When developing applications in Django, the need might arise to customize the user model. Specifically, you might want to create different types of users. In my case, I’m interested in creating a person user and a kit user. A person user can own multiple kit users, and both need to be able to authenticate to access an API. Luckily, Django’s authentication system is very flexible, and there are multiple ways to achieve this goal.
The standard way to implement this is to stick with the default user model,
django.contrib.auth.models.User, and create a complex user profile. The profile adds the desired fields and behaviors for the various user types in a new model, and links to the model through a field reference. This can get fairly complex quickly. It is especially difficult to express ownership of kits by users, without allowing ownership of users by users. Here, we will see how we can implement this using inheritance.
Though internet relay chat (IRC) is old technology, many people still use it daily. One of the most popular IRC clients for Windows is mIRC. This client is impressively feature-complete. However, it does not offer a way to satisfyingly solve one of the grievances of many IRC users: the flood of join and part messages in big channels, caused by the constant stream of people coming in and leaving. mIRC only offers the option to either show or hide all join and part messages completely; but does not allow for any fine-tuning.
What I was looking for was a way to show or hide join and part messages based on the size of the channel. If a channel has only few people, I’d like to see all joins and parts. If, however, a channel has many users (e.g., more than 25), this quickly becomes a nuisance. In addition, even in big channels, I’d like to see all joins and parts of people who were recently active in the channel, so as not to accidentally reply to someone who has left in the meantime.
With this specific need, I created a solution, which I have made available on github. It can be added to mIRC by loading it as a script (Tools -> Scripts Editor -> File -> Load). Configure it by right clicking in a chat window, and select Join/Part Filter -> Settings.
Programming this solution using mIRC’s scripting language was definitely a fun project: the scripting language is quite basic with little documentation, and as mIRC’s interpreter offers hardly any assistance when things go wrong, it quickly turns into a puzzle. However, it’s also quite powerful and allows you to access and modify many things within mIRC.
Discord is a great platform for text and voice chatting. However, one feature has been seriously lacking for a while now: the ability to see a log of people joining and leaving voice chat rooms.
Luckily, Discord provides a way to create bots. These bots have access to a variety of events occurring on a Discord server, including voice chat events. Of specific interest to us, bots can track events of people joining and leaving voice chat rooms.
I set out to create a simple bot able to log these events to a specific Discord channel. I programmed the bot in Python, using the discord.py library (v0.16.7). My code is open source and available on github. See the bot in action in the video below:
If you’d like to use this bot, feel free to use the code provided; you’ll probably need to create a new developer application on Discord and turn it into a bot user, and read the read-me.
A little while ago I found myself needing to plot a heat map table in MATLAB. Such a plot is a table where the cells have background colors; the colors depend on the value in the cell, e.g. a higher value could correspond with a warmer color. I found no existing function to do this easily, so I set out to create my own solution.
The code to plot a heat map table can be found here.
Usage is pretty simple. If you have a matrix , just pass it into the function and it will do the rest! For example:
Simple tabularHeatMap example
There are a number of options available. See the documentation in the code for more information about the options. To further adjust the generated figure, such as to add labels, proceed as you would with other plotting functions. For example: