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python: have a common header with Python format string definitions. To not have to duplicate these for each and every case, enlarging the surface for potential bugs. Also changing the signatures to number + identifier, instead of identifier repeated number of times. Means the compiler won't be able to deduplicate / overlap the literals in the binary, but is much more maintainable. For Numpy this causes the slices to be reported as actual typed Numpy arrays instead of typeless tuples, which is good I guess? Not sure why that wasn't the case before, or what is the difference, and how it will behave for sparse types such as aligned matrices.
3 years ago
#ifndef Magnum_StridedArrayViewPythonBindings_h
#define Magnum_StridedArrayViewPythonBindings_h
/*
This file is part of Magnum.
Copyright © 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019,
2020, 2021, 2022 Vladimír Vondruš <mosra@centrum.cz>
Permission is hereby granted, free of charge, to any person obtaining a
copy of this software and associated documentation files (the "Software"),
to deal in the Software without restriction, including without limitation
the rights to use, copy, modify, merge, publish, distribute, sublicense,
and/or sell copies of the Software, and to permit persons to whom the
Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included
in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
DEALINGS IN THE SOFTWARE.
*/
#include <Magnum/Magnum.h>
python: typed access to Image*.pixels. Basically mirroring what's done for MeshData and SceneData already.
3 years ago
#include "Corrade/Containers/StridedArrayViewPythonBindings.h"
python: have a common header with Python format string definitions. To not have to duplicate these for each and every case, enlarging the surface for potential bugs. Also changing the signatures to number + identifier, instead of identifier repeated number of times. Means the compiler won't be able to deduplicate / overlap the literals in the binary, but is much more maintainable. For Numpy this causes the slices to be reported as actual typed Numpy arrays instead of typeless tuples, which is good I guess? Not sure why that wasn't the case before, or what is the difference, and how it will behave for sparse types such as aligned matrices.
3 years ago
namespace Corrade { namespace Containers { namespace Implementation {
/* This expands Containers::Implementation::pythonFormatString() with Magnum
types. Using e.g. "3f" instead of "fff" as that makes Numpy understand even
the slices as arrays in a concrete type (or it at least repr()s them as
such), with "fff" it gives back an untyped tuple. */
#define _c(type, string) \
template<> constexpr const char* pythonFormatString<Magnum::type>() { return string; }
python: typed access to Image*.pixels. Basically mirroring what's done for MeshData and SceneData already.
3 years ago
_c(Half, "e")
python: have a common header with Python format string definitions. To not have to duplicate these for each and every case, enlarging the surface for potential bugs. Also changing the signatures to number + identifier, instead of identifier repeated number of times. Means the compiler won't be able to deduplicate / overlap the literals in the binary, but is much more maintainable. For Numpy this causes the slices to be reported as actual typed Numpy arrays instead of typeless tuples, which is good I guess? Not sure why that wasn't the case before, or what is the difference, and how it will behave for sparse types such as aligned matrices.
3 years ago
_c(Vector2, "2f")
python: typed access to Image*.pixels. Basically mirroring what's done for MeshData and SceneData already.
3 years ago
_c(Vector2h, "2e")
python: have a common header with Python format string definitions. To not have to duplicate these for each and every case, enlarging the surface for potential bugs. Also changing the signatures to number + identifier, instead of identifier repeated number of times. Means the compiler won't be able to deduplicate / overlap the literals in the binary, but is much more maintainable. For Numpy this causes the slices to be reported as actual typed Numpy arrays instead of typeless tuples, which is good I guess? Not sure why that wasn't the case before, or what is the difference, and how it will behave for sparse types such as aligned matrices.
3 years ago
_c(Vector2d, "2d")
_c(Vector2ub, "2B")
_c(Vector2b, "2b")
_c(Vector2us, "2H")
_c(Vector2s, "2h")
_c(Vector2ui, "2I")
_c(Vector2i, "2i")
_c(Vector3, "3f")
python: typed access to Image*.pixels. Basically mirroring what's done for MeshData and SceneData already.
