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 |
|---|---|---|---|---|---|---|---|---|---|
b2cc944e1a37eb004d38e2d09e53aea80d2f28f155c9411b04ab53541e4a9c4f | def traverse(self, word, first, last):
'\n\n :param word: the text string where to look for the\n :param first: the fist index in the lists of the patterns and scores\n :param last: the last index in the lists of the patterns and scores\n :return: a Counter (dict) with cum... | :param word: the text string where to look for the
:param first: the fist index in the lists of the patterns and scores
:param last: the last index in the lists of the patterns and scores
:return: a Counter (dict) with cumulative scores of each patterns
of the sublist of patterns between ... | exoticst/ac_automation.py | traverse | valginer0/exotic-structures | 0 | python | def traverse(self, word, first, last):
'\n\n :param word: the text string where to look for the\n :param first: the fist index in the lists of the patterns and scores\n :param last: the last index in the lists of the patterns and scores\n :return: a Counter (dict) with cum... | def traverse(self, word, first, last):
'\n\n :param word: the text string where to look for the\n :param first: the fist index in the lists of the patterns and scores\n :param last: the last index in the lists of the patterns and scores\n :return: a Counter (dict) with cum... |
35f37236a69f0a97f0fbae2ed2d0ee6bc8ff984618d8ebdabff4406e0893a157 | def main():
'Main script function.'
site = pywikibot.Site()
for pagename in load_page_list(NUMBER):
page = pywikibot.Page(site, pagename)
error = False
for line in page.text.split('\n'):
match = re.search('==+$', line)
if (not match):
continue
... | Main script function. | scripts/markers/mark_error_105.py | main | Facenapalm/NapalmBot | 4 | python | def main():
site = pywikibot.Site()
for pagename in load_page_list(NUMBER):
page = pywikibot.Page(site, pagename)
error = False
for line in page.text.split('\n'):
match = re.search('==+$', line)
if (not match):
continue
if line.sta... | def main():
site = pywikibot.Site()
for pagename in load_page_list(NUMBER):
page = pywikibot.Page(site, pagename)
error = False
for line in page.text.split('\n'):
match = re.search('==+$', line)
if (not match):
continue
if line.sta... |
9f3ee29446578564a764197150b242ceb98cecf89f8db81f8f1d8e214426b540 | def __init__(self, database: DatabaseAccessionMetabase, acs_refrep: str='tid', acs_sub_nodes: bool=False, acs_filter_method: Optional[str]=None, acs_filter_value: Optional[str]=None, **kwargs: Any):
'Mixin class constructor for :class:`.MediatorLocalAccessionMixin`\n\n Parameters\n ----------\n ... | Mixin class constructor for :class:`.MediatorLocalAccessionMixin`
Parameters
----------
database
Instance of :class:`~pmaf.database._core._base.DatabaseBase` and :class:`~pmaf.database._core._acs_base.DatabaseAccessionMixin`
acs_refrep
Taxonomy lookup level. Can be either "tid" for :term:`tids` or "rid" for :t... | pmaf/pipe/agents/mediators/_local/_components/_acs_mixin.py | __init__ | mmtechslv/PhyloMAF | 1 | python | def __init__(self, database: DatabaseAccessionMetabase, acs_refrep: str='tid', acs_sub_nodes: bool=False, acs_filter_method: Optional[str]=None, acs_filter_value: Optional[str]=None, **kwargs: Any):
'Mixin class constructor for :class:`.MediatorLocalAccessionMixin`\n\n Parameters\n ----------\n ... | def __init__(self, database: DatabaseAccessionMetabase, acs_refrep: str='tid', acs_sub_nodes: bool=False, acs_filter_method: Optional[str]=None, acs_filter_value: Optional[str]=None, **kwargs: Any):
'Mixin class constructor for :class:`.MediatorLocalAccessionMixin`\n\n Parameters\n ----------\n ... |
e7ab987f2550198a2f4bb5fac9ae8975532f3085c69797c5657f5bffa59dac9d | def get_accession_by_identifier(self, docker: DockerIdentifierMedium, factor: FactorBase, **kwargs: Any) -> DockerAccessionMedium:
'Get accession data that matches identifiers in `docker` within local\n database client.\n\n Parameters\n ----------\n docker\n A :term:`docker` :... | Get accession data that matches identifiers in `docker` within local
database client.
Parameters
----------
docker
A :term:`docker` :term:`singleton` identifier instance
factor
A :term:`factor` to accommodate matching process
kwargs
Compatibility
Returns
-------
An instance of :class:`.DockerAccession... | pmaf/pipe/agents/mediators/_local/_components/_acs_mixin.py | get_accession_by_identifier | mmtechslv/PhyloMAF | 1 | python | def get_accession_by_identifier(self, docker: DockerIdentifierMedium, factor: FactorBase, **kwargs: Any) -> DockerAccessionMedium:
'Get accession data that matches identifiers in `docker` within local\n database client.\n\n Parameters\n ----------\n docker\n A :term:`docker` :... | def get_accession_by_identifier(self, docker: DockerIdentifierMedium, factor: FactorBase, **kwargs: Any) -> DockerAccessionMedium:
'Get accession data that matches identifiers in `docker` within local\n database client.\n\n Parameters\n ----------\n docker\n A :term:`docker` :... |
2a5639cb85cba204fecd22aaaf5150c5d409073c24fe5296882d348dd169d59f | def __retrieve_accessions_by_identifier(self, docker, **kwargs):
'Actual method to retrieve accession data from identifiers.'
id_array = docker.to_array(exclude_missing=True)
tmp_accessions = dict.fromkeys(id_array, None)
tmp_metadata = dict.fromkeys(id_array, None)
if (self.configs['acs_refrep'] ==... | Actual method to retrieve accession data from identifiers. | pmaf/pipe/agents/mediators/_local/_components/_acs_mixin.py | __retrieve_accessions_by_identifier | mmtechslv/PhyloMAF | 1 | python | def __retrieve_accessions_by_identifier(self, docker, **kwargs):
id_array = docker.to_array(exclude_missing=True)
tmp_accessions = dict.fromkeys(id_array, None)
tmp_metadata = dict.fromkeys(id_array, None)
if (self.configs['acs_refrep'] == 'tid'):
tmp_db_accessions = self.client.get_accessi... | def __retrieve_accessions_by_identifier(self, docker, **kwargs):
id_array = docker.to_array(exclude_missing=True)
tmp_accessions = dict.fromkeys(id_array, None)
tmp_metadata = dict.fromkeys(id_array, None)
if (self.configs['acs_refrep'] == 'tid'):
tmp_db_accessions = self.client.get_accessi... |
0cdde3744d23e5cc77e2921cbd3bbcbf5a1699a2f8518f4bb4570ceac071cbc2 | def __filter_rids_from_tids_accessions(self, accs_dict):
'Filter matched accessions based on filtering configuration.'
tmp_accs_dict = defaultdict(list)
if ((self.configs['acs_filter_method'] == 'random') and isinstance(self.configs['acs_filter_value'], int)):
if (len(accs_dict) > self.configs['acs_... | Filter matched accessions based on filtering configuration. | pmaf/pipe/agents/mediators/_local/_components/_acs_mixin.py | __filter_rids_from_tids_accessions | mmtechslv/PhyloMAF | 1 | python | def __filter_rids_from_tids_accessions(self, accs_dict):
tmp_accs_dict = defaultdict(list)
if ((self.configs['acs_filter_method'] == 'random') and isinstance(self.configs['acs_filter_value'], int)):
if (len(accs_dict) > self.configs['acs_filter_value']):
tmp_target_ids = np.random.choic... | def __filter_rids_from_tids_accessions(self, accs_dict):
tmp_accs_dict = defaultdict(list)
if ((self.configs['acs_filter_method'] == 'random') and isinstance(self.configs['acs_filter_value'], int)):
if (len(accs_dict) > self.configs['acs_filter_value']):
tmp_target_ids = np.random.choic... |
c2d9834747ed6cd89e6dd1c0e5c107bdc50e691141e6e9e2063b77f2f249be1b | def get_identifier_by_accession(self, docker, factor, **kwargs):
'Get local database identifiers that match target accession numbers\n in `docker` within local database client.\n\n :meta private:\n\n Parameters\n ----------\n docker\n A :term:`docker` :term:`singleton` ... | Get local database identifiers that match target accession numbers
in `docker` within local database client.
:meta private:
Parameters
----------
docker
A :term:`docker` :term:`singleton` accession instance
factor
A :term:`factor` to accommodate matching process
kwargs
Compatibility
Returns
-------
A... | pmaf/pipe/agents/mediators/_local/_components/_acs_mixin.py | get_identifier_by_accession | mmtechslv/PhyloMAF | 1 | python | def get_identifier_by_accession(self, docker, factor, **kwargs):
'Get local database identifiers that match target accession numbers\n in `docker` within local database client.\n\n :meta private:\n\n Parameters\n ----------\n docker\n A :term:`docker` :term:`singleton` ... | def get_identifier_by_accession(self, docker, factor, **kwargs):
'Get local database identifiers that match target accession numbers\n in `docker` within local database client.\n\n :meta private:\n\n Parameters\n ----------\n docker\n A :term:`docker` :term:`singleton` ... |
08bab5ee5b1501a63a39c8453ccb5128d61902dc5515a425fbfb372533eaefbb | def form_string(content):
'\n Take a string or BufferedReader as argument and transform the string into a ProvDocument\n\n :param content: Takes a sting or BufferedReader\n :return: ProvDocument\n '
if isinstance(content, ProvDocument):
return content
elif isinstance(content, BufferedRea... | Take a string or BufferedReader as argument and transform the string into a ProvDocument
:param content: Takes a sting or BufferedReader
:return: ProvDocument | provdbconnector/utils/converter.py | form_string | Ama-Gi/prov-neo4j-covid19-track | 15 | python | def form_string(content):
'\n Take a string or BufferedReader as argument and transform the string into a ProvDocument\n\n :param content: Takes a sting or BufferedReader\n :return: ProvDocument\n '
if isinstance(content, ProvDocument):
return content
elif isinstance(content, BufferedRea... | def form_string(content):
'\n Take a string or BufferedReader as argument and transform the string into a ProvDocument\n\n :param content: Takes a sting or BufferedReader\n :return: ProvDocument\n '
if isinstance(content, ProvDocument):
return content
elif isinstance(content, BufferedRea... |
6ab4009bf955ede754c829f25f6cd70c572609391172fb904119653ad5433846 | def to_json(document=None):
'\n Try to convert a ProvDocument into the json representation\n\n :param document:\n :type document: prov.model.ProvDocument\n :return: Json string of the document\n :rtype: str\n '
if (document is None):
raise NoDocumentException()
return document.seri... | Try to convert a ProvDocument into the json representation
:param document:
:type document: prov.model.ProvDocument
:return: Json string of the document
:rtype: str | provdbconnector/utils/converter.py | to_json | Ama-Gi/prov-neo4j-covid19-track | 15 | python | def to_json(document=None):
'\n Try to convert a ProvDocument into the json representation\n\n :param document:\n :type document: prov.model.ProvDocument\n :return: Json string of the document\n :rtype: str\n '
if (document is None):
raise NoDocumentException()
return document.seri... | def to_json(document=None):
'\n Try to convert a ProvDocument into the json representation\n\n :param document:\n :type document: prov.model.ProvDocument\n :return: Json string of the document\n :rtype: str\n '
if (document is None):
raise NoDocumentException()
return document.seri... |
941cbafc53c0581abfeb2c386cd9ce3cae3af4ec80bbb54e95354b89d0911d2c | def from_json(json=None):
'\n Try to convert a json string into a document\n\n :param json: The json str\n :type json: str\n :return: Prov Document\n :rtype: prov.model.ProvDocument\n :raise: NoDocumentException\n '
if (json is None):
raise NoDocumentException()
return ProvDocum... | Try to convert a json string into a document
:param json: The json str
:type json: str
:return: Prov Document
:rtype: prov.model.ProvDocument
:raise: NoDocumentException | provdbconnector/utils/converter.py | from_json | Ama-Gi/prov-neo4j-covid19-track | 15 | python | def from_json(json=None):
