body_hash stringlengths 64 64 | body stringlengths 23 109k | docstring stringlengths 1 57k | path stringlengths 4 198 | name stringlengths 1 115 | repository_name stringlengths 7 111 | repository_stars float64 0 191k | lang stringclasses 1
value | body_without_docstring stringlengths 14 108k | unified stringlengths 45 133k |
|---|---|---|---|---|---|---|---|---|---|
fd5f75c2df66d54ef236b00ab00284f1e5e01c2bcda74461480d435689fa153c | def __init__(self, state_reference, entity_type, interaction_id, learner_answer_info_list, accumulated_answer_info_json_size_bytes, learner_answer_info_schema_version=feconf.CURRENT_LEARNER_ANSWER_INFO_SCHEMA_VERSION):
"Constructs a LearnerAnswerDetail domain object.\n\n Args:\n state_reference: s... | Constructs a LearnerAnswerDetail domain object.
Args:
state_reference: str. This field is used to refer to a state
in an exploration or question. For an exploration the value
will be equal to 'exp_id:state_name' & for question this will
be equal to 'question_id' only.
entity_type: str. ... | core/domain/stats_domain.py | __init__ | yashdusing/oppia | 3 | python | def __init__(self, state_reference, entity_type, interaction_id, learner_answer_info_list, accumulated_answer_info_json_size_bytes, learner_answer_info_schema_version=feconf.CURRENT_LEARNER_ANSWER_INFO_SCHEMA_VERSION):
"Constructs a LearnerAnswerDetail domain object.\n\n Args:\n state_reference: s... | def __init__(self, state_reference, entity_type, interaction_id, learner_answer_info_list, accumulated_answer_info_json_size_bytes, learner_answer_info_schema_version=feconf.CURRENT_LEARNER_ANSWER_INFO_SCHEMA_VERSION):
"Constructs a LearnerAnswerDetail domain object.\n\n Args:\n state_reference: s... |
a9408c9b8ef240e3cc2fe3ea3df8a537ad5f8e381b947cc04748b43813d51aa9 | def to_dict(self):
'Returns a dict representing LearnerAnswerDetails domain object.\n\n Returns:\n dict. A dict, mapping all fields of LearnerAnswerDetails instance.\n '
return {'state_reference': self.state_reference, 'entity_type': self.entity_type, 'interaction_id': self.interaction_... | Returns a dict representing LearnerAnswerDetails domain object.
Returns:
dict. A dict, mapping all fields of LearnerAnswerDetails instance. | core/domain/stats_domain.py | to_dict | yashdusing/oppia | 3 | python | def to_dict(self):
'Returns a dict representing LearnerAnswerDetails domain object.\n\n Returns:\n dict. A dict, mapping all fields of LearnerAnswerDetails instance.\n '
return {'state_reference': self.state_reference, 'entity_type': self.entity_type, 'interaction_id': self.interaction_... | def to_dict(self):
'Returns a dict representing LearnerAnswerDetails domain object.\n\n Returns:\n dict. A dict, mapping all fields of LearnerAnswerDetails instance.\n '
return {'state_reference': self.state_reference, 'entity_type': self.entity_type, 'interaction_id': self.interaction_... |
37d6b7fe59ef5ace067a388756d6c6ed197d4fe4f47f63b58b5c997a9a73784e | @classmethod
def from_dict(cls, learner_answer_details_dict):
'Return a LearnerAnswerDetails domain object from a dict.\n\n Args:\n learner_answer_details_dict: dict. The dict representation of\n LearnerAnswerDetails object.\n\n Returns:\n LearnerAnswerDetails. The... | Return a LearnerAnswerDetails domain object from a dict.
Args:
learner_answer_details_dict: dict. The dict representation of
LearnerAnswerDetails object.
Returns:
LearnerAnswerDetails. The corresponding LearnerAnswerDetails
domain object. | core/domain/stats_domain.py | from_dict | yashdusing/oppia | 3 | python | @classmethod
def from_dict(cls, learner_answer_details_dict):
'Return a LearnerAnswerDetails domain object from a dict.\n\n Args:\n learner_answer_details_dict: dict. The dict representation of\n LearnerAnswerDetails object.\n\n Returns:\n LearnerAnswerDetails. The... | @classmethod
def from_dict(cls, learner_answer_details_dict):
'Return a LearnerAnswerDetails domain object from a dict.\n\n Args:\n learner_answer_details_dict: dict. The dict representation of\n LearnerAnswerDetails object.\n\n Returns:\n LearnerAnswerDetails. The... |
54039963bbc67dcf66a369187aed4563daf54d8129312e7f3990ae082d728010 | def validate(self):
'Validates LearnerAnswerDetails domain object.'
if (not isinstance(self.state_reference, basestring)):
raise utils.ValidationError(('Expected state_reference to be a string, received %s' % str(self.state_reference)))
if (not isinstance(self.entity_type, basestring)):
rais... | Validates LearnerAnswerDetails domain object. | core/domain/stats_domain.py | validate | yashdusing/oppia | 3 | python | def validate(self):
if (not isinstance(self.state_reference, basestring)):
raise utils.ValidationError(('Expected state_reference to be a string, received %s' % str(self.state_reference)))
if (not isinstance(self.entity_type, basestring)):
raise utils.ValidationError(('Expected entity_type ... | def validate(self):
if (not isinstance(self.state_reference, basestring)):
raise utils.ValidationError(('Expected state_reference to be a string, received %s' % str(self.state_reference)))
if (not isinstance(self.entity_type, basestring)):
raise utils.ValidationError(('Expected entity_type ... |
52ceb9325b7f4713e88058f4e2cc4abe01cf19f1b1d039b09a3e0818bcbbaa9b | def add_learner_answer_info(self, learner_answer_info):
'Adds new learner answer info in the learner_answer_info_list.\n\n Args:\n learner_answer_info: LearnerAnswerInfo. The learner answer info\n object, which is created after the learner has submitted the\n details ... | Adds new learner answer info in the learner_answer_info_list.
Args:
learner_answer_info: LearnerAnswerInfo. The learner answer info
object, which is created after the learner has submitted the
details of the answer. | core/domain/stats_domain.py | add_learner_answer_info | yashdusing/oppia | 3 | python | def add_learner_answer_info(self, learner_answer_info):
'Adds new learner answer info in the learner_answer_info_list.\n\n Args:\n learner_answer_info: LearnerAnswerInfo. The learner answer info\n object, which is created after the learner has submitted the\n details ... | def add_learner_answer_info(self, learner_answer_info):
'Adds new learner answer info in the learner_answer_info_list.\n\n Args:\n learner_answer_info: LearnerAnswerInfo. The learner answer info\n object, which is created after the learner has submitted the\n details ... |
2384a276c0fd08433e85e05100e79719c139ae81c9e8b4f76eb3f2d010aa39ca | def delete_learner_answer_info(self, learner_answer_info_id):
'Delete the learner answer info from the learner_answer_info_list.\n\n Args:\n learner_answer_info_id: str. The learner answer info\n id, which needs to be deleted from\n the learner_answer_info_list.\n\n ... | Delete the learner answer info from the learner_answer_info_list.
Args:
learner_answer_info_id: str. The learner answer info
id, which needs to be deleted from
the learner_answer_info_list.
Raises:
Exception: If the learner answer info with the given id is not
found in the learner answ... | core/domain/stats_domain.py | delete_learner_answer_info | yashdusing/oppia | 3 | python | def delete_learner_answer_info(self, learner_answer_info_id):
'Delete the learner answer info from the learner_answer_info_list.\n\n Args:\n learner_answer_info_id: str. The learner answer info\n id, which needs to be deleted from\n the learner_answer_info_list.\n\n ... | def delete_learner_answer_info(self, learner_answer_info_id):
'Delete the learner answer info from the learner_answer_info_list.\n\n Args:\n learner_answer_info_id: str. The learner answer info\n id, which needs to be deleted from\n the learner_answer_info_list.\n\n ... |
b2d8a7bd05adb01f389804dd08fc4cfd1b0e481bcae5369b94f037173a596583 | def update_state_reference(self, new_state_reference):
'Updates the state_reference of the LearnerAnswerDetails object.\n\n Args:\n new_state_reference: str. The new state reference of the\n LearnerAnswerDetails.\n '
self.state_reference = new_state_reference | Updates the state_reference of the LearnerAnswerDetails object.
Args:
new_state_reference: str. The new state reference of the
LearnerAnswerDetails. | core/domain/stats_domain.py | update_state_reference | yashdusing/oppia | 3 | python | def update_state_reference(self, new_state_reference):
'Updates the state_reference of the LearnerAnswerDetails object.\n\n Args:\n new_state_reference: str. The new state reference of the\n LearnerAnswerDetails.\n '
self.state_reference = new_state_reference | def update_state_reference(self, new_state_reference):
'Updates the state_reference of the LearnerAnswerDetails object.\n\n Args:\n new_state_reference: str. The new state reference of the\n LearnerAnswerDetails.\n '
self.state_reference = new_state_reference<|docstring|>... |
c21bc690071b5262d616c9e0ce4cc06326409b658764127ffe0b3e96dc214a62 | def __init__(self, learner_answer_info_id, answer, answer_details, created_on):
"Constructs a LearnerAnswerInfo domain object.\n\n Args:\n learner_answer_info_id: str. The id of the LearnerAnswerInfo object.\n answer: dict or list or str or int or bool. The answer which is\n ... | Constructs a LearnerAnswerInfo domain object.
Args:
learner_answer_info_id: str. The id of the LearnerAnswerInfo object.
answer: dict or list or str or int or bool. The answer which is
submitted by the learner. Actually type of the answer is
interaction dependent, like TextInput interactions ha... | core/domain/stats_domain.py | __init__ | yashdusing/oppia | 3 | python | def __init__(self, learner_answer_info_id, answer, answer_details, created_on):
"Constructs a LearnerAnswerInfo domain object.\n\n Args:\n learner_answer_info_id: str. The id of the LearnerAnswerInfo object.\n answer: dict or list or str or int or bool. The answer which is\n ... | def __init__(self, learner_answer_info_id, answer, answer_details, created_on):
"Constructs a LearnerAnswerInfo domain object.\n\n Args:\n learner_answer_info_id: str. The id of the LearnerAnswerInfo object.\n answer: dict or list or str or int or bool. The answer which is\n ... |
dc89e4ce0435660868c35690df1b327dcee089dd3b439c1f359e9c87cfabdd34 | def to_dict(self):
'Returns the dict of learner answer info.\n\n Returns:\n dict. The learner_answer_info dict.\n '
learner_answer_info_dict = {'id': self.id, 'answer': self.answer, 'answer_details': self.answer_details, 'created_on': self.created_on.strftime('%Y-%m-%d %H:%M:%S.%f')}
... | Returns the dict of learner answer info.
Returns:
dict. The learner_answer_info dict. | core/domain/stats_domain.py | to_dict | yashdusing/oppia | 3 | python | def to_dict(self):
'Returns the dict of learner answer info.\n\n Returns:\n dict. The learner_answer_info dict.\n '
learner_answer_info_dict = {'id': self.id, 'answer': self.answer, 'answer_details': self.answer_details, 'created_on': self.created_on.strftime('%Y-%m-%d %H:%M:%S.%f')}
... | def to_dict(self):
'Returns the dict of learner answer info.\n\n Returns:\n dict. The learner_answer_info dict.\n '
learner_answer_info_dict = {'id': self.id, 'answer': self.answer, 'answer_details': self.answer_details, 'created_on': self.created_on.strftime('%Y-%m-%d %H:%M:%S.%f')}
... |
62414922402a65ca11a641a8571f0d6aedd44e04534230335f673b65da5d334d | @classmethod
def from_dict(cls, learner_answer_info_dict):
'Returns a dict representing LearnerAnswerInfo domain object.\n\n Returns:\n dict. A dict, mapping all fields of LearnerAnswerInfo instance.\n '
return cls(learner_answer_info_dict['id'], learner_answer_info_dict['answer'], lear... | Returns a dict representing LearnerAnswerInfo domain object.
Returns:
dict. A dict, mapping all fields of LearnerAnswerInfo instance. | core/domain/stats_domain.py | from_dict | yashdusing/oppia | 3 | python | @classmethod
def from_dict(cls, learner_answer_info_dict):
'Returns a dict representing LearnerAnswerInfo domain object.\n\n Returns:\n dict. A dict, mapping all fields of LearnerAnswerInfo instance.\n '
return cls(learner_answer_info_dict['id'], learner_answer_info_dict['answer'], lear... | @classmethod
def from_dict(cls, learner_answer_info_dict):
'Returns a dict representing LearnerAnswerInfo domain object.\n\n Returns:\n dict. A dict, mapping all fields of LearnerAnswerInfo instance.\n '
return cls(learner_answer_info_dict['id'], learner_answer_info_dict['answer'], lear... |
93fc9fb0bc9983de660632400eaabbb85595e5e2009f4dbed85c1fd7d352267c | @classmethod
def get_new_learner_answer_info_id(cls):
'Generates the learner answer info domain object id.\n\n Return:\n learner_answer_info_id: str. The id generated by the function.\n '
learner_answer_info_id = (utils.base64_from_int(utils.get_current_time_in_millisecs()) + utils.base... | Generates the learner answer info domain object id.
