Advent of Code 2017: Days 16 - 20

Posted on 20 December 2017 in Technology • Tagged with advent of code, python

This post is part of the series, Advent of Code 2017, where I work my way through (part of) the 2017 Advent of Code challenges in order to re-learn some of the basics of Python and reflect on some simple concepts and functionality. All my challenge code is available in cdubz/advent-of-code-2017 on GitHub.

Day 16: One Billion Permutations in 0.535 Seconds

Ok, not really (: This challenge involved making specific modifications to a list 1,000,000,000 (one billion) times. Out of curiosity, the first thing I did was set up a progress bar loop to run the actual modifications all one billion times. The resulting progress bar estimated that the entire operation would take around 133 days. So... a different approach:

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positions = [...]
movements = [...]
possibilities = [positions]
while True:
    positions = move(positions, movements)
    if positions in possibilities:
        break
    possibilities.append(positions)

Because the movements are always the same, eventually the positions list elements will return to their initial state. In this case, that happened on the 41st movement, making for a possibilities list of size 42. Armed with the answer to the ultimate question of life, the universe, and everything, it was much easier to determine the value of positions at the one billionth permutation:

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pos = 1000000000
answer_key = pos - int(pos/len(possibilities)) * len(possibilities)
print(possibilities[answer_key])  # 1,000,000,000!

This all takes a little over half a second, as opposed to the 133 days of processing that would be needed to run all one billion permutations. Phew!


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Advent of Code 2017: Days 11 - 15

Posted on 15 December 2017 in Technology • Tagged with advent of code, python

This post is part of the series, Advent of Code 2017, where I work my way through (part of) the 2017 Advent of Code challenges in order to re-learn some of the basics of Python and reflect on some simple concepts and functionality. All my challenge code is available in cdubz/advent-of-code-2017 on GitHub.

Day 11: Navigating a Hexagon Grid

I fought with how to create and navigate a hexagon grid for a bit before I stumbled on an extremely useful post by Chris Schetter: Hexagon grids: coordinate systems and distance calculations. Working in 2d grids (with four possible movement directions) in Python is fairly easy with some dictionaries, but a hexagon grid means there are six potential directions and simple calculations of x,y coordinates won't quite get the job done. What Chris explains so well is a simple concept: flip the grid and add a z-axis.

For my solution, I "flip" the grid by turning it to the right twice such that north and south become east and west:

  \ n  /
nw +--+ ne              sw +--+ nw
  /    \                \ /    \ /
-+      +-    ---->    s +      + n
  \    /                / \    / \
sw +--+ se              se +--+ ne
  / s  \

This puts north and south along the x-axis instead of the y-axis. Next, adding the z-axis and shifting all axises 120 degrees means that each hex can be made up of three points instead of two. Again, I refer to Chris's post and this image that illustrates the concept wonderfully:

Hex grid with coordinates Credit Chris Schetter (Hexagon grids: coordinate systems and distance calculations)


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Advent of Code 2017: Days 6 - 10

Posted on 10 December 2017 in Technology • Tagged with advent of code, python

This post is part of the series, Advent of Code 2017, where I work my way through (part of) the 2017 Advent of Code challenges in order to re-learn some of the basics of Python and reflect on some simple concepts and functionality. All my challenge code is available in cdubz/advent-of-code-2017 on GitHub.

Day 6: max(x, key=y)

Another simple revelation on built-ins from this challenge: the key argument can be used to modify what is evaluated by the max function. This concept is explained in detail as part of the accepted answer to this Stack Overflow post: python max function using 'key' and lambda expression.

For Python dictionaries, this means that the get() method can be used to return the key of the maximum value in a dictionary. This works because the max function sends each of the dict keys to the dict.get() method and evaluates that result instead of the key itself. Without this argument, max will actually evaluate the dictionary's keys, which isn't terribly useful:

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>>> d = {0: 1, 2: 5, 3: 4}
>>> max(d)
3  # This is the *maximum value of the dictionary's keys*.
>>> max(d, key=d.get)
2  # This is the *key of the maximum value in the dictionary*.

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Advent of Code 2017: Days 1 - 5

Posted on 05 December 2017 in Technology • Tagged with advent of code, python

This post is part of the series, Advent of Code 2017, where I work my way through (part of) the 2017 Advent of Code challenges in order to re-learn some of the basics of Python and reflect on some simple concepts and functionality. All my challenge code is available in cdubz/advent-of-code-2017 on GitHub.

