DunnBC22/codet5-small-Generate_Docstrings_for_Python-Condensed
Text2Text Generation
•
Updated
•
10
•
2
body
stringlengths 26
98.2k
| body_hash
int64 -9,222,864,604,528,158,000
9,221,803,474B
| docstring
stringlengths 1
16.8k
| path
stringlengths 5
230
| name
stringlengths 1
96
| repository_name
stringlengths 7
89
| lang
stringclasses 1
value | body_without_docstring
stringlengths 20
98.2k
|
|---|---|---|---|---|---|---|---|
def _canonicalize_experiment(exp):
'Sorts the repeated fields of an Experiment message.'
exp.hparam_infos.sort(key=operator.attrgetter('name'))
exp.metric_infos.sort(key=operator.attrgetter('name.group', 'name.tag'))
for hparam_info in exp.hparam_infos:
if hparam_info.HasField('domain_discrete'):
hparam_info.domain_discrete.values.sort(key=operator.attrgetter('string_value')) | -8,215,901,732,217,587,000 | Sorts the repeated fields of an Experiment message. | tensorboard/plugins/hparams/backend_context_test.py | _canonicalize_experiment | aryaman4/tensorboard | python | def _canonicalize_experiment(exp):
exp.hparam_infos.sort(key=operator.attrgetter('name'))
exp.metric_infos.sort(key=operator.attrgetter('name.group', 'name.tag'))
for hparam_info in exp.hparam_infos:
if hparam_info.HasField('domain_discrete'):
hparam_info.domain_discrete.values.sort(key=operator.attrgetter('string_value')) |
def __init__(self, request_id=None, return_code=None, return_message=None, total_rows=None, process_list=None):
'ResumeProcessesResponse - a model defined in Swagger'
self._request_id = None
self._return_code = None
self._return_message = None
self._total_rows = None
self._process_list = None
self.discriminator = None
if (request_id is not None):
self.request_id = request_id
if (return_code is not None):
self.return_code = return_code
if (return_message is not None):
self.return_message = return_message
if (total_rows is not None):
self.total_rows = total_rows
if (process_list is not None):
self.process_list = process_list | 2,894,216,753,114,153,500 | ResumeProcessesResponse - a model defined in Swagger | lib/services/vautoscaling/ncloud_vautoscaling/model/resume_processes_response.py | __init__ | NaverCloudPlatform/ncloud-sdk-python | python | def __init__(self, request_id=None, return_code=None, return_message=None, total_rows=None, process_list=None):
self._request_id = None
self._return_code = None
self._return_message = None
self._total_rows = None
self._process_list = None
self.discriminator = None
if (request_id is not None):
self.request_id = request_id
if (return_code is not None):
self.return_code = return_code
if (return_message is not None):
self.return_message = return_message
if (total_rows is not None):
self.total_rows = total_rows
if (process_list is not None):
self.process_list = process_list |
@property
def request_id(self):
'Gets the request_id of this ResumeProcessesResponse. # noqa: E501\n\n\n :return: The request_id of this ResumeProcessesResponse. # noqa: E501\n :rtype: str\n '
return self._request_id | -1,183,890,875,359,341,300 | Gets the request_id of this ResumeProcessesResponse. # noqa: E501
:return: The request_id of this ResumeProcessesResponse. # noqa: E501
:rtype: str | lib/services/vautoscaling/ncloud_vautoscaling/model/resume_processes_response.py | request_id | NaverCloudPlatform/ncloud-sdk-python | python | @property
def request_id(self):
'Gets the request_id of this ResumeProcessesResponse. # noqa: E501\n\n\n :return: The request_id of this ResumeProcessesResponse. # noqa: E501\n :rtype: str\n '
return self._request_id |
@request_id.setter
def request_id(self, request_id):
'Sets the request_id of this ResumeProcessesResponse.\n\n\n :param request_id: The request_id of this ResumeProcessesResponse. # noqa: E501\n :type: str\n '
self._request_id = request_id | -8,333,445,982,014,422,000 | Sets the request_id of this ResumeProcessesResponse.
:param request_id: The request_id of this ResumeProcessesResponse. # noqa: E501
:type: str | lib/services/vautoscaling/ncloud_vautoscaling/model/resume_processes_response.py | request_id | NaverCloudPlatform/ncloud-sdk-python | python | @request_id.setter
def request_id(self, request_id):
'Sets the request_id of this ResumeProcessesResponse.\n\n\n :param request_id: The request_id of this ResumeProcessesResponse. # noqa: E501\n :type: str\n '
self._request_id = request_id |
@property
def return_code(self):
'Gets the return_code of this ResumeProcessesResponse. # noqa: E501\n\n\n :return: The return_code of this ResumeProcessesResponse. # noqa: E501\n :rtype: str\n '
return self._return_code | 5,002,841,652,600,358,000 | Gets the return_code of this ResumeProcessesResponse. # noqa: E501
:return: The return_code of this ResumeProcessesResponse. # noqa: E501
:rtype: str | lib/services/vautoscaling/ncloud_vautoscaling/model/resume_processes_response.py | return_code | NaverCloudPlatform/ncloud-sdk-python | python | @property
def return_code(self):
'Gets the return_code of this ResumeProcessesResponse. # noqa: E501\n\n\n :return: The return_code of this ResumeProcessesResponse. # noqa: E501\n :rtype: str\n '
return self._return_code |
@return_code.setter
def return_code(self, return_code):
'Sets the return_code of this ResumeProcessesResponse.\n\n\n :param return_code: The return_code of this ResumeProcessesResponse. # noqa: E501\n :type: str\n '
self._return_code = return_code | 1,679,400,526,842,856,200 | Sets the return_code of this ResumeProcessesResponse.
:param return_code: The return_code of this ResumeProcessesResponse. # noqa: E501
:type: str | lib/services/vautoscaling/ncloud_vautoscaling/model/resume_processes_response.py | return_code | NaverCloudPlatform/ncloud-sdk-python | python | @return_code.setter
def return_code(self, return_code):
'Sets the return_code of this ResumeProcessesResponse.\n\n\n :param return_code: The return_code of this ResumeProcessesResponse. # noqa: E501\n :type: str\n '
self._return_code = return_code |
@property
def return_message(self):
'Gets the return_message of this ResumeProcessesResponse. # noqa: E501\n\n\n :return: The return_message of this ResumeProcessesResponse. # noqa: E501\n :rtype: str\n '
return self._return_message | -5,566,010,396,584,428,000 | Gets the return_message of this ResumeProcessesResponse. # noqa: E501
:return: The return_message of this ResumeProcessesResponse. # noqa: E501
:rtype: str | lib/services/vautoscaling/ncloud_vautoscaling/model/resume_processes_response.py | return_message | NaverCloudPlatform/ncloud-sdk-python | python | @property
def return_message(self):
'Gets the return_message of this ResumeProcessesResponse. # noqa: E501\n\n\n :return: The return_message of this ResumeProcessesResponse. # noqa: E501\n :rtype: str\n '
return self._return_message |
@return_message.setter
def return_message(self, return_message):
'Sets the return_message of this ResumeProcessesResponse.\n\n\n :param return_message: The return_message of this ResumeProcessesResponse. # noqa: E501\n :type: str\n '
self._return_message = return_message | 8,625,067,697,036,527,000 | Sets the return_message of this ResumeProcessesResponse.
:param return_message: The return_message of this ResumeProcessesResponse. # noqa: E501
:type: str | lib/services/vautoscaling/ncloud_vautoscaling/model/resume_processes_response.py | return_message | NaverCloudPlatform/ncloud-sdk-python | python | @return_message.setter
def return_message(self, return_message):
'Sets the return_message of this ResumeProcessesResponse.\n\n\n :param return_message: The return_message of this ResumeProcessesResponse. # noqa: E501\n :type: str\n '
self._return_message = return_message |
@property
def total_rows(self):
'Gets the total_rows of this ResumeProcessesResponse. # noqa: E501\n\n\n :return: The total_rows of this ResumeProcessesResponse. # noqa: E501\n :rtype: int\n '
return self._total_rows | 8,620,200,007,391,291,000 | Gets the total_rows of this ResumeProcessesResponse. # noqa: E501
:return: The total_rows of this ResumeProcessesResponse. # noqa: E501
:rtype: int | lib/services/vautoscaling/ncloud_vautoscaling/model/resume_processes_response.py | total_rows | NaverCloudPlatform/ncloud-sdk-python | python | @property
def total_rows(self):
'Gets the total_rows of this ResumeProcessesResponse. # noqa: E501\n\n\n :return: The total_rows of this ResumeProcessesResponse. # noqa: E501\n :rtype: int\n '
return self._total_rows |
@total_rows.setter
def total_rows(self, total_rows):
'Sets the total_rows of this ResumeProcessesResponse.\n\n\n :param total_rows: The total_rows of this ResumeProcessesResponse. # noqa: E501\n :type: int\n '
self._total_rows = total_rows | -3,135,320,641,953,777,000 | Sets the total_rows of this ResumeProcessesResponse.
:param total_rows: The total_rows of this ResumeProcessesResponse. # noqa: E501
:type: int | lib/services/vautoscaling/ncloud_vautoscaling/model/resume_processes_response.py | total_rows | NaverCloudPlatform/ncloud-sdk-python | python | @total_rows.setter
def total_rows(self, total_rows):
'Sets the total_rows of this ResumeProcessesResponse.\n\n\n :param total_rows: The total_rows of this ResumeProcessesResponse. # noqa: E501\n :type: int\n '
self._total_rows = total_rows |
@property
def process_list(self):
'Gets the process_list of this ResumeProcessesResponse. # noqa: E501\n\n\n :return: The process_list of this ResumeProcessesResponse. # noqa: E501\n :rtype: list[Process]\n '
return self._process_list | 3,722,111,833,422,468,600 | Gets the process_list of this ResumeProcessesResponse. # noqa: E501
:return: The process_list of this ResumeProcessesResponse. # noqa: E501
:rtype: list[Process] | lib/services/vautoscaling/ncloud_vautoscaling/model/resume_processes_response.py | process_list | NaverCloudPlatform/ncloud-sdk-python | python | @property
def process_list(self):
'Gets the process_list of this ResumeProcessesResponse. # noqa: E501\n\n\n :return: The process_list of this ResumeProcessesResponse. # noqa: E501\n :rtype: list[Process]\n '
return self._process_list |
@process_list.setter
def process_list(self, process_list):
'Sets the process_list of this ResumeProcessesResponse.\n\n\n :param process_list: The process_list of this ResumeProcessesResponse. # noqa: E501\n :type: list[Process]\n '
self._process_list = process_list | 6,121,710,043,043,419,000 | Sets the process_list of this ResumeProcessesResponse.
:param process_list: The process_list of this ResumeProcessesResponse. # noqa: E501
:type: list[Process] | lib/services/vautoscaling/ncloud_vautoscaling/model/resume_processes_response.py | process_list | NaverCloudPlatform/ncloud-sdk-python | python | @process_list.setter
def process_list(self, process_list):
'Sets the process_list of this ResumeProcessesResponse.\n\n\n :param process_list: The process_list of this ResumeProcessesResponse. # noqa: E501\n :type: list[Process]\n '
self._process_list = process_list |
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))
elif hasattr(value, 'to_dict'):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map((lambda item: ((item[0], item[1].to_dict()) if hasattr(item[1], 'to_dict') else item)), value.items()))
else:
result[attr] = value
return result | -2,772,352,302,133,010,000 | Returns the model properties as a dict | lib/services/vautoscaling/ncloud_vautoscaling/model/resume_processes_response.py | to_dict | NaverCloudPlatform/ncloud-sdk-python | 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'):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map((lambda item: ((item[0], item[1].to_dict()) if hasattr(item[1], 'to_dict') else item)), value.items()))
else:
result[attr] = value
return result |
def to_str(self):
'Returns the string representation of the model'
return pprint.pformat(self.to_dict()) | 5,849,158,643,760,736,000 | Returns the string representation of the model | lib/services/vautoscaling/ncloud_vautoscaling/model/resume_processes_response.py | to_str | NaverCloudPlatform/ncloud-sdk-python | python | def to_str(self):
return pprint.pformat(self.to_dict()) |
def __repr__(self):
'For `print` and `pprint`'
return self.to_str() | -8,960,031,694,814,905,000 | For `print` and `pprint` | lib/services/vautoscaling/ncloud_vautoscaling/model/resume_processes_response.py | __repr__ | NaverCloudPlatform/ncloud-sdk-python | python | def __repr__(self):
return self.to_str() |
def __eq__(self, other):
'Returns true if both objects are equal'
if (not isinstance(other, ResumeProcessesResponse)):
return False
return (self.__dict__ == other.__dict__) | -7,900,360,667,409,191,000 | Returns true if both objects are equal | lib/services/vautoscaling/ncloud_vautoscaling/model/resume_processes_response.py | __eq__ | NaverCloudPlatform/ncloud-sdk-python | python | def __eq__(self, other):
if (not isinstance(other, ResumeProcessesResponse)):
return False
return (self.__dict__ == other.__dict__) |
def __ne__(self, other):
'Returns true if both objects are not equal'
return (not (self == other)) | 7,764,124,047,908,058,000 | Returns true if both objects are not equal | lib/services/vautoscaling/ncloud_vautoscaling/model/resume_processes_response.py | __ne__ | NaverCloudPlatform/ncloud-sdk-python | python | def __ne__(self, other):
return (not (self == other)) |
def query_countries(countries: List[str]=[], country_ids: List[str]=[]) -> List[Region]:
' Returns a list of countries:\n If countries or country_ids are not empty, only those countries are returned (all of those in both lists)\n Otherwise, all countries are returned\n '
where = region_where_clause('s_country_label.text', countries, 'e_country.node1', country_ids)
query = f'''
SELECT e_country.node1 AS admin_id,
s_country_label.text AS admin,
'Q6256' AS region_type,
e_country.node1 AS country_id,
s_country_label.text AS country,
NULL as admin1_id,
NULL as admin1,
NULL as admin2_id,
NULL as admin2,
NULL as admin3_id,
NULL as admin3
FROM edges e_country
JOIN edges e_country_label JOIN strings s_country_label ON (e_country_label.id=s_country_label.edge_id)
ON (e_country.node1=e_country_label.node1 AND e_country_label.label='label')
WHERE e_country.label='P31' AND e_country.node2='Q6256' AND {where}
ORDER BY country
'''
return _query_regions(query) | -6,755,750,796,964,314,000 | Returns a list of countries:
If countries or country_ids are not empty, only those countries are returned (all of those in both lists)
Otherwise, all countries are returned | db/sql/dal/regions.py | query_countries | Otamio/datamart-api | python | def query_countries(countries: List[str]=[], country_ids: List[str]=[]) -> List[Region]:
' Returns a list of countries:\n If countries or country_ids are not empty, only those countries are returned (all of those in both lists)\n Otherwise, all countries are returned\n '
where = region_where_clause('s_country_label.text', countries, 'e_country.node1', country_ids)
query = f'
SELECT e_country.node1 AS admin_id,
s_country_label.text AS admin,
'Q6256' AS region_type,
e_country.node1 AS country_id,
s_country_label.text AS country,
NULL as admin1_id,
NULL as admin1,
NULL as admin2_id,
NULL as admin2,
NULL as admin3_id,
NULL as admin3
FROM edges e_country
JOIN edges e_country_label JOIN strings s_country_label ON (e_country_label.id=s_country_label.edge_id)
ON (e_country.node1=e_country_label.node1 AND e_country_label.label='label')
WHERE e_country.label='P31' AND e_country.node2='Q6256' AND {where}
ORDER BY country
'
return _query_regions(query) |
def query_admin1s(country: Optional[str]=None, country_id: Optional[str]=None, admin1s: List[str]=[], admin1_ids: List[str]=[]) -> List[Region]:
'\n Returns a list of admin1s. If country or country_id is specified, return the admin1s only of that country.\n If admin1s or admin1_ids are provided, only those admins are returned.\n If all arguments are empty, all admin1s in the system are returned.\n '
if (country and country_id):
raise ValueError('Only one of country, country_id may be specified')
if country_id:
country_where = f"e_country.node2='{country_id}'"
elif country:
country_where = f"LOWER(s_country_label.text)='{country.lower()}'"
else:
country_where = '1=1'
admin1_where = region_where_clause('s_admin1_label.text', admin1s, 'e_admin1.node1', admin1_ids)
query = f'''
SELECT e_admin1.node1 AS admin_id,
s_admin1_label.text AS admin,
'Q10864048' AS region_type,
e_country.node2 AS country_id,
s_country_label.text AS country,
e_admin1.node1 as admin1_id,
s_admin1_label.text as admin1,
NULL as admin2_id,
NULL as admin2,
NULL as admin3_id,
NULL as admin3
FROM edges e_admin1
JOIN edges e_admin1_label JOIN strings s_admin1_label ON (e_admin1_label.id=s_admin1_label.edge_id)
ON (e_admin1.node1=e_admin1_label.node1 AND e_admin1_label.label='label')
JOIN edges e_country ON (e_country.node1=e_admin1.node1 AND e_country.label='P17')
JOIN edges e_country_label JOIN strings s_country_label ON (e_country_label.id=s_country_label.edge_id)
ON (e_country.node2=e_country_label.node1 AND e_country_label.label='label')
WHERE e_admin1.label='P31' AND e_admin1.node2='Q10864048' AND {country_where} AND {admin1_where}
ORDER BY admin1
'''
return _query_regions(query) | 5,190,282,231,298,423,000 | Returns a list of admin1s. If country or country_id is specified, return the admin1s only of that country.
If admin1s or admin1_ids are provided, only those admins are returned.
If all arguments are empty, all admin1s in the system are returned. | db/sql/dal/regions.py | query_admin1s | Otamio/datamart-api | python | def query_admin1s(country: Optional[str]=None, country_id: Optional[str]=None, admin1s: List[str]=[], admin1_ids: List[str]=[]) -> List[Region]:
'\n Returns a list of admin1s. If country or country_id is specified, return the admin1s only of that country.\n If admin1s or admin1_ids are provided, only those admins are returned.\n If all arguments are empty, all admin1s in the system are returned.\n '
if (country and country_id):
raise ValueError('Only one of country, country_id may be specified')
if country_id:
country_where = f"e_country.node2='{country_id}'"
elif country:
country_where = f"LOWER(s_country_label.text)='{country.lower()}'"
else:
country_where = '1=1'
admin1_where = region_where_clause('s_admin1_label.text', admin1s, 'e_admin1.node1', admin1_ids)
query = f'
SELECT e_admin1.node1 AS admin_id,
s_admin1_label.text AS admin,
'Q10864048' AS region_type,
e_country.node2 AS country_id,
s_country_label.text AS country,
e_admin1.node1 as admin1_id,
s_admin1_label.text as admin1,
NULL as admin2_id,
NULL as admin2,
NULL as admin3_id,
NULL as admin3
FROM edges e_admin1
JOIN edges e_admin1_label JOIN strings s_admin1_label ON (e_admin1_label.id=s_admin1_label.edge_id)
ON (e_admin1.node1=e_admin1_label.node1 AND e_admin1_label.label='label')
JOIN edges e_country ON (e_country.node1=e_admin1.node1 AND e_country.label='P17')
JOIN edges e_country_label JOIN strings s_country_label ON (e_country_label.id=s_country_label.edge_id)
ON (e_country.node2=e_country_label.node1 AND e_country_label.label='label')
WHERE e_admin1.label='P31' AND e_admin1.node2='Q10864048' AND {country_where} AND {admin1_where}
ORDER BY admin1
'
return _query_regions(query) |
def query_admin2s(admin1: Optional[str]=None, admin1_id: Optional[str]=None, admin2s: List[str]=[], admin2_ids: List[str]=[]) -> List[Region]:
'\n Returns a list of admin2s. If admin1 or admin1_id is specified, return the admin2s only of that admin1.\n If admin2s or admin2_ids are provided, only those admins are returned.\n If all arguments are empty, all admin2s in the system are returned.\n '
if (admin1 and admin1_id):
raise ValueError('Only one of admin1, admin1_id may be specified')
if admin1_id:
admin1_where = f"e_admin1.node2='{admin1_id}'"
elif admin1:
admin1_where = f"LOWER(s_admin1_label.text)=LOWER('{admin1}')"
else:
admin1_where = '1=1'
admin2_where = region_where_clause('s_admin2_label.text', admin2s, 'e_admin2.node1', admin2_ids)
query = f'''
SELECT e_admin2.node1 AS admin_id,
s_admin2_label.text AS admin,
'Q13220204' AS region_type,
e_country.node2 AS country_id,
s_country_label.text AS country,
e_admin1.node2 AS admin1_id,
s_admin1_label.text AS admin1,
e_admin2.node1 AS admin2_id,
s_admin2_label.text AS admin2,
NULL as admin3_id,
NULL as admin3
FROM edges e_admin2
JOIN edges e_admin2_label JOIN strings s_admin2_label ON (e_admin2_label.id=s_admin2_label.edge_id)
ON (e_admin2.node1=e_admin2_label.node1 AND e_admin2_label.label='label')
JOIN edges e_admin1 ON (e_admin1.node1=e_admin2.node1 AND e_admin1.label='P2006190001')
JOIN edges e_admin1_label JOIN strings s_admin1_label ON (e_admin1_label.id=s_admin1_label.edge_id)
ON (e_admin1.node2=e_admin1_label.node1 AND e_admin1_label.label='label')
JOIN edges e_country ON (e_country.node1=e_admin1.node2 AND e_country.label='P17')
JOIN edges e_country_label JOIN strings s_country_label ON (e_country_label.id=s_country_label.edge_id)
ON (e_country.node2=e_country_label.node1 AND e_country_label.label='label')
WHERE e_admin2.label='P31' AND e_admin2.node2='Q13220204' AND {admin1_where} AND {admin2_where}
ORDER BY admin2
'''
return _query_regions(query) | -1,743,948,908,563,666,400 | Returns a list of admin2s. If admin1 or admin1_id is specified, return the admin2s only of that admin1.
