Mandelbrot Commands

Posted on 2023-09-29 12:42 +0100 in Python • Tagged with PyPi, Python, coding, Textual • 2 min read

I don't think I've mentioned it before on this blog, but some time back I decided it would be fun to use Textual to write a Mandelbrot explorer (simple Mandelbrot explorers have been another one of my favourite known problem to try an unknown thing problems). Doing it in the terminal seemed like a fun little hack. I started off with creating textual-canvas and then built textual-mandelbrot on top of that.

Not too long back I added a "command palette" to Textual (I'd prefer to call it a minibuffer, but I get that that's not fashionable these days), but so far I've not used it in any of my own projects; earlier today I thought it could be fun to add it to textual-mandelbrot.

Mandelbrot commands in action

Most of the commands I've added are trivial and really better covered by (and are covered by) keystrokes, but it was a good test and a way to show off how to create a command provider.

Having started this I can see some more useful things to add: for example it might be interesting to add a facility where you can bookmark a specific location, zoom level, iteration value, etc, and revisit later. The command palette would feel like a great way to pull back those bookmarks.

What I really liked though was how easy this was to do. The code to make the commands available is pretty trivial and, I believe, easy to follow. Although I do say so myself I think I managed to design a very accessible API for this.

There's more I'd like to add to that (the Textual command palette itself, I mean), of course; this was just the start. Support for commands that accept and prompt for arguments would be a neat and obvious enhancement (especially if done in a way that's reminiscent of how commands could be defined in CLIM -- I remember really liking how you could create self-documenting and self-completing commands in that).

All in good time...

Textual Query Sandbox Update

Posted on 2023-09-10 09:22 +0100 in Python • Tagged with PyPi, Python, coding, Textual • 2 min read

Since quickly hacking together textual-query-sandbox a few days back, I've made a bunch of small changes here and there. While most have been cosmetic and playing with some ideas, some have also been internal improvements that should make the tool work better.

The most prominent change is one I pondered in the previous post, where I thought it might be interesting to have a small collection of playgrounds grounded together with a TabbedContent. So as of now the tool still has the original playground which had an emphasis on nested containers:

Playground 1

There's now a playground with an emphasis on selecting widgets within containers1:

Playground 2

There's also now a playground that has an emphasis on pulling out widgets based on ID and classes:

Playground 3

The other change you will notice from the original post is the DOM tree shown in the bottom right corner. Note that that isn't there to show your query result (that's the bottom left panel), it's there to help picture how the DOM in the current playground hangs together, and will hopefully help in picturing the structure for when you write a query.

I sense there's still a lot of fun things I could add to this, and I'm still keen on the idea of having the playgrounds "soft coded" in some way, so people can make their own and load them up.

Another thing I want to try and work on is making the display as useful as possible. While I think it's actually pretty neat and clear, there's not a lot of space2 available to show the playground and the results. Finding a good balance is an interesting problem.

For a number of reasons this is turning into a really enjoyable tinker project.

  1. This is, of course, slightly nonsensical wording. Containers are widgets in Textual. Pretty much everything you see in your terminal is a widget, even a Screen is a widget. 

  2. A lot of this of course hinges on how big someone's terminal is. I tend to run a fairly high resolutions with the smallest font I find readable so my terminal windows are often pretty "big"; other people tend to have something much smaller in terms of cell with/height. 

Textual Query Sandbox

Posted on 2023-09-01 07:42 +0100 in Python • Tagged with PyPi, Python, coding, Textual • 3 min read

Sometimes I can have an idea for a Textual widget, library or application on my ideas list for weeks, months even, before I get around to it -- mostly just due to not having the clear time to make a run at getting it going -- and then other times an idea can pop into my head and it has to be created there and then. Has to be!

This happened yesterday evening.

While the tool I built is something I'd thought of before (back around November last year I think) it hadn't even made it to my "list of stuff I should make" that I keep in Apple Reminders; not sure why really. But then yesterday evening a question cropped up on the Textual Discord server that related to the subject and I was reminded of it.

The subject being: Textual DOM queries. I like to think that DOM queries in Textual are pretty easy to do, and well-explained in the docs, but it's fair to admit that they need a bit of practice first, just like any powerful tool. So I was reminded that I'd wanted to write a sandbox application, that would have a practice DOM inside it, an input field to type in a query, and a way of displaying the results.

So textual-query-sandbox was born!

