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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
9c3debde9af8074ce3390bc6a41a93df3e576cb7 | 275,123 | py | Python | intersight/api/asset_api.py | CiscoDevNet/intersight-python | 04b721f37c3044646a91c185c7259edfb991557a | [
"Apache-2.0"
] | 5 | 2021-12-16T15:13:32.000Z | 2022-03-29T16:09:54.000Z | intersight/api/asset_api.py | CiscoDevNet/intersight-python | 04b721f37c3044646a91c185c7259edfb991557a | [
"Apache-2.0"
] | 4 | 2022-01-25T19:05:51.000Z | 2022-03-29T20:18:37.000Z | intersight/api/asset_api.py | CiscoDevNet/intersight-python | 04b721f37c3044646a91c185c7259edfb991557a | [
"Apache-2.0"
] | 2 | 2020-07-07T15:01:08.000Z | 2022-01-31T04:27:35.000Z | """
Cisco Intersight
Cisco Intersight is a management platform delivered as a service with embedded analytics for your Cisco and 3rd party IT infrastructure. This platform offers an intelligent level of management that enables IT organizations to analyze, simplify, and automate their environments in more advanced ways than the prior generations of tools. Cisco Intersight provides an integrated and intuitive management experience for resources in the traditional data center as well as at the edge. With flexible deployment options to address complex security needs, getting started with Intersight is quick and easy. Cisco Intersight has deep integration with Cisco UCS and HyperFlex systems allowing for remote deployment, configuration, and ongoing maintenance. The model-based deployment works for a single system in a remote location or hundreds of systems in a data center and enables rapid, standardized configuration and deployment. It also streamlines maintaining those systems whether you are working with small or very large configurations. The Intersight OpenAPI document defines the complete set of properties that are returned in the HTTP response. From that perspective, a client can expect that no additional properties are returned, unless these properties are explicitly defined in the OpenAPI document. However, when a client uses an older version of the Intersight OpenAPI document, the server may send additional properties because the software is more recent than the client. In that case, the client may receive properties that it does not know about. Some generated SDKs perform a strict validation of the HTTP response body against the OpenAPI document. # noqa: E501
The version of the OpenAPI document: 1.0.9-4950
Contact: intersight@cisco.com
Generated by: https://openapi-generator.tech
"""
import re # noqa: F401
import sys # noqa: F401
from intersight.api_client import ApiClient, Endpoint as _Endpoint
from intersight.model_utils import ( # noqa: F401
check_allowed_values,
check_validations,
date,
datetime,
file_type,
none_type,
validate_and_convert_types
)
from intersight.model.asset_cluster_member import AssetClusterMember
from intersight.model.asset_cluster_member_response import AssetClusterMemberResponse
from intersight.model.asset_deployment import AssetDeployment
from intersight.model.asset_deployment_device import AssetDeploymentDevice
from intersight.model.asset_deployment_device_response import AssetDeploymentDeviceResponse
from intersight.model.asset_deployment_response import AssetDeploymentResponse
from intersight.model.asset_device_claim import AssetDeviceClaim
from intersight.model.asset_device_configuration import AssetDeviceConfiguration
from intersight.model.asset_device_configuration_response import AssetDeviceConfigurationResponse
from intersight.model.asset_device_connector_manager import AssetDeviceConnectorManager
from intersight.model.asset_device_connector_manager_response import AssetDeviceConnectorManagerResponse
from intersight.model.asset_device_contract_information import AssetDeviceContractInformation
from intersight.model.asset_device_contract_information_response import AssetDeviceContractInformationResponse
from intersight.model.asset_device_contract_notification import AssetDeviceContractNotification
from intersight.model.asset_device_registration import AssetDeviceRegistration
from intersight.model.asset_device_registration_response import AssetDeviceRegistrationResponse
from intersight.model.asset_subscription import AssetSubscription
from intersight.model.asset_subscription_account import AssetSubscriptionAccount
from intersight.model.asset_subscription_account_response import AssetSubscriptionAccountResponse
from intersight.model.asset_subscription_device_contract_information import AssetSubscriptionDeviceContractInformation
from intersight.model.asset_subscription_device_contract_information_response import AssetSubscriptionDeviceContractInformationResponse
from intersight.model.asset_subscription_response import AssetSubscriptionResponse
from intersight.model.asset_target import AssetTarget
from intersight.model.asset_target_response import AssetTargetResponse
from intersight.model.error import Error
class AssetApi(object):
"""NOTE: This class is auto generated by OpenAPI Generator
Ref: https://openapi-generator.tech
Do not edit the class manually.
"""
def __init__(self, api_client=None):
if api_client is None:
api_client = ApiClient()
self.api_client = api_client
def __create_asset_device_claim(
self,
asset_device_claim,
**kwargs
):
"""Create a 'asset.DeviceClaim' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_asset_device_claim(asset_device_claim, async_req=True)
>>> result = thread.get()
Args:
asset_device_claim (AssetDeviceClaim): The 'asset.DeviceClaim' resource to create.
Keyword Args:
if_match (str): For methods that apply server-side changes, and in particular for PUT, If-Match can be used to prevent the lost update problem. It can check if the modification of a resource that the user wants to upload will not override another change that has been done since the original resource was fetched. If the request cannot be fulfilled, the 412 (Precondition Failed) response is returned. When modifying a resource using POST or PUT, the If-Match header must be set to the value of the resource ModTime property after which no lost update problem should occur. For example, a client send a GET request to obtain a resource, which includes the ModTime property. The ModTime indicates the last time the resource was created or modified. The client then sends a POST or PUT request with the If-Match header set to the ModTime property of the resource as obtained in the GET request.. [optional]
if_none_match (str): For methods that apply server-side changes, If-None-Match used with the * value can be used to create a resource not known to exist, guaranteeing that another resource creation didn't happen before, losing the data of the previous put. The request will be processed only if the eventually existing resource's ETag doesn't match any of the values listed. Otherwise, the status code 412 (Precondition Failed) is used. The asterisk is a special value representing any resource. It is only useful when creating a resource, usually with PUT, to check if another resource with the identity has already been created before. The comparison with the stored ETag uses the weak comparison algorithm, meaning two resources are considered identical if the content is equivalent - they don't have to be identical byte for byte.. [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
AssetDeviceClaim
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['asset_device_claim'] = \
asset_device_claim
return self.call_with_http_info(**kwargs)
self.create_asset_device_claim = _Endpoint(
settings={
'response_type': (AssetDeviceClaim,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/DeviceClaims',
'operation_id': 'create_asset_device_claim',
'http_method': 'POST',
'servers': None,
},
params_map={
'all': [
'asset_device_claim',
'if_match',
'if_none_match',
],
'required': [
'asset_device_claim',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'asset_device_claim':
(AssetDeviceClaim,),
'if_match':
(str,),
'if_none_match':
(str,),
},
'attribute_map': {
'if_match': 'If-Match',
'if_none_match': 'If-None-Match',
},
'location_map': {
'asset_device_claim': 'body',
'if_match': 'header',
'if_none_match': 'header',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json'
]
},
api_client=api_client,
callable=__create_asset_device_claim
)
def __create_asset_device_contract_notification(
self,
asset_device_contract_notification,
**kwargs
):
"""Create a 'asset.DeviceContractNotification' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_asset_device_contract_notification(asset_device_contract_notification, async_req=True)
>>> result = thread.get()
Args:
asset_device_contract_notification (AssetDeviceContractNotification): The 'asset.DeviceContractNotification' resource to create.
Keyword Args:
if_match (str): For methods that apply server-side changes, and in particular for PUT, If-Match can be used to prevent the lost update problem. It can check if the modification of a resource that the user wants to upload will not override another change that has been done since the original resource was fetched. If the request cannot be fulfilled, the 412 (Precondition Failed) response is returned. When modifying a resource using POST or PUT, the If-Match header must be set to the value of the resource ModTime property after which no lost update problem should occur. For example, a client send a GET request to obtain a resource, which includes the ModTime property. The ModTime indicates the last time the resource was created or modified. The client then sends a POST or PUT request with the If-Match header set to the ModTime property of the resource as obtained in the GET request.. [optional]
if_none_match (str): For methods that apply server-side changes, If-None-Match used with the * value can be used to create a resource not known to exist, guaranteeing that another resource creation didn't happen before, losing the data of the previous put. The request will be processed only if the eventually existing resource's ETag doesn't match any of the values listed. Otherwise, the status code 412 (Precondition Failed) is used. The asterisk is a special value representing any resource. It is only useful when creating a resource, usually with PUT, to check if another resource with the identity has already been created before. The comparison with the stored ETag uses the weak comparison algorithm, meaning two resources are considered identical if the content is equivalent - they don't have to be identical byte for byte.. [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
AssetDeviceContractNotification
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['asset_device_contract_notification'] = \
asset_device_contract_notification
return self.call_with_http_info(**kwargs)
self.create_asset_device_contract_notification = _Endpoint(
settings={
'response_type': (AssetDeviceContractNotification,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/DeviceContractNotifications',
'operation_id': 'create_asset_device_contract_notification',
'http_method': 'POST',
'servers': None,
},
params_map={
'all': [
'asset_device_contract_notification',
'if_match',
'if_none_match',
],
'required': [
'asset_device_contract_notification',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'asset_device_contract_notification':
(AssetDeviceContractNotification,),
'if_match':
(str,),
'if_none_match':
(str,),
},
'attribute_map': {
'if_match': 'If-Match',
'if_none_match': 'If-None-Match',
},
'location_map': {
'asset_device_contract_notification': 'body',
'if_match': 'header',
'if_none_match': 'header',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json'
]
},
api_client=api_client,
callable=__create_asset_device_contract_notification
)
def __create_asset_target(
self,
asset_target,
**kwargs
):
"""Create a 'asset.Target' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_asset_target(asset_target, async_req=True)
>>> result = thread.get()
Args:
asset_target (AssetTarget): The 'asset.Target' resource to create.