If admin2s or admin2_ids are provided, only those admins are returned.
If all arguments are empty, all admin2s in the system are returned. | db/sql/dal/regions.py | query_admin2s | Otamio/datamart-api | python | def query_admin2s(admin1: Optional[str]=None, admin1_id: Optional[str]=None, admin2s: List[str]=[], admin2_ids: List[str]=[]) -> List[Region]:
'\n Returns a list of admin2s. If admin1 or admin1_id is specified, return the admin2s only of that admin1.\n If admin2s or admin2_ids are provided, only those admins are returned.\n If all arguments are empty, all admin2s in the system are returned.\n '
if (admin1 and admin1_id):
raise ValueError('Only one of admin1, admin1_id may be specified')
if admin1_id:
admin1_where = f"e_admin1.node2='{admin1_id}'"
elif admin1:
admin1_where = f"LOWER(s_admin1_label.text)=LOWER('{admin1}')"
else:
admin1_where = '1=1'
admin2_where = region_where_clause('s_admin2_label.text', admin2s, 'e_admin2.node1', admin2_ids)
query = f'
SELECT e_admin2.node1 AS admin_id,
s_admin2_label.text AS admin,
'Q13220204' AS region_type,
e_country.node2 AS country_id,
s_country_label.text AS country,
e_admin1.node2 AS admin1_id,
s_admin1_label.text AS admin1,
e_admin2.node1 AS admin2_id,
s_admin2_label.text AS admin2,
NULL as admin3_id,
NULL as admin3
FROM edges e_admin2
JOIN edges e_admin2_label JOIN strings s_admin2_label ON (e_admin2_label.id=s_admin2_label.edge_id)
ON (e_admin2.node1=e_admin2_label.node1 AND e_admin2_label.label='label')
JOIN edges e_admin1 ON (e_admin1.node1=e_admin2.node1 AND e_admin1.label='P2006190001')
JOIN edges e_admin1_label JOIN strings s_admin1_label ON (e_admin1_label.id=s_admin1_label.edge_id)
ON (e_admin1.node2=e_admin1_label.node1 AND e_admin1_label.label='label')
JOIN edges e_country ON (e_country.node1=e_admin1.node2 AND e_country.label='P17')
JOIN edges e_country_label JOIN strings s_country_label ON (e_country_label.id=s_country_label.edge_id)
ON (e_country.node2=e_country_label.node1 AND e_country_label.label='label')
WHERE e_admin2.label='P31' AND e_admin2.node2='Q13220204' AND {admin1_where} AND {admin2_where}
ORDER BY admin2
'
return _query_regions(query) |
def query_admin3s(admin2: Optional[str]=None, admin2_id: Optional[str]=None, admin3s: List[str]=[], admin3_ids: List[str]=[], debug=False) -> List[Region]:
'\n Returns a list of admin3s. If admin2 or admin2_id is specified, return the admin3s only of that admin2.\n If admin3s or admin3_ids are provided, only those admins are returned.\n If all arguments are empty, all admin3s in the system are returned.\n '
if (admin2 and admin2_id):
raise ValueError('Only one of admin2, admin2_id may be specified')
if admin2_id:
admin2_where = f"e_admin2.node2='{admin2_id}'"
elif admin2:
admin2_where = f"LOWER(s_admin2_label.text)=LOWER('{admin2}')"
else:
admin2_where = '1=1'
admin3_where = region_where_clause('s_admin3_label.text', admin3s, 'e_admin3.node1', admin3_ids)
query = f'''
SELECT e_admin3.node1 AS admin_id,
s_admin3_label.text AS admin,
'Q13221722' AS region_type,
e_country.node2 AS country_id,
s_country_label.text AS country,
e_admin1.node2 AS admin1_id,
s_admin1_label.text AS admin1,
e_admin2.node2 AS admin2_id,
s_admin2_label.text AS admin2,
e_admin2.node1 AS admin3_id,
s_admin3_label.text AS admin3
FROM
edges e_admin3
JOIN edges e_admin3_label JOIN strings s_admin3_label ON (e_admin3_label.id=s_admin3_label.edge_id)
ON (e_admin3.node1=e_admin3_label.node1 AND e_admin3_label.label='label')
JOIN edges e_admin2 ON (e_admin2.node1=e_admin3.node1 AND e_admin2.label='P2006190002')
JOIN edges e_admin2_label JOIN strings s_admin2_label ON (e_admin2_label.id=s_admin2_label.edge_id)
ON (e_admin2.node2=e_admin2_label.node1 AND e_admin2_label.label='label')
JOIN edges e_admin1 ON (e_admin1.node1=e_admin2.node1 AND e_admin1.label='P2006190001')
JOIN edges e_admin1_label JOIN strings s_admin1_label ON (e_admin1_label.id=s_admin1_label.edge_id)
ON (e_admin1.node2=e_admin1_label.node1 AND e_admin1_label.label='label')
JOIN edges e_country ON (e_country.node1=e_admin1.node2 AND e_country.label='P17')
JOIN edges e_country_label JOIN strings s_country_label ON (e_country_label.id=s_country_label.edge_id)
ON (e_country.node2=e_country_label.node1 AND e_country_label.label='label')
WHERE e_admin3.label='P31' AND e_admin3.node2='Q13221722' AND {admin2_where} AND {admin3_where}
ORDER BY admin3
'''
if debug:
print(query)
return _query_regions(query) | -7,311,637,996,641,695,000 | Returns a list of admin3s. If admin2 or admin2_id is specified, return the admin3s only of that admin2.
If admin3s or admin3_ids are provided, only those admins are returned.
If all arguments are empty, all admin3s in the system are returned. | db/sql/dal/regions.py | query_admin3s | Otamio/datamart-api | python | def query_admin3s(admin2: Optional[str]=None, admin2_id: Optional[str]=None, admin3s: List[str]=[], admin3_ids: List[str]=[], debug=False) -> List[Region]:
'\n Returns a list of admin3s. If admin2 or admin2_id is specified, return the admin3s only of that admin2.\n If admin3s or admin3_ids are provided, only those admins are returned.\n If all arguments are empty, all admin3s in the system are returned.\n '
if (admin2 and admin2_id):
raise ValueError('Only one of admin2, admin2_id may be specified')
if admin2_id:
admin2_where = f"e_admin2.node2='{admin2_id}'"
elif admin2:
admin2_where = f"LOWER(s_admin2_label.text)=LOWER('{admin2}')"
else:
admin2_where = '1=1'
admin3_where = region_where_clause('s_admin3_label.text', admin3s, 'e_admin3.node1', admin3_ids)
query = f'
SELECT e_admin3.node1 AS admin_id,
s_admin3_label.text AS admin,
'Q13221722' AS region_type,
e_country.node2 AS country_id,
s_country_label.text AS country,
e_admin1.node2 AS admin1_id,
s_admin1_label.text AS admin1,
e_admin2.node2 AS admin2_id,
s_admin2_label.text AS admin2,
e_admin2.node1 AS admin3_id,
s_admin3_label.text AS admin3
FROM
edges e_admin3
JOIN edges e_admin3_label JOIN strings s_admin3_label ON (e_admin3_label.id=s_admin3_label.edge_id)
ON (e_admin3.node1=e_admin3_label.node1 AND e_admin3_label.label='label')
JOIN edges e_admin2 ON (e_admin2.node1=e_admin3.node1 AND e_admin2.label='P2006190002')
JOIN edges e_admin2_label JOIN strings s_admin2_label ON (e_admin2_label.id=s_admin2_label.edge_id)
ON (e_admin2.node2=e_admin2_label.node1 AND e_admin2_label.label='label')
JOIN edges e_admin1 ON (e_admin1.node1=e_admin2.node1 AND e_admin1.label='P2006190001')
JOIN edges e_admin1_label JOIN strings s_admin1_label ON (e_admin1_label.id=s_admin1_label.edge_id)
ON (e_admin1.node2=e_admin1_label.node1 AND e_admin1_label.label='label')
JOIN edges e_country ON (e_country.node1=e_admin1.node2 AND e_country.label='P17')
JOIN edges e_country_label JOIN strings s_country_label ON (e_country_label.id=s_country_label.edge_id)
ON (e_country.node2=e_country_label.node1 AND e_country_label.label='label')
WHERE e_admin3.label='P31' AND e_admin3.node2='Q13221722' AND {admin2_where} AND {admin3_where}
ORDER BY admin3
'
if debug:
print(query)
return _query_regions(query) |
@simple_decorator
def error2fault(func):
'\n Catch known exceptions and translate them to\n XML-RPC faults.\n '
def catcher(*args):
try:
return func(*args)
except GameError as error:
raise xmlrpc.client.Fault(GameError.rpc_code, str(error))
except RuleError as error:
raise xmlrpc.client.Fault(RuleError.rpc_code, str(error))
except ProtocolError as error:
raise xmlrpc.client.Fault(ProtocolError.rpc_code, str(error))
return catcher | 6,223,366,847,108,657,000 | Catch known exceptions and translate them to
XML-RPC faults. | tupelo/xmlrpc.py | error2fault | jait/tupelo | python | @simple_decorator
def error2fault(func):
'\n Catch known exceptions and translate them to\n XML-RPC faults.\n '
def catcher(*args):
try:
return func(*args)
except GameError as error:
raise xmlrpc.client.Fault(GameError.rpc_code, str(error))
except RuleError as error:
raise xmlrpc.client.Fault(RuleError.rpc_code, str(error))
except ProtocolError as error:
raise xmlrpc.client.Fault(ProtocolError.rpc_code, str(error))
return catcher |
@simple_decorator
def fault2error(func):
'\n Catch known XML-RPC faults and translate them to\n custom exceptions.\n '
def catcher(*args):
try:
return func(*args)
except xmlrpc.client.Fault as error:
error_classes = (GameError, RuleError, ProtocolError)
for klass in error_classes:
if (error.faultCode == klass.rpc_code):
raise klass(error.faultString)
raise error
return catcher | 550,723,065,045,873,660 | Catch known XML-RPC faults and translate them to
custom exceptions. | tupelo/xmlrpc.py | fault2error | jait/tupelo | python | @simple_decorator
def fault2error(func):
'\n Catch known XML-RPC faults and translate them to\n custom exceptions.\n '
def catcher(*args):
try:
return func(*args)
except xmlrpc.client.Fault as error:
error_classes = (GameError, RuleError, ProtocolError)
for klass in error_classes:
if (error.faultCode == klass.rpc_code):
raise klass(error.faultString)
raise error
return catcher |
def wait_for_turn(self):
"\n Wait for this player's turn.\n "
while True:
time.sleep(0.5)
if (self.controller is not None):
events = self.controller.get_events(self.id)
for event in events:
self.handle_event(event)
if (self.game_state.turn_id == self.id):
break | -4,054,009,647,122,734,600 | Wait for this player's turn. | tupelo/xmlrpc.py | wait_for_turn | jait/tupelo | python | def wait_for_turn(self):
"\n \n "
while True:
time.sleep(0.5)
if (self.controller is not None):
events = self.controller.get_events(self.id)
for event in events:
self.handle_event(event)
if (self.game_state.turn_id == self.id):
break |
def add_collision_mesh(self, collision_mesh, options=None):
'Add a collision mesh to the planning scene.\n\n Parameters\n ----------\n collision_mesh : :class:`compas_fab.robots.CollisionMesh`\n Object containing the collision mesh to be added.\n options : dict, optional\n Unused parameter.\n\n Returns\n -------\n ``None``\n '
kwargs = {}
kwargs['collision_mesh'] = collision_mesh
kwargs['errback_name'] = 'errback'
return await_callback(self.add_collision_mesh_async, **kwargs) | -7,232,787,266,132,847,000 | Add a collision mesh to the planning scene.
Parameters
----------
collision_mesh : :class:`compas_fab.robots.CollisionMesh`
Object containing the collision mesh to be added.
options : dict, optional
Unused parameter.
Returns
-------
``None`` | src/compas_fab/backends/ros/backend_features/move_it_add_collision_mesh.py | add_collision_mesh | gramaziokohler/compas_fab | python | def add_collision_mesh(self, collision_mesh, options=None):
'Add a collision mesh to the planning scene.\n\n Parameters\n ----------\n collision_mesh : :class:`compas_fab.robots.CollisionMesh`\n Object containing the collision mesh to be added.\n options : dict, optional\n Unused parameter.\n\n Returns\n -------\n ``None``\n '
kwargs = {}
kwargs['collision_mesh'] = collision_mesh
kwargs['errback_name'] = 'errback'
return await_callback(self.add_collision_mesh_async, **kwargs) |
def coinChange(self, coins, amount):
'\n :type coins: List[int]\n :type amount: int\n :rtype: int\n '
res = ([(amount + 1)] * (amount + 1))
res[0] = 0
for i in range(1, (amount + 1)):
for j in coins:
if (j <= i):
res[i] = min(res[i], (res[(i - j)] + 1))
if (res[amount] > amount):
return (- 1)
else:
return res[amount] | 8,912,028,627,762,102,000 | :type coins: List[int]
:type amount: int
:rtype: int | Session1_2018/coinChange.py | coinChange | vedantc6/LCode | python | def coinChange(self, coins, amount):
'\n :type coins: List[int]\n :type amount: int\n :rtype: int\n '
res = ([(amount + 1)] * (amount + 1))
res[0] = 0
for i in range(1, (amount + 1)):
for j in coins:
if (j <= i):
res[i] = min(res[i], (res[(i - j)] + 1))
if (res[amount] > amount):
return (- 1)
else:
return res[amount] |
@interpolate_doc
def func():
'\n this is a docstring\n\n {interpolate_example.foo}\n\n {bar}\n\n {Foo!K}\n ' | 6,285,249,781,807,159,000 | this is a docstring
{interpolate_example.foo}
{bar}
{Foo!K} | interpolate_example.py | func | anntzer/structured-docstrings | python | @interpolate_doc
def func():
'\n this is a docstring\n\n {interpolate_example.foo}\n\n {bar}\n\n {Foo!K}\n ' |
@interpolate_doc
def bad_doc():
'\n fields {must} be preceded by whitespace\n ' | 6,683,463,889,826,569,000 | fields {must} be preceded by whitespace | interpolate_example.py | bad_doc | anntzer/structured-docstrings | python | @interpolate_doc
def bad_doc():
'\n \n ' |
@pytest.mark.django_db
def test_force_staff_sso(client):
'Test that URLs and redirects are in place.'
settings.FEATURE_FLAGS['ENFORCE_STAFF_SSO_ON'] = True
settings.AUTHBROKER_CLIENT_ID = 'debug'
settings.AUTHBROKER_CLIENT_SECRET = 'debug'
settings.AUTHBROKER_URL = 'https://test.com'
reload_urlconf()
assert (reverse('authbroker_client:login') == '/auth/login/')
assert (reverse('authbroker_client:callback') == '/auth/callback/')
response = client.get('/admin/login/')
assert (response.status_code == 302)
assert (response.url == '/auth/login/')
settings.FEATURE_FLAGS['ENFORCE_STAFF_SSO_ON'] = False
reload_urlconf() | 1,363,510,666,531,336,200 | Test that URLs and redirects are in place. | tests/users/test_views.py | test_force_staff_sso | uktrade/directory-cms | python | @pytest.mark.django_db
def test_force_staff_sso(client):
settings.FEATURE_FLAGS['ENFORCE_STAFF_SSO_ON'] = True
settings.AUTHBROKER_CLIENT_ID = 'debug'
settings.AUTHBROKER_CLIENT_SECRET = 'debug'
settings.AUTHBROKER_URL = 'https://test.com'
reload_urlconf()
assert (reverse('authbroker_client:login') == '/auth/login/')
assert (reverse('authbroker_client:callback') == '/auth/callback/')
response = client.get('/admin/login/')
assert (response.status_code == 302)
assert (response.url == '/auth/login/')
settings.FEATURE_FLAGS['ENFORCE_STAFF_SSO_ON'] = False
reload_urlconf() |
def __init__(self, db_conn):
'\n init\n :return:\n '
self.parseDbConn(db_conn)
self.__initDbClient() | -2,465,099,712,075,066,400 | init
:return: | db/dbClient.py | __init__ | dota2heqiuzhi/proxy_pool | python | def __init__(self, db_conn):
'\n init\n :return:\n '
self.parseDbConn(db_conn)
self.__initDbClient() |
def __initDbClient(self):
'\n init DB Client\n :return:\n '
__type = None
if ('SSDB' == self.db_type):
__type = 'ssdbClient'
elif ('REDIS' == self.db_type):
__type = 'redisClient'
elif ('POSTGRESQL' == self.db_type):
__type = 'postgresqlClient'
else:
pass
assert __type, 'type error, Not support DB type: {}'.format(self.db_type)
self.client = getattr(__import__(__type), ('%sClient' % self.db_type.title()))(host=self.db_host, port=self.db_port, username=self.db_user, password=self.db_pwd, db=self.db_name) | -8,341,425,307,030,236,000 | init DB Client
:return: | db/dbClient.py | __initDbClient | dota2heqiuzhi/proxy_pool | python | def __initDbClient(self):
'\n init DB Client\n :return:\n '
__type = None
if ('SSDB' == self.db_type):
__type = 'ssdbClient'
elif ('REDIS' == self.db_type):
__type = 'redisClient'
elif ('POSTGRESQL' == self.db_type):
__type = 'postgresqlClient'
else:
pass
assert __type, 'type error, Not support DB type: {}'.format(self.db_type)
self.client = getattr(__import__(__type), ('%sClient' % self.db_type.title()))(host=self.db_host, port=self.db_port, username=self.db_user, password=self.db_pwd, db=self.db_name) |
def testV1alpha1PriorityClass(self):
'\n Test V1alpha1PriorityClass\n '
pass | 8,672,129,437,725,520,000 | Test V1alpha1PriorityClass | kubernetes/test/test_v1alpha1_priority_class.py | testV1alpha1PriorityClass | MiaoRachelYu/python | python | def testV1alpha1PriorityClass(self):
'\n \n '
pass |
def fit(self, Xi_train, Xv_train, y_train, Xi_valid=None, Xv_valid=None, y_valid=None, early_stopping=False, refit=False):
'\n :param Xi_train: [[ind1_1, ind1_2, ...], [ind2_1, ind2_2, ...], ..., [indi_1, indi_2, ..., indi_j, ...], ...]\n indi_j is the feature index of feature field j of sample i in the training set\n :param Xv_train: [[val1_1, val1_2, ...], [val2_1, val2_2, ...], ..., [vali_1, vali_2, ..., vali_j, ...], ...]\n vali_j is the feature value of feature field j of sample i in the training set\n vali_j can be either binary (1/0, for binary/categorical features) or float (e.g., 10.24, for numerical features)\n :param y_train: label of each sample in the training set\n :param Xi_valid: list of list of feature indices of each sample in the validation set\n :param Xv_valid: list of list of feature values of each sample in the validation set\n :param y_valid: label of each sample in the validation set\n :param early_stopping: perform early stopping or not\n :param refit: refit the model on the train+valid dataset or not\n :return: None\n '
has_valid = (Xv_valid is not None)
Xi_train = Xi_train.copy()
Xv_train = Xv_train.copy()
y_train = y_train.copy()
for epoch in range(self.epoch):
t1 = time()
self.shuffle_in_unison_scary(Xi_train, Xv_train, y_train)
total_batch = int((len(y_train) / self.batch_size))
for i in range(total_batch):
(Xi_batch, Xv_batch, y_batch) = self.get_batch(Xi_train, Xv_train, y_train, self.batch_size, i)
(trian_out, train_loss) = self.fit_on_batch(Xi_batch, Xv_batch, y_batch)
if ((i % 1000) == 0):
print(('epoch:%d batch:%d train_loss=%.4f' % (epoch, i, train_loss)), file=sys.stderr)
train_me = self.evaluate(Xi_train, Xv_train, y_train)
self.train_result.append(train_me)
if has_valid:
valid_me = self.evaluate(Xi_valid, Xv_valid, y_valid)
self.valid_result.append(valid_me)
if ((self.verbose > 0) and ((epoch % self.verbose) == 0)):
print(('[%d] [train] auc=%.4f acc=%.4f mse=%.4f precision_1=%.4f recall_1=%.4f [%.1f s]' % ((epoch + 1), train_me['auc'], train_me['acc'], train_me['mse'], train_me['precision_1'], train_me['recall_1'], (time() - t1))))
if has_valid:
print(('[%d] [valid] auc=%.4f acc=%.4f mse=%.4f precision_1=%.4f recall_1=%.4f [%.1f s]' % ((epoch + 1), valid_me['auc'], valid_me['acc'], valid_me['mse'], valid_me['precision_1'], valid_me['recall_1'], (time() - t1))))
if (has_valid and early_stopping and self.training_termination(self.valid_result)):
break
if (has_valid and refit):
if self.greater_is_better:
best_valid_score = max(self.valid_result)
else:
best_valid_score = min(self.valid_result)
best_epoch = self.valid_result.index(best_valid_score)
best_train_score = self.train_result[best_epoch]
Xi_train = (Xi_train + Xi_valid)
Xv_train = (Xv_train + Xv_valid)
y_train = (y_train + y_valid)
for epoch in range(100):
self.shuffle_in_unison_scary(Xi_train, Xv_train, y_train)
total_batch = int((len(y_train) / self.batch_size))
for i in range(total_batch):
(Xi_batch, Xv_batch, y_batch) = self.get_batch(Xi_train, Xv_train, y_train, self.batch_size, i)
self.fit_on_batch(Xi_batch, Xv_batch, y_batch)
train_result = self.evaluate(Xi_train, Xv_train, y_train)
if ((abs((train_result - best_train_score)) < 0.001) or (self.greater_is_better and (train_result > best_train_score)) or ((not self.greater_is_better) and (train_result < best_train_score))):
break | 4,153,447,849,296,950,300 | :param Xi_train: [[ind1_1, ind1_2, ...], [ind2_1, ind2_2, ...], ..., [indi_1, indi_2, ..., indi_j, ...], ...]
