Fun with newlines

Use a typewriter lately? No? Well, who cares… except when you encounter stupidities left over from the early days of computing where people were still used to typewriters. Because typewriters had two ways of going to a new line, ASCII knows two ways of representing the newline:

  • LF (line feed, German Zeilenvorschub), represented as Unicode code point 0x0A, ASCII 00001100 and escape character \n
  • CR (carriage return, German Wagenrücklauf), represented as Unicode code point 0x0D, ASCII 00001101 and escape character \r

ASCII was the first-ever invented encoding for representing text in bits. It’s from the 1960s and at the time someone probably thought it is a good idea to have two characters for the concept of a new line. We’d think "who cares about stuff from the 1960s", it’s 2017, right? But unfortunately many later encodings base themselves on ASCII, most notably those from the Unicode family, e.g., the widely used UTF-8. So – thank you, 1960s! /sarcasm

Two characters for a new line would not be too bad if they were used consistently, but that is where the fun begins. Of course they are not! Differnt operating systems use different conventions to mark the end of a line:

  • Linux and Mac OSX use LF
  • Windows uses CR LF
  • (and to make the chaos complete, Mac OS from before version X uses CR)

So have fun reading "plain text" files! /sarcasm

Encoding in Python 2.x

One of the annoying things where I always forget the specifics. So here it is…

Reading a file line-by-line in python and writing it to another file is easy:

input_file = open("input.txt")
outputFile = open("output.txt", "w")
for line in input_file:
   outputFile.write(line + "\n")

But whenever encodings are involved, everything gets complicated. What is the encoding of line? Actually, not really anything, without specification, line is just a Byte-String (type 'str') with no encoding.

Because this is usually not what we want, the first step is to convert the read line to Unicode. This is done with the method decode. The method has two parameters. The fist is the encoding that should be used. This is a predefined String value which you can guess it for the more common encodings (or look it up in the documentation). If left out, ASCII is assumed. The second parameter defines how to handle unknown byte patterns. The value 'strict' will abort with UnicodeDecodeError, 'ignore' will leave the character out of the Unicode result, and 'replace' will replace every unknown pattern with U+FFFD. Let’s assume our input file and therefor the line we read from there is in Latin-1. We convert this to Unicode with:

lineUnicode = line.decode('latin-1','strict')

or equivalently

lineUnicode = unicode(line, encoding='latin-1', errors='strict')

After decoding, we have sometihng of type 'unicode'. If we try to print this and it is not simple English, it will probably give an error (UnicodeEncodeError: 'ascii' codec can't encode characters in position 63-66: ordinal not in range(128)). This is because Python will try to convert the characters to ASCII, which is not possible for characters that are not ASCII. So, to print out a Unicode text, we have to convert it to some encoding. Let’s say we want UTF-8 (there is no reason not to use UTF-8, so this is what you should always want):

lineUtf8 = lineUnicode.encode('utf-8')
print(lineUtf8)

Here again, there is a second parameter which defines how to handle characters that cannot be represented (which shouldn’t happen too often with UTF-8). Happy coding!

Further reading:
Unicode HOWTO in the Python documentation, Overcoming frustration: Correctly using unicode in python2 from the Python Kitchen project, The Absolute Minimum Every Software Developer Absolutely, Positively Must Know About Unicode and Character Sets (No Excuses!) by Joel Spolsky (not specific to Python, but gives a good background).

Accessing JVM arguments from inside Java

Whenever I get a ClassNotFoundException error in Java, I think to myself “but it is there!” and then I correct the typo in the classpath or get angry at Eclipse for messing up my classpath. Lately I have programmed in more complex settings where it was not always clear to me where the application gets the classpath from, so I wanted to check which of my libraries actually end up on the classpath. Turns out it is not very complicated. Here is code to print a number of useful things:

System.out.println("Working directory: " 
      + Paths.get(".").toAbsolutePath().normalize().toString());
System.out.println("Classpath: " 
      + System.getProperty("java.class.path"));
System.out.println("Library path: " 
      + System.getProperty("java.library.path"));
System.out.println("java.ext.dirs: " 
      + System.getProperty("java.ext.dirs"));

The current working directory is the starting point for all relative paths, e.g., for reading and writing files. The normalization of the path makes it a bit more readable, but is not necessary. The class Paths is from the package java.nio.file.Paths. The classpath is the place where Java looks for (bytecode for) classes. The entries should be folders or jar-files. The Java library path is where Java looks for native libraries, e.g., platform dependent things. You can of course access other environment variables with the same method, but I cannot at the moment think of a useful example.

