# Category Archives: Programming

I maintain the following programming projects:

LMFDB: (source), the code running the L-functions and Modular Forms website LMFDB. I’m one of the major contributors to the project, and the grant supporting my postdoc at the University of Warwick includes support for me to contribute to the LMFDB.

HNRSS: (source), a HackerNews RSS generator written in python. HNRSS periodically updates RSS feeds from the HN frontpage and best list. It also attempts to automatically summarize the link (if there is a link) and includes the top five comments, all to make it easier to determine whether it’s worth checking out.

LaTeX2Jax: (source), a tool to convert LaTeX documents to HTML with MathJax. This is a modification of the earlier MSE2WP, which converts Math.StackExchange flavored markdown to WordPress+MathJax compatible html. In particular, this is more general, and allows better control of the resulting html by exposing more CSS elements (that generically aren’t available on free WordPress setups). This is what is used for all math posts on this site.

MSE2WP: (source), a tool to convert Math.Stackexchange flavored markdown to WordPress+MathJax compatible html. This was once written for the Math.Stackexchange Community Blog. But as that blog is shutting down, there is much less of a purpose for this script. Note that this began as a modified version of latex2wp.

I actively contribute to:

vundle: (source), a minimalist but powerful vim plugin manager. Now that vim8 comes with a native plugin manager, there are fewer reasons to use vundle. But I find the vundle interface and code very straightforward. Users with lots of plugins, who frequently change plugins, or with complicated dependencies may want to consider vim-plug, which was inspired by vundle, but which takes a more sophisticated and complicated look at plugins and is often faster.

python-markdown2: (source),  a fast and complete python implementation of markdown, with a few additional features.

And I generally support or have contributed to:

SageMath: (main site), a free and open source system of tools for mathematics. Some think of it as a free alternative to the “Big M’s” — Maple, Mathematica, Magma.

Matplotlib: (main site), a plotting library in python. Most of the static plots on this site were creating using matplotlib.

crouton: (source), a tool for making Chromebooks, which by default are very limited in capability, into hackable linux laptops. This lets you directly run Linux on the device at the same time as having ChromeOS installed. The only cost is that there is absolutely no physical security at all (and every once in a while a ChromeOS update comes around and breaks lots of things). It’s great!

Below, you can find my most recent posts tagged “Programming” on this site.

I will note the following posts which have received lots of positive feedback.

1. A Notebook Preparing for a Talk at Quebec-Maine
2. A Brief Notebook on Cryptography
3. Computing pi with Tools from Calculus (which includes computational tidbits, though no actual programming).

## Extra comparisons in Python’s timsort

Reading through the comp.lang.python mailing list, I saw an interesting question concerning the behavior of the default sorting algorithm in python. This led to this post.

Python uses timsort, a clever hybrid sorting algorithm with ideas borrowing from merge sort and (binary) insertion sort. A major idea in timsort is to use the structure of naturally occuring runs (consecutive elements in the list that are either monotone increasing or monotone decreasing) when sorting.

Let’s look at the following simple list.

10, 20, 5

A simple sorting algorithm is insertion sort, which just advances through the list and inserts each number into the correct spot. More explicitly, insertion sort would

1. Start with the first element, 10. As a list with one element, it is correctly sorted tautologically.
2. Now consider the second element, 20. We insert this into the correct position in the already-sorted list of previous elements. Here, that just means that we verify that 20 > 10, and now we have the sorted sublist consisting of 10, 20.
3. Now consider the third element, 5. We want to insert this into the correct position in the already-sorted list of previous elements. A naively easy way to do this is to either scan the list from the right or from the left, and insert into the correct place. For example, scanning from the right would mean that we compare 5 to the last element of the sublist, 20. As 5 < 20, we shift left and compare 5 to 10. As 5 < 10, we shift left again. As there is nothing left to compare against, we insert 5 at the beginning, yielding the sorted list 5, 10, 20.

How many comparisons did this take? This took 20 > 10, 5 < 20, and 5 < 10. This is three comparisons in total.

