Merge pull request #275 from jcarpent/devel

Expose const Eigen::Ref<const ... + update pre-commit + correctly export the project version

Author: jcarpent

.pre-commit-config.yaml

@@ -3,8 +3,12 @@ repos:
     rev: v13.0.1
     hooks:
     -   id: clang-format
-        args: [-i, --style=Google]
+        args: ['-i', '--style={BasedOnStyle: Google, SortIncludes: false}']
 -   repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v4.1.0
     hooks:
     -   id: trailing-whitespace
+-   repo: https://github.com/psf/black
+    rev: 22.3.0
+    hooks:
+    -   id: black

CMakeLists.txt

@@ -1,6 +1,6 @@
 #
 # Copyright (c) 2014-2019 CNRS
-# Copyright (c) 2018-2021 INRIA
+# Copyright (c) 2018-2022 INRIA
 #
 
 CMAKE_MINIMUM_REQUIRED(VERSION 3.1)
@@ -11,6 +11,7 @@ SET(PROJECT_URL "http://github.com/stack-of-tasks/eigenpy")
 SET(PROJECT_USE_CMAKE_EXPORT TRUE)
 SET(PROJECT_USE_KEYWORD_LINK_LIBRARIES TRUE)
 SET(PROJECT_CUSTOM_HEADER_EXTENSION "hpp")
+SET(PROJECT_COMPATIBILITY_VERSION AnyNewerVersion)
 
 # Check if the submodule cmake have been initialized
 set(JRL_CMAKE_MODULES "${CMAKE_CURRENT_LIST_DIR}/cmake")

benchmarks/bench-switch.py

@@ -7,10 +7,11 @@
 import timeit
 
 from IPython import get_ipython
+
 ipython = get_ipython()
 
 quat = eigenpy.Quaternion()
-a = [0., 0., 0.]
+a = [0.0, 0.0, 0.0]
 
 cmd1 = "timeit np.array(a)"
 print("\n")
@@ -50,7 +51,7 @@
 ipython.magic(cmd5)
 print("\n")
 
-a_matrix = np.matrix(a);
+a_matrix = np.matrix(a)
 cmd6 = "timeit np.asarray(a_matrix)"
 print(cmd6)
 ipython.magic(cmd6)

cmake

@@ -68,6 +68,7 @@ void enableEigenPySpecific() {
   EigenToPyConverter<MatType>::registration();
 #if EIGEN_VERSION_AT_LEAST(3, 2, 0)
   EigenToPyConverter<Eigen::Ref<MatType> >::registration();
+  EigenToPyConverter<const Eigen::Ref<const MatType> >::registration();
 #endif
 
   // from-python

include/eigenpy/details.hpp

@@ -1,5 +1,5 @@
 /*
- * Copyright 2020 INRIA
+ * Copyright 2020-2022 INRIA
  */
 
 #ifndef __eigenpy_numpy_allocator_hpp__
@@ -116,13 +116,11 @@ template <typename MatType, int Options, typename Stride>
 struct NumpyAllocator<const Eigen::Ref<const MatType, Options, Stride> > {
   typedef const Eigen::Ref<const MatType, Options, Stride> RefType;
 
-  template <typename SimilarMatrixType>
   static PyArrayObject *allocate(RefType &mat, npy_intp nd, npy_intp *shape) {
-    typedef typename SimilarMatrixType::Scalar Scalar;
+    typedef typename RefType::Scalar Scalar;
     enum {
-      NPY_ARRAY_MEMORY_CONTIGUOUS_RO = SimilarMatrixType::IsRowMajor
-                                           ? NPY_ARRAY_CARRAY_RO
-                                           : NPY_ARRAY_FARRAY_RO
+      NPY_ARRAY_MEMORY_CONTIGUOUS_RO =
+          RefType::IsRowMajor ? NPY_ARRAY_CARRAY_RO : NPY_ARRAY_FARRAY_RO
     };
 
     if (NumpyType::sharedMemory()) {

include/eigenpy/numpy-allocator.hpp

@@ -1,11 +1,12 @@
 /*
  * Copyright 2014-2019, CNRS
- * Copyright 2018-2020, INRIA
+ * Copyright 2018-2022, INRIA
  */
 
