Back in June it was announced that the next Perl release would be called Perl 7. A quick look back at Perl's history reveals that this is a little unusual. Perl 5 was originally released in October 17, 1994 and served its users well for a number of years. In 2000 Perl 6 was announced as a ground-up redesign of the language and it spent a long time in development. It eventually surfaced in 2015 but in 2019 was renamed "Raku" to distinguish it from traditional Perl. This leaves Perl 6 as a sort of "orphaned" version number.

In the meantime while Raku née Perl 6 was being developed, the team behind Perl 5 had delivery numerous point releases and retained an active community of loyal users. Many of these had no desire to migrate to Raku and wanted a number of quality of life improvements in their language, ecosystem and libraries. The sort of changes being proposed were bigger than you'd expect from a point release, so a new major version was required. Hence Perl 7 - the real next version of Perl.

So we now have quite an interesting situation, let's take a look at the release history:

Over the years many commercial and open source projects have skipped version numbers leaving their own little version holes with their own various explanations. Today we'll take a look at a number of similar situations like this, and we'll try to understand what happened in each case and why.

A fairly typical Perl program which calculates π

PHP 6

Perl was not the first language to skip a version, in fact it wasn't even the first scripting language beginning with "P" to skip version 6. A quick glance at PHP's release history reveals the following:

So what's the story here? Well in common with most software written in the early '00s PHP did not have particularly good Unicode support. The developers behind PHP recognised this and planned a rewrite - PHP 6 - that used Unicode to represent strings internally, specifically UTF-16. Work began in 2005 and would continue for a few years. While some of the planned PHP 6 features were completed on schedule, the Unicode rewrite stalled and became a major blocker to the release. In the meantime Unicode did indeed begin to take off ...

... however UTF-16 didn't. UTF-8, a different standard, rapidly emerged as the preferred Unicode standard on the web. This was particularly problematic since it became apparent that PHP 6 had some serious performance issues dealing with converting strings to and from UTF-8, and since web applications were PHP's bread and butter this was a big deal. In 2010, five years into the PHP 6's development the project was abandoned. Much of the non-Unicode features of PHP6 were ported into PHP 5 and released in version 5.4.

In 2014 as a new major release of PHP was being planned the topic of what it should be called was raised and it was decided that for the sake of removing any confusion or ambiguity the next version of PHP would be PHP 7. There are plenty of justifications given, but the first one given in the RFC I linked really seals it

First and foremost, PHP 6 already existed and it was something completely different. The decimal system (or more accurately the infinite supply of numbers we have) makes it easy for us to skip a version, with plenty more left for future versions to come.

This makes plenty of sense - PHP 6 was already a thing, and it's not like integers are a finite resource. If this was Ubuntu and they were considering discarding a good adjective/animal pair then perhaps it'd be slightly different

ECMAScript 4

Brendan Eich developed Javascript over the course of 10 short days at Netscape, and it soon became the defacto scripting language for the web. This status was confirmed by Ecma who issued a number of standards and referred to it as "ECMAScript". Nowadays they release these standards on a yearly basis (the latest being ECMAScript 2020, published June 2020) but at the end of 2009 we had five ... kinda:

You seem to be repeating falsehoods in blogs since the Proposed ECMAScript 4th Edition Language Overview was published, claiming dissenters including Microsoft were ignored by me, or “shouted down” by the majority, in the ECMAScript standardization group
...
These ES3.1 proposals came suddenly in March after years of passive participation by Microsoft, while the rest of the group worked on ES4. They have not been touched since April.

Watcom 5.0 and earlier

Slackware 5 and 6

For some reason Slackware was the first ever Linux distro I used. I have no idea why I chose it, but looking back it would've been better if I had a more user-friendly one like RedHat or SuSE. Using software on Slackware often involved compiling it yourself, which usually meant hunting down, downloading and compiling dependencies (possibly even encountering more dependencies which require you to hunt down, download and compile sub-dependencies (possibly even encountering more dependencies ... etc)). Between May and October in 1999 they jumped from using 4.0 to 7.0

Conclusion

I only scratched the surface of this subject, really. If look around then you'll start to see missing versions everywhere. For example, what happened to Windows 9? How about .NET [Core] 4, MySQL 6 and 7 or Akumulátor 2? As soon as you apply an sequential version to some software you make a statement about how that software relates to any previous or future releases - whether or not you adhere to a strict set of rules like semver. This is meant to help users and it largely works as intended but does not really give any strong prescription for how to handle abandoned releases or forked-then-remerged projects. As a consequence we have to be cool with the idea that skipping a version or two is perhaps preferable than introducing confusion or even discarding any notion of sequence whatsoever - imagine wondering if it's safe to upgrade from PHP e1daa19 to PHP caf3c64.

I keep encountering things when setting up Jupyter! After I got it running under IIS I went to bed then picked it up the next evening, tried to install the C# and F# kernels from dotnet try. Of course I hit two snags:

Issue 1. C# and F# Kernels aren't available at all

I installed it as usual by opening up Windows Terminal, source-ing my venv, installing dotnet-try globally and installing the dotnet try kernel. I suspected I was gonna encounter something a little odd - I wasn't sure if the dotnet try would respect the venv when I installed jupyter. Turns out the kernel got installed in a weird location (under AppData/Roaming rather than in my venv dir). I did a bit of experimenting and this was how I ended up resolving the issue. First I updated the PATH so that it included both the venv directory and the dotnet tools directory:

    <aspNetCore processPath="python.exe" 
                arguments="launch.py"
                stdoutLogEnabled="true" 
                stdoutLogFile="logs\stdout">
      <environmentVariables>
        <environmentVariable name="PYTHONPATH" value="." />
        <environmentVariable name="PATH" value="%PATH%;C:\Users\sean\.dotnet\tools;.\venv\Scripts" />
      </environmentVariables>    
    </aspNetCore>

Then I opened the terminal and ran the kernel install steps once more:

Once that was in place I could create a new notebook and run F# code:

Issue 2. Notebooks weren't saving

<?xml version="1.0" encoding="utf-8"?>
<configuration>
  <system.webServer>
    <handlers>
      <add name="aspNetCore" path="*" verb="*" modules="AspNetCoreModuleV2" resourceType="Unspecified" />
      <remove name="WebDAV" />
    </handlers>

    <modules>
      <remove name="WebDAVModule" />
    </modules>
    
    <aspNetCore processPath=".\venv\Scripts\python.exe" 
                arguments="launch.py"
                stdoutLogEnabled="true" 
                stdoutLogFile="logs\stdout">
      <environmentVariables>
        <environmentVariable name="PYTHONPATH" value="." />
        <environmentVariable name="PATH" value="%PATH%;C:\Users\sean\.dotnet\tools;.\venv\Scripts" />
      </environmentVariables>    
    </aspNetCore>
  </system.webServer>
</configuration>

I use Jupyter Notebooks quite a lot for personal development, and to do this I set up an instance on a DigitalOcean droplet. However I also wanted to do something similar for some tasks at work which can access resources only available in our internal network. Since we use Windows that means I need to do something a little different. I could just use Anaconda to install and load it, but I find it a little clunky, I don't like how it tries to manage everything for you, I wanted a slightly nicer url than https://localhost:8000 and more importantly I wanted it to just be there without having to start anything up. So I set out to get Jupyter Notebook running on IIS using the ASP.NET Core Module V2 on http://notebook.local.

Make sure Python, IIS and the ASP.NET Core Hosting Bundle are installed. Open up a Terminal (I'm using bash, Powershell is probably file but I don't like it), create an new directory where our notebook application will be served, setup a venv there, and run the activation script, upgrade pip so that it doesn't complain at us and then finally install Jupyter Notebook:

$ cd ~/source/repos
$ mkdir notebook
$ python -m venv venv
$ source venv/Scripts/activate
$ pip install --upgrade pip
$ pip install notebook

$ jupyter notebook
[I 20:39:42.322 NotebookApp] The port 8888 is already in use, trying another port.
[I 20:39:42.328 NotebookApp] Serving notebooks from local directory: C:\Users\sean\source\external
[I 20:39:42.329 NotebookApp] Jupyter Notebook 6.1.4 is running at:
[I 20:39:42.329 NotebookApp] http://localhost:8889/?token=e27aa8d7754e1bb078dfdf48e7c55032ac3551360389fd65
[I 20:39:42.329 NotebookApp]  or http://127.0.0.1:8889/?token=e27aa8d7754e1bb078dfdf48e7c55032ac3551360389fd65
[I 20:39:42.329 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[C 20:39:42.476 NotebookApp]

    To access the notebook, open this file in a browser:
        file:///C:/Users/sean/AppData/Roaming/jupyter/runtime/nbserver-260-open.html
    Or copy and paste one of these URLs:
        http://localhost:8889/?token=e27aa8d7754e1bb078dfdf48e7c55032ac3551360389fd65
     or http://127.0.0.1:8889/?token=e27aa8d7754e1bb078dfdf48e7c55032ac3551360389fd65

So here you can see why I'm doing all this - http://localhost:8888 isn't quite as nice as https://notebook.local. Next let's create a logs folder where our stdout logging will live and notebooks folder where we'll keep our notebook files.

$ mkdir logs notebooks

Next we'll create a Python script called launch.py that will launch Jupyter:

import os, sys
from IPython.terminal.ipapp import launch_new_instance
from IPython.lib import passwd
import warnings

# set the password you want to use
notebook_password = "test"

sys.argv.append("notebook")
sys.argv.append("--IPKernelApp.pylab='inline'")
sys.argv.append("--NotebookApp.ip='*'")
sys.argv.append("--NotebookApp.port=" + os.environ["ASPNETCORE_PORT"])
sys.argv.append("--NotebookApp.open_browser=False")
sys.argv.append("--NotebookApp.notebook_dir=./notebooks")
sys.argv.append("--NotebookApp.password=" + passwd(notebook_password))

launch_new_instance()

This script is kind of interesting, but we'll go into it later once we've got everything up and running. Next we will need to create a Web.config file that'll be used by IIS and ANCMV2 to call this script with the Python executable and libraries from our venv. So let's create the following:

<?xml version="1.0" encoding="utf-8"?>
<configuration>
  <system.webServer>
    <handlers>
      <add name="aspNetCore" path="*" verb="*" modules="AspNetCoreModuleV2" resourceType="Unspecified" />
    </handlers>
    <aspNetCore processPath=".\venv\Scripts\python.exe" 
                arguments="launch.py"
                stdoutLogEnabled="true" 
                stdoutLogFile="logs\stdout">
      <environmentVariables>
        <environmentVariable name="PYTHONPATH" value="." />
      </environmentVariables>    
    </aspNetCore>
  </system.webServer>
</configuration>

Next we'll set up the new Site in IIS - so open up IIS Manager (hit Start and start typing "Internet Information Services" - it'll appear in the suggestions) then:

1. in the left-hand menu right click the top-level node, hit "Add Website..."
2. in the dialog that appears call the site whatever you like
   
3. in the "Physical Path" section click the "..." button, and choose the directory your notebooks live in
4. in the "Binding" section type "notebook.local" in the "Host name" section

127.0.0.1    notebook.local

So now we have a Website bound to "notebook.local" running under IIS, which is configured to use ASP.NET Core Runtime Module V2 to launch a Python script that will run Jupyter Notebook. Let's fire up a browser and navigate to http://notebook.local and enter the password test:

It loads nicely so let's just create a simple Python 3 notebook to confirm everything works end-to-end:

Excellent. Ok so let's rewind a little bit and ask the question: why do we use a special script instead of having our Web.config file launch jupyter notebook directly? Well that would be ideal, but a bit of a hack required. Notice in launch.py that we grab the ASPNETCORE_PORT environment variable and use that as our port? Well that contains the port IIS wants to communicate with our app in - so we need to hook Jupyter up to listen on that port. While it is possible to call jupyter notebook --port 8080 to specify the port we want to use, it's actually not possible to use the ASPNETCORE_PORT variable in our Web.config. These environment variables are not expanded in our arguments="..." attribute, (see issue #117 in the aspnet/AspNetCoreModule repo on GitHub) - so we need to run a script that grabs this from the Environment and sets it up. It's a bit hacky, but it gets us where we need to be.

# enable US and CZ layouts, toggle with right-win, other possible grp:* values are...
#    grp:alt_caps_toggle         Alt+Caps Lock
#    grp:alt_space_toggle        Alt+Space
#    grp:menu_toggle             Menu
#    grp:lwin_toggle             Left Win
#    grp:win_space_toggle        Win+Space
#    grp:rwin_toggle             Right Win
setxkbmap -layout us,cz                 
setxkbmap -option                       
setxkbmap -option "grp:rwin_toggle"

However I noticed that periodically this would stop working, I'd be stuck on the US layout and my right-windows key would do nothing. Re-running the three setxkbmap commands would fix things up. 

This eventually annoyed me and I resolved to figure it out. Apparently Linux has some power-management module that will cause some USB devices to disconnect, and when they reconnect they appear as a different device without these keyboard settings.

Unsuprisingly my bodge was responsible for this. The correct solution here was to set my XKBLAYOUT to "uz,cz" and to add "grp:rwin_toggle" to my XKBOPTIONS in /etc/default/keyboard:

$ cat /etc/default/keyboard 
# KEYBOARD CONFIGURATION FILE

# Consult the keyboard(5) manual page.

XKBMODEL="pc105"
XKBLAYOUT="us,cz"
XKBVARIANT=""
XKBOPTIONS="lv3:ralt_switch,terminate:ctrl_alt_bksp,grp:rwin_toggle"

BACKSPACE="guess"

Now my layout switching works a little better and my keyboard no longer has bouts of amnesia. That BACKSPACE="guess" line sure does look odd though, I'll need to come back to that at some point.

