/NMath

NCoder's Learning Aimed C++ Math Library

Primary LanguageC++MIT LicenseMIT

NMath

NCoder's Learning Aimed C++ Math Library.

Features

  • Data Types:
    • Base packed data structure templates:
      • TF_data2<F_element__, F_flag__>
      • TF_data3<F_element__, F_flag__>
      • TF_data4<F_element__, F_flag__>
    • Vectors:
      • F_vector2 = TF_data2<NMATH_DEFAULT_FP_TYPE, F_vector_flag> (use default element type)
      • F_vector2_f32 = TF_data2<f32, F_vector_flag>
      • F_vector2_i32 = TF_data2<i32, F_vector_flag>
      • F_vector2_u32 = TF_data2<u32, F_vector_flag>
      • F_vector3 = TF_data2<NMATH_DEFAULT_FP_TYPE, F_vector_flag> (use default element type)
      • F_vector3_f32 = TF_data2<f32, F_vector_flag>
      • F_vector3_i32 = TF_data2<i32, F_vector_flag>
      • F_vector3_u32 = TF_data2<u32, F_vector_flag>
      • F_vector4 = TF_data2<NMATH_DEFAULT_FP_TYPE, F_vector_flag> (use default element type)
      • F_vector4_f32 = TF_data2<f32, F_vector_flag>
    • Matrices
      • F_matrix2x2 = TF_data2<NMATH_DEFAULT_FP_TYPE, F_matrix_flag> (use default element type)
      • F_matrix2x2_f32 = TF_data2<f32, F_matrix_flag>
      • F_matrix3x3 = TF_data2<NMATH_DEFAULT_FP_TYPE, F_matrix_flag> (use default element type)
      • F_matrix3x3_f32 = TF_data2<f32, F_matrix_flag>
      • F_matrix4x4 = TF_data2<NMATH_DEFAULT_FP_TYPE, F_matrix_flag> (use default element type)
      • F_matrix4x4_f32 = TF_data2<f32, F_matrix_flag>
    • Quaternions:
      • F_quaternion = TF_data2<NMATH_DEFAULT_FP_TYPE, F_quaternion_flag> (use default element type)
      • F_quaternion_f32 = TF_data2<f32, F_quaternion_flag>
  • SIMD enabled:
    • Some basic functions support SIMD
    • Currently not fully SIMD but could be in the future
  • Cross-flag packed data functions:
    • There are functions that can be used for various packed data with different flags, such as data4_add(a, b) can be used for both vector4 and quaternion.
    • Highly reusable.
  • Basic functions and operators for scalars, vectors, matrix and quaternions.
  • Some kinds of function for rendering applications:
    • T_projection_matrix<projection_type__, F_element__>(...)

Core Dependencies

  • NCPP

Supported Platforms

  • Windows
  • MacOS
  • Unix Platforms

Supported IDEs and Text Editors (for scripts)

  • Visual Studio
  • Xcode
  • CLion
  • Visual Studio Code

Building

  • Visual Studio 2022 (Windows):
    • Step 1: Clone NMath git repository from GitHub
    • Step 2: Generate Visual Studio 2022 solution by running scripts/generate_vs2022.bat
    • Step 3: Navigate to build/vs2022
    • Step 4: Open nmath.sln
    • Step 5: Set nmath.samples.hello_nmath as startup project
    • Step 6: Run and enjoy!
  • Xcode (MacOS):
    • Step 1: Clone NMath git repository from GitHub
    • Step 2: Generate Xcode solution by running scripts/generate_xcode.sh
    • Step 3: Navigate to build/xcode
    • Step 4: Open nmath.xcodeproj
    • Step 5: Choose nmath.samples.hello_nmath for current scheme
    • Step 6: Run and enjoy!
  • CLion (Windows, MacOS, Linux):
    • Step 1: Clone NMath git repository from GitHub
    • Step 2: Open CLion by running one of these scripts based on your environment (you may need to use chmod +x command first on Linux and MacOS for execute permission):
      • Windows
        • scripts/python_open_clion.bat
        • scripts/python3_open_clion.bat
      • MacOS, Linux:
        • scripts/python_open_clion.sh
        • scripts/python3_open_clion.sh
    • Step 3: At On Project Wizard, click "OK" (if you dont see it, dont worry, go to the next step).
    • Step 4: Run and enjoy!
  • Unix makefiles:
    • Step 1: Clone NMath git repository from GitHub
    • Step 2: Generate makefiles by running scripts/generate_unix_makefiles.sh
    • Step 3: execute scripts/build_unix_makefiles.sh to build project
    • Step 4: Navigate to build/unix_makefiles/nmath/samples/hello_nmath
    • Step 5: Run "nmath.samples.hello_nmath" and enjoy!

License