Textual Query Sandbox

In this very first version (which was really quickly put together -- it was something like 15 minutes to write the main code and then probably 45 minutes tweaking styles, adding all the admin stuff to allow deployment to PyPi and writing the README) there's an Input, a display of a group of nested containers with different IDs and classes, and then a Pretty widget at the bottom to show the query result.

If you think this looks like it might be useful to you, it can be installed using either pip or (ideally) pipx:

$ pipx install textual-query-sandbox

and then you can run it with:

$ tqs

At which point load up the Textual query docs, type queries into the input field, hit enter and see what gets highlighted and which widgets end up in the result set at the bottom of the screen.

Like I say: this was a quick hack yesterday evening, I think there's a lot more can go into this. For one thing I think a more interesting practice DOM would be a good idea, with a good mix of widgets; another thing could be having a collection of different DOM playgrounds that can be switched between (a TabbedContent of different playgrounds could be fun here); this could even be taken further such that the user can create their own playground DOM to practice against.

Eventually it would be neat if this could be turned into a library that can be included in a Textual application, as a development-time debug tool, so that on-the-fly test queries can be made.

For now though, it's started, it's under way, and I think the current version probably covers 90% of the use cases for something like this; making for a really quick and easy tool to double-check how to query something.

Unbored v0.6.0

Posted on 2023-08-13 21:21 +0100 in Python • Tagged with PyPi, Python, coding, Textual • 2 min read

Late on last year I wrote about a bunch of new things that I'd added to PyPi, things mostly kicked off by an early dog-fooding session we had at textual HQ. Since then I've been slowly doing my best to keep the applications up to date with Textual.


As much as possible we try and not make breaking changes with the framework, but at the same time it is still 0.x software and there's still new ways of doing things being designed so there's going to be the odd break in approach now and again.

Unbored, my kind of silly self-populating TODO list application, has been sitting atop Textual 0.20.x for a while now and earlier today I checked how it was getting in with 0.32.0 and... actually surprisingly okay. Not perfect, there were a couple of things that had suffered from bitrot, but it wasn't crashing.

The main thing I needed to change was the ability to focus a couple of containers (they didn't used to receive focus by default, now they do so I had to tell them not to again), and that was about it.

While I was in there I also updated the application so that I dropped the nifty little slide-in error dialog I'd made, and instead embraced the new Textual notification system.

While the application itself is a bit silly, and likely of no real use to anyone, I feel it's a pretty good barometer application, helping me check what the experience is like when it comes to maintaining a Textual application and the needs to keep on top of changes to Textual.

It goes without saying, I hope, that really you should pin the Textual dependency for your applications, and upgrade in a controlled and tested way; for this though it's less crucial and is a good test of the state of the ecosystem, and on the remote chance that anyone is using it, it'll be helpful to me if it does break and they yell.

textual-canvas v0.2.0

Posted on 2023-07-16 09:00 +0100 in Python • Tagged with Python, Textual, coding, PyPi • 1 min read

Demo of textual-canvas

Given that for a good chunk of this year I've been a bit lax about writing here, there's a couple or so coding projects I've not written about (well, not on here anyway -- I have spoken lots about them over on Fosstodon). One such project is textual-canvas.

As the name might suggest, it's a "canvas" for Textual applications, which provides a pretty basic interface for drawing pixels, lines and circles -- and of course any other shape you are able to build up from those basics.

I've just released a quick update after it was requested that I add a clear method to the Canvas widget; a request that makes perfect sense.


Posted on 2022-12-16 09:30 +0000 in Python • Tagged with Python, coding, Textual, PyPi • 2 min read

Another little thing that's up on PyPi now, which is the final bit of fallout from the Textual dogfooding sessions, is a little project I'm calling OIDIA.

The application is a streak tracker. I'm quite the fan of streak trackers. I've used a few over the years, both to help keep me motivated and honest, and also to help me track that I've avoided unhelpful things too. Now, most of the apps I've used, and use now, tend to have reminders and counts and stats and are all about "DO NOT BREAK THE STREAK OR ELSE" and that's mostly fine, but...

To keep things simple and to purely concentrate on how to build Textual apps, I made this a "non-judgement" streak tracker. It's designed to be really simple: you add a streak, you bump up/down the number of times you did (or didn't do) the thing related to that streak, for each day, and that's it.

No totals. No stats. No reminders and bugging. No judgement.

Here it is in action:

When I started it, I wasn't quite sure how I wanted to store the data. Throwing it in a SQLite database held some appeal, but that also felt like a lot of faff for something so simple. Also, I wanted to make the data as easy to get at, to use elsewhere, and to hack on, as possible. So in the end I went with a simple JSON file.

