#graphviz

前一篇文章中，给大家介绍了Graphviz这个强大的工具，不过学习过程中还是遇到一些麻烦，比如说输出PNG格式图像时，中文显示为乱码。 (图源 ：pixabay) 这篇文章着手解决我们之前遇到的这个问题，毕竟作为一个讲中文的中国人，作图中肯定经常用到中文呀。 问题描述 以之前的Hello World示例为例，我们对其进行修改，将： digraph G {Hello->World} 修改为： digraph

最近发现一个很好玩的软件，叫做Graphviz，它可以通过简单的语言来描述结构图、流程图、关系图等，并自动生成相应的图像。我在考虑使用它来帮我生成一些思维导图。 安装 我们可以在 页面来下载适合我们系统的版本，我计划在Windows中使用它，所以下载的是graphviz-9.0.0 (64-bit) EXE installer。 安装过程很简单，一顿下一步即可。 （此处略作一些修改） 安装完成。

A lot of the existing packages for graph grammars seem to have GUIs for writing rules, and in some of them that seems to be your only option. Tonight I prototyped a way to export a graph grammar as a HTML

Suppose we want to create a graph grammar that makes rectangular grids. A first cut at it would be something like "expand outwards from any edge that hasn't already been used", something like

I started at doc/examples directory for the soffit project, and my first two examples are there. Unfortunately, only one of them looks any good. The way I finally convinced graphviz to lay things out the

The #1 question about graphviz seems to be "why can't I get it to lay out my clusters in the way I want", judging from the number of StackOverflow questions I see. I wrote some code in Soffit

As a follow up to the discussion from a couple days ago ( I though I'd try graphing the highest-value relationships between Steem tags. Here are the top 20 covariances and bottom 20 covariances: Green

What happens when we build a large software package? A lot! Visualizing the inputs and outputs of a build Here's a graph of every process that read or wrote a file while building the 'graphviz' toolkit,

Which Steem tags are most commonly used together? Can we learn anything from visualizing the "word cloud" of tags which are frequent neighbors? I gathered the tags and net rshares for all top-level

I modified my earlier code to use a priority queue. Starting with myself, it select nodes to include based on total incoming weight of rshares from nodes already visited. For this experiment I took the

I thought I'd play around with the Steem API and see if I could built a visualization on top of it. I installed the steem-python library and found it pretty quick to start making API calls. My goal was