* = I have no need for the "pound" symbol anymore and US-layout keyboards are much more available

An article titled "This Is What Peak Hello World Looks Like" did the rounds on Hacker News (discussion here), where someone took the plain old printf("hello, world!"); in C and transformed it step-by-step beyond recognition. This was a noble effort, but today I discovered an easier way to make Hello World more insane in a slightly different way:

    $ dotnet new console -o hello -lang F#
    The template "Console Application" was created successfully.

    Processing post-creation actions...
    Running 'dotnet restore' on hello/hello.fsproj...
      Restore completed in 209.4 ms for /home/sean/dev/dotnet/hello/hello.fsproj.

    Restore succeeded.
    
    $ cd hello
    $ cat Program.fs 
    // Learn more about F# at http://fsharp.org

    open System
    
    [<EntryPoint>]
    let main argv =
        printfn "Hello World from F#!"
        0 // return an integer exit code

That's it, an absolute monster of a program. But wait, "This is just a plain old Hello World in .NET! What's so insane about that?" I hear you ask. Well we're not quite done yet, let's get ready to share the executable with our colleague who wants to use our app so we'll add <PublishSingleFile>true</PublishSingleFile> to hello.fsproj - which will generate a single self-contained executable binary - and publish it:

    $ dotnet publish -r linux-x64 -c Release
    Microsoft (R) Build Engine version 16.5.0+d4cbfca49 for .NET Core
    Copyright (C) Microsoft Corporation. All rights reserved.

      Restore completed in 5.27 sec for /home/sean/dev/dotnet/hello/hello.fsproj.
      hello -> /home/sean/dev/dotnet/hello/bin/Release/netcoreapp3.1/linux-x64/hello.dll
      hello -> /home/sean/dev/dotnet/hello/bin/Release/netcoreapp3.1/linux-x64/publish/
    $ ./bin/Release/netcoreapp3.1/linux-x64/publish/hello
    Hello World from F#!
    $ ls -lh ./bin/Release/netcoreapp3.1/linux-x64/publish/hello
-rwxr-xr-x 1 sean sean 78M May 17 22:47 ./bin/Release/netcoreapp3.1/linux-x64/publish/hello

There she blows! An absolute monster - a 78MB Hello World executable! This is actually a bit unfair, since it includes some stuff we might not need, so let's add <PublishTrimmed>true</PublishTrimmed> to hello.fsproj and rebuild:

    $ dotnet publish -r linux-x64 -c Release
    Microsoft (R) Build Engine version 16.5.0+d4cbfca49 for .NET Core
    Copyright (C) Microsoft Corporation. All rights reserved.
    
      Restore completed in 33.8 ms for /home/sean/dev/dotnet/hello/hello.fsproj.
      hello -> /home/sean/dev/dotnet/hello/bin/Release/netcoreapp3.1/linux-x64/hello.dll
      Optimizing assemblies for size, which may change the behavior of the app. Be sure to test after publishing. See: https://aka.ms/dotnet-illink
      hello -> /home/sean/dev/dotnet/hello/bin/Release/netcoreapp3.1/linux-x64/publish/
    $ ./bin/Release/netcoreapp3.1/linux-x64/publish/hello
    Hello World from F#!
    $ ls -lh ./bin/Release/netcoreapp3.1/linux-x64/publish/hello
-rwxr-xr-x 1 sean sean 46M May 17 22:51 ./bin/Release/netcoreapp3.1/linux-x64/publish/hello

Ok, slightly better but that's still a hefty 46MB for Hello World. This is a bit disingenuous however - what's happening is that I've configured the project to be able to produce a self-contained executable, to do this the .NET Core runtime is bundled in the hello binary. So it's a little bit like distributing a little bit of application [byte]code, a bytecode interpreter, a JIT compiler and runtime libraries.

Since .NET Try supports F# officially I wanted to switch over my Jupyter notebook instance to use that instead of IFsharp. But I ran into a couple of frustrating issues that I wanted to document in case anyone else hit them and didn't know how to debug the issue.

Issue 1. dotnet-try installs but isn't usable

I followed Scott Hanselman's instructions to install dotnet-try, but when I tried to execute dotnet try jupyter install it seemed as though dotnet-try wasn't installed at all:

$ dotnet tool install dotnet-try --global
You can invoke the tool using the following command: dotnet-try
Tool 'dotnet-try' (version '1.0.19553.4') was successfully installed.

$ dotnet try jupyter install
Could not execute because the specified command or file was not found.
Possible reasons for this include:
  * You misspelled a built-in dotnet command.
  * You intended to execute a .NET Core program, but dotnet-try does not exist.
  * You intended to run a global tool, but a dotnet-prefixed executable with this name could not be found on the PATH.

After a lot of head scratching I dug into the actual docs and learned that both .NET Core 3.0 and .NET Core 2.1 SDKs should be installed, while I only had .NET Core 3.1's SDK So after a quick sudo apt install dotnet-sdk-3.0 dotnet-sdk-2.1 I was successfully able to install the kernel and list it:

$ jupyter kernelspec list
Available kernels:
  .net-csharp    /home/notebook/.local/share/jupyter/kernels/.net-csharp
  .net-fsharp    /home/notebook/.local/share/jupyter/kernels/.net-fsharp
  mit-scheme     /usr/local/share/jupyter/kernels/mit-scheme
  python3        /usr/local/share/jupyter/kernels/python3

Issue 2. Jupyter can't run dotnet-try

However even though it was installed, each time I tried to create a new F# notebook Jupyter would give an error saying that it was unable to connect to the kernel. After taking a quick look at my logs I saw the same error as before!

Feb 09 13:19:11 aviemore jupyter[837]: [I 13:19:11.169 NotebookApp] KernelRestarter: restarting kernel (1/5), new random ports
Feb 09 13:19:11 aviemore jupyter[837]: Could not execute because the specified command or file was not found.
Feb 09 13:19:11 aviemore jupyter[837]: Possible reasons for this include:
Feb 09 13:19:11 aviemore jupyter[837]: * You misspelled a built-in dotnet command.
Feb 09 13:19:11 aviemore jupyter[837]: * You intended to execute a .NET Core program, but dotnet-try does not exist.
Feb 09 13:19:11 aviemore jupyter[837]: * You intended to run a global tool, but a dotnet-prefixed executable with this name could not be found on the PATH.
Feb 09 13:19:14 aviemore jupyter[837]: [I 13:19:14.180 NotebookApp] KernelRestarter: restarting kernel (2/5), new random ports
Feb 09 13:19:14 aviemore jupyter[837]: Could not execute because the specified command or file was not found.
Feb 09 13:19:14 aviemore jupyter[837]: Possible reasons for this include:
Feb 09 13:19:14 aviemore jupyter[837]: * You misspelled a built-in dotnet command.
Feb 09 13:19:14 aviemore jupyter[837]: * You intended to execute a .NET Core program, but dotnet-try does not exist.
Feb 09 13:19:14 aviemore jupyter[837]: * You intended to run a global tool, but a dotnet-prefixed executable with this name could not be found on the PATH. 

EnvironmentFile=/home/jupyter/config/notebook-config

When reading about a Unix command or C library function it's relatively common to see it suffixed with a number in brackets. This is to make it clear what exactly you're talking about, so if someone is discussing mknod they might write mknod(1) they're talking about the shell command or mknod(2) if they mean the syscall. The number used refers to the manpage section, so to see the manpage for the shell function:

$ man 1 mknod

And to see what the syscall is all about:

$ man 2 mknod

According the man manpage on my system there are eight standard manpage sections in total, with one non-standard section:

1. Executable programs or shell commands
2. System calls (functions provided by the kernel)
3. Library calls (functions within program libraries)
4. Special files (usually found in /dev)
5. File formats and conventions eg /etc/passwd
6. Games
7. Miscellaneous (including macro packages and conventions), e.g. man(7), groff(7)
8. System administration commands (usually only for root)
9. Kernel routines [Non standard]
   

import os
import re
from collections import defaultdict

manpage_gz_pattern = "(.*)\.\w+.gz"
manpage_dir_base = "/usr/share/man"
manpage_sections = range(1, 9)
manpage_entries = defaultdict(list)

for manpage_section in manpage_sections:
    manpage_section_dir = os.path.join(manpage_dir_base, f"man{str(manpage_section)}")
    manpage_section_contents = os.listdir(manpage_section_dir)

    for manpage_entry_filename in manpage_section_contents:
        gz_entry = re.match(manpage_gz_pattern, manpage_entry_filename)
        manpage_entry = gz_entry.groups()[0] 
        manpage_entries[manpage_entry] += [(manpage_section, manpage_entry_filename)]

for section_count in manpage_sections:
    number_of_manpages = len([ m for m in manpage_entries if len(manpage_entries[m]) == section_count])
    print(f"number of manpages in {section_count} sections: {number_of_manpages}")

$ python -i mancount.py
number of manpages in 1 sections: 10763
number of manpages in 2 sections: 107
number of manpages in 3 sections: 7
number of manpages in 4 sections: 0
number of manpages in 5 sections: 0
number of manpages in 6 sections: 0
number of manpages in 7 sections: 0
number of manpages in 8 sections: 1

• mdoc (mdoc.1.gz, mdoc.5.gz, mdoc.7.gz)
• locale (locale.1.gz, locale.5.gz, locale.7.gz)
• hostname (hostname.1.gz, hostname.5.gz, hostname.7.gz)
• passwd (passwd.1ssl.gz, passwd.1.gz, passwd.5.gz)
• time (time.2.gz, time.3am.gz, time.7.gz)
• readdir (readdir.2.gz, readdir.3am.gz, readdir.3.gz)
• random (random.3.gz, random.4.gz, random.7.gz)
  

$ man 1ssl passwd
$ man 1 passwd

I used to think that each time I opened a manpage I learn something completely new and unexpected - but I never thought I'd find something interesting just by looking at the manpages' gzipped filenames!

> let foo x = x;;
val foo : x:'a -> 'a

> let foo x y = (x,y);;
val foo : x:'a -> y:'b -> 'a * 'b

> let foo x0 x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x15 x16 x17 x18 x19 x20 x21 x22 x23 x24 x25 x26 x27 x28 x29 x30 x31 x32 x33 x34 x35 x36 x37 x38 x39 = (x0,x1,x2,x- 3,x4,x5,x6,x7,x8,x9,x10,x11,x12,x13,x14,x15,x16,x17,x18,x19,x20,x21,x22,x23,x24,x25,x26,x27,x28,x29,x30,x31,x32,x33,x34,x35,x36,x37,x38,x39);;
val foo :
  x0:'a ->
    x1:'b ->
      x2:'c ->
        x3:'d ->
          x4:'e ->
            x5:'f ->
              x6:'g ->
                x7:'h ->
                  x8:'i ->
                    x9:'j ->
                      x10:'k ->
                        x11:'l ->
                          x12:'m ->
                            x13:'n ->
                              x14:'o ->
                                x15:'p ->
                                  x16:'q ->
                                    x17:'r ->
                                      x18:'s ->
                                        x19:'t ->
                                          x20:'a1 ->
                                            x21:'a2 ->
                                              x22:'a3 ->
                                                x23:'a4 ->
                                                  x24:'a5 ->
                                                    x25:'a6 ->
                                                      x26:'a7 ->
                                                        x27:'a8 ->
                                                          x28:'a9 ->
                                                            x29:'a10 ->
                                                              x30:'a11 ->
                                                                x31:'a12 ->
                                                                  x32:'a13 ->
                                                                    x33:'a14 ->
                                                                      x34:'a15 ->
                                                                        x35:'a16 ->
                                                                          x36:'a17 ->
                                                                            x37:'a18 ->
                                                                              x38:'a19 ->
                                                                                x39:'a20 ->
                                                                                  'a *
                                                                                  'b *
                                                                                  'c *
                                                                                  'd *
                                                                                  'e *
                                                                                  'f *
                                                                                  'g *
                                                                                  'h *
                                                                                  'i *
                                                                                  'j *
                                                                                  'k *
                                                                                  'l *
                                                                                  'm *
                                                                                  'n *
                                                                                  'o *
                                                                                  'p *
                                                                                  'q *
                                                                                  'r *
                                                                                  's *
                                                                                  't *
                                                                                  'a1 *
                                                                                  'a2 *
                                                                                  'a3 *
                                                                                  'a4 *
                                                                                  'a5 *
                                                                                  'a6 *
                                                                                  'a7 *
                                                                                  'a8 *
                                                                                  'a9 *
                                                                                  'a10 *
                                                                                  'a11 *
                                                                                  'a12 *
                                                                                  'a13 *
                                                                                  'a14 *
                                                                                  'a15 *
                                                                                  'a16 *
                                                                                  'a17 *
                                                                                  'a18 *
                                                                                  'a19 *
                                                                                  'a20

Warning: line too long, ignoring some characters
- 

Minor sidebar - I was going to just say we hit a restriction where 4096 bytes is the max line length, but obviously 1 character isn't necessarily 1 byte if we're using some Unicode representation, and a nice way to check that we're using Unicode is to punch in identifiers that would definitely be in different encodings in an ASCII representation - I used Georgian and Czech characters:

> let სამი = 3;;
val სამი : int = 3

> let čtyři = 4;;
val čtyři : int = 4

> სამი + čtyři;;
val it : int = 7

When I changed my program to start each parameter with the Georgian character "ა" (i.e. let bar ა0 ა1... etc) which cannot be represented in a single byte in UTF-8 we hit the error after only 162 parameters. So it's not a character limit, but a buffer of bytes that we filled - and the size of the buffer is ... uh 4096 bytes.

  x1328,
  ^^^^^

/home/sean/stdin(6636,1): error FS0039: The value or constructor 'x1328' is not defined. Maybe you want one of the following:
   x132
   x138
   x1238
   x128
   x1321

sean@seoul ~/d/s/xmms-1.2.11> ./configure
checking build system type... x86_64-unknown-linux-gnu

MANY LINES LATER

*** The glib-config script installed by GLIB could not be found
*** If GLIB was installed in PREFIX, make sure PREFIX/bin is in
*** your path, or set the GLIB_CONFIG environment variable to the
*** full path to glib-config.
configure: error: *** GLIB >= 1.2.2 not installed - please install first ***

$ git clone https://github.com/smcl/gtk-1.x
$ cd gtk-1.x
$ ./configure --prefix=/usr && make
$ sudo make install

$ git clone https://github.com/smcl/glib-1.x
$ cd glib-1.x
$ ./configure --prefix=/usr && make
$ sudo make install

$ curl -LO http://www.xmms.org/files/1.2.x/xmms-1.2.11.tar.gz
$ tar -xzf xmms-1.2.11.tar.gz
$ cd xmms-1.2.11
$ ./configure && make
$ sudo make install

Then if all is well then the original (and best!) xmms should be installed into your path, so you can go download some lovely skin of a brushed aluminium late-90s Sony CD player ... though it might be a little bit tiny if you use a HiDPI screen:

Grammatical cases are a common stumbling block for native English speakers learning another language. This might be because cases are sort of invisible in English and they're not taught in school so it's it's hard to see why they would even matter.

