Blog of Matthew Daws
Code style, testing, etc.
I've written some stuff for the University of Leeds Python Discussion Group on Code style, tooling, testing, all that Jazz.
I'll try and do some live coding, and get some debate going tomorrow. But the above can be read as a bit of blog post as well.
Read More →More formal working
I am a big fan of Jupyter notebooks and similar (e.g. R Markdown) systems which allow you to mix code and documentation, preferably in a browser (which allows sharing).
However, I've found that it's quite easy to fall into a "hacking" work pattern of developing quite a lot of code, and mixing it up with substantial data processing. This leads to a number of anti-patterns:
- The code begins to completely dominate, vs the documentation, or overview, big picture view.
- I fall into the habit of restarting the notebook, wasting time on reloading data, and then making small changes to an analysis.
- Constant minor editing and then "shift-return"ing through a load of cells.
TileMapBase
I've published my first python package on PyPi (See also the New PyPi which seems to have finally synced.)
Get it here: TileMapBase or TileMapBase on new PyPi:
Uses OpenStreetMap tiles, or other tile servers, to produce "basemaps" for use with matplotlib. Uses a SQLite database to cache the tiles, so you can experiment with map production without re-downloading the same tiles. Supports Open Data tiles from the UK Ordnance Survey.
My original aim was to produce a simple, high-level way to use OpenStreetMap style tiles as a "basemap" with MatPlotLib in Jupyter Python notebooks. Since then, I've also been working on TileWindow which uses this library to cache tiles, and provides a tkinter widget which displays a map-- sort of like GoogleMaps but in Python. Ultimately for use in my current job: PredictCode.
PyPi and use of ReStructuredText
I've in the process of putting together my first proper Python package to be uploaded to PyPi / PyPi Old. The docs around doing this are not great, but the official docs are pretty good:
Read More →On memory management
I have only ever been a hobbyist C++ programmer, while I have been paid to write Java and Python. But a common complaint I've read about C++ is that you have to manage memory manually, and worry about it. Now, I'd slightly dispute this with C++11, but perhaps I don't really have enough experience to comment.
However, I think there's a strong case that with Garbage Collected languages, you can't really forget about memory, or the difference between copy by reference and copy, but the language rather allows you to pretend that you can cease to worry. In my experience, this is only true 99% of the time, and the 1% of time it bites you, you've quite forgotten that it's a possibility, which makes debugging a real pain (the classic "unknown unknown").
Read More →Learning Python UI programming
Another new task: get going with some GUI programming!
Some references
- Tkinter 8.5 reference: a GUI for Python - Lots and lots. On the web, or a 168 page PDF file.
- TkDocs - Cross platform, but lots of Python examples. I found this pretty good!
- Official docs
- Effbot Book - A "work in progress" and old, but still, so I've found, is an excellent reference and guide.
Pandas, HD5, and large data sets
I have finally gotten around to playing with the HD5 driver for pandas (which uses, I believe, pytables under
the hood). I'm only scratching the surface, but it's easy to do what I want:
- Create a huge data frame storing an entire data set
- Efficiently query subsections of the frame
Open Street Map XML data
I want to process large amounts of XML data from Open Street Map (OSM). I.e. that obtained from GeoFrabrik or OSM.Planet. For smaller snapshots, do look at OSMnx.
My pure-Python project to read and process OSM data, currently a work in progress, can be found on GitHub, as "OSMDigest".
The XML format is documented on the OSM Wiki. There is no formal schema, but the data you can download seems to be of quite a constrained type:
Read More →