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praekeltfoundation/seed-stage-based-messaging | subscriptions/tasks.py | calculate_subscription_lifecycle | def calculate_subscription_lifecycle(subscription_id):
"""
Calculates the expected lifecycle position the subscription in
subscription_ids, and creates a BehindSubscription entry for them.
Args:
subscription_id (str): ID of subscription to calculate lifecycle for
"""
subscription = Subs... | python | def calculate_subscription_lifecycle(subscription_id):
"""
Calculates the expected lifecycle position the subscription in
subscription_ids, and creates a BehindSubscription entry for them.
Args:
subscription_id (str): ID of subscription to calculate lifecycle for
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praekeltfoundation/seed-stage-based-messaging | subscriptions/tasks.py | find_behind_subscriptions | def find_behind_subscriptions():
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"""
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"""
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praekeltfoundation/seed-stage-based-messaging | contentstore/views.py | ScheduleViewSet.send | def send(self, request, pk=None):
"""
Sends all the subscriptions for the specified schedule
"""
schedule = self.get_object()
queue_subscription_send.delay(str(schedule.id))
return Response({}, status=status.HTTP_202_ACCEPTED) | python | def send(self, request, pk=None):
"""
Sends all the subscriptions for the specified schedule
"""
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queue_subscription_send.delay(str(schedule.id))
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jreese/tasky | tasky/config.py | Config.task_config | def task_config(self, task: Task) -> Any:
'''Return the task-specific configuration.'''
return self.get(task.__class__.__name__) | python | def task_config(self, task: Task) -> Any:
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jreese/tasky | tasky/tasks/timer.py | TimerTask.run_task | async def run_task(self) -> None:
'''Execute the task inside the asyncio event loop after `DELAY`
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self.last_run = 0.0
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praekeltfoundation/seed-stage-based-messaging | seed_stage_based_messaging/decorators.py | internal_only | def internal_only(view_func):
"""
A view decorator which blocks access for requests coming through the load balancer.
"""
@functools.wraps(view_func)
def wrapper(request, *args, **kwargs):
forwards = request.META.get("HTTP_X_FORWARDED_FOR", "").split(",")
# The nginx in the docker c... | python | def internal_only(view_func):
"""
A view decorator which blocks access for requests coming through the load balancer.
"""
@functools.wraps(view_func)
def wrapper(request, *args, **kwargs):
forwards = request.META.get("HTTP_X_FORWARDED_FOR", "").split(",")
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praekeltfoundation/seed-stage-based-messaging | subscriptions/views.py | SubscriptionSend.post | def post(self, request, *args, **kwargs):
""" Validates subscription data before creating Outbound message
"""
schedule_disable.delay(kwargs["subscription_id"])
return Response({"accepted": True}, status=201) | python | def post(self, request, *args, **kwargs):
""" Validates subscription data before creating Outbound message
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praekeltfoundation/seed-stage-based-messaging | subscriptions/views.py | SubscriptionRequest.post | def post(self, request, *args, **kwargs):
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praekeltfoundation/seed-stage-based-messaging | subscriptions/views.py | BehindSubscriptionViewSet.find_behind_subscriptions | def find_behind_subscriptions(self, request):
"""
Starts a celery task that looks through active subscriptions to find
and subscriptions that are behind where they should be, and adds a
BehindSubscription for them.
"""
task_id = find_behind_subscriptions.delay()
... | python | def find_behind_subscriptions(self, request):
"""
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"""
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jreese/tasky | tasky/tasks/queue.py | QueueTask.run_task | async def run_task(self) -> None:
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praekeltfoundation/seed-stage-based-messaging | contentstore/signals.py | schedule_saved | def schedule_saved(sender, instance, **kwargs):
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sender {class} -- The model class, always Schedule
instance {Schedule} --
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praekeltfoundation/seed-stage-based-messaging | contentstore/signals.py | schedule_deleted | def schedule_deleted(sender, instance, **kwargs):
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Fires off the celery task to ensure that this schedule is deactivated
Arguments:
sender {class} -- The model class, always Schedule
instance {Schedule} --
The instance of the schedule that we want to deactivate
"""
fr... | python | def schedule_deleted(sender, instance, **kwargs):
"""
Fires off the celery task to ensure that this schedule is deactivated
Arguments:
sender {class} -- The model class, always Schedule
instance {Schedule} --
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/grouping.py | group_by | def group_by(keys, values=None, reduction=None, axis=0):
"""construct a grouping object on the given keys, optionally performing the given reduction on the given values
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keys to group by
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/grouping.py | GroupBy.split_iterable_as_iterable | def split_iterable_as_iterable(self, values):
"""Group iterable into iterables, in the order of the keys
Parameters
----------
values : iterable of length equal to keys
iterable of values to be grouped
Yields
------
iterable of items in values
... | python | def split_iterable_as_iterable(self, values):
"""Group iterable into iterables, in the order of the keys
Parameters
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values : iterable of length equal to keys
iterable of values to be grouped
Yields
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/grouping.py | GroupBy.split_iterable_as_unordered_iterable | def split_iterable_as_unordered_iterable(self, values):
"""Group iterable into iterables, without regard for the ordering of self.index.unique
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Parameters
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values : iterable of length equal to keys
it... | python | def split_iterable_as_unordered_iterable(self, values):
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values : iterable of length equal to keys
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/grouping.py | GroupBy.split_sequence_as_iterable | def split_sequence_as_iterable(self, values):
"""Group sequence into iterables
Parameters
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values : iterable of length equal to keys
iterable of values to be grouped
Yields
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iterable of items in values
Notes
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... | python | def split_sequence_as_iterable(self, values):
"""Group sequence into iterables
Parameters
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values : iterable of length equal to keys
iterable of values to be grouped
Yields
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iterable of items in values
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/grouping.py | GroupBy.split_array_as_array | def split_array_as_array(self, values):
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Parameters
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values : ndarray_like, [index.size, ...]
Returns
-------
ndarray, [groups, group_size, ...]
values grouped by key
Raises
... | python | def split_array_as_array(self, values):
"""Group ndarray into ndarray by means of reshaping
Parameters
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values : ndarray_like, [index.size, ...]
Returns
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ndarray, [groups, group_size, ...]
values grouped by key
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/grouping.py | GroupBy.split_array_as_list | def split_array_as_list(self, values):
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Parameters
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values : ndarray, [keys, ...]
Returns
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list of length self.groups of ndarray, [key_count, ...]
"""
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values : ndarray, [keys, ...]
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list of length self.groups of ndarray, [key_count, ...]
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/grouping.py | GroupBy.reduce | def reduce(self, values, operator=np.add, axis=0, dtype=None):
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all other axes are treated indepenently for the sake of this reduction
... | python | def reduce(self, values, operator=np.add, axis=0, dtype=None):
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/grouping.py | GroupBy.sum | def sum(self, values, axis=0, dtype=None):
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axis : int, optional
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values to sum per group
axis : int, optional
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/grouping.py | GroupBy.prod | def prod(self, values, axis=0, dtype=None):
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Parameters
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values : array_like, [keys, ...]
values to multiply per group
axis : int, optional
alternative reduction axis for values
dtype : output dtype
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values to multiply per group
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/grouping.py | GroupBy.mean | def mean(self, values, axis=0, weights=None, dtype=None):
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Parameters
----------
values : array_like, [keys, ...]
values to take average of per group
axis : int, optional
alternative reduction axis for values
wei... | python | def mean(self, values, axis=0, weights=None, dtype=None):
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values to take average of per group
axis : int, optional
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/grouping.py | GroupBy.var | def var(self, values, axis=0, weights=None, dtype=None):
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values : array_like, [keys, ...]
values to take variance of per group
axis : int, optional
alternative reduction axis for values
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/grouping.py | GroupBy.std | def std(self, values, axis=0, weights=None, dtype=None):
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values to take standard deviation of per group
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/grouping.py | GroupBy.median | def median(self, values, axis=0, average=True):
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Parameters
----------
values : array_like, [keys, ...]
values to compute the median of per group
axis : int, optional
alternative reduction axis for values
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/grouping.py | GroupBy.min | def min(self, values, axis=0):
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axis : int, optional
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/grouping.py | GroupBy.max | def max(self, values, axis=0):
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values to take maximum of per group
axis : int, optional
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/grouping.py | GroupBy.first | def first(self, values, axis=0):
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Parameters
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values : array_like, [keys, ...]
values to pick the first value of per group
axis : int, optional
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/grouping.py | GroupBy.last | def last(self, values, axis=0):
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values : array_like, [keys, ...]
values to pick the last value of per group
axis : int, optional
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/grouping.py | GroupBy.any | def any(self, values, axis=0):
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Parameters
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values : array_like, [keys, ...]
values to take boolean predicate over per group
axis : int, optional
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/grouping.py | GroupBy.all | def all(self, values, axis=0):
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values : array_like, [keys, ...]
values to take boolean predicate over per group
axis : int, optional
alternative reduction axis for values
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/grouping.py | GroupBy.argmin | def argmin(self, values):
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values : array_like, [keys]
values to pick the argmin of per group
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-------
unique: ndarray, [groups]
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/grouping.py | GroupBy.argmax | def argmax(self, values):
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values : array_like, [keys]
values to pick the argmax of per group
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/index.py | as_index | def as_index(keys, axis=semantics.axis_default, base=False, stable=True, lex_as_struct=False):
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except when keys is an instance of tuple,
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/index.py | Index.inverse | def inverse(self):
"""return index array that maps unique values back to original space. unique[inverse]==keys"""
inv = np.empty(self.size, np.int)
inv[self.sorter] = self.sorted_group_rank_per_key
return inv | python | def inverse(self):
"""return index array that maps unique values back to original space. unique[inverse]==keys"""
inv = np.empty(self.size, np.int)
inv[self.sorter] = self.sorted_group_rank_per_key
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/index.py | Index.rank | def rank(self):
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r[self.sorter] = np.arange(self.size)
return r | python | def rank(self):
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/index.py | LexIndex.unique | def unique(self):
"""returns a tuple of unique key columns"""
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/utility.py | as_struct_array | def as_struct_array(*columns):
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columns : sequence of key objects
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data : recarray
recarray containing the input columns as struct fields
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/utility.py | axis_as_object | def axis_as_object(arr, axis=-1):
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this is useful for efficiently sorting by the content of an axis, for instance
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arr : ndarray
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/utility.py | object_as_axis | def object_as_axis(arr, dtype, axis=-1):
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/funcs.py | count | def count(keys, axis=semantics.axis_default):
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keys : indexable object
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/funcs.py | count_table | def count_table(*keys):
"""count the number of times each key occurs in the input set
Arguments
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keys : tuple of indexable objects, each having the same number of items
Returns
-------
unique : tuple of ndarray, [groups, ...]
unique keys for each input item
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"""count the number of times each key occurs in the input set
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/funcs.py | binning | def binning(keys, start, end, count, axes=None):
"""Perform binning over the given axes of the keys
Parameters
----------
keys : indexable or tuple of indexable
Examples
--------
binning(np.random.rand(100), 0, 1, 10)
"""
if isinstance(keys, tuple):
n_keys = len(keys)
e... | python | def binning(keys, start, end, count, axes=None):
"""Perform binning over the given axes of the keys
Parameters
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keys : indexable or tuple of indexable
Examples
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binning(np.random.rand(100), 0, 1, 10)
"""
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/funcs.py | multiplicity | def multiplicity(keys, axis=semantics.axis_default):
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keys : indexable object
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keys : indexable object
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/funcs.py | rank | def rank(keys, axis=semantics.axis_default):
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----------
keys : indexable object
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/funcs.py | mode | def mode(keys, axis=semantics.axis_default, weights=None, return_indices=False):
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Parameters
----------
keys : ndarray, [n_keys, ...]
input array. elements of 'keys' can have arbitrary shape or dtype
weights : ndarray, [n_keys], opt... | python | def mode(keys, axis=semantics.axis_default, weights=None, return_indices=False):
"""compute the mode, or most frequent occuring key in a set
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keys : ndarray, [n_keys, ...]
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/funcs.py | incidence | def incidence(boundary):
"""
given an Nxm matrix containing boundary info between simplices,
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not very reusable; should probably not be in this lib
"""
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"""
given an Nxm matrix containing boundary info between simplices,
compute indidence info matrix
not very reusable; should probably not be in this lib
"""
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/funcs.py | all_unique | def all_unique(keys, axis=semantics.axis_default):
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/funcs.py | any_unique | def any_unique(keys, axis=semantics.axis_default):
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index = as_index(keys, axis)
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/funcs.py | all_equal | def all_equal(keys, axis=semantics.axis_default):
"""returns true of all keys are equal"""
index = as_index(keys, axis)
return index.groups == 1 | python | def all_equal(keys, axis=semantics.axis_default):
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/funcs.py | is_uniform | def is_uniform(keys, axis=semantics.axis_default):
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return index.uniform | python | def is_uniform(keys, axis=semantics.axis_default):
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/funcs.py | Table.get_inverses | def get_inverses(self, keys):
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Returns
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/funcs.py | Table.unique | def unique(self, values):
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return self.sum(values) | python | def unique(self, values):
"""Place each entry in a table, while asserting that each entry occurs once"""
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/arraysetops.py | unique | def unique(keys, axis=semantics.axis_default, return_index=False, return_inverse=False, return_count=False):
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Parameters
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keys : indexable key object
keys object to find unique keys within
axis : int
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/arraysetops.py | contains | def contains(this, that, axis=semantics.axis_default):
"""Returns bool for each element of `that`, indicating if it is contained in `this`
Parameters
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this : indexable key sequence
sequence of items to test against
that : indexable key sequence
sequence of items to test fo... | python | def contains(this, that, axis=semantics.axis_default):
"""Returns bool for each element of `that`, indicating if it is contained in `this`
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sequence of items to test against
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/arraysetops.py | in_ | def in_(this, that, axis=semantics.axis_default):
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sequence of items to test for
that : indexable key sequence
sequence of items to test against
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/arraysetops.py | indices | def indices(this, that, axis=semantics.axis_default, missing='raise'):
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/arraysetops.py | remap | def remap(input, keys, values, missing='ignore', inplace=False):
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/arraysetops.py | _set_preprocess | def _set_preprocess(sets, **kwargs):
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axis : int, optional
axis to view as item sequence
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/arraysetops.py | _set_concatenate | def _set_concatenate(sets):
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Parameters
----------
sets : iterable of indexable objects
Returns
-------
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handles both arrays and tuples of arrays
"""
def con(set):
# if not all():
# raise ValueError('concaten... | python | def _set_concatenate(sets):
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Parameters
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sets : iterable of indexable objects
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handles both arrays and tuples of arrays
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/arraysetops.py | _set_count | def _set_count(sets, n, **kwargs):
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number of sets the element should occur in
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/arraysetops.py | union | def union(*sets, **kwargs):
"""all unique items which occur in any one of the sets
Parameters
----------
sets : tuple of indexable objects
Returns
-------
union of all items in all sets
"""
sets = _set_preprocess(sets, **kwargs)
return as_index( _set_concatenate(sets), axis=0, ... | python | def union(*sets, **kwargs):
"""all unique items which occur in any one of the sets
Parameters
----------
sets : tuple of indexable objects
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union of all items in all sets
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sets = _set_preprocess(sets, **kwargs)
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EelcoHoogendoorn/Numpy_arraysetops_EP | numpy_indexed/arraysetops.py | difference | def difference(*sets, **kwargs):
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sets : tuple of indexable objects
first set is the head, from which we subtract
other items form the tail, which are subtracted from head
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gtnx/pandas-highcharts | pandas_highcharts/display.py | _generate_div_id_chart | def _generate_div_id_chart(prefix="chart_id", digits=8):
"""Generate a random id for div chart.
"""
choices = (random.randrange(0, 52) for _ in range(digits))
return prefix + "".join((string.ascii_letters[x] for x in choices)) | python | def _generate_div_id_chart(prefix="chart_id", digits=8):
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"""
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gtnx/pandas-highcharts | pandas_highcharts/display.py | display_charts | def display_charts(df, chart_type="default", render_to=None, **kwargs):
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df: DataFrame
chart_type: str
'default' or 'stock'
render_to: str
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df: DataFrame
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modify the Highcharts parameters.
data: dict
Serialized DataFrame in a dict for Highcharts
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gtnx/pandas-highcharts | pandas_highcharts/display.py | pretty_params | def pretty_params(data, indent=2):
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data: dict
Serialized DataFrame in a dict for Highcharts
"""
data_to_print = _series_data_filter(data)
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"""Pretty print your Highcharts params (into a JSON).
data: dict
Serialized DataFrame in a dict for Highcharts
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data_to_print = _series_data_filter(data)
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Skyscanner/skyscanner-python-sdk | skyscanner/skyscanner.py | Transport.get_additional_params | def get_additional_params(self, **params):
"""
Filter to get the additional params needed for polling
"""
# TODO: Move these params to their own vertical if needed.
polling_params = [
'locationschema',
'carrierschema',
'sorttype',
... | python | def get_additional_params(self, **params):
"""
Filter to get the additional params needed for polling
"""
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Skyscanner/skyscanner-python-sdk | skyscanner/skyscanner.py | Transport.get_result | def get_result(self, errors=GRACEFUL, **params):
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Get all results, no filtering, etc. by creating and polling the
session.
"""
additional_params = self.get_additional_params(**params)
return self.poll_session(
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Skyscanner/skyscanner-python-sdk | skyscanner/skyscanner.py | Transport.make_request | def make_request(self, service_url, method='get', headers=None, data=None,
callback=None, errors=GRACEFUL, **params):
"""
Reusable method for performing requests.
:param service_url - URL to request
:param method - request method, default is 'get'
:param hea... | python | def make_request(self, service_url, method='get', headers=None, data=None,
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Skyscanner/skyscanner-python-sdk | skyscanner/skyscanner.py | Transport.get_markets | def get_markets(self, market):
"""
Get the list of markets
http://business.skyscanner.net/portal/en-GB/Documentation/Markets
"""
url = "{url}/{market}".format(url=self.MARKET_SERVICE_URL,
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"""
Get the list of markets
http://business.skyscanner.net/portal/en-GB/Documentation/Markets
"""
url = "{url}/{market}".format(url=self.MARKET_SERVICE_URL,
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Skyscanner/skyscanner-python-sdk | skyscanner/skyscanner.py | Transport.location_autosuggest | def location_autosuggest(self, **params):
"""
Location Autosuggest Services
Doc URLs:
http://business.skyscanner.net/portal/en-GB/
Documentation/Autosuggest
http://business.skyscanner.net/portal/en-GB/
Documentation/CarHireAutoSuggest
... | python | def location_autosuggest(self, **params):
"""
Location Autosuggest Services
Doc URLs:
http://business.skyscanner.net/portal/en-GB/
Documentation/Autosuggest
http://business.skyscanner.net/portal/en-GB/
Documentation/CarHireAutoSuggest
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Skyscanner/skyscanner-python-sdk | skyscanner/skyscanner.py | Transport.poll_session | def poll_session(self, poll_url, initial_delay=2, delay=1, tries=20,
errors=GRACEFUL, **params):
"""
Poll the URL
:param poll_url - URL to poll,
should be returned by 'create_session' call
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Poll the URL
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Skyscanner/skyscanner-python-sdk | skyscanner/skyscanner.py | Transport._construct_params | def _construct_params(params, required_keys, opt_keys=None):
"""
Construct params list in order of given keys.
"""
try:
params_list = [params.pop(key) for key in required_keys]
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"""
Construct params list in order of given keys.
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Skyscanner/skyscanner-python-sdk | skyscanner/skyscanner.py | Flights.create_session | def create_session(self, **params):
"""
Create the session
date format: YYYY-mm-dd
location: ISO code
"""
return self.make_request(self.PRICING_SESSION_URL,
method='post',
headers=self._session_headers(),
... | python | def create_session(self, **params):
"""
Create the session
date format: YYYY-mm-dd
location: ISO code
"""
return self.make_request(self.PRICING_SESSION_URL,
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Skyscanner/skyscanner-python-sdk | skyscanner/skyscanner.py | Flights.request_booking_details | def request_booking_details(self, poll_url, **params):
"""
Request for booking details
URL Format:
{API_HOST}/apiservices/pricing/v1.0/{session key}/booking
?apiKey={apiKey}
"""
return self.make_request("%s/booking" % poll_url,
... | python | def request_booking_details(self, poll_url, **params):
"""
Request for booking details
URL Format:
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"""
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Skyscanner/skyscanner-python-sdk | skyscanner/skyscanner.py | FlightsCache.get_cheapest_price_by_date | def get_cheapest_price_by_date(self, **params):
"""
{API_HOST}/apiservices/browsedates/v1.0/{market}/{currency}/{locale}/
{originPlace}/{destinationPlace}/
{outboundPartialDate}/{inboundPartialDate}
?apiKey={apiKey}
"""
service_url = "{url}/{params_path}".format(
... | python | def get_cheapest_price_by_date(self, **params):
"""
{API_HOST}/apiservices/browsedates/v1.0/{market}/{currency}/{locale}/
{originPlace}/{destinationPlace}/
{outboundPartialDate}/{inboundPartialDate}
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Skyscanner/skyscanner-python-sdk | skyscanner/skyscanner.py | FlightsCache.get_cheapest_price_by_route | def get_cheapest_price_by_route(self, **params):
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{originPlace}/{destinationPlace}/
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?apiKey={apiKey}
"""
service_url = "{url}/{params_path}".format... | python | def get_cheapest_price_by_route(self, **params):
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Skyscanner/skyscanner-python-sdk | skyscanner/skyscanner.py | FlightsCache.get_cheapest_quotes | def get_cheapest_quotes(self, **params):
"""
{API_HOST}/apiservices/browsequotes/v1.0/{market}/{currency}/{locale}/
{originPlace}/{destinationPlace}/
{outboundPartialDate}/{inboundPartialDate}
?apiKey={apiKey}
"""
service_url = "{url}/{params_path}".format(
... | python | def get_cheapest_quotes(self, **params):
"""
{API_HOST}/apiservices/browsequotes/v1.0/{market}/{currency}/{locale}/
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Skyscanner/skyscanner-python-sdk | skyscanner/skyscanner.py | FlightsCache.get_grid_prices_by_date | def get_grid_prices_by_date(self, **params):
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{API_HOST}/apiservices/browsegrid/v1.0/{market}/{currency}/{locale}/
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"""
service_url = "{url}/{params_path}".format(
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Skyscanner/skyscanner-python-sdk | skyscanner/skyscanner.py | CarHire.create_session | def create_session(self, **params):
"""
Create the session
date format: YYYY-MM-DDThh:mm
location: ISO code
"""
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... | python | def create_session(self, **params):
"""
Create the session
date format: YYYY-MM-DDThh:mm
location: ISO code
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jlmadurga/django-telegram-bot | telegrambot/templatetags/telegrambot_filters.py | keyboard_field | def keyboard_field(value, args=None):
"""
Format keyboard /command field.
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qs = QueryDict(args)
per_line = qs.get('per_line', 1)
field = qs.get("field", "slug")
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group = lambda... | python | def keyboard_field(value, args=None):
"""
Format keyboard /command field.
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jlmadurga/django-telegram-bot | telegrambot/bot_views/generic/detail.py | DetailCommandView.get_queryset | def get_queryset(self):
"""
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jlmadurga/django-telegram-bot | telegrambot/bot_views/decorators.py | login_required | def login_required(view_func):
"""
Decorator for command views that checks that the chat is authenticated,
sends message with link for authenticated if necessary.
"""
@wraps(view_func)
def wrapper(bot, update, **kwargs):
chat = Chat.objects.get(id=update.message.chat.id)
if chat... | python | def login_required(view_func):
"""
Decorator for command views that checks that the chat is authenticated,
sends message with link for authenticated if necessary.
"""
@wraps(view_func)
def wrapper(bot, update, **kwargs):
chat = Chat.objects.get(id=update.message.chat.id)
if chat... | [
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0101/pipetools | pipetools/decorators.py | pipe_util | def pipe_util(func):
"""
Decorator that handles X objects and partial application for pipe-utils.
"""
@wraps(func)
def pipe_util_wrapper(function, *args, **kwargs):
if isinstance(function, XObject):
function = ~function
original_function = function
if args or kw... | python | def pipe_util(func):
"""
Decorator that handles X objects and partial application for pipe-utils.
"""
@wraps(func)
def pipe_util_wrapper(function, *args, **kwargs):
if isinstance(function, XObject):
function = ~function
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0101/pipetools | pipetools/decorators.py | auto_string_formatter | def auto_string_formatter(func):
"""
Decorator that handles automatic string formatting.
By converting a string argument to a function that does formatting on said
string.
"""
@wraps(func)
def auto_string_formatter_wrapper(function, *args, **kwargs):
if isinstance(function, string_t... | python | def auto_string_formatter(func):
"""
Decorator that handles automatic string formatting.
By converting a string argument to a function that does formatting on said
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"""
@wraps(func)
def auto_string_formatter_wrapper(function, *args, **kwargs):
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0101/pipetools | pipetools/decorators.py | data_structure_builder | def data_structure_builder(func):
"""
Decorator to handle automatic data structure creation for pipe-utils.
"""
@wraps(func)
def ds_builder_wrapper(function, *args, **kwargs):
try:
function = DSBuilder(function)
except NoBuilder:
pass
return func(funct... | python | def data_structure_builder(func):
"""
Decorator to handle automatic data structure creation for pipe-utils.
"""
@wraps(func)
def ds_builder_wrapper(function, *args, **kwargs):
try:
function = DSBuilder(function)
except NoBuilder:
pass
return func(funct... | [
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0101/pipetools | pipetools/decorators.py | regex_condition | def regex_condition(func):
"""
If a condition is given as string instead of a function, it is turned
into a regex-matching function.
"""
@wraps(func)
def regex_condition_wrapper(condition, *args, **kwargs):
if isinstance(condition, string_types):
condition = maybe | partial(r... | python | def regex_condition(func):
"""
If a condition is given as string instead of a function, it is turned
into a regex-matching function.
"""
@wraps(func)
def regex_condition_wrapper(condition, *args, **kwargs):
if isinstance(condition, string_types):
condition = maybe | partial(r... | [
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0101/pipetools | pipetools/utils.py | sort_by | def sort_by(function):
"""
Sorts an incoming sequence by using the given `function` as key.
>>> range(10) > sort_by(-X)
[9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
Supports automatic data-structure creation::
users > sort_by([X.last_name, X.first_name])
There is also a shortcut for ``sort_by(X)``... | python | def sort_by(function):
"""
Sorts an incoming sequence by using the given `function` as key.
>>> range(10) > sort_by(-X)
[9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
Supports automatic data-structure creation::
users > sort_by([X.last_name, X.first_name])
There is also a shortcut for ``sort_by(X)``... | [
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[9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
Supports automatic data-structure creation::
users > sort_by([X.last_name, X.first_name])
There is also a shortcut for ``sort_by(X)`` called ``sort``:
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0101/pipetools | pipetools/utils.py | take_first | def take_first(count):
"""
Assumes an iterable on the input, returns an iterable with first `count`
items from the input (or possibly less, if there isn't that many).
>>> range(9000) > where(X % 100 == 0) | take_first(5) | tuple
(0, 100, 200, 300, 400)
"""
def _take_first(iterable):
... | python | def take_first(count):
"""
Assumes an iterable on the input, returns an iterable with first `count`
items from the input (or possibly less, if there isn't that many).
>>> range(9000) > where(X % 100 == 0) | take_first(5) | tuple
(0, 100, 200, 300, 400)
"""
def _take_first(iterable):
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0101/pipetools | pipetools/utils.py | drop_first | def drop_first(count):
"""
Assumes an iterable on the input, returns an iterable with identical items
except for the first `count`.
>>> range(10) > drop_first(5) | tuple
(5, 6, 7, 8, 9)
"""
def _drop_first(iterable):
g = (x for x in range(1, count + 1))
return dropwhile(
... | python | def drop_first(count):
"""
Assumes an iterable on the input, returns an iterable with identical items
except for the first `count`.
>>> range(10) > drop_first(5) | tuple
(5, 6, 7, 8, 9)
"""
def _drop_first(iterable):
g = (x for x in range(1, count + 1))
return dropwhile(
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0101/pipetools | pipetools/utils.py | unless | def unless(exception_class_or_tuple, func, *args, **kwargs):
"""
When `exception_class_or_tuple` occurs while executing `func`, it will
be caught and ``None`` will be returned.
>>> f = where(X > 10) | list | unless(IndexError, X[0])
>>> f([5, 8, 12, 4])
12
>>> f([1, 2, 3])
None
"""
... | python | def unless(exception_class_or_tuple, func, *args, **kwargs):
"""
When `exception_class_or_tuple` occurs while executing `func`, it will
be caught and ``None`` will be returned.
>>> f = where(X > 10) | list | unless(IndexError, X[0])
>>> f([5, 8, 12, 4])
12
>>> f([1, 2, 3])
None
"""
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>>> f = where(X > 10) | list | unless(IndexError, X[0])
>>> f([5, 8, 12, 4])
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>>> f([1, 2, 3])
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0101/pipetools | pipetools/utils.py | group_by | def group_by(function):
"""
Groups input sequence by `function`.
Returns an iterator over a sequence of tuples where the first item is a
result of `function` and the second one a list of items matching this
result.
Ordering of the resulting iterator is undefined, but ordering of the items
... | python | def group_by(function):
"""
Groups input sequence by `function`.
Returns an iterator over a sequence of tuples where the first item is a
result of `function` and the second one a list of items matching this
result.
Ordering of the resulting iterator is undefined, but ordering of the items
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