3 years ago
_c(Vector3h, "3e")
python: have a common header with Python format string definitions. To not have to duplicate these for each and every case, enlarging the surface for potential bugs. Also changing the signatures to number + identifier, instead of identifier repeated number of times. Means the compiler won't be able to deduplicate / overlap the literals in the binary, but is much more maintainable. For Numpy this causes the slices to be reported as actual typed Numpy arrays instead of typeless tuples, which is good I guess? Not sure why that wasn't the case before, or what is the difference, and how it will behave for sparse types such as aligned matrices.
3 years ago
_c(Vector3d, "3d")
_c(Vector3ub, "3B")
_c(Vector3b, "3b")
_c(Vector3us, "3H")
_c(Vector3s, "3h")
_c(Vector3ui, "3I")
_c(Vector3i, "3i")
_c(Vector4, "4f")
python: typed access to Image*.pixels. Basically mirroring what's done for MeshData and SceneData already.
3 years ago
_c(Vector4h, "4e")
python: have a common header with Python format string definitions. To not have to duplicate these for each and every case, enlarging the surface for potential bugs. Also changing the signatures to number + identifier, instead of identifier repeated number of times. Means the compiler won't be able to deduplicate / overlap the literals in the binary, but is much more maintainable. For Numpy this causes the slices to be reported as actual typed Numpy arrays instead of typeless tuples, which is good I guess? Not sure why that wasn't the case before, or what is the difference, and how it will behave for sparse types such as aligned matrices.
3 years ago
_c(Vector4d, "4d")
_c(Vector4ub, "4B")
_c(Vector4b, "4b")
_c(Vector4us, "4H")
_c(Vector4s, "4h")
_c(Vector4ui, "4I")
_c(Vector4i, "4i")
python: data access in trade.SceneData. Just regular boring code, all the cool stuff was invented for MeshData already.
3 years ago
_c(Matrix2x2, "4f")
_c(Matrix2x2d, "4d")
_c(Matrix2x3, "6f")
_c(Matrix2x3d, "6d")
_c(Matrix2x4, "8f")
_c(Matrix2x4d, "8d")
_c(Matrix3x2, "6f")
_c(Matrix3x2d, "6d")
_c(Matrix3x3, "9f")
_c(Matrix3x3d, "9d")
_c(Matrix3x4, "12f")
_c(Matrix3x4d, "12d")
_c(Matrix4x2, "8f")
_c(Matrix4x2d, "8d")
_c(Matrix4x3, "12f")
_c(Matrix4x3d, "12d")
_c(Matrix4x4, "16f")
_c(Matrix4x4d, "16d")
_c(Range1D, "2f")
_c(Range1Dd, "2d")
_c(Range1Di, "2i")
_c(Range2D, "4f")
_c(Range2Dd, "4d")
_c(Range2Di, "4i")
_c(Range3D, "6f")
_c(Range3Dd, "6d")
_c(Range3Di, "6i")
_c(Complex, "2f")
_c(Complexd, "2d")
_c(DualComplex, "4f")
_c(DualComplexd, "4f")
_c(Quaternion, "4f")
_c(Quaterniond, "4d")
_c(DualQuaternion, "8f")
_c(DualQuaterniond, "8d")
_c(Deg, "f")
_c(Degd, "d")
_c(Rad, "f")
_c(Radd, "d")
python: have a common header with Python format string definitions. To not have to duplicate these for each and every case, enlarging the surface for potential bugs. Also changing the signatures to number + identifier, instead of identifier repeated number of times. Means the compiler won't be able to deduplicate / overlap the literals in the binary, but is much more maintainable. For Numpy this causes the slices to be reported as actual typed Numpy arrays instead of typeless tuples, which is good I guess? Not sure why that wasn't the case before, or what is the difference, and how it will behave for sparse types such as aligned matrices.
3 years ago
#undef _c
}
}}
#endif