Keyword Args:
if_match (str): For methods that apply server-side changes, and in particular for PUT, If-Match can be used to prevent the lost update problem. It can check if the modification of a resource that the user wants to upload will not override another change that has been done since the original resource was fetched. If the request cannot be fulfilled, the 412 (Precondition Failed) response is returned. When modifying a resource using POST or PUT, the If-Match header must be set to the value of the resource ModTime property after which no lost update problem should occur. For example, a client send a GET request to obtain a resource, which includes the ModTime property. The ModTime indicates the last time the resource was created or modified. The client then sends a POST or PUT request with the If-Match header set to the ModTime property of the resource as obtained in the GET request.. [optional]
if_none_match (str): For methods that apply server-side changes, If-None-Match used with the * value can be used to create a resource not known to exist, guaranteeing that another resource creation didn't happen before, losing the data of the previous put. The request will be processed only if the eventually existing resource's ETag doesn't match any of the values listed. Otherwise, the status code 412 (Precondition Failed) is used. The asterisk is a special value representing any resource. It is only useful when creating a resource, usually with PUT, to check if another resource with the identity has already been created before. The comparison with the stored ETag uses the weak comparison algorithm, meaning two resources are considered identical if the content is equivalent - they don't have to be identical byte for byte.. [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
AssetTarget
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['asset_target'] = \
asset_target
return self.call_with_http_info(**kwargs)
self.create_asset_target = _Endpoint(
settings={
'response_type': (AssetTarget,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/Targets',
'operation_id': 'create_asset_target',
'http_method': 'POST',
'servers': None,
},
params_map={
'all': [
'asset_target',
'if_match',
'if_none_match',
],
'required': [
'asset_target',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'asset_target':
(AssetTarget,),
'if_match':
(str,),
'if_none_match':
(str,),
},
'attribute_map': {
'if_match': 'If-Match',
'if_none_match': 'If-None-Match',
},
'location_map': {
'asset_target': 'body',
'if_match': 'header',
'if_none_match': 'header',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json'
]
},
api_client=api_client,
callable=__create_asset_target
)
def __delete_asset_deployment(
self,
moid,
**kwargs
):
"""Delete a 'asset.Deployment' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_asset_deployment(moid, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
Keyword Args:
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
None
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['moid'] = \
moid
return self.call_with_http_info(**kwargs)
self.delete_asset_deployment = _Endpoint(
settings={
'response_type': None,
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/Deployments/{Moid}',
'operation_id': 'delete_asset_deployment',
'http_method': 'DELETE',
'servers': None,
},
params_map={
'all': [
'moid',
],
'required': [
'moid',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
},
'attribute_map': {
'moid': 'Moid',
},
'location_map': {
'moid': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [],
},
api_client=api_client,
callable=__delete_asset_deployment
)
def __delete_asset_deployment_device(
self,
moid,
**kwargs
):
"""Delete a 'asset.DeploymentDevice' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_asset_deployment_device(moid, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
Keyword Args:
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
None
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['moid'] = \
moid
return self.call_with_http_info(**kwargs)
self.delete_asset_deployment_device = _Endpoint(
settings={
'response_type': None,
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/DeploymentDevices/{Moid}',
'operation_id': 'delete_asset_deployment_device',
'http_method': 'DELETE',
'servers': None,
},
params_map={
'all': [
'moid',
],
'required': [
'moid',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
},
'attribute_map': {
'moid': 'Moid',
},
'location_map': {
'moid': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [],
},
api_client=api_client,
callable=__delete_asset_deployment_device
)
def __delete_asset_device_claim(
self,
moid,
**kwargs
):
"""Delete a 'asset.DeviceClaim' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_asset_device_claim(moid, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
Keyword Args:
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
None
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['moid'] = \
moid
return self.call_with_http_info(**kwargs)
self.delete_asset_device_claim = _Endpoint(
settings={
'response_type': None,
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/DeviceClaims/{Moid}',
'operation_id': 'delete_asset_device_claim',
'http_method': 'DELETE',
'servers': None,
},
params_map={
'all': [
'moid',
],
'required': [
'moid',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
},
'attribute_map': {
'moid': 'Moid',
},
'location_map': {
'moid': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [],
},
api_client=api_client,
callable=__delete_asset_device_claim
)
def __delete_asset_device_contract_information(
self,
moid,
**kwargs
):
"""Delete a 'asset.DeviceContractInformation' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_asset_device_contract_information(moid, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
Keyword Args:
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
None
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['moid'] = \
moid
return self.call_with_http_info(**kwargs)
self.delete_asset_device_contract_information = _Endpoint(
settings={
'response_type': None,
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/DeviceContractInformations/{Moid}',
'operation_id': 'delete_asset_device_contract_information',
'http_method': 'DELETE',
'servers': None,
},
params_map={
'all': [
'moid',
],
'required': [
'moid',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
},
'attribute_map': {
'moid': 'Moid',
},
'location_map': {
'moid': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [],
},
api_client=api_client,
callable=__delete_asset_device_contract_information
)
def __delete_asset_device_registration(
self,
moid,
**kwargs
):
"""Deletes the resource representing the device connector. All associated REST resources will be deleted. In particular, inventory and operational data associated with this device will be deleted. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_asset_device_registration(moid, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
Keyword Args:
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
None
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['moid'] = \
moid
return self.call_with_http_info(**kwargs)
self.delete_asset_device_registration = _Endpoint(
settings={
'response_type': None,
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/DeviceRegistrations/{Moid}',
'operation_id': 'delete_asset_device_registration',
'http_method': 'DELETE',
'servers': None,
},
params_map={
'all': [
'moid',
],
'required': [
'moid',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
},
'attribute_map': {
'moid': 'Moid',
},
'location_map': {
'moid': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [],
},
api_client=api_client,
callable=__delete_asset_device_registration
)
def __delete_asset_subscription(
self,
moid,
**kwargs
):
"""Delete a 'asset.Subscription' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_asset_subscription(moid, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
Keyword Args:
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
None
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['moid'] = \
moid
return self.call_with_http_info(**kwargs)
self.delete_asset_subscription = _Endpoint(
settings={
'response_type': None,
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/Subscriptions/{Moid}',
'operation_id': 'delete_asset_subscription',
'http_method': 'DELETE',
'servers': None,
},
params_map={
'all': [
'moid',
],
'required': [
'moid',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
},
'attribute_map': {
'moid': 'Moid',
},
'location_map': {
'moid': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [],
},
api_client=api_client,
callable=__delete_asset_subscription
)
def __delete_asset_subscription_account(
self,
moid,
**kwargs
):
"""Delete a 'asset.SubscriptionAccount' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_asset_subscription_account(moid, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
Keyword Args:
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
None
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['moid'] = \
moid
return self.call_with_http_info(**kwargs)
self.delete_asset_subscription_account = _Endpoint(
settings={
'response_type': None,
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/SubscriptionAccounts/{Moid}',
'operation_id': 'delete_asset_subscription_account',
'http_method': 'DELETE',
'servers': None,
},
params_map={
'all': [
'moid',
],
'required': [
'moid',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
},
'attribute_map': {
'moid': 'Moid',
},
'location_map': {
'moid': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [],
},
api_client=api_client,
callable=__delete_asset_subscription_account
)
def __delete_asset_target(
self,
moid,
**kwargs
):
"""Delete a 'asset.Target' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_asset_target(moid, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
Keyword Args:
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
None
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['moid'] = \
moid
return self.call_with_http_info(**kwargs)
self.delete_asset_target = _Endpoint(
settings={
'response_type': None,
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/Targets/{Moid}',
'operation_id': 'delete_asset_target',
'http_method': 'DELETE',
'servers': None,
},
params_map={
'all': [
'moid',
],
'required': [
'moid',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
},
'attribute_map': {
'moid': 'Moid',
},
'location_map': {
'moid': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [],
},
api_client=api_client,
callable=__delete_asset_target
)
def __get_asset_cluster_member_by_moid(
self,
moid,
**kwargs
):
"""Read a 'asset.ClusterMember' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_asset_cluster_member_by_moid(moid, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
Keyword Args:
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
AssetClusterMember
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['moid'] = \
moid
return self.call_with_http_info(**kwargs)
self.get_asset_cluster_member_by_moid = _Endpoint(
settings={
'response_type': (AssetClusterMember,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/ClusterMembers/{Moid}',
'operation_id': 'get_asset_cluster_member_by_moid',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'moid',
],
'required': [
'moid',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
},
'attribute_map': {
'moid': 'Moid',
},
'location_map': {
'moid': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_asset_cluster_member_by_moid
)
def __get_asset_cluster_member_list(
self,
**kwargs
):
"""Read a 'asset.ClusterMember' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_asset_cluster_member_list(async_req=True)
>>> result = thread.get()
Keyword Args:
filter (str): Filter criteria for the resources to return. A URI with a $filter query option identifies a subset of the entries from the Collection of Entries. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the $filter option. The expression language that is used in $filter queries supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false).. [optional] if omitted the server will use the default value of ""
orderby (str): Determines what properties are used to sort the collection of resources.. [optional]
top (int): Specifies the maximum number of resources to return in the response.. [optional] if omitted the server will use the default value of 100
skip (int): Specifies the number of resources to skip in the response.. [optional] if omitted the server will use the default value of 0
select (str): Specifies a subset of properties to return.. [optional] if omitted the server will use the default value of ""
expand (str): Specify additional attributes or related resources to return in addition to the primary resources.. [optional]
apply (str): Specify one or more transformation operations to perform aggregation on the resources. The transformations are processed in order with the output from a transformation being used as input for the subsequent transformation. The \"$apply\" query takes a sequence of set transformations, separated by forward slashes to express that they are consecutively applied, i.e. the result of each transformation is the input to the next transformation. Supported aggregation methods are \"aggregate\" and \"groupby\". The **aggregate** transformation takes a comma-separated list of one or more aggregate expressions as parameters and returns a result set with a single instance, representing the aggregated value for all instances in the input set. The **groupby** transformation takes one or two parameters and 1. Splits the initial set into subsets where all instances in a subset have the same values for the grouping properties specified in the first parameter, 2. Applies set transformations to each subset according to the second parameter, resulting in a new set of potentially different structure and cardinality, 3. Ensures that the instances in the result set contain all grouping properties with the correct values for the group, 4. Concatenates the intermediate result sets into one result set. A groupby transformation affects the structure of the result set.. [optional]
count (bool): The $count query specifies the service should return the count of the matching resources, instead of returning the resources.. [optional]
inlinecount (str): The $inlinecount query option allows clients to request an inline count of the matching resources included with the resources in the response.. [optional] if omitted the server will use the default value of "allpages"
at (str): Similar to \"$filter\", but \"at\" is specifically used to filter versioning information properties for resources to return. A URI with an \"at\" Query Option identifies a subset of the Entries from the Collection of Entries identified by the Resource Path section of the URI. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the query option. The expression language that is used in at operators supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false) or any of the additional literal representations shown in the Abstract Type System section.. [optional]
tags (str): The 'tags' parameter is used to request a summary of the Tag utilization for this resource. When the 'tags' parameter is specified, the response provides a list of tag keys, the number of times the key has been used across all documents, and the tag values that have been assigned to the tag key.. [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
AssetClusterMemberResponse
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
return self.call_with_http_info(**kwargs)
self.get_asset_cluster_member_list = _Endpoint(
settings={
'response_type': (AssetClusterMemberResponse,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/ClusterMembers',
'operation_id': 'get_asset_cluster_member_list',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'filter',
'orderby',
'top',
'skip',
'select',
'expand',
'apply',
'count',
'inlinecount',
'at',
'tags',
],
'required': [],
'nullable': [
],
'enum': [
'inlinecount',
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
('inlinecount',): {
"ALLPAGES": "allpages",
"NONE": "none"
},
},
'openapi_types': {
'filter':
(str,),
'orderby':
(str,),
'top':
(int,),
'skip':
(int,),
'select':
(str,),
'expand':
(str,),
'apply':
(str,),
'count':
(bool,),
'inlinecount':
(str,),
'at':
(str,),
'tags':
(str,),
},
'attribute_map': {
'filter': '$filter',
'orderby': '$orderby',
'top': '$top',
'skip': '$skip',
'select': '$select',
'expand': '$expand',
'apply': '$apply',
'count': '$count',
'inlinecount': '$inlinecount',
'at': 'at',
'tags': 'tags',
},
'location_map': {
'filter': 'query',
'orderby': 'query',
'top': 'query',
'skip': 'query',
'select': 'query',
'expand': 'query',
'apply': 'query',
'count': 'query',
'inlinecount': 'query',
'at': 'query',