 #include <iostream>
 
 #include "eigenpy/eigenpy.hpp"
+#include "eigenpy/eigen-from-python.hpp"
 
 using namespace Eigen;
 using namespace eigenpy;
@@ -41,6 +42,16 @@ Eigen::Ref<MatType> asRef(const int rows, const int cols) {
   return mat;
 }
 
+template <typename MatType>
+Eigen::Ref<MatType> asRef(Eigen::Ref<MatType> mat) {
+  return Eigen::Ref<MatType>(mat);
+}
+
+template <typename MatType>
+const Eigen::Ref<const MatType> asConstRef(Eigen::Ref<MatType> mat) {
+  return Eigen::Ref<const MatType>(mat);
+}
+
 BOOST_PYTHON_MODULE(eigen_ref) {
   namespace bp = boost::python;
   eigenpy::enableEigenPy();
@@ -60,5 +71,10 @@ BOOST_PYTHON_MODULE(eigen_ref) {
   bp::def("fillVec", fill<VectorXd>);
   bp::def("fill", fill<MatrixXd>);
 
-  bp::def("asRef", asRef<MatrixXd>);
+  bp::def("asRef",
+          (Eigen::Ref<MatrixXd>(*)(const int, const int))asRef<MatrixXd>);
+  bp::def("asRef",
+          (Eigen::Ref<MatrixXd>(*)(Eigen::Ref<MatrixXd>))asRef<MatrixXd>);
+  bp::def("asConstRef", (const Eigen::Ref<const MatrixXd> (*)(
+                            Eigen::Ref<MatrixXd>))asConstRef<MatrixXd>);
 }

unittest/eigen_ref.cpp

@@ -4,20 +4,22 @@
 import numpy.linalg as la
 
 dim = 100
-A = np.random.rand(dim,dim)
+A = np.random.rand(dim, dim)
 
-A = (A + A.T)*0.5 + np.diag(10. + np.random.rand(dim))
+A = (A + A.T) * 0.5 + np.diag(10.0 + np.random.rand(dim))
 
 ldlt = eigenpy.LDLT(A)
 
 L = ldlt.matrixL()
 D = ldlt.vectorD()
 P = ldlt.transpositionsP()
 
-assert eigenpy.is_approx(np.transpose(P).dot(L.dot(np.diag(D).dot(np.transpose(L).dot(P)))),A)
+assert eigenpy.is_approx(
+    np.transpose(P).dot(L.dot(np.diag(D).dot(np.transpose(L).dot(P)))), A
+)
 
-X = np.random.rand(dim,20)
+X = np.random.rand(dim, 20)
 B = A.dot(X)
 X_est = ldlt.solve(B)
-assert eigenpy.is_approx(X,X_est)
-assert eigenpy.is_approx(A.dot(X_est),B)
+assert eigenpy.is_approx(X, X_est)
+assert eigenpy.is_approx(A.dot(X_est), B)

unittest/python/test_LDLT.py

@@ -4,17 +4,17 @@
 import numpy.linalg as la
 
 dim = 100
-A = np.random.rand(dim,dim)
+A = np.random.rand(dim, dim)
 
-A = (A + A.T)*0.5 + np.diag(10. + np.random.rand(dim))
+A = (A + A.T) * 0.5 + np.diag(10.0 + np.random.rand(dim))
 
 llt = eigenpy.LLT(A)
 
 L = llt.matrixL()
-assert eigenpy.is_approx(L.dot(np.transpose(L)),A)
+assert eigenpy.is_approx(L.dot(np.transpose(L)), A)
 
-X = np.random.rand(dim,20)
+X = np.random.rand(dim, 20)
 B = A.dot(X)
 X_est = llt.solve(B)
-assert eigenpy.is_approx(X,X_est)
-assert eigenpy.is_approx(A.dot(X_est),B)
+assert eigenpy.is_approx(X, X_est)
+assert eigenpy.is_approx(A.dot(X_est), B)

unittest/python/test_LLT.py

@@ -8,9 +8,9 @@
 
 minres = eigenpy.MINRES(A)
 
-X = np.random.rand(dim,20)
+X = np.random.rand(dim, 20)
 B = A.dot(X)
 X_est = minres.solve(B)
-print("A.dot(X_est):",A.dot(X_est))
-print("B:",B)
-assert eigenpy.is_approx(A.dot(X_est),B,1e-6)
+print("A.dot(X_est):", A.dot(X_est))
+print("B:", B)
+assert eigenpy.is_approx(A.dot(X_est), B, 1e-6)