On macOS and GNU/Linux streaks.json lives in ~/.local/share/oidia, on Windows it'll be in... I'm not sure off the top of my head actually; it'll be in whatever directory the handy xdg library has chosen. and because it's JSON that means that something like this:

OIDIA in action

ends up looking like this:

        "title": "Hack some Python",
        "days": {
            "2022-12-02": 1,
            "2022-12-03": 1,
            "2022-12-04": 1,
            "2022-12-05": 1,
            "2022-12-06": 1,
            "2022-12-07": 1,
            "2022-12-08": 1,
            "2022-12-01": 1,
            "2022-11-30": 1,
            "2022-11-29": 1,
            "2022-11-28": 1
        "title": "Brush my teeth",
        "days": {
            "2022-12-02": 2,
            "2022-12-03": 2,
            "2022-12-04": 2,
            "2022-12-05": 2,
            "2022-12-06": 2,
            "2022-12-07": 2,
            "2022-12-08": 1,
            "2022-12-01": 2,
            "2022-11-30": 2,
            "2022-11-29": 2,
            "2022-11-28": 2
        "title": "Walk",
        "days": {
            "2022-12-02": 1,
            "2022-12-03": 1,
            "2022-12-04": 1,
            "2022-12-05": 1,
            "2022-12-06": 1,
            "2022-12-07": 1,
            "2022-12-08": 1,
            "2022-12-01": 1,
            "2022-11-30": 1,
            "2022-11-29": 1,
            "2022-11-28": 1
        "title": "Run 5k",
        "days": {
            "2022-12-03": 2,
            "2022-12-05": 1,
            "2022-11-30": 1,
            "2022-11-28": 2
        "title": "Run 10k",
        "days": {
            "2022-12-03": 1,
            "2022-11-28": 1

Of course, it remains to be seen how well that actually scales; possibly not so well over a long period of time, but this was written more as another way to explore Textual than anything else. Even then, it would be pretty trivial to update to something better for holding the data.

If this seems like your thing (and I will be supporting it and onward developing it) you can find it over on PyPi, which means it can be installed with pip or the ever-handy pipx:

$ pipx install oidia

New Things On PyPi

Posted on 2022-12-01 22:13 +0000 in Python • Tagged with Python, coding, Textual, PyPi • 4 min read

An update

So, it's fast approaching 2 months now since I started the new thing and it's been a busy time. I've had to adjust to a quite a few new things, not least of which has been a longer and more involved commute. I'm actually mostly enjoying it too. While having to contend with busses isn't the best thing to be doing with my day, I do have a very fond spot for Edinburgh and it's nice to be in there most days of the week.

Part of the fallout from the new job has been that, in the last couple of weeks, I've thrown some more stuff up on PyPi. This comes about as part of a bit of a dog-fooding campaign we're on at the moment (you can read some background to this over on the company blog). While they have been, and will continue to be, mentioned on the Textualize blog, I thought I'd give a brief mention of them here on my own blog too given they are, essentially, personal projects.


This is one I'd like to tweak some more and improve on if possible. It is, in essence, a Python-coded terminal tool that does more or less the same as slstats.el. It started out as a rather silly quick hack, designed to do something different with rich-pixels.

Here's the finished version (as of the time of writing) being put through its paces:

Download from here, or install and play with it with a quick pipx install gridinfo.


The next experiment with Textual was to write a terminal-based client for the Bored-API. My initial plan for this was to just have a button or two that the user could mash on and they'd get an activity suggestion dropped into the middle of the terminal; but really that seemed a bit boring. Then I realised that it'd be a bit more silly and a bit more fun if I did it as a sort of TODO app. Bored? Run it up and use one of the activities you'd generated before. Don't like any of them? Ignore them and generate some more! Feeling bad that you've got such a backlog of reasons to not be bored? Delete a bunch!

And so on.

Here's a short video of it in action:

Download from here, or install and play with it with a quick pipx install unbored.


This one... this one I'm going to blame on the brain fog that followed flu and Covid jabs that happened the day before (and which are still kicking my arse 4 days later). Monday morning, at my desk, and I'm wondering what to next write to experiment with Textual, and I realised it would be interesting to write something that would show off that it's easy to make a third party widget library.

And... yeah, I don't know why, but I remembered qrencode.el and so textual-qrcode was born!