'tags': 'query',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_asset_cluster_member_list
)
def __get_asset_deployment_by_moid(
self,
moid,
**kwargs
):
"""Read a 'asset.Deployment' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_asset_deployment_by_moid(moid, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
Keyword Args:
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
AssetDeployment
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['moid'] = \
moid
return self.call_with_http_info(**kwargs)
self.get_asset_deployment_by_moid = _Endpoint(
settings={
'response_type': (AssetDeployment,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/Deployments/{Moid}',
'operation_id': 'get_asset_deployment_by_moid',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'moid',
],
'required': [
'moid',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
},
'attribute_map': {
'moid': 'Moid',
},
'location_map': {
'moid': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_asset_deployment_by_moid
)
def __get_asset_deployment_device_by_moid(
self,
moid,
**kwargs
):
"""Read a 'asset.DeploymentDevice' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_asset_deployment_device_by_moid(moid, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
Keyword Args:
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
AssetDeploymentDevice
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['moid'] = \
moid
return self.call_with_http_info(**kwargs)
self.get_asset_deployment_device_by_moid = _Endpoint(
settings={
'response_type': (AssetDeploymentDevice,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/DeploymentDevices/{Moid}',
'operation_id': 'get_asset_deployment_device_by_moid',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'moid',
],
'required': [
'moid',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
},
'attribute_map': {
'moid': 'Moid',
},
'location_map': {
'moid': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_asset_deployment_device_by_moid
)
def __get_asset_deployment_device_list(
self,
**kwargs
):
"""Read a 'asset.DeploymentDevice' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_asset_deployment_device_list(async_req=True)
>>> result = thread.get()
Keyword Args:
filter (str): Filter criteria for the resources to return. A URI with a $filter query option identifies a subset of the entries from the Collection of Entries. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the $filter option. The expression language that is used in $filter queries supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false).. [optional] if omitted the server will use the default value of ""
orderby (str): Determines what properties are used to sort the collection of resources.. [optional]
top (int): Specifies the maximum number of resources to return in the response.. [optional] if omitted the server will use the default value of 100
skip (int): Specifies the number of resources to skip in the response.. [optional] if omitted the server will use the default value of 0
select (str): Specifies a subset of properties to return.. [optional] if omitted the server will use the default value of ""
expand (str): Specify additional attributes or related resources to return in addition to the primary resources.. [optional]
apply (str): Specify one or more transformation operations to perform aggregation on the resources. The transformations are processed in order with the output from a transformation being used as input for the subsequent transformation. The \"$apply\" query takes a sequence of set transformations, separated by forward slashes to express that they are consecutively applied, i.e. the result of each transformation is the input to the next transformation. Supported aggregation methods are \"aggregate\" and \"groupby\". The **aggregate** transformation takes a comma-separated list of one or more aggregate expressions as parameters and returns a result set with a single instance, representing the aggregated value for all instances in the input set. The **groupby** transformation takes one or two parameters and 1. Splits the initial set into subsets where all instances in a subset have the same values for the grouping properties specified in the first parameter, 2. Applies set transformations to each subset according to the second parameter, resulting in a new set of potentially different structure and cardinality, 3. Ensures that the instances in the result set contain all grouping properties with the correct values for the group, 4. Concatenates the intermediate result sets into one result set. A groupby transformation affects the structure of the result set.. [optional]
count (bool): The $count query specifies the service should return the count of the matching resources, instead of returning the resources.. [optional]
inlinecount (str): The $inlinecount query option allows clients to request an inline count of the matching resources included with the resources in the response.. [optional] if omitted the server will use the default value of "allpages"
at (str): Similar to \"$filter\", but \"at\" is specifically used to filter versioning information properties for resources to return. A URI with an \"at\" Query Option identifies a subset of the Entries from the Collection of Entries identified by the Resource Path section of the URI. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the query option. The expression language that is used in at operators supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false) or any of the additional literal representations shown in the Abstract Type System section.. [optional]
tags (str): The 'tags' parameter is used to request a summary of the Tag utilization for this resource. When the 'tags' parameter is specified, the response provides a list of tag keys, the number of times the key has been used across all documents, and the tag values that have been assigned to the tag key.. [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
AssetDeploymentDeviceResponse
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
return self.call_with_http_info(**kwargs)
self.get_asset_deployment_device_list = _Endpoint(
settings={
'response_type': (AssetDeploymentDeviceResponse,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/DeploymentDevices',
'operation_id': 'get_asset_deployment_device_list',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'filter',
'orderby',
'top',
'skip',
'select',
'expand',
'apply',
'count',
'inlinecount',
'at',
'tags',
],
'required': [],
'nullable': [
],
'enum': [
'inlinecount',
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
('inlinecount',): {
"ALLPAGES": "allpages",
"NONE": "none"
},
},
'openapi_types': {
'filter':
(str,),
'orderby':
(str,),
'top':
(int,),
'skip':
(int,),
'select':
(str,),
'expand':
(str,),
'apply':
(str,),
'count':
(bool,),
'inlinecount':
(str,),
'at':
(str,),
'tags':
(str,),
},
'attribute_map': {
'filter': '$filter',
'orderby': '$orderby',
'top': '$top',
'skip': '$skip',
'select': '$select',
'expand': '$expand',
'apply': '$apply',
'count': '$count',
'inlinecount': '$inlinecount',
'at': 'at',
'tags': 'tags',
},
'location_map': {
'filter': 'query',
'orderby': 'query',
'top': 'query',
'skip': 'query',
'select': 'query',
'expand': 'query',
'apply': 'query',
'count': 'query',
'inlinecount': 'query',
'at': 'query',
'tags': 'query',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_asset_deployment_device_list
)
def __get_asset_deployment_list(
self,
**kwargs
):
"""Read a 'asset.Deployment' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_asset_deployment_list(async_req=True)
>>> result = thread.get()
Keyword Args:
filter (str): Filter criteria for the resources to return. A URI with a $filter query option identifies a subset of the entries from the Collection of Entries. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the $filter option. The expression language that is used in $filter queries supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false).. [optional] if omitted the server will use the default value of ""
orderby (str): Determines what properties are used to sort the collection of resources.. [optional]
top (int): Specifies the maximum number of resources to return in the response.. [optional] if omitted the server will use the default value of 100
skip (int): Specifies the number of resources to skip in the response.. [optional] if omitted the server will use the default value of 0
select (str): Specifies a subset of properties to return.. [optional] if omitted the server will use the default value of ""
expand (str): Specify additional attributes or related resources to return in addition to the primary resources.. [optional]
apply (str): Specify one or more transformation operations to perform aggregation on the resources. The transformations are processed in order with the output from a transformation being used as input for the subsequent transformation. The \"$apply\" query takes a sequence of set transformations, separated by forward slashes to express that they are consecutively applied, i.e. the result of each transformation is the input to the next transformation. Supported aggregation methods are \"aggregate\" and \"groupby\". The **aggregate** transformation takes a comma-separated list of one or more aggregate expressions as parameters and returns a result set with a single instance, representing the aggregated value for all instances in the input set. The **groupby** transformation takes one or two parameters and 1. Splits the initial set into subsets where all instances in a subset have the same values for the grouping properties specified in the first parameter, 2. Applies set transformations to each subset according to the second parameter, resulting in a new set of potentially different structure and cardinality, 3. Ensures that the instances in the result set contain all grouping properties with the correct values for the group, 4. Concatenates the intermediate result sets into one result set. A groupby transformation affects the structure of the result set.. [optional]
count (bool): The $count query specifies the service should return the count of the matching resources, instead of returning the resources.. [optional]
inlinecount (str): The $inlinecount query option allows clients to request an inline count of the matching resources included with the resources in the response.. [optional] if omitted the server will use the default value of "allpages"
at (str): Similar to \"$filter\", but \"at\" is specifically used to filter versioning information properties for resources to return. A URI with an \"at\" Query Option identifies a subset of the Entries from the Collection of Entries identified by the Resource Path section of the URI. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the query option. The expression language that is used in at operators supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false) or any of the additional literal representations shown in the Abstract Type System section.. [optional]
tags (str): The 'tags' parameter is used to request a summary of the Tag utilization for this resource. When the 'tags' parameter is specified, the response provides a list of tag keys, the number of times the key has been used across all documents, and the tag values that have been assigned to the tag key.. [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
AssetDeploymentResponse
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
return self.call_with_http_info(**kwargs)
self.get_asset_deployment_list = _Endpoint(
settings={
'response_type': (AssetDeploymentResponse,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/Deployments',
'operation_id': 'get_asset_deployment_list',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'filter',
'orderby',
'top',
'skip',
'select',
'expand',
'apply',
'count',
'inlinecount',
'at',
'tags',
],
'required': [],
'nullable': [
],
'enum': [
'inlinecount',
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
('inlinecount',): {
"ALLPAGES": "allpages",
"NONE": "none"
},
},
'openapi_types': {
'filter':
(str,),
'orderby':
(str,),
'top':
(int,),
'skip':
(int,),
'select':
(str,),
'expand':
(str,),
'apply':
(str,),
'count':
(bool,),
'inlinecount':
(str,),
'at':
(str,),
'tags':
(str,),
},
'attribute_map': {
'filter': '$filter',
'orderby': '$orderby',
'top': '$top',
'skip': '$skip',
'select': '$select',
'expand': '$expand',
'apply': '$apply',
'count': '$count',
'inlinecount': '$inlinecount',
'at': 'at',
'tags': 'tags',
},
'location_map': {
'filter': 'query',
'orderby': 'query',
'top': 'query',
'skip': 'query',
'select': 'query',
'expand': 'query',
'apply': 'query',
'count': 'query',
'inlinecount': 'query',
'at': 'query',
'tags': 'query',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_asset_deployment_list
)
def __get_asset_device_configuration_by_moid(
self,
moid,
**kwargs
):
"""Read a 'asset.DeviceConfiguration' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_asset_device_configuration_by_moid(moid, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
Keyword Args:
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
AssetDeviceConfiguration
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['moid'] = \
moid
return self.call_with_http_info(**kwargs)
self.get_asset_device_configuration_by_moid = _Endpoint(
settings={
'response_type': (AssetDeviceConfiguration,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/DeviceConfigurations/{Moid}',
'operation_id': 'get_asset_device_configuration_by_moid',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'moid',
],
'required': [
'moid',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
},
'attribute_map': {
'moid': 'Moid',
},
'location_map': {
'moid': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_asset_device_configuration_by_moid
)
def __get_asset_device_configuration_list(
self,
**kwargs
):
"""Read a 'asset.DeviceConfiguration' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_asset_device_configuration_list(async_req=True)
>>> result = thread.get()
Keyword Args:
filter (str): Filter criteria for the resources to return. A URI with a $filter query option identifies a subset of the entries from the Collection of Entries. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the $filter option. The expression language that is used in $filter queries supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false).. [optional] if omitted the server will use the default value of ""
orderby (str): Determines what properties are used to sort the collection of resources.. [optional]
top (int): Specifies the maximum number of resources to return in the response.. [optional] if omitted the server will use the default value of 100
skip (int): Specifies the number of resources to skip in the response.. [optional] if omitted the server will use the default value of 0
select (str): Specifies a subset of properties to return.. [optional] if omitted the server will use the default value of ""
expand (str): Specify additional attributes or related resources to return in addition to the primary resources.. [optional]
apply (str): Specify one or more transformation operations to perform aggregation on the resources. The transformations are processed in order with the output from a transformation being used as input for the subsequent transformation. The \"$apply\" query takes a sequence of set transformations, separated by forward slashes to express that they are consecutively applied, i.e. the result of each transformation is the input to the next transformation. Supported aggregation methods are \"aggregate\" and \"groupby\". The **aggregate** transformation takes a comma-separated list of one or more aggregate expressions as parameters and returns a result set with a single instance, representing the aggregated value for all instances in the input set. The **groupby** transformation takes one or two parameters and 1. Splits the initial set into subsets where all instances in a subset have the same values for the grouping properties specified in the first parameter, 2. Applies set transformations to each subset according to the second parameter, resulting in a new set of potentially different structure and cardinality, 3. Ensures that the instances in the result set contain all grouping properties with the correct values for the group, 4. Concatenates the intermediate result sets into one result set. A groupby transformation affects the structure of the result set.. [optional]
count (bool): The $count query specifies the service should return the count of the matching resources, instead of returning the resources.. [optional]
inlinecount (str): The $inlinecount query option allows clients to request an inline count of the matching resources included with the resources in the response.. [optional] if omitted the server will use the default value of "allpages"
