>>> tw = TaskWarrior(data_location='~/.task', create=True)
+The ``TaskWarrior`` instance will also use your .taskrc configuration (so that
+it recognizes the same UDAs as your task binary, uses the same configuration,
+etc.). To override the location of the .taskrc, use
+``taskrc_location=~/some/different/path``.
+
Creating Tasks
--------------
>>> task['id']
15
>>> task['due']
- datetime.datetime(2013, 12, 5, 0, 0)
+ datetime.datetime(2015, 2, 5, 0, 0, tzinfo=<DstTzInfo 'Europe/Berlin' CET+1:00:00 STD>)
>>> task['tags']
['work', 'servers']
>>> task3 == task1
True
+Accessing original values
+-------------------------
+
+To access the saved state of the Task, use dict-like access using the
+``original`` attribute:
+
+ >>> t = Task(tw, description="tidy up")
+ >>> t.save()
+ >>> t['description'] = "tidy up the kitchen and bathroom"
+ >>> t['description']
+ "tidy up the kitchen and bathroom"
+ >>> t.original['description']
+ "tidy up"
+
+When you save the task, original values are refreshed to reflect the
+saved state of the task:
+
+ >>> t.save()
+ >>> t.original['description']
+ "tidy up the kitchen and bathroom"
+
+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=<UTC>)
+ >>> t['due'] == now.astimezone(pytz.utc)
+ True
+
+
Working with annotations
------------------------
>>> annotation = annotated_task['annotations'][0]
>>> annotation['entry']
- datetime.datetime(2015, 1, 3, 21, 13, 55)
+ datetime.datetime(2015, 1, 3, 21, 13, 55, tzinfo=<DstTzInfo 'Europe/Berlin' CET+1:00:00 STD>)
>>> annotation['description']
u'Yeah, I am annotated!'
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
+By default, TaskWarrior uses configuration values stored in your .taskrc.
+To see what configuration value overrides 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::
+To pass your own configuration overrides, you just need to update this dictionary::
>>> tw.config.update({'hooks': 'off'}) # tasklib will not trigger hooks
This removes the need for maintaining two copies of the same code base and/or
boilerplate code.
+In ``on-modify`` events, tasklib loads both the original version and the modified
+version of the task to the returned ``Task`` object. To access the original data
+(in read-only manner), use ``original`` dict-like attribute:
+
+ >>> t = Task.from_input()
+ >>> t['description']
+ "Modified description"
+ >>> t.original['description']
+ "Original description"
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::
+Since TaskWarrior does read your .taskrc, you need not to define any UDAs
+in the TaskWarrior's config dictionary, as described above. Suppose we have
+a estimate UDA in the .taskrc::
- >>> tw = TaskWarrior()
- >>> tw.config.update({'uda.estimate.type': 'numeric'})
+ uda.estimate.type = numeric
-Now we can filter and create tasks using the estimate UDA::
+We can simply filter and create tasks using the estimate UDA out of the box::
+ >>> tw = TaskWarrior()
>>> task = Task(tw, description="Long task", estimate=1000)
>>> task.save()
>>> task['id']
$ 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::
+We can also speficy UDAs as arguments in the TaskFilter::
>>> 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::
+If you have configurated the needed config variables in your .taskrc, syncing
+is as easy as::
+
+ >>> tw = TaskWarrior()
+ >>> tw.execute_command(['sync'])
+
+If you want to use non-standard server/credentials, you'll need to provide configuration
+overrides to the ``TaskWarrior`` instance. Update the ``config`` dictionary with the
+values you desire to override, and then we can run the sync command using
+the ``execute_command()`` method::
>>> tw = TaskWarrior()
>>> sync_config = {
projects / etc / taskwarrior.git / blobdiff