body stringlengths 26 98.2k | body_hash int64 -9,222,864,604,528,158,000 9,221,803,474B | docstring stringlengths 1 16.8k | path stringlengths 5 230 | name stringlengths 1 96 | repository_name stringlengths 7 89 | lang stringclasses 1
value | body_without_docstring stringlengths 20 98.2k |
|---|---|---|---|---|---|---|---|
def registration_list_status_filter_sql():
'SQL to filter for whitelisted or null registration_list statuses.'
return sql.SQL("(status IS NULL OR status = 'whitelist')") | -2,651,173,938,659,543,600 | SQL to filter for whitelisted or null registration_list statuses. | src/dirbs/utils.py | registration_list_status_filter_sql | nealmadhu/DIRBS-Core | python | def registration_list_status_filter_sql():
return sql.SQL("(status IS NULL OR status = 'whitelist')") |
def compute_amnesty_flags(app_config, curr_date):
'Helper function to determine whether the date falls within amnesty eval or amnesty period.'
in_amnesty_eval_period = (True if (app_config.amnesty_config.amnesty_enabled and (curr_date <= app_config.amnesty_config.evaluation_period_end_date)) else False)
in_... | 4,268,033,186,415,836,700 | Helper function to determine whether the date falls within amnesty eval or amnesty period. | src/dirbs/utils.py | compute_amnesty_flags | nealmadhu/DIRBS-Core | python | def compute_amnesty_flags(app_config, curr_date):
in_amnesty_eval_period = (True if (app_config.amnesty_config.amnesty_enabled and (curr_date <= app_config.amnesty_config.evaluation_period_end_date)) else False)
in_amnesty_period = (True if (app_config.amnesty_config.amnesty_enabled and (curr_date > app_co... |
def table_exists_sql(any_schema=False):
'SQL to check for existence of a table. Note that for temp tables, any_schema should be set to True.'
if (not any_schema):
schema_filter_sql = sql.SQL('AND schemaname = current_schema()')
else:
schema_filter_sql = sql.SQL('')
return sql.SQL('SELECT... | -2,982,755,632,233,627,000 | SQL to check for existence of a table. Note that for temp tables, any_schema should be set to True. | src/dirbs/utils.py | table_exists_sql | nealmadhu/DIRBS-Core | python | def table_exists_sql(any_schema=False):
if (not any_schema):
schema_filter_sql = sql.SQL('AND schemaname = current_schema()')
else:
schema_filter_sql = sql.SQL()
return sql.SQL('SELECT EXISTS (SELECT 1\n FROM pg_tables\n ... |
def is_table_partitioned(conn, tbl_name):
'Function to determine whether a table is partitioned.'
with conn.cursor() as cursor:
cursor.execute('SELECT EXISTS (SELECT 1\n FROM pg_class\n JOIN pg_partitioned_table\n ... | 5,915,633,380,111,043,000 | Function to determine whether a table is partitioned. | src/dirbs/utils.py | is_table_partitioned | nealmadhu/DIRBS-Core | python | def is_table_partitioned(conn, tbl_name):
with conn.cursor() as cursor:
cursor.execute('SELECT EXISTS (SELECT 1\n FROM pg_class\n JOIN pg_partitioned_table\n ON pg_partitio... |
def __init__(self, msg):
'Constructor.'
super().__init__('DB schema check failure: {0}'.format(msg)) | -4,235,430,209,384,187,000 | Constructor. | src/dirbs/utils.py | __init__ | nealmadhu/DIRBS-Core | python | def __init__(self, msg):
super().__init__('DB schema check failure: {0}'.format(msg)) |
def __init__(self, msg):
'Constructor.'
super().__init__('DB role check failure: {0}'.format(msg)) | 4,190,923,084,369,278,000 | Constructor. | src/dirbs/utils.py | __init__ | nealmadhu/DIRBS-Core | python | def __init__(self, msg):
super().__init__('DB role check failure: {0}'.format(msg)) |
def default(self, obj):
'Overrides JSONEncoder.default.'
if isinstance(obj, datetime.date):
return obj.isoformat()
return JSONEncoder.default(self, obj) | 6,396,015,363,180,159,000 | Overrides JSONEncoder.default. | src/dirbs/utils.py | default | nealmadhu/DIRBS-Core | python | def default(self, obj):
if isinstance(obj, datetime.date):
return obj.isoformat()
return JSONEncoder.default(self, obj) |
def __init__(self, *args, **kwargs):
'Constructor.'
super().__init__(*args, **kwargs)
if (self.name is not None):
self.itersize = 100000 | 1,331,113,109,789,704,000 | Constructor. | src/dirbs/utils.py | __init__ | nealmadhu/DIRBS-Core | python | def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
if (self.name is not None):
self.itersize = 100000 |
def execute(self, query, params=None):
'Overrides NamedTupleCursor.execute.'
try:
return super(LoggingNamedTupleCursor, self).execute(query, params)
finally:
if (self.query is not None):
logging.getLogger('dirbs.sql').log(logging.DEBUG, str(self.query, encoding='utf-8')) | 8,880,579,946,259,191,000 | Overrides NamedTupleCursor.execute. | src/dirbs/utils.py | execute | nealmadhu/DIRBS-Core | python | def execute(self, query, params=None):
try:
return super(LoggingNamedTupleCursor, self).execute(query, params)
finally:
if (self.query is not None):
logging.getLogger('dirbs.sql').log(logging.DEBUG, str(self.query, encoding='utf-8')) |
def callproc(self, procname, params=None):
'Overrides NamedTupleCursor.callproc.'
try:
return super(LoggingNamedTupleCursor, self).callproc(procname, params)
finally:
if (self.query is not None):
logging.getLogger('dirbs.sql').log(logging.DEBUG, str(self.query, encoding='utf-8')) | 3,671,090,875,776,687,000 | Overrides NamedTupleCursor.callproc. | src/dirbs/utils.py | callproc | nealmadhu/DIRBS-Core | python | def callproc(self, procname, params=None):
try:
return super(LoggingNamedTupleCursor, self).callproc(procname, params)
finally:
if (self.query is not None):
logging.getLogger('dirbs.sql').log(logging.DEBUG, str(self.query, encoding='utf-8')) |
def __enter__(self):
'Python context manager support for use in with statement (on enter).'
self.start = time.time()
return self | -5,373,665,672,555,719,000 | Python context manager support for use in with statement (on enter). | src/dirbs/utils.py | __enter__ | nealmadhu/DIRBS-Core | python | def __enter__(self):
self.start = time.time()
return self |
def __exit__(self, *args):
'Python context manager support for use in with statement (on exit).'
self.duration = int(((time.time() - self.start) * 1000)) | 2,386,409,817,478,090,000 | Python context manager support for use in with statement (on exit). | src/dirbs/utils.py | __exit__ | nealmadhu/DIRBS-Core | python | def __exit__(self, *args):
self.duration = int(((time.time() - self.start) * 1000)) |
def test_get_backupdir_path(tmp_path):
'Returns backups Path named for default working directory.'
os.chdir(tmp_path)
Path(CONFIGFILE_NAME).write_text('config stuff')
backdir = '_backups'
datestr = '2020-01-03_1646'
workingdir = Path('agenda')
workingdir.mkdir()
os.chdir(workingdir)
... | 5,251,406,337,909,453,000 | Returns backups Path named for default working directory. | tests/returns/test_get_backupdir_path.py | test_get_backupdir_path | tombaker/mklists_old | python | def test_get_backupdir_path(tmp_path):
os.chdir(tmp_path)
Path(CONFIGFILE_NAME).write_text('config stuff')
backdir = '_backups'
datestr = '2020-01-03_1646'
workingdir = Path('agenda')
workingdir.mkdir()
os.chdir(workingdir)
actual = get_backupdir_path(backdir=backdir, now=datestr)
... |
def test_get_backupdir_path_given_datadir(tmp_path):
'Returns backups Path named for specified working directory.'
os.chdir(tmp_path)
Path(CONFIGFILE_NAME).write_text('config stuff')
workingdir = Path(tmp_path).joinpath('todolists/a')
workingdir.mkdir(parents=True, exist_ok=True)
workingdir_shor... | -8,056,138,804,211,980,000 | Returns backups Path named for specified working directory. | tests/returns/test_get_backupdir_path.py | test_get_backupdir_path_given_datadir | tombaker/mklists_old | python | def test_get_backupdir_path_given_datadir(tmp_path):
os.chdir(tmp_path)
Path(CONFIGFILE_NAME).write_text('config stuff')
workingdir = Path(tmp_path).joinpath('todolists/a')
workingdir.mkdir(parents=True, exist_ok=True)
workingdir_shortname_expected = 'todolists_a'
backdir = '_backups'
d... |
def test_get_backupdir_path_given_datadir_with_slash(tmp_path):
'Returns backups Path named for specified working directory ending with slash.'
os.chdir(tmp_path)
Path(CONFIGFILE_NAME).write_text('config stuff')
workingdir = Path(tmp_path).joinpath('todolists/a/')
workingdir.mkdir(parents=True, exis... | -3,118,882,342,985,879,600 | Returns backups Path named for specified working directory ending with slash. | tests/returns/test_get_backupdir_path.py | test_get_backupdir_path_given_datadir_with_slash | tombaker/mklists_old | python | def test_get_backupdir_path_given_datadir_with_slash(tmp_path):
os.chdir(tmp_path)
Path(CONFIGFILE_NAME).write_text('config stuff')
workingdir = Path(tmp_path).joinpath('todolists/a/')
workingdir.mkdir(parents=True, exist_ok=True)
workingdir_shortname_expected = 'todolists_a'
backdir = '_ba... |
def test_get_backupdir_path_raise_exception_if_rootdir_not_found(tmp_path):
'Raises exception if no rootdir is found (rootdir is None).'
