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        "author": "Lovely Systems",
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        "description": "Lovely Appengine Packages\n*************************\n\n================\nlovely.gae.async\n================\n\nThis package executes jobs asynchronously, it uses the appengine\ntaskqueue to exectue the jobs.\n\n    >>> from lovely.gae.async import defer, get_tasks\n\nThe defer function executes a handler asynchronously as a job. We\ncreate 3 jobs that have the same signature.\n\n    >>> import time\n    >>> for i in range(3):\n    ...     print defer(time.sleep, [0.3])\n    <google.appengine.api.labs.taskqueue.taskqueue.Task object at ...>\n    None\n    None\n\nLet us have a look on what jobs are there. Note that there is only one\nbecause all the 3 jobs we added were the same.\n\n    >>> len(get_tasks())\n    1\n\nIf we change the signature of the job, a new one will be added.\n\n    >>> added = defer(time.sleep, [0.4])\n    >>> len(get_tasks())\n    2\n\nNormally jobs are automatically executed by the taskqueueapi, we have\na test method which executes the jobs and returns the number of jobs\ndone.\n\n    >>> run_tasks()\n    2\n\nNow we can add the same signature again.\n\n    >>> added = defer(time.sleep, [0.4])\n    >>> run_tasks()\n    1\n\nWe can also set only_once to false to execute a worker many times with\nthe same signature.\n\n    >>> from pprint import pprint\n    >>> defer(pprint, ['hello'], once_only=False)\n    <google.appengine.api.labs.taskqueue.taskqueue.Task object at ...>\n    >>> defer(pprint, ['hello'], once_only=False)\n    <google.appengine.api.labs.taskqueue.taskqueue.Task object at ...>\n    >>> run_tasks()\n    'hello'\n    'hello'\n    2\n\n\n\n\n\n==========================\nDB Custom Property Classes\n==========================\n\nTyped Lists\n===========\n\nThis property converts model instances to keys and ensures a length.\n\n    >>> from lovely.gae.db.property import TypedListProperty\n    >>> from google.appengine.ext import db\n\nLet us create a model\n\n    >>> class Yummy(db.Model): pass\n    >>> class Bummy(db.Model): pass\n\nWe can now reference Yummy instances with our property. Note that we\ncan also use the kind name as an argument of the kind.\n\n    >>> class Dummy(db.Model):\n    ...     yummies = TypedListProperty(Yummy)\n    ...     bummies = TypedListProperty('Bummy', length=3)\n\nThe kind arguement needs to be a subclass kind name or a db.Model.\n\n    >>> TypedListProperty(object)\n    Traceback (most recent call last):\n    ...\n    ValueError: Kind needs to be a subclass of db.Model\n\n    >>> dummy = Dummy()\n    >>> dummy_key = dummy.put()\n    >>> yummy1 = Yummy()\n    >>> yummy1_key = yummy1.put()\n    >>> dummy.yummies = [yummy1]\n\nWe cannot set any other type.\n\n    >>> bummy1 = Bummy()\n    >>> bummy1_key = bummy1.put()\n    >>> dummy.yummies = [bummy1]\n    Traceback (most recent call last):\n    ...\n    BadValueError: Wrong kind u'Bummy'\n\nThe length needs to match if defined (see above).\n\n    >>> dummy.bummies = [bummy1]\n    Traceback (most recent call last):\n    ...\n    BadValueError: Wrong length need 3 got 1\n\n    >>> dummy.bummies = [bummy1, bummy1, bummy1]\n    >>> dummy_key == dummy.put()\n    True\n\nCase-Insensitive String Property\n================================\n\nThis property allows searching for the lowercase prefix in a\ncase-insensitive manner. This is usefull for autocomplete\nimplementations where we do not want to have a seperate property just\nfor searching.\n\n    >>> from lovely.gae.db.property import IStringProperty\n    >>> class Foo(db.Model):\n    ...     title = IStringProperty()\n\n    >>> f1 = Foo(title='Foo 1')\n    >>> kf1 = f1.put()\n    >>> f2 = Foo(title='Foo 2')\n    >>> kf2 = f2.put()\n    >>> f3 = Foo(title='foo 3')\n    >>> kf3 = f3.put()\n    >>> f4 = Foo(title=None)\n    >>> kf4 = f4.put()\n\nThe property does not allow the special seperator character which is\njust one below the  highest unicode character,\n\n    >>> f3 = Foo(title='Foo 3' + IStringProperty.SEPERATOR)\n    Traceback (most recent call last):\n    ...\n    BadValueError: Not all characters in property title\n\nNote that if we want to do an exact search, we have to use a special\nfilter that can be created by the property instance.\n\n    >>> [f.title for f in Foo.all().filter('title =', 'foo 1')]\n    []\n\nThe \"equal\" filter arguments can be computed with a special method on\nthe property.\n\n    >>> ef = Foo.title.equals_filter('Foo 1')\n    >>> ef\n    ('title =', u'foo 1\\xef\\xbf\\xbcFoo 1')\n\n    >>> [f.title for f in Foo.all().filter(*ef)]\n    [u'Foo 1']\n\nLet us try with inequallity, e.g. prefix search. Prefix search is\nnormally done with two filters using the highest unicode character.\n\nSearch for all that starts with \"fo\" case-insensitive.\n\n    >>> q = Foo.all()\n    >>> q = q.filter('title >=', 'fo')\n    >>> q = q.filter('title <', 'fo' + u'\\xEF\\xBF\\xBD')\n    >>> [f.title for f in q]\n    [u'Foo 1', u'Foo 2', u'foo 3']\n\nSearch for all that starts with 'foo 1'\n\n    >>> q = Foo.all()\n    >>> q = q.filter('title >=', 'foo 1')\n    >>> q = q.filter('title <', 'foo 1' + u'\\xEF\\xBF\\xBD')\n    >>> [f.title for f in q]\n    [u'Foo 1']\n\n    >>> q = Foo.all()\n    >>> q = q.filter('title >=', 'foo 2')\n    >>> q = q.filter('title <=', 'foo 2' + u'\\xEF\\xBF\\xBD')\n    >>> [f.title for f in q]\n    [u'Foo 2']\n\n    >>> q = Foo.all()\n    >>> q = q.filter('title >=', 'foo 3')\n    >>> q = q.filter('title <=', 'foo 3' + u'\\xEF\\xBF\\xBD')\n    >>> [f.title for f in q]\n    [u'foo 3']\n\n\nPickle Property\n===============\n\nA pickle property can hold any pickleable python object.\n\n    >>> from lovely.gae.db.property import PickleProperty\n\n    >>> class Pickie(db.Model):\n    ...     data = PickleProperty()\n\n    >>> pickie = Pickie(data={})\n    >>> pickie.data\n    {}\n    >>> kp = pickie.put()\n    >>> pickie.data\n    {}\n    >>> pickie = db.get(kp)\n    >>> pickie.data\n    {}\n    >>> pickie.data = {'key':501*\"x\"}\n    >>> kp = pickie.put()\n    >>> pickie.data\n    {'key': 'xx...xx'}\n\nIf the value is not pickleable we get a validation error.\n\n    >>> pickie = Pickie(data=dict(y=lambda x:x))\n    Traceback (most recent call last):\n    BadValueError: Property 'data' must be pickleable:\n    (Can't pickle <function <lambda> at ...>:\n    it's not found as __main__.<lambda>)\n\n\nSafe ReferenceProperty\n======================\n\n    >>> from lovely.gae.db.property import SafeReferenceProperty\n\nWe use a new model with a gae reference and our safe reference.\n\n    >>> class Refie(db.Model):\n    ...     ref   = db.ReferenceProperty(Yummy, collection_name='ref_ref')\n    ...     sfref = SafeReferenceProperty(Yummy, collection_name='sfref_ref')\n\n    >>> refie = Refie()\n    >>> refie.sfref is None\n    True\n    >>> refie.ref is None\n    True\n\nAn object to be referenced.\n\n    >>> refyummy1 = Yummy()\n    >>> ignore = refyummy1.put()\n\nSet the references to our yummy object.\n\n    >>> refie.sfref = refyummy1\n    >>> refie.sfref\n    <Yummy object at ...>\n    >>> refie.ref = refyummy1\n    >>> refie.ref\n    <Yummy object at ...>\n\n    >>> refieKey = refie.put()\n\nNow we delete the referenced object.\n\n    >>> refyummy1.delete()\n\nAnd reload our referencing object.\n\n    >>> refie = db.get(refieKey)\n\nThe gae reference raises an exception.\n\n    >>> refie.ref\n    Traceback (most recent call last):\n    Error: ReferenceProperty failed to be resolved\n\nWe catch the logs here.\n\n    >>> import logging\n    >>> from StringIO import StringIO\n    >>> log = StringIO()\n    >>> handler = logging.StreamHandler(log)\n    >>> logger = logging.getLogger('lovely.gae.db')\n    >>> logger.setLevel(logging.INFO)\n    >>> logger.addHandler(handler)\n\nOur safe reference returns None.\n\n    >>> pos = log.tell()\n    >>> refie.sfref is None\n    True\n\nLet's see what the log contains.\n\n    >>> log.seek(pos)\n    >>> print log.read()\n    Unresolved Reference for \"Refie._sfref\" set to None\n\nAccessing the stale property once again we will see it was reset to None::\n\n    >>> pos = log.tell()\n    >>> refie.sfref is None\n    True\n\n    >>> log.seek(pos)\n    >>> print log.read() == ''\n    True\n\nThe property get set to None if the reference points to a dead object but only\nif the property is not required::\n\n    >>> class Requy(db.Model):\n    ...     sfref = SafeReferenceProperty(Yummy, collection_name='req_sfref_ref',\n    ...                                   required=True)\n\n    >>> refyummy1 = Yummy()\n    >>> ignore = refyummy1.put()\n\n    >>> requy = Requy(sfref = refyummy1)\n    >>> requyKey = requy.put()\n\n    >>> requy.sfref\n    <Yummy object at ...>\n\n    >>> refyummy1.delete()\n\n    >>> requy = db.get(requyKey)\n\n    >>> pos = log.tell()\n    >>> requy.sfref is None\n    True\n\n    >>> log.seek(pos)\n    >>> print log.read()\n    Unresolved Reference for \"Requy._sfref\" will remain because it is required\n\n=====================\nBatch marker creation\n=====================\n\nThis packages provides the possibility to create markers for every N\nobjects of a given query. This is useful to create batched html pages\nor to generate jobs for every N objects.\n\nA list of attribute values are created that represent the end of a\nbatch at any given position in a given query. The result is stored in\nmemcache and the key is provided to a callback function.\n\n    >>> from lovely.gae import batch\n\nLet us create some test objects.\n\n    >>> from google.appengine.ext import db\n    >>> class Stub(db.Model):\n    ...     c_time = db.DateTimeProperty(auto_now_add=True, required=True)\n    ...     name = db.StringProperty()\n    ...     age = db.IntegerProperty()\n    ...     state = db.IntegerProperty()\n    ...     def __repr__(self):\n    ...         return '<Stub %s>' % self.name\n\n    >>> for i in range(1,13):\n    ...     s = Stub(name=str(i), age=i, state=i%2)\n    ...     sk = s.put()\n\n    >>> Stub.all().count()\n    12\n\nFirst we make sure that we have no tasks in the queue for testing.\n\n    >>> from lovely.gae.async import get_tasks\n    >>> len(get_tasks())\n    0\n\nSo for example if we want to know any 100th key of a given kind we\ncould calculate it like shown below. Note that we provide the pprint function\nas a callback, so we get the memcache key in the output.\n\nThe calculate_markers function returns the memcache key that will be\nused to store the result when the calculation is completed.\n\n    >>> from pprint import pprint\n    >>> mc_key = batch.create_markers('Stub', callback=pprint)\n    >>> mc_key\n    'create_markers:...-...-...'\n\nA task gets created.\n\n    >>> tasks = get_tasks()\n    >>> len(tasks)\n    1\n\nLet us run the task.\n\n    >>> run_tasks(1)\n    1\n\nWe now have another task left for the callback, which is actually the\npprint function.\n\n    >>> run_tasks()\n    'create_markers:...-...-...'\n    1\n\nWe should now have a result. The result shows that we need no batches\nfor the given batch size (because we only have 12 objects).\n\n    >>> from google.appengine.api import memcache\n    >>> memcache.get(mc_key)\n    []\n\nLet us use another batch size. This time without callback.\n\n    >>> mc_key = batch.create_markers('Stub', batchsize=1)\n    >>> run_tasks()\n    1\n\nWe now have exatly 12 keys, because the batch size was 1.\n\n    >>> len(memcache.get(mc_key))\n    12\n\nThe default attributes returned are the keys.\n\n    >>> memcache.get(mc_key)\n    [datastore_types.Key.fro...\n\nWe can also use other attributes. Let us get items batched by c_time\ndescending. Note that it is not checked if values are not unique, so\nif a non-unique attribute is used it might be the case that batch\nranges contains objects twice.\n\n    >>> mc_key = batch.create_markers('Stub',\n    ...                               attribute='c_time',\n    ...                               order='desc',\n    ...                               batchsize=3)\n    >>> run_tasks()\n    1\n    >>> markers = memcache.get(mc_key)\n    >>> markers\n    [datetime.datetime(...\n    >>> len(markers)\n    4\n    >>> sorted(markers, reverse=True) == markers\n    True\n\n    >>> mc_key = batch.create_markers('Stub',\n    ...                               attribute='c_time',\n    ...                               order='asc',\n    ...                               batchsize=3)\n    >>> run_tasks()\n    1\n    >>> markers = memcache.get(mc_key)\n    >>> markers\n    [datetime.datetime(...\n    >>> len(markers)\n    4\n    >>> sorted(markers) == markers\n    True\n\n\nWe can also pass filters to be applied to the query for the batch like this:\n\n    >>> mc_key = batch.create_markers('Stub',\n    ...                               filters=[('state', 0)],\n    ...                               attribute='c_time',\n    ...                               order='asc',\n    ...                               batchsize=3)\n    >>> run_tasks()\n    1\n    >>> markers = memcache.get(mc_key)\n    >>> len(markers)\n    2",
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