Setup: Add tzlocal to required packages

[etc/taskwarrior.git] / docs / index.rst

Welcome to tasklib's documentation!
===================================

tasklib is a Python library for interacting with taskwarrior_ databases, using
a queryset API similar to that of Django's ORM.

Supports Python 2.6, 2.7, 3.2, 3.3 and 3.4 with taskwarrior 2.1.x and above.
Older versions of taskwarrior are untested and may not work.

Requirements
------------

* taskwarrior_ v2.1.x or above.

Installation
------------

Install via pip (recommended)::

    pip install tasklib

Or clone from github::

    git clone https://github.com/robgolding63/tasklib.git
    cd tasklib
    python setup.py install

Initialization
--------------

Optionally initialize the ``TaskWarrior`` instance with ``data_location`` (the
database directory). If it doesn't already exist, this will be created
automatically unless ``create=False``.

The default location is the same as taskwarrior's::

    >>> tw = TaskWarrior(data_location='~/.task', create=True)

Creating Tasks
--------------

To create a task, simply create a new ``Task`` object::

    >>> new_task = Task(tw, description="throw out the trash")

This task is not yet saved to TaskWarrior (same as in Django), not until
you call ``.save()`` method::

    >>> new_task.save()

You can set any attribute as a keyword argument to the Task object::

    >>> complex_task = Task(tw, description="finally fix the shower", due=datetime(2015,2,14,8,0,0), priority='H')

or by setting the attributes one by one::

    >>> complex_task = Task(tw)
    >>> complex_task['description'] = "finally fix the shower"
    >>> complex_task['due'] = datetime(2015,2,14,8,0,0)
    >>> complex_task['priority'] = 'H'

Modifying Task
--------------

To modify a created or retrieved ``Task`` object, use dictionary-like access::

    >>> homework = tw.tasks.get(tags=['chores'])
    >>> homework['project'] = 'Home'

The change is not propagated to the TaskWarrior until you run the ``save()`` method::

    >>> homework.save()

Attributes, which map to native Python objects are converted. See Task Attributes section.

Task Attributes
---------------

Attributes of task objects are accessible through indices, like so::

    >>> task = tw.tasks.pending().get(tags__contain='work')  # There is only one pending task with 'work' tag
    >>> task['description']
    'Upgrade Ubuntu Server'
    >>> task['id']
    15
    >>> task['due']
    datetime.datetime(2015, 2, 5, 0, 0, tzinfo=<DstTzInfo 'Europe/Berlin' CET+1:00:00 STD>)
    >>> task['tags']
    ['work', 'servers']

The following fields are deserialized into Python objects:

* ``due``, ``wait``, ``scheduled``, ``until``, ``entry``: deserialized to a ``datetime`` object
* ``annotations``: deserialized to a list of ``TaskAnnotation`` objects
* ``tags``: deserialized to a list of strings
* ``depends``: deserialized to a set of ``Task`` objects

Attributes should be set using the correct Python representation, which will be
serialized into the correct format when the task is saved.

Operations on Tasks
-------------------

After modifying one or more attributes, simple call ``save()`` to write those
changes to the database::

    >>> task = tw.tasks.pending().get(tags__contain='work')
    >>> task['due'] = datetime(year=2014, month=1, day=5)
    >>> task.save()

To mark a task as complete, use ``done()``::

    >>> task = tw.tasks.pending().get(tags__contain='work')
    >>> task.done()
    >>> len(tw.tasks.pending().filter(tags__contain='work'))
    0

To delete a task, use ``delete()``::

    >>> task = tw.tasks.get(description="task added by mistake")
    >>> task.delete()

To update a task object with values from TaskWarrior database, use ``refresh()``. Example::

    >>> task = Task(tw, description="learn to cook")
    >>> task.save()
    >>> task['id']
    5
    >>> task['tags']
    []

Now, suppose the we modify the task using the TaskWarrior interface in another terminal::

    $ task 5 modify +someday
    Task 5 modified.

Switching back to the open python process::

   >>> task['tags']
   []
   >>> task.refresh()
   >>> task['tags']
   ['someday']


Retrieving Tasks
----------------

``tw.tasks`` is a ``TaskQuerySet`` object which emulates the Django QuerySet
API. To get all tasks (including completed ones)::

    >>> tw.tasks.all()
    ['First task', 'Completed task', 'Deleted task', ...]

Filtering
---------

Filter tasks using the same familiar syntax::

    >>> tw.tasks.filter(status='pending', tags__contains=['work'])
    ['Upgrade Ubuntu Server']

Filter arguments are passed to the ``task`` command (``__`` is replaced by
a period) so the above example is equivalent to the following command::

    $ task status:pending tags.contain=work

Tasks can also be filtered using raw commands, like so::

    >>> tw.tasks.filter('status:pending +work')
    ['Upgrade Ubuntu Server']

Although this practice is discouraged, as by using raw commands you may lose
some of the portablility of your commands over different TaskWarrior versions.

However, you can mix raw commands with keyword filters, as in the given example::

    >>> tw.tasks.filter('+BLOCKING', project='Home')  # Gets all blocking tasks in project Home
    ['Fix the toilette']

This can be a neat way how to use syntax not yet supported by tasklib. The above
is excellent example, since virtual tags do not work the same way as the ordinary ones, that is::

    >>> tw.tasks.filter(tags=['BLOCKING'])
    >>> []

will not work.

There are built-in functions for retrieving pending & completed tasks::

    >>> tw.tasks.pending().filter(tags__contain='work')
    ['Upgrade Ubuntu Server']
    >>> len(tw.tasks.completed())
    227

Use ``get()`` to return the only task in a ``TaskQuerySet``, or raise an
exception::

    >>> tw.tasks.get(tags__contain='work')['status']
    'pending'
    >>> tw.tasks.get(status='completed', tags__contains='work')  # Status of only task with the work tag is pending, so this should fail
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
      File "tasklib/task.py", line 224, in get
        'Lookup parameters were {0}'.format(kwargs))
    tasklib.task.DoesNotExist: Task matching query does not exist. Lookup parameters were {'status': 'completed', 'tags__contains': ['work']}
    >>> tw.tasks.get(status='pending')
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
      File "tasklib/task.py", line 227, in get
        'Lookup parameters were {1}'.format(num, kwargs))
    ValueError: get() returned more than one Task -- it returned 23! Lookup parameters were {'status': 'pending'}

Additionally, since filters return ``TaskQuerySets`` you can stack filters on top of each other::

    >>> home_tasks = tw.tasks.filter(project='Wife')
    >>> home_tasks.filter(due__before=datetime(2015,2,14,14,14,14))  # What I have to do until Valentine's day
    ['Prepare surprise birthday party']

Equality of Task objects
------------------------

Two Tasks are considered equal if they have the same UUIDs::

    >>> task1 = Task(tw, description="Pet the dog")
    >>> task1.save()
    >>> task2 = tw.tasks.get(description="Pet the dog")
    >>> task1 == task2
    True

If you compare the two unsaved tasks, they are considered equal only if it's the
same Python object::

    >>> task1 = Task(tw, description="Pet the cat")
    >>> task2 = Task(tw, description="Pet the cat")
    >>> task1 == task2
    False
    >>> task3 = task1
    >>> task3 == task1
    True

Dealing with dates and time
---------------------------

Any timestamp-like attributes of the tasks are converted to timezone-aware
datetime objects. To achieve this, Tasklib leverages ``pytz`` Python module,
which brings the Olsen timezone databaze to Python.

This shields you from annoying details of Daylight Saving Time shifts
or conversion between different timezones. For example, to list all the
tasks which are due midnight if you're currently in Berlin:

    >>> myzone = pytz.timezone('Europe/Berlin')
    >>> midnight = myzone.localize(datetime(2015,2,2,0,0,0))
    >>> tw.tasks.filter(due__before=midnight)

However, this is still a little bit tedious. That's why TaskWarrior object
is capable of automatic timezone detection, using the ``tzlocal`` Python
module. If your system timezone is set to 'Europe/Berlin', following example
will work the same way as the previous one:

    >>> tw.tasks.filter(due__before=datetime(2015,2,2,0,0,0))

You can also use simple dates when filtering:

    >>> tw.tasks.filter(due__before=date(2015,2,2))

In such case, a 00:00:00 is used as the time component.

Of course, you can use datetime naive objects when initializing Task object
or assigning values to datetime atrributes:

    >>> t = Task(tw, description="Buy new shoes", due=date(2015,2,5))
    >>> t['due']
    datetime.datetime(2015, 2, 5, 0, 0, tzinfo=<DstTzInfo 'Europe/Berlin' CET+1:00:00 STD>)
    >>> t['due'] = date(2015,2,6,15,15,15)
    >>> t['due']
    datetime.datetime(2015, 2, 6, 15, 15, 15, tzinfo=<DstTzInfo 'Europe/Berlin' CET+1:00:00 STD>)

However, since timezone-aware and timezone-naive datetimes are not comparable
in Python, this can cause some unexpected behaviour:

    >>> from datetime import datetime
    >>> now = datetime.now()
    >>> t = Task(tw, description="take out the trash now") 
    >>> t['due'] = now
    >>> now
    datetime.datetime(2015, 2, 1, 19, 44, 4, 770001)
    >>> t['due']
    datetime.datetime(2015, 2, 1, 19, 44, 4, 770001, tzinfo=<DstTzInfo 'Europe/Berlin' CET+1:00:00 STD>)
    >>> t['due'] == now
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
      TypeError: can't compare offset-naive and offset-aware datetimes

If you want to compare datetime aware value with datetime naive value, you need
to localize the naive value first:

    >>> from datetime import datetime
    >>> from tasklib.task import local_zone
    >>> now = local_zone.localize(datetime.now())
    >>> t['due'] = now
    >>> now
    datetime.datetime(2015, 2, 1, 19, 44, 4, 770001, tzinfo=<DstTzInfo 'Europe/Berlin' CET+1:00:00 STD>)
    >>> t['due'] == now
    True

Also, note that it does not matter whether the timezone aware datetime objects
are set in the same timezone:

    >>> import pytz
    >>> t['due']
    datetime.datetime(2015, 2, 1, 19, 44, 4, 770001, tzinfo=<DstTzInfo 'Europe/Berlin' CET+1:00:00 STD>)
    >>> now.astimezone(pytz.utc)
    datetime.datetime(2015, 2, 1, 18, 44, 4, 770001, tzinfo=<DstTzInfo 'Europe/Berlin' CET+1:00:00 STD>)
    >>> t['due'] == now.astimezone(pytz.utc)
    True


Working with annotations
------------------------

Annotations of the tasks are represented in tasklib by ``TaskAnnotation`` objects. These
are much like ``Task`` objects, albeit very simplified.

    >>> annotated_task = tw.tasks.get(description='Annotated task')
    >>> annotated_task['annotations']
    [Yeah, I am annotated!]

Annotations have only defined ``entry`` and ``description`` values::

    >>> annotation = annotated_task['annotations'][0]
    >>> annotation['entry']
    datetime.datetime(2015, 1, 3, 21, 13, 55, tzinfo=<DstTzInfo 'Europe/Berlin' CET+1:00:00 STD>)
    >>> annotation['description']
    u'Yeah, I am annotated!'

To add a annotation to a Task, use ``add_annotation()``::

    >>> task = Task(tw, description="new task")
    >>> task.add_annotation("we can annotate any task")
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
        File "build/bdist.linux-x86_64/egg/tasklib/task.py", line 355, in add_annotation
    tasklib.task.NotSaved: Task needs to be saved to add annotation

However, Task needs to be saved before you can add a annotation to it::

    >>> task.save()
    >>> task.add_annotation("we can annotate saved tasks")
    >>> task['annotations']
    [we can annotate saved tasks]

To remove the annotation, pass its description to ``remove_annotation()`` method::

    >>> task.remove_annotation("we can annotate saved tasks")

Alternatively, you can pass the ``TaskAnnotation`` object itself::

    >>> task.remove_annotation(task['annotations'][0])


Running custom commands
-----------------------

To run a custom commands, use ``execute_command()`` method of ``TaskWarrior`` object::

    >>> tw = TaskWarrior()
    >>> tw.execute_command(['log', 'Finish high school.'])
    [u'Logged task.']

You can use ``config_override`` keyword argument to specify a dictionary of configuration overrides::

    >>> tw.execute_command(['3', 'done'], config_override={'gc': 'off'}) # Will mark 3 as completed and it will retain its ID

Setting custom configuration values
-----------------------------------

By default, TaskWarrior does not use any of configuration values stored in
your .taskrc. To see what configuration values are passed to each executed
task command, have a peek into ``config`` attribute of ``TaskWarrior`` object::

    >>> tw.config
    {'confirmation': 'no', 'data.location': '/home/tbabej/.task'}

To pass your own configuration, you just need to update this dictionary::

    >>> tw.config.update({'hooks': 'off'})  # tasklib will not trigger hooks

Creating hook scripts
---------------------

From version 2.4.0, TaskWarrior has support for hook scripts. Tasklib provides
some very useful helpers to write those. With tasklib, writing these becomes
a breeze::

    #!/usr/bin/python

    from tasklib.task import Task
    task = Task.from_input()
    # ... <custom logic>
    print task.export_data()

For example, plugin which would assign the priority "H" to any task containing
three exclamation marks in the description, would go like this::

    #!/usr/bin/python

    from tasklib.task import Task
    task = Task.from_input()

    if "!!!" in task['description']:
        task['priority'] = "H"

    print task.export_data()

Tasklib can automatically detect whether it's running in the ``on-modify`` event,
which provides more input than ``on-add`` event and reads the data accordingly.

This means the example above works both for ``on-add`` and ``on-modify`` events!

Consenquently, you can create just one hook file for both ``on-add`` and
``on-modify`` events, and you just need to create a symlink for the other one.
This removes the need for maintaining two copies of the same code base and/or
boilerplate code.


Working with UDAs
-----------------

Since TaskWarrior does not read your .taskrc, you need to define any UDAs
in the TaskWarrior's config dictionary, as described above.

Let us demonstrate this on the same example as in the TaskWarrior's docs::

    >>> tw = TaskWarrior()
    >>> tw.config.update({'uda.estimate.type': 'numeric'})

Now we can filter and create tasks using the estimate UDA::

    >>> task = Task(tw, description="Long task", estimate=1000)
    >>> task.save()
    >>> task['id']
    1

This is saved as UDA in the TaskWarrior::

    $ task 1 export
    {"id":1,"description":"Long task","estimate":1000, ...}

As long as ``TaskWarrior``'s config is updated, we can approach UDAs as built in attributes::

    >>> tw.tasks.filter(estimate=1000)
    Long task

Syncing
-------

Syncing is not directly supported by tasklib, but it can be made to work in a similiar way
as the UDAs. First we need to update the ``config`` dictionary by the values required for
sync to work, and then we can run the sync command using the ``execute_command()`` method::

    >>> tw = TaskWarrior()
    >>> sync_config = {
    ...     'taskd.certificate': '/home/tbabej/.task/tbabej.cert.pem',
    ...     'taskd.credentials': 'Public/tbabej/34af54de-3cb2-4d3d-82be-33ddb8fd3e66',
    ...     'taskd.server': 'task.server.com:53589',
    ...     'taskd.ca': '/home/tbabej/.task/ca.cert.pem',
    ...     'taskd.trust': 'ignore hostname'}
    >>> tw.config.update(sync_config)
    >>> tw.execute_command(['sync'])


.. _taskwarrior: http://taskwarrior.org