symmetric{T,SparseMatrixCSC{T,SuiteSparse_long}},
    Hermitian{Complex{T},SparseMatrixCSC{Complex{T},SuiteSparse_long}},
    Hermitian{T,SparseMatrixCSC{T,SuiteSparse_long}}};
    kws...) = ldltfact(Sparse(A); kws...)

## Solvers

for (T, f) in ((:Dense, :solve), (:Sparse, :spsolve))
    @eval begin
        # Solve Lx = b and L'x=b where A = L*L'
        function (\){T}(L::FactorComponent{T,:L}, B::$T)
            ($f)(CHOLMOD_L, Factor(L), B)
        end
        function (\){T}(L::FactorComponent{T,:U}, B::$T)
            ($f)(CHOLMOD_Lt, Factor(L), B)
        end
        # Solve PLx = b and L'P'x=b where A = P*L*L'*P'
        function (\){T}(L::FactorComponent{T,:PtL}, B::$T)
            F = Factor(L)
            ($f)(CHOLMOD_L, F, ($f)(CHOLMOD_P, F, B)) # Confusingly, CHOLMOD_P solves P'x = b
        end
        function (\){T}(L::FactorComponent{T,:UP}, B::$T)
            F = Factor(L)
            ($f)(CHOLMOD_Pt, F, ($f)(CHOLMOD_Lt, F, B))
        end
        # Solve various equations for A = L*D*L' and A = P*L*D*L'*P'
        function (\){T}(L::FactorComponent{T,:D}, B::$T)
            ($f)(CHOLMOD_D, Factor(L), B)
        end
        function (\){T}(L::FactorComponent{T,:LD}, B::$T)
            ($f)(CHOLMOD_LD, Factor(L), B)
        end
        function (\){T}(L::FactorComponent{T,:DU}, B::$T)
            ($f)(CHOLMOD_DLt, Factor(L), B)
        end
        function (\){T}(L::FactorComponent{T,:PtLD}, B::$T)
            F = Factor(L)
            ($f)(CHOLMOD_LD, F, ($f)(CHOLMOD_P, F, B))
        end
        function (\){T}(L::FactorComponent{T,:DUP}, B::$T)
            F = Factor(L)
            ($f)(CHOLMOD_Pt, F, ($f)(CHOLMOD_DLt, F, B))
        end
    end
end

SparseVecOrMat{Tv,Ti} = Union{SparseVector{Tv,Ti}, SparseMatrixCSC{Tv,Ti}}

function (\)(L::FactorComponent, b::Vector)
    reshape(convert(Matrix, L\Dense(b)), length(b))
end
function (\)(L::FactorComponent, B::Matrix)
    convert(Matrix, L\Dense(B))
end
function (\)(L::FactorComponent, B::SparseVecOrMat)
    sparse(L\Sparse(B,0))
end

Ac_ldiv_B(L::FactorComponent, B) = ctranspose(L)\B
Ac_ldiv_B(L::FactorComponent, B::RowVector) = ctranspose(L)\B # ambiguity

(\){T<:VTypes}(L::Factor{T}, B::Dense{T}) = solve(CHOLMOD_A, L, B)
(\)(L::Factor{Float64}, B::VecOrMat{Complex{Float64}}) = complex.(L\real(B), L\imag(B))
# First explicit TypeVars are necessary to avoid ambiguity errors with definition in
# linalg/factorizations.jl
(\){T<:VTypes}(L::Factor{T}, b::StridedVector) = Vector(L\convert(Dense{T}, b))
(\){T<:VTypes}(L::Factor{T}, B::StridedMatrix) = Matrix(L\convert(Dense{T}, B))
(\)(L::Factor, B::Sparse) = spsolve(CHOLMOD_A, L, B)
# When right hand side is sparse, we have to ensure that the rhs is not marked as symmetric.
(\)(L::Factor, B::SparseVecOrMat) = sparse(spsolve(CHOLMOD_A, L, Sparse(B, 0)))