at (str): Similar to \"$filter\", but \"at\" is specifically used to filter versioning information properties for resources to return. A URI with an \"at\" Query Option identifies a subset of the Entries from the Collection of Entries identified by the Resource Path section of the URI. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the query option. The expression language that is used in at operators supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false) or any of the additional literal representations shown in the Abstract Type System section.. [optional]
tags (str): The 'tags' parameter is used to request a summary of the Tag utilization for this resource. When the 'tags' parameter is specified, the response provides a list of tag keys, the number of times the key has been used across all documents, and the tag values that have been assigned to the tag key.. [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
AssetDeviceConfigurationResponse
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
return self.call_with_http_info(**kwargs)
self.get_asset_device_configuration_list = _Endpoint(
settings={
'response_type': (AssetDeviceConfigurationResponse,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/DeviceConfigurations',
'operation_id': 'get_asset_device_configuration_list',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'filter',
'orderby',
'top',
'skip',
'select',
'expand',
'apply',
'count',
'inlinecount',
'at',
'tags',
],
'required': [],
'nullable': [
],
'enum': [
'inlinecount',
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
('inlinecount',): {
"ALLPAGES": "allpages",
"NONE": "none"
},
},
'openapi_types': {
'filter':
(str,),
'orderby':
(str,),
'top':
(int,),
'skip':
(int,),
'select':
(str,),
'expand':
(str,),
'apply':
(str,),
'count':
(bool,),
'inlinecount':
(str,),
'at':
(str,),
'tags':
(str,),
},
'attribute_map': {
'filter': '$filter',
'orderby': '$orderby',
'top': '$top',
'skip': '$skip',
'select': '$select',
'expand': '$expand',
'apply': '$apply',
'count': '$count',
'inlinecount': '$inlinecount',
'at': 'at',
'tags': 'tags',
},
'location_map': {
'filter': 'query',
'orderby': 'query',
'top': 'query',
'skip': 'query',
'select': 'query',
'expand': 'query',
'apply': 'query',
'count': 'query',
'inlinecount': 'query',
'at': 'query',
'tags': 'query',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_asset_device_configuration_list
)
def __get_asset_device_connector_manager_by_moid(
self,
moid,
**kwargs
):
"""Read a 'asset.DeviceConnectorManager' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_asset_device_connector_manager_by_moid(moid, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
Keyword Args:
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
AssetDeviceConnectorManager
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['moid'] = \
moid
return self.call_with_http_info(**kwargs)
self.get_asset_device_connector_manager_by_moid = _Endpoint(
settings={
'response_type': (AssetDeviceConnectorManager,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/DeviceConnectorManagers/{Moid}',
'operation_id': 'get_asset_device_connector_manager_by_moid',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'moid',
],
'required': [
'moid',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
},
'attribute_map': {
'moid': 'Moid',
},
'location_map': {
'moid': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_asset_device_connector_manager_by_moid
)
def __get_asset_device_connector_manager_list(
self,
**kwargs
):
"""Read a 'asset.DeviceConnectorManager' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_asset_device_connector_manager_list(async_req=True)
>>> result = thread.get()
Keyword Args:
filter (str): Filter criteria for the resources to return. A URI with a $filter query option identifies a subset of the entries from the Collection of Entries. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the $filter option. The expression language that is used in $filter queries supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false).. [optional] if omitted the server will use the default value of ""
orderby (str): Determines what properties are used to sort the collection of resources.. [optional]
top (int): Specifies the maximum number of resources to return in the response.. [optional] if omitted the server will use the default value of 100
skip (int): Specifies the number of resources to skip in the response.. [optional] if omitted the server will use the default value of 0
select (str): Specifies a subset of properties to return.. [optional] if omitted the server will use the default value of ""
expand (str): Specify additional attributes or related resources to return in addition to the primary resources.. [optional]
apply (str): Specify one or more transformation operations to perform aggregation on the resources. The transformations are processed in order with the output from a transformation being used as input for the subsequent transformation. The \"$apply\" query takes a sequence of set transformations, separated by forward slashes to express that they are consecutively applied, i.e. the result of each transformation is the input to the next transformation. Supported aggregation methods are \"aggregate\" and \"groupby\". The **aggregate** transformation takes a comma-separated list of one or more aggregate expressions as parameters and returns a result set with a single instance, representing the aggregated value for all instances in the input set. The **groupby** transformation takes one or two parameters and 1. Splits the initial set into subsets where all instances in a subset have the same values for the grouping properties specified in the first parameter, 2. Applies set transformations to each subset according to the second parameter, resulting in a new set of potentially different structure and cardinality, 3. Ensures that the instances in the result set contain all grouping properties with the correct values for the group, 4. Concatenates the intermediate result sets into one result set. A groupby transformation affects the structure of the result set.. [optional]
count (bool): The $count query specifies the service should return the count of the matching resources, instead of returning the resources.. [optional]
inlinecount (str): The $inlinecount query option allows clients to request an inline count of the matching resources included with the resources in the response.. [optional] if omitted the server will use the default value of "allpages"
at (str): Similar to \"$filter\", but \"at\" is specifically used to filter versioning information properties for resources to return. A URI with an \"at\" Query Option identifies a subset of the Entries from the Collection of Entries identified by the Resource Path section of the URI. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the query option. The expression language that is used in at operators supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false) or any of the additional literal representations shown in the Abstract Type System section.. [optional]
tags (str): The 'tags' parameter is used to request a summary of the Tag utilization for this resource. When the 'tags' parameter is specified, the response provides a list of tag keys, the number of times the key has been used across all documents, and the tag values that have been assigned to the tag key.. [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
AssetDeviceConnectorManagerResponse
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
return self.call_with_http_info(**kwargs)
self.get_asset_device_connector_manager_list = _Endpoint(
settings={
'response_type': (AssetDeviceConnectorManagerResponse,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/DeviceConnectorManagers',
'operation_id': 'get_asset_device_connector_manager_list',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'filter',
'orderby',
'top',
'skip',
'select',
'expand',
'apply',
'count',
'inlinecount',
'at',
'tags',
],
'required': [],
'nullable': [
],
'enum': [
'inlinecount',
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
('inlinecount',): {
"ALLPAGES": "allpages",
"NONE": "none"
},
},
'openapi_types': {
'filter':
(str,),
'orderby':
(str,),
'top':
(int,),
'skip':
(int,),
'select':
(str,),
'expand':
(str,),
'apply':
(str,),
'count':
(bool,),
'inlinecount':
(str,),
'at':
(str,),
'tags':
(str,),
},
'attribute_map': {
'filter': '$filter',
'orderby': '$orderby',
'top': '$top',
'skip': '$skip',
'select': '$select',
'expand': '$expand',
'apply': '$apply',
'count': '$count',
'inlinecount': '$inlinecount',
'at': 'at',
'tags': 'tags',
},
'location_map': {
'filter': 'query',
'orderby': 'query',
'top': 'query',
'skip': 'query',
'select': 'query',
'expand': 'query',
'apply': 'query',
'count': 'query',
'inlinecount': 'query',
'at': 'query',
'tags': 'query',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_asset_device_connector_manager_list
)
def __get_asset_device_contract_information_by_moid(
self,
moid,
**kwargs
):
"""Read a 'asset.DeviceContractInformation' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_asset_device_contract_information_by_moid(moid, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
Keyword Args:
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
AssetDeviceContractInformation
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['moid'] = \
moid
return self.call_with_http_info(**kwargs)
self.get_asset_device_contract_information_by_moid = _Endpoint(
settings={
'response_type': (AssetDeviceContractInformation,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/DeviceContractInformations/{Moid}',
'operation_id': 'get_asset_device_contract_information_by_moid',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'moid',
],
'required': [
'moid',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
},
'attribute_map': {
'moid': 'Moid',
},
'location_map': {
'moid': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_asset_device_contract_information_by_moid
)
def __get_asset_device_contract_information_list(
self,
**kwargs
):
"""Read a 'asset.DeviceContractInformation' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_asset_device_contract_information_list(async_req=True)
>>> result = thread.get()
Keyword Args:
filter (str): Filter criteria for the resources to return. A URI with a $filter query option identifies a subset of the entries from the Collection of Entries. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the $filter option. The expression language that is used in $filter queries supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false).. [optional] if omitted the server will use the default value of ""
orderby (str): Determines what properties are used to sort the collection of resources.. [optional]
top (int): Specifies the maximum number of resources to return in the response.. [optional] if omitted the server will use the default value of 100
skip (int): Specifies the number of resources to skip in the response.. [optional] if omitted the server will use the default value of 0
select (str): Specifies a subset of properties to return.. [optional] if omitted the server will use the default value of ""
expand (str): Specify additional attributes or related resources to return in addition to the primary resources.. [optional]
apply (str): Specify one or more transformation operations to perform aggregation on the resources. The transformations are processed in order with the output from a transformation being used as input for the subsequent transformation. The \"$apply\" query takes a sequence of set transformations, separated by forward slashes to express that they are consecutively applied, i.e. the result of each transformation is the input to the next transformation. Supported aggregation methods are \"aggregate\" and \"groupby\". The **aggregate** transformation takes a comma-separated list of one or more aggregate expressions as parameters and returns a result set with a single instance, representing the aggregated value for all instances in the input set. The **groupby** transformation takes one or two parameters and 1. Splits the initial set into subsets where all instances in a subset have the same values for the grouping properties specified in the first parameter, 2. Applies set transformations to each subset according to the second parameter, resulting in a new set of potentially different structure and cardinality, 3. Ensures that the instances in the result set contain all grouping properties with the correct values for the group, 4. Concatenates the intermediate result sets into one result set. A groupby transformation affects the structure of the result set.. [optional]
count (bool): The $count query specifies the service should return the count of the matching resources, instead of returning the resources.. [optional]
inlinecount (str): The $inlinecount query option allows clients to request an inline count of the matching resources included with the resources in the response.. [optional] if omitted the server will use the default value of "allpages"
at (str): Similar to \"$filter\", but \"at\" is specifically used to filter versioning information properties for resources to return. A URI with an \"at\" Query Option identifies a subset of the Entries from the Collection of Entries identified by the Resource Path section of the URI. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the query option. The expression language that is used in at operators supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false) or any of the additional literal representations shown in the Abstract Type System section.. [optional]
tags (str): The 'tags' parameter is used to request a summary of the Tag utilization for this resource. When the 'tags' parameter is specified, the response provides a list of tag keys, the number of times the key has been used across all documents, and the tag values that have been assigned to the tag key.. [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
AssetDeviceContractInformationResponse
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
return self.call_with_http_info(**kwargs)
self.get_asset_device_contract_information_list = _Endpoint(
settings={
'response_type': (AssetDeviceContractInformationResponse,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/DeviceContractInformations',
'operation_id': 'get_asset_device_contract_information_list',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'filter',
'orderby',
'top',
'skip',
'select',
'expand',
'apply',
'count',
'inlinecount',
'at',
'tags',
],
'required': [],
'nullable': [
],
'enum': [
'inlinecount',
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
('inlinecount',): {
"ALLPAGES": "allpages",
"NONE": "none"
},
},
'openapi_types': {
'filter':
(str,),
'orderby':
(str,),
'top':
(int,),
'skip':
(int,),
'select':
(str,),
'expand':
(str,),
'apply':
(str,),
'count':
(bool,),
'inlinecount':
(str,),
'at':
(str,),
'tags':
(str,),
},
'attribute_map': {
'filter': '$filter',
'orderby': '$orderby',
'top': '$top',
'skip': '$skip',
'select': '$select',
'expand': '$expand',
'apply': '$apply',
'count': '$count',
'inlinecount': '$inlinecount',
'at': 'at',
'tags': 'tags',
},
'location_map': {
'filter': 'query',
'orderby': 'query',
'top': 'query',
'skip': 'query',
'select': 'query',
'expand': 'query',
'apply': 'query',
'count': 'query',
'inlinecount': 'query',
'at': 'query',
'tags': 'query',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_asset_device_contract_information_list
)
def __get_asset_device_registration_by_moid(
self,
moid,
**kwargs
):
"""Read a 'asset.DeviceRegistration' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_asset_device_registration_by_moid(moid, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
Keyword Args:
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
AssetDeviceRegistration
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['moid'] = \
moid
return self.call_with_http_info(**kwargs)
self.get_asset_device_registration_by_moid = _Endpoint(
settings={
'response_type': (AssetDeviceRegistration,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/DeviceRegistrations/{Moid}',
'operation_id': 'get_asset_device_registration_by_moid',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'moid',
],
'required': [
'moid',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
},
'attribute_map': {
'moid': 'Moid',
},
'location_map': {
'moid': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_asset_device_registration_by_moid
)
def __get_asset_device_registration_list(
self,
**kwargs
):
"""Read a 'asset.DeviceRegistration' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_asset_device_registration_list(async_req=True)
>>> result = thread.get()
Keyword Args:
