criticality_score VS NumPy

TL;DR: I could extend the Criticality Score algorithm with usage metrics from Ecosyste.ms API and apply it to all open source accounts under the Open Collective, so we have a new ranking now! I also made it possible to change the weights of each parameter so that you can try the algorithm by yourself.

As mentioned in the description, the score is based on the OpenSSF criticality score. I dropped some of the features that are difficult to get from GitHub due to crawl limits, as well as changing some weights.

I am doing some research based on the criticality scores assigned by this project to different technologies: https://github.com/ossf/criticality_score

LF OpenSSF "criticality score" for 100,000 Github repos, https://github.com/ossf/criticality_score & https://docs.google.com/spreadsheets/d/1uahUIUa82J6WetAqtxCM...

> Generate a criticality score for every open source project. Create a list of critical projects that the open source community depends on. Use this data to proactively improve the security posture of these critical projects ... A project's criticality score defines the influence and importance of a project. It is a number between 0 (least-critical) and 1 (most-critical). It is based on the following algorithm by Rob Pike

Top 20 projects:

> node, kubernetes, rust, spark, nixpkgs, cmsSW, tensorflow, symfony, DefinitelyTyped, git, azure-docs, magento2, rails, ansible, pytorch, PrestaShop, framework, ceph, php-src, linux

For more information, please check the Criticality Score repo itself.

In NumPy with @, dot() or matmul():

In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.

Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:

numpy: A library for scientific computing in Python

Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.

I was looking at all the big projects like numpy, pytorch, flask, etc.

I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]