QuestPDF
NetworkX
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QuestPDF | NetworkX | |
---|---|---|
70 | 61 | |
10,462 | 14,178 | |
4.6% | 1.6% | |
9.0 | 9.6 | |
6 days ago | 1 day ago | |
C# | Python | |
GNU General Public License v3.0 or later | 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.
QuestPDF
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How do you generate pdf files with charts?
QuestPDF looks really good (I haven't used it) but I believe they changed their license recently.
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How to generate PDFs in react?
I used that same library it worked great the only issue I had was the users would often have to manually set the scaling to fit to a page. I'm sure I could've fixed this in other ways if I was more competent with CSS but ended up just switching to use https://github.com/QuestPDF/QuestPDF in a backend instead of doing everything in front end.
- Pdf export iz C# sa macOS
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Any alternate for Crystal Reports ?
Give this a try: QuestPDF
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QuestPDF will be dual licensed, no longer MIT only
I think you should ask these questions in related discussion on Github: https://github.com/QuestPDF/QuestPDF/discussions/491
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Quick question on using an HTML path for PDF creation
May I introduce you to QuestPDF!
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.NET Monthly Roundup - January 2023
➡️ James Newton-King ♔ on Twitter: "Coming in .NET 8: Route tooling for ASP.NET Core" / Twitter ➡️ Announcing .NET Community Toolkit 8.1 ➡️ Uno Platform 4.7 – New Project Template, Performance Improvements and more ➡️ C# Advent 2022 Awards | Cross Cutting Concerns ➡️ GitHub - QuestPDF ➡️ DNF Summit 2023 ➡️ .NET Frontend Day
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HTML to PDF free library? .NET 6.0
Like many have suggested, I also cast my vote on QuestPDF. No more +50MB library including a Chrome browser to render HTML so a PDF of it can be created, which took 2-3 seconds each time. But with QuestPDF, it's so much faster!
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Convert html into PDF with IE11 from WPF
Also, the library is open-source, so you can take a look at the exact implementation of the layout engine. It is inspired by Flutter and WPF, though optimized for pageable content: https://github.com/QuestPDF/QuestPDF/tree/main/Source/QuestPDF/Elements
- QuestPDF: Modern .NET library for PDF document generation
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?
WeasyPrint - The awesome document factory
Numba - NumPy aware dynamic Python compiler using LLVM
PDF.Flow.Examples - Samples, articles, issue reporting and documentation related to Gehtsoft PDF.Flow library.
Dask - Parallel computing with task scheduling
ClosedXML.Report - ClosedXML.Report is a tool for report generation with which you can easily export any data from your .NET classes to Excel using a XLSX-template.
julia - The Julia Programming Language
WeasyPrint-netcore - WeasyPrint Wrapper for .Net on Windows
RDKit - The official sources for the RDKit library
Microcharts - Create cross-platform (Xamarin, Windows, ...) simple charts.
snap - Stanford Network Analysis Platform (SNAP) is a general purpose network analysis and graph mining library.
ScottPlot - Interactive plotting library for .NET
SymPy - A computer algebra system written in pure Python