The most useless Textal widget yet

I think the most amusing part about this is that I did it in full knowledge that it would be useless; the idea being it would be a daft way of showing off how you could build a widget library as an add-on for Textual. But... more than one person actually ended up saying "yeah hold up there this could actually be handy!"

If you're one of those people... you'll find it here.


While I was on a roll putting stuff up on PyPi, I also decided to tweak up my Textual-based 5x5 and throw that up too. This was my first app built with Textual, initially written before I'd even spoken to Will about the position here. At one point I even did a version in Lisp.

It's since gone on to become one of the example apps in Textual itself but I felt it deserved being made available to the world via an easy(ish) install. So, if you fancy trying to crack the puzzle in your terminal, just do a quick:

$ pipx install fivepyfive

and click away.

You can find it over here.


Finally... for this week anyway, is a tool I've called PISpy. It's designed as a simple terminal client for looking up package information on PyPi. As of right now it's pretty straightforward, but I'd like to add more to it over time. Here's an example of it in action:

One small wrinkle with publishing it to PyPi was the fact that, once I'd chosen the name, I checked that it hadn't been used on PyPi (it hadn't) but when it came to publishing the package it got rejected because the name was too similar to another package! I don't know which, it wouldn't say, but that was a problem. So in the end the published name ended up having to be slightly different from the actual tool's name.

See over here for the package, and you can install it with a:

$ pipx install pispy-client

and then just run pispy in the terminal.


It's been a fun couple of weeks coming up with stuff to help exercise Textual, and there's more to come. Personally I've found the process really helpful in that it's help me learn more about the framework and also figure out my own approach to working with it. Each thing I've built so far has been a small step in evolution on from what I did in the previous thing. I doubt I've arrived at a plateau of understanding just yet.

Python and macOS

Posted on 2022-11-05 08:49 +0000 in Python • Tagged with Python, macOS, coding • 5 min read


On Twitter, a few weeks back, @itsBexli asked me how I go about setting up Python for development on macOS. It's a great question and one that seems to crop up in various places, and since I first got into using macOS and then subsequently got back into coding lots in Python it's absolutely an issue I ran into.

With my previous employer, while I was the only developer, I wasn't the only person writing code and more than one other person had this issue so I eventually wrote up my approach to solving this problem. That document is on their internal GitLab, but I thought it worth me writing my personal macOS/Python "rules" up again, just in case they're useful to anyone else.

I am, of course, not the first person to tackle this, to document this, to write down a good approach to this. Before and after I settled on my approach I'd seen other people write about this. So... this post isn't here to try and replace those, it's simply to write down my own approach, so if anyone asks again I can point them here. I feel it's important to stress: this isn't the only way or thoughts on this issue, there are lots of others. Do go read them too and then settle on an approach that works for you.

One other point to note, which may or may not make a difference (and may or may not affect how this changes with time): for the past few years I've been a heavy user of pipenv to manage my virtual environments. This is very likely to change from now on, but keep in mind that what follows was arrived at from the perspective of a pipenv user.

So... with that admin aside...

The Problem

When I first got back into writing Python it was on macOS and, really early on, I ran into all the usual issues: virtual environments breaking because they were based on the system Python and it got updated, virtual environments based on the Homebrew-installed Python and it got updated, etc... Simply put, an occasional, annoying, non-show-stopping breaking of my development environment which would distract me when I'd sat down to just hack on some code, not do system admin!

My Solution

For me, what's worked for me without a problem over the past few years, in short, is this:

  1. NEVER use the system version of Python. Just don't.
  2. NEVER use the Homebrew's own version of Python (I'm not even sure this is an issue any more; but it used to be).
  3. NEVER use a version of Python installed with Homebrew (or, more to the point, never install Python with Homebrew).
  4. Manage everything with pyenv; which I do install from Homebrew.

The Detail

As mentioned earlier, what I'm writing here assumes that virtual environments are being managed with pipenv (something I still do for personal projects, for now, but this may change soon). Your choices and mileage may vary, etc... This is what works well for me.

The "one time" items

These are the items that need initially installing into a new macOS machine:


Unless it comes from the Mac App Store, I try and install everything via Homebrew. It's really handy for keeping track of what I've got installed, for recreating a development environment in general, and for keeping things up to date.


With Homebrew installed the next step for me is to install pyenv. Doing so is as easy as:

$ brew install pyenv

Once installed, if it's not done it for you, you may need to make some changes to your login profile. I'm a user of fish so I have these lines in my setup. Simply put: it asks pyenv to set up my environment so my calls to Python go via its setup.

