Spock
NetworkX
Spock | NetworkX | |
---|---|---|
11 | 61 | |
3,489 | 14,178 | |
0.1% | 0.8% | |
9.4 | 9.6 | |
8 days ago | 7 days ago | |
Java | Python | |
Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Spock
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Mastering Spring Cloud Gateway Testing: Predicates (part 1)
I love using the Spock framework for its simplicity, readability, and maintainability. That's why we use Spock to drive our integration tests.
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Helidon Níma is the first Java microservices framework based on virtual threads
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.
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Ask HN: What's your favorite software testing framework and why?
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/
- 7 Awesome Libraries for Java Unit & Integration Testing
- There is framework for everything.
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Are there languages that allow to extend its syntax ?
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.
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Using Cucumber and Spock for API test Automation — What Benefits Can You Expect?
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.
- A linguagem de programação Groovy - Radar da itexto
- Gradle 7.0 Released
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HTTPS Client Certificate Authentication With Java
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.
NetworkX
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Routes to LANL from 186 sites on the Internet
Built from this data... https://github.com/networkx/networkx/blob/main/examples/grap...
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The Hunt for the Missing Data Type
I think one of the elements that author is missing here is that graphs are sparse matrices, and thus can be expressed with Linear Algebra. They mention adjacency matrices, but not sparse adjacency matrices, or incidence matrices (which can express muti and hypergraphs).
Linear Algebra is how almost all academic graph theory is expressed, and large chunks of machine learning and AI research are expressed in this language as well. There was recent thread here about PageRank and how it's really an eigenvector problem over a matrix, and the reality is, all graphs are matrices, they're typically sparse ones.
One question you might ask is, why would I do this? Why not just write my graph algorithms as a function that traverses nodes and edges? And one of the big answers is, parallelism. How are you going to do it? Fork a thread at each edge? Use a thread pool? What if you want to do it on CUDA too? Now you have many problems. How do you know how to efficiently schedule work? By treating graph traversal as a matrix multiplication, you just say Ax = b, and let the library figure it out on the specific hardware you want to target.
Here for example is a recent question on the NetworkX repo for how to find the boundary of a triangular mesh, it's one single line of GraphBLAS if you consider the graph as a matrix:
https://github.com/networkx/networkx/discussions/7326
This brings a very powerful language to the table, Linear Algebra. A language spoken by every scientist, engineer, mathematician and researcher on the planet. By treating graphs like matrices graph algorithms become expressible as mathematical formulas. For example, neural networks are graphs of adjacent layers, and the operation used to traverse from layer to layer is matrix multiplication. This generalizes to all matrices.
There is a lot of very new and powerful research and development going on around sparse graphs with linear algebra in the GraphBLAS API standard, and it's best reference implementation, SuiteSparse:GraphBLAS:
https://github.com/DrTimothyAldenDavis/GraphBLAS
SuiteSparse provides a highly optimized, parallel and CPU/GPU supported sparse Matrix Multiplication. This is relevant because traversing graph edges IS matrix multiplication when you realize that graphs are matrices.
Recently NetworkX has grown the ability to have different "graph engine" backends, and one of the first to be developed uses the python-graphblas library that binds to SuiteSparse. I'm not a directly contributor to that particular work but as I understand it there has been great results.
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Build the dependency graph of your BigQuery pipelines at no cost: a Python implementation
In the project we used Python lib networkx and a DiGraph object (Direct Graph). To detect a table reference in a Query, we use sqlglot, a SQL parser (among other things) that works well with Bigquery.
- NetworkX – Network Analysis in Python
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Custom libraries and utility tools for challenges
If you program in Python, can use NetworkX for that. But it's probably a good idea to implement the basic algorithms yourself at least one time.
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Google open-sources their graph mining library
For those wanting to play with graphs and ML I was browsing the arangodb docs recently and I saw that it includes integrations to various graph libraries and machine learning frameworks [1]. I also saw a few jupyter notebooks dealing with machine learning from graphs [2].
Integrations include:
* NetworkX -- https://networkx.org/
* DeepGraphLibrary -- https://www.dgl.ai/
* cuGraph (Rapids.ai Graph) -- https://docs.rapids.ai/api/cugraph/stable/
* PyG (PyTorch Geometric) -- https://pytorch-geometric.readthedocs.io/en/latest/
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1: https://docs.arangodb.com/3.11/data-science/adapters/
2: https://github.com/arangodb/interactive_tutorials#machine-le...
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org-roam-pygraph: Build a graph of your org-roam collection for use in Python
org-roam-ui is a great interactive visualization tool, but its main use is visualization. The hope of this library is that it could be part of a larger graph analysis pipeline. The demo provides an example graph visualization, but what you choose to do with the resulting graph certainly isn't limited to that. See for example networkx.
What are some alternatives?
Cucumber - Cucumber for the JVM
Numba - NumPy aware dynamic Python compiler using LLVM
REST Assured - Java DSL for easy testing of REST services
Dask - Parallel computing with task scheduling
AssertJ - AssertJ is a library providing easy to use rich typed assertions
julia - The Julia Programming Language
Awaitility - Awaitility is a small Java DSL for synchronizing asynchronous operations
RDKit - The official sources for the RDKit library
Mockito - Most popular Mocking framework for unit tests written in Java
snap - Stanford Network Analysis Platform (SNAP) is a general purpose network analysis and graph mining library.
ArchUnit - A Java architecture test library, to specify and assert architecture rules in plain Java
SymPy - A computer algebra system written in pure Python