Paver is meant to be a hybrid declarative/imperative system for getting stuff done. You declare things via the options in pavement.py. And, in fact, many projects can get away with nothing but options in pavement.py. Consider, for example, Paver’s own pavement file:
import paver.doctools
options(
setup=dict(
name='paver',
version="0.3",
description='Python build tool',
author='Kevin Dangoor',
author_email='dangoor+paver@gmail.com',
#url='',
packages=['paver'],
package_data=setuputils.find_package_data("paver", package="paver",
only_in_packages=False),
install_requires=[],
test_suite='nose.collector',
zip_safe=False,
entry_points="""
[console_scripts]
paver = paver.command:main
""",
),
sphinx=Bunch(
builddir="build",
sourcedir="source"
)
)
@task
@needs('paver.doctools.html')
def html():
"""Build Paver's documentation and install it into paver/docs"""
builtdocs = path("docs") / options.sphinx.builddir / "html"
destdir = path("paver") / "docs"
destdir.rmtree()
builtdocs.move(destdir)
This file has both declarative and imperative aspects. The options define enough information for distutils, setuptools and Sphinx to do their respective jobs. This would function just fine without requiring you to define any tasks.
However, for Paver’s ‘paverdoc’ built-in task to work, we need Sphinx’s generated HTML to end up inside of Paver’s package tree. So, we override the html task to move the generated files.
Tasks are just simple functions. You designate a function as being a task by using the @task decorator. Interesting tidbit: unlike many decorators you find in Python, Paver’s decorators do not actually replace the function with a new object. They just put marker data on the function object and also keep track of things in a companion paver.runtime.Task object.
You can also specify that a task depends on another task running first with the @needs decorator. As long as the options don’t change, a given task will run only once regardless of how many times it’s specified in @needs or whether the task shows up on the command line.
Sometimes, you need to do something before running another task, so the @needs decorator doesn’t quite do the job.
Of course, tasks are just Python functions. So, you can just call the other task! However, this is not ideal, because Python functions don’t do things like keeping track of their dependencies or whether they’ve been called already.
paver.runtime provides a call_task function. It’s very simple to use:
Calls the desired task, including any tasks upon which that task depends.
You can always call a task directly by calling the function directly. But, if you do so the dependencies aren’t called. call_task ensures that these are called.
Note that call_task will only call the task once during a given build as long as the options remain the same. If the options are changed, the task will be called again.
Tasks have both a long name and a short name. The short name is just the name of the function. The long name is the fully qualified Python name for the function object.
For example, the Sphinx support includes a task function called “html”. That task’s long name is “paver.sphinx.html”.
If you `import paver.sphinx` in your pavement.py, you’ll be able to use either name to refer to that task.
The last task that is defined with a given short name is the one that gets the name. Generally, this will be your pavement.py file. So, in the example at the front of this chapter, the html task in pavement.py is the one that the “html” short name refers to.
Tasks are always available unambiguously via their long names.
When you run Paver it looks for ‘pavement.py’ in the current directory. pavement.py is standard Python, with two small bits of magic:
It is purposefully not a lot of magic, and the implementation of that “magic” isn’t magical at all. Your pavement file is actually executed in the namespace of paver.defaults. After that, paver.defaults.options = runtime.options, and then the tasks are run.