namespace Magnum { namespace Math { /** @page matrix-vector Operations with matrices and vectors @brief Introduction to essential classes of the graphics pipeline. @tableofcontents Matrices and vectors are the most important part of graphics programming and one of goals of %Magnum is to make their usage as intuitive as possible. This page will overview their usage and introduce some tricks to make your life easier. @section matrix-vector-hierarchy Matrix and vector classes %Magnum has three main matrix and vector classes: RectangularMatrix, (square) Matrix and Vector. To achieve greatest code reuse, %Matrix is internally square %RectangularMatrix and %RectangularMatrix is internally array of one or more %Vector instances. Both vectors and matrices can have arbitrary size (known at compile time) and can store any arithmetic type. Each subclass brings some specialization to its superclass and for most common vector and matrix sizes there are specialized classes Matrix3 and Matrix4, implementing various transformation in 2D and 3D, Vector2, Vector3 and Vector4, implementing direct access to named components. Functions of each class try to return the most specialized type known to make subsequent operations more convenient - columns of %RectangularMatrix are returned as %Vector, but when accessing columns of e.g. %Matrix3, they are returned as %Vector3. There are also even more specialized subclasses, e.g. Color3 and Color4 for color handling and conversion. @section matrix-vector-construction Constructing matrices and vectors Default constructors of RectangularMatrix and Vector (and Vector2, Vector3, Vector4, Color3) create zero-filled objects. Matrix (and Matrix3, Matrix4) is by default constructed as identity matrix. Color4 has alpha value set to opaque. @code RectangularMatrix<2, 3, int> a; // zero-filled Vector<3, int> b; // zero-filled Matrix<3, int> identity; // diagonal set to 1 Matrix<3, int> zero(Matrix<3, int>::Zero); // zero-filled Color4 black1; // {0.0f, 0.0f, 0.0f, 1.0f} Color4 black2; // {0, 0, 0, 255} @endcode Most common and most efficient way to create vector is to pass all values to constructor, matrix is created by passing all column vectors to the constructor. @code Vector3 vec(0, 1, 2); Matrix3 mat({0, 1, 2}, {3, 4, 5}, {6, 7, 8}); @endcode All constructors check number of passed arguments and the errors are catched at compile time. You can specify all components of vector or whole diagonal of square matrix at once: @code Matrix3 diag(Matrix3::Identity, 2); // diagonal set to 2, zeros elsewhere Vector3 fill(10); // {10, 10, 10} @endcode Vectors are commonly used to specify various axes and scaling coefficients in transformations, you can use convenience functions instead of typing out all other elements: @code Matrix4::rotation(5.0_degf, Vector3::xAxis()); // {1.0f, 0.0f, 0.0f} Matrix3::translation(Vector2::yAxis(2.0f)); // {0.0f, 2.0f} Matrix4::scaling(Vector3::zScale(-10.0f)); // {1.0f, 1.0f, -10.0f} @endcode It is possible to create matrices from other matrices and vectors with the same row count; vectors from vector and scalar: @code RectangularMatrix<2, 3, int> a; Vector3 b, c; Matrix3 mat(a, b); Vector<8, int> vec(1, b, 2, c); @endcode It is also possible to create them from an C-style array. The function does simple type cast without any copying, so it's possible to conveniently operate on the array itself: @code int[] mat = { 2, 4, 6, 1, 3, 5 }; RectangularMatrix<2, 3, int>::from(mat) *= 2; // mat == { 4, 8, 12, 2, 6, 10 } @endcode Note that unlike constructors, this function has no way to check whether the array is long enough to contain all elements, so use with caution. You can also *explicitly* convert between data types: @code Vector4 floating(1.3f, 2.7f, -15.0f, 7.0f); Vector4 integral(floating); // {1, 2, -15, 7} @endcode @section matrix-vector-component-access Accessing matrix and vector components Column vectors of matrices and vector components can be accessed using square brackets, there is also round bracket operator for accessing matrix components directly: @code RectangularMatrix<3, 2, int> a; a[2] /= 2; // third column (column major indexing, see explanation below) a[0][1] = 5; // first column, second element Vector<3, int> b; b[1] = 1; // second element @endcode Fixed-size vector subclasses have functions for accessing named components and subparts: @code Vector4 a; int x = a.x(); a.y() += 5; Vector3 xyz = a.xyz(); xyz.xy() *= 5; @endcode Color3 and Color4 name their components `rgba` instead of `xyzw`. For more involved operations with components there is the swizzle() function: @code Vector<4, int> original(-1, 2, 3, 4); Vector<4, int> bgra = swizzle<'b', 'g', 'r', 'a'>(original); // { 3, 2, -1, 4 } Vector<6, int> w10xyz = swizzle<'w', '1', '0', 'x', 'y', 'z'>(original); // { 4, 1, 0, -1, 2, 3 } @endcode Matrix3 and Matrix4 have functions for accessing properties of given 2D/3D transformation: @code Matrix4 a = Matrix4::translation(Vector3::yAxis(4.0f)); Vector3 translation = a.translation(); Matrix3 b = Matrix3::rotation(15.0_degf)); Matrix<2, float> rotationScaling = b.rotationScaling(); Vector2 up = b.up(); @endcode @section matrix-vector-column-major Matrices are column-major and vectors are columns OpenGL matrices are column-major, thus it is reasonable to have matrices in %Magnum also column major (and vectors as columns). This has naturally some implications and it may differ from what is common in mathematics: - Order of template arguments in specification of RectangularMatrix is also column-major: @code RectangularMatrix<2, 3, int> mat; // two columns, three rows @endcode - Order of components in matrix constructors is also column-major, further emphasized by requirement that you have to pass directly column vectors: @code Matrix3 mat({0, 1, 2}, {3, 4, 5}, {6, 7, 8}); // first column is {0, 1, 2} @endcode - Element accessing order is also column-major, thus the bracket operator is accessing columns. Returned vector has also its own bracket operator, which is then indexing rows. @code mat[0] *= 2; // first column mat[2][0] = 5; // first element of first column @endcode - Various algorithms which commonly operate on matrix rows (such as @ref Algorithms::gaussJordanInPlace() "Gauss-Jordan elimination") have faster alternatives which operate on columns. It's then up to user decision to operate with transposed matrices or use the slower non-transposed alternative of the algorithm. */ }}