Python for Secret Agents

Download the Windows installer for Python 3.3 (or higher) for your version of Windows. When you run the installer, you'll be asked a number of questions about where to install it and what to install.

It's essential that Python be installed in a directory with a simple name. Under Windows, the common choice is C:\Python33. Using the Windows directories with spaces in their name (Program Files, My Documents, or Documents and Settings) can cause confusion.

Be sure to install the Tcl/Tk components too. This will assure that you have all the elements required to support IDLE. IDLE is a handy text editor that comes with Python. For Windows agents, this is generally bundled into the installation kit. All you need to do is be sure it has a check mark in the installation wizard.

With Windows, the python.exe program doesn't have a version number as part of its name. This is atypical.

In Mac OS X, there is already a Python installation, usually Python 2.7. This must be left intact.

Download the Mac OS X installer for Python 3.3 (or higher). When you run this, you will be adding a second version of Python. This means that many add-on modules and tools must be associated with the proper version of Python. This requires a little bit of care. It's not good to use the vaguely-named tool like easy_install. It's important to use the more specific easy_install-3.3, which identifies the version of Python you're working with.

The program named python is usually going to be an alias for python2.7. This, too, must be left intact. We'll always explicitly use python3 (also known as python3.3) for this book. You can confirm this by using the shell command.

Note that there are several versions of Tcl/Tk available for Mac OS X. The Python website directs you to a specific version. At the time of this writing, this version was ActiveTCL 8.5.14 from ActiveState software. You'll need to install this, too. This software allows us to use IDLE.

Visit http://www.activestate.com/activetcl/downloads for the proper version.

Most GNU/Linux agents have various C compilers and other developer tools available. Many Linux distributions are already configured to support developers, so the tools are already there.

Mac OS X agents will usually need Xcode. Get it from https://developer.apple.com/xcode/downloads/. Every Mac OS X agent should have this.

When installing this, be sure to also install the command line developer tools. This is another big download above and beyond the basic Xcode download.

Windows agents will generally find that pre-built binaries are available for most packages of interest. If, in a rare case, that pre-built code isn't available, tools such as Cygwin may be necessary. See http://www.cygwin.com.

In order to effectively and simply download additional Python modules, we often use a tool to get tools. There are two popular ways to add Python modules: PIP and the easy_install script.

To install easy_install, go to https://pypi.python.org/pypi/setuptools/3.6.

The setuptools package will include the easy_install script, which we'll use to add modules to Python.

If you've got multiple versions of Python installed, be sure to download and then install the correct easy install version for Python 3.3. This means that you'll generally be using the easy_install-3.3 script to add new software tools.

To install PIP, go to https://pypi.python.org/pypi/pip/1.5.6.

We'll be adding the Pillow package in Chapter 3, Encoding Secret Messages with Steganography. We'll also be adding the Beautiful Soup package in Chapter 4, Drops, Hideouts, Meetups, and Lairs.

The Python 3.4 distribution should include the PIP tool. You don't need to download it separately.

We can always enter exit to get a reminder on how to exit from interactive Python, as follows:

>>> exit

Use exit() or Ctrl + D (that is EOF (end-of-file)) to exit.

Windows agents will see that they must use Ctrl + Z and Return to exit.

Python is a programming language that also has an interactive prompt of >>>. To confirm that Python is working, we're responding to that prompt, using a feature called the Read-Execute-Print Loop (REPL).

In the long run, we'll be writing scripts to process our data. Our data might be an image or a secret message. The end of a script file will exit from Python. This is the same as pressing Ctrl + D (or Ctrl + Z and Return) to send the EOF sequence.

Python has a help mode, which is started with the help() function. The help() function provides help on a specific topic. Almost anything you see in Python can be the subject of help.

For pieces of Python syntax, such as the + operator, you'll need to use a string meaning you should enclose the syntax in quotes. For example, help("+") will provide detailed help on operator precedence.

For other objects (such as numbers, strings, functions, classes, and modules) you can simply ask for help on the object itself; quotes aren't used. Python will locate the class of the object and provide help on the class.

For example, help(3) will provide a lot of detailed, technical help on integers, as shown in the following snippet:

>>> help(3)
Help on int object:
class int(object)
|  int(x=0) -> integer
|  int(x, base=10) -> integer
|
etc.

When using the help() module from the command line, the output will be presented in pages. At the end of the first page of output, we see a new kind of non-Python prompt. This is usually :, but on Windows it may be -- More --.

Python normally prompts us with >>> or .... A non-Python prompt must come from one of the help viewers.

When we enter help() with no object or string value, we wind up in help mode. This uses Python's help> prompt to make it very clear that we're getting help, not entering Python statements. To go back to ordinary Python programming mode, and enter quit.

The prompt then changes back to >>> to confirm that we can go back to entering code.

Your next training mission is to experiment with the help() function and help mode before we can move on.

Python provides the usual mix of arithmetic and comparison operators. However, there are some important wrinkles and features. Rather than assuming you're aware of them, we'll review the details.

The conventional arithmetic operators are: +, -, *, /, //, %, and **. There are two variations on division: an exact division (/) and an integer division (//). You must choose whether you want an exact, floating-point result, or an integer result:

>>> 355/113
3.1415929203539825
>>> 355//113
3
>>> 355.0/113.0
3.1415929203539825
>>> 355.0//113.0
3.0

The exact division (/) produces a float result from two integers. The integer division produces an integer result. When we use float values, we expect exact division to produce float. Even with two floating-point values, the integer division produces a rounded-down floating-point result.

We have this extra division operator to avoid having to use wordy constructs such as int(a/b) or math.floor(a/b).

Beyond conventional arithmetic, there are some additional bit fiddling operators that are available: &, |, ^, >>, <<, and ~. These operators work on integers (and sets). These are emphatically not Boolean operators; they don't work on the narrow domain of True and False. They work on the individual bits of an integer.

We'll use binary values with the 0b prefix to show what the operators do, as shown in the following code. We'll look at details of this 0b prefix later.

>>> bin(0b0101 & 0b0110)
'0b100'
>>> bin(0b0101 ^ 0b0110)
'0b11'
>>> bin(0b0101 | 0b0110)
'0b111'
>>> bin(~0b0101)
'-0b110'

The & operator does bitwise AND. The ^ operator does bitwise exclusive OR (XOR). The | operator does inclusive OR. The ~ operator is the complement of the bits. The result has many 1 bits and is shown as a negative number.

The << and >> operators are for doing left and right shifts of the bits, as shown in the following code:

>>> bin( 0b110 << 4 )
'0b1100000'
>>> bin( 0b1100000 >> 3 )
'0b1100'

It may not be obvious, but shifting left x bits is like multiplying it by 2**x, except it may operate faster. Similarly, shifting right by b bits amounts to division by 2**b.

We also have all of the usual comparison operators: <, <=, >, >=, ==, and !=.

In Python, we can combine comparison operators without including the AND operator:

>>> 7 <= 11 < 17
True
>>> 7 <= ll and 11 < 17
True

This simplification really does implement our conventional mathematical understanding of how comparisons can be written. We don't need to say 7 <= 11 and 11 < 17.

There's another comparison operator that's used in some specialized situations: is. The is operator will appear, for now, to be the same as ==. Try it. 3 is 3 and 3 == 3 seem to do the same thing. Later, when we start using the None object, we'll see the most common use for the is operator. For more advanced Python programming, there's a need to distinguish between two references to the same object (is) and two objects which claim to have the same value (==).

Python gives us a variety of numbers, plus the ability to easily add new kinds of numbers. We'll focus on the built-in numbers here. Adding new kinds of numbers is the sort of thing that takes up whole chapters in more advanced books.

Python ranks the numbers into a kind of tower. At the top are numbers with fewest features. Each subclass extends that number with more and more features. We'll look at the tower from bottom up, starting with the integers that have the most features, and moving towards the complex numbers that have the least features. The following sections cover the various kinds of numbers we'll need to use.

48813
0xbead 
0b1011111010101101
0o137255

>>> 2**256
115792089237316195423570985008687907853269984665640564039457584007913129639936

>>> from fractions import Fraction

>>> length=4+Fraction("7/8")
>>> width=2+Fraction("1/4")
>>> length*width
Fraction(351, 32)

>>> divmod(351,32)
(10, 31)

3.1415926
6.22E12

>>> import platform
>>> platform.python_build()
('v3.3.4:7ff62415e426', 'Feb  9 2014 00:29:34')
>>> platform.python_compiler()
'GCC 4.2.1 (Apple Inc. build 5666) (dot 3)'

>>> from decimal import Decimal

>>> conversion=Decimal("247.616")
>>> conversion
Decimal('247.616')

>>> lunch=Decimal("12900")
>>> lunch/conversion
Decimal('52.09679503747738433703799431')

>>> penny=Decimal('.00')
>>> (lunch/conversion).quantize(penny)
Decimal('52.10')
That's much better. How much was the bribe we needed to pay?
>>> bribe=50000
>>> (bribe/conversion).quantize(penny)
Decimal('201.93')

>>> cab=Decimal('23.50')
That gets us to the whole calculation: lunch plus bribe, converted, plus cab.
>>> ((lunch+bribe)/conversion).quantize(penny)+cab
Decimal('277.52')

>>> 2+3j
(2+3j)

Python includes a variety of data types, which aren't numbers. In the Handling text and strings section, we'll look at Python strings. We'll look at collections in Chapter 2, Acquiring Intelligence Data.

Boolean values, True and False, form their own little domain. We can extract a Boolean value from most objects using the bool() function. Here are some examples:

>>> bool(5)
True
>>> bool(0)
False
>>> bool('')
False
>>> bool(None)
False
>>> bool('word')
True

The general pattern is that most objects have a value True and a few exceptional objects have a value False. Empty collections, 0, and None have a value False. Boolean values have their own special operators: and, or, and not. These have an additional feature. Here's an example:

>>> True and 0
0
>>> False and 0
False

When we evaluate True and 0, both sides of the and operator are evaluated; the right-hand value was the result. But when we evaluated False and 0, only the left-hand side of and was evaluated. Since it was already False, there was no reason to evaluate the right-hand side.

The and and or operators are short-circuit operators. If the left side of and is False, that's sufficient and the right-hand side is ignored. If the left-hand side of or is True, that's sufficient and the right-hand side is ignored.

Python's rules for evaluation follow mathematic practice closely. Arithmetic operations have the highest priority. Comparison operators have a lower priority than arithmetic operations. The logical operators have a very low priority. This means that a+2 > b/3 or c==15 will be done in phases: first the arithmetic, then the comparison, and finally the logic.

Mathematical rules are followed by arithmetic rules. ** has a higher priority than *, /, //, or %. The + and – operators come next. When we write 2*3+4, the 2*3 operation must be performed first. The bit fiddling is even lower in priority. When you have a sequence of operations of the same priority (a+b+c), the computations are performed from left to right. If course, if there's any doubt, it's sensible to use parenthesis.

We've been using the REPL feature of our Python toolset. In the long run, this isn't ideal. We'll be much happier writing scripts. The point behind using a computer for intelligence gathering is to automate data collection. Our scripts will require assignment to variables. It will also require explicit output and input.

We've shown the simple, obvious assignment statement in several examples previously. Note that we don't declare variables in Python. We simply assign values to variables. If the variable doesn't exist, it gets created. If the variable does exist, the previous value is replaced.

Let's look at some more sophisticated technology for creating and changing variables. We have multiple assignment statements. The following code will assign values to several variables at once:

>>> length, width = 2+Fraction(1,4), 4+Fraction(7,8)
>>> length
Fraction(9, 4)
>>> width
Fraction(39, 8)
>>> length >= width
False

We've set two variables, length and width. However, we also made a small mistake. The length isn't the larger value; we've switched the values of length and width. We can swap them very simply using a multiple assignment statement as follows:

>>> length, width = width, length
>>> length
Fraction(39, 8)
>>> width
Fraction(9, 4)

This works because the right-hand side is computed in its entirety. In this case, it's really simple. Then all of the values are broken down and assigned to the named variables. Clearly, the number of values on the right have to match the number of variables on the left or this won't work.

We also have augmented assignment statements. These couple an arithmetic operator with the assignment statement. The following code is an example of +=: using assignment augmented with addition. Here's an example of computing a sum from various bits and pieces:

>>> total= 0
>>> total += (lunch/conversion).quantize(penny)
>>> total += (bribe/conversion).quantize(penny)
>>> total += cab
>>> total
Decimal('277.53')

We don't have to write total = total +.... Instead, we can simply write total += .... It's a nice clarification of what our intent is.

All of the arithmetic operators are available as augmented assignment statements. We might have a hard time finding a use for %= or **=, but the statements are part of the language.

The idea of a nice clarification should lead to some additional thinking. For example, the variable named conversion is a perfectly opaque name. Secrecy for data is one thing: we'll look at ways to encrypt data. Obscurity through shabby processing of that data often leads to a nightmarish mess. Maybe we should have called it something that defines more clearly what it means. We'll revisit this problem of obscurity in some examples later on.

Most of our missions will involve gathering and analyzing data. We won't be creating a very sophisticated User Interface (UI). Python has tools for building websites and complex graphical user interfaces (GUIs). The complexity of those topics leads to entire books to cover GUI and web development.

We don't want to type each individual Python statement at the >>> prompt. That makes it easy to learn Python, but our goal is to create programs. In GNU/Linux parlance, our Python application programs can be called scripts. This is because Python programs fit the definition for a scripting language.

For our purposes, we'll focus on scripts that use the command-line interface (CLI) Everything we'll write will run in a simple terminal window. The advantage of this approach is speed and simplicity. We can add graphic user interfaces later. Or we can expand the essential core of a small script into a web service, once it works.

What is an application or a script? A script is simply a plain text file. We can use any text editor to create this file. A word processor is rarely a good idea, since word processors aren't good at producing plain text files.

If we're not working from the >>> REPL prompt, we'll need to explicitly display the output. We'll display output from a script using the print() function.

Here's a simple script we can use to produce a receipt for bribing (encouraging) our informant.

From decimal import Decimal:

PENNY= Decimal('.00')

grd_usd= Decimal('247.616')
lunch_grd= Decimal('12900')
bribe_grd= 50000
cab_usd= Decimal('23.50')

lunch_usd= (lunch_grd/grd_usd).quantize(PENNY)
bribe_usd= (bribe_grd/grd_usd).quantize(PENNY)

print( "Lunch", lunch_grd, "GRD", lunch_usd, "USD" )
print( "Bribe", bribe_grd, "GRD", bribe_usd, "USD" )
print( "Cab", cab_usd, "USD" )
print( "Total", lunch_usd+bribe_usd+cab_usd, "USD" )

Let's break this script down so that we can follow it. Reading a script is a lot like putting a tail on an informant. We want to see where the script goes and what it does.

First, we imported the Decimal definition. This is essential for working with currency. We defined a value, PENNY, that we'll use to round off currency calculations to the nearest penny. We used a name in all caps to make this variable distinctive. It's not an ordinary variable; we should never see it on the left-hand side of an assignment statement again in the script.

We created the currency conversion factor, and named it grd_usd. That's a name that seems meaningful than conversion in this context. Note that we also added a small suffix to our amount names. We used names such as lunch_grd, bribe_grd, and cab_usd to emphasize which currency is being used. This can help prevent head-scrambling problems.

Given the grd_usd conversion factor, we created two more variables, lunch_usd and bribe_usd, with the amounts converted to dollars and rounded to the nearest penny. If the accountants want to fiddle with the conversion factor—perhaps they can use a different bank than us spies—they can tweak the number and prepare a different receipt.

The final step was to use the print() function to write the receipt. We printed the three items we spent money on, showing the amounts in GRD and USD. We also computed the total. This will help the accountants to properly reimburse us for the mission.

We'll describe the output as primitive but acceptable. After all, they're only accountants. We'll look into pretty formatting separately.