indi_j is the feature index of feature field j of sample i in the training set
:param Xv_train: [[val1_1, val1_2, ...], [val2_1, val2_2, ...], ..., [vali_1, vali_2, ..., vali_j, ...], ...]
vali_j is the feature value of feature field j of sample i in the training set
vali_j can be either binary (1/0, for binary/categorical features) or float (e.g., 10.24, for numerical features)
:param y_train: label of each sample in the training set
:param Xi_valid: list of list of feature indices of each sample in the validation set
:param Xv_valid: list of list of feature values of each sample in the validation set
:param y_valid: label of each sample in the validation set
:param early_stopping: perform early stopping or not
:param refit: refit the model on the train+valid dataset or not
:return: None | zzh/mllib/model/_deep_fm.py | fit | zhangzhenhu/zzh | python | def fit(self, Xi_train, Xv_train, y_train, Xi_valid=None, Xv_valid=None, y_valid=None, early_stopping=False, refit=False):
'\n :param Xi_train: [[ind1_1, ind1_2, ...], [ind2_1, ind2_2, ...], ..., [indi_1, indi_2, ..., indi_j, ...], ...]\n indi_j is the feature index of feature field j of sample i in the training set\n :param Xv_train: [[val1_1, val1_2, ...], [val2_1, val2_2, ...], ..., [vali_1, vali_2, ..., vali_j, ...], ...]\n vali_j is the feature value of feature field j of sample i in the training set\n vali_j can be either binary (1/0, for binary/categorical features) or float (e.g., 10.24, for numerical features)\n :param y_train: label of each sample in the training set\n :param Xi_valid: list of list of feature indices of each sample in the validation set\n :param Xv_valid: list of list of feature values of each sample in the validation set\n :param y_valid: label of each sample in the validation set\n :param early_stopping: perform early stopping or not\n :param refit: refit the model on the train+valid dataset or not\n :return: None\n '
has_valid = (Xv_valid is not None)
Xi_train = Xi_train.copy()
Xv_train = Xv_train.copy()
y_train = y_train.copy()
for epoch in range(self.epoch):
t1 = time()
self.shuffle_in_unison_scary(Xi_train, Xv_train, y_train)
total_batch = int((len(y_train) / self.batch_size))
for i in range(total_batch):
(Xi_batch, Xv_batch, y_batch) = self.get_batch(Xi_train, Xv_train, y_train, self.batch_size, i)
(trian_out, train_loss) = self.fit_on_batch(Xi_batch, Xv_batch, y_batch)
if ((i % 1000) == 0):
print(('epoch:%d batch:%d train_loss=%.4f' % (epoch, i, train_loss)), file=sys.stderr)
train_me = self.evaluate(Xi_train, Xv_train, y_train)
self.train_result.append(train_me)
if has_valid:
valid_me = self.evaluate(Xi_valid, Xv_valid, y_valid)
self.valid_result.append(valid_me)
if ((self.verbose > 0) and ((epoch % self.verbose) == 0)):
print(('[%d] [train] auc=%.4f acc=%.4f mse=%.4f precision_1=%.4f recall_1=%.4f [%.1f s]' % ((epoch + 1), train_me['auc'], train_me['acc'], train_me['mse'], train_me['precision_1'], train_me['recall_1'], (time() - t1))))
if has_valid:
print(('[%d] [valid] auc=%.4f acc=%.4f mse=%.4f precision_1=%.4f recall_1=%.4f [%.1f s]' % ((epoch + 1), valid_me['auc'], valid_me['acc'], valid_me['mse'], valid_me['precision_1'], valid_me['recall_1'], (time() - t1))))
if (has_valid and early_stopping and self.training_termination(self.valid_result)):
break
if (has_valid and refit):
if self.greater_is_better:
best_valid_score = max(self.valid_result)
else:
best_valid_score = min(self.valid_result)
best_epoch = self.valid_result.index(best_valid_score)
best_train_score = self.train_result[best_epoch]
Xi_train = (Xi_train + Xi_valid)
Xv_train = (Xv_train + Xv_valid)
y_train = (y_train + y_valid)
for epoch in range(100):
self.shuffle_in_unison_scary(Xi_train, Xv_train, y_train)
total_batch = int((len(y_train) / self.batch_size))
for i in range(total_batch):
(Xi_batch, Xv_batch, y_batch) = self.get_batch(Xi_train, Xv_train, y_train, self.batch_size, i)
self.fit_on_batch(Xi_batch, Xv_batch, y_batch)
train_result = self.evaluate(Xi_train, Xv_train, y_train)
if ((abs((train_result - best_train_score)) < 0.001) or (self.greater_is_better and (train_result > best_train_score)) or ((not self.greater_is_better) and (train_result < best_train_score))):
break |
def predict(self, Xi, Xv):
'\n :param Xi: list of list of feature indices of each sample in the dataset\n :param Xv: list of list of feature values of each sample in the dataset\n :return: predicted probability of each sample\n '
dummy_y = ([1] * len(Xi))
batch_index = 0
(Xi_batch, Xv_batch, y_batch) = self.get_batch(Xi, Xv, dummy_y, self.batch_size, batch_index)
y_pred = None
while (len(Xi_batch) > 0):
num_batch = len(y_batch)
feed_dict = {self.feat_index: Xi_batch, self.feat_value: Xv_batch, self.dropout_keep_fm: ([1.0] * len(self.dropout_fm)), self.dropout_keep_deep: ([1.0] * len(self.dropout_deep)), self.train_phase: False}
batch_out = self.sess.run(self.out, feed_dict=feed_dict)
if (batch_index == 0):
y_pred = np.reshape(batch_out, (num_batch,))
else:
y_pred = np.concatenate((y_pred, np.reshape(batch_out, (num_batch,))))
batch_index += 1
(Xi_batch, Xv_batch, y_batch) = self.get_batch(Xi, Xv, dummy_y, self.batch_size, batch_index)
return y_pred | 4,152,048,524,689,723,000 | :param Xi: list of list of feature indices of each sample in the dataset
:param Xv: list of list of feature values of each sample in the dataset
:return: predicted probability of each sample | zzh/mllib/model/_deep_fm.py | predict | zhangzhenhu/zzh | python | def predict(self, Xi, Xv):
'\n :param Xi: list of list of feature indices of each sample in the dataset\n :param Xv: list of list of feature values of each sample in the dataset\n :return: predicted probability of each sample\n '
dummy_y = ([1] * len(Xi))
batch_index = 0
(Xi_batch, Xv_batch, y_batch) = self.get_batch(Xi, Xv, dummy_y, self.batch_size, batch_index)
y_pred = None
while (len(Xi_batch) > 0):
num_batch = len(y_batch)
feed_dict = {self.feat_index: Xi_batch, self.feat_value: Xv_batch, self.dropout_keep_fm: ([1.0] * len(self.dropout_fm)), self.dropout_keep_deep: ([1.0] * len(self.dropout_deep)), self.train_phase: False}
batch_out = self.sess.run(self.out, feed_dict=feed_dict)
if (batch_index == 0):
y_pred = np.reshape(batch_out, (num_batch,))
else:
y_pred = np.concatenate((y_pred, np.reshape(batch_out, (num_batch,))))
batch_index += 1
(Xi_batch, Xv_batch, y_batch) = self.get_batch(Xi, Xv, dummy_y, self.batch_size, batch_index)
return y_pred |
def evaluate(self, Xi, Xv, y_true):
'\n :param Xi: list of list of feature indices of each sample in the dataset\n :param Xv: list of list of feature values of each sample in the dataset\n :param y: label of each sample in the dataset\n :return: metric of the evaluation\n '
size = y_true.shape[0]
y_pred = self.predict(Xi, Xv)
error = (y_true - y_pred)
mse = ((error * error).sum() / size)
y_pred_m = y_pred.copy()
y_pred_m[(y_pred_m >= self.threshold)] = 1
y_pred_m[(y_pred_m < self.threshold)] = 0
cm = metrics.confusion_matrix(y_true, y_pred_m, labels=[1, 0])
real_1_count = cm[0, :].sum()
predict_1_count = cm[:, 0].sum()
right_1_count = cm[(0, 0)]
if (predict_1_count == 0):
precision_1 = 0
else:
precision_1 = (right_1_count / predict_1_count)
if (real_1_count == 0):
recall_1 = 0
else:
recall_1 = (right_1_count / real_1_count)
return {'size': size, 'acc': ((cm[(0, 0)] + cm[(1, 1)]) / size), 'precision_1': precision_1, 'recall_1': recall_1, 'auc': self.eval_metric(y_true, y_pred), 'mse': mse} | 7,261,298,830,425,360,000 | :param Xi: list of list of feature indices of each sample in the dataset
:param Xv: list of list of feature values of each sample in the dataset
:param y: label of each sample in the dataset
:return: metric of the evaluation | zzh/mllib/model/_deep_fm.py | evaluate | zhangzhenhu/zzh | python | def evaluate(self, Xi, Xv, y_true):
'\n :param Xi: list of list of feature indices of each sample in the dataset\n :param Xv: list of list of feature values of each sample in the dataset\n :param y: label of each sample in the dataset\n :return: metric of the evaluation\n '
size = y_true.shape[0]
y_pred = self.predict(Xi, Xv)
error = (y_true - y_pred)
mse = ((error * error).sum() / size)
y_pred_m = y_pred.copy()
y_pred_m[(y_pred_m >= self.threshold)] = 1
y_pred_m[(y_pred_m < self.threshold)] = 0
cm = metrics.confusion_matrix(y_true, y_pred_m, labels=[1, 0])
real_1_count = cm[0, :].sum()
predict_1_count = cm[:, 0].sum()
right_1_count = cm[(0, 0)]
if (predict_1_count == 0):
precision_1 = 0
else:
precision_1 = (right_1_count / predict_1_count)
if (real_1_count == 0):
recall_1 = 0
else:
recall_1 = (right_1_count / real_1_count)
return {'size': size, 'acc': ((cm[(0, 0)] + cm[(1, 1)]) / size), 'precision_1': precision_1, 'recall_1': recall_1, 'auc': self.eval_metric(y_true, y_pred), 'mse': mse} |
def test_create_valid_user_successful(self):
'Test creating user with valid payload is successful'
payload = {'email': '[email protected]', 'password': 'testpass', 'name': 'John Doe'}
res = self.client.post(CREATE_USER_URL, payload)
self.assertEqual(res.status_code, status.HTTP_201_CREATED)
user = get_user_model().objects.get(**res.data)
self.assertTrue(user.check_password(payload['password']))
self.assertNotIn('password', res.data) | -2,702,256,109,293,972,000 | Test creating user with valid payload is successful | app/user/tests/test_user_api.py | test_create_valid_user_successful | reallyusefulengine/django_rest_recipe | python | def test_create_valid_user_successful(self):
payload = {'email': '[email protected]', 'password': 'testpass', 'name': 'John Doe'}
res = self.client.post(CREATE_USER_URL, payload)
self.assertEqual(res.status_code, status.HTTP_201_CREATED)
user = get_user_model().objects.get(**res.data)
self.assertTrue(user.check_password(payload['password']))
self.assertNotIn('password', res.data) |
def test_password_too_short(self):
'tests that the password must be more than 5 characters'
payload = {'email': '[email protected]', 'password': 'pass', 'name': 'John Doe'}
res = self.client.post(CREATE_USER_URL, payload)
self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST)
user_exists = get_user_model().objects.filter(email=payload['email']).exists()
self.assertFalse(user_exists) | 5,859,076,869,366,854,000 | tests that the password must be more than 5 characters | app/user/tests/test_user_api.py | test_password_too_short | reallyusefulengine/django_rest_recipe | python | def test_password_too_short(self):
payload = {'email': '[email protected]', 'password': 'pass', 'name': 'John Doe'}
res = self.client.post(CREATE_USER_URL, payload)
self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST)
user_exists = get_user_model().objects.filter(email=payload['email']).exists()
self.assertFalse(user_exists) |
def test_create_token_for_user(self):
'Test that a token is created for a user'
payload = {'email': '[email protected]', 'password': 'testpass'}
create_user(**payload)
res = self.client.post(TOKEN_URL, payload)
self.assertTrue(res.status_code, status.HTTP_200_OK)
self.assertIn('token', res.data) | -590,296,323,168,376,700 | Test that a token is created for a user | app/user/tests/test_user_api.py | test_create_token_for_user | reallyusefulengine/django_rest_recipe | python | def test_create_token_for_user(self):
payload = {'email': '[email protected]', 'password': 'testpass'}
create_user(**payload)
res = self.client.post(TOKEN_URL, payload)
self.assertTrue(res.status_code, status.HTTP_200_OK)
self.assertIn('token', res.data) |
def test_create_token_invalid_credentials(self):
'Test that token is not created if invalid credentials are given'
create_user(email='[email protected]', password='testpass')
payload = {'email': '[email protected]', 'password': 'wrong'}
res = self.client.post(TOKEN_URL, payload)
self.assertTrue(res.status_code, status.HTTP_400_BAD_REQUEST)
self.assertNotIn('token', res.data) | -7,467,095,345,633,051,000 | Test that token is not created if invalid credentials are given | app/user/tests/test_user_api.py | test_create_token_invalid_credentials | reallyusefulengine/django_rest_recipe | python | def test_create_token_invalid_credentials(self):
create_user(email='[email protected]', password='testpass')
payload = {'email': '[email protected]', 'password': 'wrong'}
res = self.client.post(TOKEN_URL, payload)
self.assertTrue(res.status_code, status.HTTP_400_BAD_REQUEST)
self.assertNotIn('token', res.data) |
def test_create_token_no_user(self):
'Test that token is not created if user does not exist'
payload = {'email': '[email protected]', 'password': 'wrong'}
res = self.client.post(TOKEN_URL, payload)
self.assertTrue(res.status_code, status.HTTP_400_BAD_REQUEST)
self.assertNotIn('token', res.data) | -6,124,620,769,133,167,000 | Test that token is not created if user does not exist | app/user/tests/test_user_api.py | test_create_token_no_user | reallyusefulengine/django_rest_recipe | python | def test_create_token_no_user(self):
payload = {'email': '[email protected]', 'password': 'wrong'}
res = self.client.post(TOKEN_URL, payload)
self.assertTrue(res.status_code, status.HTTP_400_BAD_REQUEST)
self.assertNotIn('token', res.data) |
def test_create_token_no_missing_field(self):
'Test that token is not created if email/password not given'
res = self.client.post(TOKEN_URL, {'email': '[email protected]', 'password': ''})
self.assertTrue(res.status_code, status.HTTP_400_BAD_REQUEST)
self.assertNotIn('token', res.data) | -2,498,119,025,504,459,000 | Test that token is not created if email/password not given | app/user/tests/test_user_api.py | test_create_token_no_missing_field | reallyusefulengine/django_rest_recipe | python | def test_create_token_no_missing_field(self):
res = self.client.post(TOKEN_URL, {'email': '[email protected]', 'password': })
self.assertTrue(res.status_code, status.HTTP_400_BAD_REQUEST)
self.assertNotIn('token', res.data) |
def _softplus(x):
'Implements the softplus function.'
return torch.nn.functional.softplus(x, beta=1, threshold=10000) | -8,304,163,192,589,108,000 | Implements the softplus function. | utils/inverter.py | _softplus | Twizwei/idinvert_pytorch | python | def _softplus(x):
return torch.nn.functional.softplus(x, beta=1, threshold=10000) |
def _get_tensor_value(tensor):
'Gets the value of a torch Tensor.'
return tensor.cpu().detach().numpy() | 5,971,145,564,746,899,000 | Gets the value of a torch Tensor. | utils/inverter.py | _get_tensor_value | Twizwei/idinvert_pytorch | python | def _get_tensor_value(tensor):
return tensor.cpu().detach().numpy() |
def __init__(self, model_name, learning_rate=0.01, iteration=100, reconstruction_loss_weight=1.0, perceptual_loss_weight=5e-05, regularization_loss_weight=2.0, logger=None):
'Initializes the inverter.\n\n NOTE: Only Adam optimizer is supported in the optimization process.\n\n Args:\n model_name: Name of the model on which the inverted is based. The model\n should be first registered in `models/model_settings.py`.\n logger: Logger to record the log message.\n learning_rate: Learning rate for optimization. (default: 1e-2)\n iteration: Number of iterations for optimization. (default: 100)\n reconstruction_loss_weight: Weight for reconstruction loss. Should always\n be a positive number. (default: 1.0)\n perceptual_loss_weight: Weight for perceptual loss. 0 disables perceptual\n loss. (default: 5e-5)\n regularization_loss_weight: Weight for regularization loss from encoder.\n This is essential for in-domain inversion. However, this loss will\n automatically ignored if the generative model does not include a valid\n encoder. 0 disables regularization loss. (default: 2.0)\n '
self.logger = logger
self.model_name = model_name
self.gan_type = 'stylegan'
self.G = StyleGANGenerator(self.model_name, self.logger)
self.E = StyleGANEncoder(self.model_name, self.logger)
self.F = PerceptualModel(min_val=self.G.min_val, max_val=self.G.max_val)
self.encode_dim = [self.G.num_layers, self.G.w_space_dim]
self.run_device = self.G.run_device
assert (list(self.encode_dim) == list(self.E.encode_dim))
assert (self.G.gan_type == self.gan_type)
assert (self.E.gan_type == self.gan_type)
self.learning_rate = learning_rate
self.iteration = iteration
self.loss_pix_weight = reconstruction_loss_weight
self.loss_feat_weight = perceptual_loss_weight
self.loss_reg_weight = regularization_loss_weight
assert (self.loss_pix_weight > 0) | 4,956,284,454,006,761,000 | Initializes the inverter.
NOTE: Only Adam optimizer is supported in the optimization process.
Args:
model_name: Name of the model on which the inverted is based. The model
should be first registered in `models/model_settings.py`.
logger: Logger to record the log message.
learning_rate: Learning rate for optimization. (default: 1e-2)
iteration: Number of iterations for optimization. (default: 100)
reconstruction_loss_weight: Weight for reconstruction loss. Should always
be a positive number. (default: 1.0)
perceptual_loss_weight: Weight for perceptual loss. 0 disables perceptual
loss. (default: 5e-5)
regularization_loss_weight: Weight for regularization loss from encoder.
