Haiku Twitter Bot

Over summer 2016, I decided to make a Twitter bot just for fun. Here, I'll describe the bot's programming at a high level. But first, a little background...

I started undergraduate as an english major... then graduated with a degree in neuroscience. Although I have pretty firmly switched my career plans toward math and science, I still appreciate and respect art. As a reflection on my artistic pipedream, I designed my bot to write haikus. If you don't remember haikus from high school, hit that link in the previous sentence.

For connecting to Twitter, I used the tweepy library. I simply wrote a subclass of StreamListener to process incoming tweets and write them to a file called tweets.txt. You can read more about this process in the tweepy docs (linked above).

Each time I start my bot, most of the high-level functionality is located in one function, do_tweet().

For counting syllables and determining parts of speech, I used NLTK.

Counting syllables:

Choosing words for the haiku:

As you can see, the algorithm randomly chooses a word of syllable length sw, which is generated by the pick_syl() function. If this is hard to interpret, don't worry. It took me a while to come up with an algorithm that would randomly choose words but still adhere to the 5/7/5 haiku format. The conditions like if ix == 0 and line == 0 are there to determine which line of the haiku is being written. In this case, the first word of the first line is being chosen. Then the lines:

sdict1 = dict([(sk, sv) for sk, sv in sdict.items() if pos_tag(word_tokenize(sk),
tagset='universal')[0][1]=='NOUN']) # Chooses a noun
gsyls = syl_of_size(sdict1, sw) # Chooses a noun of syllable length sw

Then, setting up the haiku in order and writing to the file haiku.txt:

So currently this bot would write haikus with randomly chosen words, which is cool... but we can do better! ;^)
Eventually when I have a little more time, I'll give meta ai ai haiku more smarts, but for now the bot uses a simple algorithm for determining the "best" tweets, then recycles words from these tweets. Dumb, I know... it's a work in progress.

The function get_best() chooses tweets with the highest "weight" to include in the bot's corpus for writing future haikus.

Here's a link to my Twitter bot meta ai ai haiku: robcapps.com/docs/haiku. Please feel free to ask questions in the comments section!

QGIS Area of Availability Plugin

8/23/16

I'm working on a set of QGIS plugins, and one that I think should be out there for grabs is an area of availability calculator. (EDIT: you should be able to install the plugin via the public QGIS repository soon)

Given a start point and a maximum distance to travel, we want to find the boundary of accessible regions via a road network.

Boundary of area of availability, plotted as points

We build a road network graph and calculate the road distances using Dijkstra's algorithm. We can then find the points corresponding to the outer boundary of the area of availability.

The code for this calculation is below (taken from this page):

from PyQt4.QtCore import *
from PyQt4.QtGui import *

from qgis.core import *
from qgis.gui import *
from qgis.networkanalysis import *

vl = qgis.utils.iface.mapCanvas().currentLayer()
director = QgsLineVectorLayerDirector(vl, -1, '', '', '', 3)
properter = QgsDistanceArcProperter()
director.addProperter(properter)
crs = qgis.utils.iface.mapCanvas().mapRenderer().destinationCrs()
builder = QgsGraphBuilder(crs)

pStart = QgsPoint(65.5462, 57.1509)
delta = qgis.utils.iface.mapCanvas().getCoordinateTransform().mapUnitsPerPixel() * 1

rb = QgsRubberBand(qgis.utils.iface.mapCanvas(), True)
rb.setColor(Qt.green)
rb.addPoint(QgsPoint(pStart.x() - delta, pStart.y() - delta))
rb.addPoint(QgsPoint(pStart.x() + delta, pStart.y() - delta))
rb.addPoint(QgsPoint(pStart.x() + delta, pStart.y() + delta))
rb.addPoint(QgsPoint(pStart.x() - delta, pStart.y() + delta))

tiedPoints = director.makeGraph(builder, [pStart])
graph = builder.graph()
tStart = tiedPoints[0]

idStart = graph.findVertex(tStart)

(tree, cost) = QgsGraphAnalyzer.dijkstra(graph, idStart, 0)

upperBound = []
r = 2000.0
i = 0
while i < len(cost):
  if cost[i] > r and tree[i] != -1:
    outVertexId = graph.arc(tree [i]).outVertex()
    if cost[outVertexId] < r:
      upperBound.append(i)
  i = i + 1

for i in upperBound:
  centerPoint = graph.vertex(i).point()
  rb = QgsRubberBand(qgis.utils.iface.mapCanvas(), True)
  rb.setColor(Qt.red)
  rb.addPoint(QgsPoint(centerPoint.x() - delta, centerPoint.y() - delta))
  rb.addPoint(QgsPoint(centerPoint.x() + delta, centerPoint.y() - delta))
  rb.addPoint(QgsPoint(centerPoint.x() + delta, centerPoint.y() + delta))
  rb.addPoint(QgsPoint(centerPoint.x() - delta, centerPoint.y() + delta))

I'm not quite finished with my plugin implementation, but you can check out the source code on my GitHub.

If you want to try your hand at developing a QGIS plugin, I strongly recommend starting with Plugin Builder.