We can see this programmatically as well. Here is one implementation of insertion_sort, as described above.

def insertion_sort(lst):
'''
Sorts lst in-place. Note that this changes lst.
'''
for index in range(1, len(lst)):
current_value = lst[index]
position = index
while position > 0 and lst[position - 1] > current_value:
lst[position] = lst[position - 1]
position = position - 1
lst[position] = current_value

Let’s also create a simple Number class, which is just like a regular number, except that anytime a comparison is done it prints out the comparison. This will count the number of comparisons made for us.

class Number:
def __init__(self, value):
self.value = value

def __str__(self):
return str(self.value)

def __repr__(self):
return self.__str__()

def __lt__(self, other):
if self.value < other.value:
print("{} < {}".format(self, other))
return True
print("{} >= {}".format(self, other))
return False

def __eq__(self, other):
if self.value == other.value:
print("{} = {}".format(self, other))
return True
return False

def __gt__(self, other):
return not ((self == other) or (self < other))

def __le__(self, other):
return (self < other) or (self == other)

def __ge__(self, other):
return not (self < other)

def __nq__(self, other):
return not (self == other)

With this class and function, we can run

lst = [Number(10), Number(20), Number(5)]
insertion_sort(lst)
print(lst)

which will print

10 < 20
20 >= 5
10 >= 5
[5, 10, 20]

These are the three comparisons we were expecting to see.

Returning to python’s timsort — what happens if we call python’s default sorting method on this list? The code

lst = [Number(10), Number(20), Number(5)]
lst.sort()
print(lst)

prints

20 >= 10
5 < 20
5 < 20
5 < 10
[5, 10, 20]

There are four comparisons! And weirdly, the method checks that 5 < 20 twice in a row. What’s going on there?1

At its heart, this was at the core of the thread on comp.lang.python. Why are there extra comparisons in cases like this?

Poking around the implementation of timsort taught me a little bit more about timsort.2

Timsort approaches this sorting task in the following way.

1. First, timsort tries to identify how large the first run within the sequence is. So it keeps comparing terms until it finds one that is out of order. In this case, it compares 20 to 10 (finding that 20 > 10, and thus the run is increasing), and then compares 5 to 20 (finding that 5 < 20, and thus that 5 is not part of the same run as 10, 20). Now the run is identified, and there is one element left to incorporate.
2. Next timsort tries to insert 5 into the already-sorted run. It is more correct to say that timsort attempts to do a binary insertion, since one knows already that the run is sorted.3 In this binary insertion, timsort will compare 5 with the middle of the already-sorted run 10, 20. But this is a list of length 2, so what is its middle element? It turns out that timsort takes the latter element, 20, in this case. As 5 < 20, timsort concludes that 5 should be inserted somewhere in the first half of the run 10, 20, and not in the second half.
3. Of course, the first half consists entirely of 10. Thus the remaining comparison is to check that 5 < 10, and now the list is sorted.

We count4 all four of the comparisons. The doubled comparison is due to the two tasks of checking whether 5 is in the same run as 10, 20, and then of deciding through binary insertion where to place 5 in the smaller sublist of 10, 20.

Now that we’ve identified a doubled comparison, we might ask Why is it this way? Is this something that should change?

The short answer is it doesn’t really matter. A longer answer is that to apply this in general would cause additional comparisons to be made, since this applies in the case when the last element of the run agrees in value with the central value of the run (which may occur for longer lists if there are repeated values). Checking that this happens would probably either involve comparing the last element of the run with the central value (one extra comparison, so nothing is really saved anyway), or perhaps adding another data structure like a skip list (which seems sufficiently more complicated to not be worth the effort). Or it would only apply when sorting really short lists, in which case there isn’t much to worry about.

Learning a bit more about timsort made me realize that I could probably learn a lot by really understanding an implementation of timsort, or even a slightly simplified implementation. It’s a nice reminder that one can choose to optimize for certain situations or behaviors, and this might not cover all cases perfectly — and that’s ok.

## A bookmarklet to inject colorblind friendly CSS into Travis CI

In my previous post, I noted that the ability to see in color gave me an apparent superpower in quickly analyzing Travis CI and pytest logs.

I wondered: how hard is it to use colorblind friendly colors here?