This is essential for in-domain inversion. However, this loss will
automatically ignored if the generative model does not include a valid
encoder. 0 disables regularization loss. (default: 2.0) | utils/inverter.py | __init__ | Twizwei/idinvert_pytorch | python | def __init__(self, model_name, learning_rate=0.01, iteration=100, reconstruction_loss_weight=1.0, perceptual_loss_weight=5e-05, regularization_loss_weight=2.0, logger=None):
'Initializes the inverter.\n\n NOTE: Only Adam optimizer is supported in the optimization process.\n\n Args:\n model_name: Name of the model on which the inverted is based. The model\n should be first registered in `models/model_settings.py`.\n logger: Logger to record the log message.\n learning_rate: Learning rate for optimization. (default: 1e-2)\n iteration: Number of iterations for optimization. (default: 100)\n reconstruction_loss_weight: Weight for reconstruction loss. Should always\n be a positive number. (default: 1.0)\n perceptual_loss_weight: Weight for perceptual loss. 0 disables perceptual\n loss. (default: 5e-5)\n regularization_loss_weight: Weight for regularization loss from encoder.\n This is essential for in-domain inversion. However, this loss will\n automatically ignored if the generative model does not include a valid\n encoder. 0 disables regularization loss. (default: 2.0)\n '
self.logger = logger
self.model_name = model_name
self.gan_type = 'stylegan'
self.G = StyleGANGenerator(self.model_name, self.logger)
self.E = StyleGANEncoder(self.model_name, self.logger)
self.F = PerceptualModel(min_val=self.G.min_val, max_val=self.G.max_val)
self.encode_dim = [self.G.num_layers, self.G.w_space_dim]
self.run_device = self.G.run_device
assert (list(self.encode_dim) == list(self.E.encode_dim))
assert (self.G.gan_type == self.gan_type)
assert (self.E.gan_type == self.gan_type)
self.learning_rate = learning_rate
self.iteration = iteration
self.loss_pix_weight = reconstruction_loss_weight
self.loss_feat_weight = perceptual_loss_weight
self.loss_reg_weight = regularization_loss_weight
assert (self.loss_pix_weight > 0) |
def preprocess(self, image):
'Preprocesses a single image.\n\n This function assumes the input numpy array is with shape [height, width,\n channel], channel order `RGB`, and pixel range [0, 255].\n\n The returned image is with shape [channel, new_height, new_width], where\n `new_height` and `new_width` are specified by the given generative model.\n The channel order of returned image is also specified by the generative\n model. The pixel range is shifted to [min_val, max_val], where `min_val` and\n `max_val` are also specified by the generative model.\n '
if (not isinstance(image, np.ndarray)):
raise ValueError(f'Input image should be with type `numpy.ndarray`!')
if (image.dtype != np.uint8):
raise ValueError(f'Input image should be with dtype `numpy.uint8`!')
if ((image.ndim != 3) or (image.shape[2] not in [1, 3])):
raise ValueError(f'''Input should be with shape [height, width, channel], where channel equals to 1 or 3!
But {image.shape} is received!''')
if ((image.shape[2] == 1) and (self.G.image_channels == 3)):
image = np.tile(image, (1, 1, 3))
if (image.shape[2] != self.G.image_channels):
raise ValueError(f'Number of channels of input image, which is {image.shape[2]}, is not supported by the current inverter, which requires {self.G.image_channels} channels!')
if ((self.G.image_channels == 3) and (self.G.channel_order == 'BGR')):
image = image[:, :, ::(- 1)]
if (image.shape[1:3] != [self.G.resolution, self.G.resolution]):
image = cv2.resize(image, (self.G.resolution, self.G.resolution))
image = image.astype(np.float32)
image = (((image / 255.0) * (self.G.max_val - self.G.min_val)) + self.G.min_val)
image = image.astype(np.float32).transpose(2, 0, 1)
return image | 8,297,031,020,724,259,000 | Preprocesses a single image.
This function assumes the input numpy array is with shape [height, width,
channel], channel order `RGB`, and pixel range [0, 255].
The returned image is with shape [channel, new_height, new_width], where
`new_height` and `new_width` are specified by the given generative model.
The channel order of returned image is also specified by the generative
model. The pixel range is shifted to [min_val, max_val], where `min_val` and
`max_val` are also specified by the generative model. | utils/inverter.py | preprocess | Twizwei/idinvert_pytorch | python | def preprocess(self, image):
'Preprocesses a single image.\n\n This function assumes the input numpy array is with shape [height, width,\n channel], channel order `RGB`, and pixel range [0, 255].\n\n The returned image is with shape [channel, new_height, new_width], where\n `new_height` and `new_width` are specified by the given generative model.\n The channel order of returned image is also specified by the generative\n model. The pixel range is shifted to [min_val, max_val], where `min_val` and\n `max_val` are also specified by the generative model.\n '
if (not isinstance(image, np.ndarray)):
raise ValueError(f'Input image should be with type `numpy.ndarray`!')
if (image.dtype != np.uint8):
raise ValueError(f'Input image should be with dtype `numpy.uint8`!')
if ((image.ndim != 3) or (image.shape[2] not in [1, 3])):
raise ValueError(f'Input should be with shape [height, width, channel], where channel equals to 1 or 3!
But {image.shape} is received!')
if ((image.shape[2] == 1) and (self.G.image_channels == 3)):
image = np.tile(image, (1, 1, 3))
if (image.shape[2] != self.G.image_channels):
raise ValueError(f'Number of channels of input image, which is {image.shape[2]}, is not supported by the current inverter, which requires {self.G.image_channels} channels!')
if ((self.G.image_channels == 3) and (self.G.channel_order == 'BGR')):
image = image[:, :, ::(- 1)]
if (image.shape[1:3] != [self.G.resolution, self.G.resolution]):
image = cv2.resize(image, (self.G.resolution, self.G.resolution))
image = image.astype(np.float32)
image = (((image / 255.0) * (self.G.max_val - self.G.min_val)) + self.G.min_val)
image = image.astype(np.float32).transpose(2, 0, 1)
return image |
def get_init_code(self, image):
'Gets initial latent codes as the start point for optimization.\n\n The input image is assumed to have already been preprocessed, meaning to\n have shape [self.G.image_channels, self.G.resolution, self.G.resolution],\n channel order `self.G.channel_order`, and pixel range [self.G.min_val,\n self.G.max_val].\n '
x = image[np.newaxis]
x = self.G.to_tensor(x.astype(np.float32))
z = _get_tensor_value(self.E.net(x).view(1, *self.encode_dim))
return z.astype(np.float32) | -7,680,309,811,430,556,000 | Gets initial latent codes as the start point for optimization.
The input image is assumed to have already been preprocessed, meaning to
have shape [self.G.image_channels, self.G.resolution, self.G.resolution],
channel order `self.G.channel_order`, and pixel range [self.G.min_val,
self.G.max_val]. | utils/inverter.py | get_init_code | Twizwei/idinvert_pytorch | python | def get_init_code(self, image):
'Gets initial latent codes as the start point for optimization.\n\n The input image is assumed to have already been preprocessed, meaning to\n have shape [self.G.image_channels, self.G.resolution, self.G.resolution],\n channel order `self.G.channel_order`, and pixel range [self.G.min_val,\n self.G.max_val].\n '
x = image[np.newaxis]
x = self.G.to_tensor(x.astype(np.float32))
z = _get_tensor_value(self.E.net(x).view(1, *self.encode_dim))
return z.astype(np.float32) |
def invert(self, image, num_viz=0):
'Inverts the given image to a latent code.\n\n Basically, this function is based on gradient descent algorithm.\n\n Args:\n image: Target image to invert, which is assumed to have already been\n preprocessed.\n num_viz: Number of intermediate outputs to visualize. (default: 0)\n\n Returns:\n A two-element tuple. First one is the inverted code. Second one is a list\n of intermediate results, where first image is the input image, second\n one is the reconstructed result from the initial latent code, remainings\n are from the optimization process every `self.iteration // num_viz`\n steps.\n '
x = image[np.newaxis]
x = self.G.to_tensor(x.astype(np.float32))
x.requires_grad = False
init_z = self.get_init_code(image)
z = torch.Tensor(init_z).to(self.run_device)
z.requires_grad = True
optimizer = torch.optim.Adam([z], lr=self.learning_rate)
viz_results = []
viz_results.append(self.G.postprocess(_get_tensor_value(x))[0])
x_init_inv = self.G.net.synthesis(z)
viz_results.append(self.G.postprocess(_get_tensor_value(x_init_inv))[0])
pbar = tqdm(range(1, (self.iteration + 1)), leave=True)
for step in pbar:
loss = 0.0
x_rec = self.G.net.synthesis(z)
loss_pix = torch.mean(((x - x_rec) ** 2))
loss = (loss + (loss_pix * self.loss_pix_weight))
log_message = f'loss_pix: {_get_tensor_value(loss_pix):.3f}'
if self.loss_feat_weight:
x_feat = self.F.net(x)
x_rec_feat = self.F.net(x_rec)
loss_feat = torch.mean(((x_feat - x_rec_feat) ** 2))
loss = (loss + (loss_feat * self.loss_feat_weight))
log_message += f', loss_feat: {_get_tensor_value(loss_feat):.3f}'
if self.loss_reg_weight:
z_rec = self.E.net(x_rec).view(1, *self.encode_dim)
loss_reg = torch.mean(((z - z_rec) ** 2))
loss = (loss + (loss_reg * self.loss_reg_weight))
log_message += f', loss_reg: {_get_tensor_value(loss_reg):.3f}'
log_message += f', loss: {_get_tensor_value(loss):.3f}'
pbar.set_description_str(log_message)
if self.logger:
self.logger.debug(f'Step: {step:05d}, lr: {self.learning_rate:.2e}, {log_message}')
optimizer.zero_grad()
loss.backward()
optimizer.step()
if ((num_viz > 0) and ((step % (self.iteration // num_viz)) == 0)):
viz_results.append(self.G.postprocess(_get_tensor_value(x_rec))[0])
return (_get_tensor_value(z), viz_results) | -5,647,097,221,206,265,000 | Inverts the given image to a latent code.
Basically, this function is based on gradient descent algorithm.
Args:
image: Target image to invert, which is assumed to have already been
preprocessed.
num_viz: Number of intermediate outputs to visualize. (default: 0)
Returns:
A two-element tuple. First one is the inverted code. Second one is a list
of intermediate results, where first image is the input image, second
one is the reconstructed result from the initial latent code, remainings
are from the optimization process every `self.iteration // num_viz`
steps. | utils/inverter.py | invert | Twizwei/idinvert_pytorch | python | def invert(self, image, num_viz=0):
'Inverts the given image to a latent code.\n\n Basically, this function is based on gradient descent algorithm.\n\n Args:\n image: Target image to invert, which is assumed to have already been\n preprocessed.\n num_viz: Number of intermediate outputs to visualize. (default: 0)\n\n Returns:\n A two-element tuple. First one is the inverted code. Second one is a list\n of intermediate results, where first image is the input image, second\n one is the reconstructed result from the initial latent code, remainings\n are from the optimization process every `self.iteration // num_viz`\n steps.\n '
x = image[np.newaxis]
x = self.G.to_tensor(x.astype(np.float32))
x.requires_grad = False
init_z = self.get_init_code(image)
z = torch.Tensor(init_z).to(self.run_device)
z.requires_grad = True
optimizer = torch.optim.Adam([z], lr=self.learning_rate)
viz_results = []
viz_results.append(self.G.postprocess(_get_tensor_value(x))[0])
x_init_inv = self.G.net.synthesis(z)
viz_results.append(self.G.postprocess(_get_tensor_value(x_init_inv))[0])
pbar = tqdm(range(1, (self.iteration + 1)), leave=True)
for step in pbar:
loss = 0.0
x_rec = self.G.net.synthesis(z)
loss_pix = torch.mean(((x - x_rec) ** 2))
loss = (loss + (loss_pix * self.loss_pix_weight))
log_message = f'loss_pix: {_get_tensor_value(loss_pix):.3f}'
if self.loss_feat_weight:
x_feat = self.F.net(x)
x_rec_feat = self.F.net(x_rec)
loss_feat = torch.mean(((x_feat - x_rec_feat) ** 2))
loss = (loss + (loss_feat * self.loss_feat_weight))
log_message += f', loss_feat: {_get_tensor_value(loss_feat):.3f}'
if self.loss_reg_weight:
z_rec = self.E.net(x_rec).view(1, *self.encode_dim)
loss_reg = torch.mean(((z - z_rec) ** 2))
loss = (loss + (loss_reg * self.loss_reg_weight))
log_message += f', loss_reg: {_get_tensor_value(loss_reg):.3f}'
log_message += f', loss: {_get_tensor_value(loss):.3f}'
pbar.set_description_str(log_message)
if self.logger:
self.logger.debug(f'Step: {step:05d}, lr: {self.learning_rate:.2e}, {log_message}')
optimizer.zero_grad()
loss.backward()
optimizer.step()
if ((num_viz > 0) and ((step % (self.iteration // num_viz)) == 0)):
viz_results.append(self.G.postprocess(_get_tensor_value(x_rec))[0])
return (_get_tensor_value(z), viz_results) |
def easy_invert(self, image, num_viz=0):
'Wraps functions `preprocess()` and `invert()` together.'
return self.invert(self.preprocess(image), num_viz) | 8,442,911,914,438,245,000 | Wraps functions `preprocess()` and `invert()` together. | utils/inverter.py | easy_invert | Twizwei/idinvert_pytorch | python | def easy_invert(self, image, num_viz=0):
return self.invert(self.preprocess(image), num_viz) |
def diffuse(self, target, context, center_x, center_y, crop_x, crop_y, num_viz=0):
'Diffuses the target image to a context image.\n\n Basically, this function is a motified version of `self.invert()`. More\n concretely, the encoder regularizer is removed from the objectives and the\n reconstruction loss is computed from the masked region.\n\n Args:\n target: Target image (foreground).\n context: Context image (background).\n center_x: The x-coordinate of the crop center.\n center_y: The y-coordinate of the crop center.\n crop_x: The crop size along the x-axis.\n crop_y: The crop size along the y-axis.\n num_viz: Number of intermediate outputs to visualize. (default: 0)\n\n Returns:\n A two-element tuple. First one is the inverted code. Second one is a list\n of intermediate results, where first image is the direct copy-paste\n image, second one is the reconstructed result from the initial latent\n code, remainings are from the optimization process every\n `self.iteration // num_viz` steps.\n '
image_shape = (self.G.image_channels, self.G.resolution, self.G.resolution)
mask = np.zeros((1, *image_shape), dtype=np.float32)
xx = (center_x - (crop_x // 2))
yy = (center_y - (crop_y // 2))
mask[:, :, yy:(yy + crop_y), xx:(xx + crop_x)] = 1.0
target = target[np.newaxis]
context = context[np.newaxis]
x = ((target * mask) + (context * (1 - mask)))
x = self.G.to_tensor(x.astype(np.float32))
x.requires_grad = False
mask = self.G.to_tensor(mask.astype(np.float32))
mask.requires_grad = False
init_z = _get_tensor_value(self.E.net(x).view(1, *self.encode_dim))
init_z = init_z.astype(np.float32)
z = torch.Tensor(init_z).to(self.run_device)
z.requires_grad = True
optimizer = torch.optim.Adam([z], lr=self.learning_rate)
viz_results = []
viz_results.append(self.G.postprocess(_get_tensor_value(x))[0])
x_init_inv = self.G.net.synthesis(z)
viz_results.append(self.G.postprocess(_get_tensor_value(x_init_inv))[0])
pbar = tqdm(range(1, (self.iteration + 1)), leave=True)
for step in pbar:
loss = 0.0
x_rec = self.G.net.synthesis(z)
loss_pix = torch.mean((((x - x_rec) * mask) ** 2))
loss = (loss + (loss_pix * self.loss_pix_weight))
log_message = f'loss_pix: {_get_tensor_value(loss_pix):.3f}'
if self.loss_feat_weight:
x_feat = self.F.net((x * mask))
x_rec_feat = self.F.net((x_rec * mask))
loss_feat = torch.mean(((x_feat - x_rec_feat) ** 2))
loss = (loss + (loss_feat * self.loss_feat_weight))
log_message += f', loss_feat: {_get_tensor_value(loss_feat):.3f}'
log_message += f', loss: {_get_tensor_value(loss):.3f}'
pbar.set_description_str(log_message)
if self.logger:
self.logger.debug(f'Step: {step:05d}, lr: {self.learning_rate:.2e}, {log_message}')
optimizer.zero_grad()
loss.backward()
optimizer.step()
if ((num_viz > 0) and ((step % (self.iteration // num_viz)) == 0)):
viz_results.append(self.G.postprocess(_get_tensor_value(x_rec))[0])
return (_get_tensor_value(z), viz_results) | -7,616,106,853,401,418,000 | Diffuses the target image to a context image.
Basically, this function is a motified version of `self.invert()`. More
concretely, the encoder regularizer is removed from the objectives and the
reconstruction loss is computed from the masked region.
Args:
target: Target image (foreground).
context: Context image (background).
center_x: The x-coordinate of the crop center.
center_y: The y-coordinate of the crop center.
crop_x: The crop size along the x-axis.
crop_y: The crop size along the y-axis.
num_viz: Number of intermediate outputs to visualize. (default: 0)
Returns:
A two-element tuple. First one is the inverted code. Second one is a list
of intermediate results, where first image is the direct copy-paste
image, second one is the reconstructed result from the initial latent
code, remainings are from the optimization process every
`self.iteration // num_viz` steps. | utils/inverter.py | diffuse | Twizwei/idinvert_pytorch | python | def diffuse(self, target, context, center_x, center_y, crop_x, crop_y, num_viz=0):
'Diffuses the target image to a context image.\n\n Basically, this function is a motified version of `self.invert()`. More\n concretely, the encoder regularizer is removed from the objectives and the\n reconstruction loss is computed from the masked region.\n\n Args:\n target: Target image (foreground).\n context: Context image (background).\n center_x: The x-coordinate of the crop center.\n center_y: The y-coordinate of the crop center.\n crop_x: The crop size along the x-axis.\n crop_y: The crop size along the y-axis.\n num_viz: Number of intermediate outputs to visualize. (default: 0)\n\n Returns:\n A two-element tuple. First one is the inverted code. Second one is a list\n of intermediate results, where first image is the direct copy-paste\n image, second one is the reconstructed result from the initial latent\n code, remainings are from the optimization process every\n `self.iteration // num_viz` steps.\n '
image_shape = (self.G.image_channels, self.G.resolution, self.G.resolution)
mask = np.zeros((1, *image_shape), dtype=np.float32)
xx = (center_x - (crop_x // 2))
yy = (center_y - (crop_y // 2))
mask[:, :, yy:(yy + crop_y), xx:(xx + crop_x)] = 1.0
target = target[np.newaxis]
context = context[np.newaxis]
x = ((target * mask) + (context * (1 - mask)))
x = self.G.to_tensor(x.astype(np.float32))
x.requires_grad = False
mask = self.G.to_tensor(mask.astype(np.float32))
mask.requires_grad = False
init_z = _get_tensor_value(self.E.net(x).view(1, *self.encode_dim))
init_z = init_z.astype(np.float32)
z = torch.Tensor(init_z).to(self.run_device)
z.requires_grad = True
optimizer = torch.optim.Adam([z], lr=self.learning_rate)
viz_results = []
viz_results.append(self.G.postprocess(_get_tensor_value(x))[0])
x_init_inv = self.G.net.synthesis(z)
viz_results.append(self.G.postprocess(_get_tensor_value(x_init_inv))[0])
pbar = tqdm(range(1, (self.iteration + 1)), leave=True)
for step in pbar:
loss = 0.0
x_rec = self.G.net.synthesis(z)
loss_pix = torch.mean((((x - x_rec) * mask) ** 2))
loss = (loss + (loss_pix * self.loss_pix_weight))
log_message = f'loss_pix: {_get_tensor_value(loss_pix):.3f}'
if self.loss_feat_weight:
x_feat = self.F.net((x * mask))
x_rec_feat = self.F.net((x_rec * mask))
loss_feat = torch.mean(((x_feat - x_rec_feat) ** 2))
loss = (loss + (loss_feat * self.loss_feat_weight))
log_message += f', loss_feat: {_get_tensor_value(loss_feat):.3f}'
log_message += f', loss: {_get_tensor_value(loss):.3f}'
pbar.set_description_str(log_message)
if self.logger:
self.logger.debug(f'Step: {step:05d}, lr: {self.learning_rate:.2e}, {log_message}')
optimizer.zero_grad()
loss.backward()
optimizer.step()
if ((num_viz > 0) and ((step % (self.iteration // num_viz)) == 0)):
viz_results.append(self.G.postprocess(_get_tensor_value(x_rec))[0])
return (_get_tensor_value(z), viz_results) |
def easy_diffuse(self, target, context, *args, **kwargs):
'Wraps functions `preprocess()` and `diffuse()` together.'
