factoriolab
NetworkX
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factoriolab | NetworkX | |
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192 | 61 | |
431 | 14,178 | |
3.7% | 1.6% | |
8.9 | 9.6 | |
11 days ago | 1 day ago | |
TypeScript | Python | |
MIT License | GNU General Public License v3.0 or later |
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factoriolab
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TIL: Do not underestimate the production you need to make even a trickle of module 3s! (Mini base tour)
here's another: https://factoriolab.github.io/
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Vanilla 500spm Problems
Got everything built out for 500 spm using the online calculator (This one: https://factoriolab.github.io/) and when I was done I found a few issues.
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~87% of iron is used for steel and green circuits
https://factoriolab.github.io/ is the same but better than Kirk McDonalds in many ways.
- Space exploration recipes
- Beginner here, is there any tips to make this like way smaller. At this point idek how ima do the other sciences
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Is this basic rubber/plastic setup as clever as it seems ?
I recommend the factoriolab calculator for satisfactory it's very powerful.
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Blueprints - black box or bulk single?
If you build everything correctly, there are no need to concern about the ratio of proliferator. Factoriolab is your friend to help you calculate how many of those proliferators are need.
- 248k mod and insane stone requirements?
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can anyone tell some "golden ratios" for a beginner
Kirk's calc is superseded by https://factoriolab.github.io
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[IR3] Are Burner Miners actually cleaner than Steam Miners when burning charcoal?
To calculate the cost of burner inserters, I used 286 swings per coal (from my own testing), and then added additional mines to a factorio calculator to account for supplying the burners. Then I calculated the burner swings needed to supply the additional mines, and added even more mines to supply that, and repeated that process three times, after which the the tenth's place did not change. These additional miners providing fuel are not counted for useful output. I did not count the negative pollution from forestries.
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?
FactoryPlanner - A mod for Factorio. Allows you to plan out your production in detail.
Numba - NumPy aware dynamic Python compiler using LLVM
yafc - Powerful Factorio calculator/analyser that works with mods
Dask - Parallel computing with task scheduling
Foreman2 - Visual planning tool for Factorio
julia - The Julia Programming Language
factorio-lab-tools - Lua parser to build data and icons for FactorioLab base recipe sets
RDKit - The official sources for the RDKit library
factorio-blueprint-visualizer - A python library to artfully visualize Factorio Blueprints and an interactive web demo for using it.
snap - Stanford Network Analysis Platform (SNAP) is a general purpose network analysis and graph mining library.
FactorioSimulation - Multiple tools for factorio stuff, most notably the belt balancer analyzer.
SymPy - A computer algebra system written in pure Python