uproot3 | ROOT I/O in pure Python and NumPy | Data Visualization library

by scikit-hep Python Version: 3.14.4 License: BSD-3-Clause

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | uproot3 Summary

uproot3 is a Python library typically used in Analytics, Data Visualization, Numpy applications. uproot3 has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install uproot3' or download it from GitHub, PyPI.

ROOT I/O in pure Python and NumPy.

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Quality

Security

License

Reuse

Support

uproot3 has a low active ecosystem.

It has 316 star(s) with 67 fork(s). There are 22 watchers for this library.

It had no major release in the last 12 months.

There are 32 open issues and 301 have been closed. On average issues are closed in 30 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of uproot3 is 3.14.4

Quality

uproot3 has 0 bugs and 0 code smells.

Security

uproot3 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

uproot3 code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

uproot3 is licensed under the BSD-3-Clause License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

uproot3 releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

It has 13084 lines of code, 1015 functions and 56 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed uproot3 and discovered the below as its top functions. This is intended to give you an instant insight into uproot3 implemented functionality, and help decide if they suit your requirements.

Iterate over the entries in the tree
Convert a list of futures to a Pandas DataFrame
Calculate the offset of a basket entry
Returns the number of entries in the basket
Extract description from README rst file
Opens a file
Return the fields from the source
Read file contents
Initialize TStreamElement
Iterate over a tree
Write given bytes to the key
Internal method to read from the source stream
Write the file header
Internal method to read the contents of the object
Post processing
Iterate over a branch
Write streamers
Generate a unique identifier for the object
Return a numpy array of data
Return a numpy array of branches
Updates the tree
Return a list of all available eggs
Write the entry to the cursor
Create a lazy array from the file
Writes the value to the file
Return a lazyarrays

Get all kandi verified functions for this library.

uproot3 Key Features

No Key Features are available at this moment for uproot3.

uproot3 Examples and Code Snippets

No Code Snippets are available at this moment for uproot3.

Community Discussions

Trending Discussions on uproot3

TLorentz vector features in uproot4/vector when calculating invariant mass of a jet

QUESTION

TLorentz vector features in uproot4/vector when calculating invariant mass of a jet

Asked 2022-Feb-01 at 01:49

I wish to sum all the 4-momenta of the constituents in a jet. In uproot3 (+ uproot3-methods) there was the functionality of creating a TLorentzVectorArray and just doing .sum()

So this worked fine:

...

ANSWER

Answered 2022-Feb-01 at 01:49

For a solution that works equally well for flat arrays of Lorentz vectors as for jagged arrays of Lorentz vectors, try this:

Source https://stackoverflow.com/questions/70927986

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install uproot3

You can install using 'pip install uproot3' or download it from GitHub, PyPI.
You can use uproot3 like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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