Merge pull request #261 from jcarpent/devel

Remove useless setup

.github/workflows/windows-conda.yml

@@ -47,6 +47,10 @@ jobs:
         :: Build and Install
         cmake --build . --config Release --target install
 
+        :: Build stubs
+        git clone https://github.com/jcarpent/pybind11-stubgen.git
+        python "%CD%\pybind11-stubgen\pybind11_stubgen\__init__.py" -o %CONDA_PREFIX%\Lib\site-packages\eigenpy eigenpy --boost-python --ignore-invalid signature --no-setup-py --root-module-suffix ""
+
         :: Testing
         ctest --output-on-failure -C Release -V
 

CMakeLists.txt

@@ -62,16 +62,16 @@ FIND_NUMPY()
 
 IF(WIN32)
   LINK_DIRECTORIES(${PYTHON_LIBRARY_DIRS})
-  # Set default Windows build paths
-  SET(CMAKE_LIBRARY_OUTPUT_DIRECTORY
-    ${PROJECT_BINARY_DIR}/Bin
-    CACHE PATH "Single directory for all libraries")
-  SET(CMAKE_RUNTIME_OUTPUT_DIRECTORY
-    ${PROJECT_BINARY_DIR}/Bin
-    CACHE PATH "Single directory for all executables")
-  SET(CMAKE_ARCHIVE_OUTPUT_DIRECTORY
-    ${PROJECT_BINARY_DIR}/Bin
-    CACHE PATH "Sing$le directory for all archives")
+#  # Set default Windows build paths
+#  SET(CMAKE_LIBRARY_OUTPUT_DIRECTORY
+#    ${PROJECT_BINARY_DIR}/Bin
+#    CACHE PATH "Single directory for all libraries")
+#  SET(CMAKE_RUNTIME_OUTPUT_DIRECTORY
+#    ${PROJECT_BINARY_DIR}/Bin
+#    CACHE PATH "Single directory for all executables")
+#  SET(CMAKE_ARCHIVE_OUTPUT_DIRECTORY
+#    ${PROJECT_BINARY_DIR}/Bin
+#    CACHE PATH "Sing$le directory for all archives")
 ENDIF(WIN32)
 
 # ----------------------------------------------------

include/eigenpy/numpy.hpp

@@ -44,12 +44,13 @@ namespace eigenpy
   template <> struct NumpyEquivalentType<bool>    { enum { type_code = NPY_BOOL  };};
   template <> struct NumpyEquivalentType<int>     { enum { type_code = NPY_INT    };};
   template <> struct NumpyEquivalentType<unsigned int>     { enum { type_code = NPY_UINT    };};
-#if __APPLE__
-  template <> struct NumpyEquivalentType<long>    { enum { type_code = NPY_INT64    };};
-#endif
+  template <> struct NumpyEquivalentType<long>    { enum { type_code = NPY_LONG    };};
+//#if defined _WIN32 || defined __CYGWIN__
+  template <> struct NumpyEquivalentType<long long>    { enum { type_code = NPY_LONGLONG    };};
+//#else
+//  template <> struct NumpyEquivalentType<long long>    { enum { type_code = NPY_LONGLONG    };};
+//#endif
   template <> struct NumpyEquivalentType<unsigned long>    { enum { type_code = NPY_ULONG    };};
-  template <> struct NumpyEquivalentType<int64_t>    { enum { type_code = NPY_INT64 };};
-//  template <> struct NumpyEquivalentType<long long>    { enum { type_code = NPY_LONGLONG };};
 
   template<typename Scalar>
   bool isNumpyNativeType()

include/eigenpy/user-type.hpp

@@ -9,6 +9,8 @@
 #include "eigenpy/numpy-type.hpp"
 #include "eigenpy/register.hpp"
 
+#include <iostream>
+
 namespace eigenpy
 {
   /// \brief Default cast algo to cast a From to To. Can be specialized for any types.
@@ -256,6 +258,11 @@ namespace eigenpy
     assert(to_typenum >= 0 && "to_typenum is not valid");
     assert(from_array_descr != NULL && "from_array_descr is not valid");
     