filter (str): Filter criteria for the resources to return. A URI with a $filter query option identifies a subset of the entries from the Collection of Entries. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the $filter option. The expression language that is used in $filter queries supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false).. [optional] if omitted the server will use the default value of ""
orderby (str): Determines what properties are used to sort the collection of resources.. [optional]
top (int): Specifies the maximum number of resources to return in the response.. [optional] if omitted the server will use the default value of 100
skip (int): Specifies the number of resources to skip in the response.. [optional] if omitted the server will use the default value of 0
select (str): Specifies a subset of properties to return.. [optional] if omitted the server will use the default value of ""
expand (str): Specify additional attributes or related resources to return in addition to the primary resources.. [optional]
apply (str): Specify one or more transformation operations to perform aggregation on the resources. The transformations are processed in order with the output from a transformation being used as input for the subsequent transformation. The \"$apply\" query takes a sequence of set transformations, separated by forward slashes to express that they are consecutively applied, i.e. the result of each transformation is the input to the next transformation. Supported aggregation methods are \"aggregate\" and \"groupby\". The **aggregate** transformation takes a comma-separated list of one or more aggregate expressions as parameters and returns a result set with a single instance, representing the aggregated value for all instances in the input set. The **groupby** transformation takes one or two parameters and 1. Splits the initial set into subsets where all instances in a subset have the same values for the grouping properties specified in the first parameter, 2. Applies set transformations to each subset according to the second parameter, resulting in a new set of potentially different structure and cardinality, 3. Ensures that the instances in the result set contain all grouping properties with the correct values for the group, 4. Concatenates the intermediate result sets into one result set. A groupby transformation affects the structure of the result set.. [optional]
count (bool): The $count query specifies the service should return the count of the matching resources, instead of returning the resources.. [optional]
inlinecount (str): The $inlinecount query option allows clients to request an inline count of the matching resources included with the resources in the response.. [optional] if omitted the server will use the default value of "allpages"
at (str): Similar to \"$filter\", but \"at\" is specifically used to filter versioning information properties for resources to return. A URI with an \"at\" Query Option identifies a subset of the Entries from the Collection of Entries identified by the Resource Path section of the URI. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the query option. The expression language that is used in at operators supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false) or any of the additional literal representations shown in the Abstract Type System section.. [optional]
tags (str): The 'tags' parameter is used to request a summary of the Tag utilization for this resource. When the 'tags' parameter is specified, the response provides a list of tag keys, the number of times the key has been used across all documents, and the tag values that have been assigned to the tag key.. [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
AssetDeviceRegistrationResponse
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
return self.call_with_http_info(**kwargs)
self.get_asset_device_registration_list = _Endpoint(
settings={
'response_type': (AssetDeviceRegistrationResponse,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/DeviceRegistrations',
'operation_id': 'get_asset_device_registration_list',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'filter',
'orderby',
'top',
'skip',
'select',
'expand',
'apply',
'count',
'inlinecount',
'at',
'tags',
],
'required': [],
'nullable': [
],
'enum': [
'inlinecount',
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
('inlinecount',): {
"ALLPAGES": "allpages",
"NONE": "none"
},
},
'openapi_types': {
'filter':
(str,),
'orderby':
(str,),
'top':
(int,),
'skip':
(int,),
'select':
(str,),
'expand':
(str,),
'apply':
(str,),
'count':
(bool,),
'inlinecount':
(str,),
'at':
(str,),
'tags':
(str,),
},
'attribute_map': {
'filter': '$filter',
'orderby': '$orderby',
'top': '$top',
'skip': '$skip',
'select': '$select',
'expand': '$expand',
'apply': '$apply',
'count': '$count',
'inlinecount': '$inlinecount',
'at': 'at',
'tags': 'tags',
},
'location_map': {
'filter': 'query',
'orderby': 'query',
'top': 'query',
'skip': 'query',
'select': 'query',
'expand': 'query',
'apply': 'query',
'count': 'query',
'inlinecount': 'query',
'at': 'query',
'tags': 'query',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_asset_device_registration_list
)
def __get_asset_subscription_account_by_moid(
self,
moid,
**kwargs
):
"""Read a 'asset.SubscriptionAccount' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_asset_subscription_account_by_moid(moid, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
Keyword Args:
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
AssetSubscriptionAccount
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['moid'] = \
moid
return self.call_with_http_info(**kwargs)
self.get_asset_subscription_account_by_moid = _Endpoint(
settings={
'response_type': (AssetSubscriptionAccount,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/SubscriptionAccounts/{Moid}',
'operation_id': 'get_asset_subscription_account_by_moid',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'moid',
],
'required': [
'moid',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
},
'attribute_map': {
'moid': 'Moid',
},
'location_map': {
'moid': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_asset_subscription_account_by_moid
)
def __get_asset_subscription_account_list(
self,
**kwargs
):
"""Read a 'asset.SubscriptionAccount' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_asset_subscription_account_list(async_req=True)
>>> result = thread.get()
Keyword Args:
filter (str): Filter criteria for the resources to return. A URI with a $filter query option identifies a subset of the entries from the Collection of Entries. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the $filter option. The expression language that is used in $filter queries supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false).. [optional] if omitted the server will use the default value of ""
orderby (str): Determines what properties are used to sort the collection of resources.. [optional]
top (int): Specifies the maximum number of resources to return in the response.. [optional] if omitted the server will use the default value of 100
skip (int): Specifies the number of resources to skip in the response.. [optional] if omitted the server will use the default value of 0
select (str): Specifies a subset of properties to return.. [optional] if omitted the server will use the default value of ""
expand (str): Specify additional attributes or related resources to return in addition to the primary resources.. [optional]
apply (str): Specify one or more transformation operations to perform aggregation on the resources. The transformations are processed in order with the output from a transformation being used as input for the subsequent transformation. The \"$apply\" query takes a sequence of set transformations, separated by forward slashes to express that they are consecutively applied, i.e. the result of each transformation is the input to the next transformation. Supported aggregation methods are \"aggregate\" and \"groupby\". The **aggregate** transformation takes a comma-separated list of one or more aggregate expressions as parameters and returns a result set with a single instance, representing the aggregated value for all instances in the input set. The **groupby** transformation takes one or two parameters and 1. Splits the initial set into subsets where all instances in a subset have the same values for the grouping properties specified in the first parameter, 2. Applies set transformations to each subset according to the second parameter, resulting in a new set of potentially different structure and cardinality, 3. Ensures that the instances in the result set contain all grouping properties with the correct values for the group, 4. Concatenates the intermediate result sets into one result set. A groupby transformation affects the structure of the result set.. [optional]
count (bool): The $count query specifies the service should return the count of the matching resources, instead of returning the resources.. [optional]
inlinecount (str): The $inlinecount query option allows clients to request an inline count of the matching resources included with the resources in the response.. [optional] if omitted the server will use the default value of "allpages"
at (str): Similar to \"$filter\", but \"at\" is specifically used to filter versioning information properties for resources to return. A URI with an \"at\" Query Option identifies a subset of the Entries from the Collection of Entries identified by the Resource Path section of the URI. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the query option. The expression language that is used in at operators supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false) or any of the additional literal representations shown in the Abstract Type System section.. [optional]
tags (str): The 'tags' parameter is used to request a summary of the Tag utilization for this resource. When the 'tags' parameter is specified, the response provides a list of tag keys, the number of times the key has been used across all documents, and the tag values that have been assigned to the tag key.. [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
AssetSubscriptionAccountResponse
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
return self.call_with_http_info(**kwargs)
self.get_asset_subscription_account_list = _Endpoint(
settings={
'response_type': (AssetSubscriptionAccountResponse,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/SubscriptionAccounts',
'operation_id': 'get_asset_subscription_account_list',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'filter',
'orderby',
'top',
'skip',
'select',
'expand',
'apply',
'count',
'inlinecount',
'at',
'tags',
],
'required': [],
'nullable': [
],
'enum': [
'inlinecount',
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
('inlinecount',): {
"ALLPAGES": "allpages",
"NONE": "none"
},
},
'openapi_types': {
'filter':
(str,),
'orderby':
(str,),
'top':
(int,),
'skip':
(int,),
'select':
(str,),
'expand':
(str,),
'apply':
(str,),
'count':
(bool,),
'inlinecount':
(str,),
'at':
(str,),
'tags':
(str,),
},
'attribute_map': {
'filter': '$filter',
'orderby': '$orderby',
'top': '$top',
'skip': '$skip',
'select': '$select',
'expand': '$expand',
'apply': '$apply',
'count': '$count',
'inlinecount': '$inlinecount',
'at': 'at',
'tags': 'tags',
},
'location_map': {
'filter': 'query',
'orderby': 'query',
'top': 'query',
'skip': 'query',
'select': 'query',
'expand': 'query',
'apply': 'query',
'count': 'query',
'inlinecount': 'query',
'at': 'query',
'tags': 'query',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_asset_subscription_account_list
)
def __get_asset_subscription_by_moid(
self,
moid,
**kwargs
):
"""Read a 'asset.Subscription' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_asset_subscription_by_moid(moid, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
Keyword Args:
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
AssetSubscription
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['moid'] = \
moid
return self.call_with_http_info(**kwargs)
self.get_asset_subscription_by_moid = _Endpoint(
settings={
'response_type': (AssetSubscription,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/Subscriptions/{Moid}',
'operation_id': 'get_asset_subscription_by_moid',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'moid',
],
'required': [
'moid',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
},
'attribute_map': {
'moid': 'Moid',
},
'location_map': {
'moid': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_asset_subscription_by_moid
)
def __get_asset_subscription_device_contract_information_by_moid(
self,
moid,
**kwargs
):
"""Read a 'asset.SubscriptionDeviceContractInformation' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_asset_subscription_device_contract_information_by_moid(moid, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
Keyword Args:
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
AssetSubscriptionDeviceContractInformation
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['moid'] = \
moid
return self.call_with_http_info(**kwargs)
self.get_asset_subscription_device_contract_information_by_moid = _Endpoint(
settings={
'response_type': (AssetSubscriptionDeviceContractInformation,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/SubscriptionDeviceContractInformations/{Moid}',
'operation_id': 'get_asset_subscription_device_contract_information_by_moid',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'moid',
],
'required': [
'moid',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
},
'attribute_map': {
'moid': 'Moid',
},
'location_map': {
'moid': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_asset_subscription_device_contract_information_by_moid
)
def __get_asset_subscription_device_contract_information_list(
self,
**kwargs
):
"""Read a 'asset.SubscriptionDeviceContractInformation' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_asset_subscription_device_contract_information_list(async_req=True)
>>> result = thread.get()
Keyword Args:
filter (str): Filter criteria for the resources to return. A URI with a $filter query option identifies a subset of the entries from the Collection of Entries. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the $filter option. The expression language that is used in $filter queries supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false).. [optional] if omitted the server will use the default value of ""
orderby (str): Determines what properties are used to sort the collection of resources.. [optional]
top (int): Specifies the maximum number of resources to return in the response.. [optional] if omitted the server will use the default value of 100
skip (int): Specifies the number of resources to skip in the response.. [optional] if omitted the server will use the default value of 0
select (str): Specifies a subset of properties to return.. [optional] if omitted the server will use the default value of ""
expand (str): Specify additional attributes or related resources to return in addition to the primary resources.. [optional]
apply (str): Specify one or more transformation operations to perform aggregation on the resources. The transformations are processed in order with the output from a transformation being used as input for the subsequent transformation. The \"$apply\" query takes a sequence of set transformations, separated by forward slashes to express that they are consecutively applied, i.e. the result of each transformation is the input to the next transformation. Supported aggregation methods are \"aggregate\" and \"groupby\". The **aggregate** transformation takes a comma-separated list of one or more aggregate expressions as parameters and returns a result set with a single instance, representing the aggregated value for all instances in the input set. The **groupby** transformation takes one or two parameters and 1. Splits the initial set into subsets where all instances in a subset have the same values for the grouping properties specified in the first parameter, 2. Applies set transformations to each subset according to the second parameter, resulting in a new set of potentially different structure and cardinality, 3. Ensures that the instances in the result set contain all grouping properties with the correct values for the group, 4. Concatenates the intermediate result sets into one result set. A groupby transformation affects the structure of the result set.. [optional]
count (bool): The $count query specifies the service should return the count of the matching resources, instead of returning the resources.. [optional]
inlinecount (str): The $inlinecount query option allows clients to request an inline count of the matching resources included with the resources in the response.. [optional] if omitted the server will use the default value of "allpages"