os.chdir(tmp_path)
with pytest.raises(SystemExit):
get_backupdir_path() | 1,102,953,894,251,443,700 | Raises exception if no rootdir is found (rootdir is None). | tests/returns/test_get_backupdir_path.py | test_get_backupdir_path_raise_exception_if_rootdir_not_found | tombaker/mklists_old | python | def test_get_backupdir_path_raise_exception_if_rootdir_not_found(tmp_path):
os.chdir(tmp_path)
with pytest.raises(SystemExit):
get_backupdir_path() |
def __init__(self, host='127.0.0.1', port=9200):
'Create a Elasticsearch client.'
super().__init__()
self._error_container = {}
self.user = current_app.config.get('ELASTIC_USER', 'user')
self.password = current_app.config.get('ELASTIC_PASSWORD', 'pass')
self.ssl = current_app.config.get('ELASTIC... | 3,857,736,299,582,721,500 | Create a Elasticsearch client. | timesketch/lib/datastores/elastic.py | __init__ | stevengoossensB/timesketch | python | def __init__(self, host='127.0.0.1', port=9200):
super().__init__()
self._error_container = {}
self.user = current_app.config.get('ELASTIC_USER', 'user')
self.password = current_app.config.get('ELASTIC_PASSWORD', 'pass')
self.ssl = current_app.config.get('ELASTIC_SSL', False)
self.verify = ... |
@staticmethod
def _build_labels_query(sketch_id, labels):
'Build Elasticsearch query for Timesketch labels.\n\n Args:\n sketch_id: Integer of sketch primary key.\n labels: List of label names.\n\n Returns:\n Elasticsearch query as a dictionary.\n '
label_que... | -5,654,028,270,528,403,000 | Build Elasticsearch query for Timesketch labels.
Args:
sketch_id: Integer of sketch primary key.
labels: List of label names.
Returns:
Elasticsearch query as a dictionary. | timesketch/lib/datastores/elastic.py | _build_labels_query | stevengoossensB/timesketch | python | @staticmethod
def _build_labels_query(sketch_id, labels):
'Build Elasticsearch query for Timesketch labels.\n\n Args:\n sketch_id: Integer of sketch primary key.\n labels: List of label names.\n\n Returns:\n Elasticsearch query as a dictionary.\n '
label_que... |
@staticmethod
def _build_events_query(events):
'Build Elasticsearch query for one or more document ids.\n\n Args:\n events: List of Elasticsearch document IDs.\n\n Returns:\n Elasticsearch query as a dictionary.\n '
events_list = [event['event_id'] for event in events]... | 8,328,508,765,477,211,000 | Build Elasticsearch query for one or more document ids.
Args:
events: List of Elasticsearch document IDs.
Returns:
Elasticsearch query as a dictionary. | timesketch/lib/datastores/elastic.py | _build_events_query | stevengoossensB/timesketch | python | @staticmethod
def _build_events_query(events):
'Build Elasticsearch query for one or more document ids.\n\n Args:\n events: List of Elasticsearch document IDs.\n\n Returns:\n Elasticsearch query as a dictionary.\n '
events_list = [event['event_id'] for event in events]... |
@staticmethod
def _build_query_dsl(query_dsl, timeline_ids):
'Build Elastic Search DSL query by adding in timeline filtering.\n\n Args:\n query_dsl: A dict with the current query_dsl\n timeline_ids: Either a list of timeline IDs (int) or None.\n\n Returns:\n Elasticsea... | -3,096,211,081,514,344,400 | Build Elastic Search DSL query by adding in timeline filtering.
Args:
query_dsl: A dict with the current query_dsl
timeline_ids: Either a list of timeline IDs (int) or None.
Returns:
Elasticsearch query DSL as a dictionary. | timesketch/lib/datastores/elastic.py | _build_query_dsl | stevengoossensB/timesketch | python | @staticmethod
def _build_query_dsl(query_dsl, timeline_ids):
'Build Elastic Search DSL query by adding in timeline filtering.\n\n Args:\n query_dsl: A dict with the current query_dsl\n timeline_ids: Either a list of timeline IDs (int) or None.\n\n Returns:\n Elasticsea... |
@staticmethod
def _convert_to_time_range(interval):
'Convert an interval timestamp into start and end dates.\n\n Args:\n interval: Time frame representation\n\n Returns:\n Start timestamp in string format.\n End timestamp in string format.\n '
TS_FORMAT = '%... | 4,055,374,866,093,789,000 | Convert an interval timestamp into start and end dates.
Args:
interval: Time frame representation
Returns:
Start timestamp in string format.
End timestamp in string format. | timesketch/lib/datastores/elastic.py | _convert_to_time_range | stevengoossensB/timesketch | python | @staticmethod
def _convert_to_time_range(interval):
'Convert an interval timestamp into start and end dates.\n\n Args:\n interval: Time frame representation\n\n Returns:\n Start timestamp in string format.\n End timestamp in string format.\n '
TS_FORMAT = '%... |
def build_query(self, sketch_id, query_string, query_filter, query_dsl=None, aggregations=None, timeline_ids=None):
'Build Elasticsearch DSL query.\n\n Args:\n sketch_id: Integer of sketch primary key\n query_string: Query string\n query_filter: Dictionary containing filters ... | 8,189,367,095,946,872,000 | Build Elasticsearch DSL query.
Args:
sketch_id: Integer of sketch primary key
query_string: Query string
query_filter: Dictionary containing filters to apply
query_dsl: Dictionary containing Elasticsearch DSL query
aggregations: Dict of Elasticsearch aggregations
timeline_ids: Optional list of ... | timesketch/lib/datastores/elastic.py | build_query | stevengoossensB/timesketch | python | def build_query(self, sketch_id, query_string, query_filter, query_dsl=None, aggregations=None, timeline_ids=None):
'Build Elasticsearch DSL query.\n\n Args:\n sketch_id: Integer of sketch primary key\n query_string: Query string\n query_filter: Dictionary containing filters ... |
def search(self, sketch_id, query_string, query_filter, query_dsl, indices, count=False, aggregations=None, return_fields=None, enable_scroll=False, timeline_ids=None):
'Search ElasticSearch. This will take a query string from the UI\n together with a filter definition. Based on this it will execute the\n ... | -7,302,113,754,087,591,000 | Search ElasticSearch. This will take a query string from the UI
together with a filter definition. Based on this it will execute the
search request on ElasticSearch and get result back.
Args:
sketch_id: Integer of sketch primary key
query_string: Query string
query_filter: Dictionary containing filters to ... | timesketch/lib/datastores/elastic.py | search | stevengoossensB/timesketch | python | def search(self, sketch_id, query_string, query_filter, query_dsl, indices, count=False, aggregations=None, return_fields=None, enable_scroll=False, timeline_ids=None):
'Search ElasticSearch. This will take a query string from the UI\n together with a filter definition. Based on this it will execute the\n ... |
def search_stream(self, sketch_id=None, query_string=None, query_filter=None, query_dsl=None, indices=None, return_fields=None, enable_scroll=True, timeline_ids=None):
'Search ElasticSearch. This will take a query string from the UI\n together with a filter definition. Based on this it will execute the\n ... | -2,000,918,080,028,975,000 | Search ElasticSearch. This will take a query string from the UI
together with a filter definition. Based on this it will execute the
search request on ElasticSearch and get result back.
Args :
sketch_id: Integer of sketch primary key
query_string: Query string
query_filter: Dictionary containing filters to... | timesketch/lib/datastores/elastic.py | search_stream | stevengoossensB/timesketch | python | def search_stream(self, sketch_id=None, query_string=None, query_filter=None, query_dsl=None, indices=None, return_fields=None, enable_scroll=True, timeline_ids=None):
'Search ElasticSearch. This will take a query string from the UI\n together with a filter definition. Based on this it will execute the\n ... |
def get_filter_labels(self, sketch_id, indices):
'Aggregate labels for a sketch.\n\n Args:\n sketch_id: The Sketch ID\n indices: List of indices to aggregate on\n\n Returns:\n List with label names.\n '
max_labels = 10000
aggregation = {'aggs': {'nested'... | 714,276,077,707,961,900 | Aggregate labels for a sketch.
Args:
sketch_id: The Sketch ID
indices: List of indices to aggregate on
Returns:
List with label names. | timesketch/lib/datastores/elastic.py | get_filter_labels | stevengoossensB/timesketch | python | def get_filter_labels(self, sketch_id, indices):
'Aggregate labels for a sketch.\n\n Args:\n sketch_id: The Sketch ID\n indices: List of indices to aggregate on\n\n Returns:\n List with label names.\n '
max_labels = 10000
aggregation = {'aggs': {'nested'... |
def get_event(self, searchindex_id, event_id):
'Get one event from the datastore.\n\n Args:\n searchindex_id: String of ElasticSearch index id\n event_id: String of ElasticSearch event id\n\n Returns:\n Event document in JSON format\n '
METRICS['search_get_e... | 4,496,177,488,117,825,500 | Get one event from the datastore.