Return:
learner_answer_info_id: str. The id generated by the function. | core/domain/stats_domain.py | get_new_learner_answer_info_id | yashdusing/oppia | 3 | python | @classmethod
def get_new_learner_answer_info_id(cls):
'Generates the learner answer info domain object id.\n\n Return:\n learner_answer_info_id: str. The id generated by the function.\n '
learner_answer_info_id = (utils.base64_from_int(utils.get_current_time_in_millisecs()) + utils.base... | @classmethod
def get_new_learner_answer_info_id(cls):
'Generates the learner answer info domain object id.\n\n Return:\n learner_answer_info_id: str. The id generated by the function.\n '
learner_answer_info_id = (utils.base64_from_int(utils.get_current_time_in_millisecs()) + utils.base... |
04b7a77da45e40a97da886c5fbed1a1f8bce9cab15cafaf152769d11b0cfdd54 | def validate(self):
'Validates the LearnerAnswerInfo domain object.'
if (not isinstance(self.id, basestring)):
raise utils.ValidationError(('Expected id to be a string, received %s' % self.id))
if (self.answer is None):
raise utils.ValidationError('The answer submitted by the learner cannot ... | Validates the LearnerAnswerInfo domain object. | core/domain/stats_domain.py | validate | yashdusing/oppia | 3 | python | def validate(self):
if (not isinstance(self.id, basestring)):
raise utils.ValidationError(('Expected id to be a string, received %s' % self.id))
if (self.answer is None):
raise utils.ValidationError('The answer submitted by the learner cannot be empty')
if isinstance(self.answer, dict):... | def validate(self):
if (not isinstance(self.id, basestring)):
raise utils.ValidationError(('Expected id to be a string, received %s' % self.id))
if (self.answer is None):
raise utils.ValidationError('The answer submitted by the learner cannot be empty')
if isinstance(self.answer, dict):... |
b81e7b29e8b00a30fdb72aab304dcdb92c4a93b74753233fc9da1d4f8f124616 | def get_learner_answer_info_dict_size(self):
'Returns a size overestimate (in bytes) of the given learner answer\n info dict.\n\n Returns:\n int. Size of the learner_answer_info_dict in bytes.\n '
learner_answer_info_dict = self.to_dict()
return sys.getsizeof(json.dumps(learn... | Returns a size overestimate (in bytes) of the given learner answer
info dict.
Returns:
int. Size of the learner_answer_info_dict in bytes. | core/domain/stats_domain.py | get_learner_answer_info_dict_size | yashdusing/oppia | 3 | python | def get_learner_answer_info_dict_size(self):
'Returns a size overestimate (in bytes) of the given learner answer\n info dict.\n\n Returns:\n int. Size of the learner_answer_info_dict in bytes.\n '
learner_answer_info_dict = self.to_dict()
return sys.getsizeof(json.dumps(learn... | def get_learner_answer_info_dict_size(self):
'Returns a size overestimate (in bytes) of the given learner answer\n info dict.\n\n Returns:\n int. Size of the learner_answer_info_dict in bytes.\n '
learner_answer_info_dict = self.to_dict()
return sys.getsizeof(json.dumps(learn... |
886e9e2f53a1255c131e1ffb47afc04490c1ddf94a8238f8d01b5f839c8e96a8 | def __init__(self, key):
' Construct object. Requires a valid SavedMacro\n entity key. Raises exception on failure.'
self.entity = self.get_macro_entity(key)
if (not self.entity):
raise Exception(("Couldn't find macro for %s" % key)) | Construct object. Requires a valid SavedMacro
entity key. Raises exception on failure. | macro/data/appengine/savedmacro.py | __init__ | cloudmattcloud/Macro-Explain-o-Matic | 2 | python | def __init__(self, key):
' Construct object. Requires a valid SavedMacro\n entity key. Raises exception on failure.'
self.entity = self.get_macro_entity(key)
if (not self.entity):
raise Exception(("Couldn't find macro for %s" % key)) | def __init__(self, key):
' Construct object. Requires a valid SavedMacro\n entity key. Raises exception on failure.'
self.entity = self.get_macro_entity(key)
if (not self.entity):
raise Exception(("Couldn't find macro for %s" % key))<|docstring|>Construct object. Requires a valid SavedMacr... |
6a38643207591203373fa6de8b60e354635aec9d680fc2cdc78b9f38f6804665 | @classmethod
def save_macro(self, macro, notes, title, name, classes, tags, version, server=''):
' Create a new macro entry in the datastore.\n This function assumes data has already been validated.\n \n Note that this function involves two writes to the datastore\n in order to have a se... | Create a new macro entry in the datastore.
This function assumes data has already been validated.
Note that this function involves two writes to the datastore
in order to have a serialized count across all macro entites.
This sucks donkey balls but ensures a) that we can page through
macros in search results, and b) w... | macro/data/appengine/savedmacro.py | save_macro | cloudmattcloud/Macro-Explain-o-Matic | 2 | python | @classmethod
def save_macro(self, macro, notes, title, name, classes, tags, version, server=):
' Create a new macro entry in the datastore.\n This function assumes data has already been validated.\n \n Note that this function involves two writes to the datastore\n in order to have a seri... | @classmethod
def save_macro(self, macro, notes, title, name, classes, tags, version, server=):
' Create a new macro entry in the datastore.\n This function assumes data has already been validated.\n \n Note that this function involves two writes to the datastore\n in order to have a seri... |
3a97cdcbafbec2328938ba8d1e1f77422190f302febd7bdb0d757eb0d1d905c6 | @classmethod
def get_macro_entity(self, key):
' Gets a macro from its macro_id from memcached or datastore,\n unquotes it, and places it in memcached before returning the\n object.\n\n Class method, can be called without object.\n '
saved_macro = memcache.get(cache_key(key))
if (... | Gets a macro from its macro_id from memcached or datastore,
unquotes it, and places it in memcached before returning the
object.
Class method, can be called without object. | macro/data/appengine/savedmacro.py | get_macro_entity | cloudmattcloud/Macro-Explain-o-Matic | 2 | python | @classmethod
def get_macro_entity(self, key):
' Gets a macro from its macro_id from memcached or datastore,\n unquotes it, and places it in memcached before returning the\n object.\n\n Class method, can be called without object.\n '
saved_macro = memcache.get(cache_key(key))
if (... | @classmethod
def get_macro_entity(self, key):
' Gets a macro from its macro_id from memcached or datastore,\n unquotes it, and places it in memcached before returning the\n object.\n\n Class method, can be called without object.\n '
saved_macro = memcache.get(cache_key(key))
if (... |
595639cc0548014fa8312b8edb6c57f30b0badeeb24f5b0293db56832d8d987b | @classmethod
def get_rating_dict(self, rating):
' Given a float rating, translate it into a structure\n for rendering it on a page. '
int_rating = int(rating)
rating_stars = [{'id': i, 'half': False, 'on': (i <= rating), 'off': (i > rating)} for i in range((MAX_RATING + 1))[1:]]
if (int_rating !=... | Given a float rating, translate it into a structure
for rendering it on a page. | macro/data/appengine/savedmacro.py | get_rating_dict | cloudmattcloud/Macro-Explain-o-Matic | 2 | python | @classmethod
def get_rating_dict(self, rating):
' Given a float rating, translate it into a structure\n for rendering it on a page. '
int_rating = int(rating)
rating_stars = [{'id': i, 'half': False, 'on': (i <= rating), 'off': (i > rating)} for i in range((MAX_RATING + 1))[1:]]
if (int_rating !=... | @classmethod
def get_rating_dict(self, rating):
' Given a float rating, translate it into a structure\n for rendering it on a page. '
int_rating = int(rating)
rating_stars = [{'id': i, 'half': False, 'on': (i <= rating), 'off': (i > rating)} for i in range((MAX_RATING + 1))[1:]]
if (int_rating !=... |
89cf674ce97acbab57dce842f3783a20227e972a726404eeeef767859e71f6e7 | @classmethod
def get_rating_score(self, rating, num_rates, do_round=True):
' Calculate rating in float form. If specified,\n this function can round to the nearest half star.'
if (num_rates == 0):
return 0
stars = (float(rating) / float(num_rates))
if (not do_round):
return stars... | Calculate rating in float form. If specified,
this function can round to the nearest half star. | macro/data/appengine/savedmacro.py | get_rating_score | cloudmattcloud/Macro-Explain-o-Matic | 2 | python | @classmethod
def get_rating_score(self, rating, num_rates, do_round=True):
' Calculate rating in float form. If specified,\n this function can round to the nearest half star.'
if (num_rates == 0):
return 0
stars = (float(rating) / float(num_rates))
if (not do_round):
return stars... | @classmethod
def get_rating_score(self, rating, num_rates, do_round=True):
' Calculate rating in float form. If specified,\n this function can round to the nearest half star.'
if (num_rates == 0):
return 0
stars = (float(rating) / float(num_rates))
if (not do_round):
return stars... |
8de0a0c5c8fba5b438518c06595f75a3db9c0f611278a7d241fe0897ef52cb6f | def get_rating(self, rating=None, num_rates=None, do_round=True):
' Calculate rating from counters for this entity,\n returning the rating in float form. If specified,\n this function can round to the nearest half star.'
if ((rating is None) or (num_rates is None)):
num_rates = get_count(... | Calculate rating from counters for this entity,
returning the rating in float form. If specified,
this function can round to the nearest half star. | macro/data/appengine/savedmacro.py | get_rating | cloudmattcloud/Macro-Explain-o-Matic | 2 | python | def get_rating(self, rating=None, num_rates=None, do_round=True):
' Calculate rating from counters for this entity,\n returning the rating in float form. If specified,\n this function can round to the nearest half star.'
if ((rating is None) or (num_rates is None)):
num_rates = get_count(... | def get_rating(self, rating=None, num_rates=None, do_round=True):
' Calculate rating from counters for this entity,\n returning the rating in float form. If specified,\n this function can round to the nearest half star.'
if ((rating is None) or (num_rates is None)):
num_rates = get_count(... |
d91ba903d118daa8647d783665b60ac30723cc91333999d0dd61bc2707123402 | def add_rating(self, rating=0):
' Add a rating for a SavedMacro. Returns the rating rounded\n to the largest half star.'
if ((rating < 1) or (rating > MAX_RATING)):
return self.get_rating()
else:
num_rates = incr_count(self.entity.link_id, 'num_rates', update_count, entity_val=self.en... | Add a rating for a SavedMacro. Returns the rating rounded
to the largest half star. | macro/data/appengine/savedmacro.py | add_rating | cloudmattcloud/Macro-Explain-o-Matic | 2 | python | def add_rating(self, rating=0):
' Add a rating for a SavedMacro. Returns the rating rounded\n to the largest half star.'
if ((rating < 1) or (rating > MAX_RATING)):
return self.get_rating()
else:
num_rates = incr_count(self.entity.link_id, 'num_rates', update_count, entity_val=self.en... | def add_rating(self, rating=0):
' Add a rating for a SavedMacro. Returns the rating rounded\n to the largest half star.'
if ((rating < 1) or (rating > MAX_RATING)):
return self.get_rating()
else:
num_rates = incr_count(self.entity.link_id, 'num_rates', update_count, entity_val=self.en... |
c40a59395e72da055d9ab15b5fc697c09a4eec634cd5373f4342d24fbf34e5fe | def add_to_send_count(self):
' Update send count for a SavedMacro.'
try:
init_val = self.entity.sends
except:
init_val = 0
ret = incr_count(self.entity.link_id, 'sends', update_count, entity_val=init_val)
return ret | Update send count for a SavedMacro. | macro/data/appengine/savedmacro.py | add_to_send_count | cloudmattcloud/Macro-Explain-o-Matic | 2 | python | def add_to_send_count(self):
' '
try:
init_val = self.entity.sends
except:
init_val = 0
ret = incr_count(self.entity.link_id, 'sends', update_count, entity_val=init_val)
return ret | def add_to_send_count(self):
' '
try:
init_val = self.entity.sends
except:
init_val = 0
ret = incr_count(self.entity.link_id, 'sends', update_count, entity_val=init_val)
return ret<|docstring|>Update send count for a SavedMacro.<|endoftext|> |
0d1ed96d533aa10360d155b53e2f3252d5644f928ed1508820d08e3589e40c2a | def add_to_view_count(self):
' Update view count for a SavedMacro.'
try:
init_val = self.entity.views
except:
init_val = 0
ret = incr_count(self.entity.link_id, 'views', update_count, entity_val=init_val)
return ret | Update view count for a SavedMacro. | macro/data/appengine/savedmacro.py | add_to_view_count | cloudmattcloud/Macro-Explain-o-Matic | 2 | python | def add_to_view_count(self):
' '
try:
init_val = self.entity.views
except:
init_val = 0
ret = incr_count(self.entity.link_id, 'views', update_count, entity_val=init_val)
return ret | def add_to_view_count(self):
' '
try:
init_val = self.entity.views
except:
init_val = 0
ret = incr_count(self.entity.link_id, 'views', update_count, entity_val=init_val)
return ret<|docstring|>Update view count for a SavedMacro.<|endoftext|> |
ef67f3c20cdbe1499677e503a14a0db0132925403bf9deae5479bbab7b80c9ff | @classmethod
def search(self, tag, page=1, sort='-views', num=_NUM_RESULTS):
' Search macros for a given tag, return num results.\n Returns ([results], is_next_page), where is_next_page is\n True if there are more than this page of results, F otherwise.\n\n Each result in [results] is an object... | Search macros for a given tag, return num results.
Returns ([results], is_next_page), where is_next_page is
True if there are more than this page of results, F otherwise.
Each result in [results] is an object in dict form for use in
template output.
Class method, can be called without object. | macro/data/appengine/savedmacro.py | search | cloudmattcloud/Macro-Explain-o-Matic | 2 | python | @classmethod
def search(self, tag, page=1, sort='-views', num=_NUM_RESULTS):
' Search macros for a given tag, return num results.\n Returns ([results], is_next_page), where is_next_page is\n True if there are more than this page of results, F otherwise.\n\n Each result in [results] is an object... | @classmethod
def search(self, tag, page=1, sort='-views', num=_NUM_RESULTS):
' Search macros for a given tag, return num results.\n Returns ([results], is_next_page), where is_next_page is\n True if there are more than this page of results, F otherwise.\n\n Each result in [results] is an object... |
aa8ab0d5df389d190e9d3fe8ac65a66643739e54d88900f31ed6738859cd4a37 | @staticmethod
def dict_merge(self, dct, merge_dct):
' Recursive dict merge. Inspired by :meth:``dict.update()``, instead of\n updating only top-level keys, dict_merge recurses down into dicts nested\n to an arbitrary depth, updating keys. The ``merge_dct`` is merged into\n ``dct``.\n :pa... | Recursive dict merge. Inspired by :meth:``dict.update()``, instead of
updating only top-level keys, dict_merge recurses down into dicts nested
to an arbitrary depth, updating keys. The ``merge_dct`` is merged into
``dct``.