return self.diffuse(self.preprocess(target), self.preprocess(context), *args, **kwargs) | -8,924,164,457,142,377,000 | Wraps functions `preprocess()` and `diffuse()` together. | utils/inverter.py | easy_diffuse | Twizwei/idinvert_pytorch | python | def easy_diffuse(self, target, context, *args, **kwargs):
return self.diffuse(self.preprocess(target), self.preprocess(context), *args, **kwargs) |
async def async_start(hass: HomeAssistantType, discovery_topic, hass_config, config_entry=None) -> bool:
'Initialize of MQTT Discovery.'
async def async_device_message_received(topic, payload, qos):
'Process the received message.'
match = TOPIC_MATCHER.match(topic)
if (not match):
return
(_prefix_topic, component, node_id, object_id) = match.groups()
if (component not in SUPPORTED_COMPONENTS):
_LOGGER.warning('Component %s is not supported', component)
return
if payload:
try:
payload = json.loads(payload)
except ValueError:
_LOGGER.warning("Unable to parse JSON %s: '%s'", object_id, payload)
return
payload = dict(payload)
for key in list(payload.keys()):
abbreviated_key = key
key = ABBREVIATIONS.get(key, key)
payload[key] = payload.pop(abbreviated_key)
if (TOPIC_BASE in payload):
base = payload[TOPIC_BASE]
for (key, value) in payload.items():
if (isinstance(value, str) and value):
if ((value[0] == TOPIC_BASE) and key.endswith('_topic')):
payload[key] = '{}{}'.format(base, value[1:])
if ((value[(- 1)] == TOPIC_BASE) and key.endswith('_topic')):
payload[key] = '{}{}'.format(value[:(- 1)], base)
discovery_id = (' '.join((node_id, object_id)) if node_id else object_id)
discovery_hash = (component, discovery_id)
if payload:
if ((CONF_PLATFORM in payload) and ('schema' not in payload)):
platform = payload[CONF_PLATFORM]
if ((component in DEPRECATED_PLATFORM_TO_SCHEMA) and (platform in DEPRECATED_PLATFORM_TO_SCHEMA[component])):
schema = DEPRECATED_PLATFORM_TO_SCHEMA[component][platform]
payload['schema'] = schema
_LOGGER.warning('"platform": "%s" is deprecated, replace with "schema":"%s"', platform, schema)
payload[CONF_PLATFORM] = 'mqtt'
if (CONF_STATE_TOPIC not in payload):
payload[CONF_STATE_TOPIC] = '{}/{}/{}{}/state'.format(discovery_topic, component, (('%s/' % node_id) if node_id else ''), object_id)
payload[ATTR_DISCOVERY_HASH] = discovery_hash
if (ALREADY_DISCOVERED not in hass.data):
hass.data[ALREADY_DISCOVERED] = {}
if (discovery_hash in hass.data[ALREADY_DISCOVERED]):
_LOGGER.info('Component has already been discovered: %s %s, sending update', component, discovery_id)
async_dispatcher_send(hass, MQTT_DISCOVERY_UPDATED.format(discovery_hash), payload)
elif payload:
_LOGGER.info('Found new component: %s %s', component, discovery_id)
hass.data[ALREADY_DISCOVERED][discovery_hash] = None
if (component not in CONFIG_ENTRY_COMPONENTS):
(await async_load_platform(hass, component, 'mqtt', payload, hass_config))
return
config_entries_key = '{}.{}'.format(component, 'mqtt')
async with hass.data[DATA_CONFIG_ENTRY_LOCK]:
if (config_entries_key not in hass.data[CONFIG_ENTRY_IS_SETUP]):
(await hass.config_entries.async_forward_entry_setup(config_entry, component))
hass.data[CONFIG_ENTRY_IS_SETUP].add(config_entries_key)
async_dispatcher_send(hass, MQTT_DISCOVERY_NEW.format(component, 'mqtt'), payload)
hass.data[DATA_CONFIG_ENTRY_LOCK] = asyncio.Lock()
hass.data[CONFIG_ENTRY_IS_SETUP] = set()
(await mqtt.async_subscribe(hass, (discovery_topic + '/#'), async_device_message_received, 0))
return True | 2,637,539,386,003,770,000 | Initialize of MQTT Discovery. | homeassistant/components/mqtt/discovery.py | async_start | arnisoph/home-assistant | python | async def async_start(hass: HomeAssistantType, discovery_topic, hass_config, config_entry=None) -> bool:
async def async_device_message_received(topic, payload, qos):
'Process the received message.'
match = TOPIC_MATCHER.match(topic)
if (not match):
return
(_prefix_topic, component, node_id, object_id) = match.groups()
if (component not in SUPPORTED_COMPONENTS):
_LOGGER.warning('Component %s is not supported', component)
return
if payload:
try:
payload = json.loads(payload)
except ValueError:
_LOGGER.warning("Unable to parse JSON %s: '%s'", object_id, payload)
return
payload = dict(payload)
for key in list(payload.keys()):
abbreviated_key = key
key = ABBREVIATIONS.get(key, key)
payload[key] = payload.pop(abbreviated_key)
if (TOPIC_BASE in payload):
base = payload[TOPIC_BASE]
for (key, value) in payload.items():
if (isinstance(value, str) and value):
if ((value[0] == TOPIC_BASE) and key.endswith('_topic')):
payload[key] = '{}{}'.format(base, value[1:])
if ((value[(- 1)] == TOPIC_BASE) and key.endswith('_topic')):
payload[key] = '{}{}'.format(value[:(- 1)], base)
discovery_id = (' '.join((node_id, object_id)) if node_id else object_id)
discovery_hash = (component, discovery_id)
if payload:
if ((CONF_PLATFORM in payload) and ('schema' not in payload)):
platform = payload[CONF_PLATFORM]
if ((component in DEPRECATED_PLATFORM_TO_SCHEMA) and (platform in DEPRECATED_PLATFORM_TO_SCHEMA[component])):
schema = DEPRECATED_PLATFORM_TO_SCHEMA[component][platform]
payload['schema'] = schema
_LOGGER.warning('"platform": "%s" is deprecated, replace with "schema":"%s"', platform, schema)
payload[CONF_PLATFORM] = 'mqtt'
if (CONF_STATE_TOPIC not in payload):
payload[CONF_STATE_TOPIC] = '{}/{}/{}{}/state'.format(discovery_topic, component, (('%s/' % node_id) if node_id else ), object_id)
payload[ATTR_DISCOVERY_HASH] = discovery_hash
if (ALREADY_DISCOVERED not in hass.data):
hass.data[ALREADY_DISCOVERED] = {}
if (discovery_hash in hass.data[ALREADY_DISCOVERED]):
_LOGGER.info('Component has already been discovered: %s %s, sending update', component, discovery_id)
async_dispatcher_send(hass, MQTT_DISCOVERY_UPDATED.format(discovery_hash), payload)
elif payload:
_LOGGER.info('Found new component: %s %s', component, discovery_id)
hass.data[ALREADY_DISCOVERED][discovery_hash] = None
if (component not in CONFIG_ENTRY_COMPONENTS):
(await async_load_platform(hass, component, 'mqtt', payload, hass_config))
return
config_entries_key = '{}.{}'.format(component, 'mqtt')
async with hass.data[DATA_CONFIG_ENTRY_LOCK]:
if (config_entries_key not in hass.data[CONFIG_ENTRY_IS_SETUP]):
(await hass.config_entries.async_forward_entry_setup(config_entry, component))
hass.data[CONFIG_ENTRY_IS_SETUP].add(config_entries_key)
async_dispatcher_send(hass, MQTT_DISCOVERY_NEW.format(component, 'mqtt'), payload)
hass.data[DATA_CONFIG_ENTRY_LOCK] = asyncio.Lock()
hass.data[CONFIG_ENTRY_IS_SETUP] = set()
(await mqtt.async_subscribe(hass, (discovery_topic + '/#'), async_device_message_received, 0))
return True |
async def async_device_message_received(topic, payload, qos):
'Process the received message.'
match = TOPIC_MATCHER.match(topic)
if (not match):
return
(_prefix_topic, component, node_id, object_id) = match.groups()
if (component not in SUPPORTED_COMPONENTS):
_LOGGER.warning('Component %s is not supported', component)
return
if payload:
try:
payload = json.loads(payload)
except ValueError:
_LOGGER.warning("Unable to parse JSON %s: '%s'", object_id, payload)
return
payload = dict(payload)
for key in list(payload.keys()):
abbreviated_key = key
key = ABBREVIATIONS.get(key, key)
payload[key] = payload.pop(abbreviated_key)
if (TOPIC_BASE in payload):
base = payload[TOPIC_BASE]
for (key, value) in payload.items():
if (isinstance(value, str) and value):
if ((value[0] == TOPIC_BASE) and key.endswith('_topic')):
payload[key] = '{}{}'.format(base, value[1:])
if ((value[(- 1)] == TOPIC_BASE) and key.endswith('_topic')):
payload[key] = '{}{}'.format(value[:(- 1)], base)
discovery_id = (' '.join((node_id, object_id)) if node_id else object_id)
discovery_hash = (component, discovery_id)
if payload:
if ((CONF_PLATFORM in payload) and ('schema' not in payload)):
platform = payload[CONF_PLATFORM]
if ((component in DEPRECATED_PLATFORM_TO_SCHEMA) and (platform in DEPRECATED_PLATFORM_TO_SCHEMA[component])):
schema = DEPRECATED_PLATFORM_TO_SCHEMA[component][platform]
payload['schema'] = schema
_LOGGER.warning('"platform": "%s" is deprecated, replace with "schema":"%s"', platform, schema)
payload[CONF_PLATFORM] = 'mqtt'
if (CONF_STATE_TOPIC not in payload):
payload[CONF_STATE_TOPIC] = '{}/{}/{}{}/state'.format(discovery_topic, component, (('%s/' % node_id) if node_id else ''), object_id)
payload[ATTR_DISCOVERY_HASH] = discovery_hash
if (ALREADY_DISCOVERED not in hass.data):
hass.data[ALREADY_DISCOVERED] = {}
if (discovery_hash in hass.data[ALREADY_DISCOVERED]):
_LOGGER.info('Component has already been discovered: %s %s, sending update', component, discovery_id)
async_dispatcher_send(hass, MQTT_DISCOVERY_UPDATED.format(discovery_hash), payload)
elif payload:
_LOGGER.info('Found new component: %s %s', component, discovery_id)
hass.data[ALREADY_DISCOVERED][discovery_hash] = None
if (component not in CONFIG_ENTRY_COMPONENTS):
(await async_load_platform(hass, component, 'mqtt', payload, hass_config))
return
config_entries_key = '{}.{}'.format(component, 'mqtt')
async with hass.data[DATA_CONFIG_ENTRY_LOCK]:
if (config_entries_key not in hass.data[CONFIG_ENTRY_IS_SETUP]):
(await hass.config_entries.async_forward_entry_setup(config_entry, component))
hass.data[CONFIG_ENTRY_IS_SETUP].add(config_entries_key)
async_dispatcher_send(hass, MQTT_DISCOVERY_NEW.format(component, 'mqtt'), payload) | -2,159,514,262,508,901,400 | Process the received message. | homeassistant/components/mqtt/discovery.py | async_device_message_received | arnisoph/home-assistant | python | async def async_device_message_received(topic, payload, qos):
match = TOPIC_MATCHER.match(topic)
if (not match):
return
(_prefix_topic, component, node_id, object_id) = match.groups()
if (component not in SUPPORTED_COMPONENTS):
_LOGGER.warning('Component %s is not supported', component)
return
if payload:
try:
payload = json.loads(payload)
except ValueError:
_LOGGER.warning("Unable to parse JSON %s: '%s'", object_id, payload)
return
payload = dict(payload)
for key in list(payload.keys()):
abbreviated_key = key
key = ABBREVIATIONS.get(key, key)
payload[key] = payload.pop(abbreviated_key)
if (TOPIC_BASE in payload):
base = payload[TOPIC_BASE]
for (key, value) in payload.items():
if (isinstance(value, str) and value):
if ((value[0] == TOPIC_BASE) and key.endswith('_topic')):
payload[key] = '{}{}'.format(base, value[1:])
if ((value[(- 1)] == TOPIC_BASE) and key.endswith('_topic')):
payload[key] = '{}{}'.format(value[:(- 1)], base)
discovery_id = (' '.join((node_id, object_id)) if node_id else object_id)
discovery_hash = (component, discovery_id)
if payload:
if ((CONF_PLATFORM in payload) and ('schema' not in payload)):
platform = payload[CONF_PLATFORM]
if ((component in DEPRECATED_PLATFORM_TO_SCHEMA) and (platform in DEPRECATED_PLATFORM_TO_SCHEMA[component])):
schema = DEPRECATED_PLATFORM_TO_SCHEMA[component][platform]
payload['schema'] = schema
_LOGGER.warning('"platform": "%s" is deprecated, replace with "schema":"%s"', platform, schema)
payload[CONF_PLATFORM] = 'mqtt'
if (CONF_STATE_TOPIC not in payload):
payload[CONF_STATE_TOPIC] = '{}/{}/{}{}/state'.format(discovery_topic, component, (('%s/' % node_id) if node_id else ), object_id)
payload[ATTR_DISCOVERY_HASH] = discovery_hash
if (ALREADY_DISCOVERED not in hass.data):
hass.data[ALREADY_DISCOVERED] = {}
if (discovery_hash in hass.data[ALREADY_DISCOVERED]):
_LOGGER.info('Component has already been discovered: %s %s, sending update', component, discovery_id)
async_dispatcher_send(hass, MQTT_DISCOVERY_UPDATED.format(discovery_hash), payload)
elif payload:
_LOGGER.info('Found new component: %s %s', component, discovery_id)
hass.data[ALREADY_DISCOVERED][discovery_hash] = None
if (component not in CONFIG_ENTRY_COMPONENTS):
(await async_load_platform(hass, component, 'mqtt', payload, hass_config))
return
config_entries_key = '{}.{}'.format(component, 'mqtt')
async with hass.data[DATA_CONFIG_ENTRY_LOCK]:
if (config_entries_key not in hass.data[CONFIG_ENTRY_IS_SETUP]):
(await hass.config_entries.async_forward_entry_setup(config_entry, component))
hass.data[CONFIG_ENTRY_IS_SETUP].add(config_entries_key)
async_dispatcher_send(hass, MQTT_DISCOVERY_NEW.format(component, 'mqtt'), payload) |
def __init__(self, inplanes, planes, scales=4, base_width=26, base_channels=64, stage_type='normal', **kwargs):
'Bottle2neck block for Res2Net.\n\n If style is "pytorch", the stride-two layer is the 3x3 conv layer, if\n it is "caffe", the stride-two layer is the first 1x1 conv layer.\n '
super(Bottle2neck, self).__init__(inplanes, planes, **kwargs)
assert (scales > 1), 'Res2Net degenerates to ResNet when scales = 1.'
width = int(math.floor((self.planes * (base_width / base_channels))))
(self.norm1_name, norm1) = build_norm_layer(self.norm_cfg, (width * scales), postfix=1)
(self.norm3_name, norm3) = build_norm_layer(self.norm_cfg, (self.planes * self.expansion), postfix=3)
self.conv1 = build_conv_layer(self.conv_cfg, self.inplanes, (width * scales), kernel_size=1, stride=self.conv1_stride, bias=False)
self.add_module(self.norm1_name, norm1)
if ((stage_type == 'stage') and (self.conv2_stride != 1)):
self.pool = nn.AvgPool2d(kernel_size=3, stride=self.conv2_stride, padding=1)
convs = []
bns = []
fallback_on_stride = False
if self.with_dcn:
fallback_on_stride = self.dcn.pop('fallback_on_stride', False)
if ((not self.with_dcn) or fallback_on_stride):
for i in range((scales - 1)):
convs.append(build_conv_layer(self.conv_cfg, width, width, kernel_size=3, stride=self.conv2_stride, padding=self.dilation, dilation=self.dilation, bias=False))
bns.append(build_norm_layer(self.norm_cfg, width, postfix=(i + 1))[1])
self.convs = nn.ModuleList(convs)
self.bns = nn.ModuleList(bns)
else:
assert (self.conv_cfg is None), 'conv_cfg must be None for DCN'
for i in range((scales - 1)):
convs.append(build_conv_layer(self.dcn, width, width, kernel_size=3, stride=self.conv2_stride, padding=self.dilation, dilation=self.dilation, bias=False))
bns.append(build_norm_layer(self.norm_cfg, width, postfix=(i + 1))[1])
self.convs = nn.ModuleList(convs)
self.bns = nn.ModuleList(bns)
self.conv3 = build_conv_layer(self.conv_cfg, (width * scales), (self.planes * self.expansion), kernel_size=1, bias=False)
self.add_module(self.norm3_name, norm3)
self.stage_type = stage_type
self.scales = scales
self.width = width
delattr(self, 'conv2')
delattr(self, self.norm2_name) | 4,797,434,700,482,818,000 | Bottle2neck block for Res2Net.
If style is "pytorch", the stride-two layer is the 3x3 conv layer, if
it is "caffe", the stride-two layer is the first 1x1 conv layer. | detection/scrfd/mmdet/models/backbones/res2net.py | __init__ | 007gzs/insightface | python | def __init__(self, inplanes, planes, scales=4, base_width=26, base_channels=64, stage_type='normal', **kwargs):
'Bottle2neck block for Res2Net.\n\n If style is "pytorch", the stride-two layer is the 3x3 conv layer, if\n it is "caffe", the stride-two layer is the first 1x1 conv layer.\n '
super(Bottle2neck, self).__init__(inplanes, planes, **kwargs)
assert (scales > 1), 'Res2Net degenerates to ResNet when scales = 1.'
width = int(math.floor((self.planes * (base_width / base_channels))))
(self.norm1_name, norm1) = build_norm_layer(self.norm_cfg, (width * scales), postfix=1)
(self.norm3_name, norm3) = build_norm_layer(self.norm_cfg, (self.planes * self.expansion), postfix=3)
self.conv1 = build_conv_layer(self.conv_cfg, self.inplanes, (width * scales), kernel_size=1, stride=self.conv1_stride, bias=False)
self.add_module(self.norm1_name, norm1)
if ((stage_type == 'stage') and (self.conv2_stride != 1)):
self.pool = nn.AvgPool2d(kernel_size=3, stride=self.conv2_stride, padding=1)
convs = []
bns = []
fallback_on_stride = False
if self.with_dcn:
fallback_on_stride = self.dcn.pop('fallback_on_stride', False)
if ((not self.with_dcn) or fallback_on_stride):
for i in range((scales - 1)):
convs.append(build_conv_layer(self.conv_cfg, width, width, kernel_size=3, stride=self.conv2_stride, padding=self.dilation, dilation=self.dilation, bias=False))
bns.append(build_norm_layer(self.norm_cfg, width, postfix=(i + 1))[1])
self.convs = nn.ModuleList(convs)
self.bns = nn.ModuleList(bns)
else:
assert (self.conv_cfg is None), 'conv_cfg must be None for DCN'
for i in range((scales - 1)):
convs.append(build_conv_layer(self.dcn, width, width, kernel_size=3, stride=self.conv2_stride, padding=self.dilation, dilation=self.dilation, bias=False))
bns.append(build_norm_layer(self.norm_cfg, width, postfix=(i + 1))[1])
self.convs = nn.ModuleList(convs)
self.bns = nn.ModuleList(bns)
self.conv3 = build_conv_layer(self.conv_cfg, (width * scales), (self.planes * self.expansion), kernel_size=1, bias=False)
self.add_module(self.norm3_name, norm3)
self.stage_type = stage_type
self.scales = scales
self.width = width
delattr(self, 'conv2')
delattr(self, self.norm2_name) |
def forward(self, x):
'Forward function.'
