Analyzing Skiing Data
My favorite sport would have to be skiing by far, nothing can match the awesome thrill and relaxation that it provides. Over the past few years I have begun skiing with a handheld GPS so that I have been able to record things like distance traveled, max speed ect.
While this has provided some interesting stats over the years such as and overall top speed of 88.8 km/h or a total distance traveled in a single day as 81km, I have always wanted to be able to see more detail on my day's skiing.
This year I have finally got around to having a crack at making a program to analyze GPX file that my GPS records and trying to separate data from lifts and ski-runs. After a few hours procrastinating during exam week and a few hours on a bus on the way to our school ski trip i have managed to come up with a woefully inefficient but reasonably effective program that is able to do just that.
My program takes the GPX file and outputs a range of statistics on runs and lifts as well as outputting a KML with the ski-runs separated from lifts. Here is some example data from my current version of the program generated from last day of skiing on the trip.
Runs: | 28 Lifts: | 24 ------------------------------------------------------------------------------- Max run speed | 94.06 Ave run speed | 10.035 ------------------------------------------------------------------------------- Max run length: | 3568 Min run length: | 49 Ave run length: | 1459 Total run length: | 40877 ------------------------------------------------------------------------------- Max lift length: | 1763 Min lift length: | 389 Ave lift length: | 921 Total lift length: | 28319.7737391
This data is reasonably accurate except for the maximum speed which is only measured as an inaccurate point speed (I will soon average speeds over a few data-points) and the discrepancy between the number of runs and the number of lifts is caused by things like the loss of GPS reception when I went into a restaurant for lunch.
To accompany the raw data here are some screen shots from within Google Earth. In the pictures chairlifts are represented by the solid read lines and the runs are represented by the squiggly blue lines. The pins in the middle of each lift are click-able and provide statistics on the lift when clicked.
Direct top down view
A slightly side on view
A closeup of the lift data
Unfortunately there is no version of this program that i deem suitable for download at the current time. In the future i plan to work on this further making it more efficient as well as turning it into a website where people are able to upload their skiing data to generate their own maps and statistics as well as including some social aspects into the process.
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Pyweek 2011 – citySquare
This year I entered into pyweek. Pyweek is a python game programming competition where the competitors are given a theme and have a week to make a game on it. The theme that was voted in this time was "Nine Times" I wasn't really sure what I could do with this theme, and as a result my game is only very loosely linked to the theme.
The game that i came up with is a simple puzzle game where one must lay tiles within a grid to earn points. It plays out a bit of a mildly irritating single player version of Carcassonne. However I was reasonably happy with the game that i ended up with after a week of development.
I was also happy enough with the quality of the game that produced to use it as part of a project in the software design and development course that I am currently doing at school, for this I added a little bit more polish to the menus and gameplay and ironed out its few bugs. My teacher was luckily very impressed by the game and I ended up scoring full marks for the assignment (the game + a ton of documentation)
If after reading though all of that you would still like to play the game there download links and further instructions below
Downloads
The latest version of the game can be downloaded from github HERE
Objectives
The key objective to citySquare is to arrange all of the tiles in the right-hand tray on the grid to the left. The tiles that you have to match are comprised of three different types of terrain: roads, cities and plain old grass.
Controls
A tile can be picked up and put down by left clicking. While being held you can either right click or scroll to rotate the tile. The arrow keys can be used to shift all of the tiles around the grid and you can hold tab to view a breakdown of your score.
To return to the menus from gameplay simply press escape, while on menus you can also toggle full screen mode by pressing F4
Rules
Tiles must be placed so that the edges of each square match up with the squares around it. The entire edge of the grid should be grass and have NO roads or cities touching it.
Scoring
Points are scored based on completed roads and cities, however cities are scored in a way that will reward you for building larger cities. For example one city made up of four tiles would be worth much more than two cities made up of two tiles.
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pyGitBook
As part of doing the report for my Software Design and Development project i tried to find an easy tool that i could use to convert all of my git history into a basic (but nice looking) logbook that i could hand in as part of the project.
Unfortunately i couldn't find any tools that would do this for me. The closest i could find was the commit log that git-hub provided something similar to what i was after but provided alot of stuff such as avatars and commit hashes that i didn't need.
So i decided to quickly whip up a script to pass output from git-log and turn it into a nicely crafted logbook.
To interface with git i originally looked into GitPython but it proved to be rather buggy and hard to work with so i ended up going the much more low tech method of just parsing the output of a standard git-log command.
For the output i decided that using HTML via Jinja2 would be the easiest method as it would allow the template to be updated very easily down the track.
Eventually i came up with a system that i am reasonably happy with, it could do with a bit more work but it will do for now, at least until i have got all my projects out of the way.
An example of the output can be seen HERE
The script is available on github at https://github.com/Hugoagogo/pyGitBook
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Cleaning up the itunes media directory
In the process of moving my iTunes library to my new laptop (well its not so new anymore), iTunes has somehow managed to duplicate my entire library, creating a copy of every track, movie, tv show ect, and giving the duplicate some obfuscated name, i.e. ABCD.mp4
Besides being a large bunch of unnecessary files, they eat up a lot of HDD space, around 100GB.
After finally finding out what was eating up my HDD space it was easy to write a quick little python script to find and remove all of these files.
import re, shutil, os
CLEAN_FROM = r"C:\Users\Hugh\Music\iTunes\iTunes Media"
CLEAN_TO = r"C:\Users\Hugh\Desktop\junk"
FILETYPES = ["mp4","m4a"]
if not os.path.exists(CLEAN_TO):
os.makedirs(CLEAN_TO)
pat = re.compile("[A-Z]{4}[.](%s)"%"|".join(FILETYPES))
print "Starting"
for dirpath, dirnames, filenames in os.walk(CLEAN_FROM):
for fname in filenames:
if re.match(pat,fname):
shutil.move(os.path.join(dirpath,fname),os.path.join(CLEAN_TO,fname))
print "Moved: "+fname
print "Done"
I do not guarantee this script will work for you and check what you actually delete before you actually do it
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Squiglet – Now with examples
Just a little progress report to say that my vector library is nearing V1 and i will soon be able to resume work on Saga of Sol.
As of yet i just need to tweak the draw function of the vectors to fix an odd bug where the lines will have gaps near the corners of some shapes as well as adding some more functionality to the editor.
Editor View
Example View
As usual the project is available HERE
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