'\n Try to convert a json string into a document\n\n :param json: The json str\n :type json: str\n :return: Prov Document\n :rtype: prov.model.ProvDocument\n :raise: NoDocumentException\n '
if (json is None):
raise NoDocumentException()
return ProvDocum... | def from_json(json=None):
'\n Try to convert a json string into a document\n\n :param json: The json str\n :type json: str\n :return: Prov Document\n :rtype: prov.model.ProvDocument\n :raise: NoDocumentException\n '
if (json is None):
raise NoDocumentException()
return ProvDocum... |
5835841b3c8f88f8ee7086aae248e7aa96fb12dd30a96cfaa484172c7a443fdd | def to_provn(document=None):
'\n Try to convert a document into a provn representation\n\n :param document: Prov document to convert\n :type document: prov.model.ProvDocument\n :return: The prov-n str\n :rtype: str\n :raise: NoDocumentException\n '
if (document is None):
raise NoDoc... | Try to convert a document into a provn representation
:param document: Prov document to convert
:type document: prov.model.ProvDocument
:return: The prov-n str
:rtype: str
:raise: NoDocumentException | provdbconnector/utils/converter.py | to_provn | Ama-Gi/prov-neo4j-covid19-track | 15 | python | def to_provn(document=None):
'\n Try to convert a document into a provn representation\n\n :param document: Prov document to convert\n :type document: prov.model.ProvDocument\n :return: The prov-n str\n :rtype: str\n :raise: NoDocumentException\n '
if (document is None):
raise NoDoc... | def to_provn(document=None):
'\n Try to convert a document into a provn representation\n\n :param document: Prov document to convert\n :type document: prov.model.ProvDocument\n :return: The prov-n str\n :rtype: str\n :raise: NoDocumentException\n '
if (document is None):
raise NoDoc... |
91270a59ef33d647e5c08a467459e93aa25c7a3b7ddd785a58ba480d6678e25e | def from_provn(provn_str=None):
'\n Try to convert a provn string into a ProvDocument\n\n :param provn_str: The string to convert\n :type provn_str: str\n :return: The Prov document\n :rtype: ProvDocument\n :raises: NoDocumentException\n '
if (provn_str is None):
raise NoDocumentExc... | Try to convert a provn string into a ProvDocument
:param provn_str: The string to convert
:type provn_str: str
:return: The Prov document
:rtype: ProvDocument
:raises: NoDocumentException | provdbconnector/utils/converter.py | from_provn | Ama-Gi/prov-neo4j-covid19-track | 15 | python | def from_provn(provn_str=None):
'\n Try to convert a provn string into a ProvDocument\n\n :param provn_str: The string to convert\n :type provn_str: str\n :return: The Prov document\n :rtype: ProvDocument\n :raises: NoDocumentException\n '
if (provn_str is None):
raise NoDocumentExc... | def from_provn(provn_str=None):
'\n Try to convert a provn string into a ProvDocument\n\n :param provn_str: The string to convert\n :type provn_str: str\n :return: The Prov document\n :rtype: ProvDocument\n :raises: NoDocumentException\n '
if (provn_str is None):
raise NoDocumentExc... |
f832a7391525d90718d9bb20990d058ff0cda7acb919e0c02d4f0a584dff167d | def to_xml(document=None):
'\n Try to convert a document into an xml string\n\n :param document: The ProvDocument to convert\n :param document: ProvDocument\n :return: The xml string\n :rtype: str\n '
if (document is None):
raise NoDocumentException()
return document.serialize(form... | Try to convert a document into an xml string
:param document: The ProvDocument to convert
:param document: ProvDocument
:return: The xml string
:rtype: str | provdbconnector/utils/converter.py | to_xml | Ama-Gi/prov-neo4j-covid19-track | 15 | python | def to_xml(document=None):
'\n Try to convert a document into an xml string\n\n :param document: The ProvDocument to convert\n :param document: ProvDocument\n :return: The xml string\n :rtype: str\n '
if (document is None):
raise NoDocumentException()
return document.serialize(form... | def to_xml(document=None):
'\n Try to convert a document into an xml string\n\n :param document: The ProvDocument to convert\n :param document: ProvDocument\n :return: The xml string\n :rtype: str\n '
if (document is None):
raise NoDocumentException()
return document.serialize(form... |
6981adc01a247d0ee6d5dd9b9b32cc22c10f7412f408cbf33a9a42873eafe65d | def from_xml(xml_str=None):
'\n Try to convert a xml string into a ProvDocument\n\n :param xml_str: The xml string\n :type xml_str: str\n :return: The Prov document\n :rtype: ProvDocument\n '
if (xml_str is None):
raise NoDocumentException()
return ProvDocument.deserialize(source=x... | Try to convert a xml string into a ProvDocument
:param xml_str: The xml string
:type xml_str: str
:return: The Prov document
:rtype: ProvDocument | provdbconnector/utils/converter.py | from_xml | Ama-Gi/prov-neo4j-covid19-track | 15 | python | def from_xml(xml_str=None):
'\n Try to convert a xml string into a ProvDocument\n\n :param xml_str: The xml string\n :type xml_str: str\n :return: The Prov document\n :rtype: ProvDocument\n '
if (xml_str is None):
raise NoDocumentException()
return ProvDocument.deserialize(source=x... | def from_xml(xml_str=None):
'\n Try to convert a xml string into a ProvDocument\n\n :param xml_str: The xml string\n :type xml_str: str\n :return: The Prov document\n :rtype: ProvDocument\n '
if (xml_str is None):
raise NoDocumentException()
return ProvDocument.deserialize(source=x... |
08943a0e44e3d7d69ab14369d9aca0e4c6e70a5a26b1827d62bbe80ae8eedf89 | def createDegreeMethod(self, refvertices, refpoints, dofs):
' Returns a method that will create degrees of freedom'
if (len(dofs) == 0):
return (lambda vertices: None)
def createExternalDegree(vertices):
if (len(refvertices) == 0):
pullback = Pullback((lambda p: vertices[numpy.i... | Returns a method that will create degrees of freedom | src/pypyr/elements.py | createDegreeMethod | joelphillips/pypyramid | 1 | python | def createDegreeMethod(self, refvertices, refpoints, dofs):
' '
if (len(dofs) == 0):
return (lambda vertices: None)
def createExternalDegree(vertices):
if (len(refvertices) == 0):
pullback = Pullback((lambda p: vertices[numpy.ix_([0])]))
else:
map = buildaffi... | def createDegreeMethod(self, refvertices, refpoints, dofs):
' '
if (len(dofs) == 0):
return (lambda vertices: None)
def createExternalDegree(vertices):
if (len(refvertices) == 0):
pullback = Pullback((lambda p: vertices[numpy.ix_([0])]))
else:
map = buildaffi... |
771e3bd4c82b0e83106c4036317686195b5ef85e5b3aa74ad88ccc4da4e3dd03 | @app.task
def run(event_type, data):
'Everything starts here.'
installation_id = data['installation']['id']
owner = data['repository']['owner']['login']
repo = data['repository']['name']
client = github.get_client(owner, repo, installation_id)
raw_pull = get_github_pull_from_event(client, event_... | Everything starts here. | mergify_engine/tasks/engine/__init__.py | run | Madhu-1/mergify-engine | 0 | python | @app.task
def run(event_type, data):
installation_id = data['installation']['id']
owner = data['repository']['owner']['login']
repo = data['repository']['name']
client = github.get_client(owner, repo, installation_id)
raw_pull = get_github_pull_from_event(client, event_type, data)
if (not r... | @app.task
def run(event_type, data):
installation_id = data['installation']['id']
owner = data['repository']['owner']['login']
repo = data['repository']['name']
client = github.get_client(owner, repo, installation_id)
raw_pull = get_github_pull_from_event(client, event_type, data)
if (not r... |
b7e4a4dfd776ad2c322aba80ddd271683b742ea5bdbd8020dab252ec9caa8dec | def error(text):
'\n Log error `text` and produce a RuntimeError exception\n '
if (not text.startswith('Too many queries')):
print(text)
logging.error('ERROR %s', text)
raise RuntimeError(text) | Log error `text` and produce a RuntimeError exception | lib/globals.py | error | Guitar420/cheat.sh | 2 | python | def error(text):
'\n \n '
if (not text.startswith('Too many queries')):
print(text)
logging.error('ERROR %s', text)
raise RuntimeError(text) | def error(text):
'\n \n '
if (not text.startswith('Too many queries')):
print(text)
logging.error('ERROR %s', text)
raise RuntimeError(text)<|docstring|>Log error `text` and produce a RuntimeError exception<|endoftext|> |
9838cf67a063d4426ab87904c332db7c1fe9b81dfe60537e2033a0412b80c54c | def log(text):
"\n Log error `text` (if it does not start with 'Too many queries')\n "
if (not text.startswith('Too many queries')):
print(text)
logging.info(text) | Log error `text` (if it does not start with 'Too many queries') | lib/globals.py | log | Guitar420/cheat.sh | 2 | python | def log(text):
"\n \n "
if (not text.startswith('Too many queries')):
print(text)
logging.info(text) | def log(text):
"\n \n "
if (not text.startswith('Too many queries')):
print(text)
logging.info(text)<|docstring|>Log error `text` (if it does not start with 'Too many queries')<|endoftext|> |
9ab5a4a110c9ce60ef894fdd6cc24197be5f962c0a3690a67f90b6f63e30b2d9 | def parse_vit(self, data):
'\n Vit, mV * 25 -> 0x00 - 0xff\n e.g. data = 0xff -> 10.2 mV\n data = 0x01 -> 0.04 mV\n '
return (self.parse_number(data) * 25) | Vit, mV * 25 -> 0x00 - 0xff
e.g. data = 0xff -> 10.2 mV
data = 0x01 -> 0.04 mV | parsers/parser_naltec.py | parse_vit | tanupoo/lorawan-ss-as | 1 | python | def parse_vit(self, data):
'\n Vit, mV * 25 -> 0x00 - 0xff\n e.g. data = 0xff -> 10.2 mV\n data = 0x01 -> 0.04 mV\n '
return (self.parse_number(data) * 25) | def parse_vit(self, data):
'\n Vit, mV * 25 -> 0x00 - 0xff\n e.g. data = 0xff -> 10.2 mV\n data = 0x01 -> 0.04 mV\n '
return (self.parse_number(data) * 25)<|docstring|>Vit, mV * 25 -> 0x00 - 0xff
e.g. data = 0xff -> 10.2 mV
data = 0x01 -> 0.04 mV<|endoftext|> |
04aeea922d904ae344ebbae05ea78e8e91ae46693f1f2ae2930bab2339aeddd1 | def parse_temp(self, data):
'\n Temp, -40 to -1 -> 0xd8 - 0xff, 0 to 125 -> 0x00 - 0x7d\n '
return self.parse_signed_number(data) | Temp, -40 to -1 -> 0xd8 - 0xff, 0 to 125 -> 0x00 - 0x7d | parsers/parser_naltec.py | parse_temp | tanupoo/lorawan-ss-as | 1 | python | def parse_temp(self, data):
'\n \n '
return self.parse_signed_number(data) | def parse_temp(self, data):
'\n \n '
return self.parse_signed_number(data)<|docstring|>Temp, -40 to -1 -> 0xd8 - 0xff, 0 to 125 -> 0x00 - 0x7d<|endoftext|> |
fb7de2491bf7b0ee9370671891ed5a962528b6e5855c94406a9205f413ce1933 | def parse_current_20mA(self, data):
'\n 0x0000 - 0xffff -> 0mA - 20mA\n '
return round(((float(self.parse_number_le(data)) / 65535) * 20), 2) | 0x0000 - 0xffff -> 0mA - 20mA | parsers/parser_naltec.py | parse_current_20mA | tanupoo/lorawan-ss-as | 1 | python | def parse_current_20mA(self, data):
'\n \n '
return round(((float(self.parse_number_le(data)) / 65535) * 20), 2) | def parse_current_20mA(self, data):
'\n \n '
return round(((float(self.parse_number_le(data)) / 65535) * 20), 2)<|docstring|>0x0000 - 0xffff -> 0mA - 20mA<|endoftext|> |
38a95698fde6126325acbdf601dc989e58f3650eee74acebdd41214fc1e557b0 | def parse_voltage_10V(self, data):
'\n 0x0000 - 0xffff -> 0V - 10V\n '
return round(((float(self.parse_number_le(data)) / 65535) * 10), 2) | 0x0000 - 0xffff -> 0V - 10V | parsers/parser_naltec.py | parse_voltage_10V | tanupoo/lorawan-ss-as | 1 | python | def parse_voltage_10V(self, data):
'\n \n '
return round(((float(self.parse_number_le(data)) / 65535) * 10), 2) | def parse_voltage_10V(self, data):
'\n \n '
return round(((float(self.parse_number_le(data)) / 65535) * 10), 2)<|docstring|>0x0000 - 0xffff -> 0V - 10V<|endoftext|> |
463bc96cfc8597c880dbf5cf33b618eec0a1ae4e9035ec742a799e9c30b48dd8 | def parse_thermocouple(self, data):
'\n from -200 to 1820, unit 0.0625 C\n 0xf380 ... 0xfff, 0x000 ... 0x71c0\n i.e.\n int.from_bytes(data, "big", signed=True) * 0.0625\n '
return (self.parse_signed_number(data) * 0.0625) | from -200 to 1820, unit 0.0625 C
0xf380 ... 0xfff, 0x000 ... 0x71c0
i.e.
