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:

.. include:: test_ishelve.py
   :literal:

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:

.. include:: test_ishelve_more.py
   :literal:

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"):

.. include:: ishelve.placet
   :literal:

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``):

.. include:: ishelve.plac
   :literal:

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:

.. include:: ishelve2.py
   :literal:

``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:

.. include:: ishelve2.hel
   :literal:

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

.. include:: ishelve3.py
   :literal:

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:

.. include:: ishelve2.plac
   :literal:

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:

.. include:: ishelve2.placet
   :literal:

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:

.. include:: vcs.py
   :literal:

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``):

.. include:: vcs.help
   :literal:

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:

.. include:: importer1.py
   :literal:

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:

.. include:: importer2.py
   :literal:

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:

.. include:: importer_ui.py
   :literal:

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:

.. include:: picalculator.py
   :literal:

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``:

.. include:: tkmon.py
   :literal:

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):

.. include:: server_ex.py
   :literal:

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:

.. include:: annotations.py
   :literal:

You can use such annotation objects as follows:

.. include:: example11.py
   :literal:

Here is the usage message you get:

.. include:: example11.help
   :literal:

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.

.. _plac: https://pypi.org/project/plac/
.. _argparse: https://docs.python.org/library/argparse.html
.. _twill: https://github.com/twill-tools/twill
.. _shlex: https://docs.python.org/library/shlex.html
.. _multiprocessing: https://docs.python.org/library/multiprocessing.html
.. _distutils: https://docs.python.org/distutils/
.. _cmd: https://docs.python.org/library/cmd.html
.. _rlwrap: https://github.com/hanslub42/rlwrap
.. _pyreadline: https://ipython.org/pyreadline.html
