algorithmx-python
netgraph
| algorithmx-python | netgraph | |
|---|---|---|
| 1 | 5 | |
| 37 | 663 | |
| - | - | |
| 2.6 | 3.1 | |
| over 3 years ago | about 1 month ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 only |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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.
algorithmx-python
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How to run JS code locally?
There are packages written in JavaScript that have Python wrappers—many of the nice looking chart packages are like this. For network diagrams I like to use AlgorithmX.
netgraph
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NetworkX – Network Analysis in Python
You may like my Netgraph library [1], which is a Python library that aims to complement networkx, igraph, and graph-tool with publication-quality visualisations.
Netgraph implements numerous node layout algorithms and several edge routing routines. Uniquely among Python alternatives, it handles networks with multiple components gracefully (which otherwise break most node layout routines), and it post-processes the output of the node layout and edge routing algorithms with several heuristics to increase the interpretability of the visualisation (reduction of overlaps between nodes, edges, and labels; edge crossing minimisation and edge unbundling where applicable). The highly customisable plots are created using Matplotlib, and the resulting Matplotlib objects are exposed in an easily queryable format such that they can be further manipulated and/or animated using standard Matplotlib syntax. Finally, Netgraph also supports interactive changes: with the InteractiveGraph class, nodes and edges can be positioned using the mouse, and the EditableGraph class additionally supports insertion and deletion of nodes and edges as well as their (re-)labelling through standard text-entry.
[1] https://github.com/paulbrodersen/netgraph
- Modeling project dependencies using direct graph
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Ask HN: With open source software, how do I find out where my users come from?
However, last weekend, there was a huge spike in downloads: instead of the 0-5 downloads that are typical for a normal weekend day, there were 2000 downloads, both on Saturday and Sunday [2]. I would love to know what happened here, or at least, I would like to be able to find out the next time something like this happens. Obviously, unlike a normal business, I don't control the distribution, so I can't measure the traffic with Google Analytics or similar tools.
I would love to hear how other people that have open source projects are getting their intel into their user base.
[1] https://github.com/paulbrodersen/netgraph
[2] https://pepy.tech/project/netgraph
- paulbrodersen/netgraph Python drawing utilities for publication quality plots of networks.
- Show HN: Netgraph, a Python library for visualizing networks
What are some alternatives?
chaos-theory - Playing around with chaos theory simulations. Creating equilibrium graphs and visualizing the logistic maps.
grand - Your favorite Python graph libraries, scalable and interoperable. Graph databases in memory, and familiar graph APIs for cloud databases.
graph-progression - Create a progression of recommendations from a user-supplied recommender
kgsearch - Query and visualize knowledge graphs
pytransform3d - 3D transformations for Python.
ipycanvas - Interactive Canvas in Jupyter
scapy - Scapy: the Python-based interactive packet manipulation program & library.
nfstream - NFStream: a Flexible Network Data Analysis Framework.
pygraphistry - PyGraphistry is a Python library to quickly load, shape, embed, and explore big graphs with the GPU-accelerated Graphistry visual graph analyzer
Graphia - A visualisation tool for the creation and analysis of graphs
awesome-community-detection - A curated list of community detection research papers with implementations.