However in languages like Czech cases are a really important concept, and if you don't wrap your head around them you'll struggle to make yourself understood. To fully comprehend this importance we need an example - Petr and Pavel, who are not friends.

Accusative - Peter is punching Pavel

In Czech the verb "to punch" is "bít", whose third person conjugation is "bije" so if we want to describe this situation in Czech we might naively start out with something like this ...

Petr bije Pavel

However this isn't quite enough because it's not actually clear who is punching who. You should be able to rearrange the parts of this sentence in many ways in Czech without any ambiguity. But if we can rearrange the sentence as Pavel bije Petr or bije Pavel Petr then how do we tell our Czech friends who is the puncher and who is the punchee? This is where we need to modify case of some words to help clear things up a little.

In Czech we indicate the subject (puncher) using the nominative case. In our sentence this is Petr and the nominative case of Petr is simply Petr.

The object (the punchee) of the sentence is indicated using accusative case - which for Pavel is Pavla. Which gives us:

Petr bije Pavla

Genitive - Petr reads Helena's book

• what I mean by "Nominative", "Genitive" and "Accusative" case
  
• the verb "být"
  
• the noun "hodina"
• numbers from 1 - 60

Additionally plurals behave kinda funny in Czech. My friends and I all first learned this when counting beer so I'm gonna use "beer plurals" as shorthand for the following behaviour when you have ...

• one of something you give the noun in singular Nominative (jedno pivo, jedna hodina, jedna minuta)
• two, three or four of something you give the noun in plural Nominative (dvě piva, dvě hodiny, dvě minuty)
• five or more of something you give the noun in plural Genitive (pět piv, pět hodin, pět minut)

Quick Reference

Firstly in case you want to just quickly reference, here's a table that demonstrates most of the cases:

Whole Hours

If it's exactly H o'clock, we say something like "it is H hours". The word for "hours" is "hodina" and this is the first example of the beer plurals I described above. There are only 12 possibilities here so it's not too hard to just memorise the below:

Half Hours

Times that in English are exactly half past an hour in Czech are said to be a half of the next hour, so instead of "half past one" we say something like "a half of two". There's a minor complication - the hour is given in the genitive singular feminine. They behave like adjectives because ultimately they are adjectives modifying the noun "hodina". So je půl jedné means "it is half of the first (hour)"

Again there are only 12 combinations so if my description doesn't make much sense you can just memorise the below without too much trouble:

Quarter Hours

Similar to half-hours when we talk about quarter past or quarter to an hour in Czech we talk about quarters of an hour. So 12:15 is "a quarter of one" or čtvrt na jednu (the hour part is in the Accusative case) and 12:45 is "three quarters of one" or tři čtvrtě na jednu (with čtvrtě being the plural of čtvrt)

Minutes After The Hour

• "minuta" behaves like our "beer plurals" - 1 minuta, 2/3/4 minuty, 5/6/7/etc minut
• the hour is the genitive singular, and since "hodina" is feminine we have jedné, druhé, třetí, etc
  

Minutes Before The Hour

Finally for minutes before an hour, we write something like "in M minutes it is H o'clock" - so 1:50 this is "je za deset minut dve". Again we have the "beer plural" for minut - minuta, minuty, minut.

24 Hour Time. 

When reading from a watch, computer of phone it seems many Czechs will just say the hour and limit component separately. For example the other day when we were leaving the Maximus spa my girlfriend asked the lady at the counter what time the shuttle bus to the nearest tram stop left, she pulled up a schedule and said “šestnáct dvacet” - 16:20.

The only quirk is 1-9 minutes past the hour where you say the minutes with a leading nula - so 16:05 would be šestnáct nula pět.

Using Firefox on Debian 9 is a little frustrating as the packages available from default APT sources are older “ESR” releases (firefox-esr, 52.0 at the time of writing). Getting the Dev or Nightly builds is pretty straight forward but if you use Gnome you probably want a launcher, and it might not be very obvious how to do this.

First grab the sources or prebuilt binaries:

    $ curl -LO "https://download.mozilla.org/?product=firefox-devedition-latest-ssl&os=linux64&lang=en-US"

Extract them into /opt/firefox-dev:

    $ tar -xjf firefox-57.0b12.tar.bz2 && sudo mv firefox /opt/firefox-dev

Open up a text editor and create /use/share/applications/firefox-dev.desktop as follows

    [Desktop Entry]
    Name=Firefox Dev
    Comment=Browse the World Wide Web
    GenericName=Web Browser
    X-GNOME-FullName=Firefox Dev Web Browser
    Exec=/opt/firefox-dev/firefox %u
    Terminal=false
    X-MultipleArgs=false
    Type=Application
    Icon=firefox-dev
    Categories=Network;WebBrowser;
    MimeType=text/html;text/xml;application/xhtml+xml;application/xml;application/vnd.mozilla.xul+xml;application/rss+xml;application/rdf+xml;image/gif;image/jpeg;image/png;x-scheme-handler/http;x-scheme-handler/https;
    StartupNotify=true

Copy the icon and run gtk-update-icon-cache so that the icon appears as expected.

    $ sudo cp /opt/firefox-dev/browser/icons/mozicon128.png /usr/share/icons/hicolor/128x128/apps/firefox-dev.png
    $ sudo gtk-update-icon-cache -f /usr/share/icons/hicolor

And that's it! You should have a nice desktop icon for Firefox Developer Edition you can use. I also did the same for Firefox Nightly:

For updates you can clean out the directory and repeat the process with the latest tar.bz2 file ... or you can change the permissions in the firefox-dev directory so you have write access, and auto-updates will work.

A while ago I took an existing 3x8 font, converted it by hand for use with AdaFruit's graphics library and subsequently modified it to use ISO 8859-2 characters (in a presumably innocent coincidence this popped up in the Adafruit GFX Library a few months later). Anyway, I recently received a request to perform the same modification to the standard 5x8 fonts and only recently got round to looking at this. As a quick reminder, ISO 8859-2 covers ~128 extra characters for alphabets used by Central and Eastern and Southern European languages - these characters look like this:

After a little bit of experimentation I realised that the extra 2 columns aren't really all that useful for adding the extra ligatures (čarky, hačky, umlauts etc) - we're really constrained by vertical space. Many of the existing letters need to be modified or reworked entirely, since they consume the almost entire 8 rows and we need 2 rows for some ligatures. For example Ä is currently implemented as the following:

    0x7D, 0x12, 0x11, 0x12, 0x7D

Which looks like this:

This is visually a little confusing, but more importantly we cannot really re-use it for ISO 8859-2 since some of the ligatures we need to add to the "A" require at least two rows. Instead of having an "A" which jumps around depending on the ligature, I've created a single A for when a ligature is used and left the ungarnished original letter alone.

Just as another example of why this can be tricky the existing ä looks really weird to me, the umlauts are skewed off to the left and look like they're joined to the letter itself. 

I've moved this up into a central position which is the same on all letters involving umlauts. This is purely based on personal taste, but I think it looks better - below is the original style compared to my modified version:

There are similar considerations in some of the other letters that are left as an exercise for the reader - see if you can devise a neat system to fit all the letters below into 3x8 grid in a way that is consistent and legible, it's pretty tricky. I've made an initial stab at this (see GitHub gist below), but after revisiting this I've realised how flakey and error-prone this process of creating fonts is. 

To help me get used to F# and relearn the ways of functional programming I've been working through Project Euler in a Jupyter IfSharp notebook and keeping my solutions on GitHub at https://github.com/smcl/ProjectEulerJupyter

After around 50 problems so far I've spotted a handful of patterns which had either a number of possible solutions or were a pain to type out (or copy/paste) each time I used them. I explored them each in a little more detail to find the most optimal implementation of each pattern. The reason I wrote this up is that even though the problems are pretty simple, some of the results were pretty surprising.

For each of the patterns I've got a simple definition, some solutions and a set of benchmark results in a table. In each results table I've highlighted the most optimal solution that fully expands the result set (so the lazily evaluated solutions that "complete" in a millisecond don't count) so that we can have a realistic idea of what the best solution is.

Combinations of two lists 

The first item is the simplest of the four problems - if we have two lists foo and bar, produce a list of pairs featuring all combinations of elements of foo with bar. So given something like ...

let foo = [ 'a'; 'b'; 'c' ]
let bar = [ 1; 2; 3 ]

We expect to see a list like this ...

[ 
    ('a', 1); ('a', 2); ('a', 3)
    ('b', 1); ('b', 2); ('b', 3)
    ('c', 1); ('c', 2); ('c', 3)
]

I came up with only three solutions - I don't feel like I have an ideal solution to this, just the least hacky variant of the first solution that popped up in my mind.

pair_list: The first solution, call List.map for every member of one list, then in the function argument call List.map again for every member of the second - flatten the resulting list using List.concat

let pair_list l1 l2 =
    List.map (fun x -> l2 |> List.map (fun y -> (x,y))) l1
    |> List.concat    

pair_seq: as above, but assume we can have sequences as input, so we can produce the (fairly large) output array lazily:

let pair_seq s1 s2 =
    Seq.map (fun x -> s2 |> Seq.map (fun y -> (x,y))) s1
    |> Seq.concat

pair_seq_expanded: as above, expand fully to a List for an idea of how long it takes to operate on the whole output:

let pair_seq_expanded s1 s2 =
    Seq.map (fun x -> s2 |> Seq.map (fun y -> (x,y))) s1
    |> Seq.concat
    |> Seq.toList

pair_seq_for: it's pretty clear that using a sequence is preferable here, especially if you need to work with 1000 x 1000 collections, so the final variant is a slight rewrite of the second, using a for loop and yield-ing the resulting tuple.

let pair_seq_for s1 s2 = 
    [ for x in s1 do 
        for y in s2 do 
            yield (x,y) ]

To compare the performance of these three I've defined 100 and 1000 element lists/sequences and measured how long it takes to iterate through each sequence pair performing a simple operation (accumulating the difference between the pairs).

time to create n-element collection of pairs in milliseconds

So thankfully the cleanest looking pair_seq_for solution is actually the fastest when we get to larger data sets. This isn't quite where the story ends though. There's a nice discussion here on Stack Overflow about a similar but slightly different problem - finding n element combinations of a single list - so for

let someList = [ 1; 2; 3 ]

... we wanted a function combs (n:'a) (lst:'a list) which would produce something like the below for combs 2 someList

[
    ( 1, 2 ); ( 1, 3 )
    ( 2, 1 ); ( 2, 3 )
    ( 3, 1 ); ( 3, 2 )
]

This is different from the problem I posed, but I've got a GitHub gist here where I've turned them all loose on the same set of data, and performed some simple measurements. 

Pairing elements of Collections with their indexes

In a couple of places I found myself wondering if F# collections had an equivalent of python's enumerate - which is a function which wraps a list and returns an index/element pair for each loop iteration:

letters = [ "a", "b", "c", "d" ]
for i, c in enumerate(letters):
    print "%d: %s" % (i, c)

# output:
#     0: a
#     1: b
#     2: c
#     3: d

It took a little while before I spotted Array.mapi so I ended up working through and measuring a handful of different ways first - some are obviously pretty poor (particularly those using recursion) but I left them in nonetheless:

enumerate_by_for_seq - using a Seq to generate the index and return a pair

let enumerate_by_for_seq (a:string []) =
    seq { for i in 0 .. (a.Length-1) -> (i, a.[i]) }

enumerate_by_for_seq_expanded - as previous, but returning a List to fully expand the sequence

let enumerate_by_for_seq_expanded (a:string []) =
    seq { for i in 0 .. (a.Length-1) -> (i, a.[i]) }
    |> Seq.toList

enumerate_by_for_list - iterating over each index using a for loop, returning a (int * string) list

let enumerate_by_for_list (a:string []) =
    [ for i in 0 .. (a.Length-1) -> (i, a.[i]) ]

enumerate_by_for_array - as above but returning (int * string) [], note that this seems ridiculously similar, but differs surprisingly in performance (I discovered this by accident and included it in this experiment because of the difference!)

let enumerate_by_for_array (a:string []) =
    [| for i in 0 .. (a.Length-1) -> (i, a.[i]) |]

enumerate_by_map - generating a list of indexes and then using |> and List.map to create the index/element pair (i.e. the same as the first approach, but using List)

let enumerate_by_map (a:string []) =
    [0..(a.Length-1)]
    |> List.map (fun i -> (i, a.[i]))

enumerate_by_recursion_array - bonkers approach, abusing Array.append and recursing. Just don't do this...

let rec enumerate_by_recursion_array' i (a:string[]) =
    match i with
    | 0 -> [||]
    | _ -> Array.append [| (i, a.[i]) |] (enumerate_by_recursion_array' (i-1) (a.[1..]))

let enumerate_by_recursion_array (a:string[]) =
    enumerate_by_recursion_array' (a.Length-1) a

enumerate_by_recursion_list - List variant of the above. Don't do this either

let rec enumerate_by_recursion_list' i (a:string[]) =
    match i with
    | 0 -> []
    | _ -> [ (i, a.[i]) ] @ (enumerate_by_recursion_list' (i-1) (a.[1..]))
    
let enumerate_by_recursion_list (a:string[]) =
    enumerate_by_recursion_list' (a.Length-1) a

enumerate_by_for_zip - Using Array.zip - shortest solution, the best until I spotted Array.mapi

let enumerate_by_zip (a:string[]) =
    Array.zip a [|0..(a.Length-1)|]

enumerate_by_for_mapi - Probably the most "correct" solution, using Array.mapi

let enumerate_by_mapi (a:string[]) =   
    Array.mapi (fun i x -> (i,x)) a

enumerate_by_for_parallel_mapi - As above but naively switching in Array.Parallel.mapi without any other changes

let enumerate_by_parallel_mapi (a:string[]) =
    Array.Parallel.mapi (fun i x -> (i,x)) a

time taken to enumerate n element collection (milliseconds)

* = this took way too long so I killed it

Obviously Array.mapi was the fastest overall. However it wasn't as much faster than Array.zip as I would have imagined, and I suspect that I'm doing something wrong with Array.Parallel.mapi. Also interesting is that while the super-fast performance of the enumerate_by_for_seq method dissipates somewhat when fully evaluated, it is still faster than the equivalent enumerate_by_for_list version.