int.from_bytes(data, "big", signed=True) * 0.0625 | parsers/parser_naltec.py | parse_thermocouple | tanupoo/lorawan-ss-as | 1 | python | def parse_thermocouple(self, data):
'\n from -200 to 1820, unit 0.0625 C\n 0xf380 ... 0xfff, 0x000 ... 0x71c0\n i.e.\n int.from_bytes(data, "big", signed=True) * 0.0625\n '
return (self.parse_signed_number(data) * 0.0625) | def parse_thermocouple(self, data):
'\n from -200 to 1820, unit 0.0625 C\n 0xf380 ... 0xfff, 0x000 ... 0x71c0\n i.e.\n int.from_bytes(data, "big", signed=True) * 0.0625\n '
return (self.parse_signed_number(data) * 0.0625)<|docstring|>from -200 to 1820, unit 0.0625 C
0xf380 ...... |
2e2df48e9c59fe22c04dad7cc6213e2dafcff76532a86aa90e386c9d3439951d | def parse_payload_7f(self, byte_data):
'\n Hdr, Vit, Temp\n '
if (len(byte_data) != 3):
return False
return self.parse_by_format(byte_data, [('hdr', self.parse_number, 0, 1), ('vit', self.parse_vit, 1, 2), ('temp', self.parse_temp, 2, 3)]) | Hdr, Vit, Temp | parsers/parser_naltec.py | parse_payload_7f | tanupoo/lorawan-ss-as | 1 | python | def parse_payload_7f(self, byte_data):
'\n \n '
if (len(byte_data) != 3):
return False
return self.parse_by_format(byte_data, [('hdr', self.parse_number, 0, 1), ('vit', self.parse_vit, 1, 2), ('temp', self.parse_temp, 2, 3)]) | def parse_payload_7f(self, byte_data):
'\n \n '
if (len(byte_data) != 3):
return False
return self.parse_by_format(byte_data, [('hdr', self.parse_number, 0, 1), ('vit', self.parse_vit, 1, 2), ('temp', self.parse_temp, 2, 3)])<|docstring|>Hdr, Vit, Temp<|endoftext|> |
18435302fc29b0dc38e5c1b904734486bc2bfedbd20cabdfa18658fae40b3857 | def parse_payload_23(self, byte_data):
'\n Hdr, Vit, Temp, C-IN1, C-IN2, Vo-IN1, Vo-IN2\n '
if (len(byte_data) != 11):
return False
return self.parse_by_format(byte_data, [('hdr', self.parse_number, 0, 1), ('vit', self.parse_vit, 1, 2), ('temp', self.parse_temp, 2, 3), ('current_input_... | Hdr, Vit, Temp, C-IN1, C-IN2, Vo-IN1, Vo-IN2 | parsers/parser_naltec.py | parse_payload_23 | tanupoo/lorawan-ss-as | 1 | python | def parse_payload_23(self, byte_data):
'\n \n '
if (len(byte_data) != 11):
return False
return self.parse_by_format(byte_data, [('hdr', self.parse_number, 0, 1), ('vit', self.parse_vit, 1, 2), ('temp', self.parse_temp, 2, 3), ('current_input_1', self.parse_current_20mA, 3, 5), ('curren... | def parse_payload_23(self, byte_data):
'\n \n '
if (len(byte_data) != 11):
return False
return self.parse_by_format(byte_data, [('hdr', self.parse_number, 0, 1), ('vit', self.parse_vit, 1, 2), ('temp', self.parse_temp, 2, 3), ('current_input_1', self.parse_current_20mA, 3, 5), ('curren... |
13db7b880c8fa7744846702b705b3ab79cac9ee3564adbbf8cdb208b34478d50 | def parse_thermocouple_nope(self, data):
'\n it means that the terminal is not used.\n '
return 32768 | it means that the terminal is not used. | parsers/parser_naltec.py | parse_thermocouple_nope | tanupoo/lorawan-ss-as | 1 | python | def parse_thermocouple_nope(self, data):
'\n \n '
return 32768 | def parse_thermocouple_nope(self, data):
'\n \n '
return 32768<|docstring|>it means that the terminal is not used.<|endoftext|> |
fe79399d1ff967c1630b6ba9b4a8507b841a9b06835b2b06b8d8c4d891b9a628 | def parse_payload_24(self, byte_data):
'\n Hdr, Vit, Temp, TC1, TC2, TC3, TC4\n '
if (len(byte_data) != 11):
return False
return self.parse_by_format(byte_data, [('hdr', self.parse_number, 0, 1), ('vit', self.parse_vit, 1, 2), ('temp', self.parse_temp, 2, 3), ('thermocouple_1', self.pa... | Hdr, Vit, Temp, TC1, TC2, TC3, TC4 | parsers/parser_naltec.py | parse_payload_24 | tanupoo/lorawan-ss-as | 1 | python | def parse_payload_24(self, byte_data):
'\n \n '
if (len(byte_data) != 11):
return False
return self.parse_by_format(byte_data, [('hdr', self.parse_number, 0, 1), ('vit', self.parse_vit, 1, 2), ('temp', self.parse_temp, 2, 3), ('thermocouple_1', self.parse_thermocouple, 3, 5), ('thermoc... | def parse_payload_24(self, byte_data):
'\n \n '
if (len(byte_data) != 11):
return False
return self.parse_by_format(byte_data, [('hdr', self.parse_number, 0, 1), ('vit', self.parse_vit, 1, 2), ('temp', self.parse_temp, 2, 3), ('thermocouple_1', self.parse_thermocouple, 3, 5), ('thermoc... |
58515d49dbb0412f915709bd91229748ad12fd970d61969d273e15a64485c389 | def parse_payload_24x(self, byte_data):
'\n Hdr, Vit, Temp, TC1, [TC2, [TC3, [TC4]]]\n '
if (len(byte_data) not in [3, 5, 7, 9, 11]):
return False
base_format = [('hdr', self.parse_number, 0, 1), ('vit', self.parse_vit, 1, 2), ('temp', self.parse_temp, 2, 3)]
z = len(byte_data)
... | Hdr, Vit, Temp, TC1, [TC2, [TC3, [TC4]]] | parsers/parser_naltec.py | parse_payload_24x | tanupoo/lorawan-ss-as | 1 | python | def parse_payload_24x(self, byte_data):
'\n \n '
if (len(byte_data) not in [3, 5, 7, 9, 11]):
return False
base_format = [('hdr', self.parse_number, 0, 1), ('vit', self.parse_vit, 1, 2), ('temp', self.parse_temp, 2, 3)]
z = len(byte_data)
for i in range(3, z, 2):
j = (i... | def parse_payload_24x(self, byte_data):
'\n \n '
if (len(byte_data) not in [3, 5, 7, 9, 11]):
return False
base_format = [('hdr', self.parse_number, 0, 1), ('vit', self.parse_vit, 1, 2), ('temp', self.parse_temp, 2, 3)]
z = len(byte_data)
for i in range(3, z, 2):
j = (i... |
666c82ef8f1c3082f3d36d78d4a99f7d30066972b3677627b73daf52de03ab9b | def parse_bytes(self, byte_data):
'\n byte_data: payload in bytes\n return a dict object.\n '
format_tab = [{'hdr': 127, 'parser': self.parse_payload_7f}, {'hdr': 0, 'parser': self.parse_payload_00}, {'hdr': 35, 'parser': self.parse_payload_23}, {'hdr': 36, 'parser': self.parse_payload_24x}... | byte_data: payload in bytes
return a dict object. | parsers/parser_naltec.py | parse_bytes | tanupoo/lorawan-ss-as | 1 | python | def parse_bytes(self, byte_data):
'\n byte_data: payload in bytes\n return a dict object.\n '
format_tab = [{'hdr': 127, 'parser': self.parse_payload_7f}, {'hdr': 0, 'parser': self.parse_payload_00}, {'hdr': 35, 'parser': self.parse_payload_23}, {'hdr': 36, 'parser': self.parse_payload_24x}... | def parse_bytes(self, byte_data):
'\n byte_data: payload in bytes\n return a dict object.\n '
format_tab = [{'hdr': 127, 'parser': self.parse_payload_7f}, {'hdr': 0, 'parser': self.parse_payload_00}, {'hdr': 35, 'parser': self.parse_payload_23}, {'hdr': 36, 'parser': self.parse_payload_24x}... |
83206a2575f8db3278c7e477cac0e0580c8149fcc9ab37a5676d6e56e069cfa6 | def nice(v, digits=4):
'Fix v to a value with a given number of digits of precision'
if ((v == 0.0) or (not np.isfinite(v))):
return v
else:
sign = (v / abs(v))
place = floor(log10(abs(v)))
scale = (10 ** (place - (digits - 1)))
return ((sign * floor(((abs(v) / scale)... | Fix v to a value with a given number of digits of precision | bumps/gui/util.py | nice | vishalbelsare/bumps | 44 | python | def nice(v, digits=4):
if ((v == 0.0) or (not np.isfinite(v))):
return v
else:
sign = (v / abs(v))
place = floor(log10(abs(v)))
scale = (10 ** (place - (digits - 1)))
return ((sign * floor(((abs(v) / scale) + 0.5))) * scale) | def nice(v, digits=4):
if ((v == 0.0) or (not np.isfinite(v))):
return v
else:
sign = (v / abs(v))
place = floor(log10(abs(v)))
scale = (10 ** (place - (digits - 1)))
return ((sign * floor(((abs(v) / scale) + 0.5))) * scale)<|docstring|>Fix v to a value with a given ... |
d5bfe12a477cc39e69688bf7812494f007a4f9a351c8d37c9978e8358e4b4b5e | def timeout(func, args=(), kwargs={}, timeout_duration=1, default=None):
'This function will spawn a thread and run the given function using the args, kwargs and \n return the given default value if the timeout_duration is exceeded \n '
import threading
class PlayerThread(threading.Thread):
... | This function will spawn a thread and run the given function using the args, kwargs and
return the given default value if the timeout_duration is exceeded | Baroque_Chess_starter_V1.3/BaroqueGameMaster.py | timeout | aamiller/Rookoko | 0 | python | def timeout(func, args=(), kwargs={}, timeout_duration=1, default=None):
'This function will spawn a thread and run the given function using the args, kwargs and \n return the given default value if the timeout_duration is exceeded \n '
import threading
class PlayerThread(threading.Thread):
... | def timeout(func, args=(), kwargs={}, timeout_duration=1, default=None):
'This function will spawn a thread and run the given function using the args, kwargs and \n return the given default value if the timeout_duration is exceeded \n '
import threading
class PlayerThread(threading.Thread):
... |
b37edfec5a30667893a58a4383b8c928eadfd75f7c77cde9a4bbecd3da2a87f4 | def get_base_clock(self):
'Get the clock with the finest time unit, i.e. ticks the most cycles in a given time, or the highest clock_speed'
fastest_env = ps.max_by(self.envs, (lambda env: env.clock_speed))
clock = fastest_env.clock
return clock | Get the clock with the finest time unit, i.e. ticks the most cycles in a given time, or the highest clock_speed | slm_lab/env/__init__.py | get_base_clock | rhaps0dy/SLM-Lab | 1 | python | def get_base_clock(self):
fastest_env = ps.max_by(self.envs, (lambda env: env.clock_speed))
clock = fastest_env.clock
return clock | def get_base_clock(self):
fastest_env = ps.max_by(self.envs, (lambda env: env.clock_speed))
clock = fastest_env.clock
return clock<|docstring|>Get the clock with the finest time unit, i.e. ticks the most cycles in a given time, or the highest clock_speed<|endoftext|> |
e8c6443ce2fcb4f59cf03e0b590f538a4ce27bf3a46bf7129a02ffaf4769b6a3 | def preprocess(image, scale=2, max_rgb=1, to_y=True):
'Preprocess data.'
if ((image.get_shape()[(- 1)] != 1) and (image.get_shape()[(- 1)] != 3)):
image = tf.transpose(image, [0, 2, 3, 1])
(img_height, img_width) = image.get_shape()[1:3]
(crop_height, crop_width) = ((img_height - (2 * scale)), (... | Preprocess data. | vega/metrics/tensorflow/sr_metric.py | preprocess | zjzh/vega | 0 | python | def preprocess(image, scale=2, max_rgb=1, to_y=True):
if ((image.get_shape()[(- 1)] != 1) and (image.get_shape()[(- 1)] != 3)):
image = tf.transpose(image, [0, 2, 3, 1])
(img_height, img_width) = image.get_shape()[1:3]
(crop_height, crop_width) = ((img_height - (2 * scale)), (img_width - (2 * s... | def preprocess(image, scale=2, max_rgb=1, to_y=True):
if ((image.get_shape()[(- 1)] != 1) and (image.get_shape()[(- 1)] != 3)):
image = tf.transpose(image, [0, 2, 3, 1])
(img_height, img_width) = image.get_shape()[1:3]
(crop_height, crop_width) = ((img_height - (2 * scale)), (img_width - (2 * s... |
0e255188b6ade3f0e14c9185a49504480d448b6aae0cd2357e2c44861d2bf9fc | def __call__(self, output, target):
'Calculate sr metric.\n\n :param output: output of SR network\n :param target: ground truth from dataset\n :return: sr metric value\n '
shape_list = output.get_shape().as_list()
if (len(shape_list) == 5):
result = 0.0
for index ... | Calculate sr metric.
:param output: output of SR network
:param target: ground truth from dataset
:return: sr metric value | vega/metrics/tensorflow/sr_metric.py | __call__ | zjzh/vega | 0 | python | def __call__(self, output, target):
'Calculate sr metric.\n\n :param output: output of SR network\n :param target: ground truth from dataset\n :return: sr metric value\n '
shape_list = output.get_shape().as_list()
if (len(shape_list) == 5):
result = 0.0
for index ... | def __call__(self, output, target):
'Calculate sr metric.\n\n :param output: output of SR network\n :param target: ground truth from dataset\n :return: sr metric value\n '
shape_list = output.get_shape().as_list()
if (len(shape_list) == 5):
result = 0.0
for index ... |
b71a376804cee0caa335f2b5392d84d79aa2fcab6728137407624cd9e8305f02 | def __call__(self, output, target):
'Calculate sr metric.\n\n :param output: output of SR network\n :param target: ground truth from dataset\n :return: sr metric value\n '
shape_list = output.get_shape().as_list()
if (len(shape_list) == 5):
result = 0.0
for index ... | Calculate sr metric.
:param output: output of SR network
:param target: ground truth from dataset
:return: sr metric value | vega/metrics/tensorflow/sr_metric.py | __call__ | zjzh/vega | 0 | python | def __call__(self, output, target):
'Calculate sr metric.\n\n :param output: output of SR network\n :param target: ground truth from dataset\n :return: sr metric value\n '
shape_list = output.get_shape().as_list()
if (len(shape_list) == 5):
result = 0.0
for index ... | def __call__(self, output, target):
'Calculate sr metric.\n\n :param output: output of SR network\n :param target: ground truth from dataset\n :return: sr metric value\n '
shape_list = output.get_shape().as_list()
if (len(shape_list) == 5):
result = 0.0
for index ... |
9ab777ccc08b0f422008f03694853bc189059d978885c9404d1fd654eac14330 | def increase_size(sizes):
" Increase each sheep's size by 50"
for (idx, val) in enumerate(sizes):
sizes[idx] = (val + 50) | Increase each sheep's size by 50 | ss3/SE 2.py | increase_size | DuongVu39/C4E10_Duong | 0 | python | def increase_size(sizes):
" "
for (idx, val) in enumerate(sizes):
sizes[idx] = (val + 50) | def increase_size(sizes):
" "
for (idx, val) in enumerate(sizes):
sizes[idx] = (val + 50)<|docstring|>Increase each sheep's size by 50<|endoftext|> |
61e647776d5da258ffc64661ca89924627c39d9e14dc8c4b406ae0c7b47d82a8 | def initialize_analyticsreporting():
'Initializes an Analytics Reporting API V4 service object.\n\n Returns:\n An authorized Analytics Reporting API V4 service object.\n '
credentials = ServiceAccountCredentials.from_json_keyfile_name(KEY_FILE_LOCATION, SCOPES)
analytics = build('analyticsreporting', '... | Initializes an Analytics Reporting API V4 service object.
Returns:
An authorized Analytics Reporting API V4 service object. | stop_starting_start_stopping/google_analytics_api_pandas_reporting/005_google_analytics_api_pandas_reporting.py | initialize_analyticsreporting | bflaven/BlogArticlesExamples | 5 | python | def initialize_analyticsreporting():
'Initializes an Analytics Reporting API V4 service object.\n\n Returns:\n An authorized Analytics Reporting API V4 service object.\n '
credentials = ServiceAccountCredentials.from_json_keyfile_name(KEY_FILE_LOCATION, SCOPES)
analytics = build('analyticsreporting', '... | def initialize_analyticsreporting():
'Initializes an Analytics Reporting API V4 service object.\n\n Returns:\n An authorized Analytics Reporting API V4 service object.\n '
credentials = ServiceAccountCredentials.from_json_keyfile_name(KEY_FILE_LOCATION, SCOPES)
analytics = build('analyticsreporting', '... |
04da9df9e16e4024d0444f3705e1c353062de105b6c163ccba80bb9855371b37 | def get_report(analytics):
'Queries the Analytics Reporting API V4.\n\n Args:\n analytics: An authorized Analytics Reporting API V4 service object.\n Returns:\n The Analytics Reporting API V4 response.\n '
return analytics.reports().batchGet(body={'reportRequests': [{'viewId': VIEW_ID, 'dateRanges': [{... | Queries the Analytics Reporting API V4.