at (str): Similar to \"$filter\", but \"at\" is specifically used to filter versioning information properties for resources to return. A URI with an \"at\" Query Option identifies a subset of the Entries from the Collection of Entries identified by the Resource Path section of the URI. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the query option. The expression language that is used in at operators supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false) or any of the additional literal representations shown in the Abstract Type System section.. [optional]
tags (str): The 'tags' parameter is used to request a summary of the Tag utilization for this resource. When the 'tags' parameter is specified, the response provides a list of tag keys, the number of times the key has been used across all documents, and the tag values that have been assigned to the tag key.. [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
AssetSubscriptionDeviceContractInformationResponse
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
return self.call_with_http_info(**kwargs)
self.get_asset_subscription_device_contract_information_list = _Endpoint(
settings={
'response_type': (AssetSubscriptionDeviceContractInformationResponse,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/SubscriptionDeviceContractInformations',
'operation_id': 'get_asset_subscription_device_contract_information_list',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'filter',
'orderby',
'top',
'skip',
'select',
'expand',
'apply',
'count',
'inlinecount',
'at',
'tags',
],
'required': [],
'nullable': [
],
'enum': [
'inlinecount',
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
('inlinecount',): {
"ALLPAGES": "allpages",
"NONE": "none"
},
},
'openapi_types': {
'filter':
(str,),
'orderby':
(str,),
'top':
(int,),
'skip':
(int,),
'select':
(str,),
'expand':
(str,),
'apply':
(str,),
'count':
(bool,),
'inlinecount':
(str,),
'at':
(str,),
'tags':
(str,),
},
'attribute_map': {
'filter': '$filter',
'orderby': '$orderby',
'top': '$top',
'skip': '$skip',
'select': '$select',
'expand': '$expand',
'apply': '$apply',
'count': '$count',
'inlinecount': '$inlinecount',
'at': 'at',
'tags': 'tags',
},
'location_map': {
'filter': 'query',
'orderby': 'query',
'top': 'query',
'skip': 'query',
'select': 'query',
'expand': 'query',
'apply': 'query',
'count': 'query',
'inlinecount': 'query',
'at': 'query',
'tags': 'query',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_asset_subscription_device_contract_information_list
)
def __get_asset_subscription_list(
self,
**kwargs
):
"""Read a 'asset.Subscription' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_asset_subscription_list(async_req=True)
>>> result = thread.get()
Keyword Args:
filter (str): Filter criteria for the resources to return. A URI with a $filter query option identifies a subset of the entries from the Collection of Entries. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the $filter option. The expression language that is used in $filter queries supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false).. [optional] if omitted the server will use the default value of ""
orderby (str): Determines what properties are used to sort the collection of resources.. [optional]
top (int): Specifies the maximum number of resources to return in the response.. [optional] if omitted the server will use the default value of 100
skip (int): Specifies the number of resources to skip in the response.. [optional] if omitted the server will use the default value of 0
select (str): Specifies a subset of properties to return.. [optional] if omitted the server will use the default value of ""
expand (str): Specify additional attributes or related resources to return in addition to the primary resources.. [optional]
apply (str): Specify one or more transformation operations to perform aggregation on the resources. The transformations are processed in order with the output from a transformation being used as input for the subsequent transformation. The \"$apply\" query takes a sequence of set transformations, separated by forward slashes to express that they are consecutively applied, i.e. the result of each transformation is the input to the next transformation. Supported aggregation methods are \"aggregate\" and \"groupby\". The **aggregate** transformation takes a comma-separated list of one or more aggregate expressions as parameters and returns a result set with a single instance, representing the aggregated value for all instances in the input set. The **groupby** transformation takes one or two parameters and 1. Splits the initial set into subsets where all instances in a subset have the same values for the grouping properties specified in the first parameter, 2. Applies set transformations to each subset according to the second parameter, resulting in a new set of potentially different structure and cardinality, 3. Ensures that the instances in the result set contain all grouping properties with the correct values for the group, 4. Concatenates the intermediate result sets into one result set. A groupby transformation affects the structure of the result set.. [optional]
count (bool): The $count query specifies the service should return the count of the matching resources, instead of returning the resources.. [optional]
inlinecount (str): The $inlinecount query option allows clients to request an inline count of the matching resources included with the resources in the response.. [optional] if omitted the server will use the default value of "allpages"
at (str): Similar to \"$filter\", but \"at\" is specifically used to filter versioning information properties for resources to return. A URI with an \"at\" Query Option identifies a subset of the Entries from the Collection of Entries identified by the Resource Path section of the URI. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the query option. The expression language that is used in at operators supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false) or any of the additional literal representations shown in the Abstract Type System section.. [optional]
tags (str): The 'tags' parameter is used to request a summary of the Tag utilization for this resource. When the 'tags' parameter is specified, the response provides a list of tag keys, the number of times the key has been used across all documents, and the tag values that have been assigned to the tag key.. [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
AssetSubscriptionResponse
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
return self.call_with_http_info(**kwargs)
self.get_asset_subscription_list = _Endpoint(
settings={
'response_type': (AssetSubscriptionResponse,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/Subscriptions',
'operation_id': 'get_asset_subscription_list',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'filter',
'orderby',
'top',
'skip',
'select',
'expand',
'apply',
'count',
'inlinecount',
'at',
'tags',
],
'required': [],
'nullable': [
],
'enum': [
'inlinecount',
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
('inlinecount',): {
"ALLPAGES": "allpages",
"NONE": "none"
},
},
'openapi_types': {
'filter':
(str,),
'orderby':
(str,),
'top':
(int,),
'skip':
(int,),
'select':
(str,),
'expand':
(str,),
'apply':
(str,),
'count':
(bool,),
'inlinecount':
(str,),
'at':
(str,),
'tags':
(str,),
},
'attribute_map': {
'filter': '$filter',
'orderby': '$orderby',
'top': '$top',
'skip': '$skip',
'select': '$select',
'expand': '$expand',
'apply': '$apply',
'count': '$count',
'inlinecount': '$inlinecount',
'at': 'at',
'tags': 'tags',
},
'location_map': {
'filter': 'query',
'orderby': 'query',
'top': 'query',
'skip': 'query',
'select': 'query',
'expand': 'query',
'apply': 'query',
'count': 'query',
'inlinecount': 'query',
'at': 'query',
'tags': 'query',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_asset_subscription_list
)
def __get_asset_target_by_moid(
self,
moid,
**kwargs
):
"""Read a 'asset.Target' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_asset_target_by_moid(moid, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
Keyword Args:
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
AssetTarget
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['moid'] = \
moid
return self.call_with_http_info(**kwargs)
self.get_asset_target_by_moid = _Endpoint(
settings={
'response_type': (AssetTarget,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/Targets/{Moid}',
'operation_id': 'get_asset_target_by_moid',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'moid',
],
'required': [
'moid',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
},
'attribute_map': {
'moid': 'Moid',
},
'location_map': {
'moid': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_asset_target_by_moid
)
def __get_asset_target_list(
self,
**kwargs
):
"""Read a 'asset.Target' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_asset_target_list(async_req=True)
>>> result = thread.get()
Keyword Args:
filter (str): Filter criteria for the resources to return. A URI with a $filter query option identifies a subset of the entries from the Collection of Entries. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the $filter option. The expression language that is used in $filter queries supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false).. [optional] if omitted the server will use the default value of ""
orderby (str): Determines what properties are used to sort the collection of resources.. [optional]
top (int): Specifies the maximum number of resources to return in the response.. [optional] if omitted the server will use the default value of 100
skip (int): Specifies the number of resources to skip in the response.. [optional] if omitted the server will use the default value of 0
select (str): Specifies a subset of properties to return.. [optional] if omitted the server will use the default value of ""
expand (str): Specify additional attributes or related resources to return in addition to the primary resources.. [optional]
apply (str): Specify one or more transformation operations to perform aggregation on the resources. The transformations are processed in order with the output from a transformation being used as input for the subsequent transformation. The \"$apply\" query takes a sequence of set transformations, separated by forward slashes to express that they are consecutively applied, i.e. the result of each transformation is the input to the next transformation. Supported aggregation methods are \"aggregate\" and \"groupby\". The **aggregate** transformation takes a comma-separated list of one or more aggregate expressions as parameters and returns a result set with a single instance, representing the aggregated value for all instances in the input set. The **groupby** transformation takes one or two parameters and 1. Splits the initial set into subsets where all instances in a subset have the same values for the grouping properties specified in the first parameter, 2. Applies set transformations to each subset according to the second parameter, resulting in a new set of potentially different structure and cardinality, 3. Ensures that the instances in the result set contain all grouping properties with the correct values for the group, 4. Concatenates the intermediate result sets into one result set. A groupby transformation affects the structure of the result set.. [optional]
count (bool): The $count query specifies the service should return the count of the matching resources, instead of returning the resources.. [optional]
inlinecount (str): The $inlinecount query option allows clients to request an inline count of the matching resources included with the resources in the response.. [optional] if omitted the server will use the default value of "allpages"
at (str): Similar to \"$filter\", but \"at\" is specifically used to filter versioning information properties for resources to return. A URI with an \"at\" Query Option identifies a subset of the Entries from the Collection of Entries identified by the Resource Path section of the URI. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the query option. The expression language that is used in at operators supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false) or any of the additional literal representations shown in the Abstract Type System section.. [optional]
tags (str): The 'tags' parameter is used to request a summary of the Tag utilization for this resource. When the 'tags' parameter is specified, the response provides a list of tag keys, the number of times the key has been used across all documents, and the tag values that have been assigned to the tag key.. [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
AssetTargetResponse
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
return self.call_with_http_info(**kwargs)
self.get_asset_target_list = _Endpoint(
settings={
'response_type': (AssetTargetResponse,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/Targets',
'operation_id': 'get_asset_target_list',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'filter',
'orderby',
'top',
'skip',
'select',
'expand',
'apply',
'count',
'inlinecount',
'at',
'tags',
],
'required': [],
'nullable': [
],
'enum': [
'inlinecount',
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
('inlinecount',): {
"ALLPAGES": "allpages",
"NONE": "none"
},
},
'openapi_types': {
'filter':
(str,),
'orderby':
(str,),
'top':
(int,),
'skip':
(int,),
'select':
(str,),
'expand':
(str,),
'apply':
(str,),
'count':
(bool,),
'inlinecount':
(str,),
'at':
(str,),
'tags':
(str,),
},
'attribute_map': {
'filter': '$filter',
'orderby': '$orderby',
'top': '$top',
'skip': '$skip',
'select': '$select',
'expand': '$expand',
'apply': '$apply',
'count': '$count',
'inlinecount': '$inlinecount',
'at': 'at',
'tags': 'tags',
},
'location_map': {
'filter': 'query',
'orderby': 'query',
'top': 'query',
'skip': 'query',
'select': 'query',
'expand': 'query',
'apply': 'query',
'count': 'query',
'inlinecount': 'query',
'at': 'query',
'tags': 'query',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_asset_target_list
)
def __patch_asset_device_configuration(
self,
moid,
asset_device_configuration,
**kwargs
):
"""Update a 'asset.DeviceConfiguration' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.patch_asset_device_configuration(moid, asset_device_configuration, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
asset_device_configuration (AssetDeviceConfiguration): The 'asset.DeviceConfiguration' resource to update.
Keyword Args:
if_match (str): For methods that apply server-side changes, and in particular for PUT, If-Match can be used to prevent the lost update problem. It can check if the modification of a resource that the user wants to upload will not override another change that has been done since the original resource was fetched. If the request cannot be fulfilled, the 412 (Precondition Failed) response is returned. When modifying a resource using POST or PUT, the If-Match header must be set to the value of the resource ModTime property after which no lost update problem should occur. For example, a client send a GET request to obtain a resource, which includes the ModTime property. The ModTime indicates the last time the resource was created or modified. The client then sends a POST or PUT request with the If-Match header set to the ModTime property of the resource as obtained in the GET request.. [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
AssetDeviceConfiguration
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['moid'] = \
moid
kwargs['asset_device_configuration'] = \
asset_device_configuration
return self.call_with_http_info(**kwargs)
self.patch_asset_device_configuration = _Endpoint(
settings={
'response_type': (AssetDeviceConfiguration,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/DeviceConfigurations/{Moid}',
'operation_id': 'patch_asset_device_configuration',
'http_method': 'PATCH',
'servers': None,
},
params_map={
'all': [
'moid',
'asset_device_configuration',
'if_match',
],
'required': [
'moid',
'asset_device_configuration',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
'asset_device_configuration':
(AssetDeviceConfiguration,),
'if_match':
(str,),
},
'attribute_map': {
'moid': 'Moid',
'if_match': 'If-Match',
},
'location_map': {
'moid': 'path',
'asset_device_configuration': 'body',
'if_match': 'header',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json',
'application/json-patch+json'
]
},
api_client=api_client,
callable=__patch_asset_device_configuration
)
def __patch_asset_device_contract_information(
self,
moid,
asset_device_contract_information,
**kwargs
):
"""Update a 'asset.DeviceContractInformation' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.patch_asset_device_contract_information(moid, asset_device_contract_information, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
asset_device_contract_information (AssetDeviceContractInformation): The 'asset.DeviceContractInformation' resource to update.
