Tom Connors coding-to-music

Create Nice-looking Schema Diagrams in PlantUML

PlantUML is a descriptive language to generate a number of types of software diagrams, such as sequence, class, deployment and state diagrams, and many others. PlantUML does not generate very good-looking schema diagrams out of the box, but it supports themes and preprocessed macros. If you use themes and macros, you can not only use a simplified syntax, but also generate beautiful diagrams.

Here is an example of a PlantUML schema diagram, and we will build up the code to generate it.

To start, describe your schemas, tables and columns using this syntax as an example.

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Node JS Web Scraper

I this is part of the first node web scraper I created with axios and cheerio. I took out all of the logic, since I only wanted to showcase how a basic setup for a nodejs web scraper would look.

const cheerio = require('cheerio'),
      axios = require('axios'),
      url = `<url goes here>`;

Datasette for Polar Bears

I found an interesting looking dataset about Polar Bear ear tag tracking on a USGS website: https://alaska.usgs.gov/products/data.php?dataid=130

I deployed it as a datasette website and API here: https://polar-bears.now.sh/

Here's how I did it:

Install datasette and csvs-to-sqlite:

	DIAL'S ALGORITHM - PSEUDOCODE

	Input:
	Source = starting location
	TimeToTravel[u, v] = how long to travel from u to v
	C = maximum possible time a single step takes (for example, 10)

	Initialize Q = list of C+1 collections of locations
	If a location we want to expand has distance d, it will be stored in Q[d % (C+1)]
	Initialize Expanded = an array of Booleans for the map, all false

	/*
	* Battlecode 2021
	* Pathfinding Lecture - sample code
	*
	* Bug pathing and communication strategies. Run this on the map "Quadrants" for a sense of how
	* this strategy behaves: watch the red robots in the bottom-left corner.
	*
	* https://www.battlecode.org/
	* https://github.com/battlecode/battlecode21
	*/

	from flask import Flask
	app = Flask(__name__)
	import unicornhat as UH
	import time
	import os
	import signal
	import sys

	def clear_uh():
	UH.clear()

	apiVersion: rbac.authorization.k8s.io/v1
	kind: ClusterRoleBinding
	metadata:
	name: admin-user
	roleRef:
	apiGroup: rbac.authorization.k8s.io
	kind: ClusterRole
	name: cluster-admin
	subjects:
	- kind: ServiceAccount

	import re
	import pandas as pd
	from google.cloud import bigquery
	from google.colab import auth

	PROJECT_ID = 'sql-hunt' # change to your own project

	class BqTable:

	def __init__(self, table, dataset='samples', active_project='bigquery-public-data', client=None):

Tom Connors coding-to-music

Create Nice-looking Schema Diagrams in PlantUML

New DigitalOcean Pricing - Prices effective starting July 1, 2022

Contents

Node JS Web Scraper

Datasette for Polar Bears