Pandigitals

"Pandigital" numbers feature relatively frequently in Project Euler. An n-digit pandigital number will contain all digits from 0..n or 1..(n-1) once in some order. For example 41523 is a 1-5 pandigital, and 43210 is 0-4 pandigital. These numbers are mentioned in 32, 38, 41, 104, 118, 170 (and perhaps more) so a relatively efficient way to recognise them is pretty useful to have at hand. 

Again there's a few ways we can do this - in each case I can think of we start with taking the string representation of the input number and splitting it up using ToCharArray() and with this we can do a number of different things.

pandigital_strcmp - sort array, map each element to string, sort, create string + compare to "123..n"

let pandigital_strcmp (n:int) = 
    let sorted = new string (string(n).ToCharArray() |> Array.sort)
    sorted = pandigitalString

pandigital_intcmp - sort array, map each element to string, sort, create string, cast to int + compare to 123..n

let pandigital_intcmp (n:int) = 
    let sorted = new string (string(n).ToCharArray() |> Array.sort)
    int(sorted) = pandigitalInt

pandigital_arrcmp - sort array, string, cast to int + compare to existing array [| '1'; '2'; .. n |]

let pandigital_arrcmp (n:int) = 
    pandigitalArray = (string(n).ToCharArray() |> Array.sort)

pandigital_set_difference - convert to Set and compute difference from precalc'd set, pandigital if empty

let pandigital_set_difference (n:int) = 
    string(n).ToCharArray()
    |> Set.ofArray
    |> Set.difference pandigitalSet
    |> Set.isEmpty

pandigital_array_contains - for each element in precalculated pandigital array, check it's present in array and use List.fold to ensure all true

let pandigital_array_contains (n:int) = 
    let a = string(n).ToCharArray()
    pandigitalArray 
    |> Array.map (fun c -> Array.contains c a) 
    |> Array.fold (fun e acc -> e && acc) true

So I tested these against using the code to measure how quickly each method was in applying 

// where panDigitalInt is the upper limit ("n" in the table)
let testNumbers = [ 0 .. pandigitalInt ]
let bench name f =
    let sw = Stopwatch.StartNew()
    let res = testNumbers |> List.filter f |> List.length
    sw.Stop()
    printfn "%s: %f ms" name sw.Elapsed.TotalMilliseconds

time taken to filter pandigitals in [0..n] in milliseconds

So it seems Array.contains wins overall. The Set.difference approach was pretty dismal which was disappointing - it came to me when I was out walking my dog and I rushed back to write it and benchmark it. I think Set.ofArray is perhaps a little slow, but I haven't done any investigation into it.

It's worth noting that you probably shouldn't do something like [0..bigNumber] |> List.filter pandigital_array_contains to start with - maybe it's worth approaching the problem from a different angle in some cases.

Sorting a 3-element tuple

let sort_nested_if (a,b,c) =
    if a >= b then
        if b >= c then (a,b,c) else (a,c,b)
    elif b >= a then
        if a >= c then (b,a,c) else (b,c,a)
    else
        if a >= b then (c,a,b) else (c,b,a)

sort_flat_if - have a separate if for each result at the top level

let sort_flat_if (a,b,c) =
    if a >= b && b >= c then       (a,b,c)
    elif a >= b && b >= c then     (a,c,b)
    elif b >= a && a >= c then     (b,a,c)
    elif b >= a && c >= a then     (b,c,a)
    elif (*c >= b &&*) a >= b then (c,a,b)
    else (*c >= b && b >= a then*) (c,b,a)

sort_array - create an array, use Array.sort and map the results back into a tuple when returning the result

let sort_array (a,b,c) =
    let sorted = Array.sort [| a;b;c |]
    (sorted.[0], sorted.[1], sorted.[2])

To test these I generated large arrays of size 4000, 40000 and 400000 3-element tuples and timed how long each method took to sort them.

time taken to sort n 3-element tuples, in milliseconds

OK now it's probably obvious why I didn't just bin this little experiment - sort_array is surprisingly the clear winner. I would have guessed that the overhead of building an array and calling Array.sort function on a list way smaller than you'd normally need to sort would be insane. But apparently it's not!

Conclusion

It's surprising how many different ways you can write some relatively simple algorithms. Some of them are obviously pretty awful, like the recursive enumerate function (though I'm sure I can rearrange it to take advantage of tail call elimination) - and some were surprisingly performant, like the sort_array function in the final example. I've noticed that some other Project Euler people maintain a sort of library of functions they can re-use. Eventually I'd like to do something like this, but until it becomes unmanageable I'll just keep a Gist on GitHub:

When you want to do some experimentation or put together a simple code-based presentation Jupyter notebooks are a powerful tool to have at your disposal. But if you use a number of devices over a few locations it can be useful to have a single instance hosted somewhere central (Linode, Digital Ocean, wherever) that you can access from any device wherever you are. There are a handful of ways that you can achieve this:

1. log in to your remote machine, set Jupyter up and run jupyter notebook (perhaps in a tmux session) then log out - do this whenever your machine reboots
2. as above but using an existing docker image
3. spin up an Azure notebook
4. ... or we could do something like #1 - but have it setup under a separate user and administered via a systemd service

All four of the above are fine for different reasons and use-cases but here I'll talk about how I put #4 together in a little Linode instance running Fedora 25 - it's relatively simple, you can control over the kernels installed, and it's another excuse to get a bit more in-depth with another Linux subsystem (systemd).

Requirements

All you need is a Linux system which uses systemd (Fedora 15.0 or newer, Debian 8.x or newer, Ubuntu 15.04 or newer, for example) which you have sudoer level access on, and Python 3.x. It's probably pretty straight-forward to set this up on systems using the SysV init but I won't cover them here.

Install and Set Up Jupyter 

First thing we need to do is install Jupyter and set up the user context which the Jupyter will be run under - which is a user called "jupyter":

$ sudo python3 -m ensurepip
$ sudo pip install jupyter
$ sudo useradd jupyter
$ sudo passwd jupyter

Next we should switch to the new jupyter user, create the directory our notebooks will live in and generate the Jupyter config we'll mess around with:

$ su - jupyter
$ mkdir notebooks
$ jupyter notebook --generate-config

The last command will create a new file ~/.jupyter/jupyter_notebook_config.py which we'll do a little messing around with shortly, but before this we'll set up a password 

$ python3
Python 3.5.2 (default, Sep 14 2016, 11:28:32) 
[GCC 6.2.1 20160901 (Red Hat 6.2.1-1)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from notebook.auth import passwd
>>> passwd() # below I enter "password123"
Enter password: 
Verify password: 
'sha1:2eff88aac285:385c87867bd18fe852ee1d56b1010d4beed96969'

This will be used to log in to the application when its running. Open up the ~/.jupyter/jupyter_notebook_config.py file in a text editor and add/modify the following lines (using the SHA1 hash returned by the above):

c.NotebookApp.port = 8888
c.NotebookApp.ip = '0.0.0.0'
c.NotebookApp.password = 'sha1:2eff88aac285:385c87867bd18fe852ee1d56b1010d4beed96969'

Setting up a Jupyter systemd service

Now we want to create a new systemd service so we can make sure our Jupyter notebook runs on startup, handles logging nicely and has all the other bells-and-whistles afforded to us by systemd. This is surprisingly simple - we want to create a new file jupyter.service in /usr/lib/systemd/system - this will tie together our newly installed Jupyter software and our newly setup jupyter user - using your favourite text editor create it so it looks like the below:

$ sudo cat /usr/lib/systemd/system/jupyter.service
[Unit]
Description=Jupyter

[Service]
Type=simple
PIDFile=/var/run/jupyter.pid
ExecStart=/usr/bin/jupyter notebook --no-browser
WorkingDirectory=/home/jupyter/notebooks
User=jupyter
Group=jupyter

[Install]
WantedBy=multi-user.target%

Now all that's left to do is cross our fingers, enable our services, kick them off and browse to our remote box and login with our password:

$ sudo systemctl daemon-reload
$ sudo systemctl enable jupyter
$ sudo systemctl start jupyter

And if you want you can stop here - bookmark your http://www.xxx.yyy.zzz:port address and you're all set!

Conclusion

This was initially just an experiment - an excuse to test out my ability to put together a systemd .service file and do something more with a mostly-idle linux server sitting in a facility somewhere in Amsterdam. However I have found that I really like using this setup. When I was first shown Jupyter (née IPython) I was unimpressed and didn't see the point. However over the last few days I've been working through Project Euler problems again while teaching myself F# (using the IfSharp kernel) and I have found that it lends itself very well to my problem solving workflow on Project Euler.

tl;dr 

If you have weird SELinux permissions issues using Tor on Fedora, skip to "The Solution" we're basically gonna add a couple of custom SELinux policies and update the permissions on the /var/lib/tor directory.

Intro

I'm trying to get a little bit out of my cosy Debian comfort zone, and since I have a few friends working at Red Hat figured I'd try out Fedora. While I was teaching myself about systemd however I ran into an issue - starting up a Tor hidden service using systemd was fine immediately after it was installed, but after a reboot it'd repeatedly fail - the following is what is displayed when I ran systemctl status tor.service:

    tor.service - Anonymizing overlay network for TCP
       Loaded: loaded (/usr/lib/systemd/system/tor.service; enabled; vendor preset: disabled)
       Active: failed (Result: start-limit-hit) since Mon 2017-02-20 11:16:44 CET; 2min 42s ago
      Process: 1150 ExecStartPre=/usr/bin/tor --runasdaemon 0 --defaults-torrc /usr/share/tor/defaults-torrc -f /etc
    
    Feb 20 11:16:44 localhost.localdomain systemd[1]: tor.service: Service hold-off time over, scheduling restart.
    Feb 20 11:16:44 localhost.localdomain systemd[1]: Stopped Anonymizing overlay network for TCP.
    Feb 20 11:16:44 localhost.localdomain systemd[1]: tor.service: Start request repeated too quickly.
    Feb 20 11:16:44 localhost.localdomain systemd[1]: Failed to start Anonymizing overlay network for TCP.
    Feb 20 11:16:44 localhost.localdomain systemd[1]: tor.service: Unit entered failed state.
    Feb 20 11:16:44 localhost.localdomain systemd[1]: tor.service: Failed with result 'start-limit-hit'.

The Problem

Looking a little closer at the logs in journalctl it seems that the tor process is not able to access the directory structure under /var/lib/tor - the toranon user's home directory. 

    Feb 20 11:16:43 localhost.localdomain tor[1150]: Feb 20 11:16:43.033 [warn] Directory /var/lib/tor/ssh/ cannot be read: Permission denied
    Feb 20 11:16:43 localhost.localdomain tor[1150]: Feb 20 11:16:43.033 [warn] Failed to parse/validate config: Failed to configure rendezvous options. See logs for details.
    Feb 20 11:16:43 localhost.localdomain tor[1150]: Feb 20 11:16:43.034 [err] Reading config failed--see warnings above.

This appears to be down to SELinux kicking in and telling us the process is trying to do something it's not explicitly permitted to do according the the SELinux policies currently loaded. A quick google search for this error turns up a handful of results from other Fedora users:

• https://bugzilla.redhat.com/show_bug.cgi?id=1279222
• https://bugzilla.redhat.com/show_bug.cgi?id=1250893
• http://superuser.com/questions/998850/tor-hidden-service-settings-failing-to-allow-tor-service-to-start-on-centos-fedo

In each of these a couple of workarounds are proposed, like disabling SELinux and setting the hidden service directory to be the /var/lib/tor directory. Disabling SELinux would probably work fine, but I'd rather not do that - I'd rather understand what's going on and eventually fix it properly. I also don't want to use the other workaround - since that would prevent me from running two separate hidden services, and what if I want to run ssh and operate a cryptocurrency-based online drugs supermarket[1]?

After a bit more digging I found a bug report on Red Hat's Bugzilla which described exactly the problem I saw (working, ten failing after reboot). However it also confirmed that as-at 14th February 2017 this was still an open issue (poor Kyle Marek spent his Valentines Day debugging Tor) - https://bugzilla.redhat.com/show_bug.cgi?id=1375369 so there's no "proper" fix yet.

The Solution

Until there's an "official" solution there is a semi-smart workaround proposed by the SELinux Alert Browser - to generate a local policy module to permit the access that SELinux restricted. The following steps assume that you've setup a hidden service in your /etc/tor/torrc and that it's failing to start with some weird permissions error.