Args:
searchindex_id: String of ElasticSearch index id
event_id: String of ElasticSearch event id
Returns:
Event document in JSON format | timesketch/lib/datastores/elastic.py | get_event | stevengoossensB/timesketch | python | def get_event(self, searchindex_id, event_id):
'Get one event from the datastore.\n\n Args:\n searchindex_id: String of ElasticSearch index id\n event_id: String of ElasticSearch event id\n\n Returns:\n Event document in JSON format\n '
METRICS['search_get_e... |
def count(self, indices):
'Count number of documents.\n\n Args:\n indices: List of indices.\n\n Returns:\n Tuple containing number of documents and size on disk.\n '
if (not indices):
return (0, 0)
try:
es_stats = self.client.indices.stats(index=ind... | 6,281,411,345,004,881,000 | Count number of documents.
Args:
indices: List of indices.
Returns:
Tuple containing number of documents and size on disk. | timesketch/lib/datastores/elastic.py | count | stevengoossensB/timesketch | python | def count(self, indices):
'Count number of documents.\n\n Args:\n indices: List of indices.\n\n Returns:\n Tuple containing number of documents and size on disk.\n '
if (not indices):
return (0, 0)
try:
es_stats = self.client.indices.stats(index=ind... |
def set_label(self, searchindex_id, event_id, event_type, sketch_id, user_id, label, toggle=False, remove=False, single_update=True):
'Set label on event in the datastore.\n\n Args:\n searchindex_id: String of ElasticSearch index id\n event_id: String of ElasticSearch event id\n ... | -3,900,731,638,094,000,600 | Set label on event in the datastore.
Args:
searchindex_id: String of ElasticSearch index id
event_id: String of ElasticSearch event id
event_type: String of ElasticSearch document type
sketch_id: Integer of sketch primary key
user_id: Integer of user primary key
label: String with the name of t... | timesketch/lib/datastores/elastic.py | set_label | stevengoossensB/timesketch | python | def set_label(self, searchindex_id, event_id, event_type, sketch_id, user_id, label, toggle=False, remove=False, single_update=True):
'Set label on event in the datastore.\n\n Args:\n searchindex_id: String of ElasticSearch index id\n event_id: String of ElasticSearch event id\n ... |
def create_index(self, index_name=uuid4().hex, doc_type='generic_event', mappings=None):
'Create index with Timesketch settings.\n\n Args:\n index_name: Name of the index. Default is a generated UUID.\n doc_type: Name of the document type. Default id generic_event.\n mappings... | -8,882,026,856,317,529,000 | Create index with Timesketch settings.
Args:
index_name: Name of the index. Default is a generated UUID.
doc_type: Name of the document type. Default id generic_event.
mappings: Optional dict with the document mapping for Elastic.
Returns:
Index name in string format.
Document type in string forma... | timesketch/lib/datastores/elastic.py | create_index | stevengoossensB/timesketch | python | def create_index(self, index_name=uuid4().hex, doc_type='generic_event', mappings=None):
'Create index with Timesketch settings.\n\n Args:\n index_name: Name of the index. Default is a generated UUID.\n doc_type: Name of the document type. Default id generic_event.\n mappings... |
def delete_index(self, index_name):
'Delete Elasticsearch index.\n\n Args:\n index_name: Name of the index to delete.\n '
if self.client.indices.exists(index_name):
try:
self.client.indices.delete(index=index_name)
except ConnectionError as e:
rai... | 8,613,442,976,308,407,000 | Delete Elasticsearch index.
Args:
index_name: Name of the index to delete. | timesketch/lib/datastores/elastic.py | delete_index | stevengoossensB/timesketch | python | def delete_index(self, index_name):
'Delete Elasticsearch index.\n\n Args:\n index_name: Name of the index to delete.\n '
if self.client.indices.exists(index_name):
try:
self.client.indices.delete(index=index_name)
except ConnectionError as e:
rai... |
def import_event(self, index_name, event_type, event=None, event_id=None, flush_interval=DEFAULT_FLUSH_INTERVAL, timeline_id=None):
'Add event to Elasticsearch.\n\n Args:\n index_name: Name of the index in Elasticsearch\n event_type: Type of event (e.g. plaso_event)\n event: ... | 8,753,995,590,469,953,000 | Add event to Elasticsearch.
Args:
index_name: Name of the index in Elasticsearch
event_type: Type of event (e.g. plaso_event)
event: Event dictionary
event_id: Event Elasticsearch ID
flush_interval: Number of events to queue up before indexing
timeline_id: Optional ID number of a Timeline objec... | timesketch/lib/datastores/elastic.py | import_event | stevengoossensB/timesketch | python | def import_event(self, index_name, event_type, event=None, event_id=None, flush_interval=DEFAULT_FLUSH_INTERVAL, timeline_id=None):
'Add event to Elasticsearch.\n\n Args:\n index_name: Name of the index in Elasticsearch\n event_type: Type of event (e.g. plaso_event)\n event: ... |
def flush_queued_events(self, retry_count=0):
'Flush all queued events.\n\n Returns:\n dict: A dict object that contains the number of events\n that were sent to Elastic as well as information\n on whether there were any errors, and what the\n details o... | -8,373,796,784,467,723,000 | Flush all queued events.
Returns:
dict: A dict object that contains the number of events
that were sent to Elastic as well as information
on whether there were any errors, and what the
details of these errors if any.
retry_count: optional int indicating whether this is a retry. | timesketch/lib/datastores/elastic.py | flush_queued_events | stevengoossensB/timesketch | python | def flush_queued_events(self, retry_count=0):
'Flush all queued events.\n\n Returns:\n dict: A dict object that contains the number of events\n that were sent to Elastic as well as information\n on whether there were any errors, and what the\n details o... |
@property
def version(self):
'Get Elasticsearch version.\n\n Returns:\n Version number as a string.\n '
version_info = self.client.info().get('version')
return version_info.get('number') | 2,982,666,308,491,461,600 | Get Elasticsearch version.
Returns:
Version number as a string. | timesketch/lib/datastores/elastic.py | version | stevengoossensB/timesketch | python | @property
def version(self):
'Get Elasticsearch version.\n\n Returns:\n Version number as a string.\n '
version_info = self.client.info().get('version')
return version_info.get('number') |
def render(game, current):
' Displays the current room '
print(('You are in the ' + game['rooms'][current]['name']))
print(game['rooms'][current]['desc']) | 3,437,695,610,613,276,000 | Displays the current room | main.py | render | BraffordHunter/03-Text-Adventure-2 | python | def render(game, current):
' '
print(('You are in the ' + game['rooms'][current]['name']))
print(game['rooms'][current]['desc']) |
def getInput():
' Asks the user for input and returns a stripped, uppercase version of what they typed '
response = input('What would you like to do? ').strip().upper()
return response | -8,819,435,133,751,094,000 | Asks the user for input and returns a stripped, uppercase version of what they typed | main.py | getInput | BraffordHunter/03-Text-Adventure-2 | python | def getInput():
' '
response = input('What would you like to do? ').strip().upper()
return response |
def update(response, game, current):
' Process the input and update the state of the world '
for e in game['rooms'][current]['exits']:
if (response == e['verb']):
current = e['target']
return current | 4,104,156,395,958,741,000 | Process the input and update the state of the world | main.py | update | BraffordHunter/03-Text-Adventure-2 | python | def update(response, game, current):
' '
for e in game['rooms'][current]['exits']:
if (response == e['verb']):
current = e['target']
return current |
def _post_clients(self, client, user_ids, token_generator):
'\n Helper function that creates (and tests creating) a collection of Clients.\n '
headers = {'content-type': 'application/json', 'authorization': f'bearer {token_generator.get_token(client)}'}
client_ids = []
for (i, api_client) ... | -5,197,643,749,388,798,000 | Helper function that creates (and tests creating) a collection of Clients. | tests/api/test_all_apis.py | _post_clients | brighthive/authserver | python | def _post_clients(self, client, user_ids, token_generator):
'\n \n '
headers = {'content-type': 'application/json', 'authorization': f'bearer {token_generator.get_token(client)}'}
client_ids = []
for (i, api_client) in enumerate(CLIENTS):
api_client['user_id'] = user_ids[i]
... |
def multicrop_collate_fn(samples):
'Multi-crop collate function for VISSL integration.\n\n Run custom collate on a single key since VISSL transforms affect only DefaultDataKeys.INPUT\n '
result = vissl_collate_helper(samples)
inputs = [[] for _ in range(len(samples[0][DefaultDataKeys.INPUT]))]
for... | 4,826,671,954,298,192,000 | Multi-crop collate function for VISSL integration.
Run custom collate on a single key since VISSL transforms affect only DefaultDataKeys.INPUT | flash/image/embedding/vissl/transforms/utilities.py | multicrop_collate_fn | Darktex/lightning-flash | python | def multicrop_collate_fn(samples):
'Multi-crop collate function for VISSL integration.\n\n Run custom collate on a single key since VISSL transforms affect only DefaultDataKeys.INPUT\n '
result = vissl_collate_helper(samples)
inputs = [[] for _ in range(len(samples[0][DefaultDataKeys.INPUT]))]
for... |
def simclr_collate_fn(samples):
'Multi-crop collate function for VISSL integration.\n\n Run custom collate on a single key since VISSL transforms affect only DefaultDataKeys.INPUT\n '
result = vissl_collate_helper(samples)
inputs = []
num_views = len(samples[0][DefaultDataKeys.INPUT])
view_idx... | 1,590,668,760,028,334,600 | Multi-crop collate function for VISSL integration.