Plenty of help on how to set up pyenv can be found in its README.

Once I've done this I tend to go on and install the Python versions I'm likely to need. For me this tends to be the most recent "active" stable versions (as of the time of writing, 3.7 through 3.10; although I need to now start throwing 3.11 into the mix too).

I use this command:

$ pyenv install --list

to see the available versions. If I want to see what's available for a specific version I'll pipe through grep:

$ pyenv install --list | fgrep "  3.9"

This is handy if I want to check what the very latest release of a specific version of Python is.

The "Global" Python

When I'm done with the above I then tend to use pyenv to set my "global" Python. This is the version I want to get when I run python and I'm not inside a virtual environment. Doing that is as simple as:

$ pyenv global 3.10.7

Of course, you'd swap the version for whatever works for you.

When Stuff Breaks

It seems more rare these days, but on occasion I've had it such that some update somewhere still causes my environment to break. Now though I find that all it takes is a quick:

$ pyenv rehash

and everything is good again.

Setting Up A Repo

With all of the stuff above being mostly a one-off (or at least something I do once when I set up a new machine -- which isn't often), the real "work" here is when I set up a fresh repository when I start a new project. Really it's no work at all. Again, as I've said before, I've tended to use pipenv for my own work, and still do for personal stuff (but do want to change that), mileage may vary here depending on tool.

When I start a new project I think about which Python version I want to be working with, I ensure I have the latest version of it installed with pyenv, and then ask pipenv to create a new virtual environment with that:

$ pipenv --python 3.10.7

When you do this, you should see pipenv pulling the version of Python from the pyenv directories:

$ pipenv --python 3.10.7
Creating a virtualenv for this project...
Pipfile: /Users/davep/temp/cool-project/Pipfile
Using /Users/davep/.pyenv/versions/3.10.7/bin/python3 (3.10.7) to create virtualenv...
⠙ Creating virtual environment...created virtual environment in 795ms
  creator CPython3Posix(dest=/Users/davep/temp/cool-project/.venv, clear=False, no_vcs_ignore=False, global=False)
  seeder FromAppData(download=False, pip=bundle, setuptools=bundle, wheel=bundle, via=copy, app_data_dir=/Users/davep/Library/Application Support/virtualenv)
    added seed packages: pip==22.2.2, setuptools==65.3.0, wheel==0.37.1
  activators BashActivator,CShellActivator,FishActivator,NushellActivator,PowerShellActivator,PythonActivator
✔ Successfully created virtual environment!
Virtualenv location: /Users/davep/temp/cool-project/.venv
Creating a Pipfile for this project...

The key thing here is seeing that pipenv is pulling Python from ~/.pyenv/versions/. If it isn't there's a good chance you have a Python earlier in your PATH than the pyenv one -- something you generally don't want. It will work, but it's more likely to break at some point in the future. That's the key thing I look for; if I see that I know things are going to be okay.


Since I adopted these personal rules and approaches (and really, calling them "rules" is kind of grand -- there's almost nothing to this) I've found I've had near-zero issues with the stability of my Python virtual environments (and what issues I have run into tend to be trivial and of my own doing).

As I said at the start: there are, of course, other approaches to this, but this is mine and works well for me. Do feel free to comment with your own, I'm always happy to learn about new ways!

The PEP 8 hill I will die on

Posted on 2020-08-23 16:54 +0100 in Python • Tagged with Python • 3 min read

I first learnt Python back in the mid-to-late 90s, used it in place of Perl once I was comfortable with it, and then we sort of drifted apart when I first met Ruby. It's only in the last couple of years that I've got back into it, and in a huge way, thanks to my (not-quite-so-) new job. Despite the quirks and oddness (as I perceive them), I actually quite like Python and it's one of those languages that just flows off my fingers. I'm sure you know the same thing, perhaps not with Python, but there will be languages that just flow for you, and those that take a bit more effort and concentration. Python... feels okay to me.

I also appreciate that there's been a long-standing style guide. I quite like PEP 8 as a read, and think there's a lot of good ideas in there; much of the content sits with how I'd approach things if I was tasked to come up with such a document. With this in mind, I'm a fairly heavy user of pylint and it in turn leans on PEP 8 (amongst other things) and I'm happy to accept most of its judgements. Not all of its judgements, but even when I disagree with it I try and keep track of how far I'm drifting.