Firstly let's sort out the permissions for the toranon user's home directory - some people reported that the root user owned some folders in this directory which isn't really desirable:

So let's do this, and sort out the permissions for the toranon user's home directory too.

    $ sudo find /var/lib/tor ! -user toranon
    $ sudo chown toranon /var/lib/tor/some/folder
    $ sudo find /var/lib/tor ! -group toranon
    $ sudo chown :toranon /var/lib/tor/some/folder

In my case /var/lib/tor itself was owned by root - I moved it to toranon just in case. Next let's add an SELinux policy to give the Tor service the permissions it wants:

    $ sudo ausearch -c 'tor' --raw | audit2allow -M tor-workaround
    ******************** IMPORTANT ***********************
    To make this policy package active, execute:
    
    semodule -i tor-workaround.pp
    $ sudo semodule -i tor-workaround.pp

Now after a reboot we should see that the service has successfully started up without any errors

    $ sudo systemctl reboot
    (later...)
    $ sudo systemctl status tor.service
     tor.service - Anonymizing overlay network for TCP
       Loaded: loaded (/usr/lib/systemd/system/tor.service; enabled; vendor preset: 
       Active: active (running) since Sun 2017-02-19 15:49:42 CET; 18min ago
      Process: 768 ExecStartPre=/usr/bin/tor --runasdaemon 0 --defaults-torrc /usr/s
     Main PID: 825 (tor)
        Tasks: 1 (limit: 4915)
       CGroup: /system.slice/tor.service
               └─825 /usr/bin/tor --runasdaemon 0 --defaults-torrc /usr/share/tor/de
    
    Feb 19 15:49:42 localhost.localdomain Tor[825]: Parsing GEOIP IPv6 file /usr/sha
    Feb 19 15:49:42 localhost.localdomain Tor[825]: Bootstrapped 0%: Starting
    Feb 19 15:49:42 localhost.localdomain Tor[825]: Bootstrapped 80%: Connecting to 
    Feb 19 15:49:42 localhost.localdomain systemd[1]: Started Anonymizing overlay ne
    Feb 19 15:49:42 localhost.localdomain Tor[825]: Signaled readiness to systemd
    Feb 19 15:49:43 localhost.localdomain Tor[825]: Opening Control listener on /run
    Feb 19 15:49:43 localhost.localdomain Tor[825]: Bootstrapped 85%: Finishing hand
    Feb 19 15:49:43 localhost.localdomain Tor[825]: Bootstrapped 90%: Establishing a
    Feb 19 15:49:44 localhost.localdomain Tor[825]: Tor has successfully opened a ci
    Feb 19 15:49:44 localhost.localdomain Tor[825]: Bootstrapped 100%: Done

Conclusion

It was a little bit unfortunate that I bumped into this when I was trying to familiarise myself with systemd, but it was good to have it sorted and I think that the next thing I should explore is SELinux. Perhaps I could understand and contribute the proper fix since this is just a little temporary workaround. 

Every other time I run apt-get dist-upgrade my XMonad fails to rebuild with the error "Failed to load interface for âXMonad.Actions.Volumeâ"

The annoying thing is happens infrequently enough that each time I forget what the resolution is, google a bunch of things, then eventually stumble on the answer and move on. Anyway the reason it's a little tricky is that most pages or posts suggest that the correct resolution is to issue the following command

    $ sudo cabal update && sudo cabal install xmonad-extras

However for some reason I have a bunch of these Haskell packages installed in ~/.ghc and running this command using sudo doesn't update these, so instead I have to run it normally:

    $ cabal update && cabal install xmonad-extras

And that's it!

When I was puddling around in IronPython ahead of an upcoming project I spotted something interesting - when we want to deal with integers within the IronPython interpreter we frequently call a function in Microsoft.Scripting.Runtime.ScriptingRuntimeHelpers called Int32ToObject:

    public static object Int32ToObject(Int32 value) {
        // caches improves pystone by ~5-10% on MS .Net 1.1, this is a very integer intense app
        // TODO: investigate if this still helps perf. There's evidence that it's harmful on
        // .NET 3.5 and 4.0

        if (value < MAX_CACHE && value >= MIN_CACHE) {
            return cache[value - MIN_CACHE];
        }
        return (object)value;
    }

    $ cat hello.py
    print "Hello, world!"
    $ ./bin/Debug/ipy.exe hello.py | grep -c "Int32ToObject"
    1317

The code itself specifically references the pystone benchmark (which I found here) in a comment suggesting that we could see a performance improvement on pystone with versions of .NET newer than 3.5 - which appears to be the minimum version later versions of IronPython supports.

I built the Release configuration of IronPython both before and after removing this cache functionality, and tested the default pystone benchmark on my work computer (a pretty hefty 8-core Xeon E3-1275 @ 3.6 GHz, with 32GB RAM) - the results are below where I ran the test 10 times and took the average. The values output by the benchmark are "pystones per second" - where one "pystone" is an iteration through the main loop inside the Proc0() function which performs a number of integer operations and function calls:

So with the fix we see 32,661 more of these iterations per-second, which is roughly a 13.5% improvement. This makes sense - presumably casting each int to object has been improved so that it's nearly free, leaving the overhead being a simple function call.

Reflecting on 2016

It's easy to lose track of achievements and get bogged down in stress in the short term so at the end of the year I like to look back at what I've done and think about what I liked or what went well.

Debian/Linux

First off not a huge one but when I replaced my ailing MacBook Air with a Thinkpad X250 I moved to using Linux and Xmonad as my daily driver (posts one, two and three on this). This relatively minor switch actually kicked off a relatively productive year in terms of professional development - I ended up having to re-learn a lot about Linux that I had forgotten, and bit-by-bit ended up learning and creating more and more. Many times on macOS I'd find some library or utility wouldn't work without a lot of config, and I'd often just give up - once I got past the initial "setup my environment" step Debian ended up smoother than macOS.

Python

I created and released a handful of Python packages on PyPI (em73xx, rbcz, xnm, xsms) - none of them have huge widespread appeal but they're fairly well written and let me work through the process of releasing on PyPI, producing documentation. I also wrote a fair amount about Python, and worked through Philip Guo's excellent CPython Internals course which I thoroughly enjoyed.

Blog

I wrote a total of 28 blog posts in 2016, which is slightly more than one every fortnight. They've only had a few thousand views in total but I've thoroughly enjoyed learning and creating something new, then writing it up. It's good to practice any sort of technical writing (even if it's an informal blog post) and it's pretty rewarding to look back on what you've done. Here's a handful of the ones I enjoyed writing most

• ESP8266 - writing a Lua module for NodeMCU in C 
• Arduino - 8 Bit Graphics with SSD1306
• Python - Efficient String Concatenation in Python (2016 edition)
• A tiny new font for the SSD1306 128x64 OLED screen

Homepage

I also put together a new homepage @ www.mclemon.org. My company use ASP.NET MVC for all new UIs, and since I'm not always involved in the front-end development I felt like I was falling behind. Instead of shying away from this and resolving to look into it "later" I tackled it head on and set about creating a simple home page with MVC and deploy it to Azure. What started off as a simple static "Contact" page grew into a neat little aggregator of my Posthaven blogs, which I can extend pretty easily to pull in other stuff (I've tried out GitHub and Twitter, but my commits push everything out of the way, and I rarely post anything useful on Twitter!). It should degrade reasonably well on mobile, but as you can see from the screenshot there's a couple of whitespace issues I clearly need to tackle!

2017

I don't like resolutions, but I do think it's good to think ahead about some things I'd like to work on - even if I ultimately get sidetracked on something else.

IronPython

I've already written a couple of articles about IronPython, but I'd like to keep diving deeper. I have a really nice series of posts in the works which could end up being useful to the community at large so I'm pretty stoked about getting them completed. I'd ultimately like to start contributing to the project, hopefully by the time I've finished these I'll be knowledgeable enough to be of use to Alex Earl et al.

Czech

My Czech has stagnated - I've got some friends I write in Czech to but I'd like to read and write far more regularly. I've found that if I sit down I can break down most sentences with a bit of patience but my vocabulary and sentence construction is still pretty poor. 

Books

There are so many books I'd like to read - but in terms of professional development I'd like to revisit some fundamentals - so working my way through the classic SICP is top of the list, but I'd also like to work through Grokking Algorithms In Python as it looked like a nice read.

Alfie

My little dog is adorable, but he's way too boisterous - I'd like him to:

1. return on command every time I call him (without resorting to tricking him with biscuits and the frisbee!)
2. walk to "heel" better
3. not jump on strangers
4. chill out in the pub

Of course #4 is the most important one :)

Clock

I mostly cracked the Brno astronomical clock (aka the cock clock) at summer, and started writing a blog post about it - but I needed to create a couple more timelapses and visualisations to complete the picture and never found time to do it. This clock is kinda famous for being unintelligible so it'd be nice to share the knowledge with the world!

General Focus

In general I'd like to focus more on a couple of disciplines or topics and become more of an expert in them. Looking back at my posts I've covered a fairly broad spectrum of things, but I never really went into much detail on any of them. I'm spreading myself a little thinly and it doesn't help that I've got the attention span of a goldfish!

To learn more about Debian and Linux in general I'm selecting utilities at random from my PATH using the command below, learning what they do and writing a blog post about it. Previously: Part 1, Part 2, Part 3

    $ (for folder in `echo $PATH | sed "s/:/\\n/g"`; do ls -1 $folder; done; ) | shuf -n 1 | xargs man

Today's utility is xssstate, which lets your check the status of X window system's screensaver. It's written by the suckless guys, who've created a number of very good tools, such as surf (a minimalist web browser) and dmenu (autocompleting program launcher), both of which I use regularly.

The utility itself is pretty simple, there are only four command line switches including -v, so this will be pretty short post. First we can check if the screensaver is currently enabled using -t switch:

    $ xssstate -s
    off

Obviously the screensaver is off, since I am actively using this computer - however if the screensaver was active it'd print "on" and if it was disabled altogether you'd see "disabled".

To check the time idle in milliseconds, use the -i switch:

    $ xssstate -i
    2
    $ sleep 5 && xssstate -i
    4947

And to get time in milliseconds until the screensaver activates, invoke it with -t:

    $ xssstate -t
    599998
    $ sleep 10 && xssstate -t
    590052

The way the utility does this is by using some functionality provided by a X11 library, wrapped in a handful of switch statements (they have their own neat little github-style source browser if you want to check out xssstate.c in its entirety):

    // ...
    info = XScreenSaverAllocInfo();
    XScreenSaverQueryInfo(dpy, DefaultRootWindow(dpy), info);

    if (showstate) {
    	switch(info->state) {
	case ScreenSaverOn:
		printf("on\n");
		break;
	case ScreenSaverOff:
		printf("off\n");
		break;
	case ScreenSaverDisabled:
		printf("disabled\n");
		break;
	}
    } else if (showtill) {
	switch(info->state) {
	case ScreenSaverOn:
		printf("0\n");
		break;
	case ScreenSaverOff:
		printf("%lu\n", info->til_or_since);
		break;
	case ScreenSaverDisabled:
		printf("-1\n");
		break;
	}
    } else if (showidle) {
	printf("%lu\n", info->idle);
    }
    // ...

When I do these articles I like to show some practical real-life usage of the utility - in this case I decided to add a little timer to my xmobar showing how long my computer had been idle. To this I added a Run Com entry to my xmobarrc:

    -- also stick %xssstate% into the template
    Run Com "xssstate" [ "-t" ] "xssstate" 10,

This ends up showing with something like the below - apologies for shaky-cam!

Previously I'd done a bit of fiddling around with Python, revisiting some string concatenation benchmarks from an old-ish article and trying to explain some unexpected results. After playing around a bit with IronPython I was curious whether it'd be faster or slower than CPython on windows.

I installed the latest versions of both IronPython (2.7.5) and CPython (2.7.12) into my Windows 10 VM and re-ran the same set of tests.

The most interesting thing I learned was that some changes to how memory was allocated for the new buffer caused the "naive" method to be on par with the optimised version. As it turns out, IronPython doesn't actually have this - so running stest.py we get the following:

    $ ipy64 stest.py 1
    method 1
    time 2406.60858154 ms
    output size  86 kb

    $ ipy64 stest.py 6
    method 6
    time 46.9284057617 ms
    output size  86 kb

IronPython is a totally different beast to CPython, so my previous method of debugging - taking the code and examining it with the dis module doesn't yield anything useful:

This is because it compiles it into a different set of instructions to be executed using the .NET CLR (it's important to note that it does not go directly to .NET IL, there's still a level of instructions above this similar to CPythons opcodes).

However since we're on Windows with .NET we do have Visual Studio - which is arguably easier than working through python bytecode instructions in a text editor. To begin with it's extremely simple to find out where exactly we spend most of our execution time using dotTrace by JetBrains:

    [System.Security.SecuritySafeCritical]  // auto-generated
    public static String Concat(String str0, String str1) {
        Contract.Ensures(Contract.Result() != null);
        Contract.Ensures(Contract.Result().Length ==
            (str0 == null ? 0 : str0.Length) +
            (str1 == null ? 0 : str1.Length));
        Contract.EndContractBlock();

        if (IsNullOrEmpty(str0)) {
            if (IsNullOrEmpty(str1)) {
                return String.Empty;
            }
            return str1;
        }

        if (IsNullOrEmpty(str1)) {
            return str0;
        }

        int str0Length = str0.Length;
        
        String result = FastAllocateString(str0Length + str1.Length);
        
        FillStringChecked(result, 0,          str0);
        FillStringChecked(result, str0Length, str1);
        
        return result;
    }

This explains why things are so slow - when concatenating two strings there are no realloc-based tricks like CPython had - we allocate a new memory buffer every time, copy both strings into it, and let the garbage collector handle the old buffers.

