Search Results for "matplotlib" - Page 2
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Showing 95 open source projects for "matplotlib"
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1
PyVista
3D plotting and mesh analysis through a streamlined interface
...You could use any geometry to create your glyphs, or even plot the points directly. Direct access to mesh analysis and transformation routines. Intuitive plotting routines with matplotlib similar syntax. -
2
Kedro
A Python framework for creating reproducible, maintainable code
Kedro is an open sourced Python framework for creating maintainable and modular data science code. Provides the scaffolding to build more complex data and machine-learning pipelines. In addition, there's a focus on spending less time on the tedious "plumbing" required to maintain data science code; this means that you have more time to solve new problems. Standardises team workflows; the modular structure of Kedro facilitates a higher level of collaboration when teams solve problems... -
3
Homemade Machine Learning
Python examples of popular machine learning algorithms
...The purpose is pedagogical: you’ll see linear regression, logistic regression, k-means clustering, neural nets, decision trees, etc., built in Python using fundamentals like NumPy and Matplotlib, not hidden behind API calls. It is well suited for learners who want to move beyond library usage to understand how algorithms operate internally—how cost functions, gradients, updates and predictions work. -
4
seaborn
Statistical data visualization in Python
...Its dataset-oriented, declarative API lets you focus on what the different elements of your plots mean, rather than on the details of how to draw them. Behind the scenes, seaborn uses matplotlib to draw its plots. For interactive work, it’s recommended to use a Jupyter/IPython interface in matplotlib mode, or else you’ll have to call matplotlib.pyplot.show() when you want to see the plot. -
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5
theDirector
Runs sets.
...The bin and parquet files get massive, make sure to have adequate storage. Requirements: libarrow-dev, libparquet-dev, pyarrow, parquet, qt6-base-dev, pandas, matplotlib Has only been tested on Debian/Ubuntu based distros. -
6
Tellurium
Model, simulate, and analyze biochemical systems using one tool.
...It combines a number of existing libraries, including libSBML, libRoadRunner (including libStruct), libAntimony, and is extensible via tePlugins. In addition other tools kits such as matplotlib and NumPy are used to provide additional analysis and plotting support. -
7
Euler Math Toolbox
Numerical and Symbolic Math Tool
Euler is a powerful all-in-one numerical software and includes Maxima for seamless symbolic computations. Euler supports Latex for math display, Povray for photo-realistic 3D scenes, Python, Matplotlib and C for scripting, and contains a full programming language. Features include libraries for numerical algorithms, optimization, plotting in 2D and 3D, graphics export, a complete help system, tutorials and examples. Euler runs in Windows natively, or in Linux via Wine. It is completely free of royalties. The source is licensed under GPL. -
8
System Resource Monitor
SRM a lightweight desktop app for real-time CPU and Memory monitoring.
System Resource Monitor is a lightweight, Python-based desktop application designed for real-time monitoring of CPU and Memory performance. Built with Tkinter for the GUI, Matplotlib for dynamic graphing, and Plyer for cross-platform desktop notifications, this tool empowers users to keep track of CPU and memory usage efficiently. With customizable update intervals, data exporting capabilities, and built-in alerts for resource spikes, System Resource Monitor is perfect for anyone looking to keep an eye on their computer’s health without the bloat of heavier tools. ... -
9
Python Data Science Handbook
Python Data Science Handbook: full text in Jupyter Notebooks
The Python Data Science Handbook is a comprehensive collection of Jupyter notebooks written by Jake VanderPlas covering fundamental Python libraries for data science, including IPython, NumPy, Pandas, Matplotlib, Scikit-Learn and more. The project is designed for data scientists, researchers, and anyone transitioning into Python-based data work; it assumes you already know basic Python and focuses more on how to use the ecosystem effectively. Each chapter is a standalone Jupyter notebook, with runnable code, explanatory prose, visuals, and examples showing how to handle data-wrangling, exploratory data analysis, machine learning workflows, and visualization. ... -
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10
Sezioni
Python tool for section property evaluation and stress calculation
This software is a basic tool for section property evaluation and stress calculation, written in python. The section can be imported or input by points and then can be modified moved, rotated etc.. Area and section inertia can be exported. Loads can be applied (multiple load cases can be imported) and the related stresses are calculated by mean of De Saint Venant formulas. Results are plot with selectable color maps. Point results are listed and may be exported. Section can be also... -
11
vim-jukit
Jupyter-Notebook inspired Neovim/Vim Plugin
REPL plugin and Jupyter-Notebook alternative for (Neo)Vim. This plugin is aimed at users in search for a REPL plugin with lots of additional features. -
12
PyNanoLab
data analysis and Visualization with matplotlib
PyNanoLab contains a variety of tools to complete the data analysis, statistics, curve fitting, and basic machine learning application. Visualization in pynanolab is based on matplotlib. The setup tools is desinged to control and set-up all the details of the figure with a GUI. -
13
Yellowbrick
Visual analysis and diagnostic tools to facilitate ML selection