def _inner_forward(x):
identity = x
out = self.conv1(x)
out = self.norm1(out)
out = self.relu(out)
if self.with_plugins:
out = self.forward_plugin(out, self.after_conv1_plugin_names)
spx = torch.split(out, self.width, 1)
sp = self.convs[0](spx[0].contiguous())
sp = self.relu(self.bns[0](sp))
out = sp
for i in range(1, (self.scales - 1)):
if (self.stage_type == 'stage'):
sp = spx[i]
else:
sp = (sp + spx[i])
sp = self.convs[i](sp.contiguous())
sp = self.relu(self.bns[i](sp))
out = torch.cat((out, sp), 1)
if ((self.stage_type == 'normal') or (self.conv2_stride == 1)):
out = torch.cat((out, spx[(self.scales - 1)]), 1)
elif (self.stage_type == 'stage'):
out = torch.cat((out, self.pool(spx[(self.scales - 1)])), 1)
if self.with_plugins:
out = self.forward_plugin(out, self.after_conv2_plugin_names)
out = self.conv3(out)
out = self.norm3(out)
if self.with_plugins:
out = self.forward_plugin(out, self.after_conv3_plugin_names)
if (self.downsample is not None):
identity = self.downsample(x)
out += identity
return out
if (self.with_cp and x.requires_grad):
out = cp.checkpoint(_inner_forward, x)
else:
out = _inner_forward(x)
out = self.relu(out)
return out | 1,846,693,992,825,146,000 | Forward function. | detection/scrfd/mmdet/models/backbones/res2net.py | forward | 007gzs/insightface | python | def forward(self, x):
def _inner_forward(x):
identity = x
out = self.conv1(x)
out = self.norm1(out)
out = self.relu(out)
if self.with_plugins:
out = self.forward_plugin(out, self.after_conv1_plugin_names)
spx = torch.split(out, self.width, 1)
sp = self.convs[0](spx[0].contiguous())
sp = self.relu(self.bns[0](sp))
out = sp
for i in range(1, (self.scales - 1)):
if (self.stage_type == 'stage'):
sp = spx[i]
else:
sp = (sp + spx[i])
sp = self.convs[i](sp.contiguous())
sp = self.relu(self.bns[i](sp))
out = torch.cat((out, sp), 1)
if ((self.stage_type == 'normal') or (self.conv2_stride == 1)):
out = torch.cat((out, spx[(self.scales - 1)]), 1)
elif (self.stage_type == 'stage'):
out = torch.cat((out, self.pool(spx[(self.scales - 1)])), 1)
if self.with_plugins:
out = self.forward_plugin(out, self.after_conv2_plugin_names)
out = self.conv3(out)
out = self.norm3(out)
if self.with_plugins:
out = self.forward_plugin(out, self.after_conv3_plugin_names)
if (self.downsample is not None):
identity = self.downsample(x)
out += identity
return out
if (self.with_cp and x.requires_grad):
out = cp.checkpoint(_inner_forward, x)
else:
out = _inner_forward(x)
out = self.relu(out)
return out |
def init_weights(self, pretrained=None):
'Initialize the weights in backbone.\n\n Args:\n pretrained (str, optional): Path to pre-trained weights.\n Defaults to None.\n '
if isinstance(pretrained, str):
logger = get_root_logger()
load_checkpoint(self, pretrained, strict=False, logger=logger)
elif (pretrained is None):
for m in self.modules():
if isinstance(m, nn.Conv2d):
kaiming_init(m)
elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
constant_init(m, 1)
if (self.dcn is not None):
for m in self.modules():
if isinstance(m, Bottle2neck):
for n in m.convs:
if hasattr(n, 'conv_offset'):
constant_init(n.conv_offset, 0)
if self.zero_init_residual:
for m in self.modules():
if isinstance(m, Bottle2neck):
constant_init(m.norm3, 0)
else:
raise TypeError('pretrained must be a str or None') | -399,503,817,821,927,300 | Initialize the weights in backbone.
Args:
pretrained (str, optional): Path to pre-trained weights.
Defaults to None. | detection/scrfd/mmdet/models/backbones/res2net.py | init_weights | 007gzs/insightface | python | def init_weights(self, pretrained=None):
'Initialize the weights in backbone.\n\n Args:\n pretrained (str, optional): Path to pre-trained weights.\n Defaults to None.\n '
if isinstance(pretrained, str):
logger = get_root_logger()
load_checkpoint(self, pretrained, strict=False, logger=logger)
elif (pretrained is None):
for m in self.modules():
if isinstance(m, nn.Conv2d):
kaiming_init(m)
elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
constant_init(m, 1)
if (self.dcn is not None):
for m in self.modules():
if isinstance(m, Bottle2neck):
for n in m.convs:
if hasattr(n, 'conv_offset'):
constant_init(n.conv_offset, 0)
if self.zero_init_residual:
for m in self.modules():
if isinstance(m, Bottle2neck):
constant_init(m.norm3, 0)
else:
raise TypeError('pretrained must be a str or None') |
def parse_assets(lines):
' Parse asset list\n\n :param string paste_string: An asset list string\n '
(matches, bad_lines) = regex_match_lines(ASSET_LIST_RE, lines)
result = [{'name': name, 'quantity': (f_int(quantity) or 1), 'group': group, 'category': category, 'size': size, 'slot': slot, 'volume': volume, 'meta_level': meta_level, 'tech_level': tech_level} for (name, quantity, _, group, _, category, _, size, _, slot, _, volume, _, meta_level, _, tech_level) in matches]
return (result, bad_lines) | 6,874,263,680,403,479,000 | Parse asset list
:param string paste_string: An asset list string | eveparser/parsers/assets.py | parse_assets | Nothing4You/eveparser | python | def parse_assets(lines):
' Parse asset list\n\n :param string paste_string: An asset list string\n '
(matches, bad_lines) = regex_match_lines(ASSET_LIST_RE, lines)
result = [{'name': name, 'quantity': (f_int(quantity) or 1), 'group': group, 'category': category, 'size': size, 'slot': slot, 'volume': volume, 'meta_level': meta_level, 'tech_level': tech_level} for (name, quantity, _, group, _, category, _, size, _, slot, _, volume, _, meta_level, _, tech_level) in matches]
return (result, bad_lines) |
def __init__(self, compute_client):
'Instantiate ZoneResourceFetcher and embed all required data into it.\n\n ZoneResourceFetcher is a class depending on "base_classes"\n class layout (properties side-derived from one of base_class class). This\n function can be used to avoid unfeasible inheritance and use composition\n instead when refactoring away from base_classes into stateless style.\n\n This constructor embeds following properties into ZoneResourceFetcher\n instance:\n - compute\n - messages\n - http\n - batch_url\n\n Example:\n compute_holder = base_classes.ComputeApiHolder(self.ReleaseTrack())\n client = compute_holder.client\n\n zone_resource_fetcher = ZoneResourceFetcher(client)\n or\n zone_resource_fetcher = ZoneResourceFetcher(self.compute_client)\n to use in a class derived from some of base_classes\n\n zone_resource_fetcher.WarnForZonalCreation(...)\n\n Args:\n compute_client: compute_holder.client\n '
self._compute = compute_client.apitools_client
self._messages = compute_client.messages
self._http = compute_client.apitools_client.http
self._batch_url = compute_client.batch_url | 4,449,017,669,771,330,600 | Instantiate ZoneResourceFetcher and embed all required data into it.
ZoneResourceFetcher is a class depending on "base_classes"
class layout (properties side-derived from one of base_class class). This
function can be used to avoid unfeasible inheritance and use composition
instead when refactoring away from base_classes into stateless style.
This constructor embeds following properties into ZoneResourceFetcher
instance:
- compute
- messages
- http
- batch_url
Example:
compute_holder = base_classes.ComputeApiHolder(self.ReleaseTrack())
client = compute_holder.client
zone_resource_fetcher = ZoneResourceFetcher(client)
or
zone_resource_fetcher = ZoneResourceFetcher(self.compute_client)
to use in a class derived from some of base_classes
zone_resource_fetcher.WarnForZonalCreation(...)
Args:
compute_client: compute_holder.client | gcloud/google-cloud-sdk/.install/.backup/lib/googlecloudsdk/api_lib/compute/zone_utils.py | __init__ | bopopescu/JobSniperRails | python | def __init__(self, compute_client):
'Instantiate ZoneResourceFetcher and embed all required data into it.\n\n ZoneResourceFetcher is a class depending on "base_classes"\n class layout (properties side-derived from one of base_class class). This\n function can be used to avoid unfeasible inheritance and use composition\n instead when refactoring away from base_classes into stateless style.\n\n This constructor embeds following properties into ZoneResourceFetcher\n instance:\n - compute\n - messages\n - http\n - batch_url\n\n Example:\n compute_holder = base_classes.ComputeApiHolder(self.ReleaseTrack())\n client = compute_holder.client\n\n zone_resource_fetcher = ZoneResourceFetcher(client)\n or\n zone_resource_fetcher = ZoneResourceFetcher(self.compute_client)\n to use in a class derived from some of base_classes\n\n zone_resource_fetcher.WarnForZonalCreation(...)\n\n Args:\n compute_client: compute_holder.client\n '
self._compute = compute_client.apitools_client
self._messages = compute_client.messages
self._http = compute_client.apitools_client.http
self._batch_url = compute_client.batch_url |
def GetZones(self, resource_refs):
'Fetches zone resources.'
errors = []
requests = []
zone_names = set()
for resource_ref in resource_refs:
if (resource_ref.zone not in zone_names):
zone_names.add(resource_ref.zone)
requests.append((self._compute.zones, 'Get', self._messages.ComputeZonesGetRequest(project=resource_ref.project, zone=resource_ref.zone)))
res = list(request_helper.MakeRequests(requests=requests, http=self._http, batch_url=self._batch_url, errors=errors))
if errors:
return None
else:
return res | -4,250,735,275,922,151,000 | Fetches zone resources. | gcloud/google-cloud-sdk/.install/.backup/lib/googlecloudsdk/api_lib/compute/zone_utils.py | GetZones | bopopescu/JobSniperRails | python | def GetZones(self, resource_refs):
errors = []
requests = []
zone_names = set()
for resource_ref in resource_refs:
if (resource_ref.zone not in zone_names):
zone_names.add(resource_ref.zone)
requests.append((self._compute.zones, 'Get', self._messages.ComputeZonesGetRequest(project=resource_ref.project, zone=resource_ref.zone)))
res = list(request_helper.MakeRequests(requests=requests, http=self._http, batch_url=self._batch_url, errors=errors))
if errors:
return None
else:
return res |
def WarnForZonalCreation(self, resource_refs):
'Warns the user if a zone has upcoming deprecation.'
zones = self.GetZones(resource_refs)
if (not zones):
return
prompts = []
zones_with_deprecated = []
for zone in zones:
if zone.deprecated:
zones_with_deprecated.append(zone)
if (not zones_with_deprecated):
return
if zones_with_deprecated:
phrases = []
if (len(zones_with_deprecated) == 1):
phrases = ('zone is', 'this zone', 'the')
else:
phrases = ('zones are', 'these zones', 'their')
title = '\nWARNING: The following selected {0} deprecated. All resources in {1} will be deleted after {2} turndown date.'.format(phrases[0], phrases[1], phrases[2])
printable_deprecated_zones = []
for zone in zones_with_deprecated:
if zone.deprecated.deleted:
printable_deprecated_zones.append('[{0}] {1}'.format(zone.name, zone.deprecated.deleted))
else:
printable_deprecated_zones.append('[{0}]'.format(zone.name))
prompts.append(utils.ConstructList(title, printable_deprecated_zones))
final_message = ' '.join(prompts)
if (not console_io.PromptContinue(message=final_message)):
raise calliope_exceptions.ToolException('Creation aborted by user.') | -5,013,486,056,412,300,000 | Warns the user if a zone has upcoming deprecation. | gcloud/google-cloud-sdk/.install/.backup/lib/googlecloudsdk/api_lib/compute/zone_utils.py | WarnForZonalCreation | bopopescu/JobSniperRails | python | def WarnForZonalCreation(self, resource_refs):
zones = self.GetZones(resource_refs)
if (not zones):
return
prompts = []
zones_with_deprecated = []
for zone in zones:
if zone.deprecated:
zones_with_deprecated.append(zone)
if (not zones_with_deprecated):
return
if zones_with_deprecated:
phrases = []
if (len(zones_with_deprecated) == 1):
phrases = ('zone is', 'this zone', 'the')
else:
phrases = ('zones are', 'these zones', 'their')
title = '\nWARNING: The following selected {0} deprecated. All resources in {1} will be deleted after {2} turndown date.'.format(phrases[0], phrases[1], phrases[2])
printable_deprecated_zones = []
for zone in zones_with_deprecated:
if zone.deprecated.deleted:
printable_deprecated_zones.append('[{0}] {1}'.format(zone.name, zone.deprecated.deleted))
else:
printable_deprecated_zones.append('[{0}]'.format(zone.name))
prompts.append(utils.ConstructList(title, printable_deprecated_zones))
final_message = ' '.join(prompts)
if (not console_io.PromptContinue(message=final_message)):
raise calliope_exceptions.ToolException('Creation aborted by user.') |
def run_forever():
'Runs the asyncio event loop with and\n ensures state machines are exited upon a KeyboardInterrupt.\n '
loop = asyncio.get_event_loop()
try:
loop.run_forever()
except KeyboardInterrupt:
Framework.stop()
loop.close() | -6,814,674,388,628,781,000 | Runs the asyncio event loop with and
ensures state machines are exited upon a KeyboardInterrupt. | farc/__init__.py | run_forever | SzeMengTan/farc | python | def run_forever():
'Runs the asyncio event loop with and\n ensures state machines are exited upon a KeyboardInterrupt.\n '
loop = asyncio.get_event_loop()
try:
loop.run_forever()
except KeyboardInterrupt:
Framework.stop()
loop.close() |
@staticmethod
def enable_spy(spy_cls):
'Sets the Spy to use the given class\n and calls its initializer.\n '
Spy._actv_cls = spy_cls
spy_cls.init() | 2,137,537,250,594,512,100 | Sets the Spy to use the given class
and calls its initializer. | farc/__init__.py | enable_spy | SzeMengTan/farc | python | @staticmethod
def enable_spy(spy_cls):
'Sets the Spy to use the given class\n and calls its initializer.\n '
Spy._actv_cls = spy_cls
spy_cls.init() |
def __getattr__(*args):
'Returns\n 1) the enable_spy static method if requested by name, or\n 2) the attribute from the active class (if active class was set), or\n 3) a function that swallows any arguments and does nothing.\n '
if (args[1] == 'enable_spy'):
return Spy.enable_spy
if Spy._actv_cls:
return getattr(Spy._actv_cls, args[1])
return (lambda *x: None) | 6,580,064,896,499,551,000 | Returns
1) the enable_spy static method if requested by name, or
2) the attribute from the active class (if active class was set), or
3) a function that swallows any arguments and does nothing. | farc/__init__.py | __getattr__ | SzeMengTan/farc | python | def __getattr__(*args):
'Returns\n 1) the enable_spy static method if requested by name, or\n 2) the attribute from the active class (if active class was set), or\n 3) a function that swallows any arguments and does nothing.\n '
if (args[1] == 'enable_spy'):
return Spy.enable_spy
if Spy._actv_cls:
return getattr(Spy._actv_cls, args[1])
return (lambda *x: None) |
@staticmethod
def exists(signame):
'Returns True if signame is in the Signal registry.\n '
return (signame in Signal._registry) | -1,967,252,211,100,177,000 | Returns True if signame is in the Signal registry. | farc/__init__.py | exists | SzeMengTan/farc | python | @staticmethod
def exists(signame):
'\n '
return (signame in Signal._registry) |
@staticmethod
def register(signame):
'Registers the signame if it is not already registered.\n Returns the signal number for the signame.\n '
assert (type(signame) is str)
if (signame in Signal._registry):
return Signal._registry[signame]
else:
sigid = len(Signal._lookup)
Signal._registry[signame] = sigid
Signal._lookup.append(signame)
Spy.on_signal_register(signame, sigid)
return sigid | -5,932,514,422,443,632,000 | Registers the signame if it is not already registered.
Returns the signal number for the signame. | farc/__init__.py | register | SzeMengTan/farc | python | @staticmethod
def register(signame):
'Registers the signame if it is not already registered.\n Returns the signal number for the signame.\n '
assert (type(signame) is str)
if (signame in Signal._registry):
return Signal._registry[signame]
else:
sigid = len(Signal._lookup)
Signal._registry[signame] = sigid
Signal._lookup.append(signame)
Spy.on_signal_register(signame, sigid)
return sigid |
def __init__(self):
"Sets this Hsm's current state to Hsm.top(), the default state\n and stores the given initial state.\n "
self.state = self.top
self.initial_state = self._initial | -1,334,007,641,773,020,400 | Sets this Hsm's current state to Hsm.top(), the default state
and stores the given initial state. | farc/__init__.py | __init__ | SzeMengTan/farc | python | def __init__(self):
"Sets this Hsm's current state to Hsm.top(), the default state\n and stores the given initial state.\n "
self.state = self.top
self.initial_state = self._initial |
def _initial(self, event):
'Raises a NotImplementedError to force the derived class\n to implement its own initial state.\n '
raise NotImplementedError | 6,814,616,314,063,496,000 | Raises a NotImplementedError to force the derived class
to implement its own initial state. | farc/__init__.py | _initial | SzeMengTan/farc | python | def _initial(self, event):
'Raises a NotImplementedError to force the derived class\n to implement its own initial state.\n '
raise NotImplementedError |
def state(func):
'A decorator that identifies which methods are states.\n The presence of the farc_state attr, not the value of the attr,\n determines statehood.\n The Spy debugging system uses the farc_state attribute\n to determine which methods inside a class are actually states.\n Other uses of the attribute may come in the future.\n '
@wraps(func)
def func_wrap(self, evt):
result = func(self, evt)
Spy.on_state_handler_called(func_wrap, evt, result)
return result
setattr(func_wrap, 'farc_state', True)
return staticmethod(func_wrap) | 4,006,958,211,435,342,000 | A decorator that identifies which methods are states.
The presence of the farc_state attr, not the value of the attr,
determines statehood.
The Spy debugging system uses the farc_state attribute
to determine which methods inside a class are actually states.
Other uses of the attribute may come in the future. | farc/__init__.py | state | SzeMengTan/farc | python | def state(func):
'A decorator that identifies which methods are states.\n The presence of the farc_state attr, not the value of the attr,\n determines statehood.\n The Spy debugging system uses the farc_state attribute\n to determine which methods inside a class are actually states.\n Other uses of the attribute may come in the future.\n '
@wraps(func)
def func_wrap(self, evt):
result = func(self, evt)
Spy.on_state_handler_called(func_wrap, evt, result)
return result
setattr(func_wrap, 'farc_state', True)
return staticmethod(func_wrap) |
@state
def top(me, event):
"This is the default state handler.\n This handler ignores all signals except\n the POSIX-like events, SIGINT/SIGTERM.\n Handling SIGINT/SIGTERM here causes the Exit path\n to be executed from the application's active state\n to top/here.\n The application may put something useful\n or nothing at all in the Exit path.\n "
if (Event.SIGINT == event):
return Hsm.RET_HANDLED
if (Event.SIGTERM == event):
return Hsm.RET_HANDLED
return Hsm.RET_IGNORED | 4,357,347,042,863,078,000 | This is the default state handler.
This handler ignores all signals except
the POSIX-like events, SIGINT/SIGTERM.
Handling SIGINT/SIGTERM here causes the Exit path
to be executed from the application's active state
to top/here.
The application may put something useful
or nothing at all in the Exit path. | farc/__init__.py | top | SzeMengTan/farc | python | @state
def top(me, event):
"This is the default state handler.\n This handler ignores all signals except\n the POSIX-like events, SIGINT/SIGTERM.\n Handling SIGINT/SIGTERM here causes the Exit path\n to be executed from the application's active state\n to top/here.\n The application may put something useful\n or nothing at all in the Exit path.\n "
if (Event.SIGINT == event):
return Hsm.RET_HANDLED
if (Event.SIGTERM == event):
return Hsm.RET_HANDLED
return Hsm.RET_IGNORED |
@staticmethod
def _perform_init_chain(me, current):
'Act on the chain of initializations required starting from current.\n '
t = current
while (Hsm.trig(me, (t if (t != Hsm.top) else me.initial_state), Signal.INIT) == Hsm.RET_TRAN):
path = []
while (me.state != t):
path.append(me.state)
Hsm.trig(me, me.state, Signal.EMPTY)
me.state = path[0]
assert (len(path) < 32)
path.reverse()
for s in path:
Hsm.enter(me, s)
t = path[(- 1)]
return t | -3,732,168,619,316,651,000 | Act on the chain of initializations required starting from current. | farc/__init__.py | _perform_init_chain | SzeMengTan/farc | python | @staticmethod
def _perform_init_chain(me, current):
'\n '
t = current
while (Hsm.trig(me, (t if (t != Hsm.top) else me.initial_state), Signal.INIT) == Hsm.RET_TRAN):
path = []
while (me.state != t):
path.append(me.state)
Hsm.trig(me, me.state, Signal.EMPTY)
me.state = path[0]
assert (len(path) < 32)
path.reverse()
for s in path:
Hsm.enter(me, s)
t = path[(- 1)]
return t |
@staticmethod
def init(me, event=None):
'Transitions to the initial state. Follows any INIT transitions\n from the inital state and performs ENTRY actions as it proceeds.\n Use this to pass any parameters to initialize the state machine.\n p. 172\n '
me.state = Hsm._perform_init_chain(me, Hsm.top) | -8,697,070,345,986,381,000 | Transitions to the initial state. Follows any INIT transitions
from the inital state and performs ENTRY actions as it proceeds.