I had in the back of my mind the thought of the next time I sit down and pair program with someone who is colorblind (which will definitely happen). Pair programming is largely about sharing experiences and ideas, and color disambiguation shouldn’t be a wedge.

I decided that loading customized CSS is the way to go. There are different ways to do this, but an easy method for quick replicability is to create a bookmarklet that adds CSS into the page. So, I did that.

You can get that bookmarklet here. (Due to very sensible security reasons, WordPress doesn’t want to allow me to provide a link which is actually a javascript function. So I make it available on a static, handwritten page).1

Here’s how it works. A Travis log looks typically like this:

After clicking on the bookmarklet, it looks like

This is not beautiful, but it works and it’s very noticable. Nonetheless, when the goal is just to be able to quickly recognize if errors are occuring, or to recognize exceptional lines on a quick scroll-by, the black-text-on-white-box wins the standout crown.

The LMFDB uses pytest, which conveniently produces error summaries at the end of the test. (We used to use nosetest, and we hadn’t set it up to have nice summaries before transitioning to pytest). This bookmark will also effect the error summary, so that it now looks like

Again, I would say this is not beautiful, but definitely noticeable.

As an aside, I also looked through the variety of colorschemes that I have collected over the years. And it turns out that 100 percent of them are unkind to colorblind users, with the exception of the monotone or monochromatic schemes (which are equal in the Harrison Bergeron sense).

We should do better.

## Seeing color shouldn’t feel like a superpower

In the last month, I have found myself pair programming with three different people. All three times involved working on the LMFDB. I rarely pair program outside a mentor-mentee or instructor-student situation.1

This is fun. It’s fun seeing other people’s workflows. (In these cases, it happened to be that the other person was usually the one at the keyboard and typing, and I was backseat driving). I live in the terminal, subscribe to the Unix-is-my-IDE general philosophy: vim is my text editor; a mixture of makefiles, linters, and fifos with tmux perform automated building, testing, and linting; git for source control; and a medium-sized but consistently growing set of homegrown bash/python/c tools and scripts make it fun and work how I want.

I’m distinctly interested in seeing tools other people have made for their own workflows. Those scripts that aren’t polished, but get the work done. There is a whole world of git-hooks and aliases that amaze me.

But my recent encounters with pair programming exposed me to a totally different and unexpected experience: two of my programming partners were color blind.2

At first, I didn’t think much of it. I had thought that you might set some colorblind-friendly colorschemes, and otherwise configure your way around it. But as is so often the case with accessibility problems, I underestimated both the number of challenges and the difficulty in solving them (lousy but true aside: most companies almost completely ignore problems with accessibility).

I first noticed differences while trying to fix bugs and review bugfixes in the LMFDB. We use Travis CI for automated testing, and we were examining a build that had failed. We brought up the Travic CI interface and scroll through the log. I immediately point out the failure, since I see something like this.3

How do you know something failed? asks John, my partner for the day. Oh, it’s because the output is colored, isn’t it? I didn’t know. With the help of the color-blindness.com color-blindness simulator, I now see that John saw something like
With red-green colorblindness, there is essentially no difference in the shades of PASSED and FAILED. That’s sort of annoying.

We’d make a few changes, and then rerun some tests. Now we were running tests in a terminal, and the testlogs are scolling by. We’re chatting about emacs wizardy (or c++ magic, or compiler differences between gcc and clang, or something), and I point out that we can stop the tests since three tests have already failed.

He stared at me a bit dumbfoundedly. It was like I had superpowers. I could recognize failures without paying almost any attention, since flashes of red stand out.

But if you don’t recognize differences in color, how would you even know that the terminal outputs different colors for PASSED and FAILED? (We use pytest, which does). A quick look for different colorschemes led to frustration, as there are different sorts of colorblindness and no single solution that will work for everyone (and changing colorschemes is sort of annoying anyway).4

I should say that the Travis team has made some accessibility improvements for colorblind users in the past. The build-passing and build-failing icons used to be little circles that were red or green, as shown here.

That means the build status was effectively invisible to colorblind users. After an issue was raised and discussed, they moved to the current green-checkmark-circle for passing and red-exed-circle for failing, which is a big improvement.