+    std::cout << "From: " << bp::type_info(typeid(From)).name() << " " << Register::getTypeCode<From>()
+    << " to: " << bp::type_info(typeid(To)).name() << " " << Register::getTypeCode<To>()
+    << "\n to_typenum: " << to_typenum
+    << std::endl;
+    
     if(call_PyArray_RegisterCastFunc(from_array_descr,
                                      to_typenum,
                                      static_cast<PyArray_VectorUnaryFunc *>(&eigenpy::internal::cast<From,To>)) < 0)

unittest/python/test_MINRES.py

@@ -11,4 +11,6 @@ minres = eigenpy.MINRES(A)
 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)

unittest/python/test_user_type.py

@@ -6,13 +6,13 @@ rows = 10
 cols = 20
 
 def test(dtype):
-  mat = np.ones((rows,cols),dtype=dtype)
+  mat = np.array(np.ones((rows,cols)).astype(np.intc),dtype=dtype)
   mat = np.random.rand(rows,cols).astype(dtype)
   mat_copy = mat.copy()
   assert (mat == mat_copy).all()
   assert not (mat != mat_copy).all()
 
-#  if version.parse(np.__version__) >= version.parse("1.21.0"): # check if it fixes for new versio of NumPy 
+#  if version.parse(np.__version__) >= version.parse("1.21.0"): # check if it fixes for new versio of NumPy
 #    mat.fill(mat.dtype.type(20.))
 #    mat_copy = mat.copy()
 #    assert((mat == mat_copy).all())
@@ -20,7 +20,7 @@ def test(dtype):
 
   mat_op = mat + mat
   mat_op = mat.copy(order='F') + mat.copy(order='C')
-  
+
   mat_op = mat - mat
   mat_op = mat * mat
   mat_op = mat.dot(mat.T)
@@ -45,7 +45,7 @@ def test_cast(from_dtype,to_dtype):
 
   from_mat = np.zeros((rows,cols),dtype=from_dtype)
   to_mat = from_mat.astype(dtype=to_dtype)
-  
+
 test(user_type.CustomDouble)
 
 test_cast(user_type.CustomDouble,np.double)

unittest/user_type.cpp

@@ -196,14 +196,20 @@ BOOST_PYTHON_MODULE(user_type)
   
   eigenpy::registerCast<DoubleType,double>(true);
   eigenpy::registerCast<double,DoubleType>(true);
-  eigenpy::registerCast<DoubleType,int32_t>(false);
-  eigenpy::registerCast<int32_t,DoubleType>(true);
-  eigenpy::registerCast<DoubleType,int64_t>(false);
-  eigenpy::registerCast<int64_t,DoubleType>(true);
+  eigenpy::registerCast<DoubleType,int>(false);
+  eigenpy::registerCast<int,DoubleType>(true);
+  eigenpy::registerCast<DoubleType,long long>(false);
+  eigenpy::registerCast<long long,DoubleType>(true);
+  eigenpy::registerCast<DoubleType,long>(false);
+  eigenpy::registerCast<long,DoubleType>(true);
   eigenpy::registerCast<FloatType,double>(true);
   eigenpy::registerCast<double,FloatType>(false);
-  eigenpy::registerCast<FloatType,int64_t>(false);
-  eigenpy::registerCast<int64_t,FloatType>(true);
+  eigenpy::registerCast<FloatType,long long>(false);
+  eigenpy::registerCast<long long,FloatType>(true);
+  eigenpy::registerCast<FloatType,int>(false);
+  eigenpy::registerCast<int,FloatType>(true);
+  eigenpy::registerCast<FloatType,long>(false);
+  eigenpy::registerCast<long,FloatType>(true);
 
   bp::implicitly_convertible<double,DoubleType>();
   bp::implicitly_convertible<DoubleType,double>();