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What is broot?
The ROOT data analysis framework is a prominent tool in High Energy Physics (HEP) that utilizes its own specialized file format (.root) for data storage. It boasts seamless integration with C++ programs, and for those who prefer Python, it offers an interface known as pyROOT. Unfortunately, pyROOT faces challenges with compatibility for Python 3.4, which has led to the development of a new library called broot. This streamlined library is designed to convert data contained in Python's numpy ndarrays into ROOT files, organizing data by creating a branch for each array. The primary goal of this library is to provide a consistent method for exporting numpy data structures to ROOT files efficiently. Additionally, broot is crafted to be both portable and compatible across Python 2 and 3, as well as with ROOT versions 5 and 6, requiring no modifications to the existing ROOT components—only a standard installation is sufficient. Users will appreciate the straightforward installation process, as they can either compile the library once or install it conveniently as a Python package, making it an attractive option for data analysis tasks. This user-friendly approach is likely to encourage an increasing number of researchers to incorporate ROOT into their data analysis routines. Overall, the accessibility and functionality of broot enhance the versatility of using ROOT in various research settings.
What is Cython?
Cython functions as a powerful static compiler that optimizes both the Python language and its extended variant, Cython, which has roots in Pyrex. It greatly simplifies the creation of C extensions for Python, making the process as easy as writing in Python itself. Through Cython, developers are able to leverage the advantages of both Python and C, facilitating smooth interactions between Python code and C or C++ code whenever necessary. By implementing static type declarations in a syntax similar to Python, users can significantly boost the performance of their easily understandable Python code to match that of standard C. Additionally, it offers integrated source code level debugging, which helps developers pinpoint problems within their Python, Cython, and C code efficiently. Cython excels at handling extensive datasets, including multi-dimensional NumPy arrays, which enhances the development of applications in the comprehensive CPython ecosystem. Importantly, Cython enriches Python's capabilities by enabling direct access to C functions and the ability to declare C types for variables and class attributes, thereby improving the overall development experience. This integration of programming languages not only expands the opportunities available to developers but also makes the optimization of Python applications more efficient and streamlined. Consequently, Cython represents a significant tool for anyone looking to maximize performance while maintaining the simplicity of Python's syntax.
Integrations Supported
Axis LMS
C
C++
Fortran
Gensim
NumPy
PyCharm
Python
scikit-image
Integrations Supported
Axis LMS
C
C++
Fortran
Gensim
NumPy
PyCharm
Python
scikit-image
API Availability
Has API
API Availability
Has API
Pricing Information
Free
Free Trial Offered?
Free Version
Pricing Information
Free
Free Trial Offered?
Free Version
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
broot
Company Website
pypi.org/project/broot/
Company Facts
Organization Name
Cython
Company Location
United States
Company Website
cython.org