The colorscheme used for Travic CI’s online logs is based on the nord color palette, and there is no colorscheme-switching option. It’s a beautiful and well-researched theme for me, but not for everybody.

The colors on the page are controllable by CSS, but not in a uniform way that works on many sites. (Or at least, not to my knowledge. I would be interested if someone else knew more about this and knew a generic approach. The people I was pair-programming with didn’t have a good solution to this problem).

Should you really need to write your own solution to every colorblind accessibility problem?

In the next post, I’ll give a (lousy but functional) bookmarklet that injects CSS into the page to see Travis CI FAILs immediately.

## Ghosts of Forums Past

This is the second in a miniseries of posts on internet fora, and Math.SE and StackOverflow in particular. In the previous entry in the miniseries, I described some of the common major problems facing community cohesion. I claimed that when communities get large, they tend to fracture and the ratio of meaningful communication to noise plummets. To combat this tendency, communities use some mixture of core moderation, peer moderation, membership requirements, or creating subcommunities/splitting off in to other communities.

In this chapter I focus more on Math.SE and StackOverflow. Math.SE is now experiencing growing pains and looking for solutions. But many users of Math.SE have little involvement in the rest of the StackExchange network and are mostly unaware of the fact that StackOverflow has already encountered and passed many of the same trials and tribulations (with varying degrees of success).

Thinking more broadly, many communities have faced these same challenges. Viewed from the point of view from the last chapter, it may appear that there are only a handful of tools a community might use to try to retain group cohesion. Yet it is possible to craft clever mixtures of these tools synergistically. The major reason the StackExchange model has succeeded where other fora have stalled (or failed outright) is through its innovations on the implementation of communition cohesion strategies while still allowing essentially anyone to visit the site.

## Imaginary Internet Points

Slashdot1 popularized the idea of associating imaginary internet points to different users. It was called karma. You got karma if other users rated your comments or submissions well, and lost karma if they rated your posts as poor. But perhaps most importantly, each user can set a threshold for minimum scores of content to see. Thus if people have reasonable thresholds and you post crap, then most people won’t even see it after it’s scored badly.

Posted in Programming, SE | | 1 Comment

## Challenges facing community cohesion (and Math.StackExchange in particular)

In about a month, Math.StackExchange will turn 8. Way back when I was an undergrad, I joined the site. This was 7 years ago, during the site’s first year.

Now with some perspective as a frequent contributor/user/moderator of various online newsgroups and fora, I want to take a moment to examine the current state of Math.SE.

To a certain extent, this is inspired by Joel Spolsky’s series of posts on StackOverflow (which he is currently writing and sending out). But this is also inspired by recent discussion on Meta.Math.SE. As I began to collect my thoughts to make a coherent reply, I realized that I have a lot of thoughts, and a lot to say.

So this is chapter one of a miniseries of writings on internet fora, and Math.SE and StackOverflow in particular.

Posted in Programming, SE | | 2 Comments

## Cracking Codes with Python: A Book Review

How do you begin to learn a technical subject?

My first experience in “programming” was following a semi-tutorial on how to patch the Starcraft exe in order to make it understand replays from previous versions. I was about 10, and I cobbled together my understanding from internet mailing lists and chatrooms. The documentation was awful and the original description was flawed, and to make it worse, I didn’t know anything about any sort of programming yet. But I trawled these lists and chatroom logs and made it work, and learned a few things. Each time Starcraft was updated, the old replay system broke completely and it was necessary to make some changes, and I got pretty good at figuring out what changes were necessary and how to perform these patches.

On the other hand, my first formal experience in programming was taking a course at Georgia Tech many years later, in which a typical activity would revolve around an exciting topic like concatenating two strings or understanding object polymorphism. These were dry topics presented to us dryly, but I knew that I wanted to understand what was going on and so I suffered the straight-faced-ness of the class and used the course as an opportunity to build some technical depth.

Now I recognize that these two approaches cover most first experiences learning a technical subject: a motivated survey versus monographic study. At the heart of the distinction is a decision to view and alight on many topics (but not delving deeply in most) or to spend as much time as is necessary to understand completely each topic (and hence to not touch too many different topics). Each has their place, but each draws a very different crowd.

