Use lapacke library, drop linking to LAPACKE implementation

.gitignore

@@ -10,3 +10,7 @@ Cargo.lock
 
 # cargo fmt
 *.bk
+
+# IntelliJ Rust
+.idea
+*.iml

Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name    = "ndarray-linalg"
-version = "0.7.1"
+version = "0.8.0"
 authors = ["Toshiki Teramura <[email protected]>"]
 
 description   = "Linear algebra package for rust-ndarray using LAPACK"
@@ -11,40 +11,20 @@ license       = "MIT"
 readme        = "README.md"
 categories    = ["algorithms", "science"]
 
-[features]
-default  = ["openblas-static"]
-openblas-shared = ["lapack/openblas"]
-openblas-static = ["lapack/openblas", "openblas-src/static"]
-openblas-system = ["lapack/openblas", "openblas-src/system"]
-netlib-shared   = ["lapack/netlib"]
-netlib-static   = ["lapack/netlib", "netlib-src/static"]
-netlib-system   = ["lapack/netlib", "netlib-src/system"]
-intel-mkl       = ["intel-mkl-src"]
-
 [dependencies]
 rand = "0.3"
-derive-new = "0.4"
+derive-new = "0.5"
 procedurals = "0.2"
 num-traits  = "0.1"
-num-complex = "0.1"
-lapack = { version = "0.13", default-features = false }
-lapack-sys = { version = "0.11", default-features = false }
+num-complex = { version = "0.1", default-features = false }
+lapacke = "0.1.4"
+lapack-src = { git = "https://github.com/alexbool/lapack-src", rev = "da33354", optional = true }
 
 [dependencies.ndarray]
 version = "0.10"
 default-features = false
 
-[dependencies.openblas-src]
-version = "0.5.3"
-default-features = false
-optional = true
-
-[dependencies.netlib-src]
-version = "0.7.0"
-default-features = false
-optional = true
-
-[dependencies.intel-mkl-src]
-version = "0.2.5"
-default-features = false
-optional = true
+[features]
+openblas = ["lapack-src", "lapack-src/openblas"]
+netlib = ["lapack-src", "lapack-src/netlib"]
+intel-mkl = ["lapack-src", "lapack-src/intel-mkl"]

README.md

@@ -10,23 +10,38 @@ Dependencies
 -------------
 
 - [bluss/rust-ndarray](https://github.com/bluss/rust-ndarray)
-- [stainless-steel/lapack](https://github.com/stainless-steel/lapack)
+- [blas-lapack-rs/lapacke](https://github.com/blas-lapack-rs/lapacke)
 
 and more (See Cargo.toml).
 
-Feature flags
---------------
+Choosing LAPACKE implementation
+--------------------------------
+
+For the sake of linking flexibility, you must provide LAPACKE implementation (as an `extern crate`) yourself.
+Currently three LAPACKE implementations are supported and tested:
 
 - [OpenBLAS](https://github.com/cmr/openblas-src)
-  - `openblas-static`: use OpenBLAS with static link (default)
-  - `openblas-shared`: use OpenBLAS with shared link
-  - `openblas-system`: use system OpenBLAS (experimental)
 - [Netlib](https://github.com/cmr/netlib-src)
-  - `netlib-static`: use Netlib with static link (default)
-  - `netlib-shared`: use Netlib with shared link
-  - `netlib-system`: use system Netlib (experimental)
 - [Intel MKL](https://github.com/termoshtt/rust-intel-mkl) (non-free license, see the linked page)
-  - `intel-mkl`: use Intel MKL shared link (experimental)
+
+You should link a LAPACKE implementation to a final crate (like binary executable or dylib) only, not to a Rust library.
+Example:
+
+`Cargo.toml`:
+```toml
+[depencdencies]
+ndarray = "0.10"
+ndarray-linalg = "0.8"
+openblas-src = "0.5" # or another backend of your choice
+
+```
+
+`main.rs`:
+```rust
+extern crate ndarray;
+extern crate ndarray_linalg;
+extern crate openblas_src; // or another backend of your choice
+```
 
 Examples
 ---------

examples/eigh.rs

@@ -1,6 +1,8 @@
 
 extern crate ndarray;
 extern crate ndarray_linalg;
+#[cfg(feature = "lapack-src")]
+extern crate lapack_src;
 
 use ndarray::*;
 use ndarray_linalg::*;

examples/solve.rs

@@ -1,6 +1,8 @@
 
 extern crate ndarray;
 extern crate ndarray_linalg;
+#[cfg(feature = "lapack-src")]
+extern crate lapack_src;
 
 use ndarray::*;
 use ndarray_linalg::*;

examples/solveh.rs

@@ -1,6 +1,8 @@
 
 extern crate ndarray;
 extern crate ndarray_linalg;
+#[cfg(feature = "lapack-src")]
+extern crate lapack_src;
 
 use ndarray::*;
 use ndarray_linalg::*;

src/cholesky.rs

@@ -9,7 +9,7 @@
 //! Using the Cholesky decomposition of `A` for various operations, where `A`
 //! is a Hermitian (or real symmetric) positive definite matrix:
 //!
-//! ```
+//! ```no_run
 //! #[macro_use]
 //! extern crate ndarray;
 //! extern crate ndarray_linalg;

src/lapack_traits/cholesky.rs

@@ -1,6 +1,6 @@
 //! Cholesky decomposition
 
-use lapack::c;
+use lapacke;
 
 use error::*;
 use layout::MatrixLayout;
@@ -46,7 +46,7 @@ impl Cholesky_ for $scalar {
 }
 }} // end macro_rules
 
-impl_cholesky!(f64, c::dpotrf, c::dpotri, c::dpotrs);
-impl_cholesky!(f32, c::spotrf, c::spotri, c::spotrs);
-impl_cholesky!(c64, c::zpotrf, c::zpotri, c::zpotrs);
-impl_cholesky!(c32, c::cpotrf, c::cpotri, c::cpotrs);
+impl_cholesky!(f64, lapacke::dpotrf, lapacke::dpotri, lapacke::dpotrs);
+impl_cholesky!(f32, lapacke::spotrf, lapacke::spotri, lapacke::spotrs);
+impl_cholesky!(c64, lapacke::zpotrf, lapacke::zpotri, lapacke::zpotrs);
+impl_cholesky!(c32, lapacke::cpotrf, lapacke::cpotri, lapacke::cpotrs);

src/lapack_traits/eigh.rs

@@ -1,6 +1,6 @@
 //! Eigenvalue decomposition for Hermite matrices
 
-use lapack::c;
+use lapacke;
 use num_traits::Zero;
 
 use error::*;
@@ -27,7 +27,7 @@ impl Eigh_ for $scalar {
 }
 }} // impl_eigh!
 
-impl_eigh!(f64, c::dsyev);
-impl_eigh!(f32, c::ssyev);
-impl_eigh!(c64, c::zheev);
-impl_eigh!(c32, c::cheev);
+impl_eigh!(f64, lapacke::dsyev);
+impl_eigh!(f32, lapacke::ssyev);
+impl_eigh!(c64, lapacke::zheev);
+impl_eigh!(c32, lapacke::cheev);

src/lapack_traits/opnorm.rs

@@ -1,7 +1,7 @@
 //! Operator norms of matrices
 
-use lapack::c;
-use lapack::c::Layout::ColumnMajor as cm;
+use lapacke;
+use lapacke::Layout::ColumnMajor as cm;
 
 use layout::MatrixLayout;
 use types::*;
@@ -24,7 +24,7 @@ impl OperatorNorm_ for $scalar {
 }
 }} // impl_opnorm!
 