Run custom collate on a single key since VISSL transforms affect only DefaultDataKeys.INPUT | flash/image/embedding/vissl/transforms/utilities.py | simclr_collate_fn | Darktex/lightning-flash | python | def simclr_collate_fn(samples):
'Multi-crop collate function for VISSL integration.\n\n Run custom collate on a single key since VISSL transforms affect only DefaultDataKeys.INPUT\n '
result = vissl_collate_helper(samples)
inputs = []
num_views = len(samples[0][DefaultDataKeys.INPUT])
view_idx... |
def moco_collate_fn(samples):
'MOCO collate function for VISSL integration.\n\n Run custom collate on a single key since VISSL transforms affect only DefaultDataKeys.INPUT\n '
result = vissl_collate_helper(samples)
inputs = []
for batch_ele in samples:
inputs.append(torch.stack(batch_ele[D... | -102,752,008,453,979,340 | MOCO collate function for VISSL integration.
Run custom collate on a single key since VISSL transforms affect only DefaultDataKeys.INPUT | flash/image/embedding/vissl/transforms/utilities.py | moco_collate_fn | Darktex/lightning-flash | python | def moco_collate_fn(samples):
'MOCO collate function for VISSL integration.\n\n Run custom collate on a single key since VISSL transforms affect only DefaultDataKeys.INPUT\n '
result = vissl_collate_helper(samples)
inputs = []
for batch_ele in samples:
inputs.append(torch.stack(batch_ele[D... |
@abstractmethod
def __call__(self, location):
'Evaluate the time-continuous posterior for a given location\n\n Parameters\n ----------\n location : float\n Location, or time, at which to evaluate the posterior.\n\n Returns\n -------\n rv : `RandomVariable`\n ... | 2,588,504,303,512,299,000 | Evaluate the time-continuous posterior for a given location
Parameters
----------
location : float
Location, or time, at which to evaluate the posterior.
Returns
-------
rv : `RandomVariable` | src/probnum/filtsmooth/filtsmoothposterior.py | __call__ | admdev8/probnum | python | @abstractmethod
def __call__(self, location):
'Evaluate the time-continuous posterior for a given location\n\n Parameters\n ----------\n location : float\n Location, or time, at which to evaluate the posterior.\n\n Returns\n -------\n rv : `RandomVariable`\n ... |
@abstractmethod
def __len__(self):
'Length of the discrete-time solution\n\n Corresponds to the number of filtering/smoothing steps\n '
raise NotImplementedError | 7,496,453,161,260,714,000 | Length of the discrete-time solution
Corresponds to the number of filtering/smoothing steps | src/probnum/filtsmooth/filtsmoothposterior.py | __len__ | admdev8/probnum | python | @abstractmethod
def __len__(self):
'Length of the discrete-time solution\n\n Corresponds to the number of filtering/smoothing steps\n '
raise NotImplementedError |
@abstractmethod
def __getitem__(self, idx):
'Return the corresponding index/slice of the discrete-time solution'
raise NotImplementedError | -1,963,588,614,465,622,800 | Return the corresponding index/slice of the discrete-time solution | src/probnum/filtsmooth/filtsmoothposterior.py | __getitem__ | admdev8/probnum | python | @abstractmethod
def __getitem__(self, idx):
raise NotImplementedError |
def sample(self, locations=None, size=()):
'\n Draw samples from the filtering/smoothing posterior.\n\n If nothing is specified, a single sample is drawn (supported on self.locations).\n If locations are specified, the samples are drawn on those locations.\n If size is specified, more th... | 4,466,780,101,186,818,600 | Draw samples from the filtering/smoothing posterior.
If nothing is specified, a single sample is drawn (supported on self.locations).
If locations are specified, the samples are drawn on those locations.
If size is specified, more than a single sample is drawn.
Parameters
----------
locations : array_like, optional
... | src/probnum/filtsmooth/filtsmoothposterior.py | sample | admdev8/probnum | python | def sample(self, locations=None, size=()):
'\n Draw samples from the filtering/smoothing posterior.\n\n If nothing is specified, a single sample is drawn (supported on self.locations).\n If locations are specified, the samples are drawn on those locations.\n If size is specified, more th... |
def create_app(config_object='code_runner.settings'):
'Creates and returns flask app instance as well as register all the extensions and blueprints'
app = Flask(__name__)
register_environment()
app.config.from_object(config_object)
register_blueprints(app=app)
register_views(app=app)
registe... | 3,818,114,167,602,064,400 | Creates and returns flask app instance as well as register all the extensions and blueprints | code_runner/app.py | create_app | thephilomaths/code-runner-as-a-service | python | def create_app(config_object='code_runner.settings'):
app = Flask(__name__)
register_environment()
app.config.from_object(config_object)
register_blueprints(app=app)
register_views(app=app)
register_extensions(app=app)
configure_logger(app=app)
return app |
def register_blueprints(app):
'Registers the blueprints'
app.register_blueprint(code.views.blueprint) | -6,392,716,567,037,836,000 | Registers the blueprints | code_runner/app.py | register_blueprints | thephilomaths/code-runner-as-a-service | python | def register_blueprints(app):
app.register_blueprint(code.views.blueprint) |
def register_views(app):
'Registers the pluggable views'
run_view = code.views.RunCode.as_view('run')
run_async_view = code.views.RunCodeAsync.as_view('run-async')
app.add_url_rule('/run', view_func=run_view, methods=['POST'])
app.add_url_rule('/run-async', view_func=run_async_view, methods=['POST']... | 2,637,482,684,825,603,000 | Registers the pluggable views | code_runner/app.py | register_views | thephilomaths/code-runner-as-a-service | python | def register_views(app):
run_view = code.views.RunCode.as_view('run')
run_async_view = code.views.RunCodeAsync.as_view('run-async')
app.add_url_rule('/run', view_func=run_view, methods=['POST'])
app.add_url_rule('/run-async', view_func=run_async_view, methods=['POST'])
app.add_url_rule('/get-re... |
def register_extensions(app):
'Register Flask extensions'
with app.app_context():
db.init_app(app=app)
db.create_all()
limiter.init_app(app=app) | 1,989,962,585,448,259,600 | Register Flask extensions | code_runner/app.py | register_extensions | thephilomaths/code-runner-as-a-service | python | def register_extensions(app):
with app.app_context():
db.init_app(app=app)
db.create_all()
limiter.init_app(app=app) |
def register_environment():
'Register environment'
dotenv_path = (Path('./') / '.env.development.local')
load_dotenv(dotenv_path=dotenv_path) | 4,229,727,122,486,207,000 | Register environment | code_runner/app.py | register_environment | thephilomaths/code-runner-as-a-service | python | def register_environment():
dotenv_path = (Path('./') / '.env.development.local')
load_dotenv(dotenv_path=dotenv_path) |
def configure_logger(app):
'Configure loggers.'
handler = logging.StreamHandler(sys.stdout)
if (not app.logger.handlers):
app.logger.addHandler(handler) | 3,422,815,523,629,059,000 | Configure loggers. | code_runner/app.py | configure_logger | thephilomaths/code-runner-as-a-service | python | def configure_logger(app):
handler = logging.StreamHandler(sys.stdout)
if (not app.logger.handlers):
app.logger.addHandler(handler) |
def computeLPPTransitMetric(data, mapInfo):
'\n This function takes a data class with light curve info\n and the mapInfo with information about the mapping to use.\n It then returns a lpp metric value.\n '
(binFlux, binPhase) = foldBinLightCurve(data, mapInfo.ntrfr, mapInfo.npts)
(rawTLpp, trans... | 7,073,059,742,202,364,000 | This function takes a data class with light curve info
and the mapInfo with information about the mapping to use.
It then returns a lpp metric value. | lpp/newlpp/lppTransform.py | computeLPPTransitMetric | barentsen/dave | python | def computeLPPTransitMetric(data, mapInfo):
'\n This function takes a data class with light curve info\n and the mapInfo with information about the mapping to use.\n It then returns a lpp metric value.\n '
(binFlux, binPhase) = foldBinLightCurve(data, mapInfo.ntrfr, mapInfo.npts)
(rawTLpp, trans... |
def runningMedian(t, y, dt, runt):
'\n Take a running median of size dt\n Return values at times given in runt\n '
newy = np.zeros(len(y))
newt = np.zeros(len(y))
srt = np.argsort(t)
newt = t[srt]
newy = y[srt]
runy = []
for i in range(len(runt)):
tmp = []
for j ... | -5,922,158,501,723,082,000 | Take a running median of size dt
Return values at times given in runt | lpp/newlpp/lppTransform.py | runningMedian | barentsen/dave | python | def runningMedian(t, y, dt, runt):
'\n Take a running median of size dt\n Return values at times given in runt\n '
newy = np.zeros(len(y))
newt = np.zeros(len(y))
srt = np.argsort(t)
newt = t[srt]
newy = y[srt]
runy = []
for i in range(len(runt)):
tmp = []
for j ... |
def foldBinLightCurve(data, ntrfr, npts):
'\n Fold and bin light curve for input to LPP metric calculation\n \n data contains time, tzero, dur, priod,mes and flux (centered around zero)\n \n ntrfr -- number of transit fraction for binning around transit ~1.5\n npts -- number of points in the final... | 281,194,665,893,503,000 | Fold and bin light curve for input to LPP metric calculation
data contains time, tzero, dur, priod,mes and flux (centered around zero)
ntrfr -- number of transit fraction for binning around transit ~1.5
npts -- number of points in the final binning. | lpp/newlpp/lppTransform.py | foldBinLightCurve | barentsen/dave | python | def foldBinLightCurve(data, ntrfr, npts):
'\n Fold and bin light curve for input to LPP metric calculation\n \n data contains time, tzero, dur, priod,mes and flux (centered around zero)\n \n ntrfr -- number of transit fraction for binning around transit ~1.5\n npts -- number of points in the final... |
def computeRawLPPTransitMetric(binFlux, mapInfo):
'\n Perform the matrix transformation with LPP\n Do the knn test to get a raw LPP transit metric number.\n '
Yorig = mapInfo.YmapMapped
lpp = LocalityPreservingProjection(n_components=mapInfo.n_dim)
lpp.projection_ = mapInfo.YmapM
normBinFlu... | 8,917,899,535,312,045,000 | Perform the matrix transformation with LPP
Do the knn test to get a raw LPP transit metric number. | lpp/newlpp/lppTransform.py | computeRawLPPTransitMetric | barentsen/dave | python | def computeRawLPPTransitMetric(binFlux, mapInfo):
'\n Perform the matrix transformation with LPP\n Do the knn test to get a raw LPP transit metric number.\n '
Yorig = mapInfo.YmapMapped
lpp = LocalityPreservingProjection(n_components=mapInfo.n_dim)
lpp.projection_ = mapInfo.YmapM
normBinFlu... |
def knnDistance_fromKnown(knownTransits, new, knn):
'\n For a group of known transits and a new one.\n Use knn to determine how close the new one is to the known transits\n using knn minkowski p = 3 ()\n Using scipy signal to do this.\n '
nbrs = NearestNeighbors(n_neighbors=int(knn), algorithm='k... | -6,694,463,733,298,679,000 | For a group of known transits and a new one.