The book Cracking Codes with Python: An Introduction to Building and Breaking Ciphers by Al Sweigart1 is very much a motivated flight through various topics in programming and cryptography, and not at all a deep technical study of any individual topic. A more accurate (though admittedly less beckoning) title might be An Introduction to Programming Concepts Through Building and Breaking Ciphers in Python. The main goal is to promote programmatical thinking by exploring basic ciphers, and the medium happens to be python.

But ciphers are cool. And breaking them is cool. And if you think you might want to learn something about programming and you might want to learn something about ciphers, then this is a great book to read.

Sweigart has a knack for writing approachable descriptions of what’s going on without delving into too many details. In fact, in some sense Sweigart has already written this book before: his other books Automate the Boring Stuff with Python and Invent your own Computer Games with Python are similarly survey material using python as the medium, though with different motivating topics.

Each chapter of this book is centered around exploring a different aspect of a cipher, and introduces additional programming topics to do so. For example, one chapter introduces the classic Caesar cipher, as well as the “if”, “else”, and “elif” conditionals (and a few other python functions). Another chapter introduces brute-force breaking the Caesar cipher (as well as string formatting in python).

In each chapter, Sweigart begins by giving a high-level overview of the topics in that chapter, followed by python code which accomplishes the goal of the chapter, followed by a detailed description of what each block of code accomplishes. Readers get to see fully written code that does nontrivial things very quickly, but on the other hand the onus of code generation is entirely on the book and readers may have trouble adapting the concepts to other programming tasks. (But remember, this is more survey, less technical description). Further, Sweigart uses a number of good practices in his code that implicitly encourages good programming behaviors: modules are written with many well-named functions and well-named variables, and sufficiently modularly that earlier modules are imported and reused later.

But this book is not without faults. As with any survey material, one can always disagree on what topics are or are not included. The book covers five classical ciphers (Caesar, transposition, substitution, Vigenere, and affine) and one modern cipher (textbook-RSA), as well as the write-backwards cipher (to introduce python concepts) and the one-time-pad (presented oddly as a Vigenere cipher whose key is the same length as the message). For some unknown reason, Sweigart chooses to refer to RSA almost everywhere as “the public key cipher”, which I find both misleading (there are other public key ciphers) and giving insufficient attribution (the cipher is implemented in chapter 24, but “RSA” appears only once as a footnote in that chapter. Hopefully the reader was paying lots of attention, as otherwise it would be rather hard to find out more about it).

Further, the choice of python topics (and their order) is sometimes odd. In truth, this book is almost language agnostic and one could very easily adapt the presentation to any other scripting language, such as C.

In summary, this book is an excellent resource for the complete beginner who wants to learn something about programming and wants to learn something about ciphers. After reading this book, the reader will be a mid-beginner student of python (knee-deep is apt) and well-versed in classical ciphers. Should the reader feel inspired to learn more python, then he or she would probably feel comfortable diving into a tutorial or reference for their area of interest (like Full Stack Python if interested in web dev, or Python for Data Analysis if interested in data science). Or he or she might dive into a more complete monograph like Dive into Python or the monolithic Learn Python. Many fundamental topics (such as classes and objects, list comprehensions, data structures or algorithms) are not covered, and so “advanced” python resources would not be appropriate.

Further, should the reader feel inspired to learn more about cryptography, then I recommend that he or she consider Cryptanalysis by Gaines, which is a fun book aimed at diving deeper into classical pre-computer ciphers, or the slightly heavier but still fun resource would “Codebreakers” by Kahn. For much further cryptography, it’s necessary to develop a bit of mathematical maturity, which is its own hurdle.

This book is not appropriate for everyone. An experienced python programmer could read this book in an hour, skipping the various descriptions of how python works along the way. An experienced programmer who doesn’t know python could similarly read this book in a lazy afternoon. Both would probably do better reading either a more advanced overview of either cryptography or python, based on what originally drew them to the book.