Yellowbrick extends the Scikit-Learn API to make model selection and hyperparameter tuning easier. Under the hood, it’s using Matplotlib. Yellowbrick is a suite of visual diagnostic tools called "Visualizers" that extend the scikit-learn API to allow human steering of the model selection process. In a nutshell, Yellowbrick combines scikit-learn with matplotlib in the best tradition of the scikit-learn documentation, but to produce visualizations for your machine learning workflow. -
14
Python ML Jupyter Notebooks
Practice and tutorial-style notebooks
...The project provides numerous examples and tutorials covering classical machine learning techniques such as regression, classification, clustering, and dimensionality reduction. It includes code implementations that show how to build models using popular libraries like scikit-learn, NumPy, pandas, and Matplotlib. The repository is designed to help learners understand both the theory and practical implementation of machine learning algorithms through step-by-step code examples. Many notebooks include explanations of algorithm behavior, data preparation techniques, and evaluation methods for machine learning models. The project also includes examples that demonstrate how to apply machine learning to real-world datasets and practical business problems. -
15
pyprobml
Python code for "Probabilistic Machine learning" book by Kevin Murphy
Python 3 code to reproduce the figures in the books Probabilistic Machine Learning: An Introduction (aka "book 1") and Probabilistic Machine Learning: Advanced Topics (aka "book 2"). The code uses the standard Python libraries, such as numpy, scipy, matplotlib, sklearn, etc. Some of the code (especially in book 2) also uses JAX, and in some parts of book 1, we also use Tensorflow 2 and a little bit of Torch. See also probml-utils for some utility code that is shared across multiple notebooks. -
16
tikzplotlib
Save matplotlib figures as TikZ/PGFplots for integration into LaTeX
This is tikzplotlib, a Python tool for converting matplotlib figures into PGFPlots (PGF/TikZ) figures. The output of tikzplotlib is in PGFPlots, a TeX library that sits on top of PGF/TikZ and describes graphs in terms of axes, data etc. -
17
IdleX - IDLE Extensions for Python
IDLE Extensions
A collection of extensions for Python's IDLE, the Python IDE built with the tkinter GUI toolkit. -
18
Python Tutorials
Machine Learning Tutorials
...This includes foundational Python concepts, data processing with libraries like NumPy and pandas, threading and multiprocessing for concurrency, and practical use of libraries such as Matplotlib for data visualization. It also provides tutorials on machine learning frameworks and concepts, including TensorFlow, PyTorch, Keras, Scikit-Learn, and reinforcement learning techniques. Each section contains organized code and explanations designed to help learners understand the underlying mechanics of Python and common computational approaches. -
19
Zipline
Zipline, a Pythonic algorithmic trading library
Zipline is a Pythonic algorithmic trading library. It is an event-driven system for backtesting. Zipline is currently used in production as the backtesting and live-trading engine powering Quantopian -- a free, community-centered, hosted platform for building and executing trading strategies. Quantopian also offers a fully managed service for professionals that includes Zipline, Alphalens, Pyfolio, FactSet data, and more. Installing Zipline is slightly more involved than the average Python... -
20
Brand new cheatsheets and handouts
Matplotlib 3.1 cheat sheet
...For practitioners working on data-heavy projects, dashboards, or research code where plotting is frequent, it helps speed up development by reducing context-switching and documentation navigation overhead. It is especially useful when you know roughly what you want (e.g. “I need a scatter + histogram marginal plot”) but don’t remember the exact Matplotlib call. -
21
cv2_plt_imshow
Using matplotlib_imshow for images read by cv2
## Introduction One of the major issue faced while using `cv2`, especially when you are using `jupyter-notebooks`, is to perform `cv2.imshow` the kernel breaks. Apart from this, most of the users are comfortable using matplotlib for display, specially its display in notebook using `%matplotlib inline` magic. This package provides two of the main function, converting the image to a format more suitable in matplotlib, and plotting the image using matplotlib in the notebooks. ## Table of contents * [Setup](#setup) * [Dependencies](#dependencies) * [Contact](#contact) ## Setup ```pip install cv2_plt_imshow``` ## Dependencies * [matplotlib](https://pypi.org/project/matplotlib/) * [cv2](https://pypi.org/project/opencv-python/) ## Contact * [Author](https://rupesh.info/) * [Github](https://github.com/rs9899/cv2_plt_imshow) -
22
ombakwarna
Spreadsheet based graphical and analysis software
OmbakWarna is a spreadsheet based graphical analysis software utilizing numpy and matplotlib as analysis and graphic tools. Its design is inspired by IgorPro software. It is licensed under GNU General Public License, version 2. This is still works in progress. Some widgets are without functionality. There is no manual yet, but a few videos were uploaded to the files section showing simple tutorials for some plotting capabilities. -
23
Python4Proteomics Course
Python course for Proteomics analysis
Python course (in Spanish) for Proteomics analysis using basically Jupyter NoteBooks. For more information, you can have a look at the readme.md file in the source code tree: https://sourceforge.net/p/lp-csic-uab/p4p/code/ci/default/tree/readme.md -
24
The Neural Process Family
This repository contains notebook implementations
...Each notebook includes theoretical explanations, key building blocks, and executable code that runs directly in Google Colab, requiring no local setup. Implementations rely only on standard dependencies such as NumPy, TensorFlow, and Matplotlib, and provide visualizations of model performance. -
25
MCNPydE
MCNP data extraction and display software library
MCNPydE is a Python library for extracting data from MCNP output file. It requires Python, Matplotlib and Numpy. It is a data reduction tool for MCNP output for ease of results analysis and viewing.