:param dct: dict onto which the merge is executed
:param merge_dct: dct merged into dct
:return: ... | webdjango/Tools.py | dict_merge | myog-io/WebDjangular | 1 | python | @staticmethod
def dict_merge(self, dct, merge_dct):
' Recursive dict merge. Inspired by :meth:``dict.update()``, instead of\n updating only top-level keys, dict_merge recurses down into dicts nested\n to an arbitrary depth, updating keys. The ``merge_dct`` is merged into\n ``dct``.\n :pa... | @staticmethod
def dict_merge(self, dct, merge_dct):
' Recursive dict merge. Inspired by :meth:``dict.update()``, instead of\n updating only top-level keys, dict_merge recurses down into dicts nested\n to an arbitrary depth, updating keys. The ``merge_dct`` is merged into\n ``dct``.\n :pa... |
59eca8d4c7adcb2951b7c4ba0662cbe412f844ab9008bcd99f46d8ed27efa092 | @plot_gridworld_heatmap_ex.config
def default_config():
'Default configuration values.'
normalize = False
log_root = serialize.get_output_dir()
discount = 0.99
reward_subset = None
kind = 'npec'
styles = ['paper', 'heatmap', 'heatmap-1col', 'heatmap-1col-fatlabels', 'tex']
save_kwargs = ... | Default configuration values. | src/evaluating_rewards/analysis/distances/plot_gridworld_heatmap.py | default_config | HumanCompatibleAI/evaluating_rewards | 42 | python | @plot_gridworld_heatmap_ex.config
def default_config():
normalize = False
log_root = serialize.get_output_dir()
discount = 0.99
reward_subset = None
kind = 'npec'
styles = ['paper', 'heatmap', 'heatmap-1col', 'heatmap-1col-fatlabels', 'tex']
save_kwargs = {'fmt': 'pdf'}
_ = locals()... | @plot_gridworld_heatmap_ex.config
def default_config():
normalize = False
log_root = serialize.get_output_dir()
discount = 0.99
reward_subset = None
kind = 'npec'
styles = ['paper', 'heatmap', 'heatmap-1col', 'heatmap-1col-fatlabels', 'tex']
save_kwargs = {'fmt': 'pdf'}
_ = locals()... |
4166b122209315074907236bfba83dd3a71624e8fd82257c905677a4ae2f6388 | @plot_gridworld_heatmap_ex.named_config
def test():
'Unit tests/debugging.'
styles = ['paper', 'heatmap', 'heatmap-2col']
reward_subset = ['sparse_goal', 'dense_goal']
_ = locals()
del _ | Unit tests/debugging. | src/evaluating_rewards/analysis/distances/plot_gridworld_heatmap.py | test | HumanCompatibleAI/evaluating_rewards | 42 | python | @plot_gridworld_heatmap_ex.named_config
def test():
styles = ['paper', 'heatmap', 'heatmap-2col']
reward_subset = ['sparse_goal', 'dense_goal']
_ = locals()
del _ | @plot_gridworld_heatmap_ex.named_config
def test():
styles = ['paper', 'heatmap', 'heatmap-2col']
reward_subset = ['sparse_goal', 'dense_goal']
_ = locals()
del _<|docstring|>Unit tests/debugging.<|endoftext|> |
168bba919cd2f9447556ff7b4986c5343a38a5bcac2e595b41fd944ccdfd90fa | @plot_gridworld_heatmap_ex.named_config
def paper():
'Figure for paper appendix.'
reward_subset = ['sparse_goal', 'transformed_goal', 'center_goal', 'sparse_penalty', 'dirt_path', 'cliff_walk']
heatmap_kwargs = {'cbar_kws': dict(fraction=0.05)}
_ = locals()
del _ | Figure for paper appendix. | src/evaluating_rewards/analysis/distances/plot_gridworld_heatmap.py | paper | HumanCompatibleAI/evaluating_rewards | 42 | python | @plot_gridworld_heatmap_ex.named_config
def paper():
reward_subset = ['sparse_goal', 'transformed_goal', 'center_goal', 'sparse_penalty', 'dirt_path', 'cliff_walk']
heatmap_kwargs = {'cbar_kws': dict(fraction=0.05)}
_ = locals()
del _ | @plot_gridworld_heatmap_ex.named_config
def paper():
reward_subset = ['sparse_goal', 'transformed_goal', 'center_goal', 'sparse_penalty', 'dirt_path', 'cliff_walk']
heatmap_kwargs = {'cbar_kws': dict(fraction=0.05)}
_ = locals()
del _<|docstring|>Figure for paper appendix.<|endoftext|> |
797903316fde42d7d2943bf3255dea9308216d5e2d21ed0d8e6942b8a3c5ddff | def state_to_3d(reward: np.ndarray, ns: int, na: int) -> np.ndarray:
"Convert state-only reward R[s] to 3D reward R[s,a,s'].\n\n Args:\n - reward: state only reward.\n - ns: number of states.\n - na: number of actions.\n\n Returns:\n State-action-next state reward from tiling `rewa... | Convert state-only reward R[s] to 3D reward R[s,a,s'].
Args:
- reward: state only reward.
- ns: number of states.
- na: number of actions.
Returns:
State-action-next state reward from tiling `reward`. | src/evaluating_rewards/analysis/distances/plot_gridworld_heatmap.py | state_to_3d | HumanCompatibleAI/evaluating_rewards | 42 | python | def state_to_3d(reward: np.ndarray, ns: int, na: int) -> np.ndarray:
"Convert state-only reward R[s] to 3D reward R[s,a,s'].\n\n Args:\n - reward: state only reward.\n - ns: number of states.\n - na: number of actions.\n\n Returns:\n State-action-next state reward from tiling `rewa... | def state_to_3d(reward: np.ndarray, ns: int, na: int) -> np.ndarray:
"Convert state-only reward R[s] to 3D reward R[s,a,s'].\n\n Args:\n - reward: state only reward.\n - ns: number of states.\n - na: number of actions.\n\n Returns:\n State-action-next state reward from tiling `rewa... |
e07135012b30ce19738091250106bad679b0f3e78c4a41ff26e6b0726e17a6fb | def grid_to_3d(reward: np.ndarray) -> np.ndarray:
"Convert gridworld state-only reward R[i,j] to 3D reward R[s,a,s']."
assert (reward.ndim == 2)
reward = reward.flatten()
ns = reward.shape[0]
return state_to_3d(reward, ns, 5) | Convert gridworld state-only reward R[i,j] to 3D reward R[s,a,s']. | src/evaluating_rewards/analysis/distances/plot_gridworld_heatmap.py | grid_to_3d | HumanCompatibleAI/evaluating_rewards | 42 | python | def grid_to_3d(reward: np.ndarray) -> np.ndarray:
assert (reward.ndim == 2)
reward = reward.flatten()
ns = reward.shape[0]
return state_to_3d(reward, ns, 5) | def grid_to_3d(reward: np.ndarray) -> np.ndarray:
assert (reward.ndim == 2)
reward = reward.flatten()
ns = reward.shape[0]
return state_to_3d(reward, ns, 5)<|docstring|>Convert gridworld state-only reward R[i,j] to 3D reward R[s,a,s'].<|endoftext|> |
eb31e4746e4cca6171ecfee8c1501e8d394adadc6e3bafb8c33c6f54b47f5e54 | def make_reward(cfg: Dict[(str, np.ndarray)], discount: float) -> np.ndarray:
'Create reward from state-only reward and potential.'
state_reward = grid_to_3d(cfg['state_reward'])
potential = cfg['potential']
assert (potential.ndim == 2)
potential = potential.flatten()
return tabular.shape(state_... | Create reward from state-only reward and potential. | src/evaluating_rewards/analysis/distances/plot_gridworld_heatmap.py | make_reward | HumanCompatibleAI/evaluating_rewards | 42 | python | def make_reward(cfg: Dict[(str, np.ndarray)], discount: float) -> np.ndarray:
state_reward = grid_to_3d(cfg['state_reward'])
potential = cfg['potential']
assert (potential.ndim == 2)
potential = potential.flatten()
return tabular.shape(state_reward, potential, discount) | def make_reward(cfg: Dict[(str, np.ndarray)], discount: float) -> np.ndarray:
state_reward = grid_to_3d(cfg['state_reward'])
potential = cfg['potential']
assert (potential.ndim == 2)
potential = potential.flatten()
return tabular.shape(state_reward, potential, discount)<|docstring|>Create rewar... |
f9b6c6c3fb106f7d32c54c48b9b2f8b2dedf13feecdabc4afbd68af74f46dcb6 | def build_dist(rew: np.ndarray, xlen: int, ylen: int) -> np.ndarray:
'Computes uniform visitation distribution compatible with gridworld dynamics.\n\n Args:\n rew: A three-dimensional reward (needed for dimensionality).\n xlen: width of gridworld.\n ylen: height of gridworld.\n\n Returns:... | Computes uniform visitation distribution compatible with gridworld dynamics.
Args:
rew: A three-dimensional reward (needed for dimensionality).
xlen: width of gridworld.
ylen: height of gridworld.
Returns:
A distribution | src/evaluating_rewards/analysis/distances/plot_gridworld_heatmap.py | build_dist | HumanCompatibleAI/evaluating_rewards | 42 | python | def build_dist(rew: np.ndarray, xlen: int, ylen: int) -> np.ndarray:
'Computes uniform visitation distribution compatible with gridworld dynamics.\n\n Args:\n rew: A three-dimensional reward (needed for dimensionality).\n xlen: width of gridworld.\n ylen: height of gridworld.\n\n Returns:... | def build_dist(rew: np.ndarray, xlen: int, ylen: int) -> np.ndarray:
'Computes uniform visitation distribution compatible with gridworld dynamics.\n\n Args:\n rew: A three-dimensional reward (needed for dimensionality).\n xlen: width of gridworld.\n ylen: height of gridworld.\n\n Returns:... |
dfe6a0e1c952b12e93a57eea2e82573347d05fbc4c2194c63c1a861c237b2302 | def compute_divergence(reward_cfg: Dict[(str, Any)], discount: float, kind: str) -> pd.Series:
'Compute divergence for each pair of rewards in `reward_cfg`.'
rewards = {name: make_reward(cfg, discount) for (name, cfg) in reward_cfg.items()}
divergence = collections.defaultdict(dict)
for (src_name, src_r... | Compute divergence for each pair of rewards in `reward_cfg`. | src/evaluating_rewards/analysis/distances/plot_gridworld_heatmap.py | compute_divergence | HumanCompatibleAI/evaluating_rewards | 42 | python | def compute_divergence(reward_cfg: Dict[(str, Any)], discount: float, kind: str) -> pd.Series:
rewards = {name: make_reward(cfg, discount) for (name, cfg) in reward_cfg.items()}
divergence = collections.defaultdict(dict)
for (src_name, src_reward) in rewards.items():
for (target_name, target_re... | def compute_divergence(reward_cfg: Dict[(str, Any)], discount: float, kind: str) -> pd.Series:
rewards = {name: make_reward(cfg, discount) for (name, cfg) in reward_cfg.items()}
divergence = collections.defaultdict(dict)
for (src_name, src_reward) in rewards.items():
for (target_name, target_re... |
07b2a9e1d4e1ea23a8cd5e61453864f8093c1ec0fe8ecdefe8a11c58bec0b5a0 | def normalize_dissimilarity(s: pd.Series) -> pd.Series:
'Divides by distance from Zero reward, an upper bound on the distance.'
df = s.unstack(level=['source_reward_type', 'source_reward_path'])
zero_col_name = (serialize.ZERO_REWARD, 'dummy')
zero_dissimilarity = df.pop(zero_col_name)
df = df.apply... | Divides by distance from Zero reward, an upper bound on the distance. | src/evaluating_rewards/analysis/distances/plot_gridworld_heatmap.py | normalize_dissimilarity | HumanCompatibleAI/evaluating_rewards | 42 | python | def normalize_dissimilarity(s: pd.Series) -> pd.Series:
df = s.unstack(level=['source_reward_type', 'source_reward_path'])
zero_col_name = (serialize.ZERO_REWARD, 'dummy')
zero_dissimilarity = df.pop(zero_col_name)
df = df.apply((lambda x: (x / zero_dissimilarity)))
return df.unstack(level=df.i... | def normalize_dissimilarity(s: pd.Series) -> pd.Series:
df = s.unstack(level=['source_reward_type', 'source_reward_path'])
zero_col_name = (serialize.ZERO_REWARD, 'dummy')
zero_dissimilarity = df.pop(zero_col_name)
df = df.apply((lambda x: (x / zero_dissimilarity)))
return df.unstack(level=df.i... |
f943f5d07aeb13f6413907db6a314fc677a2ae901b460e79c84db31423a1eb8e | @plot_gridworld_heatmap_ex.main
def plot_gridworld_heatmap(normalize: bool, styles: Iterable[str], reward_subset: Optional[Iterable[str]], heatmap_kwargs: Dict[(str, Any)], kind: str, discount: float, log_dir: str, save_kwargs: Mapping[(str, Any)]) -> None:
'Entry-point into script to produce divergence heatmaps.\n... | Entry-point into script to produce divergence heatmaps.
Args:
normalize: whether to divide by distance from Zero.
styles: styles to apply from `evaluating_rewards.analysis.stylesheets`.
reward_subset: if specified, subset of keys to plot.
discount: discount rate of MDP.
log_dir: directory to write ... | src/evaluating_rewards/analysis/distances/plot_gridworld_heatmap.py | plot_gridworld_heatmap | HumanCompatibleAI/evaluating_rewards | 42 | python | @plot_gridworld_heatmap_ex.main
def plot_gridworld_heatmap(normalize: bool, styles: Iterable[str], reward_subset: Optional[Iterable[str]], heatmap_kwargs: Dict[(str, Any)], kind: str, discount: float, log_dir: str, save_kwargs: Mapping[(str, Any)]) -> None:
'Entry-point into script to produce divergence heatmaps.\n... | @plot_gridworld_heatmap_ex.main
def plot_gridworld_heatmap(normalize: bool, styles: Iterable[str], reward_subset: Optional[Iterable[str]], heatmap_kwargs: Dict[(str, Any)], kind: str, discount: float, log_dir: str, save_kwargs: Mapping[(str, Any)]) -> None:
'Entry-point into script to produce divergence heatmaps.\n... |
86bbd6f3d90103ecefdb6ba35dbd06c92c73feca1cbc8108d3fdae35c267730e | @staticmethod
def prepare(message):
' Assign unique hashes to messages ready for transport.\n Returns (new hashed message) -> str '
out = ''
timestamp = str(datetime.datetime.utcnow())
out += timestamp
out += message
sig = sha3_224(out.encode()).hexdigest()[:16]
out = ''
out +... | Assign unique hashes to messages ready for transport.
