Python Fire
NetworkX
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Python Fire | NetworkX | |
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37 | 61 | |
26,309 | 14,178 | |
1.1% | 1.4% | |
6.8 | 9.6 | |
1 day ago | about 12 hours ago | |
Python | 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.
Python Fire
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CLI tools hidden in the Python standard library
The cli tool [fire](https://github.com/google/python-fire/blob/master/docs/guide...) has a nifty feature where it can generate a cli for any file for you.
So random and math are somewhat usable that way
$ python -m fire random uniform 0 1
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Build CLI blazingly fast with python-fire 🔥
With python-fire you can use either function or class to create your subcommands. But I find working with classes more intuitive and manageable. Our first command is going to be a sub-command that shows us the UTC time.
- What is the status of Python 3.11?
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I am sick of writing argparse boilerplate code, so I made "duckargs" to do it for me
Have you checked out fire? Personally, I think it's a really elegant solution to turning a callable object into command line. Plus, the chaining function calls feature lets you build some pretty complex command line patterns likes you never seen with other frameworks. Definitely worth giving it a try!
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What is your favorite ,most underrated 3rd party python module that made your programming 10 times more easier and less code ? so we can also try that out :-) .as a beginner , mine is pyinputplus
I started with click but found python fire to be so much easier to use.
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Best way to get data into python scripts
I highly recommend checking out fire for adding a CLI quickly to little utility scripts that aren't going to be published to the world but just for you.
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What are your coolest tools for one-liners ?
python fire autogenerates CLI wrappers for python modules, which really synergizes with method-chaining APIs like pandas.
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Show HN: Rocketry – Modern scheduler to power your Python projects
Fire can basically do the first step (object -> CLI):
https://github.com/google/python-fire
Gooey can do (CLI -> GUI):
https://github.com/chriskiehl/Gooey
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What packages replaced standard library modules in your workflow?
also, while we're on the subject, fire may not be the same kind of workhorse as argparse or click, but for really simple stuff it's pretty awesome
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Eclipse: python-fire inspired library to simplify creating CLIs in Go, on top of Cobra
I'm relatively new to Go (coming from Python) so I haven't been using Cobra (or Go, for that matter) for long but it's clearly very polished -- only friction I was experiencing with it is there's a lot of boilerplate to creating commands and subcommands, that IMO (idea as proven by python-fire) can be naturally (better) expressed as types / fields / methods that are already built into the language.
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?
click - Python composable command line interface toolkit
Numba - NumPy aware dynamic Python compiler using LLVM
typer - Typer, build great CLIs. Easy to code. Based on Python type hints.
Dask - Parallel computing with task scheduling
Gooey - Turn (almost) any Python command line program into a full GUI application with one line
julia - The Julia Programming Language
PyInquirer - A Python module for common interactive command line user interfaces
RDKit - The official sources for the RDKit library
docopt - This project is no longer maintained. Please see https://github.com/jazzband/docopt-ng
snap - Stanford Network Analysis Platform (SNAP) is a general purpose network analysis and graph mining library.
pydantic-cli - Turn Pydantic defined Data Models into CLI Tools
SymPy - A computer algebra system written in pure Python