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Description
Utilize open-source machine learning tools and data visualization techniques to create dynamic data analysis workflows in a visual format, supported by a broad and varied collection of resources. Conduct straightforward data assessments accompanied by insightful visual representations, and investigate statistical distributions through box plots and scatter plots; for more complex inquiries, utilize decision trees, hierarchical clustering, heatmaps, multidimensional scaling, and linear projections. Even intricate multidimensional datasets can be effectively represented in 2D, particularly through smart attribute selection and ranking methods. Engage in interactive data exploration for swift qualitative analysis, enhanced by clear visual displays. The user-friendly graphic interface enables a focus on exploratory data analysis rather than programming, while intelligent defaults facilitate quick prototyping of data workflows. Simply position widgets on your canvas, link them together, import your datasets, and extract valuable insights! When it comes to teaching data mining concepts, we prefer to demonstrate rather than merely describe, and Orange excels in making this approach effective and engaging. The platform not only simplifies the process but also enriches the learning experience for users at all levels.
Description
PyQtGraph is a graphics and GUI library developed in pure Python, utilizing PyQt/PySide alongside NumPy, designed primarily for applications in mathematics, science, and engineering. Despite its complete implementation in Python, the library achieves impressive speed by effectively utilizing NumPy for numerical computations and the Qt GraphicsView framework for efficient rendering. Released under the MIT open-source license, PyQtGraph supports fundamental 2D plotting through interactive view boxes, enabling line and scatter plots with user-friendly mouse control for panning and scaling. Its ability to handle various data types, including integers, floats, and different bit depths, is complemented by functionalities for slicing multidimensional images at various angles, making it particularly useful for MRI data analysis. Furthermore, it facilitates rapid updates suitable for video display or real-time interactions, along with image display features that include interactive lookup tables and level adjustments. The library also provides mesh rendering capabilities with isosurface generation, while interactive viewports allow users to rotate and zoom with ease using the mouse. Additionally, it incorporates a basic 3D scenegraph, simplifying the programming process for three-dimensional data visualization. With its robust set of features, PyQtGraph caters to a wide range of visualization needs and enhances user experience through interactivity.
API Access
Has API
API Access
Has API
Integrations
Python
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
Free
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
University of Ljubljana
Country
Slovenia
Website
orange.biolab.si
Vendor Details
Company Name
PyQtGraph
Website
www.pyqtgraph.org
Product Features
Data Mining
Data Extraction
Data Visualization
Fraud Detection
Linked Data Management
Machine Learning
Predictive Modeling
Semantic Search
Statistical Analysis
Text Mining
Data Visualization
Analytics
Content Management
Dashboard Creation
Filtered Views
OLAP
Relational Display
Simulation Models
Visual Discovery
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization