vnlog VS matplotplusplus

For simple analyses (i.e. what most people do most of the time) doing this on the commandline gets you there faster. I use vnlog (https://github.com/dkogan/vnlog/). By the time you fired up your editor to write your Python code, I already have analyses and plots ready.

Alternative very appropriate for some uses cases: `vnl-join` from the vnlog toolkit (https://github.com/dkogan/vnlog). Uses the `join` tool from coreutils (works well, has been around forever), and `vnlog` for nice column labelling

There's also https://github.com/dkogan/vnlog/ which is a wrapper around the existing coreutils, so all the options work, and there's nothing to learn

And to improve the ergonomics, the vnlog wrappers are available to operate on field names, while retaining the internals of the core tools:

  https://github.com/dkogan/vnlog/

I sorta, kinda agree. Tools written in AWK (and friends) are indeed somewhat unmaintainable, but they're really close to being just right for a LOT of applications. The vnlog toolkit (https://github.com/dkogan/vnlog) adds just a little bit of syntactic sugar to the usual commandline tools to make processing scripts robust and easy to read and write. This was not my intent initially, but I now do most of my data processing with the shell and vnl-wrapped awk (and sort and join, ...) It's really nice. If you write stuff in awk, you should check it out. (Disclaimer: I'm the author)

cmake_minimum_required(VERSION 3.5) project(knn_cpp CXX) # Set up C++ version and properties include(CheckIncludeFileCXX) check_include_file_cxx(any HAS_ANY) check_include_file_cxx(string_view HAS_STRING_VIEW) check_include_file_cxx(coroutine HAS_COROUTINE) set(CMAKE_CXX_STANDARD 20) set(CMAKE_BUILD_TYPE Debug) set(CMAKE_CXX_STANDARD_REQUIRED ON) set(CMAKE_CXX_EXTENSIONS OFF) # Copy data file to build directory file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/iris.data DESTINATION ${CMAKE_CURRENT_BINARY_DIR}) # Download library usinng FetchContent include(FetchContent) FetchContent_Declare(matplotplusplus GIT_REPOSITORY https://github.com/alandefreitas/matplotplusplus GIT_TAG origin/master) FetchContent_GetProperties(matplotplusplus) if(NOT matplotplusplus_POPULATED) FetchContent_Populate(matplotplusplus) add_subdirectory(${matplotplusplus_SOURCE_DIR} ${matplotplusplus_BINARY_DIR} EXCLUDE_FROM_ALL) endif() FetchContent_Declare( fmt GIT_REPOSITORY https://github.com/fmtlib/fmt.git GIT_TAG 7.1.3 # Adjust the version as needed ) FetchContent_MakeAvailable(fmt) # Add executable and link project libraries and folders add_executable(${PROJECT_NAME} main.cc) target_link_libraries(${PROJECT_NAME} PUBLIC matplot fmt::fmt) aux_source_directory(lib LIB_SRC) target_include_directories(${PROJECT_NAME} PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}) target_sources(${PROJECT_NAME} PRIVATE ${LIB_SRC}) add_subdirectory(tests)

If you want a C++ solution you can use a library like matplot++.

If you want a strict C++ equivalent to SDL the clear answer is SFML. If you just want to visualize 2D/3D data there's matplot++. If you want something slightly higher-level than SDL/SFML (with pre-made UI widgets and such) there's imGUI. If you need an all-in-one GUI solution for desktop or mobile apps there's Qt.

If not, then ditch Python and matplotlib and use a different C++ native plotting framework such as matplot++

The best way to visualize most mathematical concepts is by plotting a 2D graph. To do that you can use e.g. Matplot++

If you just want to plot graphs in C++ check out https://alandefreitas.github.io/matplotplusplus/. There is extensive documentation on how to use it. But if you haven't used a library before you should start here:

Using a 3rd party UI library, you certainly can. E.g. with MatPlot++

Maybe Matplot++ is the solution. You can check more info in https://github.com/alandefreitas/matplotplusplus