Returns (new hashed message) -> str | src/server/server.py | prepare | D3P-Dell-Part-Picker/Axonet | 0 | python | @staticmethod
def prepare(message):
' Assign unique hashes to messages ready for transport.\n Returns (new hashed message) -> str '
out =
timestamp = str(datetime.datetime.utcnow())
out += timestamp
out += message
sig = sha3_224(out.encode()).hexdigest()[:16]
out =
out += si... | @staticmethod
def prepare(message):
' Assign unique hashes to messages ready for transport.\n Returns (new hashed message) -> str '
out =
timestamp = str(datetime.datetime.utcnow())
out += timestamp
out += message
sig = sha3_224(out.encode()).hexdigest()[:16]
out =
out += si... |
fdb23429db92b20a0fdbb0765e0a9f8578dea62932500eac033d054fb4f9abea | def permute_network_tuple(self):
" Permute the network tuple. Repetitive permutation after each call\n of respond() functionally allows the network to inherit many of the anonymous\n aspects of a mixing network. Packets are sent sequentially in the order of the\n network tuple, whic... | Permute the network tuple. Repetitive permutation after each call
of respond() functionally allows the network to inherit many of the anonymous
aspects of a mixing network. Packets are sent sequentially in the order of the
network tuple, which when permuted, thwarts many timing attacks. ''
Doesn't return | src/server/server.py | permute_network_tuple | D3P-Dell-Part-Picker/Axonet | 0 | python | def permute_network_tuple(self):
" Permute the network tuple. Repetitive permutation after each call\n of respond() functionally allows the network to inherit many of the anonymous\n aspects of a mixing network. Packets are sent sequentially in the order of the\n network tuple, whic... | def permute_network_tuple(self):
" Permute the network tuple. Repetitive permutation after each call\n of respond() functionally allows the network to inherit many of the anonymous\n aspects of a mixing network. Packets are sent sequentially in the order of the\n network tuple, whic... |
d31a3be5a74db18f8ec21759eaffc856db433dcf77dc116ffcd1ff938640a4e8 | def lookup_socket(self, address):
'Do a brute force search for a specific socket.\n Maybe this can be optimized by caching the indexes of commonly-used connections?'
net_tuple = self.read_nodestate(0)
for item in net_tuple:
discovered_address = item[1]
if (address == discovered_add... | Do a brute force search for a specific socket.
Maybe this can be optimized by caching the indexes of commonly-used connections? | src/server/server.py | lookup_socket | D3P-Dell-Part-Picker/Axonet | 0 | python | def lookup_socket(self, address):
'Do a brute force search for a specific socket.\n Maybe this can be optimized by caching the indexes of commonly-used connections?'
net_tuple = self.read_nodestate(0)
for item in net_tuple:
discovered_address = item[1]
if (address == discovered_add... | def lookup_socket(self, address):
'Do a brute force search for a specific socket.\n Maybe this can be optimized by caching the indexes of commonly-used connections?'
net_tuple = self.read_nodestate(0)
for item in net_tuple:
discovered_address = item[1]
if (address == discovered_add... |
93887ce0b7bde1490992fc77bd4524fb08cac440dd04d1c6b76999897db049bc | def lookup_address(self, in_sock):
'Do a brute force search for a specific socket.\n Maybe this can be optimized by caching the indexes of commonly-used connections?'
net_tuple = self.read_nodestate(0)
for item in net_tuple:
discovered_socket = item[0]
if (in_sock == discovered_soc... | Do a brute force search for a specific socket.
Maybe this can be optimized by caching the indexes of commonly-used connections? | src/server/server.py | lookup_address | D3P-Dell-Part-Picker/Axonet | 0 | python | def lookup_address(self, in_sock):
'Do a brute force search for a specific socket.\n Maybe this can be optimized by caching the indexes of commonly-used connections?'
net_tuple = self.read_nodestate(0)
for item in net_tuple:
discovered_socket = item[0]
if (in_sock == discovered_soc... | def lookup_address(self, in_sock):
'Do a brute force search for a specific socket.\n Maybe this can be optimized by caching the indexes of commonly-used connections?'
net_tuple = self.read_nodestate(0)
for item in net_tuple:
discovered_socket = item[0]
if (in_sock == discovered_soc... |
eab7945b7f64a3c41b62144f2fbd9d332709e5231ead5189468932b8a7a613c2 | def append(self, in_socket, address):
" Add a connection to the network tuple. Doesn't return."
net_tuple = self.read_nodestate(0)
network_list = list(net_tuple)
connection = (in_socket, address)
network_list.append(connection)
self.write_nodestate(nodeState, 0, tuple(network_list)) | Add a connection to the network tuple. Doesn't return. | src/server/server.py | append | D3P-Dell-Part-Picker/Axonet | 0 | python | def append(self, in_socket, address):
" "
net_tuple = self.read_nodestate(0)
network_list = list(net_tuple)
connection = (in_socket, address)
network_list.append(connection)
self.write_nodestate(nodeState, 0, tuple(network_list)) | def append(self, in_socket, address):
" "
net_tuple = self.read_nodestate(0)
network_list = list(net_tuple)
connection = (in_socket, address)
network_list.append(connection)
self.write_nodestate(nodeState, 0, tuple(network_list))<|docstring|>Add a connection to the network tuple. Doesn't return.... |
055f2ca9347d72782a0252c68000742c217d08ed1c835ebefe07b914eb3cae9a | def remove(self, connection):
"Remove a connection from the network tuple. Doesn't return"
net_tuple = self.read_nodestate(0)
network_list = list(net_tuple)
try:
index = network_list.index(connection)
network_list.pop(index)
except ValueError:
log_msg = str(('Not removing non... | Remove a connection from the network tuple. Doesn't return | src/server/server.py | remove | D3P-Dell-Part-Picker/Axonet | 0 | python | def remove(self, connection):
net_tuple = self.read_nodestate(0)
network_list = list(net_tuple)
try:
index = network_list.index(connection)
network_list.pop(index)
except ValueError:
log_msg = str(('Not removing non-existent connection: ' + str(connection)))
Primitiv... | def remove(self, connection):
net_tuple = self.read_nodestate(0)
network_list = list(net_tuple)
try:
index = network_list.index(connection)
network_list.pop(index)
except ValueError:
log_msg = str(('Not removing non-existent connection: ' + str(connection)))
Primitiv... |
81440a707ed2b76ae626444ffaa3379c449857ceaf30a8f035a26499a667236b | def stop(self):
' Attempt to gracefully disconnect and terminate,\n but resort to brute force if needed. '
net_tuple = self.read_nodestate(0)
try:
localhost_socket = self.lookup_socket('127.0.0.1')
localhost_connection = (localhost_socket, '127.0.0.1')
self.send(localhost_conn... | Attempt to gracefully disconnect and terminate,
but resort to brute force if needed. | src/server/server.py | stop | D3P-Dell-Part-Picker/Axonet | 0 | python | def stop(self):
' Attempt to gracefully disconnect and terminate,\n but resort to brute force if needed. '
net_tuple = self.read_nodestate(0)
try:
localhost_socket = self.lookup_socket('127.0.0.1')
localhost_connection = (localhost_socket, '127.0.0.1')
self.send(localhost_conn... | def stop(self):
' Attempt to gracefully disconnect and terminate,\n but resort to brute force if needed. '
net_tuple = self.read_nodestate(0)
try:
localhost_socket = self.lookup_socket('127.0.0.1')
localhost_connection = (localhost_socket, '127.0.0.1')
self.send(localhost_conn... |
4d393a51abb24c39524529d37aa6c6ed7f65cd04b08ba6216f3d0f2dd26404a5 | def disconnect(self, connection, disallow_local_disconnect=True):
"Try to disconnect from a socket as cleanly as possible.\n Doesn't return anything. "
sock = connection[0]
address = connection[1]
terminated = self.read_nodestate(2)
try:
if disallow_local_disconnect:
Pr... | Try to disconnect from a socket as cleanly as possible.
Doesn't return anything. | src/server/server.py | disconnect | D3P-Dell-Part-Picker/Axonet | 0 | python | def disconnect(self, connection, disallow_local_disconnect=True):
"Try to disconnect from a socket as cleanly as possible.\n Doesn't return anything. "
sock = connection[0]
address = connection[1]
terminated = self.read_nodestate(2)
try:
if disallow_local_disconnect:
Pr... | def disconnect(self, connection, disallow_local_disconnect=True):
"Try to disconnect from a socket as cleanly as possible.\n Doesn't return anything. "
sock = connection[0]
address = connection[1]
terminated = self.read_nodestate(2)
try:
if disallow_local_disconnect:
Pr... |
1ef695b7ffda0da50503009c40479536b29a825fc21293ea15b5559a49d36149 | def listen(self, connection):
"Listen for incoming messages in one thread, manage the network injector in another.\n Doesn't return anything. "
global receive_lock
def listener(conn):
_terminated = self.read_nodestate(2)
listener_terminated = False
while (not (_terminated or ... | Listen for incoming messages in one thread, manage the network injector in another.
Doesn't return anything. | src/server/server.py | listen | D3P-Dell-Part-Picker/Axonet | 0 | python | def listen(self, connection):
"Listen for incoming messages in one thread, manage the network injector in another.\n Doesn't return anything. "
global receive_lock
def listener(conn):
_terminated = self.read_nodestate(2)
listener_terminated = False
while (not (_terminated or ... | def listen(self, connection):
"Listen for incoming messages in one thread, manage the network injector in another.\n Doesn't return anything. "
global receive_lock
def listener(conn):
_terminated = self.read_nodestate(2)
listener_terminated = False
while (not (_terminated or ... |
68d6a4c4726c7e9f930644edde04bdd091638773141362151623a7684521c703 | def accuracy(output, target, topk=(1,)):
'Computes the precision@k for the specified values of k\n Arguments\n '
maxk = max(topk)
batch_size = target.size(0)
(_, pred) = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, (- 1)).expand_as(pred))
res = []
... | Computes the precision@k for the specified values of k
Arguments | workshops/sagemaker/pytorch_utils/trainers/train_utils.py | accuracy | MLDA-NTU/AWS-Workshop-2020 | 4 | python | def accuracy(output, target, topk=(1,)):
'Computes the precision@k for the specified values of k\n Arguments\n '
maxk = max(topk)
batch_size = target.size(0)
(_, pred) = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, (- 1)).expand_as(pred))
res = []
... | def accuracy(output, target, topk=(1,)):
'Computes the precision@k for the specified values of k\n Arguments\n '
maxk = max(topk)
batch_size = target.size(0)
(_, pred) = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, (- 1)).expand_as(pred))
res = []
... |
8ffefed52666e89dbadf052b7284159b2e5f52d6e26e54981b5442d9f994518b | def longestValidParentheses(self, s):
'\n :type s: str\n :rtype: int\n '
dp = [0 for _ in xrange(0, len(s))]
left = 0
ans = 0
for i in xrange(0, len(s)):
if (s[i] == '('):
left += 1
elif (left > 0):
left -= 1
dp[i] = (dp[(i - 1... | :type s: str
:rtype: int | LeetcodeAlgorithms/032. Longest Valid Parentheses/longest-valid-parentheses.py | longestValidParentheses | Fenghuapiao/PyLeetcode | 3 | python | def longestValidParentheses(self, s):
'\n :type s: str\n :rtype: int\n '
dp = [0 for _ in xrange(0, len(s))]
left = 0
ans = 0
for i in xrange(0, len(s)):
if (s[i] == '('):
left += 1
elif (left > 0):
left -= 1
dp[i] = (dp[(i - 1... | def longestValidParentheses(self, s):
'\n :type s: str\n :rtype: int\n '
dp = [0 for _ in xrange(0, len(s))]
left = 0
ans = 0
for i in xrange(0, len(s)):
if (s[i] == '('):
left += 1
elif (left > 0):
left -= 1
dp[i] = (dp[(i - 1... |
4eff0f0e70543e837a975e2abe9a945314663d776102623eb7213d4781568ddd | @stateful_op_impl(op)
def elementwise_op(types, args=(), kwargs=None, pg=None):
'\n Handles ``__torch_function__`` dispatch for the elementwise op such\n as ``torch.nn.functional.gelu`` or ``torch.nn.functional.relu``.\n This method computes on either a normal tensor or a sharded tensor.\n ... | Handles ``__torch_function__`` dispatch for the elementwise op such
as ``torch.nn.functional.gelu`` or ``torch.nn.functional.relu``.