Use knn to determine how close the new one is to the known transits
using knn minkowski p = 3 ()
Using scipy signal to do this. | lpp/newlpp/lppTransform.py | knnDistance_fromKnown | barentsen/dave | python | def knnDistance_fromKnown(knownTransits, new, knn):
'\n For a group of known transits and a new one.\n Use knn to determine how close the new one is to the known transits\n using knn minkowski p = 3 ()\n Using scipy signal to do this.\n '
nbrs = NearestNeighbors(n_neighbors=int(knn), algorithm='k... |
def periodNormalLPPTransitMetric(rawTLpp, newPerMes, mapInfo):
'\n Normalize the rawTransitMetric value by those with the closest period.\n This part removes the period dependence of the metric at short periods.\n Plus it makes a value near one be the threshold between good and bad.\n \n newPerMes is... | -4,829,747,934,316,969,000 | Normalize the rawTransitMetric value by those with the closest period.
This part removes the period dependence of the metric at short periods.
Plus it makes a value near one be the threshold between good and bad.
newPerMes is the np.array([period, mes]) of the new sample | lpp/newlpp/lppTransform.py | periodNormalLPPTransitMetric | barentsen/dave | python | def periodNormalLPPTransitMetric(rawTLpp, newPerMes, mapInfo):
'\n Normalize the rawTransitMetric value by those with the closest period.\n This part removes the period dependence of the metric at short periods.\n Plus it makes a value near one be the threshold between good and bad.\n \n newPerMes is... |
def lpp_onetransit(tcedata, mapInfo, ntransit):
'\n Chop down the full time series to one orbital period.\n Then gather the lpp value for that one transit.\n '
startTime = (tcedata.time[0] + (ntransit * tcedata.period))
endTime = ((tcedata.time[0] + ((ntransit + 1) * tcedata.period)) + (3 / 24.0))
... | -5,569,252,872,100,213,000 | Chop down the full time series to one orbital period.
Then gather the lpp value for that one transit. | lpp/newlpp/lppTransform.py | lpp_onetransit | barentsen/dave | python | def lpp_onetransit(tcedata, mapInfo, ntransit):
'\n Chop down the full time series to one orbital period.\n Then gather the lpp value for that one transit.\n '
startTime = (tcedata.time[0] + (ntransit * tcedata.period))
endTime = ((tcedata.time[0] + ((ntransit + 1) * tcedata.period)) + (3 / 24.0))
... |
def lpp_averageIndivTransit(tcedata, mapInfo):
'\n \n Create the loop over individual transits and return \n array normalized lpp values, mean and std.\n Input TCE object and mapInfo object.\n \n It is unclear that this individual transit approach\n separates out several new false positives.\n ... | 4,539,365,381,711,080,400 | Create the loop over individual transits and return
array normalized lpp values, mean and std.
Input TCE object and mapInfo object.
It is unclear that this individual transit approach
separates out several new false positives.
It probably would require retuning for low SNR signals. | lpp/newlpp/lppTransform.py | lpp_averageIndivTransit | barentsen/dave | python | def lpp_averageIndivTransit(tcedata, mapInfo):
'\n \n Create the loop over individual transits and return \n array normalized lpp values, mean and std.\n Input TCE object and mapInfo object.\n \n It is unclear that this individual transit approach\n separates out several new false positives.\n ... |
def get_pkg_details(in_file):
'For the new pkg format, we return the size and hashes of the inner pkg part of the file'
for ext in SUPPORTED_EXTENSIONS:
if in_file.endswith(ext):
details = SUPPORTED_EXTENSIONS[ext].get_pkg_details(in_file)
break
else:
raise ValueError... | -1,385,206,209,404,265,200 | For the new pkg format, we return the size and hashes of the inner pkg part of the file | src/conda_package_handling/api.py | get_pkg_details | katietz/conda-package-handling | python | def get_pkg_details(in_file):
for ext in SUPPORTED_EXTENSIONS:
if in_file.endswith(ext):
details = SUPPORTED_EXTENSIONS[ext].get_pkg_details(in_file)
break
else:
raise ValueError("Don't know what to do with file {}".format(in_file))
return details |
def __init__(self, cfg, vis_highest_scoring=True, output_dir='./vis'):
'\n Args:\n cfg (CfgNode):\n vis_highest_scoring (bool): If set to True visualizes only\n the highest scoring prediction\n '
self.metadata = MetadataCatalog.get(cfg.D... | 281,400,471,534,412,000 | Args:
cfg (CfgNode):
vis_highest_scoring (bool): If set to True visualizes only
the highest scoring prediction | demo/demo.py | __init__ | ishanic/MeshRCNN-keypoints | python | def __init__(self, cfg, vis_highest_scoring=True, output_dir='./vis'):
'\n Args:\n cfg (CfgNode):\n vis_highest_scoring (bool): If set to True visualizes only\n the highest scoring prediction\n '
self.metadata = MetadataCatalog.get(cfg.D... |
def run_on_image(self, image, focal_length=10.0):
'\n Args:\n image (np.ndarray): an image of shape (H, W, C) (in BGR order).\n This is the format used by OpenCV.\n focal_length (float): the focal_length of the image\n\n Returns:\n predictions (dict): th... | 7,762,340,422,223,548,000 | Args:
image (np.ndarray): an image of shape (H, W, C) (in BGR order).
This is the format used by OpenCV.
focal_length (float): the focal_length of the image
Returns:
predictions (dict): the output of the model. | demo/demo.py | run_on_image | ishanic/MeshRCNN-keypoints | python | def run_on_image(self, image, focal_length=10.0):
'\n Args:\n image (np.ndarray): an image of shape (H, W, C) (in BGR order).\n This is the format used by OpenCV.\n focal_length (float): the focal_length of the image\n\n Returns:\n predictions (dict): th... |
def __init__(self, host, port=9000, schema=hdfs_schema):
' 目前只需要host和port '
self.host = host
self.port = port
self.schema = schema
self._path = '/'
self._status = None | 4,427,913,885,585,468,000 | 目前只需要host和port | hdfshell/cluster.py | __init__ | alingse/hdfshell | python | def __init__(self, host, port=9000, schema=hdfs_schema):
' '
self.host = host
self.port = port
self.schema = schema
self._path = '/'
self._status = None |
@property
def uri_head(self):
' 返回 uri 的 head'
head = (self.schema + '{}:{}'.format(self.host, self.port))
return head | -5,477,964,233,584,933,000 | 返回 uri 的 head | hdfshell/cluster.py | uri_head | alingse/hdfshell | python | @property
def uri_head(self):
' '
head = (self.schema + '{}:{}'.format(self.host, self.port))
return head |
@property
def uri(self):
' 返回当前路径'
_uri = (self.schema + '{}:{}{}'.format(self.host, self.port, self._path))
return _uri | -1,669,485,123,415,073,300 | 返回当前路径 | hdfshell/cluster.py | uri | alingse/hdfshell | python | @property
def uri(self):
' '
_uri = (self.schema + '{}:{}{}'.format(self.host, self.port, self._path))
return _uri |
@click.command(epilog='\x08\nExamples:\n bdt gitlab update-bob -vv\n bdt gitlab update-bob -vv --stable\n')
@click.option('--stable/--beta', help='To use the stable versions in the list and pin packages.')
@verbosity_option()
@bdt.raise_on_error
def update_bob(stable):
'Updates the Bob meta package with new p... | -5,205,953,134,817,273,000 | Updates the Bob meta package with new packages. | bob/devtools/scripts/update_bob.py | update_bob | bioidiap/bob.devtools | python | @click.command(epilog='\x08\nExamples:\n bdt gitlab update-bob -vv\n bdt gitlab update-bob -vv --stable\n')
@click.option('--stable/--beta', help='To use the stable versions in the list and pin packages.')
