matplotplusplus
plotly
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matplotplusplus | plotly | |
---|---|---|
26 | 65 | |
3,925 | 15,247 | |
- | 2.3% | |
6.5 | 9.4 | |
19 days ago | 10 days ago | |
C++ | Python | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
matplotplusplus
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Creating k-NN with C++ (from Scratch)
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)
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Help making plot for experiment
If you want a C++ solution you can use a library like matplot++.
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Widely-used graphics library
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.
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Embedding matplotlibcpp plot into QT QWidget
If not, then ditch Python and matplotlib and use a different C++ native plotting framework such as matplot++
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Best Library to Visualize Mathematical Concepts
The best way to visualize most mathematical concepts is by plotting a 2D graph. To do that you can use e.g. Matplot++
- Update on C++ DataFrame project
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How to implement Matplotlib in C++
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:
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2D Animation for algorithms
Using a 3rd party UI library, you certainly can. E.g. with MatPlot++
- I want to make a program that draws a graphical function to a png and I don't know how.
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C++ plotting library for Windows + MinGW similar to matplotlib in Python?
Maybe Matplot++ is the solution. You can check more info in https://github.com/alandefreitas/matplotplusplus
plotly
- Yes, Python and Matplotlib can make pretty charts
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Top 10 growing data visualization libraries in Python in 2023
Github: https://github.com/plotly/plotly.py
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How to Create a Pareto Chart 📐
First we need to install the Plotly. To create some very dynamic graphics, this tool helps a lot.
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For all you computational people: What’s your favorite plotting software?
my good dude wake up and smell the plotly. Knowing the ins and outs of matplotlib is helpful but doing interactive stuff with jupyter I always use plotly.
- What does Power BI offer?
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Other programing options?
Plotly documentation (https://plotly.com/python/)
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Advice on upgrading my Presentation template
I don´t know your workflow, but I use 2 markdown based presentations: obsidian advance slides and Quarto presentations. The former is a plugin for Obsidian, which is the software I use to take all my notes, write my thesis, etc., so It makes it extremely easy to make presentations since all my information is in Obsidian. In the other hand, Quarto is a publishing system (articles, presentations, websites books) that can be easily integrated with python and R. This makes it supper convenient for showing my data to my PI since I can analyze my data and at the same time make a presentation for the data. Besides this, Quarto also integrates with my Zotero library, so I can insert citations. Lastly, one thing that made my Quarto presentations infinitely better that the powerpoints, Is that I can insert interactive graphs with plotly, so when I'm showing my data, my PI is able to explore the data inside the presentation.
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[OC] Clustering Images with OpenAI CLIP, T-SNE, UMAP & Plotly
Plotly GitHub repository: https://github.com/plotly/plotly.py
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Could you recommend some graphing GitHub Repo. for JupyterLab?
I'm using plotly.py now. This is why I love this community.
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Anyone else feel ‘trapped’ in power bi?
Depending on the nature of your reporting requirements, you could output a formatted Excel document with Python and a library such as openpyxl, and shove that into your SharePoint environment. This would be less dynamic than PBI reports can be, but may be sufficient. If you want viz as well, you can use something like ggplot or Plotly. Again, less dynamic than PBI for the same effort.
What are some alternatives?
matplotlib - C++ wrappers around python's matplotlib
Altair - Declarative statistical visualization library for Python
matplotlib-cpp - Extremely simple yet powerful header-only C++ plotting library built on the popular matplotlib
bokeh - Interactive Data Visualization in the browser, from Python
implot - Immediate Mode Plotting
matplotlib - matplotlib: plotting with Python
manim - Animation engine for explanatory math videos
PyQtGraph - Fast data visualization and GUI tools for scientific / engineering applications
volbx - Graphical tool for data manipulation written in C++/Qt.
folium - Python Data. Leaflet.js Maps.
bauh - Graphical user interface for managing your Linux applications. Supports AppImage, Debian and Arch packages (including AUR), Flatpak, Snap and native Web applications
Apache Superset - Apache Superset is a Data Visualization and Data Exploration Platform [Moved to: https://github.com/apache/superset]