This method computes on either a normal tensor or a sharded tensor. | colossalai/gemini/tensor/_ops/element_wise.py | elementwise_op | weiplanet/ColossalAI | 0 | python | @stateful_op_impl(op)
def elementwise_op(types, args=(), kwargs=None, pg=None):
'\n Handles ``__torch_function__`` dispatch for the elementwise op such\n as ``torch.nn.functional.gelu`` or ``torch.nn.functional.relu``.\n This method computes on either a normal tensor or a sharded tensor.\n ... | @stateful_op_impl(op)
def elementwise_op(types, args=(), kwargs=None, pg=None):
'\n Handles ``__torch_function__`` dispatch for the elementwise op such\n as ``torch.nn.functional.gelu`` or ``torch.nn.functional.relu``.\n This method computes on either a normal tensor or a sharded tensor.\n ... |
21a81b34594ab2443a62f450cb1d56d2fcc14eacf6ff0788761ae016701fc3e9 | def ExampleModule(Layer):
'\n An example outline module that implements all possible\n abtract functions.\n '
def __init__(self, name, **args):
Layer.__init__(self, name, **args)
def after_init(self):
'\n Executed automatically after the constructor. This method\n e... | An example outline module that implements all possible
abtract functions. | samples/outline-modules/example.py | ExampleModule | danielkrajnik/OpenCue | 334 | python | def ExampleModule(Layer):
'\n An example outline module that implements all possible\n abtract functions.\n '
def __init__(self, name, **args):
Layer.__init__(self, name, **args)
def after_init(self):
'\n Executed automatically after the constructor. This method\n e... | def ExampleModule(Layer):
'\n An example outline module that implements all possible\n abtract functions.\n '
def __init__(self, name, **args):
Layer.__init__(self, name, **args)
def after_init(self):
'\n Executed automatically after the constructor. This method\n e... |
5821d83980e253113f2a32482c16eaf35acb6de710e9ec3527f28f902fcdc26e | def after_init(self):
'\n Executed automatically after the constructor. This method\n exists because the parent outline is not known in the constructor.\n '
outline = self.get_outline() | Executed automatically after the constructor. This method
exists because the parent outline is not known in the constructor. | samples/outline-modules/example.py | after_init | danielkrajnik/OpenCue | 334 | python | def after_init(self):
'\n Executed automatically after the constructor. This method\n exists because the parent outline is not known in the constructor.\n '
outline = self.get_outline() | def after_init(self):
'\n Executed automatically after the constructor. This method\n exists because the parent outline is not known in the constructor.\n '
outline = self.get_outline()<|docstring|>Executed automatically after the constructor. This method
exists because the parent outline... |
2f0a9344a7302376a8df54e8d4f51a0a75baf99be63364c6e52f3864621ba6c0 | def after_parented(self):
'\n Executed automatically after the layer has been parented\n to another layer. This only happens when building\n composite layers, or, layers that contain other layers.\n '
parent_layer = self.get_parent() | Executed automatically after the layer has been parented
to another layer. This only happens when building
composite layers, or, layers that contain other layers. | samples/outline-modules/example.py | after_parented | danielkrajnik/OpenCue | 334 | python | def after_parented(self):
'\n Executed automatically after the layer has been parented\n to another layer. This only happens when building\n composite layers, or, layers that contain other layers.\n '
parent_layer = self.get_parent() | def after_parented(self):
'\n Executed automatically after the layer has been parented\n to another layer. This only happens when building\n composite layers, or, layers that contain other layers.\n '
parent_layer = self.get_parent()<|docstring|>Executed automatically after the laye... |
f2bf106f69c7262a44f37055fef5c659be97066a29a43de7f8274c809fe3f6ca | def _setup(self):
'\n Should contain any operations that should be run before the job\n is launched. This is the first time the session becomes\n available, so its possible to write data into the cue_archive.\n '
pass | Should contain any operations that should be run before the job
is launched. This is the first time the session becomes
available, so its possible to write data into the cue_archive. | samples/outline-modules/example.py | _setup | danielkrajnik/OpenCue | 334 | python | def _setup(self):
'\n Should contain any operations that should be run before the job\n is launched. This is the first time the session becomes\n available, so its possible to write data into the cue_archive.\n '
pass | def _setup(self):
'\n Should contain any operations that should be run before the job\n is launched. This is the first time the session becomes\n available, so its possible to write data into the cue_archive.\n '
pass<|docstring|>Should contain any operations that should be run befo... |
2d4321e0252e1208002b9f5429121b9671583e463852ae2aab55f39e46975399 | def _before_execute(self):
'\n Run before execute. Generally used to create objects that do\n not serialize to pickle properly for job launch.\n ' | Run before execute. Generally used to create objects that do
not serialize to pickle properly for job launch. | samples/outline-modules/example.py | _before_execute | danielkrajnik/OpenCue | 334 | python | def _before_execute(self):
'\n Run before execute. Generally used to create objects that do\n not serialize to pickle properly for job launch.\n ' | def _before_execute(self):
'\n Run before execute. Generally used to create objects that do\n not serialize to pickle properly for job launch.\n '<|docstring|>Run before execute. Generally used to create objects that do
not serialize to pickle properly for job launch.<|endoftext|> |
d9160cbe839e9e63c8bd9dc9702ce815f7549eee1383dc4f3b54d48aeff2348b | def _execute(self, frames):
'\n The core module behavior should be implemented here. The\n frames argument contains an array of frames that the current\n instance is responsible for.\n '
pass | The core module behavior should be implemented here. The
frames argument contains an array of frames that the current
instance is responsible for. | samples/outline-modules/example.py | _execute | danielkrajnik/OpenCue | 334 | python | def _execute(self, frames):
'\n The core module behavior should be implemented here. The\n frames argument contains an array of frames that the current\n instance is responsible for.\n '
pass | def _execute(self, frames):
'\n The core module behavior should be implemented here. The\n frames argument contains an array of frames that the current\n instance is responsible for.\n '
pass<|docstring|>The core module behavior should be implemented here. The
frames argument conta... |
e9b5ff409167f327b43200eb94fc4912bbdf5637e28e148924b2be9f5eff3502 | def _after_execute(self):
'\n Run after execute even if execute throws an exception. Used for\n cleanup and implementing extra output checks like checking for\n black frames or log parsing.\n '
pass | Run after execute even if execute throws an exception. Used for
cleanup and implementing extra output checks like checking for
black frames or log parsing. | samples/outline-modules/example.py | _after_execute | danielkrajnik/OpenCue | 334 | python | def _after_execute(self):
'\n Run after execute even if execute throws an exception. Used for\n cleanup and implementing extra output checks like checking for\n black frames or log parsing.\n '
pass | def _after_execute(self):
'\n Run after execute even if execute throws an exception. Used for\n cleanup and implementing extra output checks like checking for\n black frames or log parsing.\n '
pass<|docstring|>Run after execute even if execute throws an exception. Used for
cleanup... |
87b331b7b6727abc70e1c8362d65841f69fb5fdbaa9941b34d55b6f0fca310d0 | @staticmethod
def __watcher__(fd_out, fd_err):
' Static method that checks the stderr file descriptor looking\n for new lines added at the end.\n It is enabled to also look into the stdout file descriptor, but\n currently not being used.\n\n :param fd_out: Standard output file descriptor... | Static method that checks the stderr file descriptor looking
for new lines added at the end.
It is enabled to also look into the stdout file descriptor, but
currently not being used.
:param fd_out: Standard output file descriptor.
:param fd_err: Standard error file descriptor.
:return: Yields each line found in the fd... | compss/programming_model/bindings/python/src/pycompss/util/interactive/outwatcher.py | __watcher__ | alexbarcelo/compss | 31 | python | @staticmethod
def __watcher__(fd_out, fd_err):
' Static method that checks the stderr file descriptor looking\n for new lines added at the end.\n It is enabled to also look into the stdout file descriptor, but\n currently not being used.\n\n :param fd_out: Standard output file descriptor... | @staticmethod
def __watcher__(fd_out, fd_err):
' Static method that checks the stderr file descriptor looking\n for new lines added at the end.\n It is enabled to also look into the stdout file descriptor, but\n currently not being used.\n\n :param fd_out: Standard output file descriptor... |
3a69163d1f91e0bc62097be4d77abac04950dc07e1ad7b7fae816c7ee3bb2084 | def __std_follower__(self, out_file_name, err_file_name):
' Opens the out and error files and looks inside them thanks to the\n __watcher__ generator. This function puts into the queue any line\n of the error file which starts with "[ERRMGR]".\n\n :param out_file_name: Output file name.\n ... | Opens the out and error files and looks inside them thanks to the
__watcher__ generator. This function puts into the queue any line
of the error file which starts with "[ERRMGR]".
:param out_file_name: Output file name.
:param err_file_name: Error file name.
:return: None | compss/programming_model/bindings/python/src/pycompss/util/interactive/outwatcher.py | __std_follower__ | alexbarcelo/compss | 31 | python | def __std_follower__(self, out_file_name, err_file_name):
' Opens the out and error files and looks inside them thanks to the\n __watcher__ generator. This function puts into the queue any line\n of the error file which starts with "[ERRMGR]".\n\n :param out_file_name: Output file name.\n ... | def __std_follower__(self, out_file_name, err_file_name):
' Opens the out and error files and looks inside them thanks to the\n __watcher__ generator. This function puts into the queue any line\n of the error file which starts with "[ERRMGR]".\n\n :param out_file_name: Output file name.\n ... |
579e65979d2e3abfdb0b33f98cd9b641ebf161e8ca87e0779dd59a8937bb6097 | def start_watching(self):
' Starts a new thread in charge of monitoring the stdout and stderr\n files provided by the redirector.\n\n :return: None\n '
if is_redirected():
self.running = True
(out_file_name, err_file_name) = get_redirection_file_names()
thread = thre... | Starts a new thread in charge of monitoring the stdout and stderr
files provided by the redirector.
:return: None | compss/programming_model/bindings/python/src/pycompss/util/interactive/outwatcher.py | start_watching | alexbarcelo/compss | 31 | python | def start_watching(self):
' Starts a new thread in charge of monitoring the stdout and stderr\n files provided by the redirector.\n\n :return: None\n '
if is_redirected():
self.running = True
(out_file_name, err_file_name) = get_redirection_file_names()
thread = thre... | def start_watching(self):
' Starts a new thread in charge of monitoring the stdout and stderr\n files provided by the redirector.\n\n :return: None\n '
if is_redirected():
self.running = True
(out_file_name, err_file_name) = get_redirection_file_names()
thread = thre... |
7858f509a605f5eb126e1d3d21bd81a34c96951e87f1954cdd083ac17ef19b6e | def stop_watching(self, clean=True):
' Stops the monitoring thread and cleans the redirection files\n if clean is True.\n\n :param clean: Remove the redirection files.\n :return: None\n '
self.running = False
if clean:
(out_file_name, err_file_name) = get_redirection_file... | Stops the monitoring thread and cleans the redirection files
if clean is True.
:param clean: Remove the redirection files.
:return: None | compss/programming_model/bindings/python/src/pycompss/util/interactive/outwatcher.py | stop_watching | alexbarcelo/compss | 31 | python | def stop_watching(self, clean=True):
' Stops the monitoring thread and cleans the redirection files\n if clean is True.\n\n :param clean: Remove the redirection files.\n :return: None\n '
self.running = False
if clean:
(out_file_name, err_file_name) = get_redirection_file... | def stop_watching(self, clean=True):
' Stops the monitoring thread and cleans the redirection files\n if clean is True.\n\n :param clean: Remove the redirection files.\n :return: None\n '
self.running = False
if clean:
(out_file_name, err_file_name) = get_redirection_file... |
667ce74100e4bb3f44ff591cf18d7cd8a986001c018c92525cf4683a4a61667e | def get_messages(self):
' Retrieves the current messages stored in the queue as a list\n of strings (one per line reported by the stdout and stderr files).\n\n :return: A list with the reported messages.\n '
current_messages = []
while (not self.messages.empty()):
current_messag... | Retrieves the current messages stored in the queue as a list
of strings (one per line reported by the stdout and stderr files).
:return: A list with the reported messages. | compss/programming_model/bindings/python/src/pycompss/util/interactive/outwatcher.py | get_messages | alexbarcelo/compss | 31 | python | def get_messages(self):
' Retrieves the current messages stored in the queue as a list\n of strings (one per line reported by the stdout and stderr files).\n\n :return: A list with the reported messages.\n '
current_messages = []
while (not self.messages.empty()):
current_messag... | def get_messages(self):
' Retrieves the current messages stored in the queue as a list\n of strings (one per line reported by the stdout and stderr files).\n\n :return: A list with the reported messages.\n '
current_messages = []
while (not self.messages.empty()):
current_messag... |
e848765dd6740a09440856b4ece802fb8505692e00005a7b8257568c96e7a77d | def get_network_endpoint_group(name=None, self_link=None, zone=None, opts=None):
'\n Use this data source to access information about an existing resource.\n \n '
__args__ = dict()
__args__['name'] = name
__args__['selfLink'] = self_link
__args__['zone'] = zone
if (opts is None):
... | Use this data source to access information about an existing resource. | sdk/python/pulumi_gcp/compute/get_network_endpoint_group.py | get_network_endpoint_group | 23doors/pulumi-gcp | 1 | python | def get_network_endpoint_group(name=None, self_link=None, zone=None, opts=None):
'\n \n \n '
__args__ = dict()
__args__['name'] = name
__args__['selfLink'] = self_link
__args__['zone'] = zone
if (opts is None):
opts = pulumi.InvokeOptions()
if (opts.version is None):
... | def get_network_endpoint_group(name=None, self_link=None, zone=None, opts=None):
'\n \n \n '
__args__ = dict()
__args__['name'] = name
__args__['selfLink'] = self_link
__args__['zone'] = zone
if (opts is None):
opts = pulumi.InvokeOptions()
if (opts.version is None):
... |
a46db432b847ac4ef5b9ed39cd42229e767ccba20ac95b205347cbf6b1681d20 | def calc(self, request, **args):
'\n\n :param request: StatsComputeOptions\n :param args: dict\n :return:\n '
start_time = datetime.now()
(ds, bounding_polygon, start_seconds_from_epoch, end_seconds_from_epoch, apply_seasonal_cycle_filter, apply_low_pass_filter, nparts_requested,... | :param request: StatsComputeOptions
:param args: dict
:return: | analysis/webservice/algorithms_spark/TimeSeriesSpark.py | calc | kevinmarlis/incubator-sdap-nexus | 17 | python | def calc(self, request, **args):
'\n\n :param request: StatsComputeOptions\n :param args: dict\n :return:\n '
start_time = datetime.now()
(ds, bounding_polygon, start_seconds_from_epoch, end_seconds_from_epoch, apply_seasonal_cycle_filter, apply_low_pass_filter, nparts_requested,... | def calc(self, request, **args):
'\n\n :param request: StatsComputeOptions\n :param args: dict\n :return:\n '
start_time = datetime.now()
(ds, bounding_polygon, start_seconds_from_epoch, end_seconds_from_epoch, apply_seasonal_cycle_filter, apply_low_pass_filter, nparts_requested,... |
de5d5c0000027eff2cd91997189ffd6e65b020fdd87554076c60614b382b9434 | def describe(source, *, source_type=None, **options):
'Describe the data source\n\n API | Usage\n -------- | --------\n Public | `from frictionless import describe`\n\n Parameters:\n source (any): data source\n source_type (str): source type - `schema`, `resource` or `package`\n ... | Describe the data source
API | Usage
-------- | --------
Public | `from frictionless import describe`
Parameters:
source (any): data source
source_type (str): source type - `schema`, `resource` or `package`
**options (dict): options for the underlaying describe function
Returns:
Package|Resour... | frictionless/describe/main.py | describe | kant/frictionless-py | 0 | python | def describe(source, *, source_type=None, **options):
'Describe the data source\n\n API | Usage\n -------- | --------\n Public | `from frictionless import describe`\n\n Parameters:\n source (any): data source\n source_type (str): source type - `schema`, `resource` or `package`\n ... | def describe(source, *, source_type=None, **options):
'Describe the data source\n\n API | Usage\n -------- | --------\n Public | `from frictionless import describe`\n\n Parameters:\n source (any): data source\n source_type (str): source type - `schema`, `resource` or `package`\n ... |
9c90461c01f4e15e4a8c8b3c7fc728916eb5a99dc06fc1cbff437ce1b7bf8ff7 | @abstractmethod
def run(self, event: EventMessage) -> EventMessage:
'Runs the function when it is called directly\n :param event: Event which function wants running on, for which, this should be true:\n (is_prefixed is not false and command_args is not None)\n '
raise NotImplementedError | Runs the function when it is called directly
:param event: Event which function wants running on, for which, this should be true:
(is_prefixed is not false and command_args is not None) | hallo/function.py | run | joshcoales/Hallo | 1 | python | @abstractmethod
def run(self, event: EventMessage) -> EventMessage:
'Runs the function when it is called directly\n :param event: Event which function wants running on, for which, this should be true:\n (is_prefixed is not false and command_args is not None)\n '
raise NotImplementedError | @abstractmethod
def run(self, event: EventMessage) -> EventMessage:
'Runs the function when it is called directly\n :param event: Event which function wants running on, for which, this should be true:\n (is_prefixed is not false and command_args is not None)\n '
raise NotImplementedError<|d... |
b94a5f33989c7d1c5589f4d6eb0b4ba514327c00ad53aa215941027a18f576e2 | @staticmethod
def is_persistent() -> bool:
'Returns boolean representing whether this function is supposed to be persistent or not'
return False | Returns boolean representing whether this function is supposed to be persistent or not | hallo/function.py | is_persistent | joshcoales/Hallo | 1 | python | @staticmethod
def is_persistent() -> bool:
return False | @staticmethod
def is_persistent() -> bool:
return False<|docstring|>Returns boolean representing whether this function is supposed to be persistent or not<|endoftext|> |
a64f36f7d92b21b4bdff7ab33df2f73b205154ad455516ffc249d5fd0e11100c | @staticmethod
def load_function() -> 'Function':
'Loads the function, persistent functions only.'