@verbosity_option()
@bdt.raise_on_error
def update_bob(stable):
import tempfile
from ..ci impor... |
def _testUploadFileToItem(self, item, name, user, contents):
'\n Uploads a non-empty file to the server.\n '
resp = self.request(path='/file', method='POST', user=user, params={'parentType': 'item', 'parentId': item['_id'], 'name': name, 'size': len(contents)})
self.assertStatusOk(resp)
up... | 7,241,848,628,900,935,000 | Uploads a non-empty file to the server. | tests/cases/item_test.py | _testUploadFileToItem | RemiCecchinato/girder | python | def _testUploadFileToItem(self, item, name, user, contents):
'\n \n '
resp = self.request(path='/file', method='POST', user=user, params={'parentType': 'item', 'parentId': item['_id'], 'name': name, 'size': len(contents)})
self.assertStatusOk(resp)
uploadId = resp.json['_id']
resp = se... |
def _testDownloadSingleFileItem(self, item, user, contents):
'\n Downloads a single-file item from the server\n :param item: The item to download.\n :type item: dict\n :param contents: The expected contents.\n :type contents: str\n '
resp = self.request(path=('/item/%s/... | -7,198,199,060,866,721,000 | Downloads a single-file item from the server
:param item: The item to download.
:type item: dict
:param contents: The expected contents.
:type contents: str | tests/cases/item_test.py | _testDownloadSingleFileItem | RemiCecchinato/girder | python | def _testDownloadSingleFileItem(self, item, user, contents):
'\n Downloads a single-file item from the server\n :param item: The item to download.\n :type item: dict\n :param contents: The expected contents.\n :type contents: str\n '
resp = self.request(path=('/item/%s/... |
def testItemCrud(self):
'\n Test Create, Read, Update, and Delete of items.\n '
self.ensureRequiredParams(path='/item', method='POST', required=('folderId',), user=self.users[1])
params = {'name': ' ', 'description': ' a description ', 'folderId': self.publicFolder['_id']}
resp = self.requ... | 648,732,367,353,630,800 | Test Create, Read, Update, and Delete of items. | tests/cases/item_test.py | testItemCrud | RemiCecchinato/girder | python | def testItemCrud(self):
'\n \n '
self.ensureRequiredParams(path='/item', method='POST', required=('folderId',), user=self.users[1])
params = {'name': ' ', 'description': ' a description ', 'folderId': self.publicFolder['_id']}
resp = self.request(path='/item', method='POST', params=params,... |
def testItemMetadataCrud(self):
'\n Test CRUD of metadata.\n '
params = {'name': 'item with metadata', 'description': ' a description ', 'folderId': self.privateFolder['_id']}
resp = self.request(path='/item', method='POST', params=params, user=self.users[0])
self.assertStatusOk(resp)
... | -7,451,827,177,975,639,000 | Test CRUD of metadata. | tests/cases/item_test.py | testItemMetadataCrud | RemiCecchinato/girder | python | def testItemMetadataCrud(self):
'\n \n '
params = {'name': 'item with metadata', 'description': ' a description ', 'folderId': self.privateFolder['_id']}
resp = self.request(path='/item', method='POST', params=params, user=self.users[0])
self.assertStatusOk(resp)
item = resp.json
r... |
def testItemFiltering(self):
'\n Test filtering private metadata from items.\n '
params = {'name': 'item with metadata', 'description': ' a description ', 'folderId': self.privateFolder['_id']}
resp = self.request(path='/item', method='POST', params=params, user=self.users[0])
self.assertS... | -5,949,870,336,204,574,000 | Test filtering private metadata from items. | tests/cases/item_test.py | testItemFiltering | RemiCecchinato/girder | python | def testItemFiltering(self):
'\n \n '
params = {'name': 'item with metadata', 'description': ' a description ', 'folderId': self.privateFolder['_id']}
resp = self.request(path='/item', method='POST', params=params, user=self.users[0])
self.assertStatusOk(resp)
item = Item().load(resp.j... |
def testLazyFieldComputation(self):
'\n Demonstrate that an item that is saved in the database without\n derived fields (like lowerName or baseParentId) get those values\n computed at load() time.\n '
item = Item().createItem('My Item Name', creator=self.users[0], folder=self.publicF... | 5,206,396,976,131,922,000 | Demonstrate that an item that is saved in the database without
derived fields (like lowerName or baseParentId) get those values
computed at load() time. | tests/cases/item_test.py | testLazyFieldComputation | RemiCecchinato/girder | python | def testLazyFieldComputation(self):
'\n Demonstrate that an item that is saved in the database without\n derived fields (like lowerName or baseParentId) get those values\n computed at load() time.\n '
item = Item().createItem('My Item Name', creator=self.users[0], folder=self.publicF... |
def testParentsToRoot(self):
'\n Demonstrate that forcing parentsToRoot will cause it to skip the\n filtering process.\n '
item = Item().createItem('My Item Name', creator=self.users[0], folder=self.publicFolder)
parents = Item().parentsToRoot(item, force=True)
for parent in parents... | 4,241,321,206,045,767,700 | Demonstrate that forcing parentsToRoot will cause it to skip the
filtering process. | tests/cases/item_test.py | testParentsToRoot | RemiCecchinato/girder | python | def testParentsToRoot(self):
'\n Demonstrate that forcing parentsToRoot will cause it to skip the\n filtering process.\n '
item = Item().createItem('My Item Name', creator=self.users[0], folder=self.publicFolder)
parents = Item().parentsToRoot(item, force=True)
for parent in parents... |
def testCookieAuth(self):
"\n We make sure a cookie is sufficient for authentication for the item\n download endpoint. Also, while we're at it, we make sure it's not\n sufficient for other endpoints.\n "
item = self._createItem(self.privateFolder['_id'], 'cookie_auth_download', '', s... | -6,878,945,270,122,662,000 | We make sure a cookie is sufficient for authentication for the item
download endpoint. Also, while we're at it, we make sure it's not
sufficient for other endpoints. | tests/cases/item_test.py | testCookieAuth | RemiCecchinato/girder | python | def testCookieAuth(self):
"\n We make sure a cookie is sufficient for authentication for the item\n download endpoint. Also, while we're at it, we make sure it's not\n sufficient for other endpoints.\n "
item = self._createItem(self.privateFolder['_id'], 'cookie_auth_download', , sel... |
def subtract_signal(t, signal, fit_params=3):
'\n\n Returns the subtracted signal\n\n '
coef = np.polynomial.polynomial.polyfit(t, signal, (fit_params - 1))
delta_signal = np.einsum('n,nj->j', coef, np.asarray([np.power(t, n) for n in range(fit_params)]))
ht = (signal - delta_signal)
return ht | -3,028,313,951,607,885,000 | Returns the subtracted signal | src/signals.py | subtract_signal | delos/dm-pta-mc | python | def subtract_signal(t, signal, fit_params=3):
'\n\n \n\n '
coef = np.polynomial.polynomial.polyfit(t, signal, (fit_params - 1))
delta_signal = np.einsum('n,nj->j', coef, np.asarray([np.power(t, n) for n in range(fit_params)]))
ht = (signal - delta_signal)
return ht |
def dphi_dop_chunked(t, profile, r0_vec, v_vec, d_hat, use_form=False, use_chunk=False, chunk_size=10000, verbose=False, form_fun=None, interp_table=None, time_end=np.inf):
'\n\n Compute dphi but in chunks over the subhalos, use when Nt x N is too large an array to\n store in memory\n\n '
num_objects =... | -6,609,646,367,769,294,000 | Compute dphi but in chunks over the subhalos, use when Nt x N is too large an array to
store in memory | src/signals.py | dphi_dop_chunked | delos/dm-pta-mc | python | def dphi_dop_chunked(t, profile, r0_vec, v_vec, d_hat, use_form=False, use_chunk=False, chunk_size=10000, verbose=False, form_fun=None, interp_table=None, time_end=np.inf):
'\n\n Compute dphi but in chunks over the subhalos, use when Nt x N is too large an array to\n store in memory\n\n '
num_objects =... |
def dphi_dop_chunked_vec(t, profile, r0_vec, v_vec, use_form=False, use_chunk=False, chunk_size=10000, verbose=False, form_fun=None, interp_table=None, time_end=np.inf):
'\n\n Compute dphi but in chunks over the subhalos, use when Nt x N is too large an array to\n store in memory\n\n '
num_objects = le... | 2,014,472,338,040,164,900 | Compute dphi but in chunks over the subhalos, use when Nt x N is too large an array to
store in memory | src/signals.py | dphi_dop_chunked_vec | delos/dm-pta-mc | python | def dphi_dop_chunked_vec(t, profile, r0_vec, v_vec, use_form=False, use_chunk=False, chunk_size=10000, verbose=False, form_fun=None, interp_table=None, time_end=np.inf):
'\n\n Compute dphi but in chunks over the subhalos, use when Nt x N is too large an array to\n store in memory\n\n '
num_objects = le... |
def dphi_dop_vec(t, profile, r0_vec, v_vec, use_form=False, form_fun=None, interp_table=None):
'\n\n Returns the vector phase shift due to the Doppler delay for subhalos of mass, mass.\n Dot with d_hat to get dphi_I\n\n TODO: add use_closest option\n\n '
v_mag = np.linalg.norm(v_vec, axis=1)
r0_... | -1,128,212,848,609,852,800 | Returns the vector phase shift due to the Doppler delay for subhalos of mass, mass.