Sadly it's pretty non-trivial for someone like me to implement a similar optimisation here - since we depend on the underlying string implementation in .NET we're stuck with how string concatenation was implemented there. I toyed with the idea of re-writing a hacky reimplementation of FastAllocateString as FastReallocateString specifically for the += operator (it's possible to do - we need to change PythonBinaryOperationBinder.BindDelegate() to handle Add and AddAssign differently) this would've involved getting stuck into the mscorlib sources in coreclr - and I'd rather stay in Python-land for the time being.

However since it's possible to access the .NET standard libraries from IronPython we can at least test how System.Text.StringBuilder performs, since it is designed to solve this very problem. So I setup the stest.py code I previously used, and re-ran them on my Windows 10 VM for both CPython 2.7.12 and IronPython 2.7.5. Just to quickly recap, here are the string concatenation methods I tested:

Method 1: simple concatenation: s1 += s2

Method 2: concatenation using MutableString (s1 += s2, where s1 is a MutableString)

Method 3: appending to a long array of char

Method 4: building a list of strings, then calling "".join()

Method 5: writing to a cStringIO buffer in memory using write()

Method 6: same as Method 4 but using a list comprehension inline

Method 7: using System.Text.StringBuilder (IronPython only)

Conclusion

So in IronPython the most efficient way to join strings together is to hook into .NET's System.Text library and use StringBuilder, no surprises there. What was surprising was how much slower IronPython was than CPython. I'm curious if this is just a freak result or if IronPython is known to be pretty slow. I'll probably not attempt to speed up the AddAssign op in IronPython, however I would like to investigate why IronPython is so slow, and if there are any plans to improve things. In addition I was surprised that the realloc trick had little-to-no effect on CPython in Windows (i.e. method 1 was slow even on 2.7.12).

I am a little sick of this benchmark now - I might revisit it one final time to compare it across CPython, IronPython, PyPy and Pyjion to complete the picture, but only if I'm really bored :)

After I listened to an episode of Talk Python To Me featuring Alex Earl from the IronPython project I learned that not only is IronPython not dead/dying, but it's actually seeing a bit of a resurgence recently. I grabbed the sources from the IronLanguages GitHub, setup my dev environment, opened it up and launched the IronPythonConsole project hoping to see the familiar python REPL.

However instead I saw that it had hit an exception:

I was frustrated at first, thinking I'd done something wrong, but realised that getting to the bottom of an Exception was a fine way to introduce yourself to a new codebase.

The exception itself is a ZipImportError with the text "not a Zip file" and is thrown in the constructor for zipimporter.

Python Confession #1: I had never heard of or used zipimporter before.

Since I'd never heard of the class before I had no idea why the IronPython runtime would be calling this and especially on something which didn't appear to exist. So it's time to dig through the call stack to see where this comes from:

It appears that PythonCommandLine.ImportSite kicks this process off so that's where I started looking:

    private void ImportSite() {
        if (Options.SkipImportSite)
            return;
    
        try {
            Importer.ImportModule(PythonContext.SharedContext, null, "site", false, -1);
        } catch (Exception e) {
            Console.Write(Language.FormatException(e), Style.Error);
        }
    }

It turns out that site is a special Python module which is imported by default when the interpreter starts (for all implementations - IronPython, Jython, PyPy and good old vanilla CPython). It's responsible for doing some platform-specific module path setup.

Python Confession #2: I had never heard of the site module before.

So how does importing site cause us to execute some code relating to zipimporter? Searching through the call stack at the point of the Exception shows that all this seems to come from FindImporterForPath which takes every function in path_hooks and attempts to apply it to the path we're importing.

    /// 
    /// Finds a user defined importer for the given path or returns null if no importer
    /// handles this path.
    /// 
    private static object FindImporterForPath(CodeContext/*!*/ context, string dirname) {
        List pathHooks = PythonContext.GetContext(context).GetSystemStateValue("path_hooks") as List;

        foreach (object hook in (IEnumerable)pathHooks) {
            try {
               object handler = PythonCalls.Call(context, hook, dirname);

                if (handler != null) {
                    return handler;
                }
            } catch (ImportException) {
                // we can't handle the path
            }
        }

So we call every path_hook with the module we're importing as an argument using PythonCalls.Call(). The path_hooks themselves come from the sys module:

    sys.path_hooks

Python Confession #3: I had never heard of or used path_hooks before.

So what is in path_hooks? If I keep hitting continue on the Visual Studio debugger the Exception is caught, I reach the python REPL and can inspect what is in sys.path_hooks:

And there it is - zipimporter. Now we're approaching an explanation - when the IronPython interpreter is initialised it imports the site module which takes the everything in path_hooks and applies them to all the modules in our path - but since there are no .zip files anywhere in our path zipimporter (the only path hook) cannot find anything to operate on, so throws an exception which is normally caught and handled.

So this is normal behaviour - the exception is even expected, since path_hooks' documentation states that if a path_hook fails it raises an Exception.

Conclusion

OK nothing special has happened here since IronPython is behaving exactly as it should, however unexpected it was to me. That said, this is a very nice way to learn some new Python concepts, like:

1. the zipimport module
2. the site module
3. sys.path_hooks 

I even have a half-working clone of zipimporter for tarballs called tgzimporter, but there's little need for such functionality as I suspect that even zipimporter is seldom used. 

It would've been easy to just keep hitting the "F5" key until I hit the REPL, but then I would likely have struggled to find a way to approach the source code and perhaps would've put it off indefinitely. Hopefully now I'll find some way to continue to improve my understanding and contribute to the IronPython project.

When I was messing around trying to get the 4G modem working on my Thinkpad X250 for this post I ended up doing a little debugging by connecting to the modem over serial using cu:

    $ cu -h -l /dev/ttyACM0
    Connected.
    AT^M
    OK

It turns out that the Sierra EM7345 modem in my Thinkpad can be controlled through AT commands sent over serial. Over at zukota.com there's a guy who's done a seriously good job of experimenting with this modem and documenting what was previously not very well documented.  

For example if we wanted to use a SIM locked with the PIN "1234", then send the message "Hello, world!" to the Czech number 775123456, we would connect as above and enter the following:

    AT+CPIN="1234"^M

    OK
    AT+CMGS="+420775356278"^M
    > Hello, world!^Z^M
    +CMGS: 40

    OK

Getting the GPS position involved issuing the XLCSLSR at command and parsing it's output. This is a little more complicated, since there's a slight delay in the response, and the response contains dozens of fields (I've included the request/response in its entirety below so you can see):

    AT+XLCSLSR=1,1,,,,,,,,,,^M
    +XLCSLSR: request id 2

    OK

    +XLCSLSR: 2, 49.195669 N, 16.606075 E, 119.996932, 48.743179, 39.616302,143, 100.997169,67,2016/05/10,18:38:22,0,1,75.45,2.28,-0.25,2.20,0.64,239919,239919.74,,,4.50,2.50,3.50,118.92,62.80,100.98,,,1,1896,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,73.04,70.04,21.00,1,,,,21 ,61.03,283.14,41.00,1,,,,16 ,20.01,173.09,30.00,1,,,,10 ,17.01,100.05,32.00,1,,,,29 

    OK

I was delighted when I found this out, as I had recently discovered the pyserial library, and realised I could interact with the modem and explore/expose some of the modem's functionality via a Python library.

em73xx

I rolled up my sleeves and wrote a library called em73xx (PyPI, GitHub) which will issue the necessary AT commands to send and receive SMS messages, and retrieve the current location using the modem's GPS functionality.

To install the library either retrieve from pypi using pip:

    $ pip install em73xx

... or you can clone the smcl/em73xx repo and install using setup.py:

    $ git clone https://github.com/smcl/py-em73xx
    $ cd py-em73xx
    $ python setup.py install

Once it's installed you can use em73xx by importing the Modem class and instantiating it with the path to the serial device on your system (probably /dev/ttyACM0, as below) and optionally specify the PIN for the sim inserted:

    from em73xx import Modem
    modem = Modem("/dev/ttyACM0", pin="1234")

To retrieve all the SMS messages on the SIM, and print them to stdout we can use the getSMS method:

    messages = modem.getSMS()
    for m in messages:
        print("from %s: % (m.sender))
        print("\t%s" % (m.message))

If we want to send a message we can use the sendSMS method:

    # request an 60 minute tram ticket in brno!
    modem.sendSMS("90206", "BRNO")

And finally if we want to retrieve the current GPS position, we can use the getGPS method - note that the modem can fail to get a GPS fix, and will return None if so:

    gps = modem.getGPS()
    if gps:
        print(gps.latitude)
        print(gps.longitude)

xsms

Ultimately I started this because I wanted to have a simple utility that integrated with xmobar which would inform me whether I had received a new SMS, and would allow me to reply to existing messages or compose an entirely new one. To achieve this I wrote xsms which is a utility that will either print to stdout the number of unread messages (for xmobar) or launch a GUI, depending on the command line switches used.

Again you can either retrieve xsms from pypi using pip:

    $ pip install xsms

... or clone the smcl/xsms repo from github and install using setup.py:

    $ git clone https://github.com/smcl/xsms
    $ cd xsms
    $ python setup.py install

Once xsms is installed you can either launch it standalone.

    $ python -m xsms --device=/dev/ttyACM0

And if you want to have a little indicator in your xmobar you can use something like the below (which takes advantage of the ability to specify the font via tags to easily get some icons from Font Awesome):

    -- assumes you have Font Awesome installed and used here:
    -- additionalFonts = ["xft:FontAwesome-10"],
    Run Com "/usr/bin/python" [ "-m", "xsms", "-d", "/dev/ttyACM0", "-p", "1234", "-r", "", "-u", " %d" ] "xsms" 600,

So when you add %sms% to your xmobarrc's template and restart you'll see something like this:

... and if you want to be able to click the icon to raise the GUI, you can surround it with an <action> which invokes xsms:

  template = "%StdinReader% }{ ... stuff ... <action=`python -m xsms -g -d /dev/ttyACM0 -p 1234`>%xsms%</action> ... "

Conclusion

This is an idea that sat half-finished in my ~/dev folder for about 6 months, and I'm really glad that I was able to take it from a single hacky one-shot script to two fully-fledged packages released on PyPI and ready to be used. There is still some additional functionality I'd like to add to em73xx, for example I only grab longitude/latitude from the GPS data and I'd like to be able to reset or power-cycle the modem in case of problems, however it's in pretty good shape overall.

As for xsms, it's the second Tkinter application I've put together, and while I'm finding it easier each time I write something (with fewer cut-paste lines from effbot.org's Tkinter docs) it's starting to be a little unwieldy. For example the ttk module allows you to add widgets to your application with a unified theme, but it's missing a multi-line text entry widget - which is something I use for inputting and displaying the SMS messages. This meant that I had to either decide to put off styling xsms or add some hacks to customise the Tkinter.Text widget that I ultimately used. In addition programatically constructing the UI using the grid() and pack() layout managers feels a little like creating a webpage laid out using nested <table> elements in the late 90's. Ultimately if I find myself writing a Python desktop app in future I'll spend a little more time investigating the frameworks available and weighing them up against using Tkinter, now that I'm broadly familiar with it.

Useful Links

• Sierra EM73xx AT Command Reference (requires free registration) 
• Nice article from Zukota.com on the EM7345 AT commands
  
• xsms on GitHub
  
• py-em73xx on GitHub

To learn more about Debian and Linux in general I'm selecting utilities at random from my PATH using the command below, learning what they do and writing a blog post about it. Previously: Part 1, Part 2

    $ (for folder in `echo $PATH | sed "s/:/\\n/g"`; do ls -1 $folder; done; ) | shuf -n 1 | xargs man

    $ jjs
    jjs> println("hello, world!")
    :1 ReferenceError: "println" is not defined
    jjs> print("hello, world!") 
    hello, world!
    jjs> function add(x, y) { return x + y }
    function add(x, y) { return x + y }
    jjs> add(10, 20)
    30
    jjs> function fib(n) { if (n < 1) { return 0 } else if (n <= 2) { return 1 } else { return fib (n - 1) + fib (n - 2)} }
    function fib(n) { if (n < 1) { return 0 } else if (n <= 2) { return 1 } else { return fib (n - 1) + fib (n - 2)} }
    jjs> fib(3)
    2
    jjs> fib(50)
    12586269025
    jjs>

    jjs> var str = new java.lang.String("Hello, world!")
    jjs> java.lang.System.out.println(str)
    Hello, world!
    jjs> var dict = new java.util.HashMap()
    jjs> dict.put("foo", "bar")
    null
    jjs> dict.put("baf", "baz")
    null
    jjs> dict.forEach(function(k,v) { print(k + v); }) 
    bafbaz
    foobar

1. shell scripting
2. unix system utilities
3. visualisation
4. web services

Demo 1 - a simple shell script

    $ jjs findexec.js -- /usr/local/bin
    /usr/local/bin/fsv
    /usr/local/bin/n-m
    /usr/local/bin/behave
    /usr/local/bin/tzupdate
    /usr/local/bin/dotnet

Demo 2 - a unix-y utility

1. a list of lines read in from stdin (piped or input)
2. a list of files supplied as arguments, which are each written reversed

    $ cat > foo << EOF
    > herp
    > derp
    > blorp
    > EOF
    $ jjs filerev.js -- foo
    blorp
    derp
    herp
    $ cat foo | jjs filerev.js 
    blorp
    derp
    herp

Demo 3 - a JavaFX utility

Demo 4 - a web service

You can ran the following to start the server:

    $ git clone https://github.com/smcl/thumbooo
    Cloning into 'thumbooo'...
    remote: Counting objects: 42, done.
    remote: Compressing objects: 100% (37/37), done.
    remote: Total 42 (delta 18), reused 17 (delta 4), pack-reused 0
    Unpacking objects: 100% (42/42), done.
    Checking connectivity... done.
    $ cd thumbooo
    $ jjs -scripting nasven.js -- src

and in another xterm use cURL to POST an image to the /thumb endpoint:

    $ curl -L -o thumb.png --form "file=@lenna.png" http://localhost:8080/thumb
      % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                     Dload  Upload   Total   Spent    Left  Speed
    100  494k    0 32358  100  462k   638k  9353k --:--:-- --:--:-- --:--:-- 9447k

Conclusion

To learn more about Debian and Linux in general I'm selecting utilities at random from my PATH using the command below, learning what they do and writing a blog post about it. Previously: Part 1

    $ (for folder in `echo $PATH | sed "s/:/\\n/g"`; do ls -1 $folder; done; ) | shuf -n 1 | xargs man

Today's randomly selected utility is called slabtop - according to the man page it should "display kernel slab cache information in real time". I was pretty pleased about this one actually, I had no idea about slab allocators so it was good to have something to explore on a stormy day:

Put simply, Linux permits us to manage and allocate chunks of memory from a set of fixed-size buffers - called slabs - for different purposes. And slaptop gives us a view into the state of each slab cache: what they're called, how many elements are allocated in total and how full each slab is:

So for the highlighted row, we can know the following information:

- name ("kmalloc-32")

- number of slabs allocated (163)

- number of objects allocated (20007)

- percentage of slab space allocated (98%)

- total size of the slab space allocated (652K)

... and so on.