## Hosting a Flask App on WebFaction on a Non-root Domain

Since I came to Warwick, I’ve been working extensively on the LMFDB, which uses python, sage, flask, and mongodb at its core. Thus I’ve become very familiar with flask. Writing a simple flask application is very quick and easy. So I thought it would be a good idea to figure out how to deploy a flask app on the server which runs this website, which is currently at WebFaction.

In short, it was not too hard, and now the app is set up for use. (It’s not a public tool, so I won’t link to it).

But there were a few things that I had to think figure out which I would quickly forget. Following the variety of information I found online, the only nontrivial aspect was configuring the site to run on a non-root domain (like davidlowryduda.com/subdomain instead of at davidlowryduda.com). I’m writing this so as to not need to figure this out when I write and hoost more flask apps. (Which I’ll almost certainly do, as it’s so straightforward).

There are some uninteresting things one must do on WebFaction.

2. Add a new application of type mod_wsgi (and the desired version of python, which is hopefully 3.6+).
3. Add this application to the desired website and subdomain in the WebFaction control panel.

After this, WebFaction will set up a skeleton “Hello World” mod_wsgi application with many reasonable server setting defaults. The remainder of the setup is done on the server itself.

In ~/webapps/application_name there will now appear

apache2/    # Apache config files and bin
htdocs/     # Default location where Apache looks for the app

We won’t change that structure. In htdocs1 there is a file index.py, which is where apache expects to find a python wsgi application called application. We will place the flask app along this structure and point to it in htdocs/index.py.

Usually I will use a virtualenv here. So in ~/webapps/application_name, I will run something like virtualenv flask_app_venv and virtualenv activate (or actually out of habit I frequently source the flask_app_venv/bin/activate file). Then pip install flask and whatever other python modules are necessary for the application to run. We will configure the server to use this virtual environment to run the app in a moment.

Copy the flask app, so that the resulting structure looks something like

~/webapps/application_name:

- apache2/
- htdocs/
- config.py
- libs/
- main/
- static/
- templates/
- __init__.py
- views.py
- models.my

I find it conceptually easiest if I have flask_app/main/__init__.py to directly contain the flask app to reference it by name in htdocs/index.py. It can be made elsewhere (for instance, perhaps in a file like flask_app/main/app.py, which appears to be a common structure), but I assume that it is at least imported in __init__.py.

For example, __init__.py might look something like

# ... other import statements from project if necessary

app.config.from_object('config')

# Importing the views for the rest of our site
# We do this here to avoid circular imports
# Note that I call it "main" where many call it "app"
from main import views

if __name__ == '__main__':
app.run()

The Flask constructor returns exactly the sort of wsgi application that apache expects. With this structure, we can edit the htdocs/index.py file to look like

# application_name/htdocs/index.py

import sys

# launching our app
from main import app as application

Now the server knows the correct wsgi_application to serve.

We must configure it to use our python virtual environment (and we’ll add a few additional convenience pieces). We edit /apache2/conf/httpd.conf as follows. Near the top of the file, certain modules are loaded. Add in the alias module, so that the modules look something like

#... other modules

This allows us to alias the root of the site. Since all site functionality is routed through htdocs/index.py, we want to think of the root / as beginning with /htdocs/index.py. At the end of the file

We now set the virtual environment to be used properly. There will be a set of lines containing names like WSGIDaemonProcess and WSGIProcessGroup. We edit these to refer to the correct python. WebFaction will have configured WSGIDaemonProcess to point to a local version of python by setting the python-path. Remove that, making that line look like

(or similar). We set the python path below, adding the line

I believe that this could also actually be done by setting puthon-path in WSGIDaemonProcess, but I find this more aesthetically pleasing.

We must also modify the “ section. Edit it to look like

<Directory>

It may very well be that I don't use the RewriteEngine at all, but if I do then this is where it's done. Script reloading is a nice convenience, especially while reloading and changing the app.

I note that it may be convenient to add an additional alias for static file hosting,

though I have not used this so far. (I get the same functionality through controlling the flask views appropriately).

The rest of this file has been setup by WebFaction for us upon creating the wsgi application.

## If the application is on a non-root domain...

If the application is to be run on a non-root domain, such as davidlowryduda.com/subdomain, then there is currently a problem. In flask, when using url getters like url_for, urls will be returned as though there is no subdomain. And thus all urls will be incorrect. It is necessary to alter provided urls in some way.