Dot with d_hat to get dphi_I
TODO: add use_closest option | src/signals.py | dphi_dop_vec | delos/dm-pta-mc | python | def dphi_dop_vec(t, profile, r0_vec, v_vec, use_form=False, form_fun=None, interp_table=None):
'\n\n Returns the vector phase shift due to the Doppler delay for subhalos of mass, mass.\n Dot with d_hat to get dphi_I\n\n TODO: add use_closest option\n\n '
v_mag = np.linalg.norm(v_vec, axis=1)
r0_... |
def dphi_dop(t, profile, r0_vec, v_vec, d_hat, use_form=False, form_fun=None, interp_table=None):
'\n\n Returns the phase shift due to the Doppler delay for subhalos of mass, mass\n\n TODO: add use_closest option\n\n '
v_mag = np.linalg.norm(v_vec, axis=1)
r0_v = np.einsum('ij, ij -> i', r0_vec, v_... | 1,391,388,544,967,027,700 | Returns the phase shift due to the Doppler delay for subhalos of mass, mass
TODO: add use_closest option | src/signals.py | dphi_dop | delos/dm-pta-mc | python | def dphi_dop(t, profile, r0_vec, v_vec, d_hat, use_form=False, form_fun=None, interp_table=None):
'\n\n Returns the phase shift due to the Doppler delay for subhalos of mass, mass\n\n TODO: add use_closest option\n\n '
v_mag = np.linalg.norm(v_vec, axis=1)
r0_v = np.einsum('ij, ij -> i', r0_vec, v_... |
@property
def inserted(self):
'Provide the "inserted" namespace for an ON DUPLICATE KEY UPDATE statement\n\n MySQL\'s ON DUPLICATE KEY UPDATE clause allows reference to the row\n that would be inserted, via a special function called ``VALUES()``.\n This attribute provides all columns in this ro... | -8,385,649,932,417,646,000 | Provide the "inserted" namespace for an ON DUPLICATE KEY UPDATE statement
MySQL's ON DUPLICATE KEY UPDATE clause allows reference to the row
that would be inserted, via a special function called ``VALUES()``.
This attribute provides all columns in this row to be referenceable
such that they will render within a ``VALU... | virtual/lib/python3.8/site-packages/sqlalchemy/dialects/mysql/dml.py | inserted | Ag-nes/Blog | python | @property
def inserted(self):
'Provide the "inserted" namespace for an ON DUPLICATE KEY UPDATE statement\n\n MySQL\'s ON DUPLICATE KEY UPDATE clause allows reference to the row\n that would be inserted, via a special function called ``VALUES()``.\n This attribute provides all columns in this ro... |
@_generative
@_exclusive_against('_post_values_clause', msgs={'_post_values_clause': 'This Insert construct already has an ON DUPLICATE KEY clause present'})
def on_duplicate_key_update(self, *args, **kw):
'\n Specifies the ON DUPLICATE KEY UPDATE clause.\n\n :param \\**kw: Column keys linked to UPDA... | 7,189,407,818,811,196,000 | Specifies the ON DUPLICATE KEY UPDATE clause.
:param \**kw: Column keys linked to UPDATE values. The
values may be any SQL expression or supported literal Python
values.
.. warning:: This dictionary does **not** take into account
Python-specified default UPDATE values or generation functions,
e.g. those spe... | virtual/lib/python3.8/site-packages/sqlalchemy/dialects/mysql/dml.py | on_duplicate_key_update | Ag-nes/Blog | python | @_generative
@_exclusive_against('_post_values_clause', msgs={'_post_values_clause': 'This Insert construct already has an ON DUPLICATE KEY clause present'})
def on_duplicate_key_update(self, *args, **kw):
'\n Specifies the ON DUPLICATE KEY UPDATE clause.\n\n :param \\**kw: Column keys linked to UPDA... |
def _compare_text_filters(self, first: TextFilter, second: TextFilter):
'\n\n :param first: TextFilter\n :param second: TextFilter\n :return: bool\n '
self.assertEqual(str(first.x), str(second.x))
self.assertEqual(str(first.y), str(second.y))
self.assertEqual(first.text, seco... | 2,669,470,008,177,207,300 | :param first: TextFilter
:param second: TextFilter
:return: bool | tests/bitmovin/services/filters/text_filter_tests.py | _compare_text_filters | bitmovin/bitmovin-python | python | def _compare_text_filters(self, first: TextFilter, second: TextFilter):
'\n\n :param first: TextFilter\n :param second: TextFilter\n :return: bool\n '
self.assertEqual(str(first.x), str(second.x))
self.assertEqual(str(first.y), str(second.y))
self.assertEqual(first.text, seco... |
@classmethod
def what_cached(self, model_name: str, path=None, learn=None):
'\n Shows what keys are cached\n '
if (isNone(path) and isNone(learn)):
print('path and learn cannot be None at the same time')
return
elif isNone(path):
path = learn.path
name = f'{model_na... | 208,750,910,187,950,180 | Shows what keys are cached | fastinference/tabular/pd.py | what_cached | floleuerer/fastinference | python | @classmethod
def what_cached(self, model_name: str, path=None, learn=None):
'\n \n '
if (isNone(path) and isNone(learn)):
print('path and learn cannot be None at the same time')
return
elif isNone(path):
path = learn.path
name = f'{model_name}_part_dep'
folder =... |
@classmethod
def empty_cache(self, model_name: str, path=None, learn=None):
'\n deletes the cache file\n '
if (isNone(path) and isNone(learn)):
print('path and learn cannot be None at the same time')
return
elif isNone(path):
path = learn.path
name = f'{model_name}_... | 5,048,076,303,994,254,000 | deletes the cache file | fastinference/tabular/pd.py | empty_cache | floleuerer/fastinference | python | @classmethod
def empty_cache(self, model_name: str, path=None, learn=None):
'\n \n '
if (isNone(path) and isNone(learn)):
print('path and learn cannot be None at the same time')
return
elif isNone(path):
path = learn.path
name = f'{model_name}_part_dep'
folder =... |
def _cont_into_buckets(self, df_init, CONT_COLS):
"\n Categorical values can be easily distiguished one from another\n But that doesn't work with continious values, we have to divede it's\n values into buckets and then use all values in a bucket as a single value\n that avarages the buck... | 2,281,857,222,200,926,000 | Categorical values can be easily distiguished one from another
But that doesn't work with continious values, we have to divede it's
values into buckets and then use all values in a bucket as a single value
that avarages the bucket. This way we convert cont feture into pseudo categorical
and are able to apply partial de... | fastinference/tabular/pd.py | _cont_into_buckets | floleuerer/fastinference | python | def _cont_into_buckets(self, df_init, CONT_COLS):
"\n Categorical values can be easily distiguished one from another\n But that doesn't work with continious values, we have to divede it's\n values into buckets and then use all values in a bucket as a single value\n that avarages the buck... |
def _get_field_uniq_x_coef(self, df: pd.DataFrame, fields: list, coef: float) -> list:
"\n This function outputs threshold to number of occurrences different variants of list of columns (fields)\n In short if coef for ex. is 0.9, then function outputs number of occurrences for all but least 10%\n ... | 5,400,113,811,807,088,000 | This function outputs threshold to number of occurrences different variants of list of columns (fields)
In short if coef for ex. is 0.9, then function outputs number of occurrences for all but least 10%
of the least used
If coef is more 1.0, then 'coef' itself is used as threshold | fastinference/tabular/pd.py | _get_field_uniq_x_coef | floleuerer/fastinference | python | def _get_field_uniq_x_coef(self, df: pd.DataFrame, fields: list, coef: float) -> list:
"\n This function outputs threshold to number of occurrences different variants of list of columns (fields)\n In short if coef for ex. is 0.9, then function outputs number of occurrences for all but least 10%\n ... |
def _get_part_dep_one(self, fields: list, masterbar=None) -> pd.DataFrame:
"\n Function calculate partial dependency for column in fields.\n Fields is a list of lists of what columns we want to test. The inner items are treated as connected fields.\n For ex. fields = [['Store','StoreType']] mea... | 2,957,460,990,702,026,000 | Function calculate partial dependency for column in fields.
Fields is a list of lists of what columns we want to test. The inner items are treated as connected fields.
For ex. fields = [['Store','StoreType']] mean that Store and StoreType is treated as one entity
(it's values are substitute as a pair, not as separate v... | fastinference/tabular/pd.py | _get_part_dep_one | floleuerer/fastinference | python | def _get_part_dep_one(self, fields: list, masterbar=None) -> pd.DataFrame:
"\n Function calculate partial dependency for column in fields.\n Fields is a list of lists of what columns we want to test. The inner items are treated as connected fields.\n For ex. fields = [['Store','StoreType']] mea... |
def _get_part_dep(self):
'\n Makes a datafreme with partial dependencies for every pair of columns in fields\n '
fields = self.fields
learn = self.learn
cache_path = self.cache_path
dep_name = self._get_dep_var()
is_continue = self.is_continue
l2k = self._list_to_key
result... | -488,242,411,160,210,560 | Makes a datafreme with partial dependencies for every pair of columns in fields | fastinference/tabular/pd.py | _get_part_dep | floleuerer/fastinference | python | def _get_part_dep(self):
'\n \n '
fields = self.fields
learn = self.learn
cache_path = self.cache_path
dep_name = self._get_dep_var()
is_continue = self.is_continue
l2k = self._list_to_key
result = []
to_save = {}
from_saved = {}
if (is_continue == True):
... |
def _save_cached(self):
'\n Saves calculated PartDep df into path.\n Can be saved more than one with as an dict with fields as key\n '
path = self.cache_path
path.mkdir(parents=True, exist_ok=True)
name = self.save_name
sv_dict = self._load_dict(name=name, path=path)
key = s... | 7,031,681,797,881,425,000 | Saves calculated PartDep df into path.