Related (at least related enough to put it into the same post), this is how you can print the space used and available on the JVM heap:

int mbfactor = 1024*1024;
System.out.println("Memory free/used/total/max " 
      + Runtime.getRuntime().freeMemory()/mbfactor + "/"
      + (Runtime.getRuntime().totalMemory()-Runtime.getRuntime().freeMemory())/mbfactor + "/"
      + Runtime.getRuntime().totalMemory()/mbfactor + "/"
      + Runtime.getRuntime().maxMemory()/mbfactor + " MB"
);

Histograms of category frequencies in R

I am learning R, so this is my first attempt to create histograms in R. The data that I have is a vector of one category for each data point. For this example we will use a vector of a random sample of letters. The important thing is that we want a histogram of the frequencies of texts, not numbers. And the texts are longer than just one letter. So let’s start with this:

labels <- sample(letters[1:20],100,replace=TRUE)
labels <- vapply(seq_along(labels), 
                 function(x) paste(rep(labels[x],10), collapse = ""),
                 character(1L)) # Repeat each letter 10 times
library(plyr) # for the function 'count'
distribution <- count(labels)
distribution_sorted <- 
   distribution[order(distribution[,"freq"], decreasing=TRUE),]

I use the function count from the package plyr to get a matrix distribution with the different categories in column one (called "x") and the number of times this label occurs in column two (called "freq"). As I would like the histogram to display the categories from the most frequent to the least frequent one, I then sort this matrix by frequency with the function order. The function gives back a vector of indices in the correct order, so I need to plug this into the original matrix as row numbers.

Now let's do the histogram:

mp <- barplot(distribution_sorted[,"freq"],
         names.arg=distribution_sorted[,1], # X-axis names
         las=2,  # turn labels by 90 degrees
         col=c("blue"), # blue bars (just for fun)
         xlab="Kategorie", ylab="Häufigkeit", # Axis labels
         )

There are many more settings to adapt, e.g., you can use cex to increase the font size for the numerical y-axis values (cex.axis), the categorical x-axis names (cex.names), and axis labels (cex.lab).

In my plot there is one problem. My categorie names are much longer than the values on the y-axis and so the axis labels are positioned incorrectly. This is the point to give up and do the plot in Excel (ahem, LaTeX!) - or take input from fellow bloggers. They explain the issues way better than me, so I will just post my final solution. I took the x-axis label out of the plot and inserted it separately with mtext. I then wanted a line for the x-axis as well and in the end I took out the x-axis names from the plot again and put them into a separate axis at the bottom (side=1) with zero-length ticks (tcl=0) intersecting the y-axis at pos=-0.3.

# mai = space around the plot: bottom - left - top - right
# mgp = spacing for axis title - axis labels - axis line
par(mai=c(2.5,1,0.3,0.15), mgp=c(2.5,0.75,0))
mp <- barplot(distribution_sorted[,"freq"],
         #names.arg=distribution_sorted[,1], # X-axis names/labels
         las=2,  # turn labels by 90 degrees
         col=c("blue"), # blue bars (just for fun)
         ylab="Häufigkeit", # Axis title
         )
axis(side=1, at=mp, pos=-0.3, 
     tick=TRUE, tcl=0, 
     labels=distribution_sorted[,1], las=2, 
     )
mtext("Kategorie", side=1, line=8.5) # x-axis label

There has to be an easier way !?

Sorting a HashMap by Value in Java

Sometimes Java drives you nuts… you want to save word bigrams and their frequencies as an example. A HashMap<String, Integer> is very convenient. Now we want to sort it by value. And the fun begins! Of course we do not want to lose the connection between keys and values, so we cannot just use Collections.sort(map.keySet()) (or the same for values). Also using a TreeMap with a custom-wrote Comparator for our pairs does not work, because there you cannot have identical values (another fun fact).

Here is a generic method that sorts the given HashMap by value. V can be anything as long as it can be compared with itself.

public static <K, V extends Comparable<V>> List<Entry<K, V>> sortHashMapByValue (HashMap<K, V> theMap) {
   List<Entry<K, V>> resultList = new ArrayList<Entry<K, V>>(theMap.entrySet());
   Collections.sort(resultList,
          new Comparator<Entry<K, V>>() {
            @Override
            public int compare(Entry<K, V> o1, Entry<K, V> o2) {
               return o1.getValue().compareTo(o2.getValue());
            }
       });
   return resultList;