The way that worked for me was to insert a tiny bit of middleware in the wsgi_application. Alter htdocs/index.py to read

#application_name/htdocs/index.py

import sys

# subdomain url rerouting middleware
from webfaction_middleware import Middleware

from main import app

# set app through middleware
application = Middleware(app)

Now of course we need to write this middleware.

class Middleware(object):  # python2 aware
def __init__(self, app):
self.app = app

def __call__(self, environ, start_response):
app_url = '/subdomain'
if app_url != '/':
environ['SCRIPT_NAME'] = app_url
return self.app(environ, start_response)

I now have a template file in which I keep app_url = '/' so that I can forget this and not worry, but that is where the subdomain url is prepended. Note that the leading slash is necessary. When I first tried using this, I omitted the leading slash. The application worked sometimes, and horribly failed in some other places. Some urls were correcty constructed, but most were not. I didn't try to figure out which ones were doomed to fail --- but it took me an embarassingly long time to realize that prepending a slash solved all problems.

The magical-names of environ and start_response are because the flask app is a wsgi_application, and this is the api of wsgi_applications generically.

Restart the apache server (/apache2/bin/restart) and go. Note that when incrementally making changes above, some changes can take a few minutes to fully propogate. It's only doing it the first time which takes some thought.

## Segregation, Gerrymandering, and Schelling’s Model

[This note is more about modeling some of the mathematics behind political events than politics themselves. And there are pretty pictures.]

Gerrymandering has become a recurring topic in the news. The Supreme Court of the US, as well as more state courts and supreme courts, is hearing multiple cases on partisan gerrymandering (all beginning with a case in Wisconsin).

Intuitively, it is clear that gerrymandering is bad. It allows politicians to choose their voters, instead of the other way around. And it allows the majority party to quash minority voices.

But how can one identify a gerrymandered map? To quote Justice Kennedy in his Concurrence the 2004 Supreme Court case Vieth v. Jubelirer:

When presented with a claim of injury from partisan gerrymandering, courts confront two obstacles. First is the lack of comprehensive and neutral principles for drawing electoral boundaries. No substantive definition of fairness in districting seems to command general assent. Second is the absence of rules to limit and confine judicial intervention. With uncertain limits, intervening courts–even when proceeding with best intentions–would risk assuming political, not legal, responsibility for a process that often produces ill will and distrust.

Later, he adds to the first obstacle, saying:

The object of districting is to establish “fair and effective representation for all citizens.” Reynolds v. Sims, 377 U.S. 533, 565—568 (1964). At first it might seem that courts could determine, by the exercise of their own judgment, whether political classifications are related to this object or instead burden representational rights. The lack, however, of any agreed upon model of fair and effective representation makes this analysis difficult to pursue.

From Justice Kennedy’s Concurrence emerges a theme — a “workable standard” of identifying gerrymandering would open up the possibility of limiting partisan gerrymandering through the courts. Indeed, at the core of the Wisconsin gerrymandering case is a proposed “workable standard”, based around the efficiency gap.

## Thomas Schelling and Segregation

In 1971, American economist Thomas Schelling (who later won the Nobel Prize in Economics in 2005) published Dynamic Models of Segregation (Journal of Mathematical Sociology, 1971, Vol 1, pp 143–186). He sought to understand why racial segregation in the United States seems so difficult to combat.

He introduced a simple model of segregation suggesting that even if each individual person doesn’t mind living with others of a different race, they might still choose to segregate themselves through mild preferences. As each individual makes these choices, overall segregation increases.

I write this post because I wondered what happens if we adapt Schelling’s model to instead model a state and its district voting map. In place of racial segregation, I consider political segregation. Supposing the district voting map does not change, I wondered how the efficiency gap will change over time as people further segregate themselves.

It seemed intuitive to me that political segregation (where people who had the same political beliefs stayed largely together and separated from those with different political beliefs) might correspond to more egregious cases of gerrymandering. But to my surprise, I was (mostly) wrong.

Let’s set up and see the model.

