Spock VS NumPy

I love using the Spock framework for its simplicity, readability, and maintainability. That's why we use Spock to drive our integration tests.

Well I care a lot that it exists. And many other people I know do as well. Just because you don't seem to like it, you shouldn't imagine everyone else is like you.

Maybe Grails is no longer used as much (like Rails itself), but Groovy found other usages since then, like https://spockframework.org/ and Jenkins pipelines (https://www.jenkins.io/doc/book/pipeline/syntax/). It's not going anywhere, and I see no reason for anyone to be upset about it.

In my opinion it is Spock for Java/Groovy [1]. The amount of functionality and readability you can squeeze from Groovy's DSLesque is absurd. Is basically a full fledged new test language with Java sprinkled as the test contents code

[1]: https://spockframework.org/

Groovy allows you to perform transforms on it's AST. If you look at the Spock framework, they used AST transforms to pull off a lot of the DSL.

Spock and Cucumber exemplify the philosophy of behavior-driven development (BDD). The principle behind BDD is that you must first define the desired result of the added feature in a subject-oriented language before writing any tests. The developers are then given the final documentation.

As a quick demonstration, the following (Spock) test asserts that the client JVM code fails to create an SSL connection with the service. Note that I chose to use Vert.x Web Client to handle interacting with the service, but don't let this decision distract from the core content of this post. Nevertheless, if you haven't used Vert.x, I encourage you to try it out -- especially for building server-side network applications.

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]