Ac_ldiv_B(L::Factor, B::Dense) = solve(CHOLMOD_A, L, B)
Ac_ldiv_B(L::Factor, B::VecOrMat) = convert(Matrix, solve(CHOLMOD_A, L, Dense(B)))
Ac_ldiv_B(L::Factor, B::Sparse) = spsolve(CHOLMOD_A, L, B)
Ac_ldiv_B(L::Factor, B::SparseVecOrMat) = Ac_ldiv_B(L, Sparse(B))

for f in (:\, :Ac_ldiv_B)
    @eval function ($f)(A::Union{Symmetric{Float64,SparseMatrixCSC{Float64,SuiteSparse_long}},
                          Hermitian{Float64,SparseMatrixCSC{Float64,SuiteSparse_long}},
                          Hermitian{Complex{Float64},SparseMatrixCSC{Complex{Float64},SuiteSparse_long}}}, B::StridedVecOrMat)
        try
            return ($f)(cholfact(A), B)
        catch e
            isa(e, LinAlg.PosDefException) || rethrow(e)
            return ($f)(ldltfact(A) , B)
        end
    end
end

## Other convenience methods
function diag{Tv}(F::Factor{Tv})
    f = unsafe_load(get(F.p))
    fsuper = f.super
    fpi = f.pi
    res = Base.zeros(Tv, Int(f.n))
    xv  = f.x
    if f.is_super!=0
        px = f.px
        pos = 1
        for i in 1:f.nsuper
            base = unsafe_load(px, i) + 1
            res[pos] = unsafe_load(xv, base)
            pos += 1
            for j in 1:unsafe_load(fsuper, i + 1) - unsafe_load(fsuper, i) - 1
                res[pos] = unsafe_load(xv, base + j*(unsafe_load(fpi, i + 1) -
                    unsafe_load(fpi, i) + 1))
                pos += 1
            end
        end
    else
        c0 = f.p
        r0 = f.i
        xv = f.x
        for j in 1:f.n
            jj = unsafe_load(c0, j) + 1
            assert(unsafe_load(r0, jj) == j - 1)
            res[j] = unsafe_load(xv, jj)
        end
    end
    res
end

function logdet{Tv<:VTypes}(F::Factor{Tv})
    f = unsafe_load(get(F.p))
    res = zero(Tv)
    for d in diag(F); res += log(abs(d)) end
    f.is_ll!=0 ? 2res : res
end

det(L::Factor) = exp(logdet(L))

function isposdef(A::SparseMatrixCSC{<:VTypes,SuiteSparse_long})
    if !ishermitian(A)
        return false
    end
    try
        f = cholfact(A)
    catch e
        isa(e, LinAlg.PosDefException) || rethrow(e)
        return false
    end
    true
end

function ishermitian(A::Sparse{Float64})
    s = unsafe_load(A.p)
    if s.stype != 0
        return true
    else
        i = symmetry(A, 1)[1]
        if i < 0
            throw(CHOLMODException("negative value returned from CHOLMOD's symmetry function. This
                is either because the indices are not sorted or because of a memory error"))
        end
        return i == MM_SYMMETRIC || i == MM_SYMMETRIC_POSDIAG
    end
end
function ishermitian(A::Sparse{Complex{Float64}})
    s = unsafe_load(A.p)
    if s.stype != 0
        return true
    else
        i = symmetry(A, 1)[1]
        if i < 0
            throw(CHOLMODException("negative value returned from CHOLMOD's symmetry function. This
                is either because the indices are not sorted or because of a memory error"))
        end
        return i == MM_HERMITIAN || i == MM_HERMITIAN_POSDIAG
    end
end

(*){Ti}(A::Symmetric{Float64,SparseMatrixCSC{Float64,Ti}},
    B::SparseVecOrMat{Float64,Ti}) = sparse(Sparse(A)*Sparse(B))
(*){Ti}(A::Hermitian{Complex{Float64},SparseMatrixCSC{Complex{Float64},Ti}},
    B::SparseVecOrMat{Complex{Float64},Ti}) = sparse(Sparse(A)*Sparse(B))
(*){Ti}(A::Hermitian{Float64,SparseMatrixCSC{Float64,Ti}},
    B::SparseVecOrMat{Float64,Ti}) = sparse(Sparse(A)*Sparse(B))

end #module