def to_internal_value(self, data):
question = self.get_question()
polling = question.polling
now = _djtz.localtime(timezone=_djtz.get_default_timezone())
if now < polling.start_time:
self.fail('polling_not_started')
if polling.start_time + polling.duration <= now:
self.fail('polling_ended')
data = super().to_internal_value(data)
data['question'] = question
data = F.select_values(F.complement(F.isnone), data)
logger.debug('QuestionAnswerSerializer.to_internal_value(%r)', data)
if isinstance(question, models.TextQuestion):
kwargs = F.project(data, 'guest_id question text'.split())
rest = F.project(data, F.without(data, *kwargs.keys()))
elif isinstance(question, models.ChoiceQuestion):
kwargs = F.project(data, 'guest_id question choices'.split())
rest = F.project(data, F.without(data, *kwargs.keys()))
else:
self.fail('unknown_question', question=question)
if rest:
self.fail('unknown_fields', fields=dict(rest))
return data
def take(self, limit=5):
""" Take up to n (n = limit) posts/comments at a time.
You can call this method as many times as you want. Once
there are no more posts to take, it will return [].
Returns:
List of posts/comments in a batch of size up to `limit`.
"""
# get main posts only
comment_filter = is_comment if self.comments_only else complement(
is_comment)
hist = filter(comment_filter, self.history)
# filter out reblogs
hist2 = filter(lambda x: x["author"] == self.account.name, hist)
# post edits will re-appear in history
# we should therefore filter out already seen posts
def ensure_unique(post):
if post["permlink"] not in self.seen_items:
self.seen_items.add(post["permlink"])
return True
unique = filter(ensure_unique, hist2)
serialized = filter(bool, map(silent(Post), unique))
batch = take(limit, serialized)
return batch
def PNot(value_or_stepf: Union[Callable, Any]) -> bool:
"Unary negate for `LogicPiping`."
if callable(value_or_stepf):
stepf = value_or_stepf
else:
stepf = funcy.identity
return funcy.complement(stepf)
def reduce_scenario(scenario, scenario_id, stream):
def reducer(acc, ioTuple):
return acc + (ioTuple.msg,)
return stream.take_while(complement(end_of_scenario(scenario,scenario_id)))\
.reduce(reducer,())\
.map(lambda events: Scenario(scenario,scenario_id,events))
def get_accepted_features(features: Collection[Feature],
proposed_feature: Feature) -> List[Feature]:
"""Deselect candidate features from list of all features
Args:
features: collection of all features in the ballet project: both
accepted features and candidate ones that have not been accepted
proposed_feature: candidate feature that has not been accepted
Returns:
list of features with the proposed feature not in it.
Raises:
ballet.exc.BalletError: Could not deselect exactly the proposed
feature.
"""
def eq(feature):
"""Features are equal if they have the same source
At least in this implementation...
"""
return feature.source == proposed_feature.source
# deselect features that match the proposed feature
result = lfilter(complement(eq), features)
if len(features) - len(result) == 1:
return result
elif len(result) == len(features):
raise BalletError(
'Did not find match for proposed feature within \'contrib\'')
else:
raise BalletError(f'Unexpected condition (n_features={len(features)}, '
f'n_result={len(result)})')
def push_branches_to_remote(repo: git.Repo, remote_name: str,
branches: Iterable[str]):
"""Push selected branches to origin
Similar to::
$ git push origin branch1:branch1 branch2:branch2
Raises:
ballet.exc.BalletError: Push failed in some way
"""
remote = repo.remote(remote_name)
result = remote.push([f'{b}:{b}' for b in branches])
failures = lfilter(complement(did_git_push_succeed), result)
if failures:
for push_info in failures:
logger.error(f'Failed to push ref {push_info.local_ref.name} to '
f'{push_info.remote_ref.name}')
raise BalletError('Push failed')
def _push(project):
"""Push default branch and project template branch to remote
With default config (i.e. remote and branch names), equivalent to::
$ git push origin master:master project-template:project-template
Raises:
ballet.exc.BalletError: Push failed in some way
"""
repo = project.repo
remote_name = project.config.get('github.remote')
remote = repo.remote(remote_name)
result = _call_remote_push(remote)
failures = lfilter(complement(did_git_push_succeed), result)
if failures:
for push_info in failures:
logger.error('Failed to push ref {from_ref} to {to_ref}'.format(
from_ref=push_info.local_ref.name,
to_ref=push_info.remote_ref.name))
raise BalletError('Push failed')
def needs_path(f):
"""Wraps a function that accepts path-like to give it a pathlib.Path"""
@wraps(f)
def wrapped(pathlike, *args, **kwargs):
path = pathlib.Path(pathlike)
return f(path, *args, **kwargs)
return wrapped
def warn(msg):
"""Issue a warning message of category BalletWarning"""
warnings.warn(msg, category=BalletWarning)
@decorator
def raiseifnone(call):
"""Decorate a function to raise a ValueError if result is None"""
result = call()
if result is None:
raise ValueError
else:
return result
def falsy(o):
return isinstance(o, str) and (o.lower() == 'false' or o == '')
truthy = complement(falsy)
self.append(obj)
else:
self.extend(obj)
@property
def children(self):
return self
def as_node_set(self):
return self
def as_json(self):
return [node.as_json() for node in self]
is_nset = isa(NodeSet)
is_single = any_fn(is_leaf,
complement(isa(Node, NodeSet, list)))
def nodify(name, data):
_nodify = lambda (name, data): nodify(name, data)
if isinstance(data, list):
return Branch(name, NodeSet.from_seq(map(_nodify, enumerate(data))))
elif isinstance(data, dict):
return Branch(name, NodeSet.from_seq(map(_nodify, data.iteritems())))
else:
return Leaf(name, data)
# ____ ___ __ __ ____ ___ _ _ _ _____ ___ ____ ____
# / ___/ _ \| \/ | __ )_ _| \ | | / \|_ _/ _ \| _ \/ ___|
else:
self.extend(obj)
@property
def children(self):
return self
def as_node_set(self):
return self
def as_json(self):
return [node.as_json() for node in self]
is_nset = isa(NodeSet)
is_single = any_fn(is_leaf, complement(isa(Node, NodeSet, list)))
def nodify(name, data):
_nodify = lambda (name, data): nodify(name, data)
if isinstance(data, list):
return Branch(name, NodeSet.from_seq(map(_nodify, enumerate(data))))
elif isinstance(data, dict):
return Branch(name, NodeSet.from_seq(map(_nodify, data.iteritems())))
else:
return Leaf(name, data)
# ____ ___ __ __ ____ ___ _ _ _ _____ ___ ____ ____
# / ___/ _ \| \/ | __ )_ _| \ | | / \|_ _/ _ \| _ \/ ___|
# | | | | | | |\/| | _ \| || \| | / _ \ | || | | | |_) \___ \
def remove_values(pred, col):
return select_values(complement(pred), col)
def __neg__(self, stepf) -> None:
"Does __ge__"
return self.logically(funcy.complement(stepf), False)
return fn
def mkclass(name: str, bases: Tuple = (), **clsattrs: Any) -> Any:
"Does mkclass"
Gen = type(name, (Base, ) + bases, clsattrs)
return Gen
def arity(fn: Callable) -> int:
"Returns the number of arguments required by `fn`."
return len(inspect.signature(fn).parameters)
always_tup = funcy.iffy(funcy.complement(funcy.is_seqcont), lambda x: (x, ))
class Piping(pipelib.BasePiping):
"""Piping objects is for (ab)using Python operator overloading to
build small pipeline-DSL's.
The most basic one will simply refuse to do anything - you have to
give it instructions/permissions on everything it's made for ;-).
"""
class Fresh(object):
"Marker for Piping instances that never has been run"
pass
class Executed(object):