return Function() | Loads the function, persistent functions only. | hallo/function.py | load_function | joshcoales/Hallo | 1 | python | @staticmethod
def load_function() -> 'Function':
return Function() | @staticmethod
def load_function() -> 'Function':
return Function()<|docstring|>Loads the function, persistent functions only.<|endoftext|> |
43b7be5eeb7de78e97b9ac48070f44f0bad9b79be668692772e72bb3707cf8d3 | def save_function(self) -> None:
'Saves the function, persistent functions only.'
return None | Saves the function, persistent functions only. | hallo/function.py | save_function | joshcoales/Hallo | 1 | python | def save_function(self) -> None:
return None | def save_function(self) -> None:
return None<|docstring|>Saves the function, persistent functions only.<|endoftext|> |
2a08ebe7a8e4bbfe32593c61111ec62771669c6962fb6e49fd8bb6ee3dbbc81c | def get_passive_events(self) -> Set[Type[Event]]:
'Returns a list of events which this function may want to respond to in a passive way'
return set() | Returns a list of events which this function may want to respond to in a passive way | hallo/function.py | get_passive_events | joshcoales/Hallo | 1 | python | def get_passive_events(self) -> Set[Type[Event]]:
return set() | def get_passive_events(self) -> Set[Type[Event]]:
return set()<|docstring|>Returns a list of events which this function may want to respond to in a passive way<|endoftext|> |
bace9d17aefe562f6de091fbc52f6ccc33de08368002071f106b434df5126e6f | def passive_run(self, event: Event, hallo_obj) -> Optional[ServerEvent]:
'Replies to an event not directly addressed to the bot.\n :param event: Event which has called the function\n :param hallo_obj: Hallo object which fired the event.\n '
pass | Replies to an event not directly addressed to the bot.
:param event: Event which has called the function
:param hallo_obj: Hallo object which fired the event. | hallo/function.py | passive_run | joshcoales/Hallo | 1 | python | def passive_run(self, event: Event, hallo_obj) -> Optional[ServerEvent]:
'Replies to an event not directly addressed to the bot.\n :param event: Event which has called the function\n :param hallo_obj: Hallo object which fired the event.\n '
pass | def passive_run(self, event: Event, hallo_obj) -> Optional[ServerEvent]:
'Replies to an event not directly addressed to the bot.\n :param event: Event which has called the function\n :param hallo_obj: Hallo object which fired the event.\n '
pass<|docstring|>Replies to an event not directly ... |
c830bb914bf1c985427eb861f9a527b1fd871fbc8c7568794b146a71d1ce222b | def get_help_name(self) -> str:
'Returns the name to be printed for help documentation'
if (self.help_name is None):
raise NotImplementedError
return self.help_name | Returns the name to be printed for help documentation | hallo/function.py | get_help_name | joshcoales/Hallo | 1 | python | def get_help_name(self) -> str:
if (self.help_name is None):
raise NotImplementedError
return self.help_name | def get_help_name(self) -> str:
if (self.help_name is None):
raise NotImplementedError
return self.help_name<|docstring|>Returns the name to be printed for help documentation<|endoftext|> |
2e6bfb8e59540150fe75b71777d54585c351610d442e21ca9ea39d99b0fa8bb9 | def get_help_docs(self) -> str:
'\n Returns the help documentation, specific to given arguments, if supplied\n '
if (self.help_docs is None):
raise NotImplementedError
return self.help_docs | Returns the help documentation, specific to given arguments, if supplied | hallo/function.py | get_help_docs | joshcoales/Hallo | 1 | python | def get_help_docs(self) -> str:
'\n \n '
if (self.help_docs is None):
raise NotImplementedError
return self.help_docs | def get_help_docs(self) -> str:
'\n \n '
if (self.help_docs is None):
raise NotImplementedError
return self.help_docs<|docstring|>Returns the help documentation, specific to given arguments, if supplied<|endoftext|> |
b0c669b0e6f51366bec3424e7808d3dc0d55b61fc8a5650a16fd7330dc4dd63e | def get_names(self) -> Set[str]:
'Returns the list of names for directly addressing the function'
self.names.add(self.help_name)
return self.names | Returns the list of names for directly addressing the function | hallo/function.py | get_names | joshcoales/Hallo | 1 | python | def get_names(self) -> Set[str]:
self.names.add(self.help_name)
return self.names | def get_names(self) -> Set[str]:
self.names.add(self.help_name)
return self.names<|docstring|>Returns the list of names for directly addressing the function<|endoftext|> |
533732a788b776d4d611ff86574a5b1e0bb1c208ebc3d92b6f271e485a84fdf8 | def fill_insn(self, insn: Insn, model: Model) -> Optional[ProgInsn]:
"Try to fill out an instruction\n\n This might fail if, for example, the model doesn't have enough\n registers with architectural values. In that case, return None.\n\n "
if (insn.lsu is None):
op_vals = []
... | Try to fill out an instruction
This might fail if, for example, the model doesn't have enough
registers with architectural values. In that case, return None. | hw/ip/otbn/util/rig/gens/straight_line_insn.py | fill_insn | vanwinkeljan/opentitan | 0 | python | def fill_insn(self, insn: Insn, model: Model) -> Optional[ProgInsn]:
"Try to fill out an instruction\n\n This might fail if, for example, the model doesn't have enough\n registers with architectural values. In that case, return None.\n\n "
if (insn.lsu is None):
op_vals = []
... | def fill_insn(self, insn: Insn, model: Model) -> Optional[ProgInsn]:
"Try to fill out an instruction\n\n This might fail if, for example, the model doesn't have enough\n registers with architectural values. In that case, return None.\n\n "
if (insn.lsu is None):
op_vals = []
... |
e8126722ac44199184bdd1a60d25b0d42b491bf59cc48d4735ce27def17a4cfa | @click.group()
@click.pass_context
def config(context):
'create config files required for running the pipeline.'
pass | create config files required for running the pipeline. | BALSAMIC/commands/config/base.py | config | Clinical-Genomics/BALSAMIC | 39 | python | @click.group()
@click.pass_context
def config(context):
pass | @click.group()
@click.pass_context
def config(context):
pass<|docstring|>create config files required for running the pipeline.<|endoftext|> |
8a94c49d7c2faee4e8c146081461acb9e07b2ec2fbff67664fc169300bedfaa2 | def Score(self, result, score_aggregator=aggregators.Multiplier(), reasons_aggregator=aggregators.IdentityAggregator(), changed_files_aggregator=aggregators.ChangedFilesAggregator()):
'Aggregates score, reasons and changed_files from all the scorers.\n\n Note: This method sets confidence, reasons and changed_fil... | Aggregates score, reasons and changed_files from all the scorers.
Note: This method sets confidence, reasons and changed_files of results. | appengine/findit/crash/scorers/aggregated_scorer.py | Score | mithro/chromium-infra | 0 | python | def Score(self, result, score_aggregator=aggregators.Multiplier(), reasons_aggregator=aggregators.IdentityAggregator(), changed_files_aggregator=aggregators.ChangedFilesAggregator()):
'Aggregates score, reasons and changed_files from all the scorers.\n\n Note: This method sets confidence, reasons and changed_fil... | def Score(self, result, score_aggregator=aggregators.Multiplier(), reasons_aggregator=aggregators.IdentityAggregator(), changed_files_aggregator=aggregators.ChangedFilesAggregator()):
'Aggregates score, reasons and changed_files from all the scorers.\n\n Note: This method sets confidence, reasons and changed_fil... |
780a48e42bf18d934bc46056cde00621d2a2118984bb89a7bba1161536079982 | def elemsens(teffs=[3500, 4500, 5500], loggs=[1.0, 3.0, 5.0], mhs=[0.0], delta=0.1, suffix='', complement=False):
' create sample with small delta of each element at grid of [teff,logg,mh] to see sensitivities\n '
els = np.array(['O', 'Na', 'Mg', 'Al', 'Si', 'P', 'S', 'K', 'Ca', 'Ti', 'V', 'Cr', 'Mn', 'Co', ... | create sample with small delta of each element at grid of [teff,logg,mh] to see sensitivities | python/apogee/speclib/sample.py | elemsens | sdss/apogee | 5 | python | def elemsens(teffs=[3500, 4500, 5500], loggs=[1.0, 3.0, 5.0], mhs=[0.0], delta=0.1, suffix=, complement=False):
' \n '
els = np.array(['O', 'Na', 'Mg', 'Al', 'Si', 'P', 'S', 'K', 'Ca', 'Ti', 'V', 'Cr', 'Mn', 'Co', 'Fe', 'Ni', 'Cu', 'Ge', 'Rb', 'Ce', 'Nd'])
els_alpha = els[np.where(((((((els == 'O') | (el... | def elemsens(teffs=[3500, 4500, 5500], loggs=[1.0, 3.0, 5.0], mhs=[0.0], delta=0.1, suffix=, complement=False):
' \n '
els = np.array(['O', 'Na', 'Mg', 'Al', 'Si', 'P', 'S', 'K', 'Ca', 'Ti', 'V', 'Cr', 'Mn', 'Co', 'Fe', 'Ni', 'Cu', 'Ge', 'Rb', 'Ce', 'Nd'])
els_alpha = els[np.where(((((((els == 'O') | (el... |
f08fc2aa5de80be086ea7c13695cb0fa299c075c9a022aec97ae9467c6bce02e | def sample(name='test', gridclass=None, eps=0.01, tefflim=[3000, 8000], dtlo=100.0, logglim=[(- 0.5), 5.5], mhlim=[(- 2.5), 0.75], nmlim=[(- 0.5), 2.0], cmlim=[(- 1.5), 1.0], emlim=[(- 0.5), 1.0], vmicrolim=[0.3, 4.8], amlim=[(- 0.5), 1.0], vrotlim=[1.5, 96.0], rot=True, nsamp=1, niso=None, elems='all', fact=1.0, offgr... | Generate a test sample of parameters and abundances from isochrones | python/apogee/speclib/sample.py | sample | sdss/apogee | 5 | python | def sample(name='test', gridclass=None, eps=0.01, tefflim=[3000, 8000], dtlo=100.0, logglim=[(- 0.5), 5.5], mhlim=[(- 2.5), 0.75], nmlim=[(- 0.5), 2.0], cmlim=[(- 1.5), 1.0], emlim=[(- 0.5), 1.0], vmicrolim=[0.3, 4.8], amlim=[(- 0.5), 1.0], vrotlim=[1.5, 96.0], rot=True, nsamp=1, niso=None, elems='all', fact=1.0, offgr... | def sample(name='test', gridclass=None, eps=0.01, tefflim=[3000, 8000], dtlo=100.0, logglim=[(- 0.5), 5.5], mhlim=[(- 2.5), 0.75], nmlim=[(- 0.5), 2.0], cmlim=[(- 1.5), 1.0], emlim=[(- 0.5), 1.0], vmicrolim=[0.3, 4.8], amlim=[(- 0.5), 1.0], vrotlim=[1.5, 96.0], rot=True, nsamp=1, niso=None, elems='all', fact=1.0, offgr... |
3689fabfe43bbb694ec4e3cb31572f166ce0d14ed59fdc462b95bebb424969d9 | def comp(file, true=None, truespec=None, hard=False, plot=False, minchi2=0.0, testid=None, rot=False):
' Compare input parameters with output results\n '
if (true is None):
true = (file + '.ipf')
if rot:
names = ['id', 'vmicro', 'cm', 'nm', 'am', 'vrot', 'mh', 'logg', 'teff']
name... | Compare input parameters with output results | python/apogee/speclib/sample.py | comp | sdss/apogee | 5 | python | def comp(file, true=None, truespec=None, hard=False, plot=False, minchi2=0.0, testid=None, rot=False):
' \n '
if (true is None):
true = (file + '.ipf')
if rot:
names = ['id', 'vmicro', 'cm', 'nm', 'am', 'vrot', 'mh', 'logg', 'teff']
names_spm = ['id', 'vmicro', 'cm', 'nm', 'am', '... | def comp(file, true=None, truespec=None, hard=False, plot=False, minchi2=0.0, testid=None, rot=False):
' \n '
if (true is None):
true = (file + '.ipf')
if rot:
names = ['id', 'vmicro', 'cm', 'nm', 'am', 'vrot', 'mh', 'logg', 'teff']
names_spm = ['id', 'vmicro', 'cm', 'nm', 'am', '... |
01847c495c20dee0e8206b946a5cae573dbe8c57231b2e7b3c3051492bfaca11 | def clip(x, lim, eps=None):
' Utility routine to clip values within limits, and move slightly off edges if requested\n '
if np.isclose(x, 0.0):
x = 0.0
tmp = np.max([lim[0], np.min([lim[1], x])])
if (eps is not None):
if np.isclose(tmp, lim[0]):
tmp += eps
if np.is... | Utility routine to clip values within limits, and move slightly off edges if requested | python/apogee/speclib/sample.py | clip | sdss/apogee | 5 | python | def clip(x, lim, eps=None):
' \n '
if np.isclose(x, 0.0):
x = 0.0
tmp = np.max([lim[0], np.min([lim[1], x])])
if (eps is not None):
if np.isclose(tmp, lim[0]):
tmp += eps
if np.isclose(tmp, lim[1]):
tmp -= eps
return tmp | def clip(x, lim, eps=None):
' \n '
if np.isclose(x, 0.0):
x = 0.0
tmp = np.max([lim[0], np.min([lim[1], x])])
if (eps is not None):
if np.isclose(tmp, lim[0]):
tmp += eps
if np.isclose(tmp, lim[1]):
tmp -= eps
return tmp<|docstring|>Utility routine ... |
47aaa5af025a50f1453b3242ea118ade9c7a1deeb1aa227480d0f26e4476c363 | def upgrade():
'Add ProposedTags.'