But there is absolutely one hill I will happily die on when it comes to PEP 8: the concept of "extraneous whitespace" in lists and expressions. Just.... no! Oh gods no!

To borrow a line of code from the journey problem I dabbled with a while back, PEP 8 would have me write something like this:

def perform(commands: List[str],state: State) -> State:

Now, I'm sure plenty of people won't see a problem with this at all; but all I can see is an almost-claustrophobic parameter list. What's with the parameters being jammed up against the opening and closing parens? Why have the dinky little comma lost between two different things? Why have it look like a long stream of letters and punctuation? Why....


Just no.

I can't.

Rightly or wrongly, I just need for the code to breathe a bit. When I type this:

def perform( commands: List[ str ], state: State ) -> State:

suddenly if feels like there's fresh air in the code, like it flows gently out of my head, off my fingers, through the keyboard and into the buffer.

In my head, and to my eyes, the code is.... relaxed.

Do I have a rational reason for this? Nope. Then again I don't see one for doing it the other way either; I can't think of one and I don't see one in the source document. So, that's a warning I always turn off with pylint and it's a style I carry through all my Python code; and I think that's the important point here: anyone reading and working with my code should see the same style all the way through. It might differ from PEP 8 on this point, but at least it's the same all the way.

And, really, that's okay: PEP 8 is there to be ignored. ;-)

PS: This is a small part of another blog post I was meaning to write, and might still do, about my (still ongoing) experience of getting lsp-mode up and running in Emacs and having it play nice with Python projects. I have that working, but it was a bit of a learning curve and epic battle over a couple of days, and one that had me first encounter pycodestyle. I may still tell the tale...

git2gantt -- Simple tool to visualise coding runs

Posted on 2019-12-08 13:44 +0000 in Python • Tagged with Python, documentation • 3 min read

At the start of this year, as part of a much bigger process to review the work that had taken place over the previous 12 months, I was asked (at work) to provide some information about how much time I'd spent on various projects. Now, for me, there's really only one project, but there's lots of different tools and libraries that I've written to support the main work I do. All of these are split into different repositories in the company-internal instance of GitLab. This meant that getting a rough idea of what I was working on and when would be easy enough -- it's all there in the commit history.

Given that this information would make up a couple of slides at most during a far bigger presentation, I wanted something that would be snappy and easy for non-developers to follow and understand. I spent a bit of time pondering some options and decided that (ab)using a gantt chart layout would make sense.

That choice was made all the more easier given that GitLab supports the use of mermaid charts within its Markdown. This meant I could quickly write some code that took the git log of each repository, turned it into mermaid code, and then render it (by hand, this was all about getting things done quickly) via GitLab.

This sounded like it could be a fun personal project. The result was some Python code called git2gantt.

As mentioned above, the output isn't anything too clever, it's just code that can be used to create a plot via mermaid. For example, running git2gannt over itself:

  title git2gantt output
  dateFormat YYYY-MM-DD

  section git2gantt
  Development: devgit2gantt20190208, 2019-02-08, 2019-02-13
  Development: devgit2gantt20190214, 2019-02-14, 2019-02-15
  Development: devgit2gantt20190303, 2019-03-03, 2019-03-04
  Development: devgit2gantt20191203, 2019-12-03, 2019-12-04

Usage is pretty straightforward: Screenshot 2019-12-08 at
13.18.12.png As you can see, it can be run over multiple repos at once, and there's also an option to have it consider every branch within each repository. Another handy option is the ability to limit the output to just one author -- perhaps you just want to document what you've done on a repo, not the contributions of other people.

Also especially handy, if you don't want to bore people with too much detail, is the "fuzz" option. This lets you tell git2gannt how relaxed you want it to be when it tries to decide how long a run of work on a repo lasted. So, perhaps, you're working on and off on a library that supports some other system you're documenting, but you might only be making changes every other day or so. With the correct fuzz value you can make it clear you were working on the library for a couple of weeks, despite there only being a commit every other day.

An example of running the output over a handful of projects would look something like this:

Screenshot 2019-12-08 at 13.34.41.png

This is one of those tools I knocked up quickly to get a job done, and haven't quite got round to finishing off fully. One thing I'd really like to do is add mermaid support directly within it, so that it actually has the option to emit plots, not just mermaid code (or, perhaps, drop the mermaid approach and use something else entirely).

Meanwhile though, if you're looking for something quick and dirty that will help you visualise what you've been working on and when for a good period of time... perhaps this will help.