Day 1: zip()

This challenge taught me about Python's built-in zip function. The basic goal is to compare values in a list with specific positional relationships to other values (e.g. next to, X steps from, etc.) in the same list. zip assists with this task by combining multiple lists in to tuples. My initial solution used a construct similar to:

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digits = [1, 2, 3, 4, 5]
total = 0
for a, b in zip(digits[::1], digits[1::1]):
    if a == b:
        total += a

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Advent of Code 2017

Posted on 30 November 2017 in Technology • Tagged with advent of code, python

I taught myself Python a few years ago by following the wonderful Learn Python the Hard Way (LPTHW) series by Zed A. Shaw. Since then, I have spent a decent amount of time in Python largely in the Django framework. Django is a lot of fun to work with because it abstracts away much of the complexities of developing a full-featured web application in Python. In this way, however, it has also led me to forget some of the basic Python that I learned through LPTHW.

In order to recapture some of those early lessons (and maybe learn a few more Python 3 specific ones), I worked through (part of) the 2017 Advent of Code, a 25-day, language agnostic programming challenge series developed by Eric Watsl. I originally thought about using the series to learn a new language, but eventually realized that I still have a long way to go in Python.

This series explores my (often very basic) revelations and lessons learned while completing 20 of the 25 days of challenges (vacation travel cut the series short for me). The code I used for each day is available on GitHub. Also check out the advent-of-code and advent-of-code-2017 GitHub topics to see the many solutions people have developed in various languages.


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Consistent Selenium Testing in Python

Posted on 01 September 2017 in Technology • Tagged with django, python, saucelabs, selenium, testing, timestrap

Back in April, I learned about Timestrap, a self-hostable, Django-based time-tracking project from a post on HackerNews by Isaac Bythewood. As I have been learning Python in the past year or so, I reached out to Isaac and started contributing to the project. After getting familiar with the core application, I turned my attention to testing and eventually found my way to Selenium, a collection of browser automation tools used for frontend testing.

I had never worked with Selenium or other automated testing products, so it struck me as a great opportunity to get my feet wet in something new. After getting things up and running, we quickly learned that the test results were quite inconsistent across development environments - even to a point that occasionally tests would succeed when run individually, but fail with the full test case.

After much trial and error, we have settled on a (mostly) consistent setup for testing with Selenium, Python and SauceLabs. This produces much better results than testing in development environments and crossing fingers during CI. Hopefully this primer will help others facing similar challenges (as we had a lot of trouble finding good material on the subject).


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RDAP Explorer

Posted on 06 February 2017 in Technology • Tagged with django, ip, ipv4, ipv6, ipwhois, nginx, python, rdap, uwsgi, whois

Having fallen behind a bit on Takeout Inspector, the 12 Years of Gmail series and some other projects, I decided to try to put something very simple together from beginning to end and actually launch it. One of my previous posts, Examining the Remnants of a Small DDoS Attack introduced me to the Python package ipwhois and the alternative WHOIS system RDAP. This eventually led me to a quick and simple project called RDAP Explorer...


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12 Years of Gmail, Part 5: Mail

Posted on 05 December 2016 in Technology • Tagged with 12 years of gmail, email, graphing, plotly, python, takeout inspector, wordcloud

After taking a look at the chat data in my export, I am finally ready to move on to some of the actual mail! Much of what I will look at here is pretty similar to what I was able to turn up with chat data. I tried to branch out a bit, bringing in a new package to create word clouds, and also refactored some of the Takeout Inspector code to form the beginning of a more "formal" report generating process (instead of just spitting out a single HTML file with only a certain subset of the data). Hopefully I can continue to improve this to a point allowing for easier report generation for any user. Anyway, on to the mail data!


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12 Years of Gmail, Part 4: Chat

Posted on 18 November 2016 in Technology • Tagged with 12 years of gmail, chat, graphing, plotly, python, takeout inspector

With the Finishing Touches in place, it's finally time to start looking at some of the data in my Google Takeout Mail export file. What better to start with than the Google Talk (or Google Chat, as I will refer to it) content stored within!

I am starting with Chat because I was surprised to find it all stored in the export file. It makes sense as chat history is accessible from the Chats link in the old Gmail interface (I couldn't find an equivalent in Inbox). My surprise led to curiosity and my curiosity led to obsession with trying to figure how the Chat data is stored and what information each messages contains. It turns out there are quite a few things that can be gleaned from these chat messages -


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12 Years of Gmail, Part 3: Finishing Touches

Posted on 12 November 2016 in Technology • Tagged with 12 years of gmail, configparser, names, graphing, plotly, python, takeout inspector

After spending last week Bootstrapping things and, somewhat related, working my way around Pelican, today I have tried to tie up loose ends so I can start spending more time thinking about what information I can get from all this data. While the package is far from complete, these "finishing touches" ended up being the three themes of this morning's work -

  1. Implementing a settings file
  2. Customising Plotly graphs
  3. Generating random names

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