op.create_table('proposed_tags', sa.Column('id', sa.Integer(), nullable=False), sa.Column('tags', sa.String(), nullable=True), sa.Column('created_at', sa.DateTime(), server_default=sa.text('now()'), nullable=False), sa.Column('user_id', sa.Integer(), nullable=True), sa.Column(... | Add ProposedTags. | migrations/versions/2019_05_13_a80fa524a2ba_add_proposed_tags.py | upgrade | fan-tom/sticker-finder | 82 | python | def upgrade():
op.create_table('proposed_tags', sa.Column('id', sa.Integer(), nullable=False), sa.Column('tags', sa.String(), nullable=True), sa.Column('created_at', sa.DateTime(), server_default=sa.text('now()'), nullable=False), sa.Column('user_id', sa.Integer(), nullable=True), sa.Column('sticker_file_id', ... | def upgrade():
op.create_table('proposed_tags', sa.Column('id', sa.Integer(), nullable=False), sa.Column('tags', sa.String(), nullable=True), sa.Column('created_at', sa.DateTime(), server_default=sa.text('now()'), nullable=False), sa.Column('user_id', sa.Integer(), nullable=True), sa.Column('sticker_file_id', ... |
d34dcb8439f3d4d9415bd95ef2bf9fec12dc4b13cab697a2426f26847c082d2a | def downgrade():
'Remove ProposedTags.'
op.drop_index(op.f('ix_proposed_tags_user_id'), table_name='proposed_tags')
op.drop_index(op.f('ix_proposed_tags_sticker_file_id'), table_name='proposed_tags')
op.drop_index(op.f('ix_proposed_tags_chat_id'), table_name='proposed_tags')
op.drop_table('proposed_... | Remove ProposedTags. | migrations/versions/2019_05_13_a80fa524a2ba_add_proposed_tags.py | downgrade | fan-tom/sticker-finder | 82 | python | def downgrade():
op.drop_index(op.f('ix_proposed_tags_user_id'), table_name='proposed_tags')
op.drop_index(op.f('ix_proposed_tags_sticker_file_id'), table_name='proposed_tags')
op.drop_index(op.f('ix_proposed_tags_chat_id'), table_name='proposed_tags')
op.drop_table('proposed_tags') | def downgrade():
op.drop_index(op.f('ix_proposed_tags_user_id'), table_name='proposed_tags')
op.drop_index(op.f('ix_proposed_tags_sticker_file_id'), table_name='proposed_tags')
op.drop_index(op.f('ix_proposed_tags_chat_id'), table_name='proposed_tags')
op.drop_table('proposed_tags')<|docstring|>Rem... |
858c7d58b0de3540a7fc08982be9112042af7fee6582c7a0987b31e1d7656bf2 | def get_native_encoding_type():
"Returns the encoding type that matches Python's native strings."
if (sys.maxunicode == 65535):
return 'UTF16'
else:
return 'UTF32' | Returns the encoding type that matches Python's native strings. | ml/language/python/mlapi.py | get_native_encoding_type | obulpathi/cloud | 4 | python | def get_native_encoding_type():
if (sys.maxunicode == 65535):
return 'UTF16'
else:
return 'UTF32' | def get_native_encoding_type():
if (sys.maxunicode == 65535):
return 'UTF16'
else:
return 'UTF32'<|docstring|>Returns the encoding type that matches Python's native strings.<|endoftext|> |
2a770e5e29395293c979158098606286e32eaa34bc097a2538bf67ea6876305f | def read(fileToInput, outputPath=None, badFrameStrikes=BAD_FRAME_STRIKES, assembleHold=False, blockHeightOverride=False, blockWidthOverride=False, encryptionKey=None, scryptN=SCRYPT_N_DEFAULT, scryptR=SCRYPT_R_DEFAULT, scryptP=SCRYPT_P_DEFAULT, loggingLevel='info', loggingScreenOutput=True, loggingSaveOutput=False):
... | This is the high level function that decodes BitGlitter encoded images and video back into the files/folders
contained within them. This along with write() are the two primary functions of this library. | bitglitter/read/read.py | read | Drunpy/BitGlitter | 0 | python | def read(fileToInput, outputPath=None, badFrameStrikes=BAD_FRAME_STRIKES, assembleHold=False, blockHeightOverride=False, blockWidthOverride=False, encryptionKey=None, scryptN=SCRYPT_N_DEFAULT, scryptR=SCRYPT_R_DEFAULT, scryptP=SCRYPT_P_DEFAULT, loggingLevel='info', loggingScreenOutput=True, loggingSaveOutput=False):
... | def read(fileToInput, outputPath=None, badFrameStrikes=BAD_FRAME_STRIKES, assembleHold=False, blockHeightOverride=False, blockWidthOverride=False, encryptionKey=None, scryptN=SCRYPT_N_DEFAULT, scryptR=SCRYPT_R_DEFAULT, scryptP=SCRYPT_P_DEFAULT, loggingLevel='info', loggingScreenOutput=True, loggingSaveOutput=False):
... |
34c990759e041df56361a47e05ffe5d22a586c19ccb9fe105ce3435fef9f17bd | def sim_busy_all_clusters(trace):
'\n Returns DataFrame of simultaneously busy cores irrespectively of cluster.\n '
data = {num_cores: trace.cpu.simultaneously_busy_time(num_cores, interval=None) for num_cores in xrange((len(ALL_CPUS) + 1))}
total_duration = (trace.duration if (not INTERVAL) else INTE... | Returns DataFrame of simultaneously busy cores irrespectively of cluster. | examples/power_perf_tool.py | sim_busy_all_clusters | steven-eckhoff/ftrace | 62 | python | def sim_busy_all_clusters(trace):
'\n \n '
data = {num_cores: trace.cpu.simultaneously_busy_time(num_cores, interval=None) for num_cores in xrange((len(ALL_CPUS) + 1))}
total_duration = (trace.duration if (not INTERVAL) else INTERVAL.duration)
return (Series(data=data.values(), index=data.keys(), ... | def sim_busy_all_clusters(trace):
'\n \n '
data = {num_cores: trace.cpu.simultaneously_busy_time(num_cores, interval=None) for num_cores in xrange((len(ALL_CPUS) + 1))}
total_duration = (trace.duration if (not INTERVAL) else INTERVAL.duration)
return (Series(data=data.values(), index=data.keys(), ... |
5cd808588ebada934b414ceba472d00a362ed2a6d362da80fe411469ded316bf | def sim_busy_by_clusters(trace, cpus):
'\n Returns Series of simultaneously busy cores per `cpus` in cluster.\n '
data = {num_cores: trace.cpu.simultaneously_busy_time(num_cores, cpus=list(cpus), interval=None) for num_cores in xrange((len(cpus) + 1))}
total_duration = (trace.duration if (not INTERVAL... | Returns Series of simultaneously busy cores per `cpus` in cluster. | examples/power_perf_tool.py | sim_busy_by_clusters | steven-eckhoff/ftrace | 62 | python | def sim_busy_by_clusters(trace, cpus):
'\n \n '
data = {num_cores: trace.cpu.simultaneously_busy_time(num_cores, cpus=list(cpus), interval=None) for num_cores in xrange((len(cpus) + 1))}
total_duration = (trace.duration if (not INTERVAL) else INTERVAL.duration)
return (Series(data=data.values(), i... | def sim_busy_by_clusters(trace, cpus):
'\n \n '
data = {num_cores: trace.cpu.simultaneously_busy_time(num_cores, cpus=list(cpus), interval=None) for num_cores in xrange((len(cpus) + 1))}
total_duration = (trace.duration if (not INTERVAL) else INTERVAL.duration)
return (Series(data=data.values(), i... |
3b3d71001f38286f83646ecd1f68d32536ca950becb1bd5673df50fe0a577263 | def __init__(self):
'\n transform: 4x4 matrix to transform from local system to world system\n vertices_L: point locations expressed in local coordinate system in centimeters. vertices matrix will have shape\n 4xN\n vertices_W: point locations expressed in world coordinate system... | transform: 4x4 matrix to transform from local system to world system
vertices_L: point locations expressed in local coordinate system in centimeters. vertices matrix will have shape
4xN
vertices_W: point locations expressed in world coordinate system | simulator/util/LaneMarking.py | __init__ | Iftimie/ChauffeurNet | 99 | python | def __init__(self):
'\n transform: 4x4 matrix to transform from local system to world system\n vertices_L: point locations expressed in local coordinate system in centimeters. vertices matrix will have shape\n 4xN\n vertices_W: point locations expressed in world coordinate system... | def __init__(self):
'\n transform: 4x4 matrix to transform from local system to world system\n vertices_L: point locations expressed in local coordinate system in centimeters. vertices matrix will have shape\n 4xN\n vertices_W: point locations expressed in world coordinate system... |
8840dc0c180fe0fef40ea7f077885d4f1448be44c8d6d9dc5f061a26ebce8bab | def render(self, image, C):
'\n :param image: image on which this actor will be renderd on\n :param C: camera matrix\n :return: image with this object renderd\n '
if (self.vertices_W.shape[1] > 1):
(x, y) = C.project(self.vertices_W)
pts = np.array([x, y]).T
... | :param image: image on which this actor will be renderd on
:param C: camera matrix
:return: image with this object renderd | simulator/util/LaneMarking.py | render | Iftimie/ChauffeurNet | 99 | python | def render(self, image, C):
'\n :param image: image on which this actor will be renderd on\n :param C: camera matrix\n :return: image with this object renderd\n '
if (self.vertices_W.shape[1] > 1):
(x, y) = C.project(self.vertices_W)
pts = np.array([x, y]).T
... | def render(self, image, C):
'\n :param image: image on which this actor will be renderd on\n :param C: camera matrix\n :return: image with this object renderd\n '
if (self.vertices_W.shape[1] > 1):
(x, y) = C.project(self.vertices_W)
pts = np.array([x, y]).T
... |
01e8b9692e65a936364e2ccafb1843b0ea6369637d5c5db81e42f9abd0eb9fa8 | @property
def n_words(self):
' The dictionary size. '
if (not hasattr(self, 'word2idx')):
raise Exception('Dictionary not built yet!')
return len(self.word2idx) | The dictionary size. | data_utils/dataset_msrvtt.py | n_words | ahjeongseo/MASN---Attend-What-You-Need-Motion-Appearance-Synergistic-Networks-for-Video-Question-Answering | 18 | python | @property
def n_words(self):
' '
if (not hasattr(self, 'word2idx')):
raise Exception('Dictionary not built yet!')
return len(self.word2idx) | @property
def n_words(self):
' '
if (not hasattr(self, 'word2idx')):
raise Exception('Dictionary not built yet!')
return len(self.word2idx)<|docstring|>The dictionary size.<|endoftext|> |
182dc42f9d670c653b3da584838b5fbf4e10117bc3815297cdac72dd9c8d57cf | def split_sentence_into_words(self, sentence, eos=True):
'\n Split the given sentence (str) and enumerate the words as strs.\n Each word is normalized, i.e. lower-cased, non-alphabet characters\n like period (.) or comma (,) are stripped.\n When tokenizing, I use ``data_util.clean_str``\... | Split the given sentence (str) and enumerate the words as strs.
Each word is normalized, i.e. lower-cased, non-alphabet characters
like period (.) or comma (,) are stripped.
When tokenizing, I use ``data_util.clean_str`` | data_utils/dataset_msrvtt.py | split_sentence_into_words | ahjeongseo/MASN---Attend-What-You-Need-Motion-Appearance-Synergistic-Networks-for-Video-Question-Answering | 18 | python | def split_sentence_into_words(self, sentence, eos=True):
'\n Split the given sentence (str) and enumerate the words as strs.\n Each word is normalized, i.e. lower-cased, non-alphabet characters\n like period (.) or comma (,) are stripped.\n When tokenizing, I use ``data_util.clean_str``\... | def split_sentence_into_words(self, sentence, eos=True):
'\n Split the given sentence (str) and enumerate the words as strs.\n Each word is normalized, i.e. lower-cased, non-alphabet characters\n like period (.) or comma (,) are stripped.\n When tokenizing, I use ``data_util.clean_str``\... |
91ba23e5685790be31435cab1d5dc44cc51e2ec15d0f2aaeaf57cdd72dec0c84 | def create_answerset(self, ans_df):
'Generate 1000 answer set from train_qa.json.\n Args:\n trainqa_path: path to train_qa.json.\n answerset_path: generate answer set of mc_qa\n '
ans_num = 4000
answer_freq = ans_df['answer'].value_counts()
answer_freq = list(answer_f... | Generate 1000 answer set from train_qa.json.