Args:
analytics: An authorized Analytics Reporting API V4 service object.
Returns:
The Analytics Reporting API V4 response. | stop_starting_start_stopping/google_analytics_api_pandas_reporting/005_google_analytics_api_pandas_reporting.py | get_report | bflaven/BlogArticlesExamples | 5 | python | def get_report(analytics):
'Queries the Analytics Reporting API V4.\n\n Args:\n analytics: An authorized Analytics Reporting API V4 service object.\n Returns:\n The Analytics Reporting API V4 response.\n '
return analytics.reports().batchGet(body={'reportRequests': [{'viewId': VIEW_ID, 'dateRanges': [{... | def get_report(analytics):
'Queries the Analytics Reporting API V4.\n\n Args:\n analytics: An authorized Analytics Reporting API V4 service object.\n Returns:\n The Analytics Reporting API V4 response.\n '
return analytics.reports().batchGet(body={'reportRequests': [{'viewId': VIEW_ID, 'dateRanges': [{... |
74a4591989de53d077f1637db4a0f3b2f4bf97d9495a444d2992912982ffe9df | def print_response(response):
'Parses and prints the Analytics Reporting API V4 response.\n\n Args:\n response: An Analytics Reporting API V4 response.\n '
for report in response.get('reports', []):
columnHeader = report.get('columnHeader', {})
dimensionHeaders = columnHeader.get('dimension... | Parses and prints the Analytics Reporting API V4 response.
Args:
response: An Analytics Reporting API V4 response. | stop_starting_start_stopping/google_analytics_api_pandas_reporting/005_google_analytics_api_pandas_reporting.py | print_response | bflaven/BlogArticlesExamples | 5 | python | def print_response(response):
'Parses and prints the Analytics Reporting API V4 response.\n\n Args:\n response: An Analytics Reporting API V4 response.\n '
for report in response.get('reports', []):
columnHeader = report.get('columnHeader', {})
dimensionHeaders = columnHeader.get('dimension... | def print_response(response):
'Parses and prints the Analytics Reporting API V4 response.\n\n Args:\n response: An Analytics Reporting API V4 response.\n '
for report in response.get('reports', []):
columnHeader = report.get('columnHeader', {})
dimensionHeaders = columnHeader.get('dimension... |
a5b28106c0740fef22abef50545d241857d1e6bf4afbb17fd1bd2b6554769b59 | def should_set_tablename(cls):
'\n Determine whether ``__tablename__`` should be automatically generated\n for a model.\n\n * If no class in the MRO sets a name, one should be generated.\n * If a declared attr is found, it should be used instead.\n * If a name is found, it should be used if the class... | Determine whether ``__tablename__`` should be automatically generated
for a model.
* If no class in the MRO sets a name, one should be generated.
* If a declared attr is found, it should be used instead.
* If a name is found, it should be used if the class is a mixin, otherwise
one should be generated.
* Abstract mo... | sqlalchemy_unchained/base_model_metaclass.py | should_set_tablename | barseghyanartur/sqlalchemy-unchained | 6 | python | def should_set_tablename(cls):
'\n Determine whether ``__tablename__`` should be automatically generated\n for a model.\n\n * If no class in the MRO sets a name, one should be generated.\n * If a declared attr is found, it should be used instead.\n * If a name is found, it should be used if the class... | def should_set_tablename(cls):
'\n Determine whether ``__tablename__`` should be automatically generated\n for a model.\n\n * If no class in the MRO sets a name, one should be generated.\n * If a declared attr is found, it should be used instead.\n * If a name is found, it should be used if the class... |
28061dd04b9ffa30160285eeb527339336045e9fb2380876ab267bb9de6154a4 | def __table_cls__(cls, *args, **kwargs):
'This is called by SQLAlchemy during mapper setup. It determines the\n final table object that the model will use.\n\n If no primary key is found, that indicates single-table inheritance,\n so no table will be created and ``__tablename__`` will be unset.... | This is called by SQLAlchemy during mapper setup. It determines the
final table object that the model will use.
If no primary key is found, that indicates single-table inheritance,
so no table will be created and ``__tablename__`` will be unset. | sqlalchemy_unchained/base_model_metaclass.py | __table_cls__ | barseghyanartur/sqlalchemy-unchained | 6 | python | def __table_cls__(cls, *args, **kwargs):
'This is called by SQLAlchemy during mapper setup. It determines the\n final table object that the model will use.\n\n If no primary key is found, that indicates single-table inheritance,\n so no table will be created and ``__tablename__`` will be unset.... | def __table_cls__(cls, *args, **kwargs):
'This is called by SQLAlchemy during mapper setup. It determines the\n final table object that the model will use.\n\n If no primary key is found, that indicates single-table inheritance,\n so no table will be created and ``__tablename__`` will be unset.... |
01e8196b4e3995f955db2802b9951a262141c3b201c360e36f9df1d7279cb99e | def _pre_mcs_init(cls):
'\n Callback for BaseModelMetaclass subclasses to run code just before a\n concrete Model class gets registered with SQLAlchemy.\n\n This is intended to be used for advanced meta options implementations.\n ' | Callback for BaseModelMetaclass subclasses to run code just before a
concrete Model class gets registered with SQLAlchemy.
This is intended to be used for advanced meta options implementations. | sqlalchemy_unchained/base_model_metaclass.py | _pre_mcs_init | barseghyanartur/sqlalchemy-unchained | 6 | python | def _pre_mcs_init(cls):
'\n Callback for BaseModelMetaclass subclasses to run code just before a\n concrete Model class gets registered with SQLAlchemy.\n\n This is intended to be used for advanced meta options implementations.\n ' | def _pre_mcs_init(cls):
'\n Callback for BaseModelMetaclass subclasses to run code just before a\n concrete Model class gets registered with SQLAlchemy.\n\n This is intended to be used for advanced meta options implementations.\n '<|docstring|>Callback for BaseModelMetaclass subclasses t... |
542d0b3e0eb0311e463ce0968945f59945441c105b8753207f659ea1a30a927c | def _post_mcs_init(cls):
'\n Callback for BaseModelMetaclass subclasses to run code just after a\n concrete Model class gets registered with SQLAlchemy.\n\n This is intended to be used for advanced meta options implementations.\n ' | Callback for BaseModelMetaclass subclasses to run code just after a
concrete Model class gets registered with SQLAlchemy.
This is intended to be used for advanced meta options implementations. | sqlalchemy_unchained/base_model_metaclass.py | _post_mcs_init | barseghyanartur/sqlalchemy-unchained | 6 | python | def _post_mcs_init(cls):
'\n Callback for BaseModelMetaclass subclasses to run code just after a\n concrete Model class gets registered with SQLAlchemy.\n\n This is intended to be used for advanced meta options implementations.\n ' | def _post_mcs_init(cls):
'\n Callback for BaseModelMetaclass subclasses to run code just after a\n concrete Model class gets registered with SQLAlchemy.\n\n This is intended to be used for advanced meta options implementations.\n '<|docstring|>Callback for BaseModelMetaclass subclasses t... |
b6e2c49464a3b485d4ed5c3fc7634069e717981b4ac7ebdb1d8db1e0537c81ec | def get_upload_time_str(assignment, user):
"Returns a datetime object with upload time user's last submission"
location = vmcheckerpaths.dir_user(assignment, user)
config_file = os.path.join(location, 'config')
if (not os.path.isdir(location)):
return None
if (not os.path.isfile(config_file)... | Returns a datetime object with upload time user's last submission | bin/submissions.py | get_upload_time_str | ironmissy/vmchecker | 1 | python | def get_upload_time_str(assignment, user):
location = vmcheckerpaths.dir_user(assignment, user)
config_file = os.path.join(location, 'config')
if (not os.path.isdir(location)):
return None
if (not os.path.isfile(config_file)):
_logger.warn('%s found, but config (%s) is missing', loc... | def get_upload_time_str(assignment, user):
location = vmcheckerpaths.dir_user(assignment, user)
config_file = os.path.join(location, 'config')
if (not os.path.isdir(location)):
return None
if (not os.path.isfile(config_file)):
_logger.warn('%s found, but config (%s) is missing', loc... |
b48c12a3d556c404b32562b8b2403628fd3a0e472d9208a445f552c0d92625c9 | def __init__(self, tmp_dir: TmpDir, organizer: Organizer, username: Optional[str], password: Optional[str]):
'Create a new http downloader.'
self._organizer = organizer
self._tmp_dir = tmp_dir
self._username = username
self._password = password
self._session = self._build_session() | Create a new http downloader. | PFERD/downloaders.py | __init__ | pavelzw/PFERD | 0 | python | def __init__(self, tmp_dir: TmpDir, organizer: Organizer, username: Optional[str], password: Optional[str]):
self._organizer = organizer
self._tmp_dir = tmp_dir
self._username = username
self._password = password
self._session = self._build_session() | def __init__(self, tmp_dir: TmpDir, organizer: Organizer, username: Optional[str], password: Optional[str]):
self._organizer = organizer
self._tmp_dir = tmp_dir
self._username = username
self._password = password
self._session = self._build_session()<|docstring|>Create a new http downloader.<|e... |
9d0e476fe5bc1cb1fc8900b8eb226375931c67770e0c47876a002ce87e43a78b | def download_all(self, infos: List[HttpDownloadInfo]) -> None:
'\n Download multiple files one after the other.\n '
for info in infos:
self.download(info) | Download multiple files one after the other. | PFERD/downloaders.py | download_all | pavelzw/PFERD | 0 | python | def download_all(self, infos: List[HttpDownloadInfo]) -> None:
'\n \n '
for info in infos:
self.download(info) | def download_all(self, infos: List[HttpDownloadInfo]) -> None:
'\n \n '
for info in infos:
self.download(info)<|docstring|>Download multiple files one after the other.<|endoftext|> |
5a1b7bfe971a760b5cb97a77837306db4823be31d1b8870bbdb50ad665f21b52 | def download(self, info: HttpDownloadInfo) -> None:
'\n Download a single file.\n '
with self._session.get(info.url, params=info.parameters, stream=True) as response:
if (response.status_code == 200):
tmp_file = self._tmp_dir.new_path()
stream_to_path(response, tmp_... | Download a single file. | PFERD/downloaders.py | download | pavelzw/PFERD | 0 | python | def download(self, info: HttpDownloadInfo) -> None:
'\n \n '
with self._session.get(info.url, params=info.parameters, stream=True) as response:
if (response.status_code == 200):
tmp_file = self._tmp_dir.new_path()
stream_to_path(response, tmp_file, info.path.name)
... | def download(self, info: HttpDownloadInfo) -> None:
'\n \n '
with self._session.get(info.url, params=info.parameters, stream=True) as response:
if (response.status_code == 200):
tmp_file = self._tmp_dir.new_path()
stream_to_path(response, tmp_file, info.path.name)
... |
3684202c3c5c1cf67a04dfcb477b1e350cfacfaf8f4ebf21c46c9f50a02a72ef | def scraping_mg_initial_period(masp, senha, stop_period, headless, pdf):
'\n Função responsável pela busca de informações dos contracheques dos servidores do Estado de Minas Gerais até o período desejado.\n Parâmetros:\n -------\n masp: string\n Masp do servidor do Estado de Minas Gerais\n senha: string\n ... | Função responsável pela busca de informações dos contracheques dos servidores do Estado de Minas Gerais até o período desejado.
Parâmetros:
-------
masp: string
Masp do servidor do Estado de Minas Gerais
senha: string
Senha de acesso ao Portal do servidor do Estado de Minas Gerais
stop-period: string
Período fina... | meu_contracheque/mg_initial_period.py | scraping_mg_initial_period | gabrielbdornas/meu-contracheque | 0 | python | def scraping_mg_initial_period(masp, senha, stop_period, headless, pdf):
'\n Função responsável pela busca de informações dos contracheques dos servidores do Estado de Minas Gerais até o período desejado.\n Parâmetros:\n -------\n masp: string\n Masp do servidor do Estado de Minas Gerais\n senha: string\n ... | def scraping_mg_initial_period(masp, senha, stop_period, headless, pdf):
'\n Função responsável pela busca de informações dos contracheques dos servidores do Estado de Minas Gerais até o período desejado.\n Parâmetros:\n -------\n masp: string\n Masp do servidor do Estado de Minas Gerais\n senha: string\n ... |
d24f7c4e6f10936f8906c9350267b3f336f4e529229a355b87d59a15f112979b | @click.command(name='ate-periodo-inicial')
@click.pass_context
@click.option('--stop-period', '-sp', required=True, help='Último período a ser pesquisado. Exemplo: 02/2008')
def scraping_mg_initial_period_cli(ctx, stop_period):
'\n Função CLI responsável pela busca de informações dos contracheques dos servidores d... | Função CLI responsável pela busca de informações dos contracheques dos servidores do Estado de Minas Gerais até o período desejado.
Por padrão, função buscará masp e senha nas variáveis de ambiente MASP e PORTAL_PWD cadastradas na máquina ou
em arquivo .env.