Use this to pass any parameters to initialize the state machine.
p. 172 | farc/__init__.py | init | SzeMengTan/farc | python | @staticmethod
def init(me, event=None):
'Transitions to the initial state. Follows any INIT transitions\n from the inital state and performs ENTRY actions as it proceeds.\n Use this to pass any parameters to initialize the state machine.\n p. 172\n '
me.state = Hsm._perform_init_chain(me, Hsm.top) |
@staticmethod
def dispatch(me, event):
"Dispatches the given event to this Hsm.\n Follows the application's state transitions\n until the event is handled or top() is reached\n p. 174\n "
Spy.on_hsm_dispatch_event(event)
t = me.state
exit_path = []
r = Hsm.RET_SUPER
while (r == Hsm.RET_SUPER):
s = me.state
exit_path.append(s)
Spy.on_hsm_dispatch_pre(s)
r = s(me, event)
Spy.on_hsm_dispatch_post(exit_path)
if (r == Hsm.RET_TRAN):
t = me.state
for st in exit_path[:(- 1)]:
r = Hsm.exit(me, st)
assert ((r == Hsm.RET_SUPER) or (r == Hsm.RET_HANDLED))
s = exit_path[(- 1)]
Hsm._perform_transition(me, s, t)
t = Hsm._perform_init_chain(me, t)
me.state = t | -8,821,831,792,432,782,000 | Dispatches the given event to this Hsm.
Follows the application's state transitions
until the event is handled or top() is reached
p. 174 | farc/__init__.py | dispatch | SzeMengTan/farc | python | @staticmethod
def dispatch(me, event):
"Dispatches the given event to this Hsm.\n Follows the application's state transitions\n until the event is handled or top() is reached\n p. 174\n "
Spy.on_hsm_dispatch_event(event)
t = me.state
exit_path = []
r = Hsm.RET_SUPER
while (r == Hsm.RET_SUPER):
s = me.state
exit_path.append(s)
Spy.on_hsm_dispatch_pre(s)
r = s(me, event)
Spy.on_hsm_dispatch_post(exit_path)
if (r == Hsm.RET_TRAN):
t = me.state
for st in exit_path[:(- 1)]:
r = Hsm.exit(me, st)
assert ((r == Hsm.RET_SUPER) or (r == Hsm.RET_HANDLED))
s = exit_path[(- 1)]
Hsm._perform_transition(me, s, t)
t = Hsm._perform_init_chain(me, t)
me.state = t |
@staticmethod
def post(event, act):
"Posts the event to the given Ahsm's event queue.\n The argument, act, is an Ahsm instance.\n "
assert isinstance(act, Ahsm)
act.postFIFO(event) | 1,225,549,252,378,367,700 | Posts the event to the given Ahsm's event queue.
The argument, act, is an Ahsm instance. | farc/__init__.py | post | SzeMengTan/farc | python | @staticmethod
def post(event, act):
"Posts the event to the given Ahsm's event queue.\n The argument, act, is an Ahsm instance.\n "
assert isinstance(act, Ahsm)
act.postFIFO(event) |
@staticmethod
def post_by_name(event, act_name):
"Posts the event to the given Ahsm's event queue.\n The argument, act, is a string of the name of the class\n to which the event is sent. The event will post to all actors\n having the given classname.\n "
assert (type(act_name) is str)
for act in Framework._ahsm_registry:
if (act.__class__.__name__ == act_name):
act.postFIFO(event) | -6,567,935,478,147,241,000 | Posts the event to the given Ahsm's event queue.
The argument, act, is a string of the name of the class
to which the event is sent. The event will post to all actors
having the given classname. | farc/__init__.py | post_by_name | SzeMengTan/farc | python | @staticmethod
def post_by_name(event, act_name):
"Posts the event to the given Ahsm's event queue.\n The argument, act, is a string of the name of the class\n to which the event is sent. The event will post to all actors\n having the given classname.\n "
assert (type(act_name) is str)
for act in Framework._ahsm_registry:
if (act.__class__.__name__ == act_name):
act.postFIFO(event) |
@staticmethod
def publish(event):
"Posts the event to the message queue of every Ahsm\n that is subscribed to the event's signal.\n "
if (event.signal in Framework._subscriber_table):
for act in Framework._subscriber_table[event.signal]:
act.postFIFO(event)
Framework._event_loop.call_soon_threadsafe(Framework.run) | -3,316,009,976,515,112,000 | Posts the event to the message queue of every Ahsm
that is subscribed to the event's signal. | farc/__init__.py | publish | SzeMengTan/farc | python | @staticmethod
def publish(event):
"Posts the event to the message queue of every Ahsm\n that is subscribed to the event's signal.\n "
if (event.signal in Framework._subscriber_table):
for act in Framework._subscriber_table[event.signal]:
act.postFIFO(event)
Framework._event_loop.call_soon_threadsafe(Framework.run) |
@staticmethod
def subscribe(signame, act):
'Adds the given Ahsm to the subscriber table list\n for the given signal. The argument, signame, is a string of the name\n of the Signal to which the Ahsm is subscribing. Using a string allows\n the Signal to be created in the registry if it is not already.\n '
sigid = Signal.register(signame)
if (sigid not in Framework._subscriber_table):
Framework._subscriber_table[sigid] = []
Framework._subscriber_table[sigid].append(act) | 7,131,938,376,864,232,000 | Adds the given Ahsm to the subscriber table list
for the given signal. The argument, signame, is a string of the name
of the Signal to which the Ahsm is subscribing. Using a string allows
the Signal to be created in the registry if it is not already. | farc/__init__.py | subscribe | SzeMengTan/farc | python | @staticmethod
def subscribe(signame, act):
'Adds the given Ahsm to the subscriber table list\n for the given signal. The argument, signame, is a string of the name\n of the Signal to which the Ahsm is subscribing. Using a string allows\n the Signal to be created in the registry if it is not already.\n '
sigid = Signal.register(signame)
if (sigid not in Framework._subscriber_table):
Framework._subscriber_table[sigid] = []
Framework._subscriber_table[sigid].append(act) |
@staticmethod
def addTimeEvent(tm_event, delta):
"Adds the TimeEvent to the list of time events in the Framework.\n The event will fire its signal (to the TimeEvent's target Ahsm)\n after the delay, delta.\n "
expiration = (Framework._event_loop.time() + delta)
Framework.addTimeEventAt(tm_event, expiration) | -1,549,828,390,167,262,000 | Adds the TimeEvent to the list of time events in the Framework.
The event will fire its signal (to the TimeEvent's target Ahsm)
after the delay, delta. | farc/__init__.py | addTimeEvent | SzeMengTan/farc | python | @staticmethod
def addTimeEvent(tm_event, delta):
"Adds the TimeEvent to the list of time events in the Framework.\n The event will fire its signal (to the TimeEvent's target Ahsm)\n after the delay, delta.\n "
expiration = (Framework._event_loop.time() + delta)
Framework.addTimeEventAt(tm_event, expiration) |
@staticmethod
def addTimeEventAt(tm_event, abs_time):
"Adds the TimeEvent to the list of time events in the Framework.\n The event will fire its signal (to the TimeEvent's target Ahsm)\n at the given absolute time (_event_loop.time()).\n "
assert (tm_event not in Framework._time_events.values())
Framework._insortTimeEvent(tm_event, abs_time) | 5,152,183,799,103,508,000 | Adds the TimeEvent to the list of time events in the Framework.
The event will fire its signal (to the TimeEvent's target Ahsm)
at the given absolute time (_event_loop.time()). | farc/__init__.py | addTimeEventAt | SzeMengTan/farc | python | @staticmethod
def addTimeEventAt(tm_event, abs_time):
"Adds the TimeEvent to the list of time events in the Framework.\n The event will fire its signal (to the TimeEvent's target Ahsm)\n at the given absolute time (_event_loop.time()).\n "
assert (tm_event not in Framework._time_events.values())
Framework._insortTimeEvent(tm_event, abs_time) |
@staticmethod
def _insortTimeEvent(tm_event, expiration):
'Inserts a TimeEvent into the list of time events,\n sorted by the next expiration of the timer.\n If the expiration time matches an existing expiration,\n we add the smallest amount of time to the given expiration\n to avoid a key collision in the Dict\n and make the identically-timed events fire in a FIFO fashion.\n '
now = Framework._event_loop.time()
if (expiration < now):
tm_event.act.postFIFO(tm_event)
while (expiration in Framework._time_events.keys()):
(m, e) = math.frexp(expiration)
expiration = ((m + sys.float_info.epsilon) * (2 ** e))
Framework._time_events[expiration] = tm_event
if (len(Framework._time_events) == 1):
Framework._tm_event_handle = Framework._event_loop.call_at(expiration, Framework.timeEventCallback, tm_event, expiration)
elif (expiration < min(Framework._time_events.keys())):
Framework._tm_event_handle.cancel()
Framework._tm_event_handle = Framework._event_loop.call_at(expiration, Framework.timeEventCallback, tm_event, expiration) | 9,202,879,888,713,282,000 | Inserts a TimeEvent into the list of time events,
sorted by the next expiration of the timer.
If the expiration time matches an existing expiration,
we add the smallest amount of time to the given expiration
to avoid a key collision in the Dict
and make the identically-timed events fire in a FIFO fashion. | farc/__init__.py | _insortTimeEvent | SzeMengTan/farc | python | @staticmethod
def _insortTimeEvent(tm_event, expiration):
'Inserts a TimeEvent into the list of time events,\n sorted by the next expiration of the timer.\n If the expiration time matches an existing expiration,\n we add the smallest amount of time to the given expiration\n to avoid a key collision in the Dict\n and make the identically-timed events fire in a FIFO fashion.\n '
now = Framework._event_loop.time()
if (expiration < now):
tm_event.act.postFIFO(tm_event)
while (expiration in Framework._time_events.keys()):
(m, e) = math.frexp(expiration)
expiration = ((m + sys.float_info.epsilon) * (2 ** e))
Framework._time_events[expiration] = tm_event
if (len(Framework._time_events) == 1):
Framework._tm_event_handle = Framework._event_loop.call_at(expiration, Framework.timeEventCallback, tm_event, expiration)
elif (expiration < min(Framework._time_events.keys())):
Framework._tm_event_handle.cancel()
Framework._tm_event_handle = Framework._event_loop.call_at(expiration, Framework.timeEventCallback, tm_event, expiration) |
@staticmethod
def removeTimeEvent(tm_event):
"Removes the TimeEvent from the list of active time events.\n Cancels the TimeEvent's callback if there is one.\n Schedules the next event's callback if there is one.\n "
for (k, v) in Framework._time_events.items():
if (v is tm_event):
if (k == min(Framework._time_events.keys())):
del Framework._time_events[k]
if Framework._tm_event_handle:
Framework._tm_event_handle.cancel()
if (len(Framework._time_events) > 0):
next_expiration = min(Framework._time_events.keys())
next_event = Framework._time_events[next_expiration]
Framework._tm_event_handle = Framework._event_loop.call_at(next_expiration, Framework.timeEventCallback, next_event, next_expiration)
else:
Framework._tm_event_handle = None
else:
del Framework._time_events[k]
break | 9,140,786,555,416,009,000 | Removes the TimeEvent from the list of active time events.
Cancels the TimeEvent's callback if there is one.
Schedules the next event's callback if there is one. | farc/__init__.py | removeTimeEvent | SzeMengTan/farc | python | @staticmethod
def removeTimeEvent(tm_event):
"Removes the TimeEvent from the list of active time events.\n Cancels the TimeEvent's callback if there is one.\n Schedules the next event's callback if there is one.\n "
for (k, v) in Framework._time_events.items():
if (v is tm_event):
if (k == min(Framework._time_events.keys())):
del Framework._time_events[k]
if Framework._tm_event_handle:
Framework._tm_event_handle.cancel()
if (len(Framework._time_events) > 0):
next_expiration = min(Framework._time_events.keys())
next_event = Framework._time_events[next_expiration]
Framework._tm_event_handle = Framework._event_loop.call_at(next_expiration, Framework.timeEventCallback, next_event, next_expiration)
else:
Framework._tm_event_handle = None
else:
del Framework._time_events[k]
break |
@staticmethod
def timeEventCallback(tm_event, expiration):
"The callback function for all TimeEvents.\n Posts the event to the event's target Ahsm.\n If the TimeEvent is periodic, re-insort the event\n in the list of active time events.\n "
assert (expiration in Framework._time_events.keys()), ('Exp:%d _time_events.keys():%s' % (expiration, Framework._time_events.keys()))
del Framework._time_events[expiration]
Framework._tm_event_handle = None
tm_event.act.postFIFO(tm_event)
if (tm_event.interval > 0):
Framework._insortTimeEvent(tm_event, (expiration + tm_event.interval))
if ((Framework._tm_event_handle == None) and (len(Framework._time_events) > 0)):
next_expiration = min(Framework._time_events.keys())
next_event = Framework._time_events[next_expiration]
Framework._tm_event_handle = Framework._event_loop.call_at(next_expiration, Framework.timeEventCallback, next_event, next_expiration)
Framework._event_loop.call_soon_threadsafe(Framework.run) | 2,536,676,419,023,421,000 | The callback function for all TimeEvents.
Posts the event to the event's target Ahsm.
If the TimeEvent is periodic, re-insort the event
in the list of active time events. | farc/__init__.py | timeEventCallback | SzeMengTan/farc | python | @staticmethod
def timeEventCallback(tm_event, expiration):
"The callback function for all TimeEvents.\n Posts the event to the event's target Ahsm.\n If the TimeEvent is periodic, re-insort the event\n in the list of active time events.\n "
assert (expiration in Framework._time_events.keys()), ('Exp:%d _time_events.keys():%s' % (expiration, Framework._time_events.keys()))
del Framework._time_events[expiration]
Framework._tm_event_handle = None
tm_event.act.postFIFO(tm_event)
if (tm_event.interval > 0):
Framework._insortTimeEvent(tm_event, (expiration + tm_event.interval))
if ((Framework._tm_event_handle == None) and (len(Framework._time_events) > 0)):
next_expiration = min(Framework._time_events.keys())
next_event = Framework._time_events[next_expiration]
Framework._tm_event_handle = Framework._event_loop.call_at(next_expiration, Framework.timeEventCallback, next_event, next_expiration)
Framework._event_loop.call_soon_threadsafe(Framework.run) |
@staticmethod
def add(act):
'Makes the framework aware of the given Ahsm.\n '
Framework._ahsm_registry.append(act)
assert (act.priority not in Framework._priority_dict), 'Priority MUST be unique'
Framework._priority_dict[act.priority] = act
Spy.on_framework_add(act) | 4,218,832,176,318,824,000 | Makes the framework aware of the given Ahsm. | farc/__init__.py | add | SzeMengTan/farc | python | @staticmethod
def add(act):
'\n '
Framework._ahsm_registry.append(act)
assert (act.priority not in Framework._priority_dict), 'Priority MUST be unique'
Framework._priority_dict[act.priority] = act
Spy.on_framework_add(act) |
@staticmethod
def run():
'Dispatches an event to the highest priority Ahsm\n until all event queues are empty (i.e. Run To Completion).\n '
getPriority = (lambda x: x.priority)
while True:
allQueuesEmpty = True
sorted_acts = sorted(Framework._ahsm_registry, key=getPriority)
for act in sorted_acts:
if act.has_msgs():
event_next = act.pop_msg()
act.dispatch(act, event_next)
allQueuesEmpty = False
break
if allQueuesEmpty:
return | 7,207,906,900,246,715,000 | Dispatches an event to the highest priority Ahsm
until all event queues are empty (i.e. Run To Completion). | farc/__init__.py | run | SzeMengTan/farc | python | @staticmethod
def run():
'Dispatches an event to the highest priority Ahsm\n until all event queues are empty (i.e. Run To Completion).\n '
getPriority = (lambda x: x.priority)
while True:
allQueuesEmpty = True
sorted_acts = sorted(Framework._ahsm_registry, key=getPriority)
for act in sorted_acts:
if act.has_msgs():
event_next = act.pop_msg()
act.dispatch(act, event_next)
allQueuesEmpty = False
break
if allQueuesEmpty:
return |
@staticmethod
def stop():
'EXITs all Ahsms and stops the event loop.\n '
if Framework._tm_event_handle:
Framework._tm_event_handle.cancel()
Framework._tm_event_handle = None
for act in Framework._ahsm_registry:
Framework.post(Event.EXIT, act)
Framework.run()
Framework._event_loop.stop()
Spy.on_framework_stop() | -4,242,969,735,239,040,500 | EXITs all Ahsms and stops the event loop. | farc/__init__.py | stop | SzeMengTan/farc | python | @staticmethod
def stop():
'\n '
if Framework._tm_event_handle:
Framework._tm_event_handle.cancel()
Framework._tm_event_handle = None
for act in Framework._ahsm_registry:
Framework.post(Event.EXIT, act)
Framework.run()
Framework._event_loop.stop()
Spy.on_framework_stop() |
@staticmethod
def print_info():
'Prints the name and current state\n of each actor in the framework.\n Meant to be called when ctrl+T (SIGINFO/29) is issued.\n '
for act in Framework._ahsm_registry:
print(act.__class__.__name__, act.state.__name__) | -2,474,237,011,219,255,300 | Prints the name and current state
of each actor in the framework.
Meant to be called when ctrl+T (SIGINFO/29) is issued. | farc/__init__.py | print_info | SzeMengTan/farc | python | @staticmethod
def print_info():
'Prints the name and current state\n of each actor in the framework.\n Meant to be called when ctrl+T (SIGINFO/29) is issued.\n '
for act in Framework._ahsm_registry:
print(act.__class__.__name__, act.state.__name__) |
def postAt(self, act, abs_time):
'Posts this TimeEvent to the given Ahsm at a specified time.\n '
assert issubclass(type(act), Ahsm)
self.act = act
self.interval = 0
Framework.addTimeEventAt(self, abs_time) | -3,514,636,557,849,529,300 | Posts this TimeEvent to the given Ahsm at a specified time. | farc/__init__.py | postAt | SzeMengTan/farc | python | def postAt(self, act, abs_time):
'\n '
assert issubclass(type(act), Ahsm)
self.act = act
self.interval = 0
Framework.addTimeEventAt(self, abs_time) |
def postIn(self, act, delta):
'Posts this TimeEvent to the given Ahsm after the time delta.\n '
assert issubclass(type(act), Ahsm)
self.act = act
self.interval = 0
Framework.addTimeEvent(self, delta) | 7,578,574,746,659,476,000 | Posts this TimeEvent to the given Ahsm after the time delta. | farc/__init__.py | postIn | SzeMengTan/farc | python | def postIn(self, act, delta):
'\n '
assert issubclass(type(act), Ahsm)
self.act = act
self.interval = 0
Framework.addTimeEvent(self, delta) |
def postEvery(self, act, delta):
'Posts this TimeEvent to the given Ahsm after the time delta\n and every time delta thereafter until disarmed.\n '
assert issubclass(type(act), Ahsm)
self.act = act
self.interval = delta
Framework.addTimeEvent(self, delta) | 8,641,827,318,052,131,000 | Posts this TimeEvent to the given Ahsm after the time delta
and every time delta thereafter until disarmed. | farc/__init__.py | postEvery | SzeMengTan/farc | python | def postEvery(self, act, delta):
'Posts this TimeEvent to the given Ahsm after the time delta\n and every time delta thereafter until disarmed.\n '
assert issubclass(type(act), Ahsm)
self.act = act
self.interval = delta
Framework.addTimeEvent(self, delta) |
def disarm(self):
"Removes this TimeEvent from the Framework's active time events.\n "
self.act = None
Framework.removeTimeEvent(self) | 7,122,821,650,057,362,000 | Removes this TimeEvent from the Framework's active time events. | farc/__init__.py | disarm | SzeMengTan/farc | python | def disarm(self):
"\n "
self.act = None
Framework.removeTimeEvent(self) |
@staticmethod
def calcVariation(onetick, oneposition):
'\n (tick: OneTick, position: OnePosition) -> float\n '
created = oneposition.priceMean()
if (oneposition.side == OnePosition.SideLong):
current = onetick.bid
else:
current = onetick.ask
return (current / created) | 2,954,959,345,308,836,000 | (tick: OneTick, position: OnePosition) -> float | apps/trade/src/PositionsManager.py | calcVariation | kikei/btc-bot-ai | python | @staticmethod
def calcVariation(onetick, oneposition):
'\n \n '
created = oneposition.priceMean()
if (oneposition.side == OnePosition.SideLong):
current = onetick.bid
else:
current = onetick.ask
return (current / created) |
def _fit_imaging_from(fit: af.Fit, galaxies: List[ag.Galaxy], settings_imaging: aa.SettingsImaging=None, settings_pixelization: aa.SettingsPixelization=None, settings_inversion: aa.SettingsInversion=None, use_preloaded_grid: bool=True, use_hyper_scaling: bool=True) -> FitImaging:
'\n Returns a `FitImaging` object from a PyAutoFit database `Fit` object and an instance of galaxies from a non-linear\n search model-fit.\n\n This function adds the `hyper_model_image` and `hyper_galaxy_image_path_dict` to the galaxies before performing the\n fit, if they were used.\n\n Parameters\n ----------\n fit\n A PyAutoFit database Fit object containing the generators of the results of PyAutoGalaxy model-fits.\n galaxies\n A list of galaxies corresponding to a sample of a non-linear search and model-fit.\n\n Returns\n -------\n FitImaging\n The fit to the imaging dataset computed via an instance of galaxies.\n '
imaging = _imaging_from(fit=fit, settings_imaging=settings_imaging)
tracer = _tracer_from(fit=fit, galaxies=galaxies)
settings_pixelization = (settings_pixelization or fit.value(name='settings_pixelization'))
settings_inversion = (settings_inversion or fit.value(name='settings_inversion'))
preloads = Preloads(use_w_tilde=False)
if use_preloaded_grid:
sparse_grids_of_planes = fit.value(name='preload_sparse_grids_of_planes')
if (sparse_grids_of_planes is not None):
preloads = Preloads(sparse_image_plane_grid_pg_list=sparse_grids_of_planes, use_w_tilde=False)
if (len(preloads.sparse_image_plane_grid_pg_list) == 2):
if (type(preloads.sparse_image_plane_grid_pg_list[1]) != list):
preloads.sparse_image_plane_grid_pg_list[1] = [preloads.sparse_image_plane_grid_pg_list[1]]
return FitImaging(dataset=imaging, tracer=tracer, settings_pixelization=settings_pixelization, settings_inversion=settings_inversion, preloads=preloads, use_hyper_scaling=use_hyper_scaling) | -6,554,503,904,327,956,000 | Returns a `FitImaging` object from a PyAutoFit database `Fit` object and an instance of galaxies from a non-linear
search model-fit.
