Category: Software development

Both Ruby and Python offer great interactive shells, also known as REPLs (Read Eval Print Loops). These are handy for verifying snippets of code. You can invoke Python’s by simply running python or Ruby’s by running irb, jirb (for jRuby), or rails c (for Rails).

Sometimes, however, one can be mystified as to what one can do with an object or module. Lately, I’ve been finding the Ruby API documentation especially frustrating.

Fortunately, both Python and Ruby let you see what’s available. In Python, you can call the dir() function, while Ruby has the handy .methods() method.

Python:

>>> dir("some string")
['__add__', '__class__', '__contains__', '__delattr__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__getnewargs__', '__getslice__', '__gt__', '__hash__', '__init__', '__le__', '__len__', '__lt__', '__mod__', '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__rmod__', '__rmul__', '__setattr__','__sizeof__', '__str__', '__subclasshook__', '_formatter_field_name_split', '_formatter_parser', 'capitalize', 'center', 'count', 'decode', 'encode', 'endswith', 'expandtabs', 'find', 'format', 'index', 'isalnum', 'isalpha', 'isdigit', 'islower', 'isspace', 'istitle', 'isupper', 'join', 'ljust', 'lower', 'lstrip', 'partition', 'replace', 'rfind', 'rindex', 'rjust', 'rpartition', 'rsplit', 'rstrip', 'split', 'splitlines', 'startswith', 'strip', 'swapcase', 'title', 'translate', 'upper', 'zfill']

And Ruby:

irb(main):003:0> "some string".methods
=> [:to_java_bytes, :upcase!, :ascii_only?, :lstrip, :upto, :lines, :encoding, :prepend, :scan, :==, :clear, :squeeze!,:chop, :next!, :casecmp, :start_with?, :split, :to_f, :center, :reverse, :sub, :byteslice, :>, :upcase, :next, :strip!,:count, :sub!, :hash, :bytesize, :lstrip!, :to_sym, :<=, :replace, :length, :swapcase, :gsub, :intern, :succ, :capitalize, :each_codepoint, :oct, :delete!, :+, :initialize_copy, :to_java_string, :match, :unpack, :index, :rstrip!, :*, :each_char, :gsub!, :to_s, :empty?, :size, :swapcase!, :ljust, :downcase, :rpartition, :to_str, :getbyte, :sum, :crypt, :partition, :reverse!, :=~, :force_encoding, :each_byte, :tr!, :inspect, :to_c, :rstrip, :succ!, :<, :[]=, :valid_encoding?, :slice!, :slice, :insert, :tr_s!, :unseeded_hash, :squeeze, :dump, :===, :end_with?, :hex, :strip, :capitalize!, :bytes, :setbyte, :chop!, :each_line, :[], :encode, :include?, :chomp!, :<<, :encode!, :chomp, :rindex, :to_i, :<=>, :eql?, :tr_s, :chars, :codepoints, :delete, :chr, :to_r, :rjust, :%, :>=, :concat, :ord, :tr, :downcase!, :between?, :handle_different_imports, :include_class, :java_kind_of?, :java_signature, :methods, :define_singleton_method, :initialize_clone, :freeze, :extend, :nil?, :tainted?, :method, :is_a?, :instance_variable_defined?, :instance_variable_get, :singleton_class, :instance_variable_set, :public_method, :display, :send, :private_methods, :enum_for, :com, :to_java, :public_send, :instance_of?, :taint, :class, :java_annotation, :instance_variables, :!~, :org, :untrust, :protected_methods, :trust, :java_implements, :tap, :frozen?, :initialize_dup, :java, :respond_to?, :java_package, :untaint, :respond_to_missing?, :clone, :java_name, :to_enum, :singleton_methods, :untrusted?, :dup, :kind_of?, :javafx, :java_require, :javax, :public_methods, :instance_exec, :__send__, :instance_eval, :equal?, :object_id, :__id__, :!, :!=]

TeXnicCenter is an excellent IDE for developing TeX documents on Windows. It follows the usual interface conventions and is quite helpful in getting started and debugging. TeX is the standard page layout language for writing mathematical and scientific papers.

Here are some excellent tutorials for getting started with TeX.

The IDE requires MikTeX for actually compiling TeX files into PDF, PS, and so on.

When writing Python scripts which rely on python-ldap and openLDAP, it is often useful to turn on debug messages as follows:

import ldap;

# enable python-ldap logging
ldap.set_option(ldap.OPT_DEBUG_LEVEL, 4095)

# enable openLDAP logging
l = ldap.initialize('ldap://yourserver:port', trace_level=2)

This is also useful when debugging the LDAP Plugin for Trac.

(This applies to Java and C, but the code is given in Python for readability.)

Is it faster to iterate over multiple separate arrays (tuples) of simple variables?

for i in range(0, n):
	phone = phones[i];
	# ...
	email = emails[i];

Or over a single array of complex variables?

for i in range(0, n):
	phone = people[i].phone;
	# ...
	email = people[i].email;

One array is faster than multiple arrays. This is because an array is stored in a contiguous block of memory. Accessing data in different arrays at the same time can require several different pages to be loaded from virtual memory. Memory access, especially hard drive access, is slow. As your application and data set grows, a significant performance difference may manifest itself.

Arrays from the Second Dimension

When iterating over a multidimensional array with indexes i and j, is it faster to iterate over j inside i?

for i in range(0, n):
	for j in range(0, m):
		cell = cells[i][j];

Or over i inside j?

for j in range(0, m):
	for i in range(0, n):
		cell = cells[i][j];

In Java and C, a multidimensional array[n][m] is stored as contiguous m-block of contiguous n-blocks. Let i be in n and j be in m. For a given i-cell, j-cells will be far apart. For a given j-cell, i-cells will be adjacent. Accessing adjacent values in memory is always faster.

For an array[i][j], putting j in the outer loop and i in the inner loop will significantly reduce potential virtual memory slowdowns.

This is the right way:

for j in range(0, m):
	for i in range(0, n):
		cell = cells[i][j];

The above only makes a difference with large data sets, but I like to cultivate good habits.