Args:
trainqa_path: path to train_qa.json.
answerset_path: generate answer set of mc_qa | data_utils/dataset_msrvtt.py | create_answerset | ahjeongseo/MASN---Attend-What-You-Need-Motion-Appearance-Synergistic-Networks-for-Video-Question-Answering | 18 | python | def create_answerset(self, ans_df):
'Generate 1000 answer set from train_qa.json.\n Args:\n trainqa_path: path to train_qa.json.\n answerset_path: generate answer set of mc_qa\n '
ans_num = 4000
answer_freq = ans_df['answer'].value_counts()
answer_freq = list(answer_f... | def create_answerset(self, ans_df):
'Generate 1000 answer set from train_qa.json.\n Args:\n trainqa_path: path to train_qa.json.\n answerset_path: generate answer set of mc_qa\n '
ans_num = 4000
answer_freq = ans_df['answer'].value_counts()
answer_freq = list(answer_f... |
bc8fff6663a19e8267a8c71d01b590974d5c48e030a08c4b840e253356e76c54 | def build_word_vocabulary(self, all_sen=None, ans_df=None, word_count_threshold=0):
"\n borrowed this implementation from @karpathy's neuraltalk.\n "
log.infov('Building word vocabulary (%s) ...', self.dataset_name)
if ((all_sen is None) or (ans_df is None)):
(all_sen, ans_df) = self.g... | borrowed this implementation from @karpathy's neuraltalk. | data_utils/dataset_msrvtt.py | build_word_vocabulary | ahjeongseo/MASN---Attend-What-You-Need-Motion-Appearance-Synergistic-Networks-for-Video-Question-Answering | 18 | python | def build_word_vocabulary(self, all_sen=None, ans_df=None, word_count_threshold=0):
"\n \n "
log.infov('Building word vocabulary (%s) ...', self.dataset_name)
if ((all_sen is None) or (ans_df is None)):
(all_sen, ans_df) = self.get_all_captions()
all_captions_source = all_sen
w... | def build_word_vocabulary(self, all_sen=None, ans_df=None, word_count_threshold=0):
"\n \n "
log.infov('Building word vocabulary (%s) ...', self.dataset_name)
if ((all_sen is None) or (ans_df is None)):
(all_sen, ans_df) = self.get_all_captions()
all_captions_source = all_sen
w... |
1ff68f36a84c05b1f76231b1d3c0fc1cf1ce29047a5a5b2403882511cdc5567b | def get_all_captions(self):
'\n Iterate caption strings associated in the vid/gifs.\n '
data_path = ('%s/train_qa.json' % self.csv_dir)
with open(data_path, 'r') as f:
data_df = pd.read_json(f)
all_sents = list(data_df['question'])
return (all_sents, data_df) | Iterate caption strings associated in the vid/gifs. | data_utils/dataset_msrvtt.py | get_all_captions | ahjeongseo/MASN---Attend-What-You-Need-Motion-Appearance-Synergistic-Networks-for-Video-Question-Answering | 18 | python | def get_all_captions(self):
'\n \n '
data_path = ('%s/train_qa.json' % self.csv_dir)
with open(data_path, 'r') as f:
data_df = pd.read_json(f)
all_sents = list(data_df['question'])
return (all_sents, data_df) | def get_all_captions(self):
'\n \n '
data_path = ('%s/train_qa.json' % self.csv_dir)
with open(data_path, 'r') as f:
data_df = pd.read_json(f)
all_sents = list(data_df['question'])
return (all_sents, data_df)<|docstring|>Iterate caption strings associated in the vid/gifs.<|endo... |
c5b40a2d11eaf69c7e17b9d401b3f7932dd076ff62234bbd0a99f996cf354eb4 | def convert_sentence_to_matrix(self, sentence, eos=True):
'\n Convert the given sentence into word indices and masks.\n WARNING: Unknown words (not in vocabulary) are revmoed.\n\n Args:\n sentence: A str for unnormalized sentence, containing T words\n\n Returns:\n s... | Convert the given sentence into word indices and masks.
WARNING: Unknown words (not in vocabulary) are revmoed.
Args:
sentence: A str for unnormalized sentence, containing T words
Returns:
sentence_word_indices : list of (at most) length T,
each being a word index | data_utils/dataset_msrvtt.py | convert_sentence_to_matrix | ahjeongseo/MASN---Attend-What-You-Need-Motion-Appearance-Synergistic-Networks-for-Video-Question-Answering | 18 | python | def convert_sentence_to_matrix(self, sentence, eos=True):
'\n Convert the given sentence into word indices and masks.\n WARNING: Unknown words (not in vocabulary) are revmoed.\n\n Args:\n sentence: A str for unnormalized sentence, containing T words\n\n Returns:\n s... | def convert_sentence_to_matrix(self, sentence, eos=True):
'\n Convert the given sentence into word indices and masks.\n WARNING: Unknown words (not in vocabulary) are revmoed.\n\n Args:\n sentence: A str for unnormalized sentence, containing T words\n\n Returns:\n s... |
c574caa08e1df0afa449b657f8a57a13e85b79bcdb625f02bd939112310a0d7f | def get_question(self, key):
'\n Return question index for given key.\n '
question = self.data_df.loc[(key, ['question'])].values
question = question[0]
question = self.split_sentence_into_words(question, eos=False)
q_refine = []
for w in question:
q_refine.append(w)
q_... | Return question index for given key. | data_utils/dataset_msrvtt.py | get_question | ahjeongseo/MASN---Attend-What-You-Need-Motion-Appearance-Synergistic-Networks-for-Video-Question-Answering | 18 | python | def get_question(self, key):
'\n \n '
question = self.data_df.loc[(key, ['question'])].values
question = question[0]
question = self.split_sentence_into_words(question, eos=False)
q_refine = []
for w in question:
q_refine.append(w)
q_refine = self.convert_sentence_to_ma... | def get_question(self, key):
'\n \n '
question = self.data_df.loc[(key, ['question'])].values
question = question[0]
question = self.split_sentence_into_words(question, eos=False)
q_refine = []
for w in question:
q_refine.append(w)
q_refine = self.convert_sentence_to_ma... |
09b0a57baf3dbe6456e6b414bf63628df9fabf2144023ab50755230a040d5230 | def tag_to_wn(self, tag):
' Convert between a Penn Treebank tag to a simplified Wordnet tag '
if tag.startswith('N'):
return 'n'
if tag.startswith('V'):
return 'v'
if tag.startswith('J'):
return 'a'
if tag.startswith('R'):
return 'r'
return None | Convert between a Penn Treebank tag to a simplified Wordnet tag | text_similarity/wordnet.py | tag_to_wn | sorindragan/ChatBot_Th | 0 | python | def tag_to_wn(self, tag):
' '
if tag.startswith('N'):
return 'n'
if tag.startswith('V'):
return 'v'
if tag.startswith('J'):
return 'a'
if tag.startswith('R'):
return 'r'
return None | def tag_to_wn(self, tag):
' '
if tag.startswith('N'):
return 'n'
if tag.startswith('V'):
return 'v'
if tag.startswith('J'):
return 'a'
if tag.startswith('R'):
return 'r'
return None<|docstring|>Convert between a Penn Treebank tag to a simplified Wordnet tag<|endo... |
260a56c475c86ba09b486087048c90f6dab7e8f393bedf444ea18efbbaadd140 | def tagged_to_synset(self, word, tag):
' Returns the first synset of the word given as parameter'
wn_tag = self.tag_to_wn(tag)
if (wn_tag is None):
return None
try:
return wn.synsets(word, wn_tag)[0]
except:
return None | Returns the first synset of the word given as parameter | text_similarity/wordnet.py | tagged_to_synset | sorindragan/ChatBot_Th | 0 | python | def tagged_to_synset(self, word, tag):
' '
wn_tag = self.tag_to_wn(tag)
if (wn_tag is None):
return None
try:
return wn.synsets(word, wn_tag)[0]
except:
return None | def tagged_to_synset(self, word, tag):
' '
wn_tag = self.tag_to_wn(tag)
if (wn_tag is None):
return None
try:
return wn.synsets(word, wn_tag)[0]
except:
return None<|docstring|>Returns the first synset of the word given as parameter<|endoftext|> |
2a418c1841f0d3052ed3bf7a14e24fcd8120d0e73fee86d41a57e7724c0c4446 | def sentence_similarity(self, sentence1, sentence2):
' compute the sentence similarity using Wordnet '
if (sentence1.lower() == sentence2.lower()):
return 2
sentence1 = pos_tag(word_tokenize(sentence1.lower()))
sentence2 = pos_tag(word_tokenize(sentence2.lower()))
synsets1 = reduce((lambda a... | compute the sentence similarity using Wordnet | text_similarity/wordnet.py | sentence_similarity | sorindragan/ChatBot_Th | 0 | python | def sentence_similarity(self, sentence1, sentence2):
' '
if (sentence1.lower() == sentence2.lower()):
return 2
sentence1 = pos_tag(word_tokenize(sentence1.lower()))
sentence2 = pos_tag(word_tokenize(sentence2.lower()))
synsets1 = reduce((lambda acc, w: (acc if (self.tagged_to_synset(*w) is ... | def sentence_similarity(self, sentence1, sentence2):
' '
if (sentence1.lower() == sentence2.lower()):
return 2
sentence1 = pos_tag(word_tokenize(sentence1.lower()))
sentence2 = pos_tag(word_tokenize(sentence2.lower()))
synsets1 = reduce((lambda acc, w: (acc if (self.tagged_to_synset(*w) is ... |
5afe441d6cd5584d2d5acd37681a6dcbc720d7bcfb84808fd2f7df60230a84ea | def GetMessages():
'Import and return the appropriate projects messages module.'
return apis.GetMessagesModule('projects', 'v1beta1') | Import and return the appropriate projects messages module. | lib/googlecloudsdk/api_lib/projects/util.py | GetMessages | eyalev/gcloud | 0 | python | def GetMessages():
return apis.GetMessagesModule('projects', 'v1beta1') | def GetMessages():
return apis.GetMessagesModule('projects', 'v1beta1')<|docstring|>Import and return the appropriate projects messages module.<|endoftext|> |
4ecc8cda2cf55a7fd1b64e72f6f15fa81e88340e46e0ff6fb0470bd85de28423 | def GetClient():
'Import and return the appropriate projects client.\n\n Returns:\n Cloud Resource Manager client for the appropriate release track.\n '
return apis.GetClientInstance('projects', 'v1beta1') | Import and return the appropriate projects client.
Returns:
Cloud Resource Manager client for the appropriate release track. | lib/googlecloudsdk/api_lib/projects/util.py | GetClient | eyalev/gcloud | 0 | python | def GetClient():
'Import and return the appropriate projects client.\n\n Returns:\n Cloud Resource Manager client for the appropriate release track.\n '
return apis.GetClientInstance('projects', 'v1beta1') | def GetClient():
'Import and return the appropriate projects client.\n\n Returns:\n Cloud Resource Manager client for the appropriate release track.\n '
return apis.GetClientInstance('projects', 'v1beta1')<|docstring|>Import and return the appropriate projects client.
Returns:
Cloud Resource Manager cli... |
b7b2a4efc6e650318c1722740d34e40b688ee94d8132ef7cf80e67cd73f03c16 | def IsActive(project):
"Returns true if the project's lifecycle state is 'active'.\n\n Args:\n project: A Project\n Returns:\n True if the Project's lifecycle state is 'active,' else False.\n "
lifecycle_enum = GetMessages().Project.LifecycleStateValueValuesEnum
return (project.lifecycleState == li... | Returns true if the project's lifecycle state is 'active'.
Args:
project: A Project
Returns:
True if the Project's lifecycle state is 'active,' else False. | lib/googlecloudsdk/api_lib/projects/util.py | IsActive | eyalev/gcloud | 0 | python | def IsActive(project):
"Returns true if the project's lifecycle state is 'active'.\n\n Args:\n project: A Project\n Returns:\n True if the Project's lifecycle state is 'active,' else False.\n "
lifecycle_enum = GetMessages().Project.LifecycleStateValueValuesEnum
return (project.lifecycleState == li... | def IsActive(project):
"Returns true if the project's lifecycle state is 'active'.\n\n Args:\n project: A Project\n Returns:\n True if the Project's lifecycle state is 'active,' else False.\n "
lifecycle_enum = GetMessages().Project.LifecycleStateValueValuesEnum
return (project.lifecycleState == li... |
0a8be7ecaf6622a484da1597e212338ae538c27a0c236fa12fe4100b25e29990 | def GetError(error):
"Returns a more specific Projects error from an HttpError.\n\n Args:\n error: HttpError resulting from unsuccessful call to API.\n\n Returns:\n Specific error based on error reason in HttpError.\n\n First line will parse project ID out of error url.\n Example:\n URL = .../v1beta1/pr... | Returns a more specific Projects error from an HttpError.
Args:
error: HttpError resulting from unsuccessful call to API.
Returns:
Specific error based on error reason in HttpError.
First line will parse project ID out of error url.
Example:
URL = .../v1beta1/projects/BAD_ID?prettyPrint=True&alt=json
project_i... | lib/googlecloudsdk/api_lib/projects/util.py | GetError | eyalev/gcloud | 0 | python | def GetError(error):
"Returns a more specific Projects error from an HttpError.\n\n Args:\n error: HttpError resulting from unsuccessful call to API.\n\n Returns:\n Specific error based on error reason in HttpError.\n\n First line will parse project ID out of error url.\n Example:\n URL = .../v1beta1/pr... | def GetError(error):
"Returns a more specific Projects error from an HttpError.\n\n Args:\n error: HttpError resulting from unsuccessful call to API.\n\n Returns:\n Specific error based on error reason in HttpError.\n\n First line will parse project ID out of error url.\n Example:\n URL = .../v1beta1/pr... |
efc24e5ee0c9ca76506bb2fbcee8b2e647c22828b0d7206d737f7236d8c17623 | def HandleKnownHttpErrors(func):
'Decorator that catches HttpError and raises corresponding error.'
@functools.wraps(func)
def CatchHTTPErrorRaiseProjectError(*args, **kwargs):
try:
return func(*args, **kwargs)
except exceptions.HttpError as error:
processed_error = ... | Decorator that catches HttpError and raises corresponding error. | lib/googlecloudsdk/api_lib/projects/util.py | HandleKnownHttpErrors | eyalev/gcloud | 0 | python | def HandleKnownHttpErrors(func):
@functools.wraps(func)
def CatchHTTPErrorRaiseProjectError(*args, **kwargs):
try:
return func(*args, **kwargs)
except exceptions.HttpError as error:
processed_error = GetError(error)
if (not processed_error):
... | def HandleKnownHttpErrors(func):
@functools.wraps(func)
def CatchHTTPErrorRaiseProjectError(*args, **kwargs):
try:
return func(*args, **kwargs)
except exceptions.HttpError as error:
processed_error = GetError(error)
if (not processed_error):
... |
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