This function adds the `hyper_model_image` and `hyper_galaxy_image_path_dict` to the galaxies before performing the
fit, if they were used.
Parameters
----------
fit
A PyAutoFit database Fit object containing the generators of the results of PyAutoGalaxy model-fits.
galaxies
A list of galaxies corresponding to a sample of a non-linear search and model-fit.
Returns
-------
FitImaging
The fit to the imaging dataset computed via an instance of galaxies. | autolens/aggregator/fit_imaging.py | _fit_imaging_from | Jammy2211/AutoLens | python | def _fit_imaging_from(fit: af.Fit, galaxies: List[ag.Galaxy], settings_imaging: aa.SettingsImaging=None, settings_pixelization: aa.SettingsPixelization=None, settings_inversion: aa.SettingsInversion=None, use_preloaded_grid: bool=True, use_hyper_scaling: bool=True) -> FitImaging:
'\n Returns a `FitImaging` object from a PyAutoFit database `Fit` object and an instance of galaxies from a non-linear\n search model-fit.\n\n This function adds the `hyper_model_image` and `hyper_galaxy_image_path_dict` to the galaxies before performing the\n fit, if they were used.\n\n Parameters\n ----------\n fit\n A PyAutoFit database Fit object containing the generators of the results of PyAutoGalaxy model-fits.\n galaxies\n A list of galaxies corresponding to a sample of a non-linear search and model-fit.\n\n Returns\n -------\n FitImaging\n The fit to the imaging dataset computed via an instance of galaxies.\n '
imaging = _imaging_from(fit=fit, settings_imaging=settings_imaging)
tracer = _tracer_from(fit=fit, galaxies=galaxies)
settings_pixelization = (settings_pixelization or fit.value(name='settings_pixelization'))
settings_inversion = (settings_inversion or fit.value(name='settings_inversion'))
preloads = Preloads(use_w_tilde=False)
if use_preloaded_grid:
sparse_grids_of_planes = fit.value(name='preload_sparse_grids_of_planes')
if (sparse_grids_of_planes is not None):
preloads = Preloads(sparse_image_plane_grid_pg_list=sparse_grids_of_planes, use_w_tilde=False)
if (len(preloads.sparse_image_plane_grid_pg_list) == 2):
if (type(preloads.sparse_image_plane_grid_pg_list[1]) != list):
preloads.sparse_image_plane_grid_pg_list[1] = [preloads.sparse_image_plane_grid_pg_list[1]]
return FitImaging(dataset=imaging, tracer=tracer, settings_pixelization=settings_pixelization, settings_inversion=settings_inversion, preloads=preloads, use_hyper_scaling=use_hyper_scaling) |
def __init__(self, aggregator: af.Aggregator, settings_imaging: Optional[aa.SettingsImaging]=None, settings_pixelization: Optional[aa.SettingsPixelization]=None, settings_inversion: Optional[aa.SettingsInversion]=None, use_preloaded_grid: bool=True, use_hyper_scaling: bool=True):
'\n Wraps a PyAutoFit aggregator in order to create generators of fits to imaging data, corresponding to the\n results of a non-linear search model-fit.\n '
super().__init__(aggregator=aggregator)
self.settings_imaging = settings_imaging
self.settings_pixelization = settings_pixelization
self.settings_inversion = settings_inversion
self.use_preloaded_grid = use_preloaded_grid
self.use_hyper_scaling = use_hyper_scaling | -2,135,738,284,995,956,000 | Wraps a PyAutoFit aggregator in order to create generators of fits to imaging data, corresponding to the
results of a non-linear search model-fit. | autolens/aggregator/fit_imaging.py | __init__ | Jammy2211/AutoLens | python | def __init__(self, aggregator: af.Aggregator, settings_imaging: Optional[aa.SettingsImaging]=None, settings_pixelization: Optional[aa.SettingsPixelization]=None, settings_inversion: Optional[aa.SettingsInversion]=None, use_preloaded_grid: bool=True, use_hyper_scaling: bool=True):
'\n Wraps a PyAutoFit aggregator in order to create generators of fits to imaging data, corresponding to the\n results of a non-linear search model-fit.\n '
super().__init__(aggregator=aggregator)
self.settings_imaging = settings_imaging
self.settings_pixelization = settings_pixelization
self.settings_inversion = settings_inversion
self.use_preloaded_grid = use_preloaded_grid
self.use_hyper_scaling = use_hyper_scaling |
def make_object_for_gen(self, fit, galaxies) -> FitImaging:
'\n Creates a `FitImaging` object from a `ModelInstance` that contains the galaxies of a sample from a non-linear\n search.\n\n Parameters\n ----------\n fit\n A PyAutoFit database Fit object containing the generators of the results of PyAutoGalaxy model-fits.\n galaxies\n A list of galaxies corresponding to a sample of a non-linear search and model-fit.\n\n Returns\n -------\n FitImaging\n A fit to imaging data whose galaxies are a sample of a PyAutoFit non-linear search.\n '
return _fit_imaging_from(fit=fit, galaxies=galaxies, settings_imaging=self.settings_imaging, settings_pixelization=self.settings_pixelization, settings_inversion=self.settings_inversion, use_preloaded_grid=self.use_preloaded_grid, use_hyper_scaling=self.use_hyper_scaling) | -5,418,655,718,074,749,000 | Creates a `FitImaging` object from a `ModelInstance` that contains the galaxies of a sample from a non-linear
search.
Parameters
----------
fit
A PyAutoFit database Fit object containing the generators of the results of PyAutoGalaxy model-fits.
galaxies
A list of galaxies corresponding to a sample of a non-linear search and model-fit.
Returns
-------
FitImaging
A fit to imaging data whose galaxies are a sample of a PyAutoFit non-linear search. | autolens/aggregator/fit_imaging.py | make_object_for_gen | Jammy2211/AutoLens | python | def make_object_for_gen(self, fit, galaxies) -> FitImaging:
'\n Creates a `FitImaging` object from a `ModelInstance` that contains the galaxies of a sample from a non-linear\n search.\n\n Parameters\n ----------\n fit\n A PyAutoFit database Fit object containing the generators of the results of PyAutoGalaxy model-fits.\n galaxies\n A list of galaxies corresponding to a sample of a non-linear search and model-fit.\n\n Returns\n -------\n FitImaging\n A fit to imaging data whose galaxies are a sample of a PyAutoFit non-linear search.\n '
return _fit_imaging_from(fit=fit, galaxies=galaxies, settings_imaging=self.settings_imaging, settings_pixelization=self.settings_pixelization, settings_inversion=self.settings_inversion, use_preloaded_grid=self.use_preloaded_grid, use_hyper_scaling=self.use_hyper_scaling) |
def _velocity_to_redshift(velocity):
'\n Convert a velocity to a relativistic redshift.\n '
beta = (velocity / C_KMS)
return (np.sqrt(((1 + beta) / (1 - beta))) - 1) | -4,264,383,338,760,901,600 | Convert a velocity to a relativistic redshift. | LI/lib/python3.8/site-packages/astropy/coordinates/spectral_coordinate.py | _velocity_to_redshift | honeybhardwaj/Language_Identification | python | def _velocity_to_redshift(velocity):
'\n \n '
beta = (velocity / C_KMS)
return (np.sqrt(((1 + beta) / (1 - beta))) - 1) |
def _redshift_to_velocity(redshift):
'\n Convert a relativistic redshift to a velocity.\n '
zponesq = ((1 + redshift) ** 2)
return ((C_KMS * (zponesq - 1)) / (zponesq + 1)) | -3,123,915,812,359,021,000 | Convert a relativistic redshift to a velocity. | LI/lib/python3.8/site-packages/astropy/coordinates/spectral_coordinate.py | _redshift_to_velocity | honeybhardwaj/Language_Identification | python | def _redshift_to_velocity(redshift):
'\n \n '
zponesq = ((1 + redshift) ** 2)
return ((C_KMS * (zponesq - 1)) / (zponesq + 1)) |
def _apply_relativistic_doppler_shift(scoord, velocity):
'\n Given a `SpectralQuantity` and a velocity, return a new `SpectralQuantity`\n that is Doppler shifted by this amount.\n\n Note that the Doppler shift applied is the full relativistic one, so\n `SpectralQuantity` currently expressed in velocity and not using the\n relativistic convention will temporarily be converted to use the\n relativistic convention while the shift is applied.\n\n Positive velocities are assumed to redshift the spectral quantity,\n while negative velocities blueshift the spectral quantity.\n '
squantity = scoord.view(SpectralQuantity)
beta = (velocity / c)
doppler_factor = np.sqrt(((1 + beta) / (1 - beta)))
if squantity.unit.is_equivalent(u.m):
return (squantity * doppler_factor)
elif (squantity.unit.is_equivalent(u.Hz) or squantity.unit.is_equivalent(u.eV) or squantity.unit.is_equivalent((1 / u.m))):
return (squantity / doppler_factor)
elif squantity.unit.is_equivalent(KMS):
return (squantity.to(u.Hz) / doppler_factor).to(squantity.unit)
else:
raise RuntimeError(f'Unexpected units in velocity shift: {squantity.unit}. This should not happen, so please report this in the astropy issue tracker!') | -837,969,382,452,347,500 | Given a `SpectralQuantity` and a velocity, return a new `SpectralQuantity`
that is Doppler shifted by this amount.
Note that the Doppler shift applied is the full relativistic one, so
`SpectralQuantity` currently expressed in velocity and not using the
relativistic convention will temporarily be converted to use the
relativistic convention while the shift is applied.
Positive velocities are assumed to redshift the spectral quantity,
while negative velocities blueshift the spectral quantity. | LI/lib/python3.8/site-packages/astropy/coordinates/spectral_coordinate.py | _apply_relativistic_doppler_shift | honeybhardwaj/Language_Identification | python | def _apply_relativistic_doppler_shift(scoord, velocity):
'\n Given a `SpectralQuantity` and a velocity, return a new `SpectralQuantity`\n that is Doppler shifted by this amount.\n\n Note that the Doppler shift applied is the full relativistic one, so\n `SpectralQuantity` currently expressed in velocity and not using the\n relativistic convention will temporarily be converted to use the\n relativistic convention while the shift is applied.\n\n Positive velocities are assumed to redshift the spectral quantity,\n while negative velocities blueshift the spectral quantity.\n '
squantity = scoord.view(SpectralQuantity)
beta = (velocity / c)
doppler_factor = np.sqrt(((1 + beta) / (1 - beta)))
if squantity.unit.is_equivalent(u.m):
return (squantity * doppler_factor)
elif (squantity.unit.is_equivalent(u.Hz) or squantity.unit.is_equivalent(u.eV) or squantity.unit.is_equivalent((1 / u.m))):
return (squantity / doppler_factor)
elif squantity.unit.is_equivalent(KMS):
return (squantity.to(u.Hz) / doppler_factor).to(squantity.unit)
else:
raise RuntimeError(f'Unexpected units in velocity shift: {squantity.unit}. This should not happen, so please report this in the astropy issue tracker!') |
def update_differentials_to_match(original, velocity_reference, preserve_observer_frame=False):
'\n Given an original coordinate object, update the differentials so that\n the final coordinate is at the same location as the original coordinate\n but co-moving with the velocity reference object.\n\n If preserve_original_frame is set to True, the resulting object will be in\n the frame of the original coordinate, otherwise it will be in the frame of\n the velocity reference.\n '
if (not velocity_reference.data.differentials):
raise ValueError('Reference frame has no velocities')
if (('obstime' in velocity_reference.frame_attributes) and hasattr(original, 'obstime')):
velocity_reference = velocity_reference.replicate(obstime=original.obstime)
original_icrs = original.transform_to(ICRS())
velocity_reference_icrs = velocity_reference.transform_to(ICRS())
differentials = velocity_reference_icrs.data.represent_as(CartesianRepresentation, CartesianDifferential).differentials
data_with_differentials = original_icrs.data.represent_as(CartesianRepresentation).with_differentials(differentials)
final_icrs = original_icrs.realize_frame(data_with_differentials)
if preserve_observer_frame:
final = final_icrs.transform_to(original)
else:
final = final_icrs.transform_to(velocity_reference)
return final.replicate(representation_type=CartesianRepresentation, differential_type=CartesianDifferential) | 8,652,156,971,554,723,000 | Given an original coordinate object, update the differentials so that
the final coordinate is at the same location as the original coordinate
but co-moving with the velocity reference object.
If preserve_original_frame is set to True, the resulting object will be in
the frame of the original coordinate, otherwise it will be in the frame of
the velocity reference. | LI/lib/python3.8/site-packages/astropy/coordinates/spectral_coordinate.py | update_differentials_to_match | honeybhardwaj/Language_Identification | python | def update_differentials_to_match(original, velocity_reference, preserve_observer_frame=False):
'\n Given an original coordinate object, update the differentials so that\n the final coordinate is at the same location as the original coordinate\n but co-moving with the velocity reference object.\n\n If preserve_original_frame is set to True, the resulting object will be in\n the frame of the original coordinate, otherwise it will be in the frame of\n the velocity reference.\n '
if (not velocity_reference.data.differentials):
raise ValueError('Reference frame has no velocities')
if (('obstime' in velocity_reference.frame_attributes) and hasattr(original, 'obstime')):
velocity_reference = velocity_reference.replicate(obstime=original.obstime)
original_icrs = original.transform_to(ICRS())
velocity_reference_icrs = velocity_reference.transform_to(ICRS())
differentials = velocity_reference_icrs.data.represent_as(CartesianRepresentation, CartesianDifferential).differentials
data_with_differentials = original_icrs.data.represent_as(CartesianRepresentation).with_differentials(differentials)
final_icrs = original_icrs.realize_frame(data_with_differentials)
if preserve_observer_frame:
final = final_icrs.transform_to(original)
else:
final = final_icrs.transform_to(velocity_reference)
return final.replicate(representation_type=CartesianRepresentation, differential_type=CartesianDifferential) |
def attach_zero_velocities(coord):
'\n Set the differentials to be stationary on a coordinate object.\n '
new_data = coord.cartesian.with_differentials(ZERO_VELOCITIES)
return coord.realize_frame(new_data) | 624,241,151,493,268,200 | Set the differentials to be stationary on a coordinate object. | LI/lib/python3.8/site-packages/astropy/coordinates/spectral_coordinate.py | attach_zero_velocities | honeybhardwaj/Language_Identification | python | def attach_zero_velocities(coord):
'\n \n '
new_data = coord.cartesian.with_differentials(ZERO_VELOCITIES)
return coord.realize_frame(new_data) |
@staticmethod
def _validate_coordinate(coord, label=''):
'\n Checks the type of the frame and whether a velocity differential and a\n distance has been defined on the frame object.\n\n If no distance is defined, the target is assumed to be "really far\n away", and the observer is assumed to be "in the solar system".\n\n Parameters\n ----------\n coord : `~astropy.coordinates.BaseCoordinateFrame`\n The new frame to be used for target or observer.\n label : str, optional\n The name of the object being validated (e.g. \'target\' or \'observer\'),\n which is then used in error messages.\n '
if (coord is None):
return
if (not issubclass(coord.__class__, BaseCoordinateFrame)):
if isinstance(coord, SkyCoord):
coord = coord.frame
else:
raise TypeError(f'{label} must be a SkyCoord or coordinate frame instance')
with np.errstate(all='ignore'):
distance = getattr(coord, 'distance', None)
if ((distance is not None) and (distance.unit.physical_type == 'dimensionless')):
coord = SkyCoord(coord, distance=DEFAULT_DISTANCE)
warnings.warn(f'Distance on coordinate object is dimensionless, an abritrary distance value of {DEFAULT_DISTANCE} will be set instead.', NoDistanceWarning)
if ('s' not in coord.data.differentials):
warnings.warn('No velocity defined on frame, assuming {}.'.format(ZERO_VELOCITIES), NoVelocityWarning)
coord = attach_zero_velocities(coord)
return coord | 4,801,924,817,370,988,000 | Checks the type of the frame and whether a velocity differential and a
distance has been defined on the frame object.
If no distance is defined, the target is assumed to be "really far
away", and the observer is assumed to be "in the solar system".
Parameters
----------
coord : `~astropy.coordinates.BaseCoordinateFrame`
The new frame to be used for target or observer.
label : str, optional
The name of the object being validated (e.g. 'target' or 'observer'),
which is then used in error messages. | LI/lib/python3.8/site-packages/astropy/coordinates/spectral_coordinate.py | _validate_coordinate | honeybhardwaj/Language_Identification | python | @staticmethod
def _validate_coordinate(coord, label=):
'\n Checks the type of the frame and whether a velocity differential and a\n distance has been defined on the frame object.\n\n If no distance is defined, the target is assumed to be "really far\n away", and the observer is assumed to be "in the solar system".\n\n Parameters\n ----------\n coord : `~astropy.coordinates.BaseCoordinateFrame`\n The new frame to be used for target or observer.\n label : str, optional\n The name of the object being validated (e.g. \'target\' or \'observer\'),\n which is then used in error messages.\n '
if (coord is None):
return
if (not issubclass(coord.__class__, BaseCoordinateFrame)):
if isinstance(coord, SkyCoord):
coord = coord.frame
else:
raise TypeError(f'{label} must be a SkyCoord or coordinate frame instance')
with np.errstate(all='ignore'):
distance = getattr(coord, 'distance', None)
if ((distance is not None) and (distance.unit.physical_type == 'dimensionless')):
coord = SkyCoord(coord, distance=DEFAULT_DISTANCE)
warnings.warn(f'Distance on coordinate object is dimensionless, an abritrary distance value of {DEFAULT_DISTANCE} will be set instead.', NoDistanceWarning)
if ('s' not in coord.data.differentials):
warnings.warn('No velocity defined on frame, assuming {}.'.format(ZERO_VELOCITIES), NoVelocityWarning)
coord = attach_zero_velocities(coord)
return coord |
End of preview. Expand
in Dataset Viewer.
- Downloads last month
- 60
Size of downloaded dataset files:
18.6 MB
Size of the auto-converted Parquet files:
18.6 MB
Number of rows:
24,616