Parâmetros:
----------
masp: string
Masp do servidor do E... | meu_contracheque/mg_initial_period.py | scraping_mg_initial_period_cli | gabrielbdornas/meu-contracheque | 0 | python | @click.command(name='ate-periodo-inicial')
@click.pass_context
@click.option('--stop-period', '-sp', required=True, help='Último período a ser pesquisado. Exemplo: 02/2008')
def scraping_mg_initial_period_cli(ctx, stop_period):
'\n Função CLI responsável pela busca de informações dos contracheques dos servidores d... | @click.command(name='ate-periodo-inicial')
@click.pass_context
@click.option('--stop-period', '-sp', required=True, help='Último período a ser pesquisado. Exemplo: 02/2008')
def scraping_mg_initial_period_cli(ctx, stop_period):
'\n Função CLI responsável pela busca de informações dos contracheques dos servidores d... |
5ea7edf25257aabfbd20a0e01a48b756ad0b33b6bdaa2380f2480952469bc44c | def __init__(self, id=None, url=None, links=None):
'BatchWebhook - a model defined in Swagger'
self._id = None
self._url = None
self._links = None
self.discriminator = None
if (id is not None):
self.id = id
if (url is not None):
self.url = url
if (links is not None):
... | BatchWebhook - a model defined in Swagger | mailchimp_marketing_asyncio/models/batch_webhook.py | __init__ | john-parton/mailchimp-asyncio | 0 | python | def __init__(self, id=None, url=None, links=None):
self._id = None
self._url = None
self._links = None
self.discriminator = None
if (id is not None):
self.id = id
if (url is not None):
self.url = url
if (links is not None):
self.links = links | def __init__(self, id=None, url=None, links=None):
self._id = None
self._url = None
self._links = None
self.discriminator = None
if (id is not None):
self.id = id
if (url is not None):
self.url = url
if (links is not None):
self.links = links<|docstring|>BatchWeb... |
b18eac0b77304fbcb9c2f79e9a082c48bd50026eaddb8a5e2baadd872b36c35c | @property
def id(self):
'Gets the id of this BatchWebhook. # noqa: E501\n\n A string that uniquely identifies this Batch Webhook. # noqa: E501\n\n :return: The id of this BatchWebhook. # noqa: E501\n :rtype: str\n '
return self._id | Gets the id of this BatchWebhook. # noqa: E501
A string that uniquely identifies this Batch Webhook. # noqa: E501
:return: The id of this BatchWebhook. # noqa: E501
:rtype: str | mailchimp_marketing_asyncio/models/batch_webhook.py | id | john-parton/mailchimp-asyncio | 0 | python | @property
def id(self):
'Gets the id of this BatchWebhook. # noqa: E501\n\n A string that uniquely identifies this Batch Webhook. # noqa: E501\n\n :return: The id of this BatchWebhook. # noqa: E501\n :rtype: str\n '
return self._id | @property
def id(self):
'Gets the id of this BatchWebhook. # noqa: E501\n\n A string that uniquely identifies this Batch Webhook. # noqa: E501\n\n :return: The id of this BatchWebhook. # noqa: E501\n :rtype: str\n '
return self._id<|docstring|>Gets the id of this BatchWebhook. # ... |
95c8a4f4880f4139c0552c874313b528bf454e0d1ff5309bc68b8a41c7e5999d | @id.setter
def id(self, id):
'Sets the id of this BatchWebhook.\n\n A string that uniquely identifies this Batch Webhook. # noqa: E501\n\n :param id: The id of this BatchWebhook. # noqa: E501\n :type: str\n '
self._id = id | Sets the id of this BatchWebhook.
A string that uniquely identifies this Batch Webhook. # noqa: E501
:param id: The id of this BatchWebhook. # noqa: E501
:type: str | mailchimp_marketing_asyncio/models/batch_webhook.py | id | john-parton/mailchimp-asyncio | 0 | python | @id.setter
def id(self, id):
'Sets the id of this BatchWebhook.\n\n A string that uniquely identifies this Batch Webhook. # noqa: E501\n\n :param id: The id of this BatchWebhook. # noqa: E501\n :type: str\n '
self._id = id | @id.setter
def id(self, id):
'Sets the id of this BatchWebhook.\n\n A string that uniquely identifies this Batch Webhook. # noqa: E501\n\n :param id: The id of this BatchWebhook. # noqa: E501\n :type: str\n '
self._id = id<|docstring|>Sets the id of this BatchWebhook.
A string tha... |
379e580dfd5a225d467cf9e54313150604836a65fe434c2477527830a1111a8f | @property
def url(self):
'Gets the url of this BatchWebhook. # noqa: E501\n\n A valid URL for the Webhook. # noqa: E501\n\n :return: The url of this BatchWebhook. # noqa: E501\n :rtype: str\n '
return self._url | Gets the url of this BatchWebhook. # noqa: E501
A valid URL for the Webhook. # noqa: E501
:return: The url of this BatchWebhook. # noqa: E501
:rtype: str | mailchimp_marketing_asyncio/models/batch_webhook.py | url | john-parton/mailchimp-asyncio | 0 | python | @property
def url(self):
'Gets the url of this BatchWebhook. # noqa: E501\n\n A valid URL for the Webhook. # noqa: E501\n\n :return: The url of this BatchWebhook. # noqa: E501\n :rtype: str\n '
return self._url | @property
def url(self):
'Gets the url of this BatchWebhook. # noqa: E501\n\n A valid URL for the Webhook. # noqa: E501\n\n :return: The url of this BatchWebhook. # noqa: E501\n :rtype: str\n '
return self._url<|docstring|>Gets the url of this BatchWebhook. # noqa: E501
A valid ... |
f80e817d5d23feb7be47159d998dfdaea3e09493615631c71048efe636413f4b | @url.setter
def url(self, url):
'Sets the url of this BatchWebhook.\n\n A valid URL for the Webhook. # noqa: E501\n\n :param url: The url of this BatchWebhook. # noqa: E501\n :type: str\n '
self._url = url | Sets the url of this BatchWebhook.
A valid URL for the Webhook. # noqa: E501
:param url: The url of this BatchWebhook. # noqa: E501
:type: str | mailchimp_marketing_asyncio/models/batch_webhook.py | url | john-parton/mailchimp-asyncio | 0 | python | @url.setter
def url(self, url):
'Sets the url of this BatchWebhook.\n\n A valid URL for the Webhook. # noqa: E501\n\n :param url: The url of this BatchWebhook. # noqa: E501\n :type: str\n '
self._url = url | @url.setter
def url(self, url):
'Sets the url of this BatchWebhook.\n\n A valid URL for the Webhook. # noqa: E501\n\n :param url: The url of this BatchWebhook. # noqa: E501\n :type: str\n '
self._url = url<|docstring|>Sets the url of this BatchWebhook.
A valid URL for the Webhook.... |
2b24d940032510de2981cd7b4f18b90a0569430ac44c4c493bc2c484ff5d2925 | @property
def links(self):
'Gets the links of this BatchWebhook. # noqa: E501\n\n A list of link types and descriptions for the API schema documents. # noqa: E501\n\n :return: The links of this BatchWebhook. # noqa: E501\n :rtype: list[ResourceLink]\n '
return self._links | Gets the links of this BatchWebhook. # noqa: E501
A list of link types and descriptions for the API schema documents. # noqa: E501
:return: The links of this BatchWebhook. # noqa: E501
:rtype: list[ResourceLink] | mailchimp_marketing_asyncio/models/batch_webhook.py | links | john-parton/mailchimp-asyncio | 0 | python | @property
def links(self):
'Gets the links of this BatchWebhook. # noqa: E501\n\n A list of link types and descriptions for the API schema documents. # noqa: E501\n\n :return: The links of this BatchWebhook. # noqa: E501\n :rtype: list[ResourceLink]\n '
return self._links | @property
def links(self):
'Gets the links of this BatchWebhook. # noqa: E501\n\n A list of link types and descriptions for the API schema documents. # noqa: E501\n\n :return: The links of this BatchWebhook. # noqa: E501\n :rtype: list[ResourceLink]\n '
return self._links<|docstri... |
576e24cd3ace855f682aab9cb85bb8ec15ff384d4d4a2b8b46119e5e3f12a554 | @links.setter
def links(self, links):
'Sets the links of this BatchWebhook.\n\n A list of link types and descriptions for the API schema documents. # noqa: E501\n\n :param links: The links of this BatchWebhook. # noqa: E501\n :type: list[ResourceLink]\n '
self._links = links | Sets the links of this BatchWebhook.
A list of link types and descriptions for the API schema documents. # noqa: E501
:param links: The links of this BatchWebhook. # noqa: E501
:type: list[ResourceLink] | mailchimp_marketing_asyncio/models/batch_webhook.py | links | john-parton/mailchimp-asyncio | 0 | python | @links.setter
def links(self, links):
'Sets the links of this BatchWebhook.\n\n A list of link types and descriptions for the API schema documents. # noqa: E501\n\n :param links: The links of this BatchWebhook. # noqa: E501\n :type: list[ResourceLink]\n '
self._links = links | @links.setter
def links(self, links):
'Sets the links of this BatchWebhook.\n\n A list of link types and descriptions for the API schema documents. # noqa: E501\n\n :param links: The links of this BatchWebhook. # noqa: E501\n :type: list[ResourceLink]\n '
self._links = links<|docst... |
35f2145ce70e6111ab658e06b69b2a58db1e250fae53ae5766deccf3b7131c41 | def to_dict(self):
'Returns the model properties as a dict'
result = {}
for (attr, _) in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map((lambda x: (x.to_dict() if hasattr(x, 'to_dict') else x)), value))
e... | Returns the model properties as a dict | mailchimp_marketing_asyncio/models/batch_webhook.py | to_dict | john-parton/mailchimp-asyncio | 0 | python | def to_dict(self):
result = {}
for (attr, _) in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map((lambda x: (x.to_dict() if hasattr(x, 'to_dict') else x)), value))
elif hasattr(value, 'to_dict'):
... | def to_dict(self):
result = {}
for (attr, _) in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map((lambda x: (x.to_dict() if hasattr(x, 'to_dict') else x)), value))
elif hasattr(value, 'to_dict'):
... |
cbb19eaa2fc8a113d9e32f924ef280a7e97563f8915f94f65dab438997af2e99 | def to_str(self):
'Returns the string representation of the model'
return pprint.pformat(self.to_dict()) | Returns the string representation of the model | mailchimp_marketing_asyncio/models/batch_webhook.py | to_str | john-parton/mailchimp-asyncio | 0 | python | def to_str(self):
return pprint.pformat(self.to_dict()) | def to_str(self):
return pprint.pformat(self.to_dict())<|docstring|>Returns the string representation of the model<|endoftext|> |
772243a2c2b3261a9b954d07aaf295e3c1242a579a495e2d6a5679c677861703 | def __repr__(self):
'For `print` and `pprint`'
return self.to_str() | For `print` and `pprint` | mailchimp_marketing_asyncio/models/batch_webhook.py | __repr__ | john-parton/mailchimp-asyncio | 0 | python | def __repr__(self):
return self.to_str() | def __repr__(self):
return self.to_str()<|docstring|>For `print` and `pprint`<|endoftext|> |
f2b4b8f2cf64966388213fa2d6d3d50a84976aaf5e7d902dec624670887680a6 | def __eq__(self, other):
'Returns true if both objects are equal'
if (not isinstance(other, BatchWebhook)):
return False
return (self.__dict__ == other.__dict__) | Returns true if both objects are equal | mailchimp_marketing_asyncio/models/batch_webhook.py | __eq__ | john-parton/mailchimp-asyncio | 0 | python | def __eq__(self, other):
if (not isinstance(other, BatchWebhook)):
return False
return (self.__dict__ == other.__dict__) | def __eq__(self, other):
if (not isinstance(other, BatchWebhook)):
return False
return (self.__dict__ == other.__dict__)<|docstring|>Returns true if both objects are equal<|endoftext|> |
43dc6740163eb9fc1161d09cb2208a64c7ad0cc8d9c8637ac3264522d3ec7e42 | def __ne__(self, other):
'Returns true if both objects are not equal'
return (not (self == other)) | Returns true if both objects are not equal | mailchimp_marketing_asyncio/models/batch_webhook.py | __ne__ | john-parton/mailchimp-asyncio | 0 | python | def __ne__(self, other):
return (not (self == other)) | def __ne__(self, other):
return (not (self == other))<|docstring|>Returns true if both objects are not equal<|endoftext|> |
2f87d754d95e5109330f278f03d38ad595abffbf450cb892611fce17477f9c99 | def equalValue(self, size):
'\n x 等间距划分分箱 -> (0-0.1,0.1-0.2...)\n :param size:\n :return:\n '
self.range_dict = {}
self.bins = np.linspace(min(self.x), max(self.x), (size + 1))
for i in range((len(self.bins) - 1)):
self.range_dict[(self.bins[i], self.bins[(i + 1)])] ... | x 等间距划分分箱 -> (0-0.1,0.1-0.2...)