The simplest way to gather input is to copy and paste it into the script. That's what we did previously. We pasted the Greek Drachma conversion into the script: grd_usd= Decimal('247.616'). We could annotate this with a comment to help the accountants make any changes.

Additional comments come at the end of the line, after a # sign. They look like this:

grd_usd= Decimal('247.616') # Conversion from Mihalis Bank 5/15/14

This extra text is part of the application, but it doesn't actually do anything. It's a note to ourselves, our accountants, our handler, or the person who takes over our assignments when we disappear.

This kind of data line is easy to edit. But sometimes the people we work with want more flexibility. In that case, we can gather this value as input from a person. For this, we'll use the input() function.

We often break user input down into two steps like this:

        entry= input("GRD conversion: ")
        grd_usd= Decimal(entry)

The first line will write a prompt and wait for the user to enter the amount. The amount will be a string of characters, assigned to the variable entry. Python can't use the characters directly in arithmetic statements, so we need to explicitly convert them to a useful numeric type.

The second line will try to convert the user's input to a useful Decimal object. We have to emphasize the try part of this. If the user doesn't enter a string that represents valid Decimal number, there will be a major crisis. Try it.

The crisis will look like this:

>>> entry= input("GRD conversion: ")
GRD conversion: 123.%$6
>>> grd_usd= Decimal(entry)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
decimal.InvalidOperation: [<class 'decimal.ConversionSyntax'>]

Rather than this, enter a good number. We entered 123.%$6.

The bletch starting with Traceback indicates that Python raised an exception. A crisis in Python always results in an exception being raised. Python defines a variety of exceptions to make it possible for us to write scripts that deal with these kinds of crises.

Once we've seen how to deal with crises, we can look at string data and some simple clean-up steps that can make the user's life a little easier. We can't fix their mistakes, but we can handle a few common problems that stem from trying to type numbers on a keyboard.

    entry= input("GRD conversion: ")
    try:
        grd_usd= Decimal(entry)
    except decimal.InvalidOperation:
        print("Invalid: ", entry)

grd_usd= None
while grd_usd is None:
    entry= input("GRD conversion: ")
    try:
        grd_usd= Decimal(entry)
    except decimal.InvalidOperation:
        print("Invalid: ", entry)
print( grd_usd, "GRD = 1 USD" )

We have two kinds of interesting string conversions: strings to numbers and numbers to strings.

We've seen functions such as Decimal() to convert a string to a number. We also have the functions: int(), float(), fractions.Fraction(), and complex(). When we have numbers that aren't in base 10, we can also use int() to convert those, as shown in the following code:

>>> int( 'dead', 16 )
57005
>>> int( '0b1101111010101101', 2 )
57005

We can create strings from numbers too. We can use functions such as hex(), oct(), and bin() to create strings in base 16, 8, and 2. We also have the str() function, which is the most general-purpose function to convert any Python object into a string of some kind.

More valuable than these is the format() method of a string. This performs a variety of value-to-string conversions. It uses a conversion format specification or template string to define what the resulting string will look like.

Here's an example of using format() to convert several values into a single string. It uses a rather complex format specification string:

 >>> "{0:12s} {1:6.2f} USD {2:8.0f} GRD".format( "lunch", lunch_usd, lunch_grd )
'lunch         52.10 USD    12900 GRD'

The format string has three conversion specifications: {0:12s}, {1:6.2f}, and {2:8.0f}. It also has some literal text, mostly spaces, but USD and GRD are part of the background literal text into which the data will be merged.

Each conversion specification has two parts: the item to convert and the format for that item. These two parts separated by a : inside {}. We'll look at each conversion:

We can do something like the following to improve our receipt:

receipt_1 = "{0:12s}              {1:6.2f} USD"
receipt_2 = "{0:12s} {1:8.0f} GRD {2:6.2f} USD"
print( receipt_2.format("Lunch", lunch_grd, lunch_usd) )
print( receipt_2.format("Bribe", bribe_grd, bribe_usd) )
print( receipt_1.format("Cab", cab_usd) )
print( receipt_1.format("Total", lunch_usd+bribe_usd+cab_usd) )

We've used two parallel format specifications. The receipt_1 string can be used to format a label and a single dollar value. The receipt_2 string can be used to format a label and two numeric values: one in dollars and the other in Greek Drachma.

This makes a better-looking receipt. That should keep the accountants off our back and let us focus on the real work: working on data files and folders.

String objects can also be decomposed or parsed into substrings. We could easily write an entire chapter on all the various parsing methods that string objects offer. A common transformation is to strip extraneous whitespace from the beginning and end of a string. The idea is to remove spaces and tabs (and a few other nonobvious characters). It looks like this:

entry= input("GRD conversion: ").strip()

We've applied the input() function to get a string from the user. Then we've applied the strip() method of that string object to create a new string, stripped bare of whitespace characters. We can try it from the >>> prompt like this:

>>> "   123.45     ".strip()
'123.45'

This shows how a string with junk was pared down to the essentials. This can simplify a user's life; a few extra spaces won't be a problem.

Another transformation might be to split a string into pieces. Here's just one of the many techniques available:

>>> amount, space, currency = "123.45 USD".partition(" ")
>>> amount
'123.45'
>>> space
' '
>>> currency
'USD'

Let's look at this in detail. First, it's a multiple-assignment statement, where three variables are going to be set: amount, space, and currency.

The expression, "123.45 USD".partition(" "), works by applying the partition() method to a literal string value. We're going to partition the string on the space character. The partition() method returns three things: the substring in front of the partition, the partition character, and the substring after the partition.

The actual partition variable may also be assigned an empty string, ''. Try this:

amount, space, currency = "word".partition(" ") 

What are the values for amount, space, and currency?

If you use help(str), you'll see all of the various kinds of things a string can do. The names that have __ around them map to Python operators. __add__(), for example, is how the + operator is implemented.

We'll start by creating a text file that we can work with. There are several interesting aspects to working with files. We'll focus on the following two aspects:

Our general template, with open("message1.txt", "w") as target, for creating a file looks like this:

    print( "Message to HQ", file=target )
    print( "Device Size 10 31/32", file=target )

We'll open the file with the open() function. In this case, the file is opened in write mode. We've used the print() function to write some data into the file.

Once the program finishes the indented context of the with statement, the file is properly closed and the OS resources are released. We don't need to explicitly close the file object.

We can also use something like this to create our file:

text="""Message to HQ\n Device Size 10 31/32\n"""
with open("message1.txt", "w") as target:
    target.write(text)

Note the important difference here. The print() function automatically ends each line with a \n character. The write() method of a file object doesn't add anything.

In many cases, we may have more complex physical formatting for a file. We'll look at JSON or CSV files in a later section. We'll also look at reading and writing image files in Chapter 3, Encoding Secret Messages with Steganography.

Our general template for reading a file looks like this:

with open("message1.txt", "r") as source:
    text= source.read()
print( text )

This will create the file object, but it will be in read mode. If the file doesn't exist, we'll get an exception. The read() function will slurp the entire file into a single block of text. Once we're done reading the content of the file, we're also done with the with context. The file can be closed and the resources can be released. The text variable that we created will have the file's contents ready for further processing.

In many cases, we want to process the lines of the text separately. For this, Python gives us the for loop. This statement interacts with files to iterate through each line of the file, as shown in the following code:

with open("message1.txt", "r") as source:
    for line in source:
        print(line)

The output looks a bit odd, doesn't it?

It's double-spaced because each line read from the file contains a \n character at the end. The print() function automatically includes a \n character. This leads to double-spaced output.

We have two candidate fixes. We can tell the print() function not to include a \n character. For example, print(line, end="") does this.

A slightly better fix is to use the rstrip() method to remove the trailing whitespace from the right-hand end of line. This is slightly better because it's something we'll do often in a number of contexts. Attempting to suppress the output of the extra \n character in the print() function is too specialized to this one situation.

In some cases, we may have files where we need to filter the lines, looking for particular patterns. We might have a loop that includes conditional processing via the if statement, as shown in the following code:

with open("message1.txt", "r") as source:
    for line in source:
        junk1, keyword, size= line.rstrip().partition("Size")
        if keyword != '':
            print( size )

This shows a typical structure for text processing programs. First, we open the file via a with statement context; this assures us that the file will be closed properly no matter what happens.

We use the for statement to iterate through all lines of the file. Each line has a two-step process: the rstrip() method removes trailing whitespace, the partition() method breaks the line around the keyword Size.

The if statement defines a condition (keyword != '') and some processing that's done only if the condition is True. If the condition is False (the value of keyword is ''), the indented body of the if statement is silently skipped.

The assignment and if statements form the body of the for statement. These two statements are executed once for every line in the file. When we get to the end of the for statement, we can be assured that all lines were processed.

We have to note that we can create an exception to the usual for all lines assumption about processing files with the for statement. We can use the break statement to exit early from the loop, breaking the usual assumption. We'd prefer to avoid the break statement, making it easy to see that a for statement works for all lines of a file.

At the end of the for statement, we're done processing the file. We're done with the with context, too. The file will be closed.

What if we have more patterns than what we're looking for? What if we're processing more complex data?

Let's say we've got something like this in a file:

Message to Field Agent 006 1/2 
Proceed to Rendezvous FM16uu62
Authorization to Pay $250 USD

We're looking for two keywords: Rendezvous and Pay. Python gives us the elif clause as part of the if statement. This clause provides a tidy way to handle multiple conditions gracefully. Here's a script to parse a message to us from the headquarters:

amount= None
location= None
with open("message2.txt", "r") as source:
    for line in source:
        clean= line.lower().rstrip()
        junk, pay, pay_data= clean.partition("pay")
        junk, meet, meet_data= clean.partition("rendezvous")
        if pay != '':
            amount= pay_data
        elif meet != '':
            location= meet_data
        else:
            pass # ignore this line 
print("Budget", amount, "Meet", location)

We're searching the contents in the file for two pieces of information: the rendezvous location and the amount we can use to bribe our contact. In effect, we're going to summarize this file down to two short facts, discarding the parts we don't care about.

As with the previous examples, we're using a with statement to create a processing context. We're also using the for statement to iterate through all lines of the file.

We've used a two-step process to clean each line. First, we used the lower() method to create a string in lowercase. Then we used the rstrip() method to remove any trailing whitespace from the line.

We applied the partition() method to the cleaned line twice. One partition looked for pay and the other partition looked for rendezvous. If the line could be partitioned on pay, the pay variable (and pay_data) would have values not equal to a zero-length string. If the line could be partitioned on rendezvous, then the meet variable (and meet_data) would have values not equal to a zero-length string. The else, if is abbreviated elif in Python.

If none of the previous conditions are true, we don't need to do anything. We don't need an else: clause. But we decided to include the else: clause in case we later needed to add some processing. For now, there's nothing more to do. In Python, the pass statement does nothing. It's a syntactic placeholder; a thing to write when we must write something.

The first thing we need to do is read our spell-check corpus. We'll call it a corpus—a body of words—not a dictionary. The examples will be based on web2 (Webster's Second International) all 234,936 words worth. This is generally available in BSD Unix and Mac OS X.

Here's a typical script that will examine a corpus:

count= 0
corpus_file = "/usr/share/dict/words" 
with open( corpus_file ) as corpus:
    for line in corpus:
        word= line.strip()
        if len(word) == 10:
            print(word)
            count += 1
print( count )

We've opened the corpus file and read all of the lines. The word was located by stripping whitespace from the line; this removes the trailing \n character. An if statement was used to filter the 10-letter words. There are 30,878 of those, from abalienate to Zyzzogeton.

This little script isn't really part of any larger application. It's a kind of technology spike—something we're using to nail down a detail. When writing little scripts like this, we'll often skip careful design of classes or functions and just slap some Python statements into a file.

In POSIX-compliant OSes, we can do two more things to make a script easy to work with. First, we can add a special comment on the very first line of the file to help the OS figure out what to do with it. The line looks like this:

#!/usr/bin/env python3

This tells the OS how to handle the script. Specifically, it tells the OS to use the env program. The env program will then locate our installation of Python 3. Responsibility will be handed off to the python3 program.

The second step is to mark the script as executable. We use the OS command, chmod +x some_file.py, to mark a Python file as an executable script.

If we've done these two steps, we can execute a script by simply typing its name at the command prompt.

In Windows, the file extension (.py) is associated with the Python program. There is an Advanced Settings panel that defines these file associations. When you installed Python, the association was built by the installer. This means that you can enter the name of a Python script and Windows will search through the directories named in your PATH value and execute that script properly.

We'll use Python's zipfile module to work with a ZIP archive. This means we'll need to use import zipfile before we can do anything else. Since a ZIP archive contains multiple files, we'll often want to get a listing of the available files in the archive. Here's how we can survey an archive:

import zipfile
with zipfile.ZipFile( "demo.zip", "r" ) as archive:
    archive.printdir()

We've opened the archive, creating a file processing context. We then used the archive's printdir() method to dump the members of the archive.

We can't, however, extract any of the files because the ZIP archive was encrypted and we lost the password. Here's a script that will try to read the first member:

import zipfile
with zipfile.ZipFile( "demo.zip", "r" ) as archive:
    archive.printdir()
    first = archive.infolist()[0]
    with archive.open(first) as member:
        text= member.read()
        print( text )

We've created a file processing context using the open archive. We used the infolist() method to get information on each member. The archive.infolist()[0] statement will pick item zero from the list, that is, the first item.

We tried to create a file processing context for this specific member. Instead of seeing the content of the member, we get an exception. The details will vary, but your exception message will look like this:

RuntimeError: File <zipfile.ZipInfo object at 0x1007e78e8> is encrypted, password required for extraction

The hexadecimal number (0x1007e78e8) may not match your output, but you'll still get an error trying to read an encrypted ZIP file.

To recover the files, we'll need to resort to brute-force search for a workable password. This means inserting our corpora reading loop into our archive processing context. It's a bit of flashy copy-and-paste that leads to a script like the following:

import zipfile
import zlib
corpus_file = "/usr/share/dict/words"

with zipfile.ZipFile( "demo.zip", "r" ) as archive:
    first = archive.infolist()[0]
    print( "Reading", first.filename )
    with open( corpus_file ) as corpus:
        for line in corpus:
            word= line.strip().encode("ASCII")
            try:
                with archive.open(first, 'r', pwd=word) as member:
                    text= member.read()
                print( "Password", word )
                print( text )
                break
            except (RuntimeError, zlib.error, zipfile.BadZipFile):
                pass

We've imported two libraries: zipfile as well as zlib. We added zlib because it turns out that we'll sometimes see zlib.error exceptions when guessing passwords. We created a context for our open archive file. We used the infolist() method to get names of members and fetched just the first file from that list. If we can read one file, we can read them all.

Then we opened our corpus file, and created a file processing context for that file. For each line in the corpora, we used two methods of the line: the strip() method will remove the trailing "\n", and the encode("ASCII") method will transform the line from Unicode characters to ASCII bytes. We need this because ZIP library passwords are ASCII bytes, not proper Unicode character strings.

The try: block attempts to open and read the first member. We created a file processing context for this member within the archive. We tried to read the member. If anything goes wrong while we are trying to read the encrypted member, an exception will be raised. The usual culprit, of course, is attempting to read the member with the wrong password.

If everything works well, then we guessed the correct password. We can print the recovered password, as well as the text of the member as a confirmation.

Note that we've used a break statement to end the corpora processing for loop. This changes the for loop's semantics from for all words to there exists a word. The break statement means the loop ends as soon as a valid password is found. No further words in the corpus need to be processed.

We've listed three kinds of exceptions that might be raised from attempting to use a bad password. It's not obvious why different kinds of exceptions may be raised by wrong passwords. But it's easy to run some experiments to confirm that a variety of different exceptions really are raised by a common underlying problem.