Merge pull request #437 from jcarpent/devel

Fix bug in Sparse module with zero-size matrices

Author: jcarpent

.github/workflows/check-changelog.yml

@@ -1,7 +1,7 @@
 name: Check-changelog
 on:
   pull_request:
-    types: [assigned, opened, synchronize, reopened, labeled, unlabeled]
+    types: [assigned, opened, synchronize, reopened, labeled, unlabeled, edited]
     branches:
       - devel
 jobs:

CHANGELOG.md

@@ -16,6 +16,7 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
 ### Fixed
 - Fix the issue of missing exposition of Eigen types with __int64 scalar type ([#426](https://github.com/stack-of-tasks/eigenpy/pull/426))
 - Fix namespace use in unittest/std_pair.cpp ([#429](https://github.com/stack-of-tasks/eigenpy/pull/429))
+- Fix case of zero-size sparse matrices ([#437](https://github.com/stack-of-tasks/eigenpy/pull/437))
 
 ## [3.3.0] - 2024-01-23
 

cmake

@@ -85,6 +85,9 @@ struct scipy_allocator_impl_sparse_matrix {
       //      scipy_sparse_matrix_type(*bp::make_tuple(0,0),**args);
       scipy_sparse_matrix = scipy_sparse_matrix_type(
           Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic>(0, 0));
+    } else if (mat.nonZeros() == 0) {
+      scipy_sparse_matrix =
+          scipy_sparse_matrix_type(bp::make_tuple(mat.rows(), mat.cols()));
     } else {
       scipy_sparse_matrix = scipy_sparse_matrix_type(bp::make_tuple(
           DataVector(data),

include/eigenpy/scipy-allocator.hpp

@@ -146,8 +146,15 @@ void eigen_sparse_matrix_from_py_construct(
     const Eigen::Index m = bp::extract<Eigen::Index>(shape[0]),
                        n = bp::extract<Eigen::Index>(shape[1]),
                        nnz = bp::extract<Eigen::Index>(obj.attr("nnz"));
-    MapMatOrRefType sparse_map(m, n, nnz, indptr.data(), indices.data(),
-                               data.data());
+
+    // Handle the specific case of the null matrix
+    Scalar *data_ptr = nullptr;
+    StorageIndex *indices_ptr = nullptr;
+    if (nnz > 0) {
+      data_ptr = data.data();
+      indices_ptr = indices.data();
+    }
+    MapMatOrRefType sparse_map(m, n, nnz, indptr.data(), indices_ptr, data_ptr);
 
     new (raw_ptr) MatOrRefType(sparse_map);
   }

include/eigenpy/sparse/eigen-from-python.hpp

@@ -16,8 +16,7 @@
 v = sparse_matrix.vector1x1(2)
 assert v.toarray() == np.array([2])
 
-size = 100
-diag_values = np.random.rand(100)
+diag_values = np.random.rand(10)
 diag_mat = sparse_matrix.diagonal(diag_values)
 assert (diag_mat.toarray() == np.diag(diag_values)).all()
 
@@ -26,3 +25,8 @@
 
 diag_mat_csr = csr_matrix(diag_mat)
 assert (sparse_matrix.copy(diag_mat_csr) != diag_mat_csr).nnz == 0
+
+# test zero matrix
+zero_mat = csr_matrix(np.zeros((10, 1)))
+zero_mat_copy = sparse_matrix.copy(zero_mat)
+assert (zero_mat_copy != zero_mat).nnz == 0

unittest/python/test_sparse_matrix.py

@@ -26,7 +26,8 @@ Eigen::SparseMatrix<Scalar, Options> matrix1x1(const Scalar& value) {
 
 template <typename Scalar, int Options>
 Eigen::SparseMatrix<Scalar, Options> diagonal(
-    const Eigen::Ref<Eigen::Matrix<Scalar, Eigen::Dynamic, 1> >& diag_values) {
+    const Eigen::Ref<const Eigen::Matrix<Scalar, Eigen::Dynamic, 1> >&
+        diag_values) {
   typedef Eigen::SparseMatrix<Scalar, Options> ReturnType;
   ReturnType mat(diag_values.size(), diag_values.size());
   for (Eigen::Index k = 0; k < diag_values.size(); ++k)