:param size:
:return: | lapras/utils/simpleMethods.py | equalValue | yhangang/Lapras | 13 | python | def equalValue(self, size):
'\n x 等间距划分分箱 -> (0-0.1,0.1-0.2...)\n :param size:\n :return:\n '
self.range_dict = {}
self.bins = np.linspace(min(self.x), max(self.x), (size + 1))
for i in range((len(self.bins) - 1)):
self.range_dict[(self.bins[i], self.bins[(i + 1)])] ... | def equalValue(self, size):
'\n x 等间距划分分箱 -> (0-0.1,0.1-0.2...)\n :param size:\n :return:\n '
self.range_dict = {}
self.bins = np.linspace(min(self.x), max(self.x), (size + 1))
for i in range((len(self.bins) - 1)):
self.range_dict[(self.bins[i], self.bins[(i + 1)])] ... |
9ff3fe928c0b465e88d8872d7bbcdc63eab9a43081e928a1907789667e2bd5ad | def equalHist(self, size):
'\n 基于np.histogram分箱\n :param size: bin数目\n :return:\n '
self.down = {}
(self.hist, self.bins) = np.histogram(self.x, bins=size)
for i in range((len(self.bins) - 1)):
start = self.bins[i]
end = self.bins[(i + 1)]
self.range_d... | 基于np.histogram分箱
:param size: bin数目
:return: | lapras/utils/simpleMethods.py | equalHist | yhangang/Lapras | 13 | python | def equalHist(self, size):
'\n 基于np.histogram分箱\n :param size: bin数目\n :return:\n '
self.down = {}
(self.hist, self.bins) = np.histogram(self.x, bins=size)
for i in range((len(self.bins) - 1)):
start = self.bins[i]
end = self.bins[(i + 1)]
self.range_d... | def equalHist(self, size):
'\n 基于np.histogram分箱\n :param size: bin数目\n :return:\n '
self.down = {}
(self.hist, self.bins) = np.histogram(self.x, bins=size)
for i in range((len(self.bins) - 1)):
start = self.bins[i]
end = self.bins[(i + 1)]
self.range_d... |
6198a08e39ed5e258a8735b96824ee7a683ded8c3a2d4465185041bc9bdbb2b8 | def equalSize(self, size):
'\n 每个分箱样本数平均\n :param size:\n :return:\n '
self.range_dict = {}
breakpoints = ((np.arange(0, (size + 1)) / size) * 100)
self.bins = [np.percentile(self.x, b) for b in breakpoints]
for i in range((len(self.bins) - 1)):
start = self.bins[... | 每个分箱样本数平均
:param size:
:return: | lapras/utils/simpleMethods.py | equalSize | yhangang/Lapras | 13 | python | def equalSize(self, size):
'\n 每个分箱样本数平均\n :param size:\n :return:\n '
self.range_dict = {}
breakpoints = ((np.arange(0, (size + 1)) / size) * 100)
self.bins = [np.percentile(self.x, b) for b in breakpoints]
for i in range((len(self.bins) - 1)):
start = self.bins[... | def equalSize(self, size):
'\n 每个分箱样本数平均\n :param size:\n :return:\n '
self.range_dict = {}
breakpoints = ((np.arange(0, (size + 1)) / size) * 100)
self.bins = [np.percentile(self.x, b) for b in breakpoints]
for i in range((len(self.bins) - 1)):
start = self.bins[... |
e7a9e45e233e602cd6ddd3c5b3932532f496a48fa0935b639ad637d7264d347b | def everysplit(self):
'\n 最细粒度切分\n :return:\n '
if ((len(set(self.x)) <= 10) and (not self.force)):
self.bins = np.array(list(self.x))
else:
x_sort = sorted(list(set(self.x)), reverse=False)
bins = [x_sort[0]]
for i in range((len(x_sort) - 1)):
... | 最细粒度切分
:return: | lapras/utils/simpleMethods.py | everysplit | yhangang/Lapras | 13 | python | def everysplit(self):
'\n 最细粒度切分\n :return:\n '
if ((len(set(self.x)) <= 10) and (not self.force)):
self.bins = np.array(list(self.x))
else:
x_sort = sorted(list(set(self.x)), reverse=False)
bins = [x_sort[0]]
for i in range((len(x_sort) - 1)):
... | def everysplit(self):
'\n 最细粒度切分\n :return:\n '
if ((len(set(self.x)) <= 10) and (not self.force)):
self.bins = np.array(list(self.x))
else:
x_sort = sorted(list(set(self.x)), reverse=False)
bins = [x_sort[0]]
for i in range((len(x_sort) - 1)):
... |
0845ee0a7d594cd25492604286418432869017bfe011625ddb8e35cb03eaf790 | def __init__(self, logger):
'\n Create a :class:`Kmeans_model` instance.\n\n Parameters\n ----------\n logger: :class:`mylogging.Logger`\n Logging object instance.\n '
self.logger = logger
self.is_trained = False
self.supported_formats = ['pkl', 'onnx', 'pmm... | Create a :class:`Kmeans_model` instance.
Parameters
----------
logger: :class:`mylogging.Logger`
Logging object instance. | MMLL/models/POM1/Kmeans/Kmeans.py | __init__ | Musketeer-H2020/MMLL-Robust | 0 | python | def __init__(self, logger):
'\n Create a :class:`Kmeans_model` instance.\n\n Parameters\n ----------\n logger: :class:`mylogging.Logger`\n Logging object instance.\n '
self.logger = logger
self.is_trained = False
self.supported_formats = ['pkl', 'onnx', 'pmm... | def __init__(self, logger):
'\n Create a :class:`Kmeans_model` instance.\n\n Parameters\n ----------\n logger: :class:`mylogging.Logger`\n Logging object instance.\n '
self.logger = logger
self.is_trained = False
self.supported_formats = ['pkl', 'onnx', 'pmm... |
43464c7b3631948aa81ffd9ea8dd73ee8964706f6b8577308232f6f70fedf22c | def predict(self, X_b):
'\n Uses the Kmeans model to predict new outputs given the inputs.\n\n Parameters\n ----------\n X_b: ndarray\n Array containing the input patterns.\n\n Returns\n -------\n preds: ndarray\n Array containing the prediction... | Uses the Kmeans model to predict new outputs given the inputs.
Parameters
----------
X_b: ndarray
Array containing the input patterns.
Returns
-------
preds: ndarray
Array containing the predictions. | MMLL/models/POM1/Kmeans/Kmeans.py | predict | Musketeer-H2020/MMLL-Robust | 0 | python | def predict(self, X_b):
'\n Uses the Kmeans model to predict new outputs given the inputs.\n\n Parameters\n ----------\n X_b: ndarray\n Array containing the input patterns.\n\n Returns\n -------\n preds: ndarray\n Array containing the prediction... | def predict(self, X_b):
'\n Uses the Kmeans model to predict new outputs given the inputs.\n\n Parameters\n ----------\n X_b: ndarray\n Array containing the input patterns.\n\n Returns\n -------\n preds: ndarray\n Array containing the prediction... |
2ebda6fb93b4607450d46ce4e435b54bb7ceb98c760a23a4e49c27ebe1509aff | def __init__(self, comms, logger, verbose=False, NC=None, Nmaxiter=None, tolerance=None):
'\n Create a :class:`Kmeans_Master` instance.\n\n Parameters\n ----------\n comms: :class:`Comms_master`\n Object providing communication functionalities.\n\n logger: :class:`mylog... | Create a :class:`Kmeans_Master` instance.
Parameters
----------
comms: :class:`Comms_master`
Object providing communication functionalities.
logger: :class:`mylogging.Logger`
Logging object instance.
verbose: boolean
Indicates whether to print messages on screen nor not.
NC: int
Number of clusters.
... | MMLL/models/POM1/Kmeans/Kmeans.py | __init__ | Musketeer-H2020/MMLL-Robust | 0 | python | def __init__(self, comms, logger, verbose=False, NC=None, Nmaxiter=None, tolerance=None):
'\n Create a :class:`Kmeans_Master` instance.\n\n Parameters\n ----------\n comms: :class:`Comms_master`\n Object providing communication functionalities.\n\n logger: :class:`mylog... | def __init__(self, comms, logger, verbose=False, NC=None, Nmaxiter=None, tolerance=None):
'\n Create a :class:`Kmeans_Master` instance.\n\n Parameters\n ----------\n comms: :class:`Comms_master`\n Object providing communication functionalities.\n\n logger: :class:`mylog... |
cd6cdac46b8b652c8dd83b9932bcd6a71f8e6b89dafb8813987d8e37ddb57a21 | def Update_State_Master(self):
'\n Function to control the state of the execution.\n\n Parameters\n ----------\n None\n '
if (self.state_dict['CN'] == 'START_TRAIN'):
self.state_dict['CN'] = 'SEND_CENTROIDS'
if self.checkAllStates('INIT_CENTROIDS', self.state_dict)... | Function to control the state of the execution.
Parameters
----------
None | MMLL/models/POM1/Kmeans/Kmeans.py | Update_State_Master | Musketeer-H2020/MMLL-Robust | 0 | python | def Update_State_Master(self):
'\n Function to control the state of the execution.\n\n Parameters\n ----------\n None\n '
if (self.state_dict['CN'] == 'START_TRAIN'):
self.state_dict['CN'] = 'SEND_CENTROIDS'
if self.checkAllStates('INIT_CENTROIDS', self.state_dict)... | def Update_State_Master(self):
'\n Function to control the state of the execution.\n\n Parameters\n ----------\n None\n '
if (self.state_dict['CN'] == 'START_TRAIN'):
self.state_dict['CN'] = 'SEND_CENTROIDS'
if self.checkAllStates('INIT_CENTROIDS', self.state_dict)... |
9290b7015a18dea49ff1169b456516301a58e445e504d612becdf80fc6f06f52 | def TakeAction_Master(self):
'\n Function to take actions according to the state.\n\n Parameters\n ----------\n None\n '
to = 'MLmodel'
if (self.state_dict['CN'] == 'SEND_CENTROIDS'):
action = 'SEND_CENTROIDS'
data = {'num_centroids': self.num_centroids}
... | Function to take actions according to the state.
Parameters
----------
None | MMLL/models/POM1/Kmeans/Kmeans.py | TakeAction_Master | Musketeer-H2020/MMLL-Robust | 0 | python | def TakeAction_Master(self):
'\n Function to take actions according to the state.\n\n Parameters\n ----------\n None\n '
to = 'MLmodel'
if (self.state_dict['CN'] == 'SEND_CENTROIDS'):
action = 'SEND_CENTROIDS'
data = {'num_centroids': self.num_centroids}
... | def TakeAction_Master(self):
'\n Function to take actions according to the state.\n\n Parameters\n ----------\n None\n '
to = 'MLmodel'
if (self.state_dict['CN'] == 'SEND_CENTROIDS'):
action = 'SEND_CENTROIDS'
data = {'num_centroids': self.num_centroids}
... |
2686a0ad2a637332430d325935ebb52a17cc150186f935df054caa69329e8ef1 | def ProcessReceivedPacket_Master_(self, packet, sender):
'\n Process the received packet at master.\n\n Parameters\n ----------\n packet: dictionary\n Packet received from a worker.\n\n sender: string\n Identification of the sender.\n '
if (packet[... | Process the received packet at master.
Parameters
----------
packet: dictionary
Packet received from a worker.
sender: string
Identification of the sender. | MMLL/models/POM1/Kmeans/Kmeans.py | ProcessReceivedPacket_Master_ | Musketeer-H2020/MMLL-Robust | 0 | python | def ProcessReceivedPacket_Master_(self, packet, sender):
'\n Process the received packet at master.\n\n Parameters\n ----------\n packet: dictionary\n Packet received from a worker.\n\n sender: string\n Identification of the sender.\n '
if (packet[... | def ProcessReceivedPacket_Master_(self, packet, sender):
'\n Process the received packet at master.\n\n Parameters\n ----------\n packet: dictionary\n Packet received from a worker.\n\n sender: string\n Identification of the sender.\n '
if (packet[... |
5b86c58c0d9e4f7cff1693a84714d7c27cfb9fdb20cdb57ee52cc4b0c213157c | def check_empty_clusters(self, array):
'\n Function to check if there are empty clusters in array.\n \n Parameters\n ----------\n array: numpy array\n Array with centroids.\n\n Returns\n -------\n flag: boolean\n Flag indicating whether t... | Function to check if there are empty clusters in array.
Parameters
----------
array: numpy array
Array with centroids.
Returns
-------
flag: boolean
Flag indicating whether there are empty clusters. | MMLL/models/POM1/Kmeans/Kmeans.py | check_empty_clusters | Musketeer-H2020/MMLL-Robust | 0 | python | def check_empty_clusters(self, array):
'\n Function to check if there are empty clusters in array.\n \n Parameters\n ----------\n array: numpy array\n Array with centroids.\n\n Returns\n -------\n flag: boolean\n Flag indicating whether t... | def check_empty_clusters(self, array):
'\n Function to check if there are empty clusters in array.\n \n Parameters\n ----------\n array: numpy array\n Array with centroids.\n\n Returns\n -------\n flag: boolean\n Flag indicating whether t... |
50db99f2415aa404a602f0cc212f064bfd51da096708d40563869025ac0d5ae2 | def __init__(self, master_address, comms, logger, verbose=False, Xtr_b=None):
'\n Create a :class:`Kmeans_Worker` instance.\n\n Parameters\n ----------\n master_address: string\n Identifier of the master instance.\n\n comms: :class:`Comms_worker`\n Object pro... | Create a :class:`Kmeans_Worker` instance.
Parameters
----------
master_address: string
Identifier of the master instance.
comms: :class:`Comms_worker`
Object providing communication functionalities.
logger: :class:`mylogging.Logger`
Logging object instance.
verbose: boolean
Indicates whether to prin... | MMLL/models/POM1/Kmeans/Kmeans.py | __init__ | Musketeer-H2020/MMLL-Robust | 0 | python | def __init__(self, master_address, comms, logger, verbose=False, Xtr_b=None):
'\n Create a :class:`Kmeans_Worker` instance.\n\n Parameters\n ----------\n master_address: string\n Identifier of the master instance.\n\n comms: :class:`Comms_worker`\n Object pro... | def __init__(self, master_address, comms, logger, verbose=False, Xtr_b=None):
'\n Create a :class:`Kmeans_Worker` instance.\n\n Parameters\n ----------\n master_address: string\n Identifier of the master instance.\n\n comms: :class:`Comms_worker`\n Object pro... |
e6eceaea742da58c6bc436eae0899d2db274774ced44028cb2a45bb2ce80a339 | def ProcessReceivedPacket_Worker(self, packet):
'\n Process the received packet at worker.\n\n Parameters\n ----------\n packet: dictionary\n Packet received from the master.\n '
if (packet['action'] == 'SEND_CENTROIDS'):
self.display((self.name + (' %s: Ini... | Process the received packet at worker.
Parameters
----------
packet: dictionary
Packet received from the master. | MMLL/models/POM1/Kmeans/Kmeans.py | ProcessReceivedPacket_Worker | Musketeer-H2020/MMLL-Robust | 0 | python | def ProcessReceivedPacket_Worker(self, packet):
'\n Process the received packet at worker.\n\n Parameters\n ----------\n packet: dictionary\n Packet received from the master.\n '
if (packet['action'] == 'SEND_CENTROIDS'):
self.display((self.name + (' %s: Ini... | def ProcessReceivedPacket_Worker(self, packet):
'\n Process the received packet at worker.\n\n Parameters\n ----------\n packet: dictionary\n Packet received from the master.\n '
if (packet['action'] == 'SEND_CENTROIDS'):
self.display((self.name + (' %s: Ini... |
426f866b17e5d87c6d2ac8ba44c70d5922814e7fb956188227aed088007fee24 | def naive_sharding(self, ds, k):
'\n Initialize cluster centroids using deterministic naive sharding algorithm.\n \n Parameters\n ----------\n ds: numpy array\n The dataset to be used for centroid initialization.\n k: int\n The desired number of clusters f... | Initialize cluster centroids using deterministic naive sharding algorithm.
