matplotplusplus
plotly
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matplotplusplus | plotly | |
---|---|---|
26 | 64 | |
3,871 | 15,067 | |
- | 1.9% | |
6.5 | 9.4 | |
about 1 month ago | 7 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.
- 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:
- 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
- Plotting graphs
- How can I create animation of mathematical function that changes over time in c++ and save it as video
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How to plot graphs in C++
I've also recently found out about matplotplusplus.
plotly
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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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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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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.
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FiftyOne Computer Vision Model Evaluation Tips and Tricks – Feb 03, 2023
Because the confusion matrix is implemented in plotly, it is interactive! To interact visually with your data via the confusion matrix, attach the plot to a session launched with the dataset:
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Create interactive plots with Python and Plotly
I've created a notebook in this Github repo to demo some of Plotly basic capabilities and I highly recommend checking out the official documentations for examples of each plot type and to discover lots of cool stuff that you can put in your notebook/site 🙂.
- GUI for a Dynamically Created Dataframe
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Инструменты Python. Библиотеки для анализа данных
- plotly (https://plotly.com/python/);
What are some alternatives?
Altair - Declarative statistical visualization library for Python
bokeh - Interactive Data Visualization in the browser, from Python
matplotlib - matplotlib: plotting with Python
PyQtGraph - Fast data visualization and GUI tools for scientific / engineering applications
folium - Python Data. Leaflet.js Maps.
Apache Superset - Apache Superset is a Data Visualization and Data Exploration Platform [Moved to: https://github.com/apache/superset]
matplotlib - C++ wrappers around python's matplotlib
seaborn - Statistical data visualization in Python
matplotlib-cpp - Extremely simple yet powerful header-only C++ plotting library built on the popular matplotlib
bqplot - Plotting library for IPython/Jupyter notebooks
pygal - PYthon svg GrAph plotting Library
Graphviz - Simple Python interface for Graphviz