NetworkX
WeasyPrint
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NetworkX | WeasyPrint | |
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61 | 43 | |
14,113 | 6,610 | |
1.1% | 2.1% | |
9.6 | 9.4 | |
7 days ago | 7 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" License |
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NetworkX
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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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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.
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Network Graph Layer3 Topology
I had some success using Networkx in the past: https://networkx.org/
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PageRank Algorithm for Graph Databases
Common graph databases are network-based for scaling purposes. Sqlite is a in-file database. So just run graph algorithms on a stringifed json stored as a text on sqlite.
See the networkx module for the common graph algorithms https://networkx.org/
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NetworkX 3.0
A good place to start specifically for NetworkX would be to go through the new contributor documentation: https://networkx.org/documentation/latest/developer/new_cont...
We also have some structured projects https://networkx.org/documentation/latest/developer/projects... but they are usually for programs like GSoC/Outreachy.
Feel free to start a discussion https://github.com/networkx/networkx/discussions if you are looking for something specific :)
[I am one of the NetworkX devs]
A key feature of this release is pluggable backends incl gpu based ones which should greatly affect performance
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-🎄- 2022 Day 12 Solutions -🎄-
Sure! I didn't actually use any path-finding algorithm -- I used networkx to do the pathfinding. Essentially, I created a directed graph in networkx which allowed me to model each location as a node and then place a directed edge between them if I was allowed to move from one to the next following the rules (wasn't jumping up more than one step at a time). Once I had built the map, I used the shortest_path_length command in networkx to find the shortest path and compute its length. Let me know if this makes sense or if you want more explanation!
WeasyPrint
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Launch HN: Onedoc (YC W24) – A better way to create PDFs
Is there a reason you didn't consider something like Weasyprint?
I've gone through a number of systems to convert CV's, business cards, and other docs and it hasn't let me down yet.
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CSS for Printing to Paper
You don't _have_ to use a browser. I had very good results with Weasyprint [0]. And there's also PrinceXML [1] if you're willing to pay.
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Show HN: A new open-source library to design PDF using React
Thanks for your answer! I imagined you would be using PrinceXML behind the scenes since that is probably the gold standard in HTML+CSS rendering.
The only open source alternative I know of is WeasyPrint at https://weasyprint.org/. I'm not sure how well it fares against PrinceXML, though.
And thanks for the pointer to Taffy - I didn't know it before!
- 1.5M PDFs in 25 Minutes
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Htmldocs: Typeset and Generate PDFs with HTML/CSS
Flexbox support has been [included][1] since 2018, although my use case was the prototypical one - a single row w/ 3 columns - so YMMV with how it handles more complex layouts.
Some people might be interested in https://weasyprint.org/
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How to Simply Generate a PDF From HTML in Symfony With WeasyPrint
Performance is not the strength of WeasyPrint, meaning that heavy HTML files will increase generation time. You should always compress images before attaching them, as they are not compressed by default. Generating a 50-page-long PDF may take up to a minute in extreme cases, although multi-page documents generated on my project take fewer than 2 seconds to generate.
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Show HN: Invoice Dragon – An Open Source App to Create PDF Invoices for Free
For Python there is Weasyprint: you prepare the invoice as an HTML document, and Weasyprint turns it into a PDF
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The Gemini protocol seen by this HTTP client person (curl dev)
Well yes, but you can implement HTML+CSS. WeasyPrint did from scratch, and independent implementations of HTML+CSS are considerably more numerous than HTML+CSS+JS.
What are some alternatives?
ReportLab
PyPDF2 - A pure-python PDF library capable of splitting, merging, cropping, and transforming the pages of PDF files
Numba - NumPy aware dynamic Python compiler using LLVM
WKHTMLToPDF - Convert HTML to PDF using Webkit (QtWebKit)
Dask - Parallel computing with task scheduling
julia - The Julia Programming Language
QuestPDF - QuestPDF is a modern open-source .NET library for PDF document generation. Offering comprehensive layout engine powered by concise and discoverable C# Fluent API. Easily generate PDF reports, invoices, exports, etc.
PDFMiner - Python PDF Parser (Not actively maintained). Check out pdfminer.six.
RDKit - The official sources for the RDKit library
snap - Stanford Network Analysis Platform (SNAP) is a general purpose network analysis and graph mining library.
MathJax - Beautiful and accessible math in all browsers
SymPy - A computer algebra system written in pure Python