Keyword Args:
if_match (str): For methods that apply server-side changes, and in particular for PUT, If-Match can be used to prevent the lost update problem. It can check if the modification of a resource that the user wants to upload will not override another change that has been done since the original resource was fetched. If the request cannot be fulfilled, the 412 (Precondition Failed) response is returned. When modifying a resource using POST or PUT, the If-Match header must be set to the value of the resource ModTime property after which no lost update problem should occur. For example, a client send a GET request to obtain a resource, which includes the ModTime property. The ModTime indicates the last time the resource was created or modified. The client then sends a POST or PUT request with the If-Match header set to the ModTime property of the resource as obtained in the GET request.. [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
AssetDeviceContractInformation
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['moid'] = \
moid
kwargs['asset_device_contract_information'] = \
asset_device_contract_information
return self.call_with_http_info(**kwargs)
self.patch_asset_device_contract_information = _Endpoint(
settings={
'response_type': (AssetDeviceContractInformation,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/DeviceContractInformations/{Moid}',
'operation_id': 'patch_asset_device_contract_information',
'http_method': 'PATCH',
'servers': None,
},
params_map={
'all': [
'moid',
'asset_device_contract_information',
'if_match',
],
'required': [
'moid',
'asset_device_contract_information',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
'asset_device_contract_information':
(AssetDeviceContractInformation,),
'if_match':
(str,),
},
'attribute_map': {
'moid': 'Moid',
'if_match': 'If-Match',
},
'location_map': {
'moid': 'path',
'asset_device_contract_information': 'body',
'if_match': 'header',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json',
'application/json-patch+json'
]
},
api_client=api_client,
callable=__patch_asset_device_contract_information
)
def __patch_asset_device_registration(
self,
moid,
asset_device_registration,
**kwargs
):
"""Updates the resource representing the device connector. For example, this can be used to annotate the device connector resource with user-specified tags. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.patch_asset_device_registration(moid, asset_device_registration, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
asset_device_registration (AssetDeviceRegistration): The 'asset.DeviceRegistration' resource to update.
Keyword Args:
if_match (str): For methods that apply server-side changes, and in particular for PUT, If-Match can be used to prevent the lost update problem. It can check if the modification of a resource that the user wants to upload will not override another change that has been done since the original resource was fetched. If the request cannot be fulfilled, the 412 (Precondition Failed) response is returned. When modifying a resource using POST or PUT, the If-Match header must be set to the value of the resource ModTime property after which no lost update problem should occur. For example, a client send a GET request to obtain a resource, which includes the ModTime property. The ModTime indicates the last time the resource was created or modified. The client then sends a POST or PUT request with the If-Match header set to the ModTime property of the resource as obtained in the GET request.. [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
AssetDeviceRegistration
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['moid'] = \
moid
kwargs['asset_device_registration'] = \
asset_device_registration
return self.call_with_http_info(**kwargs)
self.patch_asset_device_registration = _Endpoint(
settings={
'response_type': (AssetDeviceRegistration,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/DeviceRegistrations/{Moid}',
'operation_id': 'patch_asset_device_registration',
'http_method': 'PATCH',
'servers': None,
},
params_map={
'all': [
'moid',
'asset_device_registration',
'if_match',
],
'required': [
'moid',
'asset_device_registration',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
'asset_device_registration':
(AssetDeviceRegistration,),
'if_match':
(str,),
},
'attribute_map': {
'moid': 'Moid',
'if_match': 'If-Match',
},
'location_map': {
'moid': 'path',
'asset_device_registration': 'body',
'if_match': 'header',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json',
'application/json-patch+json'
]
},
api_client=api_client,
callable=__patch_asset_device_registration
)
def __patch_asset_target(
self,
moid,
asset_target,
**kwargs
):
"""Update a 'asset.Target' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.patch_asset_target(moid, asset_target, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
asset_target (AssetTarget): The 'asset.Target' resource to update.
Keyword Args:
if_match (str): For methods that apply server-side changes, and in particular for PUT, If-Match can be used to prevent the lost update problem. It can check if the modification of a resource that the user wants to upload will not override another change that has been done since the original resource was fetched. If the request cannot be fulfilled, the 412 (Precondition Failed) response is returned. When modifying a resource using POST or PUT, the If-Match header must be set to the value of the resource ModTime property after which no lost update problem should occur. For example, a client send a GET request to obtain a resource, which includes the ModTime property. The ModTime indicates the last time the resource was created or modified. The client then sends a POST or PUT request with the If-Match header set to the ModTime property of the resource as obtained in the GET request.. [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
AssetTarget
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['moid'] = \
moid
kwargs['asset_target'] = \
asset_target
return self.call_with_http_info(**kwargs)
self.patch_asset_target = _Endpoint(
settings={
'response_type': (AssetTarget,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/Targets/{Moid}',
'operation_id': 'patch_asset_target',
'http_method': 'PATCH',
'servers': None,
},
params_map={
'all': [
'moid',
'asset_target',
'if_match',
],
'required': [
'moid',
'asset_target',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
'asset_target':
(AssetTarget,),
'if_match':
(str,),
},
'attribute_map': {
'moid': 'Moid',
'if_match': 'If-Match',
},
'location_map': {
'moid': 'path',
'asset_target': 'body',
'if_match': 'header',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json',
'application/json-patch+json'
]
},
api_client=api_client,
callable=__patch_asset_target
)
def __update_asset_device_configuration(
self,
moid,
asset_device_configuration,
**kwargs
):
"""Update a 'asset.DeviceConfiguration' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_asset_device_configuration(moid, asset_device_configuration, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
asset_device_configuration (AssetDeviceConfiguration): The 'asset.DeviceConfiguration' resource to update.
Keyword Args:
if_match (str): For methods that apply server-side changes, and in particular for PUT, If-Match can be used to prevent the lost update problem. It can check if the modification of a resource that the user wants to upload will not override another change that has been done since the original resource was fetched. If the request cannot be fulfilled, the 412 (Precondition Failed) response is returned. When modifying a resource using POST or PUT, the If-Match header must be set to the value of the resource ModTime property after which no lost update problem should occur. For example, a client send a GET request to obtain a resource, which includes the ModTime property. The ModTime indicates the last time the resource was created or modified. The client then sends a POST or PUT request with the If-Match header set to the ModTime property of the resource as obtained in the GET request.. [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
AssetDeviceConfiguration
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['moid'] = \
moid
kwargs['asset_device_configuration'] = \
asset_device_configuration
return self.call_with_http_info(**kwargs)
self.update_asset_device_configuration = _Endpoint(
settings={
'response_type': (AssetDeviceConfiguration,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/DeviceConfigurations/{Moid}',
'operation_id': 'update_asset_device_configuration',
'http_method': 'POST',
'servers': None,
},
params_map={
'all': [
'moid',
'asset_device_configuration',
'if_match',
],
'required': [
'moid',
'asset_device_configuration',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
'asset_device_configuration':
(AssetDeviceConfiguration,),
'if_match':
(str,),
},
'attribute_map': {
'moid': 'Moid',
'if_match': 'If-Match',
},
'location_map': {
'moid': 'path',
'asset_device_configuration': 'body',
'if_match': 'header',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json',
'application/json-patch+json'
]
},
api_client=api_client,
callable=__update_asset_device_configuration
)
def __update_asset_device_contract_information(
self,
moid,
asset_device_contract_information,
**kwargs
):
"""Update a 'asset.DeviceContractInformation' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_asset_device_contract_information(moid, asset_device_contract_information, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
asset_device_contract_information (AssetDeviceContractInformation): The 'asset.DeviceContractInformation' resource to update.
Keyword Args:
if_match (str): For methods that apply server-side changes, and in particular for PUT, If-Match can be used to prevent the lost update problem. It can check if the modification of a resource that the user wants to upload will not override another change that has been done since the original resource was fetched. If the request cannot be fulfilled, the 412 (Precondition Failed) response is returned. When modifying a resource using POST or PUT, the If-Match header must be set to the value of the resource ModTime property after which no lost update problem should occur. For example, a client send a GET request to obtain a resource, which includes the ModTime property. The ModTime indicates the last time the resource was created or modified. The client then sends a POST or PUT request with the If-Match header set to the ModTime property of the resource as obtained in the GET request.. [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
AssetDeviceContractInformation
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['moid'] = \
moid
kwargs['asset_device_contract_information'] = \
asset_device_contract_information
return self.call_with_http_info(**kwargs)
self.update_asset_device_contract_information = _Endpoint(
settings={
'response_type': (AssetDeviceContractInformation,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/DeviceContractInformations/{Moid}',
'operation_id': 'update_asset_device_contract_information',
'http_method': 'POST',
'servers': None,
},
params_map={
'all': [
'moid',
'asset_device_contract_information',
'if_match',
],
'required': [
'moid',
'asset_device_contract_information',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
'asset_device_contract_information':
(AssetDeviceContractInformation,),
'if_match':
(str,),
},
'attribute_map': {
'moid': 'Moid',
'if_match': 'If-Match',
},
'location_map': {
'moid': 'path',
'asset_device_contract_information': 'body',
'if_match': 'header',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json',
'application/json-patch+json'
]
},
api_client=api_client,
callable=__update_asset_device_contract_information
)
def __update_asset_device_registration(
self,
moid,
asset_device_registration,
**kwargs
):
"""Updates the resource representing the device connector. For example, this can be used to annotate the device connector resource with user-specified tags. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_asset_device_registration(moid, asset_device_registration, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
asset_device_registration (AssetDeviceRegistration): The 'asset.DeviceRegistration' resource to update.
Keyword Args:
if_match (str): For methods that apply server-side changes, and in particular for PUT, If-Match can be used to prevent the lost update problem. It can check if the modification of a resource that the user wants to upload will not override another change that has been done since the original resource was fetched. If the request cannot be fulfilled, the 412 (Precondition Failed) response is returned. When modifying a resource using POST or PUT, the If-Match header must be set to the value of the resource ModTime property after which no lost update problem should occur. For example, a client send a GET request to obtain a resource, which includes the ModTime property. The ModTime indicates the last time the resource was created or modified. The client then sends a POST or PUT request with the If-Match header set to the ModTime property of the resource as obtained in the GET request.. [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
AssetDeviceRegistration
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['moid'] = \
moid
kwargs['asset_device_registration'] = \
asset_device_registration
return self.call_with_http_info(**kwargs)
self.update_asset_device_registration = _Endpoint(
settings={
'response_type': (AssetDeviceRegistration,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/DeviceRegistrations/{Moid}',
'operation_id': 'update_asset_device_registration',
'http_method': 'POST',
'servers': None,
},
params_map={
'all': [
'moid',
'asset_device_registration',
'if_match',
],
'required': [
'moid',
'asset_device_registration',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
'asset_device_registration':
(AssetDeviceRegistration,),
'if_match':
(str,),
},
'attribute_map': {
'moid': 'Moid',
'if_match': 'If-Match',
},
'location_map': {
'moid': 'path',
'asset_device_registration': 'body',
'if_match': 'header',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json',
'application/json-patch+json'
]
},
api_client=api_client,
callable=__update_asset_device_registration
)
def __update_asset_target(
self,
moid,
asset_target,
**kwargs
):
"""Update a 'asset.Target' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_asset_target(moid, asset_target, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
asset_target (AssetTarget): The 'asset.Target' resource to update.