-impl_opnorm!(f64, c::dlange);
-impl_opnorm!(f32, c::slange);
-impl_opnorm!(c64, c::zlange);
-impl_opnorm!(c32, c::clange);
+impl_opnorm!(f64, lapacke::dlange);
+impl_opnorm!(f32, lapacke::slange);
+impl_opnorm!(c64, lapacke::zlange);
+impl_opnorm!(c32, lapacke::clange);

src/lapack_traits/qr.rs

@@ -1,6 +1,6 @@
 //! QR decomposition
 
-use lapack::c;
+use lapacke;
 use num_traits::Zero;
 use std::cmp::min;
 
@@ -44,7 +44,7 @@ impl QR_ for $scalar {
 }
 }} // endmacro
 
-impl_qr!(f64, c::dgeqrf, c::dorgqr);
-impl_qr!(f32, c::sgeqrf, c::sorgqr);
-impl_qr!(c64, c::zgeqrf, c::zungqr);
-impl_qr!(c32, c::cgeqrf, c::cungqr);
+impl_qr!(f64, lapacke::dgeqrf, lapacke::dorgqr);
+impl_qr!(f32, lapacke::sgeqrf, lapacke::sorgqr);
+impl_qr!(c64, lapacke::zgeqrf, lapacke::zungqr);
+impl_qr!(c32, lapacke::cgeqrf, lapacke::cungqr);

src/lapack_traits/solve.rs

@@ -1,6 +1,6 @@
 //! Solve linear problem using LU decomposition
 
-use lapack::c;
+use lapacke;
 
 use error::*;
 use layout::MatrixLayout;
@@ -65,7 +65,7 @@ impl Solve_ for $scalar {
 
 }} // impl_solve!
 
-impl_solve!(f64, c::dgetrf, c::dgetri, c::dgecon, c::dgetrs);
-impl_solve!(f32, c::sgetrf, c::sgetri, c::sgecon, c::sgetrs);
-impl_solve!(c64, c::zgetrf, c::zgetri, c::zgecon, c::zgetrs);
-impl_solve!(c32, c::cgetrf, c::cgetri, c::cgecon, c::cgetrs);
+impl_solve!(f64, lapacke::dgetrf, lapacke::dgetri, lapacke::dgecon, lapacke::dgetrs);
+impl_solve!(f32, lapacke::sgetrf, lapacke::sgetri, lapacke::sgecon, lapacke::sgetrs);
+impl_solve!(c64, lapacke::zgetrf, lapacke::zgetri, lapacke::zgecon, lapacke::zgetrs);
+impl_solve!(c32, lapacke::cgetrf, lapacke::cgetri, lapacke::cgecon, lapacke::cgetrs);

src/lapack_traits/solveh.rs

@@ -2,7 +2,7 @@
 //!
 //! See also [the manual of dsytrf](http://www.netlib.org/lapack/lapack-3.1.1/html/dsytrf.f.html)
 
-use lapack::c;
+use lapacke;
 
 use error::*;
 use layout::MatrixLayout;
@@ -55,7 +55,7 @@ impl Solveh_ for $scalar {
 
 }} // impl_solveh!
 
-impl_solveh!(f64, c::dsytrf, c::dsytri, c::dsytrs);
-impl_solveh!(f32, c::ssytrf, c::ssytri, c::ssytrs);
-impl_solveh!(c64, c::zhetrf, c::zhetri, c::zhetrs);
-impl_solveh!(c32, c::chetrf, c::chetri, c::chetrs);
+impl_solveh!(f64, lapacke::dsytrf, lapacke::dsytri, lapacke::dsytrs);
+impl_solveh!(f32, lapacke::ssytrf, lapacke::ssytri, lapacke::ssytrs);
+impl_solveh!(c64, lapacke::zhetrf, lapacke::zhetri, lapacke::zhetrs);
+impl_solveh!(c32, lapacke::chetrf, lapacke::chetri, lapacke::chetrs);

src/lapack_traits/svd.rs

@@ -1,6 +1,6 @@
 //! Singular-value decomposition
 
-use lapack::c;
+use lapacke;
 use num_traits::Zero;
 
 use error::*;
@@ -63,7 +63,7 @@ impl SVD_ for $scalar {
 
 }} // impl_svd!
 