Parameters
----------
ds: numpy array
The dataset to be used for centroid initialization.
k: int
The desired number of clusters for which centroids are required.
Returns
-------
centroids : numpy array
Collection of k centroids as ... | MMLL/models/POM1/Kmeans/Kmeans.py | naive_sharding | Musketeer-H2020/MMLL-Robust | 0 | python | def naive_sharding(self, ds, k):
'\n Initialize cluster centroids using deterministic naive sharding algorithm.\n \n Parameters\n ----------\n ds: numpy array\n The dataset to be used for centroid initialization.\n k: int\n The desired number of clusters f... | def naive_sharding(self, ds, k):
'\n Initialize cluster centroids using deterministic naive sharding algorithm.\n \n Parameters\n ----------\n ds: numpy array\n The dataset to be used for centroid initialization.\n k: int\n The desired number of clusters f... |
88ed7a633db1a14f07fcc52eb3c7c9a555c6fbe792e4679e787f1dfccd0721ce | def _get_mean(self, sums, step):
'\n Vectorizable ufunc for getting means of summed shard columns.\n \n Parameters\n ----------\n sums: float\n The summed shard columns.\n step: int\n The number of instances per shard.\n\n Returns\n -----... | Vectorizable ufunc for getting means of summed shard columns.
Parameters
----------
sums: float
The summed shard columns.
step: int
The number of instances per shard.
Returns
-------
sums/step (means): numpy array
The means of the shard columns. | MMLL/models/POM1/Kmeans/Kmeans.py | _get_mean | Musketeer-H2020/MMLL-Robust | 0 | python | def _get_mean(self, sums, step):
'\n Vectorizable ufunc for getting means of summed shard columns.\n \n Parameters\n ----------\n sums: float\n The summed shard columns.\n step: int\n The number of instances per shard.\n\n Returns\n -----... | def _get_mean(self, sums, step):
'\n Vectorizable ufunc for getting means of summed shard columns.\n \n Parameters\n ----------\n sums: float\n The summed shard columns.\n step: int\n The number of instances per shard.\n\n Returns\n -----... |
bd06a346d1d30b7aa02de6e8f917d120057581213ef3704c4d235a36860ef97c | def client(self, name: str) -> records.Database:
'\n \n :param name: \n :return:\n '
if (name in self.__instance.__dict__.keys()):
return self.__instance.__getattribute__(name)
else:
logging.debug(f'database client {name} do not exist!') | :param name:
:return: | itest2/core/database_client.py | client | hzhang123/Itest2 | 0 | python | def client(self, name: str) -> records.Database:
'\n \n :param name: \n :return:\n '
if (name in self.__instance.__dict__.keys()):
return self.__instance.__getattribute__(name)
else:
logging.debug(f'database client {name} do not exist!') | def client(self, name: str) -> records.Database:
'\n \n :param name: \n :return:\n '
if (name in self.__instance.__dict__.keys()):
return self.__instance.__getattribute__(name)
else:
logging.debug(f'database client {name} do not exist!')<|docstring|>:param name:
... |
3b3e9fea530a115f5f926006fabbe375e9113530237afdb81dca64ba751ff9e3 | def create_clients(self, conn_config: list=None):
'\n 根据传入的配置创建records.Database\n :param conn_config: (\n {\n "name": "pg",\n "uri": "postgresql://user:password@host:port/database"\n }\n )\n :return:\n '
if (conn_config is no... | 根据传入的配置创建records.Database
:param conn_config: (
{
"name": "pg",
"uri": "postgresql://user:password@host:port/database"
}
)
:return: | itest2/core/database_client.py | create_clients | hzhang123/Itest2 | 0 | python | def create_clients(self, conn_config: list=None):
'\n 根据传入的配置创建records.Database\n :param conn_config: (\n {\n "name": "pg",\n "uri": "postgresql://user:password@host:port/database"\n }\n )\n :return:\n '
if (conn_config is no... | def create_clients(self, conn_config: list=None):
'\n 根据传入的配置创建records.Database\n :param conn_config: (\n {\n "name": "pg",\n "uri": "postgresql://user:password@host:port/database"\n }\n )\n :return:\n '
if (conn_config is no... |
d173b042d64eb6a64ddf51544d3432edfecb0e25b291f17fc6abcc0a67c66779 | def create_client(self, name: str, uri: str):
'\n 指定名称和连接串创建records.Database\n :param name: records.Database对象名称\n :param uri: 数据库连接串\n :return:\n '
if (name in self.__instance.__dict__.values()):
if self.__instance.__getattribute__(name).open:
return self.... | 指定名称和连接串创建records.Database
:param name: records.Database对象名称
:param uri: 数据库连接串
:return: | itest2/core/database_client.py | create_client | hzhang123/Itest2 | 0 | python | def create_client(self, name: str, uri: str):
'\n 指定名称和连接串创建records.Database\n :param name: records.Database对象名称\n :param uri: 数据库连接串\n :return:\n '
if (name in self.__instance.__dict__.values()):
if self.__instance.__getattribute__(name).open:
return self.... | def create_client(self, name: str, uri: str):
'\n 指定名称和连接串创建records.Database\n :param name: records.Database对象名称\n :param uri: 数据库连接串\n :return:\n '
if (name in self.__instance.__dict__.values()):
if self.__instance.__getattribute__(name).open:
return self.... |
afd9a2a2bdccd99eed4a95b57c181efb0182921efa8fd0d972a1ac98f3aaa1ca | def np_euclidean_distance(p, q=0):
' Euclidean distance, useful for plotting results.'
return np.sqrt(np.sum(np.square((p - q)), axis=(- 1))) | Euclidean distance, useful for plotting results. | sample_code/utils_nn.py | np_euclidean_distance | tkrivachy/neural-network-for-nonlocality-in-networks | 6 | python | def np_euclidean_distance(p, q=0):
' '
return np.sqrt(np.sum(np.square((p - q)), axis=(- 1))) | def np_euclidean_distance(p, q=0):
' '
return np.sqrt(np.sum(np.square((p - q)), axis=(- 1)))<|docstring|>Euclidean distance, useful for plotting results.<|endoftext|> |
ee666960e7aa5bf520a3d3f00ce6205e41e242b01d9919b8ba123d67c6185261 | def np_distance(p, q=0):
' Same as the distance used in the loss function, just written for numpy arrays.'
if (cf.pnn.loss.lower() == 'l2'):
return np.sum(np.square((p - q)), axis=(- 1))
elif (cf.pnn.loss.lower() == 'l1'):
return (0.5 * np.sum(np.abs((p - q)), axis=(- 1)))
elif (cf.pnn.l... | Same as the distance used in the loss function, just written for numpy arrays. | sample_code/utils_nn.py | np_distance | tkrivachy/neural-network-for-nonlocality-in-networks | 6 | python | def np_distance(p, q=0):
' '
if (cf.pnn.loss.lower() == 'l2'):
return np.sum(np.square((p - q)), axis=(- 1))
elif (cf.pnn.loss.lower() == 'l1'):
return (0.5 * np.sum(np.abs((p - q)), axis=(- 1)))
elif (cf.pnn.loss.lower() == 'kl'):
p = np.clip(p, K.epsilon(), 1)
q = np.cl... | def np_distance(p, q=0):
' '
if (cf.pnn.loss.lower() == 'l2'):
return np.sum(np.square((p - q)), axis=(- 1))
elif (cf.pnn.loss.lower() == 'l1'):
return (0.5 * np.sum(np.abs((p - q)), axis=(- 1)))
elif (cf.pnn.loss.lower() == 'kl'):
p = np.clip(p, K.epsilon(), 1)
q = np.cl... |
f185898511380c4a2b84b78645a2efdf9608273dd27a17ea11a0186d6a5bf0b1 | def keras_distance(p, q):
' Distance used in loss function. '
if (cf.pnn.loss.lower() == 'l2'):
return K.sum(K.square((p - q)), axis=(- 1))
elif (cf.pnn.loss.lower() == 'l1'):
return (0.5 * K.sum(K.abs((p - q)), axis=(- 1)))
elif (cf.pnn.loss.lower() == 'kl'):
p = K.clip(p, K.eps... | Distance used in loss function. | sample_code/utils_nn.py | keras_distance | tkrivachy/neural-network-for-nonlocality-in-networks | 6 | python | def keras_distance(p, q):
' '
if (cf.pnn.loss.lower() == 'l2'):
return K.sum(K.square((p - q)), axis=(- 1))
elif (cf.pnn.loss.lower() == 'l1'):
return (0.5 * K.sum(K.abs((p - q)), axis=(- 1)))
elif (cf.pnn.loss.lower() == 'kl'):
p = K.clip(p, K.epsilon(), 1)
q = K.clip(q... | def keras_distance(p, q):
' '
if (cf.pnn.loss.lower() == 'l2'):
return K.sum(K.square((p - q)), axis=(- 1))
elif (cf.pnn.loss.lower() == 'l1'):
return (0.5 * K.sum(K.abs((p - q)), axis=(- 1)))
elif (cf.pnn.loss.lower() == 'kl'):
p = K.clip(p, K.epsilon(), 1)
q = K.clip(q... |
5d161ef35b115232db5d88b878e82ff1165792fc0a7e1c945015381cd243aa82 | def customLoss_distr(y_pred):
' Converts the output of the neural network to a probability vector.\n That is from a shape of (batch_size, a_outputsize + b_outputsize + c_outputsize) to a shape of (a_outputsize * b_outputsize * c_outputsize,)\n '
a_probs = y_pred[(:, 0:cf.pnn.a_outputsize)]
b_probs = y... | Converts the output of the neural network to a probability vector.
That is from a shape of (batch_size, a_outputsize + b_outputsize + c_outputsize) to a shape of (a_outputsize * b_outputsize * c_outputsize,) | sample_code/utils_nn.py | customLoss_distr | tkrivachy/neural-network-for-nonlocality-in-networks | 6 | python | def customLoss_distr(y_pred):
' Converts the output of the neural network to a probability vector.\n That is from a shape of (batch_size, a_outputsize + b_outputsize + c_outputsize) to a shape of (a_outputsize * b_outputsize * c_outputsize,)\n '
a_probs = y_pred[(:, 0:cf.pnn.a_outputsize)]
b_probs = y... | def customLoss_distr(y_pred):
' Converts the output of the neural network to a probability vector.\n That is from a shape of (batch_size, a_outputsize + b_outputsize + c_outputsize) to a shape of (a_outputsize * b_outputsize * c_outputsize,)\n '
a_probs = y_pred[(:, 0:cf.pnn.a_outputsize)]
b_probs = y... |
34259cb0add42747062583d6748a33a9b5213d76adda182c57af19cdf7416371 | def customLoss(y_true, y_pred):
' Custom loss function.'
return keras_distance(y_true[(0, :)], customLoss_distr(y_pred)) | Custom loss function. | sample_code/utils_nn.py | customLoss | tkrivachy/neural-network-for-nonlocality-in-networks | 6 | python | def customLoss(y_true, y_pred):
' '
return keras_distance(y_true[(0, :)], customLoss_distr(y_pred)) | def customLoss(y_true, y_pred):
' '
return keras_distance(y_true[(0, :)], customLoss_distr(y_pred))<|docstring|>Custom loss function.<|endoftext|> |
56e19d9fa9475af28556c34efdf852217ce0c7f84ddb9c08bbf25be33692e57a | def single_evaluation(model):
' Evaluates the model and returns the resulting distribution as a numpy array. '
test_pred = model.predict_generator(generate_x_test(), steps=1, max_queue_size=10, workers=1, use_multiprocessing=False, verbose=0)
result = K.eval(customLoss_distr(test_pred))
return result | Evaluates the model and returns the resulting distribution as a numpy array. | sample_code/utils_nn.py | single_evaluation | tkrivachy/neural-network-for-nonlocality-in-networks | 6 | python | def single_evaluation(model):
' '
test_pred = model.predict_generator(generate_x_test(), steps=1, max_queue_size=10, workers=1, use_multiprocessing=False, verbose=0)
result = K.eval(customLoss_distr(test_pred))
return result | def single_evaluation(model):
' '
test_pred = model.predict_generator(generate_x_test(), steps=1, max_queue_size=10, workers=1, use_multiprocessing=False, verbose=0)
result = K.eval(customLoss_distr(test_pred))
return result<|docstring|>Evaluates the model and returns the resulting distribution as a nu... |
948b8d91ba7a99ac090d36bca962089f74e2f8e67fb27930d547ecef8c4ef707 | def single_run():
' Runs training algorithm for a single target distribution. Returns model.'
K.clear_session()
model = build_model()
if (cf.pnn.start_from is not None):
print('LOADING MODEL WEIGHTS FROM', cf.pnn.start_from)
model = load_model(cf.pnn.start_from, custom_objects={'customLo... | Runs training algorithm for a single target distribution. Returns model. | sample_code/utils_nn.py | single_run | tkrivachy/neural-network-for-nonlocality-in-networks | 6 | python | def single_run():
' '
K.clear_session()
model = build_model()
if (cf.pnn.start_from is not None):
print('LOADING MODEL WEIGHTS FROM', cf.pnn.start_from)
model = load_model(cf.pnn.start_from, custom_objects={'customLoss': customLoss})
if (cf.pnn.optimizer.lower() == 'adadelta'):
... | def single_run():
' '
K.clear_session()
model = build_model()
if (cf.pnn.start_from is not None):
print('LOADING MODEL WEIGHTS FROM', cf.pnn.start_from)
model = load_model(cf.pnn.start_from, custom_objects={'customLoss': customLoss})
if (cf.pnn.optimizer.lower() == 'adadelta'):
... |
074058ae2288a99a5c8eb8e4b88d5f32385b49973d53df4a3ea9274b24e8019c | def compare_models(model1, model2):
' Evaluates two models for p_target distribution and return one which is closer to it.'
result1 = single_evaluation(model1)
result2 = single_evaluation(model2)
if (np_distance(result1, cf.pnn.p_target) < np_distance(result2, cf.pnn.p_target)):
return (model1, ... | Evaluates two models for p_target distribution and return one which is closer to it. | sample_code/utils_nn.py | compare_models | tkrivachy/neural-network-for-nonlocality-in-networks | 6 | python | def compare_models(model1, model2):
' '
result1 = single_evaluation(model1)
result2 = single_evaluation(model2)
if (np_distance(result1, cf.pnn.p_target) < np_distance(result2, cf.pnn.p_target)):
return (model1, 1)
else:
return (model2, 2) | def compare_models(model1, model2):
' '
result1 = single_evaluation(model1)
result2 = single_evaluation(model2)
if (np_distance(result1, cf.pnn.p_target) < np_distance(result2, cf.pnn.p_target)):
return (model1, 1)
else:
return (model2, 2)<|docstring|>Evaluates two models for p_targe... |
33ee43416d801bd8e493dc3b9895912f0e49f78134786115163b3872715847b3 | def update_results(model_new, i):
' Updates plots and results if better than the one I loaded the model from in this round.\n If I am in last sample of the sweep I will plot no matter one, so that there is at least one plot per sweep.\n '
result_new = single_evaluation(model_new)
distance_new = np_dis... | Updates plots and results if better than the one I loaded the model from in this round.