Can be saved more than one with as an dict with fields as key | fastinference/tabular/pd.py | _save_cached | floleuerer/fastinference | python | def _save_cached(self):
'\n Saves calculated PartDep df into path.\n Can be saved more than one with as an dict with fields as key\n '
path = self.cache_path
path.mkdir(parents=True, exist_ok=True)
name = self.save_name
sv_dict = self._load_dict(name=name, path=path)
key = s... |
def _load_cached(self):
'\n Load calculated PartDep df if hash exist.\n '
name = self.save_name
path = self.cache_path
if (not Path(f'{(path / name)}.pkl').exists()):
return None
ld_dict = self._ld_var(name=name, path=path)
key = self._list_to_key((self.fields + [self.coef]... | -5,927,804,199,348,323,000 | Load calculated PartDep df if hash exist. | fastinference/tabular/pd.py | _load_cached | floleuerer/fastinference | python | def _load_cached(self):
'\n \n '
name = self.save_name
path = self.cache_path
if (not Path(f'{(path / name)}.pkl').exists()):
return None
ld_dict = self._ld_var(name=name, path=path)
key = self._list_to_key((self.fields + [self.coef]))
if (key not in ld_dict):
r... |
def _load_or_calculate(self):
'\n Calculates part dep or load it from cache if possible\n '
if ((self.is_use_cache == False) or isNone(self._load_cached())):
self._get_part_dep()
return self._save_cached()
else:
self.part_dep_df = self._load_cached() | 4,629,582,466,019,069,000 | Calculates part dep or load it from cache if possible | fastinference/tabular/pd.py | _load_or_calculate | floleuerer/fastinference | python | def _load_or_calculate(self):
'\n \n '
if ((self.is_use_cache == False) or isNone(self._load_cached())):
self._get_part_dep()
return self._save_cached()
else:
self.part_dep_df = self._load_cached() |
def plot_raw(self, field, sample=1.0):
'\n Plot dependency graph from data itself\n field must be list of exactly one feature\n sample is a coef to len(df). Lower if kernel use to shut down on that\n '
df = self.df
df = df.sample(int((len(df) * sample)))
field = field[0]
... | -5,214,618,900,920,584,000 | Plot dependency graph from data itself
field must be list of exactly one feature
sample is a coef to len(df). Lower if kernel use to shut down on that | fastinference/tabular/pd.py | plot_raw | floleuerer/fastinference | python | def plot_raw(self, field, sample=1.0):
'\n Plot dependency graph from data itself\n field must be list of exactly one feature\n sample is a coef to len(df). Lower if kernel use to shut down on that\n '
df = self.df
df = df.sample(int((len(df) * sample)))
field = field[0]
... |
def plot_model(self, field, strict_recalc=False, sample=1.0):
'\n Plot dependency graph from the model.\n It also take into account times, so plot becomes much more resilient, cause not every value treats as equal\n (more occurences means more power)\n field must be list of exactly one f... | 1,374,911,187,912,990,700 | Plot dependency graph from the model.
It also take into account times, so plot becomes much more resilient, cause not every value treats as equal
(more occurences means more power)
field must be list of exactly one feature
strict_recalc=True ignores precalculated `part_dep_df` and calculate it anyway
sample is a coef t... | fastinference/tabular/pd.py | plot_model | floleuerer/fastinference | python | def plot_model(self, field, strict_recalc=False, sample=1.0):
'\n Plot dependency graph from the model.\n It also take into account times, so plot becomes much more resilient, cause not every value treats as equal\n (more occurences means more power)\n field must be list of exactly one f... |
def get_pd(self, feature, min_tm=1):
'\n Gets particular feature subtable from the whole one (min times is optional parameter)\n '
if isNone(self.part_dep_df):
return None
df = self.part_dep_df.query(f'(feature == "{feature}") and (times > {min_tm})')
return self._general2partial(d... | 8,928,306,377,913,288,000 | Gets particular feature subtable from the whole one (min times is optional parameter) | fastinference/tabular/pd.py | get_pd | floleuerer/fastinference | python | def get_pd(self, feature, min_tm=1):
'\n \n '
if isNone(self.part_dep_df):
return None
df = self.part_dep_df.query(f'(feature == "{feature}") and (times > {min_tm})')
return self._general2partial(df=df) |
def get_pd_main_chained_feat(self, main_feat_idx=0, show_min=1):
'\n Transforms whole features table to get_part_dep_one output table format\n '
def get_xth_el(str_list: str, indexes: list):
lst = (str_list if is_listy(str_list) else ast.literal_eval(str_list))
lst = listify(lst)
... | -4,872,721,693,105,727,000 | Transforms whole features table to get_part_dep_one output table format | fastinference/tabular/pd.py | get_pd_main_chained_feat | floleuerer/fastinference | python | def get_pd_main_chained_feat(self, main_feat_idx=0, show_min=1):
'\n \n '
def get_xth_el(str_list: str, indexes: list):
lst = (str_list if is_listy(str_list) else ast.literal_eval(str_list))
lst = listify(lst)
if (len(lst) == 1):
return lst[0]
elif (len... |
def plot_part_dep(self, fields, limit=20, asc=False):
'\n Plots partial dependency plot for sublist of connected `fields`\n `fields` must be sublist of `fields` given on initalization calculation\n '
def prepare_colors(df_pd: pd.DataFrame):
heat_min = df_pd['times'].min()
h... | 8,386,247,731,553,893,000 | Plots partial dependency plot for sublist of connected `fields`
`fields` must be sublist of `fields` given on initalization calculation | fastinference/tabular/pd.py | plot_part_dep | floleuerer/fastinference | python | def plot_part_dep(self, fields, limit=20, asc=False):
'\n Plots partial dependency plot for sublist of connected `fields`\n `fields` must be sublist of `fields` given on initalization calculation\n '
def prepare_colors(df_pd: pd.DataFrame):
heat_min = df_pd['times'].min()
h... |
def _parse_content(response):
'parse the response body as JSON, raise on errors'
if (response.status_code != 200):
raise ApiError(f'unknown error: {response.content.decode()}')
result = json.loads(response.content)
if (not result['ok']):
raise ApiError(f"{result['error']}: {result.get('d... | -553,374,406,510,625,340 | parse the response body as JSON, raise on errors | examples/slack/query.py | _parse_content | ariebovenberg/snug | python | def _parse_content(response):
if (response.status_code != 200):
raise ApiError(f'unknown error: {response.content.decode()}')
result = json.loads(response.content)
if (not result['ok']):
raise ApiError(f"{result['error']}: {result.get('detail')}")
return result |
def paginated_retrieval(methodname, itemtype):
'decorator factory for retrieval queries from query params'
return compose(reusable, basic_interaction, map_yield(partial(_params_as_get, methodname))) | -6,033,415,841,283,409,000 | decorator factory for retrieval queries from query params | examples/slack/query.py | paginated_retrieval | ariebovenberg/snug | python | def paginated_retrieval(methodname, itemtype):
return compose(reusable, basic_interaction, map_yield(partial(_params_as_get, methodname))) |
def json_post(methodname, rtype, key):
'decorator factory for json POST queries'
return compose(reusable, map_return(registry(rtype), itemgetter(key)), basic_interaction, map_yield(partial(_json_as_post, methodname)), oneyield) | 4,652,402,797,051,923,000 | decorator factory for json POST queries | examples/slack/query.py | json_post | ariebovenberg/snug | python | def json_post(methodname, rtype, key):
return compose(reusable, map_return(registry(rtype), itemgetter(key)), basic_interaction, map_yield(partial(_json_as_post, methodname)), oneyield) |
def retinanet_target_assign(bbox_pred, cls_logits, anchor_box, anchor_var, gt_boxes, gt_labels, is_crowd, im_info, num_classes=1, positive_overlap=0.5, negative_overlap=0.4):
"\n **Target Assign Layer for the detector RetinaNet.**\n\n This OP finds out positive and negative samples from all anchors\n for t... | 4,884,496,934,939,049,000 | **Target Assign Layer for the detector RetinaNet.**
This OP finds out positive and negative samples from all anchors
for training the detector `RetinaNet <https://arxiv.org/abs/1708.02002>`_ ,
and assigns target labels for classification along with target locations for
regression to each sample, then takes out the par... | python/paddle/fluid/layers/detection.py | retinanet_target_assign | 92lqllearning/Paddle | python | def retinanet_target_assign(bbox_pred, cls_logits, anchor_box, anchor_var, gt_boxes, gt_labels, is_crowd, im_info, num_classes=1, positive_overlap=0.5, negative_overlap=0.4):
"\n **Target Assign Layer for the detector RetinaNet.**\n\n This OP finds out positive and negative samples from all anchors\n for t... |
def rpn_target_assign(bbox_pred, cls_logits, anchor_box, anchor_var, gt_boxes, is_crowd, im_info, rpn_batch_size_per_im=256, rpn_straddle_thresh=0.0, rpn_fg_fraction=0.5, rpn_positive_overlap=0.7, rpn_negative_overlap=0.3, use_random=True):
"\n **Target Assign Layer for region proposal network (RPN) in Faster-RC... | -5,902,719,678,806,247,000 | **Target Assign Layer for region proposal network (RPN) in Faster-RCNN detection.**
This layer can be, for given the Intersection-over-Union (IoU) overlap
between anchors and ground truth boxes, to assign classification and
regression targets to each each anchor, these target labels are used for
train RPN. The classi... | python/paddle/fluid/layers/detection.py | rpn_target_assign | 92lqllearning/Paddle | python | def rpn_target_assign(bbox_pred, cls_logits, anchor_box, anchor_var, gt_boxes, is_crowd, im_info, rpn_batch_size_per_im=256, rpn_straddle_thresh=0.0, rpn_fg_fraction=0.5, rpn_positive_overlap=0.7, rpn_negative_overlap=0.3, use_random=True):
"\n **Target Assign Layer for region proposal network (RPN) in Faster-RC... |
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