-impl_svd!(f64, c::dgesvd);
-impl_svd!(f32, c::sgesvd);
-impl_svd!(c64, c::zgesvd);
-impl_svd!(c32, c::cgesvd);
+impl_svd!(f64, lapacke::dgesvd);
+impl_svd!(f32, lapacke::sgesvd);
+impl_svd!(c64, lapacke::zgesvd);
+impl_svd!(c32, lapacke::cgesvd);

src/lapack_traits/triangular.rs

@@ -1,6 +1,6 @@
 //! Implement linear solver and inverse matrix
 
-use lapack::c;
+use lapacke;
 
 use super::{Transpose, UPLO, into_result};
 use error::*;
@@ -50,7 +50,7 @@ impl Triangular_ for $scalar {
 
 }} // impl_triangular!
 
-impl_triangular!(f64, c::dtrtri, c::dtrtrs);
-impl_triangular!(f32, c::strtri, c::strtrs);
-impl_triangular!(c64, c::ztrtri, c::ztrtrs);
-impl_triangular!(c32, c::ctrtri, c::ctrtrs);
+impl_triangular!(f64, lapacke::dtrtri, lapacke::dtrtrs);
+impl_triangular!(f32, lapacke::strtri, lapacke::strtrs);
+impl_triangular!(c64, lapacke::ztrtri, lapacke::ztrtrs);
+impl_triangular!(c32, lapacke::ctrtri, lapacke::ctrtrs);

src/layout.rs

@@ -1,6 +1,6 @@
 //! Memory layout of matrices
 
-use lapack::c;
+use lapacke;
 use ndarray::*;
 
 use super::error::*;
@@ -44,10 +44,10 @@ impl MatrixLayout {
         }
     }
 
-    pub fn lapacke_layout(&self) -> c::Layout {
+    pub fn lapacke_layout(&self) -> lapacke::Layout {
         match *self {
-            MatrixLayout::C(_) => c::Layout::RowMajor,
-            MatrixLayout::F(_) => c::Layout::ColumnMajor,
+            MatrixLayout::C(_) => lapacke::Layout::RowMajor,
+            MatrixLayout::F(_) => lapacke::Layout::ColumnMajor,
         }
     }
 

src/lib.rs

@@ -17,8 +17,7 @@
 //!  - [generator functions](generate/index.html)
 //!  - [Scalar trait](types/trait.Scalar.html)
 
-extern crate lapack;
-extern crate lapack_sys;
+extern crate lapacke;
 extern crate num_traits;
 extern crate num_complex;
 extern crate rand;
@@ -29,8 +28,8 @@ extern crate procedurals;
 #[macro_use]
 extern crate derive_new;
 
-#[cfg(feature = "intel-mkl")]
-extern crate intel_mkl_src;
+//#[cfg(feature = "intel-mkl")]
+//extern crate intel_mkl_src;
 
 pub mod assert;
 pub mod cholesky;

src/solve.rs

@@ -4,7 +4,7 @@
 //!
 //! Solve `A * x = b`:
 //!
-//! ```
+//! ```no_run
 //! #[macro_use]
 //! extern crate ndarray;
 //! extern crate ndarray_linalg;
@@ -28,9 +28,10 @@
 //! coefficient matrix `A`, it's faster to compute the LU factorization once at
 //! the beginning than solving directly using `A`:
 //!
-//! ```
+//! ```no_run
 //! # extern crate ndarray;
 //! # extern crate ndarray_linalg;
+//!
 //! use ndarray::prelude::*;
 //! use ndarray_linalg::*;
 //! # fn main() {

src/solveh.rs

@@ -7,7 +7,7 @@
 //!
 //! Solve `A * x = b`, where `A` is a Hermitian (or real symmetric) matrix:
 //!
-//! ```
+//! ```no_run
 //! #[macro_use]
 //! extern crate ndarray;
 //! extern crate ndarray_linalg;
@@ -32,7 +32,7 @@
 //! Hermitian or real symmetric coefficient matrix `A`, it's faster to compute
 //! the factorization once at the beginning than solving directly using `A`:
 //!
-//! ```
+//! ```no_run
 //! # extern crate ndarray;
 //! # extern crate ndarray_linalg;
 //! use ndarray::prelude::*;