## Advent of Code: Day 4

This is a very short post in my collection working through this year’s Advent of Code challenges. Unlike the previous ones, this has no mathematical comments, as it was a very short exercise. This notebook is available in its original format on my github.

# Day 4: High Entropy Passphrases¶

Given a list of strings, determine how many strings have no duplicate words.

This is a classic problem, and it’s particularly easy to solve this in python. Some might use collections.Counter, but I think it’s more straightforward to use sets.

The key idea is that the set of words in a sentence will not include duplicates. So if taking the set of a sentence reduces its length, then there was a duplicate word.

In [1]:
with open("input.txt", "r") as f:

def count_lines_with_unique_words(lines):
num_pass = 0
for line in lines:
s = line.split()
if len(s) == len(set(s)):
num_pass += 1
return num_pass

count_lines_with_unique_words(lines)
Out[1]:
455

I think this is the first day where I would have had a shot at the leaderboard if I’d been gunning for it.

# Part 2¶

Let’s add in another constraint. Determine how many strings have no duplicate words, even after anagramming. Thus the string

abc bac

is not valid, since the second word is an anagram of the first. There are many ways to tackle this as well, but I will handle anagrams by sorting the letters in each word first, and then running the bit from part 1 to identify repeated words.

In [2]:
with open("input.txt", "r") as f:

sorted_lines = []
for line in lines:
sorted_line = ' '.join([''.join(l) for l in map(sorted, line.split())])
sorted_lines.append(sorted_line)

sorted_lines[:2]

Out[2]:
['bddjjow acimrv bcjjm anr flmmos fiosv',
'bcmnoxy dfinyzz dgmp dfgioy hinrrv eeklpuu adgpw kqv']
In [3]:
count_lines_with_unique_words(sorted_lines)
Out[3]:
186
Posted in Expository, Programming, Python | Tagged , , | 1 Comment

## Advent of Code: Day 3

This is the third notebook in my posts on the Advent of Code challenges. The notebook in its original format can be found on my github.

# Day 3: Spiral Memory¶

Numbers are arranged in a spiral

17  16  15  14  13
18   5   4   3  12
19   6   1   2  11
20   7   8   9  10
21  22  23---> ...

Given an integer n, what is its Manhattan Distance from the center (1) of the spiral? For instance, the distance of 3 is $2 = 1 + 1$, since it’s one space to the right and one space up from the center.

Here’s my idea. The bottom right corner of the $k$th layer is the integer $(2k+1)^2$, since that’s how many integers are contained within that square. The other three corners in that layer are $(2k+1)^2 – 2k, (2k+1)^2 – 4k$, and $(2k+1)^2 – 6k$. Finally, the closest spot on the $k$th layer to the origin is at distance $k$: these are the four “axis locations” halfway between the corners, at $(2k+1)^2 – k, (2k+1)^2 – 3k, (2k+1)^2 – 5k$, and $(2k+1)^2 – 7k$.

For instance when $k = 1$, the bottom right is $(2 + 1)^2 = 9$, and the four “axis locations” are $9 – 1, 9 – 3, 9-5$, and $9-7$. The “axis locations” are $k$ away, and the corners are $2k$ away.

So I will first find which layer the number is on. Then I’ll figure out which side it’s on, and then how far away it is from the nearest “axis location” or “corner”.

My given number happens to be 289326.

In [1]:
import math

def find_lowest_larger_odd_square(n):
upper = math.ceil(n**.5)
if upper %2 == 0:
upper += 1
return upper
In [2]:
assert find_lowest_larger_odd_square(39) == 7
assert find_lowest_larger_odd_square(26) == 7
assert find_lowest_larger_odd_square(25) == 5
In [3]:
find_lowest_larger_odd_square(289326)
Out[3]:
539
In [4]:
539**2 - 289326
Out[4]:
1195

It happens to be that our integer is very close to an odd square.
The square is $539^2$, and the distance to that square is $538$ from the center.

Note that $539 = 2(269) + 1$, so this is the $269$th layer of the square.
The previous corner to $539^2$ is $539^2 – 538$, and the previous corner to that is $539^2 – 2\cdot538 = 539^2 – 1076$.
This is the nearest corner.
How far away from the square is this corner?