Advanced usages of plac

Introduction

One of the design goals of plac is to make it dead easy to write a scriptable and testable interface for an application. You can use plac whenever you have an API with strings in input and strings in output, and that includes a huge domain of applications.

A string-oriented interface is a scriptable interface by construction. That means that you can define a command language for your application and that it is possible to write scripts which are interpretable by plac and can be run as batch scripts.

Actually, at the most general level, you can see plac as a generic tool to write domain specific languages (DSL). With plac you can test your application interactively as well as with batch scripts, and even with the analogous of Python doctests for your defined language.

You can easily replace the cmd module of the standard library and you could easily write an application like twill with plac. Or you could use it to script your building procedure. plac also supports parallel execution of multiple commands and can be used as task manager. It is also quite easy to build a GUI or a Web application on top of plac. When speaking of things you can do with plac, your imagination is the only limit!

From scripts to interactive applications

Command-line scripts have many advantages, but they are no substitute for interactive applications.

In particular, if you have a script with a large startup time which must be run multiple times, it is best to turn it into an interactive application, so that the startup is performed only once. plac provides an Interpreter class just for this purpose.

The Interpreter class wraps the main function of a script and provides an .interact method to start an interactive interpreter reading commands from the console.

The .interact method reads commands from the console and send them to the underlying interpreter, until the user send a CTRL-D command (CTRL-Z in Windows). There is a default argument prompt='i> ' which can be used to change the prompt. The text displayed at the beginning of the interactive session is the docstring of the main function. plac also understands command abbreviations: in this example del is an abbreviation for delete. In case of ambiguous abbreviations plac raises a NameError.

Finally I must notice that plac.Interpreter is available only if you are using a recent version of Python (>= 2.5), because it is a context manager object which uses extended generators internally.

Testing a plac application

You can conveniently test your application in interactive mode. However manual testing is a poor substitute for automatic testing.

In principle, one could write automatic tests for the ishelve application by using plac.call directly:

# test_ishelve.py
import plac
import ishelve


def test():
    assert plac.call(ishelve.main, ['.clear']) == ['cleared the shelve']
    assert plac.call(ishelve.main, ['a=1']) == ['setting a=1']
    assert plac.call(ishelve.main, ['a']) == ['1']
    assert plac.call(ishelve.main, ['.delete=a']) == ['deleted a']
    assert plac.call(ishelve.main, ['a']) == ['a: not found']


if __name__ == '__main__':
    test()

However, using plac.call is not especially nice. The big issue is that plac.call responds to invalid input by printing an error message on stderr and by raising a SystemExit: this is certainly not a nice thing to do in a test.

As a consequence of this behavior it is impossible to test for invalid commands, unless you wrap the SystemExit exception by hand each time (and possibly you do something with the error message in stderr too). Luckily, plac offers a better testing support through the check method of Interpreter objects:

# test_ishelve_more.py
from __future__ import with_statement
import ishelve
import plac


def test():
    with plac.Interpreter(ishelve.main) as i:
        i.check('.clear', 'cleared the shelve')
        i.check('a=1', 'setting a=1')
        i.check('a', '1')
        i.check('.delete=a', 'deleted a')
        i.check('a', 'a: not found')

The method .check(given_input, expected_output) works on strings and raises an AssertionError if the output produced by the interpreter is different from the expected output for the given input. Notice that AssertionError is caught by tools like pytest and nosetests and actually plac tests are intended to be run with such tools.

Interpreters offer a minor syntactic advantage with respect to calling plac.call directly, but they offer a major semantic advantage when things go wrong (read exceptions): an Interpreter object internally invokes something like plac.call, but it wraps all exceptions, so that i.check is guaranteed not to raise any exception except AssertionError.

Even the SystemExit exception is captured and you can write your test as

i.check('-cler', 'SystemExit: unrecognized arguments: -cler')

without risk of exiting from the Python interpreter.

There is a second advantage of interpreters: if the main function contains some initialization code and finalization code (__enter__ and __exit__ functions) they will be run at the beginning and at the end of the interpreter loop, whereas plac.call ignores the initialization/finalization code.

Plac easy tests

Writing your tests in terms of Interpreter.check is certainly an improvement over writing them in terms of plac.call, but they are still too low-level for my taste. The Interpreter class provides support for doctest-style tests, a.k.a. plac easy tests.

By using plac easy tests you can cut and paste your interactive session and turn it into a runnable automatics test. Consider for instance the following file ishelve.placet (the .placet extension is a mnemonic for “plac easy tests”):

#!ishelve.py
i> .clear # start from a clean state
cleared the shelve
i> a=1
setting a=1
i> a
1
i> .del a
deleted a
i> a
a: not found
i> .cler # spelling error
.cler: not found

Notice the presence of the shebang line containing the name of the plac tool to test (a plac tool is just a Python module with a function called main). The shebang is ignored by the interpreter (it looks like a comment to it) but it is there so that external tools (say a test runner) can infer the plac interpreter to use to test the file.

You can run the ishelve.placet file by calling the .doctest method of the interpreter, as in this example:

$ python -c "import plac, ishelve
plac.Interpreter(ishelve.main).doctest(open('ishelve.placet'), verbose=True)"

Internally Interpreter.doctests invokes things like Interpreter.check multiple times inside the same context and compares the output with the expected output: if even one check fails, the whole test fails.

You should realize that the easy tests supported by plac are not unittests: they are functional tests. They model the user interaction and the order of the operations generally matters. The single subtests in a .placet file are not independent and it makes sense to exit immediately at the first failure.

The support for doctests in plac comes nearly for free, thanks to the shlex module in the standard library, which is able to parse simple languages as the ones you can implement with plac. In particular, thanks to shlex, plac is able to recognize comments (the default comment character is #), escape sequences and more. Look at the shlex documentation if you need to customize how the language is interpreted. For more flexibility, it is even possible to pass the interpreter a custom split function with signature split(line, commentchar).

In addition, I have implemented some support for line number recognition, so that if a test fails you get the line number of the failing command. This is especially useful if your tests are stored in external files, though they do not need to be in a file: you can just pass to the .doctest method a list of strings corresponding to the lines of the file.

At the present plac does not use any code from the doctest module, but the situation may change in the future (it would be nice if plac could reuse doctests directives like ELLIPSIS).

It is straightforward to integrate your .placet tests with standard testing tools. For instance, you can integrate your doctests with nose or py.test as follow:

import os, shlex, plac

def test_doct():
   """
   Find all the doctests in the current directory and run them with the
   corresponding plac interpreter (the shebang rules!)
   """
   placets = [f for f in os.listdir('.') if f.endswith('.placet')]
   for placet in placets:
       lines = list(open(placet))
       assert lines[0].startswith('#!'), 'Missing or incorrect shebang line!'
       firstline = lines[0][2:] # strip the shebang
       main = plac.import_main(*shlex.split(firstline))
       yield plac.Interpreter(main).doctest, lines[1:]

Here you should notice that usage of plac.import_main, a utility which is able to import the main function of the script specified in the shebang line. You can use both the full path name of the tool, or a relative path name. In this case the runner looks at the environment variable PLACPATH and it searches the plac tool in the directories specified there (PLACPATH is just a string containing directory names separated by colons). If the variable PLACPATH is not defined, it just looks in the current directory. If the plac tool is not found, an ImportError is raised.

Plac batch scripts

It is pretty easy to realize that an interactive interpreter can also be used to run batch scripts: instead of reading the commands from the console, it is enough to read the commands from a file. plac interpreters provide an .execute method to perform just that.

There is just a subtle point to notice: whereas in an interactive loop one wants to manage all exceptions, a batch script should not continue in the background in case of unexpected errors. The implementation of Interpreter.execute makes sure that any error raised by plac.call internally is re-raised. In other words, plac interpreters wrap the errors, but does not eat them: the errors are always accessible and can be re-raised on demand.

The exception is the case of invalid commands, which are skipped. Consider for instance the following batch file, which contains a misspelled command (.dl instead of .del):

#!ishelve.py
.clear
a=1 b=2
.show
.del a
.dl b
.show

If you execute the batch file, the interpreter will print a .dl: not found at the .dl line and will continue:

$ python -c "import plac, ishelve
plac.Interpreter(ishelve.main).execute(open('ishelve.plac'), verbose=True)"
i> .clear
cleared the shelve
i> a=1 b=2
setting a=1
setting b=2
i> .show
b=2
a=1
i> .del a
deleted a
i> .dl b
2
.dl: not found
i> .show
b=2

The verbose flag is there to show the lines which are being interpreted (prefixed by i>). This is done on purpose, so that you can cut and paste the output of the batch script and turn it into a .placet test (cool, isn’t it?).

Implementing subcommands

When I discussed the ishelve implementation, I said that it looked like the poor man implementation of an object system as a chain of elifs; I also said that plac was able to do much better than that. Here I will substantiate my claim.

plac is actually able to infer a set of subparsers from a generic container of commands. This is useful if you want to implement subcommands (a familiar example of a command-line application featuring subcommands is version control system). Technically a container of commands is any object with a .commands attribute listing a set of functions or methods which are valid commands. A command container may have initialization/finalization hooks (__enter__/__exit__) and dispatch hooks (__missing__, invoked for invalid command names). Moreover, only when using command containers is plac able to provide automatic autocompletion of commands.

The shelve interface can be rewritten in an object-oriented way as follows:

# ishelve2.py
import os
import shelve
import plac


class ShelveInterface(object):
    "A minimal interface over a shelve object."
    commands = 'set', 'show', 'showall', 'delete'

    @plac.annotations(
        configfile=('path name of the shelve', 'option'))
    def __init__(self, configfile):
        self.configfile = configfile or 'conf.shelve'
        self.fname = os.path.expanduser(self.configfile)
        self.__doc__ += ('\nOperating on %s.\nUse help to see '
                         'the available commands.\n' % self.fname)

    def __enter__(self):
        self.sh = shelve.open(self.fname)
        return self

    def __exit__(self, etype, exc, tb):
        self.sh.close()

    def set(self, name, value):
        "set name value"
        yield 'setting %s=%s' % (name, value)
        self.sh[name] = value

    def show(self, *names):
        "show given parameters"
        for name in names:
            yield '%s = %s' % (name, self.sh[name])  # no error checking

    def showall(self):
        "show all parameters"
        for name in self.sh:
            yield '%s = %s' % (name, self.sh[name])

    def delete(self, name=''):
        "delete given parameter (or everything)"
        if name == '':
            yield 'deleting everything'
            self.sh.clear()
        else:
            yield 'deleting %s' % name
            del self.sh[name]  # no error checking


if __name__ == '__main__':
    plac.Interpreter(plac.call(ShelveInterface)).interact()

plac.Interpreter objects wrap context manager objects consistently. In other words, if you wrap an object with __enter__ and __exit__ methods, they are invoked in the right order (__enter__ before the interpreter loop starts and __exit__ after the interpreter loop ends, both in the regular and in the exceptional case). In our example, the methods __enter__ and __exit__ make sure the the shelve is opened and closed correctly even in the case of exceptions. Notice that I have not implemented any error checking in the show and delete methods on purpose, to verify that plac works correctly in the presence of exceptions.

When working with command containers, plac automatically adds two special commands to the set of provided commands: help and .last_tb. The help command is the easier to understand: when invoked without arguments it displays the list of available commands with the same formatting of the cmd module; when invoked with the name of a command it displays the usage message for that command. The .last_tb command is useful when debugging: in case of errors, it allows you to display the traceback of the last executed command.

Here is the usage message:

usage: ishelve2.py [-h] [-configfile CONFIGFILE]

A minimal interface over a shelve object.

optional arguments:
  -h, --help            show this help message and exit
  -configfile CONFIGFILE
                        path name of the shelve

Here is a session of usage on a Unix-like operating system:

$ python ishelve2.py -c test.shelve
A minimal interface over a shelve object.
Operating on test.shelve.
Use help to see the available commands.
i> help

special commands
================
.last_tb

custom commands
===============
delete  set  show  showall

i> delete
deleting everything
i> set a pippo
setting a=pippo
i> set b lippo
setting b=lippo
i> showall
b = lippo
a = pippo
i> show a b
a = pippo
b = lippo
i> del a
deleting a
i> showall
b = lippo
i> delete a
deleting a
KeyError: 'a'
i> .last_tb
 File "/usr/local/lib/python2.6/dist-packages/plac-0.6.0-py2.6.egg/plac_ext.py", line 190, in _wrap
    for value in genobj:
  File "./ishelve2.py", line 37, in delete
    del self.sh[name] # no error checking
  File "/usr/lib/python2.6/shelve.py", line 136, in __delitem__
    del self.dict[key]
i>

Notice that in interactive mode the traceback is hidden, unless you pass the verbose flag to the Interpreter.interact method.

CHANGED IN VERSION 0.9: if you have an old version of plac the help command must be prefixed with a dot, i.e. you must write .help. The old behavior was more consistent in my opinion, since it made it clear that the help command was special and threated differently from the regular commands. Notice that if you implement a custom help command in the commander class the default help will not be added, as you would expect.

In version 0.9 an exception `plac.Interpreter.Exit was added. Its purpose is to make it easy to define commands to exit from the command loop. Just define something like:

def quit(self):
   raise plac.Interpreter.Exit

and the interpreter will be closed properly when the quit command is entered.

plac.Interpreter.call

At the core of plac there is the call function which invokes a callable with the list of arguments passed at the command-line (sys.argv[1:]). Thanks to plac.call you can launch your module by simply adding the lines:

if __name__ == '__main__':
    plac.call(main)

Everything works fine if main is a simple callable performing some action; however, in many cases, one has a main “function” which is actually a factory returning a command container object. For instance, in my second shelve example the main function is the class ShelveInterface, and the two lines needed to run the module are a bit ugly:

if __name__ == '__main__':
   plac.Interpreter(plac.call(ShelveInterface)).interact()

Moreover, now the program runs, but only in interactive mode, i.e. it is not possible to run it as a script. Instead, it would be nice to be able to specify the command to execute on the command-line and have the interpreter start, execute the command and finish properly (I mean by calling __enter__ and __exit__) without needing user input. Then the script could be called from a batch shell script working in the background. In order to provide such functionality plac.Interpreter provides a classmethod named .call which takes the factory, instantiates it with the arguments read from the command line, wraps the resulting container object as an interpreter and runs it with the remaining arguments found in the command line. Here is the code to turn the ShelveInterface into a script

# ishelve3.py
from ishelve2 import ShelveInterface

if __name__ == '__main__':
    import plac; plac.Interpreter.call(ShelveInterface)

## try the following:
# $ python ishelve3.py delete
# $ python ishelve3.py set a 1
# $ python ishelve3.py showall

and here are a few examples of usage:

$ python ishelve3.py help

special commands
================
.last_tb

custom commands
===============
delete  set  show  showall

$ python ishelve3.py set a 1
setting a=1
$ python ishelve3.py show a
a = 1

If you pass the -i flag in the command line, then the script will enter in interactive mode and ask the user for the commands to execute:

$ python ishelve3.py -i
A minimal interface over a shelve object.
Operating on conf.shelve.
Use help to see the available commands.

i>

In a sense, I have closed the circle: at the beginning of this document I discussed how to turn a script into an interactive application (the shelve_interpreter.py example), whereas here I have show how to turn an interactive application into a script.

The complete signature of plac.Interpreter.call is the following:

call(factory, arglist=sys.argv[1:],
     commentchar='#', split=shlex.split,
     stdin=sys.stdin, prompt='i> ', verbose=False)

The factory must have a fixed number of positional arguments (no default arguments, no varargs, no kwargs), otherwise a TypeError is raised: the reason is that we want to be able to distinguish the command-line arguments needed to instantiate the factory from the remaining arguments that must be sent to the corresponding interpreter object. It is also possible to specify a list of arguments different from sys.argv[1:] (useful in tests), the character to be recognized as a comment, the splitting function, the input source, the prompt to use while in interactive mode, and a verbose flag.

Readline support

Starting from release 0.6 plac offers full readline support. That means that if your Python was compiled with readline support you get autocompletion and persistent command history for free. By default all commands autocomplete in a case sensitive way. If you want to add new words to the autocompletion set, or you want to change the location of the .history file, or to change the case sensitivity, the way to go is to pass a plac.ReadlineInput object to the interpreter.

If the readline library is not available, my suggestion is to use the rlwrap tool which provides similar features, at least on Unix-like platforms. plac should also work fine on Windows with the pyreadline library (I do not use Windows, so this part is very little tested: I tried it only once and it worked, but your mileage may vary). For people worried about licenses, I will notice that plac uses the readline library only if available, it does not include it and it does not rely on it in any fundamental way, so that the plac licence does not need to be the GPL (actually it is a BSD do-whatever-you-want-with-it licence).

The interactive mode of plac can be used as a replacement of the cmd module in the standard library. It is actually better than cmd: for instance, the help command is more powerful, since it provides information about the arguments accepted by the given command:

i> help set
usage:  set name value

set name value

positional arguments:
  name
  value

i> help delete
usage:  delete [name]

delete given parameter (or everything)

positional arguments:
  name        [None]

i> help show
usage:  show [names ...]

show given parameters

positional arguments:
  names

As you can imagine, the help message is provided by the underlying argparse subparser: there is a subparser for each command. plac commands accept options, flags, varargs, keyword arguments, arguments with defaults, arguments with a fixed number of choices, type conversion and all the features provided of argparse .

Moreover at the moment plac also understands command abbreviations. However, this feature may disappear in future releases. It was meaningful in the past, when plac did not support readline.

Notice that if an abbreviation is ambiguous, plac warns you:

i> sh
NameError: Ambiguous command 'sh': matching ['showall', 'show']

The plac runner

The distribution of plac includes a runner script named plac_runner.py, which will be installed in a suitable directory in your system by distutils (say in /usr/local/bin/plac_runner.py in a Unix-like operative system). The runner provides many facilities to run .plac scripts and .placet files, as well as Python modules containing a main object, which can be a function, a command container object or even a command container class.

For instance, suppose you want to execute a script containing commands defined in the ishelve2 module like the following one:

#!ishelve2.py:ShelveInterface -c conf.shelve
set a 1
del a
del a # intentional error

The first line of the .plac script contains the name of the python module containing the plac interpreter and the arguments which must be passed to its main function in order to be able to instantiate an interpreter object. In this case I appended :ShelveInterface to the name of the module to specify the object that must be imported: if not specified, by default the object named ‘main’ is imported. The other lines contains commands. You can run the script as follows:

$ plac_runner.py --batch ishelve2.plac
setting a=1
deleting a
Traceback (most recent call last):
  ...
_bsddb.DBNotFoundError: (-30988, 'DB_NOTFOUND: No matching key/data pair found')

The last command intentionally contained an error, to show that the plac runner does not eat the traceback.

The runner can also be used to run Python modules in interactive mode and non-interactive mode. If you put this alias in your bashrc

alias plac="plac_runner.py"

(or you define a suitable plac.bat script in Windows) you can run the ishelve2.py script in interactive mode as follows:

$ plac -i ishelve2.py:ShelveInterface
A minimal interface over a shelve object.
Operating on conf.shelve.
.help to see the available commands.

i> del
deleting everything
i> set a 1
setting a=1
i> set b 2
setting b=2
i> show b
b = 2

Now you can cut and paste the interactive session and turn it into a .placet file like the following:

#!ishelve2.py:ShelveInterface -configfile=test.shelve
# an example of a .placet file for the ShelveInterface
i> del
deleting everything
i> set a 1
setting a=1
i> set b 2
setting b=2
i> show a
a = 1

Notice that the first line specifies a test database test.shelve, to avoid clobbering your default shelve. If you misspell the arguments in the first line plac will give you an argparse error message (just try).

You can run placets following the shebang convention directly with the plac runner:

$ plac --test ishelve2.placet
run 1 plac test(s)

If you want to see the output of the tests, pass the -v/--verbose flag. Notice that he runner ignores the extension, so you can actually use any extension your like, but it relies on the first line of the file to invoke the corresponding plac tool with the given arguments.

The plac runner does not provide any test discovery facility, but you can use standard Unix tools to help. For instance, you can run all the .placet files into a directory and its subdirectories as follows:

$ find . -name \*.placet | xargs plac_runner.py -t

The plac runner expects the main function of your script to return a plac tool, i.e. a function or an object with a .commands attribute. If this is not the case the runner exits gracefully.

It also works in non-interactive mode, if you call it as

$ plac module.py args ...

Here is an example:

$ plac ishelve.py a=1
setting a=1
$ plac ishelve.py .show
a=1

Notice that in non-interactive mode the runner just invokes plac.call on the main object of the Python module.

A non class-based example

plac does not force you to use classes to define command containers. Even a simple function can be a valid command container, it is enough to add a .commands attribute to it, and possibly __enter__ and/or __exit__ attributes too.

In particular, a Python module is a perfect container of commands. As an example, consider the following module implementing a fake Version Control System:

"A Fake Version Control System"

import plac  # this implementation also works with Python 2.4

commands = 'checkout', 'commit', 'status'


@plac.annotations(url='url of the source code')
def checkout(url):
    "A fake checkout command"
    return ('checkout ', url)


@plac.annotations(message=('commit message', 'option'))
def commit(message):
    "A fake commit command"
    return ('commit ', message)


@plac.annotations(quiet=('summary information', 'flag', 'q'))
def status(quiet):
    "A fake status command"
    return ('status ', quiet)


def __missing__(name):
    return ('Command %r does not exist' % name,)


def __exit__(etype, exc, tb):
    "Will be called automatically at the end of the interpreter loop"
    if etype in (None, GeneratorExit):  # success
        print('ok')

main = __import__(__name__)  # the module imports itself!

if __name__ == '__main__':
    import plac
    for out in plac.call(main, version='0.1.0'):
        print(out)

Notice that I have defined both an __exit__ hook and a __missing__ hook, invoked for non-existing commands. The real trick here is the line main = __import__(__name__), which define main to be an alias for the current module.

The vcs module can be run through the plac runner (try plac vcs.py -h):

usage: plac_runner.py vcs.py [-h] {status,commit,checkout} ...

A Fake Version Control System

optional arguments:
  -h, --help            show this help message and exit

subcommands:
  {status,commit,checkout}
    checkout            A fake checkout command
    commit              A fake commit command
    status              A fake status command

You can get help for the subcommands by inserting an -h after the name of the command:

$ plac vcs.py status -h
usage: plac_runner.py vcs.py status [-h] [-q]

A fake status command

optional arguments:
  -h, --help   show this help message and exit
  -q, --quiet  summary information

Notice how the docstring of the command is automatically shown in the usage message, as well as the documentation for the sub flag -q.

Here is an example of a non-interactive session:

$ plac vcs.py check url
checkout
url
$ plac vcs.py st -q
status
True
$ plac vcs.py co
commit
None

and here is an interactive session:

$ plac -i vcs.py
usage: plac_runner.py vcs.py [-h] {status,commit,checkout} ...
i> check url
checkout
url
i> st -q
status
True
i> co
commit
None
i> sto
Command 'sto' does not exist
i> [CTRL-D]
ok

Notice the invocation of the __missing__ hook for non-existing commands. Notice also that the __exit__ hook gets called only in interactive mode.

If the commands are completely independent, a module is a good fit for a method container. In other situations, it is best to use a custom class.

Writing your own plac runner

The runner included in the plac distribution is intentionally kept small (around 50 lines of code) so that you can study it and write your own runner if you want to. If you need to go to such level of detail, you should know that the most important method of the Interpreter class is the .send method, which takes strings as input and returns a four elements tuple with attributes .str, .etype, .exc and .tb:

• .str is the output of the command, if successful (a string);

• .etype is the class of the exception, if the command fails;

• .exc is the exception instance;

• .tb is the traceback.

Moreover, the __str__ representation of the output object is redefined to return the output string if the command was successful, or the error message (preceded by the name of the exception class) if the command failed.

For instance, if you send a misspelled option to the interpreter a SystemExit will be trapped:

>>> import plac
>>> from ishelve import ishelve
>>> with plac.Interpreter(ishelve) as i:
...     print(i.send('.cler'))
...
SystemExit: unrecognized arguments: .cler

It is important to invoke the .send method inside the context manager, otherwise you will get a RuntimeError.

For instance, suppose you want to implement a graphical runner for a plac-based interpreter with two text widgets: one to enter the commands and one to display the results. Suppose you want to display the errors with tracebacks in red. You will need to code something like that (pseudocode follows):

input_widget = WidgetReadingInput()
output_widget = WidgetDisplayingOutput()

def send(interpreter, line):
    out = interpreter.send(line)
    if out.tb: # there was an error
        output_widget.display(out.tb, color='red')
    else:
        output_widget.display(out.str)

main = plac.import_main(tool_path) # get the main object

with plac.Interpreter(main) as i:
   def callback(event):
      if event.user_pressed_ENTER():
           send(i, input_widget.last_line)
   input_widget.addcallback(callback)
   gui_mainloop.start()

You can adapt the pseudocode to your GUI toolkit of choice and you can also change the file associations in such a way that the graphical user interface starts when clicking on a plac tool file.

An example of a GUI program built on top of plac is given later on, in the paragraph Managing the output of concurrent commands (using Tkinter for simplicity and portability).

There is a final caveat: since the plac interpreter loop is implemented via extended generators, plac interpreters are single threaded: you will get an error if you .send commands from separated threads. You can circumvent the problem by using a queue. If EXIT is a sentinel value to signal exiting from the interpreter loop, you can write code like this:

with interpreter:
    for input_value in iter(input_queue.get, EXIT):
        output_queue.put(interpreter.send(input_value))

The same trick also works for processes; you could run the interpreter loop in a separate process and send commands to it via the Queue class provided by the multiprocessing module.

Long running commands

As we saw, by default a plac interpreter blocks until the command terminates. This is an issue, in the sense that it makes the interactive experience quite painful for long running commands. An example is better than a thousand words, so consider the following fake importer:

import time
import plac

class FakeImporter(object):
    "A fake importer with an import_file command"
    commands = ['import_file']
    def __init__(self, dsn):
        self.dsn = dsn
    def import_file(self, fname):
        "Import a file into the database"
        try:
            for n in range(10000):
                time.sleep(.01)
                if n % 100 == 99:
                    yield 'Imported %d lines' % (n+1)
        finally:
            print('closing the file')

if __name__ == '__main__':
    plac.Interpreter.call(FakeImporter)

If you run the import_file command, you will have to wait for 200 seconds before entering a new command:

$ python importer1.py dsn -i
A fake importer with an import_file command
i> import_file file1
... <wait 3+ minutes>
Imported 100 lines
Imported 200 lines
Imported 300 lines
...
Imported 10000 lines
closing the file

Being unable to enter any other command is quite annoying: in those situations one would like to run the long running commands in the background, to keep the interface responsive. plac provides two ways to reach this goal: threads and processes.

Threaded commands

The most familiar way to execute a task in the background (even if not necessarily the best way) is to run it into a separate thread. In our example it is sufficient to replace the line

commands = ['import_file']

with

thcommands = ['import_file']

to tell to the plac interpreter that the command import_file should be run into a separated thread. Here is an example session:

i> import_file file1
<ThreadedTask 1 [import_file file1] RUNNING>

The import task started in a separated thread. You can see the progress of the task by using the special command .output:

i> .output 1
<ThreadedTask 1 [import_file file1] RUNNING>
Imported 100 lines
Imported 200 lines

If you look after a while, you will get more lines of output:

i> .output 1
<ThreadedTask 1 [import_file file1] RUNNING>
Imported 100 lines
Imported 200 lines
Imported 300 lines
Imported 400 lines

If you look after a time long enough, the task will be finished:

i> .output 1
<ThreadedTask 1 [import_file file1] FINISHED>

It is possible to store the output of a task into a file, to be read later (this is useful for tasks with a large output):

i> .output 1 out.txt
saved output of 1 into out.txt

You can even skip the number argument: then .output will the return the output of the last launched command (the special commands like .output do not count).

You can launch many tasks one after the other:

i> import_file file2
<ThreadedTask 5 [import_file file2] RUNNING>
i> import_file file3
<ThreadedTask 6 [import_file file3] RUNNING>

The .list command displays all the running tasks:

i> .list
<ThreadedTask 5 [import_file file2] RUNNING>
<ThreadedTask 6 [import_file file3] RUNNING>

It is even possible to kill a task:

i> .kill 5
<ThreadedTask 5 [import_file file2] TOBEKILLED>
# wait a bit ...
closing the file
i> .output 5
<ThreadedTask 5 [import_file file2] KILLED>

Note that since at the Python level it is impossible to kill a thread, the .kill command works by setting the status of the task to TOBEKILLED. Internally the generator corresponding to the command is executed in the thread and the status is checked at each iteration: when the status becomes TOBEKILLED, a GeneratorExit exception is raised and the thread terminates (softly, so that the finally clause is honored). In our example the generator is yielding back control once every 100 iterations, i.e. every two seconds (not much). In order to get a responsive interface it is a good idea to yield more often, for instance every 10 iterations (i.e. 5 times per second), as in the following code:

import time
import plac

class FakeImporter(object):
    "A fake importer with an import_file command"
    thcommands = ['import_file']
    def __init__(self, dsn):
        self.dsn = dsn
    def import_file(self, fname):
        "Import a file into the database"
        try:
            for n in range(10000):
                time.sleep(.02)
                if n % 100 == 99: # every two seconds
                    yield 'Imported %d lines' % (n+1)
                if n % 10 == 9: # every 0.2 seconds
                    yield # go back and check the TOBEKILLED status
        finally:
            print('closing the file')

if __name__ == '__main__':
    plac.Interpreter.call(FakeImporter)

Running commands as external processes

Threads are not loved much in the Python world and actually most people prefer to use processes instead. For this reason plac provides the option to execute long running commands as external processes. Unfortunately the current implementation only works on Unix-like operating systems (including Mac OS/X) because it relies on fork via the multiprocessing module.

In our example, to enable the feature it is sufficient to replace the line

thcommands = ['import_file']

with

mpcommands = ['import_file'].

The user experience is exactly the same as with threads and you will not see any difference at the user interface level:

i> import_file file3
<MPTask 1 [import_file file3] SUBMITTED>
i> .kill 1
<MPTask 1 [import_file file3] RUNNING>
closing the file
i> .output 1
<MPTask 1 [import_file file3] KILLED>
Imported 100 lines
Imported 200 lines
i>

Still, using processes is quite different than using threads: in particular, when using processes you can only yield pickleable values and you cannot re-raise an exception first raised in a different process, because traceback objects are not pickleable. Moreover, you cannot rely on automatic sharing of your objects.

On the plus side, when using processes you do not need to worry about killing a command: they are killed immediately using a SIGTERM signal, and there is no TOBEKILLED mechanism. Moreover, the killing is guaranteed to be soft: internally a command receiving a SIGTERM raises a TerminatedProcess exception which is trapped in the generator loop, so that the command is closed properly.

Using processes allows one to take full advantage of multicore machines and it is safer than using threads, so it is the recommended approach unless you are working on Windows.

Managing the output of concurrent commands

plac acts as a command-line task launcher and can be used as the base to build a GUI-based task launcher and task monitor. To this aim the interpreter class provides a .submit method which returns a task object and a .tasks method returning the list of all the tasks submitted to the interpreter. The submit method does not start the task and thus it is nonblocking. Each task has an .outlist attribute which is a list storing the value yielded by the generator underlying the task (the None values are skipped though): the .outlist grows as the task runs and more values are yielded. Accessing the .outlist is nonblocking and can be done freely. Finally there is a .result property which waits for the task to finish and returns the last yielded value or raises an exception. The code below provides an example of how you could implement a GUI over the importer example:

from __future__ import with_statement
from Tkinter import *
from importer3 import FakeImporter

def taskwidget(root, task, tick=500):
    "A Label widget showing the output of a task every 500 ms"
    sv = StringVar(root)
    lb = Label(root, textvariable=sv)
    def show_outlist():
        try:
            out = task.outlist[-1]
        except IndexError: # no output yet
            out = ''
        sv.set('%s %s' % (task, out))
        root.after(tick, show_outlist)
    root.after(0, show_outlist)
    return lb

def monitor(tasks):
    root = Tk()
    for task in tasks:
        task.run()
        taskwidget(root, task).pack()
    root.mainloop()

if __name__ == '__main__':
    import plac
    with plac.Interpreter(plac.call(FakeImporter)) as i:
        tasks = [i.submit('import_file f1'), i.submit('import_file f2')]
        monitor(tasks)

Experimental features

The distribution of plac includes a few experimental features which I am not committed to fully support and that may go away in future versions. They are included as examples of things that you may build on top of plac: the aim is to give you ideas. Some of the experimental features might grow to become external projects built on plac.

Parallel computing with plac

plac is certainly not intended as a tool for parallel computing, but still you can use it to launch a set of commands and collect the results, similarly to the MapReduce pattern popularized by Google. In order to give an example, I will consider the “Hello World” of parallel computing, i.e. the computation of pi with independent processes. There is a huge number of algorithms to compute pi; here I will describe a trivial one chosen for simplicity, not for efficiency. The trick is to consider the first quadrant of a circle with radius 1 and to extract a number of points (x, y) with x and y random variables in the interval [0,1]. The probability of extracting a number inside the quadrant (i.e. with x^2 + y^2 < 1) is proportional to the area of the quadrant (i.e. pi/4). The value of pi therefore can be extracted by multiplying by 4 the ratio between the number of points in the quadrant versus the total number of points N, for N large:

def calc_pi(N):
    inside = 0
    for j in xrange(N):
        x, y = random(), random()
        if x*x + y*y < 1:
            inside += 1
    return (4.0 * inside) / N

The algorithm is trivially parallelizable: if you have n CPUs, you can compute pi n times with N/n iterations, sum the results and divide the total by n. I have a Macbook with two cores, therefore I would expect a speedup factor of 2 with respect to a sequential computation. Moreover, I would expect a threaded computation to be even slower than a sequential computation, due to the GIL and the scheduling overhead.

Here is a script implementing the algorithm and working in three different modes (parallel mode, threaded mode and sequential mode) depending on a mode option:

#  -*- coding: utf-8 -*-
from __future__ import with_statement
from __future__ import division
import math
from random import random
import multiprocessing
import plac


class PiCalculator(object):
    """Compute \u03C0 in parallel with threads or processes"""

    @plac.annotations(
        npoints=('number of integration points', 'positional', None, int),
        mode=('sequential|parallel|threaded', 'option', 'm', str, 'SPT'))
    def __init__(self, npoints, mode='S'):
        self.npoints = npoints
        if mode == 'P':
            self.mpcommands = ['calc_pi']
        elif mode == 'T':
            self.thcommands = ['calc_pi']
        elif mode == 'S':
            self.commands = ['calc_pi']
        self.n_cpu = multiprocessing.cpu_count()

    def submit_tasks(self):
        npoints = math.ceil(self.npoints / self.n_cpu)
        self.i = plac.Interpreter(self).__enter__()
        return [self.i.submit('calc_pi %d' % npoints)
                for _ in range(self.n_cpu)]

    def close(self):
        self.i.close()

    @plac.annotations(npoints=('npoints', 'positional', None, int))
    def calc_pi(self, npoints):
        counts = 0
        for j in range(npoints):
            n, r = divmod(j, 1000000)
            if r == 0:
                yield '%dM iterations' % n
            x, y = random(), random()
            if x*x + y*y < 1:
                counts += 1
        yield (4.0 * counts) / npoints

    def run(self):
        tasks = self.i.tasks()
        for t in tasks:
            t.run()
        try:
            total = 0
            for task in tasks:
                total += task.result
        except:  # the task was killed
            print(tasks)
            return
        return total / self.n_cpu

if __name__ == '__main__':
    pc = plac.call(PiCalculator)
    pc.submit_tasks()
    try:
        import time
        t0 = time.time()
        print('%f in %f seconds ' % (pc.run(), time.time() - t0))
    finally:
        pc.close()

Notice the submit_tasks method, which instantiates and initializes a plac.Interpreter object and submits a number of commands corresponding to the number of available CPUs. The calc_pi command yields a log message for each million interactions, in order to monitor the progress of the computation. The run method starts all the submitted commands in parallel and sums the results. It returns the average value of pi after the slowest CPU has finished its job (if the CPUs are equal and equally busy they should finish more or less at the same time).

Here are the results on my old Macbook with Ubuntu 10.04 and Python 2.6, for 10 million of iterations:

$ python picalculator.py -mP 10000000 # two processes
3.141904 in 5.744545 seconds
$ python picalculator.py -mT 10000000 # two threads
3.141272 in 13.875645 seconds
$ python picalculator.py -mS 10000000 # sequential
3.141586 in 11.353841 seconds

As you see using processes one gets a 2x speedup indeed, where the threaded mode is some 20% slower than the sequential mode.

Since the pattern “submit a bunch of tasks, start them and collect the results” is so common, plac provides a utility function runp(genseq, mode='p') to start a bunch of generators and return a list of results. By default runp use processes, but you can use threads by passing mode='t'. With runp the parallel pi calculation becomes a one-liner:

sum(task.result for task in plac.runp(calc_pi(N) for i in range(ncpus)))/ncpus

The file test_runp in the doc directory of the plac distribution shows another usage example. Note that if one of the tasks fails for some reason, you will get the exception object instead of the result.

Monitor support

plac provides experimental support for monitoring the output of concurrent commands, at least for platforms where multiprocessing is fully supported. You can define your own monitor class, simply by inheriting from plac.Monitor and overriding the methods add_listener(self, taskno), del_listener(self, taskno), notify_listener(self, taskno, msg), read_queue(self), start(self) and stop(self). Then you can add a monitor object to any plac.Interpreter object by calling the add_monitor method. For convenience, plac comes with a very simple TkMonitor based on Tkinter (I chose Tkinter because it is easy to use and in the standard library, but you can use any GUI): you can look at how the TkMonitor is implemented in plac_tk.py and adapt it. Here is a usage example of the TkMonitor:

from __future__ import with_statement
import plac

class Hello(object):
    mpcommands = ['hello', 'quit']
    def hello(self):
        yield 'hello'
    def quit(self):
        raise plac.Interpreter.Exit

if __name__ == '__main__':
    i = plac.Interpreter(Hello())
    i.add_monitor(plac.TkMonitor('tkmon'))
    i.interact()

Try to run the hello command in the interactive interpreter: each time, a new text widget will be added displaying the output of the command. Note that if Tkinter is not installed correctly on your system, the TkMonitor class will not be available.

The plac server

A command-line oriented interface can be easily converted into a socket-based interface. Starting from release 0.7 plac features a built-in server which is able to accept commands from multiple clients and execute them. The server works by instantiating a separate interpreter for each client, so that if a client interpreter dies for any reason, the other interpreters keep working. To avoid external dependencies the server is based on the asynchat module in the standard library, but it would not be difficult to replace the server with a different one (for instance, a Twisted server). Notice that at the moment the plac server does not work with to Python 3.2+ due to changes to asynchat. In time I will fix this and other known issues. You should consider the server functionality still experimental and subject to changes. Also, notice that since asynchat-based servers are asynchronous, any blocking command in the interpreter should be run in a separated process or thread. The default port for the plac server is 2199, and the command to signal end-of-connection is EOF. For instance, here is how you could manage remote import on a database (say an SQLite db):

import plac
from importer2 import FakeImporter

def main(port=2199):
    main = FakeImporter('dsn')
    plac.Interpreter(main).start_server(port)

if __name__ == '__main__':
   plac.call(main)

You can connect to the server with telnet on port 2199, as follows:

$ telnet localhost 2199
Trying ::1...
Trying 127.0.0.1...
Connected to localhost.
Escape character is '^]'.
i> import_file f1
i> .list
<ThreadedTask 1 [import_file f1] RUNNING>
i> .out
Imported 100 lines
Imported 200 lines
i> EOF
Connection closed by foreign host.

Summary

Once plac claimed to be the easiest command-line arguments parser in the world. Having read this document you may think that it is not so easy after all. But it is a false impression. Actually the rules are quite simple:

1. if you want to implement a command-line script, use plac.call;

2. if you want to implement a command interpreter, use plac.Interpreter:

   • for an interactive interpreter, call the .interact method;

   • for a batch interpreter, call the .execute method;

3. for testing call the Interpreter.check method in the appropriate context or use the Interpreter.doctest feature;

4. if you need to go to a lower level, you may need to call the Interpreter.send method which returns a (finished) Task object;

5. long running commands can be executed in the background as threads or processes: just declare them in the lists thcommands and mpcommands respectively;

6. the .start_server method starts an asynchronous server on the given port number (default 2199).

Moreover, remember that plac_runner.py is your friend.

---

Appendix: custom annotation objects

Internally plac uses an Annotation class to convert the tuples in the function signature to annotation objects, i.e. objects with six attributes: help, kind, short, type, choices, metavar.

Advanced users can implement their own annotation objects. For instance, here is an example of how you could implement annotations for positional arguments:

# annotations.py
class Positional(object):
    def __init__(self, help='', type=None, choices=None, metavar=None):
        self.help = help
        self.kind = 'positional'
        self.abbrev = None
        self.type = type
        self.choices = choices
        self.metavar = metavar

You can use such annotation objects as follows:

# example11.py
import plac
from annotations import Positional

@plac.annotations(
    i=Positional("This is an int", int),
    n=Positional("This is a float", float),
    rest=Positional("Other arguments"))
def main(i, n, *rest):
    print(i, n, rest)

if __name__ == '__main__':
    import plac; plac.call(main)

Here is the usage message you get:

usage: example11.py [-h] i n [rest ...]

positional arguments:
  i           This is an int
  n           This is a float
  rest        Other arguments

options:
  -h, --help  show this help message and exit

You can go on and define Option and Flag classes, if you like. Using custom annotation objects you could do advanced things like extracting the annotations from a configuration file or from a database, but I expect such use cases to be quite rare: the default mechanism should work pretty well for most users.