Notes on codes, projects and everything
While JSON is a fine data-interchange format, however it does have some limitations. It is well-known for its simplicity, that even a non-programmer can easily compose a JSON file (but humanity will surprise you IRL). Therefore, it is found almost everywhere, from numerous web APIs, to geospatial data (GeoJSON), and even semantic web (RDF/JSON).
Previously, I started practising recursions by implementing a type check on lat (list of atoms), and ismember (whether an atom is a member of a given lat). Then in the third chapter, named “Cons the Magnificent”, more list manipulation methods are being introduced.
Recently I find some of my pet projects share a common pattern, they all are based on some kind of grids. So I find myself writing similar piece of code over and over again. While re-inventing wheels is quite fun, especially when you learn new way of getting things done with every iteration, it is actually quite tedious after a while.
In the last part, I implemented a couple of primitive functions so that they can be applied in the following chapters. The second chapter of the book, is titled “Do it again, and again, and again…”. The title already hints that readers will deal with repetitions throughout the chapter.
I saw this article from alistapart, which is about Javascript’s prototypal object orientation. So the article mentioned Douglas Crawford, and I was immediately reminded about my struggle in understanding the language itself. Back then I used to also refer to his site for a lot of notes in Javascript. So I went back to have a quick read, and found this article that discusses the similarity between Javascript and Lisp.
Sometimes, letting a piece of code evolving by itself without much planning does not usually end well. However I was quite pleased with a by-product of it and I am currently formalizing it. So the by-product is some sort of DSL for a rule engine that I implemented to process records. It started as some lambda functions in Python but eventually becomes something else.
So my cheat with dask worked fine and dandy, until I started inspecting the output (which was to be used as an input for another script). While the script seemed to work fine, however when I started to parse each line I was hit with some funny syntax errors. After some quick inspection I found some of the lines was not printed completely.
Often times, I am dealing with JSONL files, though panda’s DataFrame is great (and blaze to certain extend), however it is offering too much for the job. Most of the received data is in the form of structured text and I do all sorts of work with them. For example checking for consistency, doing replace based on values of other columns, stripping whitespace etc.
I came across a video on Youtube on Pi day. Coincidently it was about estimating the value of Pi produced by Matt Parker aka standupmaths. While I am not quite interested in knowing the best way to estimate Pi, I am quite interested in the algorithm he showed in the video however. Specifically, I am interested to find out how easy it is to implement in Python.
Traversing a tree structure often involves writing a recursive function. However, Python isn’t the best language for this purpose. Therefore I started flattening the tree into a key-value dictonary structure. Logically it is still a tree, but it is physically stored as a dictionary. Therefore it is now easier to write a simple loop to traverse it.
After the last post, I found that it may be fun to write a wrapper for YUI in order to make it behave like jQuery. Therefore, the code below is clearly mainly for self-amusement and is not intended to be used in production projects. However, through coding this, I found that although the difference in design, but YUI is obviously capable to do what jQuery offers (if not more). I will not continue working on this so whoever interested may just copy and paste the code to further developing it.
One of my recent tasks involving crawling a lot of geo-tagged data from a given service. The most recent one is crawling files containing a point cloud for a given location. So I began by observing the behavior in the browser. After exporting the list of HTTP requests involved in loading the application, I noticed there are a lot of requests fetching resources with a common rXXX pattern.
I really don’t know how to start explaining what is a Dragon Curve. However, I find it is interesting enough after finding out that there’s actually a fixed pattern of occurrence. Therefore I spent some time writing a series of scripts to plot the generated fractal into a graph. What I didn’t expect is, the series get really complicated after a while.
Should have done this earlier, I was just being lazy to go through all the steps to publish it properly. So here it is, the full source is published to bitbucket. Feel free to fork the project if you are interested. I have not attach a licence to it but it will most probably be BSD licence. I have also uploaded the latest 0.0.2 release to bitbucket and would update the download link posted previously soon.
A new day, and a new post on job application. So this time instead of asking a snippet, I was actually asked to deliver some sort of a full application. Not sure why this was required, but I had fun creating them nonetheless. Though I would say I am not really a fan of creating visual stuff though (oh the crappy animation nearly killed me).