What is slab allocation

So what does all this mean and why do we need it? During the execution of some program we may have requested and released  lots of differently sized objects from the heap. This could result in a pretty fragmented heap.

This could mean that allocating new buffers is either a little slow or unpredictable, since we need to search for an appropriately sized buffer before we can mark it as allocated and return it to the calling code. If any part of our application depends on a particular object being allocated quickly and predictably this is not ideal. 

Slab allocation involves setting up a slab which can hold certain amount of objects of the same size and type. Since we're dealing with fixed-size objects allocating space for a new object is quick - we just need to scan through an array to find an unused slot, mark it as used, then calculate the address (start_addr + size * index) and return it.

We could roll our own implementation since it's pretty straightforward to implement - and that would be quite an interesting project for an evening or so. However if we're writing Linux kernel or module code there's already an existing set of functions which are well understood and battle-hardened. In addition these functions hide a lot of underlying complexity by abstracting a lot of the memory management (we can actually have numerous slabs, but Linux manages this for us).

How Linux supports slab allocation

To start with, if we want to create a new slab cache (a managed collection of slabs) we can use kmem_cache_create:

    struct kmem_cache *kmem_cache_create(
	    const char *,      // name of cache
	    size_t,            // size of each object
	    size_t,            // object alignment
	    unsigned long,     // any special flags
	    void (*)(void *)   // slab constructor
    );

To allocate a new object from this cache we can use kmem_cache_alloc:

    void *kmem_cache_alloc( 
    	struct kmem_cache *, // ptr to the cache 
    	gfp_t flags          // flags to use if we need new slab
    );

Then when we need to free any, kmem_cache_free which will mark the space as unused and make it available to the allocator:

    void kmem_cache_free(
    	struct kmem_cache *, // ptr to the cache
    	void *               // ptr to object to free
    );

And finally when we're done and we want to remove the cache entirely there is kmem_cache_destroy which will free and release all the slabs related to a given cache:

	void kmem_cache_destroy(
		struct kmem_cache *  // ptr to the cache
	);

How to use slab allocation

As an example of how this could be used we can think about it in the context of a simple kernel module. I'll roughly go over the individual parts, and then present a full-blown module to explore. Firstly we'll want to defined the type we want to create the slab cache for  - imaginatively named slabthing - and use it to declare a variable s:

    typedef struct
    {
      char foo; 
      char bar; 
      char baz;
    } slabthing;

    slabthing *s;

Inside our modules init_module we can use kmem_cache_create - so that our slab cache will be called "slabdemo", it'll allocate objects with the required size. Since I don't really care much about performance (specifying an appropriate alignment could permit us to use different/faster load instructions) so we'll just request it to be single byte-aligned.

    struct kmem_cache *slabthing_cache;

    int init_module(void)
    {
      slabthing_cache = kmem_cache_create("slabdemo", sizeof(slabthing), 1, NULL, NULL);
      // other module stuff...
    }

If our module wants to allocate space for the variable s in our cache we can call kmem_cache_alloc:

    s = kmem_cache_alloc(slabthing_cache, NULL);

When we want to free the cache, in this case in our module cleanup code, we can call kmem_cache_free and kmem_cache_destroy. I don't think the free is necessary in this case, but I've just included it anyway:

    void cleanup_module(void) {
        kmem_cache_free(slabthing_cache, s);
        kmem_cache_destroy(slabthing_cache);
     }

To see this in action I've created a little demo module that creates a cache and then allocates 128 objects when it's loaded - then frees all the objects and destroys them when it's unloaded. You'll need to make sure you have the sources checked out for your current kernel inside /usr/src/linux-<version>:

    $ git clone http://github.com/smcl/slabdemo
    $ cd slabdemo
    $ make 

If our module builds successfully we can load it using insmod and then fire up slabtop to see how things look:

    $ sudo insmod slabdemo.ko
    $ sudo slabtop

After working through Philip Guo's excellent series of lectures on the CPython VM I found myself looking at the performance of string concatenation. Strings are immutable in python - so they cannot be modified in-place, you have to create a new string. This also affects string concatenation, so if we want to tack one string onto the end of another we might have some code like this:

    x = "hello"
    x += ", world!"

When the CPython interpreter is running the above code, it's doing something conceptually similar to the following:

1. allocate a buffer for the string "hello" 6 bytes => len("hello") + 1, and copy the string into it
2. make x reference this
3. allocate a new buffer with length 14 bytes => len("hello") + len(", world") + 1
4. copy "hello", then ", world" into this new buffer, and make x reference it
5. decrement reference on the old string, possibly free it

Note even though Python is unlike C in that it doesn't rely on a trailing NULL to terminate strings (we store each string's length), it does by convention still null-terminate them - which is where the extra "+ 1" byte comes from.

Anyway, if we have to repeatedly join strings together we have to be careful how we do it, just in case we accidentally introduce some slow, inefficient code. This has been long understood in the python community, for instance here is an article from 2004 which explored different methods of concatenating strings, and compared their performance. 

I was curious if there was any difference on how things shaped up on modern hardware (the test used an old 400 MHz Celeron) and a newer version of Python (this used python 2.2.1, the latest version of python 2 is 2.7.12) - so I grabbed the source code and started playing around.

It needed a tiny bit of tweaking however - the timing module it uses doesn't seem exist in the standard library and current version in pypi isn't compatible. I modified it to use the time module, the source code is here on GitHub if you're interested: stest-new.py. 

As a quick recap, there are 6 methods of concatenating strings which we're putting under the microscope:

Method 1: simple concatenation: s1 += s2

Method 2: concatenation using MutableString (s1 += s2, where s1 is a MutableString)

Method 3: appending to a long array of char

Method 4: building a list of strings, then calling "".join()

Method 5: writing to a cStringIO buffer in memory using write()

Method 6: same as Method 4 but using a list comprehension inline

The original tests were performed using Python 2.2.1, for comparison's sake I've re-run them on my computer just to see:

    $ for i in {1..6}; do python stest.py $i; done

The results for Python 2.2.1 are below:

So in Python 2.2.1 the list comprehension method was the fastest by a pretty clear margin. However when I re-ran using 2.7.12 things turned out very differently:

In the time since 2.2.1 the performance of the naïve string concatenation method has improved hugely, it's now it's the fastest method (note: this is using a Python interpreter I built, using the Python 2,7.12 package that comes with Debian it's actually the fastest). This is surprising, since I thought it was relatively well-established and understood that it was slow, or just not ... pythonic. I was curious exactly why Method 6 was now slower than Method 1, so I disassembled the functions using the dis module.

There were a number of SET_LINENO instructions in the 2.2.1 version which I've not shown - it makes the disassembly a little easier to read and the performance impact would have been negligible - when tracing is turned off (which it is) all this instruction did was set the current frame's f_lineno and continue executing the next instruction.

Disassembly - Method 1 

 6 LOAD_CONST               1 ('')
 9 STORE_FAST               0 (out_str)
15 SETUP_LOOP              37 (to 55)
18 LOAD_GLOBAL              1 (xrange)
21 LOAD_GLOBAL              2 (loop_count)
24 CALL_FUNCTION            1
27 GET_ITER            
31 FOR_ITER                20 (to 54)
34 STORE_FAST               1 (num)
40 LOAD_FAST                0 (out_str)
43 LOAD_FAST                1 (num)
46 UNARY_CONVERT       
47 INPLACE_ADD         
48 STORE_FAST               0 (out_str)
51 JUMP_ABSOLUTE           28
54 POP_BLOCK           
58 LOAD_FAST                0 (out_str)
61 RETURN_VALUE              

 0 LOAD_CONST               1 ('')
 3 STORE_FAST               0 (out_str)
 6 SETUP_LOOP              31 (to 40)
 9 LOAD_GLOBAL              0 (xrange)
12 LOAD_GLOBAL              1 (loop_count)
15 CALL_FUNCTION            1
18 GET_ITER            
19 FOR_ITER                17 (to 39)
22 STORE_FAST               1 (num)
25 LOAD_FAST                0 (out_str)
28 LOAD_FAST                1 (num)
31 UNARY_CONVERT       
32 INPLACE_ADD         
33 STORE_FAST               0 (out_str)
36 JUMP_ABSOLUTE           19
39 POP_BLOCK           
40 LOAD_FAST                0 (out_str)
43 RETURN_VALUE             

Disassembly - Method 6

 6 LOAD_CONST               1 ('') 
 9 LOAD_ATTR                0 (join)
12 BUILD_LIST               0
15 DUP_TOP             
16 LOAD_ATTR                1 (append)
19 STORE_FAST               0 (_[1])
22 LOAD_GLOBAL              3 (xrange)
25 LOAD_GLOBAL              4 (loop_count)
28 CALL_FUNCTION            1
31 GET_ITER            
35 FOR_ITER                17 (to 55)
38 STORE_FAST               2 (num)
41 LOAD_FAST                0 (_[1])
44 LOAD_FAST                2 (num)
47 UNARY_CONVERT       
48 CALL_FUNCTION            1
51 POP_TOP             
52 JUMP_ABSOLUTE           32
55 DELETE_FAST              0 (_[1])
58 CALL_FUNCTION            1
61 STORE_FAST               1 (out_str)
67 LOAD_FAST                1 (out_str)
70 RETURN_VALUE        

 0 LOAD_CONST               1 ('')
 3 LOAD_ATTR                0 (join)
 6 BUILD_LIST               0
 9 LOAD_GLOBAL              1 (xrange)
12 LOAD_GLOBAL              2 (loop_count)
15 CALL_FUNCTION            1
18 GET_ITER            
19 FOR_ITER                13 (to 35)
22 STORE_FAST               0 (num)
25 LOAD_FAST                0 (num)
28 UNARY_CONVERT       
29 LIST_APPEND              2
32 JUMP_ABSOLUTE           19
35 CALL_FUNCTION            1
38 STORE_FAST               1 (out_str)

41 LOAD_FAST                1 (out_str)
44 RETURN_VALUE        

There are slightly more differences when comparing how 2.2.1 and 2.7.12 generate the bytecode for the list comprehension method. However, aside from a couple of quirks in the 2.2.1 version (i'm not sure why we call DUP_TOP on the list we created, and I have no idea what _[1] is) much of the bytecode is broadly the same - we produce a list of integers by applying CALL_FUNCTION to xrange with argument loop_count, and then iterate over the results, calling UNARY_CONVERT on each and assembling either a list or string using INPLACE_ADD or LIST_APPEND. 

Since the generated bytecode contains no substantial differences, if we want to understand why the naive concatenation method (which uses INPLACE_ADD) became super-fast over the last 14 years we'll need to inspect how the Python VM interprets this code.

Analysis - INPLACE_ADD in Python 2.2.1

To save some space and time I'll skip straight to the meat and bones of where the actual string concatenation occurs - which is in Objects/stringobject.c, in the string_concat() function. It's quite small, so I've incuded it below - but I've stripped some macros for easier reading:

static PyObject *
string_concat(register PyStringObject *a, register PyObject *bb)
{
        register unsigned int size;
        register PyStringObject *op;
        if (!PyString_Check(bb)) {
                PyErr_Format(PyExc_TypeError,
                             "cannot concatenate 'str' and '%.200s' objects",
                             bb->ob_type->tp_name);
                return NULL;
        }
#define b ((PyStringObject *)bb)
        /* Optimize cases with empty left or right operand */
        if ((a->ob_size == 0 || b->ob_size == 0) &&
            PyString_CheckExact(a) && PyString_CheckExact(b)) {
                if (a->ob_size == 0) {
                        Py_INCREF(bb);
                        return bb;
                }
                Py_INCREF(a);
                return (PyObject *)a;
        }
        size = a->ob_size + b->ob_size;
        /* PyObject_NewVar is inlined */
        op = (PyStringObject *)
                PyObject_MALLOC(sizeof(PyStringObject) + size * sizeof(char));
        if (op == NULL)
                return PyErr_NoMemory();
        PyObject_INIT_VAR(op, &PyString_Type, size);
        memcpy(op->ob_sval, a->ob_sval, (int) a->ob_size);
        memcpy(op->ob_sval + a->ob_size, b->ob_sval, (int) b->ob_size);
        op->ob_sval[size] = '\0';
        return (PyObject *) op;
#undef b
}

This is pretty much exactly the algorithm I described in my first paragraph - after a couple of checks to ensure we're definitely  dealing with strings we malloc a new buffer and memcpy both strings into it.