If I am in last sample of the sweep I will plot no matter one, so that there is at least one plot per sweep. | sample_code/utils_nn.py | update_results | tkrivachy/neural-network-for-nonlocality-in-networks | 6 | python | def update_results(model_new, i):
' Updates plots and results if better than the one I loaded the model from in this round.\n If I am in last sample of the sweep I will plot no matter one, so that there is at least one plot per sweep.\n '
result_new = single_evaluation(model_new)
distance_new = np_dis... | def update_results(model_new, i):
' Updates plots and results if better than the one I loaded the model from in this round.\n If I am in last sample of the sweep I will plot no matter one, so that there is at least one plot per sweep.\n '
result_new = single_evaluation(model_new)
distance_new = np_dis... |
3d252545edcad8aa25655328270be8e440794d7ff5c95b805ad0c35ddc088b6a | def __init__(self, function_space, depth, options, test_function=None):
'\n :arg function_space: :class:`FunctionSpace` where the solution belongs\n :arg depth: :class: `DepthExpression` containing depth info\n :arg options: :class`ModelOptions2d` containing parameters\n '
super(Cons... | :arg function_space: :class:`FunctionSpace` where the solution belongs
:arg depth: :class: `DepthExpression` containing depth info
:arg options: :class`ModelOptions2d` containing parameters | thetis/conservative_tracer_eq_2d.py | __init__ | connorjward/thetis | 45 | python | def __init__(self, function_space, depth, options, test_function=None):
'\n :arg function_space: :class:`FunctionSpace` where the solution belongs\n :arg depth: :class: `DepthExpression` containing depth info\n :arg options: :class`ModelOptions2d` containing parameters\n '
super(Cons... | def __init__(self, function_space, depth, options, test_function=None):
'\n :arg function_space: :class:`FunctionSpace` where the solution belongs\n :arg depth: :class: `DepthExpression` containing depth info\n :arg options: :class`ModelOptions2d` containing parameters\n '
super(Cons... |
28f9c212ed47d8671c69b715860d0442791b744e3d9cd28a7a43ed96c9815fdd | def init(self, target_rule, x='x', y='y'):
"Initializes the grammar.\n\n A grammar can only be used for sampling after initialization.\n The initialization takes time linear in the size of the grammar.\n\n Parameters\n ----------\n target_rule : str\n Rule to be sampled... | Initializes the grammar.
A grammar can only be used for sampling after initialization.
The initialization takes time linear in the size of the grammar.
Parameters
----------
target_rule : str
Rule to be sampled from (sampling will also be possible for all
rules the target depends on).
x : str, optional (defau... | pyboltzmann/decomposition_grammar.py | init | towink/boltzmann-planar-graph | 0 | python | def init(self, target_rule, x='x', y='y'):
"Initializes the grammar.\n\n A grammar can only be used for sampling after initialization.\n The initialization takes time linear in the size of the grammar.\n\n Parameters\n ----------\n target_rule : str\n Rule to be sampled... | def init(self, target_rule, x='x', y='y'):
"Initializes the grammar.\n\n A grammar can only be used for sampling after initialization.\n The initialization takes time linear in the size of the grammar.\n\n Parameters\n ----------\n target_rule : str\n Rule to be sampled... |
234ab42c58fa9536186aa2351b2d6c2d3faa41c4225e3f281878ef82ad4f803b | def _init_alias_samplers(self):
'Sets the grammar in the alias samplers.'
def apply_to_each(sampler):
if isinstance(sampler, pybo.AliasSampler):
sampler.grammar = self
sampler._referenced_sampler = self[sampler.sampled_class]
sampler.children = (sampler._referenced_s... | Sets the grammar in the alias samplers. | pyboltzmann/decomposition_grammar.py | _init_alias_samplers | towink/boltzmann-planar-graph | 0 | python | def _init_alias_samplers(self):
def apply_to_each(sampler):
if isinstance(sampler, pybo.AliasSampler):
sampler.grammar = self
sampler._referenced_sampler = self[sampler.sampled_class]
sampler.children = (sampler._referenced_sampler,)
for alias in self._rules:
... | def _init_alias_samplers(self):
def apply_to_each(sampler):
if isinstance(sampler, pybo.AliasSampler):
sampler.grammar = self
sampler._referenced_sampler = self[sampler.sampled_class]
sampler.children = (sampler._referenced_sampler,)
for alias in self._rules:
... |
34787d3ff52556bc42d3dd5fca6b48e72132d30a2b05fdaa27aa5a2c66e92947 | def _find_recursive_rules(self):
'Analyses the grammar to find out which rules are recursive and saves\n them.\n '
rec_rules = []
for alias in self.rules:
sampler = self[alias]
def apply_to_each(s):
if (isinstance(s, pybo.AliasSampler) and (s.sampled_class == alias... | Analyses the grammar to find out which rules are recursive and saves
them. | pyboltzmann/decomposition_grammar.py | _find_recursive_rules | towink/boltzmann-planar-graph | 0 | python | def _find_recursive_rules(self):
'Analyses the grammar to find out which rules are recursive and saves\n them.\n '
rec_rules = []
for alias in self.rules:
sampler = self[alias]
def apply_to_each(s):
if (isinstance(s, pybo.AliasSampler) and (s.sampled_class == alias... | def _find_recursive_rules(self):
'Analyses the grammar to find out which rules are recursive and saves\n them.\n '
rec_rules = []
for alias in self.rules:
sampler = self[alias]
def apply_to_each(s):
if (isinstance(s, pybo.AliasSampler) and (s.sampled_class == alias... |
4fba5c435b0ca8a6755c648ca7eb2c96321ecf68f615df314ef23fed7aca7ee1 | def _infer_target_class_labels(self):
'Automatically tries to infer class labels if they are not given\n explicitly.\n '
for alias in self.rules:
sampler = self[alias]
while isinstance(sampler, pybo.BijectionSampler):
sampler = sampler.get_children()[0]
if isins... | Automatically tries to infer class labels if they are not given
explicitly. | pyboltzmann/decomposition_grammar.py | _infer_target_class_labels | towink/boltzmann-planar-graph | 0 | python | def _infer_target_class_labels(self):
'Automatically tries to infer class labels if they are not given\n explicitly.\n '
for alias in self.rules:
sampler = self[alias]
while isinstance(sampler, pybo.BijectionSampler):
sampler = sampler.get_children()[0]
if isins... | def _infer_target_class_labels(self):
'Automatically tries to infer class labels if they are not given\n explicitly.\n '
for alias in self.rules:
sampler = self[alias]
while isinstance(sampler, pybo.BijectionSampler):
sampler = sampler.get_children()[0]
if isins... |
2a1076a2cb6e893f940a85f43120e5e136612ecfcbb3b198dbbaccaea9d5b5c3 | def _collect_oracle_queries(self):
'Returns all oracle queries that may be needed when sampling from\n the rule identified by alias.\n '
visitor = self._CollectOracleQueriesVisitor(self._target_x, self._target_y)
self[self._target_rule].accept(visitor)
return sorted(visitor.result) | Returns all oracle queries that may be needed when sampling from
the rule identified by alias. | pyboltzmann/decomposition_grammar.py | _collect_oracle_queries | towink/boltzmann-planar-graph | 0 | python | def _collect_oracle_queries(self):
'Returns all oracle queries that may be needed when sampling from\n the rule identified by alias.\n '
visitor = self._CollectOracleQueriesVisitor(self._target_x, self._target_y)
self[self._target_rule].accept(visitor)
return sorted(visitor.result) | def _collect_oracle_queries(self):
'Returns all oracle queries that may be needed when sampling from\n the rule identified by alias.\n '
visitor = self._CollectOracleQueriesVisitor(self._target_x, self._target_y)
self[self._target_rule].accept(visitor)
return sorted(visitor.result)<|docstr... |
abab91e3ed9ae5d055dc10664bb617887bf230d40180aa6e3d856719d5c26943 | def _precompute_evals(self):
'Precomputes all evaluations needed for sampling from the given\n class with the symbolic x and y values.\n '
visitor = self._PrecomputeEvaluationsVisitor(self._target_x, self._target_y)
self[self._target_rule].accept(visitor) | Precomputes all evaluations needed for sampling from the given
class with the symbolic x and y values. | pyboltzmann/decomposition_grammar.py | _precompute_evals | towink/boltzmann-planar-graph | 0 | python | def _precompute_evals(self):
'Precomputes all evaluations needed for sampling from the given\n class with the symbolic x and y values.\n '
visitor = self._PrecomputeEvaluationsVisitor(self._target_x, self._target_y)
self[self._target_rule].accept(visitor) | def _precompute_evals(self):
'Precomputes all evaluations needed for sampling from the given\n class with the symbolic x and y values.\n '
visitor = self._PrecomputeEvaluationsVisitor(self._target_x, self._target_y)
self[self._target_rule].accept(visitor)<|docstring|>Precomputes all evaluation... |
c8f8010200e9f225501a782d2de17c7f96cd860783abcddd3b4119e52a33e394 | def restart_sampler(self):
'Restarts the iterative sampler.'
self._restart_flag = True | Restarts the iterative sampler. | pyboltzmann/decomposition_grammar.py | restart_sampler | towink/boltzmann-planar-graph | 0 | python | def restart_sampler(self):
self._restart_flag = True | def restart_sampler(self):
self._restart_flag = True<|docstring|>Restarts the iterative sampler.<|endoftext|> |
f0cd66af226b7d3c6c388b2166f2314aeb5705f600511563e37687400e58bd37 | def set_builder(self, rules=None, builder=pybo.DefaultBuilder()):
'Sets a builder for a given set of rules.\n\n Parameters\n ----------\n rules : str or iterable, optional (default=all rules in the grammar)\n Rules for which the builder should be set.\n builder : Combinatorial... | Sets a builder for a given set of rules.
Parameters
----------
rules : str or iterable, optional (default=all rules in the grammar)
Rules for which the builder should be set.
builder : CombinatorialClassBuilder, optional (default=DefaultBuilder)
The builder object itself.
Returns
-------
v : SetBuilderVisitor | pyboltzmann/decomposition_grammar.py | set_builder | towink/boltzmann-planar-graph | 0 | python | def set_builder(self, rules=None, builder=pybo.DefaultBuilder()):
'Sets a builder for a given set of rules.\n\n Parameters\n ----------\n rules : str or iterable, optional (default=all rules in the grammar)\n Rules for which the builder should be set.\n builder : Combinatorial... | def set_builder(self, rules=None, builder=pybo.DefaultBuilder()):
'Sets a builder for a given set of rules.\n\n Parameters\n ----------\n rules : str or iterable, optional (default=all rules in the grammar)\n Rules for which the builder should be set.\n builder : Combinatorial... |
13fb210208ec1c04fa211a6c04b0a95f441cb22a871eabda3750e03a11971af4 | def add_rule(self, alias, sampler):
'Adds a decomposition rule to this grammar.\n\n Parameters\n ----------\n alias : str\n sampler : BoltzmannSamplerBase\n '
self._rules[alias] = sampler | Adds a decomposition rule to this grammar.
Parameters
----------
alias : str
sampler : BoltzmannSamplerBase | pyboltzmann/decomposition_grammar.py | add_rule | towink/boltzmann-planar-graph | 0 | python | def add_rule(self, alias, sampler):
'Adds a decomposition rule to this grammar.\n\n Parameters\n ----------\n alias : str\n sampler : BoltzmannSamplerBase\n '
self._rules[alias] = sampler | def add_rule(self, alias, sampler):
'Adds a decomposition rule to this grammar.\n\n Parameters\n ----------\n alias : str\n sampler : BoltzmannSamplerBase\n '
self._rules[alias] = sampler<|docstring|>Adds a decomposition rule to this grammar.
Parameters
----------
alias : str... |
50fc32e429fed6fdc44cf9c3cc5fec43f355c245ee3c156a46a4bc41e33d3873 | def __setitem__(self, key, value):
'Shorthand for add_rule.'
self.add_rule(key, value) | Shorthand for add_rule. | pyboltzmann/decomposition_grammar.py | __setitem__ | towink/boltzmann-planar-graph | 0 | python | def __setitem__(self, key, value):
self.add_rule(key, value) | def __setitem__(self, key, value):
self.add_rule(key, value)<|docstring|>Shorthand for add_rule.<|endoftext|> |
35ff6b266aa18f036078b8797956fcef6440c24c39d072e61f4211c2d2efb19d | def get_rule(self, alias):
'Returns the rule corresponding to the given alias.\n\n Parameters\n ----------\n alias : str\n\n Returns\n -------\n BoltzmannSamplerBase\n '
return self._rules[alias] | Returns the rule corresponding to the given alias.
Parameters
----------
alias : str
Returns
-------
BoltzmannSamplerBase | pyboltzmann/decomposition_grammar.py | get_rule | towink/boltzmann-planar-graph | 0 | python | def get_rule(self, alias):
'Returns the rule corresponding to the given alias.\n\n Parameters\n ----------\n alias : str\n\n Returns\n -------\n BoltzmannSamplerBase\n '
return self._rules[alias] | def get_rule(self, alias):
'Returns the rule corresponding to the given alias.\n\n Parameters\n ----------\n alias : str\n\n Returns\n -------\n BoltzmannSamplerBase\n '
return self._rules[alias]<|docstring|>Returns the rule corresponding to the given alias.
Par... |
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