catala
NetworkX
catala | NetworkX | |
---|---|---|
35 | 61 | |
1,922 | 14,200 | |
1.1% | 0.9% | |
9.7 | 9.6 | |
4 days ago | 5 days ago | |
OCaml | Python | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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catala
- Co to znaczy być edżajlowi?
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Hey, Computer, Make Me a Font
Programming and law can go together tho https://github.com/CatalaLang/catala
- GitHub - CatalaLang/catala: Programming language for literate programming law specification
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CatalaLang/catala: Programming language for law specification
Law is a mess, in part because its authors take shortcuts. For example, from the first example on CatalaLang's README.md:
> If the property was acquired by gift [and various conditions apply], then for the purpose of determining loss the basis shall be such fair market value. [emphasis added]
I think (and I'm not a lawyer or a tax expert) that this means that the basis of an asset can have a different value for the purpose of determining gain or determining loss. Wow, basis isn't just a number, although one might not notice this if one didn't read the six emphasized words.
But the Catala code seems to completely ignore this. Oops. I filed an issue:
https://github.com/CatalaLang/catala/issues/514
In a real use case, I imagine that substantial refactoring of the parts that consume basis might be needed when one notices that the basis is not a number.
- Catala – Programming language for literate programming law specification
- Code source du calcul de la taxe foncière
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Looking for a language to visualize logic relationships
Catals is a language trying to exactly this.
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Have any of you considered law school with a math background?
There's even a programming language for that: https://catala-lang.org/
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?
mlang - Compiler for the M language, used to compute the income tax of French taxpayers
Numba - NumPy aware dynamic Python compiler using LLVM
Les-codes-en-vigueur - Ce dépôt des Codes en vigueur permet à tout un chacun de consulter, modifier (_fork_) et proposer leurs changements (_Pull Request_) qui seront examinés systématiquement par les instances legislatives de la République Française. Ces dernières mettront en place dans les plus brefs délais un système de validation par les citoyens (_peers_) afin de pouvoir répondre à toutes les demandes. Nous travaillons de concert avec l'équipe de Github pour rendre disponible en Français l'interface de cette plateforme.
Dask - Parallel computing with task scheduling
alaptorveny - Magyarország Alaptörvénye
julia - The Julia Programming Language
nl-covid19-notification-app-website - Project website
RDKit - The official sources for the RDKit library
leyes - La Constitución Española en git
snap - Stanford Network Analysis Platform (SNAP) is a general purpose network analysis and graph mining library.
france.code-penal - Le Code pénal français, sous Git
SymPy - A computer algebra system written in pure Python