Analysis - INPLACE_ADD in Python 2.7.12

Again I'll skip straight to where the concatenation occurs - which is in Python/ceval.c in the string_concatenate() function: 

static PyObject *
string_concatenate(PyObject *v, PyObject *w,
                   PyFrameObject *f, unsigned char *next_instr)
{
    /* This function implements 'variable += expr' when both arguments                         
       are strings. */
    Py_ssize_t v_len = PyString_GET_SIZE(v);
    Py_ssize_t w_len = PyString_GET_SIZE(w);
    Py_ssize_t new_len = v_len + w_len;
    if (new_len < 0) {
        PyErr_SetString(PyExc_OverflowError,
                        "strings are too large to concat");
        return NULL;
    }

    if (v->ob_refcnt == 2) {
        /* In the common case, there are 2 references to the value                             
         * stored in 'variable' when the += is performed: one on the                           
         * value stack (in 'v') and one still stored in the                                    
         * 'variable'.  We try to delete the variable now to reduce                            
         * the refcnt to 1.                                                                    
         */
        switch (*next_instr) {
        case STORE_FAST:
        {
            int oparg = PEEKARG();
            PyObject **fastlocals = f->f_localsplus;
            if (GETLOCAL(oparg) == v)
                SETLOCAL(oparg, NULL);
            break;
        }
        case STORE_DEREF:
        {
            PyObject **freevars = (f->f_localsplus +
                                   f->f_code->co_nlocals);
            PyObject *c = freevars[PEEKARG()];
            if (PyCell_GET(c) == v)
                PyCell_Set(c, NULL);
            break;
        }
        case STORE_NAME:
        {
            PyObject *names = f->f_code->co_names;
            PyObject *name = GETITEM(names, PEEKARG());
            PyObject *locals = f->f_locals;
            if (PyDict_CheckExact(locals) &&
                PyDict_GetItem(locals, name) == v) {
                if (PyDict_DelItem(locals, name) != 0) {
                    PyErr_Clear();
                }
            }
            break;
        }
        }
    }

    if (v->ob_refcnt == 1 && !PyString_CHECK_INTERNED(v)) {
        /* Now we own the last reference to 'v', so we can resize it                           
         * in-place.                                                                           
         */
        if (_PyString_Resize(&v, new_len) != 0) {
            /* XXX if _PyString_Resize() fails, 'v' has been                                   
             * deallocated so it cannot be put back into                                       
             * 'variable'.  The MemoryError is raised when there                               
             * is no value in 'variable', which might (very                                    
             * remotely) be a cause of incompatibilities.                                      
             */
            return NULL;
        }
        /* copy 'w' into the newly allocated area of 'v' */
        memcpy(PyString_AS_STRING(v) + v_len,
               PyString_AS_STRING(w), w_len);
        return v;
    }
    else {
        /* When in-place resizing is not an option. */
        PyString_Concat(&v, w);
        return v;
    }
}

This one is a little more complex because there are a couple of neat optimisations. 

Firstly, if the next instruction to be executed is one of a handful (STORE_FAST, STORE_DEREF, STORE_NAME) we save ourselves a few cycles by doing a little setup ahead of time.

However more importantly, if there's only a single reference to the destination string we attempt to resize it in-place using PyString_Resize() instead of allocating an entirely new buffer. We can check this is the case by forcing this condition to be false:

    //if (v->ob_refcnt == 1 && !PyString_CHECK_INTERNED(v)) {                                  
    if (0) {
        /* Now we own the last reference to 'v', so we can resize it                           
         * in-place.                                                                           
         */

If we build a Python 2.7.12 compiler without this resize optimisation, and retest:

We're right back where we started, with Method 1 being the second-slowest and the list comprehension used in Method 6 outperforming everyone else. 

Conclusion

In the intervening years since the original benchmark was performed, an optimisation has been introduced to improve how the CPython VM handles string concatenation. In the context of this specific benchmark it means that if you need to concatenate strings you can go with a naive, slightly more intuitive implementation and performance very close to the recommended, more-pythonic implementation. 

It's very likely that this is not applicable in all cases, I'm sure that if we have a number of objects allocated on the heap then it'll get slower very quickly (we won't be able to extend that buffer every time, and will need to find new locations) - however it's still an interesting and surprising result nonetheless.

When I open up dmenu and start typing out the program I want to open it provides some autocomplete suggestions based on the programs in my PATH.

I realised that there are hundreds of these, most of which I have never heard of before in my life. I realised that without a concerted effort I'd actually never end up learning anything about most of them - so I wrote a quick one liner to choose a random utility from my PATH and open up the man page:

    $ (for folder in `echo $PATH | sed "s/:/\\n/g"`; do ls -1 $folder; done; ) | shuf -n 1 | xargs man

    $ echo $PATH
    /usr/bin:/bin
    $ ls /
    bin  boot  dev  etc  home  initrd.img  lib  lib64  lost+found  media  mnt  opt  proc  root  run  sbin  srv  sys  tmp  usr  var  vmlinuz

So when we run a command like 'ls' it'll look for the utility in the two folders in our PATH (/bin and /usr/bin), then execute it on root directory of our file system. If we want however we can do a little trickery, and use the utility chroot so that when ls runs it sees an entirely different root altogether.

To try this we'll need to prepare our fake root directory:

    $ sudo mkdir /opt/fakeroot
    $ sudo debootstrap --arch i386 jessie /opt/fakeroot http://httpredir.debian.org/debian
    I: Retrieving Release 
    I: Retrieving Release.gpg 
    I: Checking Release signature
    I: Valid Release signature (key id 75DDC3C4A499F1A18CB5F3C8CBF8D6FD518E17E1)
    ... many lines later ...
    I: Configuring tasksel...
    I: Configuring tasksel-data...
    I: Configuring libc-bin...
    I: Configuring systemd...
    I: Base system installed successfully.
    $ ls /
    bin  boot  dev  etc  home  initrd.img  lib  lib64  lost+found  media  mnt  opt  proc  root  run  sbin  srv  sys  tmp  usr  var  vmlinuz
    $ ls /opt/fakeroot/
    bin  boot  dev  etc  home  lib  media  mnt  opt  proc  root  run  sbin  srv  sys  tmp  usr  var

OK so we now have a similar looking set of folders in /opt/fakeroot that we'd have in a freshly built Debian Jessie system - however we can run ls / using the chroot command so that it sees /opt/fakeroot as its root directory:

    $ sudo chroot /opt/fakeroot ls /
    bin  boot  dev	etc  home  lib	media  mnt  opt  proc  root  run  sbin	srv  sys  tmp  usr  var

OK nice, I can trick a process I've launched into thinking that any directory is root. However if I am launching a process like ls using chroot then surely I have no need for a tool like ischroot? Well the painful truth is that you may be running in a chroot "jail" without even knowing it. Some crafty (or security conscious) sysadmin may have set the system up so that, for example, when you connect via SSH your session is run inside a chroot. So assuming you've been entrusted with sudo priviledges, you can run ischroot and you'll be able to find out whether you are or not.

So just to replay the man page information, the return value of running ischroot should indicate whether we're running inside a chroot or not - the possible return values are:

So to test this out, let's run ischroot outside a chroot jail ...

    root@hanoi:/opt# ischroot
    root@hanoi:/opt# echo $?
    1

Cool, so that is exactly as expected. So if we switch over to our newly created chroot and run the same command...

    root@hanoi:/opt# sudo chroot /opt/fakeroot
    root@hanoi:/# ischroot
    root@hanoi:/# echo $?
    2

Ah, so instead of returning 1 we got 2 - indicating an error. After a bit of messing around it turns out we have just hit debian bug #685034 -" debianutils: ischroot fails to detect if it is running in a chroot". However we can determine if we definitely are not running in a chroot, but we have a bit of difficulty determining whether or not we are - a return value of 2 could be thought of as a "maybe". Since we don't like "maybe" in some cases, we can force ischroot to interpret "maybe" as "yes" using the -t flag:

    root@hanoi:/# ischroot -t
    root@hanoi:/# echo $?
    0

So not exactly a great start! However I am glad I learned about creating a chroot jail, even if ischroot isn't working as expected. Hopefully the next utility I pick up works a little better (and involves a smaller writeup).

The Playstation 4 came out in 2013 and came with the DualShock 4 controller. I bought one in the midst of a particularly tough hangover, and when I received it two days later the part that most intrigued me was the controller. It's quite an amazing piece of equipment - I originally wanted to claim that the controller alone sported higher specs than the original PSX, and while the CPU is beefier (ARM Cortex M3 @ 160MHz vs MIPS R3K @ 33.8MHz) however the controller has a hundred or so Kb of RAM versus the PSX' 2MB.

That said the interfaces are pretty cool - in addition to the standard d-pad and X, O, triangle + square  buttons there's a couple of analog sticks, shoulder buttons (2 x analogue) and a handful of accelerometers for motion sensing. All in all a nice little controller, no wonder mine needs charged every couple of days.

I thought that it'd be nice to play with some of these sensors and write OpenGL code in python. So I put together a quick demo - a 3D cube which is controlled by the accelerometers. 

If we want to read events from the controller we can use pygame which can handle a lot of the fiddly parts (preventing the need for us to mess with something like evdev directly) - assuming we only have only one controller connected we can do this:

    import pygame
    controller = pygame.joystick.Joystick(0)
    controller.init()

What we'll do is repeatedly look for events, and then save the data we've read back into a couple of dictionaries - this roughly looks like the below:

    axis = {}
    button = {}

    # these are the identifiers for the PS4's accelerometers
    AXIS_X = 3
    AXIS_Y = 4

    # variables we'll store the rotations in, initialised to zero
    rot_x = 0.0
    rot_y = 0.0

    # main loop
    while True:

        # copy rot_x/rot_y into axis[] in case we don't read any
        axis[AXIS_X] = rot_x
        axis[AXIS_Y] = rot_y

        # retrieve any events ...
        for event in pygame.event.get():

            if event.type == pygame.JOYAXISMOTION:
                axis[event.axis] = round(event.value,2)
            elif event.type == pygame.JOYBUTTONDOWN:
                button[event.button] = True
            elif event.type == pygame.JOYBUTTONUP:
                button[event.button] = False

        rot_x = axis[AXIS_X]
        rot_y = axis[AXIS_Y]            

        # do something with this ...

It's important to note that we don't necessarily receive updated values each time we call pygame.event.get() - so we need to be careful about how we handle this.

Now that we've got some rotation values from the controller, we can use them to render and rotate a simple cube on screen:

    glPushMatrix()   
    glRotatef(roty_scale * rot_y, 1, 0, 0)
    glRotatef(rotx_scale * rot_x, 0, 0, 1)
    glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT)
    glBegin(GL_LINES)
    for edge in edges:
        for vertex in edge:
            glColor3fv((1,1,1))
            glVertex3fv(vertices[vertex])
    glEnd()
    glPopMatrix()

However I could only see accelerometers for two axes - to perform the rotation through the third axis I wanted to use the L2 and R2 shoulder buttons. However these really are a funny beast. After a bit of experimenting I noticed that there's some extremely weird quirk. Joystick input using pygame can either return analogue ("axis") or boolean ("button") data - L2 and R2 buttons are unique in that they have both. The peculiar thing is how these values change based on how far the button is pressed - the below shows how the axis/button data changes as you press the shoulder button:

There's an ambiguity between the button being halfway and fully pressed. I'm not sure if this is a bug or just a missing feature in pygame. However since I was just using the buttons to control rotation, I was able to carefully select a scaling value (1.0 = 90 degrees) to apply to the value we read so that it's not possible to see this. Computer graphics has a long and storied history of such careful hacks to give the illusion of something working, so I was happy to let this slide!

I added a couple more things (shifting the cube around using the touchpad, lighting the cube depending on the buttons pressed) and the results are here this gist if anyone's curious.

Finally I wanted to use the lightbar - each DualShock 4 has an array of Red, Green and Blue LEDs which can be set independently to 256 different brightness levels. This is put to neat use in things like Grand Theft Auto - when the police are on you the lightbar flashes blue and red.

Sadly there doesn't appear to be any way to handle this through the pygame interface, and when I tried dropping to the lower-level evdev interface the leds() method didn't return anything.

I did however notice that a couple of devices appeared in the /sys/class/leds directory, so I could play around with them by writing 0 .. 255 values to these devices. Sadly they're not so easy to detect (the device name changes each time it's connected) so I have to find the device names using a slightly nasty regex:

I recently moved to using XMonad semi-fulltime, which is working out nicely most of the time. However the one sticking point is that when I try to work somewhere other than my flat or my office I had to drop to the command line and use nmcli to connect to wifi or to enable my builtin 4G modem.

This is less than ideal, there doesn't appear to by any simple NetworkManager popup/interface I could integrate easily with my xmobar setup - I wanted to have a little icon launch a UI that allowed me to the wifi network to connect to or switch on my 4G modem.

To address this I put together a little python app using Tkinter which updates network connectivity settings through NetworkManager's D-Bus API. The app is launched by clicking an area on Xmobar containing the dynnetwork widget beside a neat little old-school TTY icon:

Clicking this area will raise a menu like the below - listing WiFi and Modem interfaces

Above you can see that /dev/cdc-wdm0 is connected to my "Vodafone CZ" connection - there's a little chain icon beside it. Clicking on this connection would disconnect. Selecting one of the WiFi networks would have either connected automatically (if it was Open) or raised a popup asking for a password (if it was password-protected).

To achieve this you need to do a couple of simple things. Firstly ensure that the necessary dependencies are installed, and checkout the code

    $ sudo apt-get install python-tk python-networkmanager
    $ git clone https://github.com/smcl/xnm

Then ensure your xmobar template has the following line, which will display the dynnetwork info beside a TTY (assuming Font Awesome is installed as Additional Font #1 in xmobar):

<action=`/home/sean/.xmonad/xnm.py`>%dynnetwork% <fn=1></fn></action>

And that's it!