Keyword Args:
if_match (str): For methods that apply server-side changes, and in particular for PUT, If-Match can be used to prevent the lost update problem. It can check if the modification of a resource that the user wants to upload will not override another change that has been done since the original resource was fetched. If the request cannot be fulfilled, the 412 (Precondition Failed) response is returned. When modifying a resource using POST or PUT, the If-Match header must be set to the value of the resource ModTime property after which no lost update problem should occur. For example, a client send a GET request to obtain a resource, which includes the ModTime property. The ModTime indicates the last time the resource was created or modified. The client then sends a POST or PUT request with the If-Match header set to the ModTime property of the resource as obtained in the GET request.. [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (float/tuple): timeout setting for this request. If one
number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
AssetTarget
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['moid'] = \
moid
kwargs['asset_target'] = \
asset_target
return self.call_with_http_info(**kwargs)
self.update_asset_target = _Endpoint(
settings={
'response_type': (AssetTarget,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/asset/Targets/{Moid}',
'operation_id': 'update_asset_target',
'http_method': 'POST',
'servers': None,
},
params_map={
'all': [
'moid',
'asset_target',
'if_match',
],
'required': [
'moid',
'asset_target',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
'asset_target':
(AssetTarget,),
'if_match':
(str,),
},
'attribute_map': {
'moid': 'Moid',
'if_match': 'If-Match',
},
'location_map': {
'moid': 'path',
'asset_target': 'body',
'if_match': 'header',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json',
'application/json-patch+json'
]
},
api_client=api_client,
callable=__update_asset_target
)
| 46.54424 | 1,678 | 0.514297 | 26,106 | 275,123 | 5.253658 | 0.022447 | 0.018833 | 0.015545 | 0.016143 | 0.964332 | 0.954722 | 0.940788 | 0.93699 | 0.932455 | 0.930304 | 0 | 0.002697 | 0.411034 | 275,123 | 5,910 | 1,679 | 46.552115 | 0.843722 | 0.43591 | 0 | 0.756191 | 0 | 0 | 0.227561 | 0.058502 | 0 | 0 | 0 | 0 | 0 | 1 | 0.010099 | false | 0 | 0.006973 | 0 | 0.02717 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
92d17b4eabf894df33ad8215346f04e59d9e6579 | 4,631 | py | Python | test/python/inspectors/test_filter_duplicate_issues.py | hyperskill/hyperstyle | bf3c6e2dc42290ad27f2d30ce42d84a53241544b | [
"Apache-2.0"
] | 18 | 2020-10-05T16:48:11.000Z | 2022-03-22T04:15:38.000Z | test/python/inspectors/test_filter_duplicate_issues.py | hyperskill/hyperstyle | bf3c6e2dc42290ad27f2d30ce42d84a53241544b | [
"Apache-2.0"
] | 60 | 2020-10-05T17:01:05.000Z | 2022-01-27T12:46:14.000Z | test/python/inspectors/test_filter_duplicate_issues.py | hyperskill/hyperstyle | bf3c6e2dc42290ad27f2d30ce42d84a53241544b | [
"Apache-2.0"
] | 6 | 2021-02-09T09:31:19.000Z | 2021-08-13T07:45:51.000Z | from pathlib import Path
from hyperstyle.src.python.review.inspectors.inspector_type import InspectorType
from hyperstyle.src.python.review.inspectors.issue import CodeIssue, IssueDifficulty, IssueType
from hyperstyle.src.python.review.reviewers.utils.issues_filter import filter_duplicate_issues
def test_filter_duplicate_issues_when_single_inspector() -> None:
issues = [
CodeIssue(
file_path=Path('code.py'),
line_no=10,
description='',
inspector_type=InspectorType.FLAKE8,
type=IssueType.CODE_STYLE,
column_no=1,
origin_class='',
difficulty=IssueDifficulty.EASY,
),
CodeIssue(
file_path=Path('code.py'),
line_no=11,
description='',
inspector_type=InspectorType.FLAKE8,
type=IssueType.CODE_STYLE,
column_no=1,
origin_class='',
difficulty=IssueDifficulty.EASY,
),
CodeIssue(
file_path=Path('code.py'),
line_no=11,
description='',
inspector_type=InspectorType.FLAKE8,
type=IssueType.CODE_STYLE,
column_no=1,
origin_class='',
difficulty=IssueDifficulty.EASY,
),
CodeIssue(
file_path=Path('code.py'),
line_no=11,
description='',
type=IssueType.CODE_STYLE,
inspector_type=InspectorType.FLAKE8,
column_no=1,
origin_class='',
difficulty=IssueDifficulty.EASY,
),
]
filtered_issues = filter_duplicate_issues(issues)
assert set(filtered_issues) == set(issues)
def test_filter_duplicate_issues_when_several_inspectors() -> None:
issues = [
CodeIssue(
file_path=Path('code.py'),
line_no=10,
description='',
inspector_type=InspectorType.PYLINT,
column_no=1,
origin_class='',
type=IssueType.COMPLEXITY,
difficulty=IssueDifficulty.HARD,
),
CodeIssue(
file_path=Path('code.py'),
line_no=10,
description='',
inspector_type=InspectorType.FLAKE8,
column_no=1,
origin_class='',
type=IssueType.COMPLEXITY,
difficulty=IssueDifficulty.HARD,
),
CodeIssue(
file_path=Path('code.py'),
line_no=11,
description='',
type=IssueType.CODE_STYLE,
inspector_type=InspectorType.PYLINT,
column_no=1,
origin_class='',
difficulty=IssueDifficulty.EASY,
),
CodeIssue(
file_path=Path('code.py'),
line_no=11,
description='',
type=IssueType.BEST_PRACTICES,
inspector_type=InspectorType.FLAKE8,
column_no=1,
origin_class='',
difficulty=IssueDifficulty.MEDIUM,
),
]
filtered_issues = filter_duplicate_issues(issues)
assert set(filtered_issues) == {issues[0], issues[2], issues[3]}
def test_filter_duplicate_issues_when_several_issues_in_line_no() -> None:
issues = [
CodeIssue(
file_path=Path('code.py'),
line_no=10,
description='',
type=IssueType.CODE_STYLE,
inspector_type=InspectorType.PYLINT,
column_no=1,
origin_class='',
difficulty=IssueDifficulty.EASY,
),
CodeIssue(
file_path=Path('code.py'),
line_no=10,
description='',
type=IssueType.CODE_STYLE,
inspector_type=InspectorType.FLAKE8,
column_no=1,
origin_class='',
difficulty=IssueDifficulty.EASY,
),
CodeIssue(
file_path=Path('code.py'),
line_no=10,
description='',
type=IssueType.CODE_STYLE,
inspector_type=InspectorType.FLAKE8,
column_no=1,
origin_class='',
difficulty=IssueDifficulty.EASY,
),
CodeIssue(
file_path=Path('code.py'),
line_no=10,
description='',
inspector_type=InspectorType.FLAKE8,
column_no=1,
origin_class='',
type=IssueType.COMPLEXITY,
difficulty=IssueDifficulty.HARD,
),
]
filtered_issues = filter_duplicate_issues(issues)
assert set(filtered_issues) == {issues[1], issues[2], issues[3]}
| 30.267974 | 95 | 0.557547 | 424 | 4,631 | 5.839623 | 0.132075 | 0.068255 | 0.082391 | 0.101777 | 0.905089 | 0.893376 | 0.861874 | 0.812601 | 0.812601 | 0.810178 | 0 | 0.016782 | 0.34377 | 4,631 | 152 | 96 | 30.467105 | 0.79796 | 0 | 0 | 0.892086 | 0 | 0 | 0.018139 | 0 | 0 | 0 | 0 | 0 | 0.021583 | 1 | 0.021583 | false | 0 | 0.028777 | 0 | 0.05036 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 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 | 7 |
92f50b9b15057e8658695d0e3121cbcac3c7e45f | 1,135 | py | Python | asyncapi_schema_pydantic/v2_3_0/redis_bindings.py | albertnadal/asyncapi-schema-pydantic | 83966bdc11f2d465a10b52cec5ff79d18fa6f5fe | [
"MIT"
] | null | null | null | asyncapi_schema_pydantic/v2_3_0/redis_bindings.py | albertnadal/asyncapi-schema-pydantic | 83966bdc11f2d465a10b52cec5ff79d18fa6f5fe | [
"MIT"
] | null | null | null | asyncapi_schema_pydantic/v2_3_0/redis_bindings.py | albertnadal/asyncapi-schema-pydantic | 83966bdc11f2d465a10b52cec5ff79d18fa6f5fe | [
"MIT"
] | null | null | null | from pydantic import BaseModel, Extra
class RedisChannelBinding(BaseModel):
"""
This document defines how to describe Redis-specific information on AsyncAPI.
This object MUST NOT contain any properties. Its name is reserved for future use.
"""
class Config:
extra = Extra.forbid
class RedisMessageBinding(BaseModel):
"""
This document defines how to describe Redis-specific information on AsyncAPI.
This object MUST NOT contain any properties. Its name is reserved for future use.
"""
class Config:
extra = Extra.forbid
class RedisOperationBinding(BaseModel):
"""
This document defines how to describe Redis-specific information on AsyncAPI.
This object MUST NOT contain any properties. Its name is reserved for future use.
"""
class Config:
extra = Extra.forbid
class RedisServerBinding(BaseModel):
"""
This document defines how to describe Redis-specific information on AsyncAPI.
This object MUST NOT contain any properties. Its name is reserved for future use.
"""
class Config:
extra = Extra.forbid
| 22.7 | 85 | 0.705727 | 137 | 1,135 | 5.846715 | 0.262774 | 0.064919 | 0.104869 | 0.139825 | 0.857678 | 0.857678 | 0.857678 | 0.857678 | 0.857678 | 0.857678 | 0 | 0 | 0.237004 | 1,135 | 49 | 86 | 23.163265 | 0.924942 | 0.56652 | 0 | 0.615385 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.076923 | 0 | 0.692308 | 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 | 1 | 0 | 0 | 7 |
92fda23454b9c50f2eece34cf327700891ae7c86 | 15,014 | py | Python | api/client/swagger_client/api/credential_service_api.py | Zachary-Fernandes/mlx | d5117c5585b969ca0de5f321d14b5a27cd468280 | [
"Apache-2.0"
] | null | null | null | api/client/swagger_client/api/credential_service_api.py | Zachary-Fernandes/mlx | d5117c5585b969ca0de5f321d14b5a27cd468280 | [
"Apache-2.0"
] | null | null | null | api/client/swagger_client/api/credential_service_api.py | Zachary-Fernandes/mlx | d5117c5585b969ca0de5f321d14b5a27cd468280 | [
"Apache-2.0"
] | null | null | null | # Copyright 2021 The MLX Contributors
#
# SPDX-License-Identifier: Apache-2.0
# coding: utf-8
"""
MLX API
MLX API Extension for Kubeflow Pipelines # noqa: E501
OpenAPI spec version: 0.1.29-filter-categories
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import re # noqa: F401
# python 2 and python 3 compatibility library
import six
from swagger_client.api_client import ApiClient
class CredentialServiceApi(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
Ref: https://github.com/swagger-api/swagger-codegen
"""
def __init__(self, api_client=None):
if api_client is None:
api_client = ApiClient()
self.api_client = api_client
def create_credential(self, body, **kwargs): # noqa: E501
"""create_credential # noqa: E501
Creates a credential associated with a pipeline. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_credential(body, async_req=True)
>>> result = thread.get()
:param async_req bool
:param ApiCredential body: (required)
:return: ApiCredential
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.create_credential_with_http_info(body, **kwargs) # noqa: E501
else:
(data) = self.create_credential_with_http_info(body, **kwargs) # noqa: E501
return data
def create_credential_with_http_info(self, body, **kwargs): # noqa: E501
"""create_credential # noqa: E501
Creates a credential associated with a pipeline. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_credential_with_http_info(body, async_req=True)
>>> result = thread.get()
:param async_req bool
:param ApiCredential body: (required)
:return: ApiCredential
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['body'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method create_credential" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'body' is set
if ('body' not in params or
params['body'] is None):
raise ValueError("Missing the required parameter `body` when calling `create_credential`") # noqa: E501
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'body' in params:
body_params = params['body']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/credentials', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ApiCredential', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def delete_credential(self, id, **kwargs): # noqa: E501
"""delete_credential # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_credential(id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str id: (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.delete_credential_with_http_info(id, **kwargs) # noqa: E501
else:
(data) = self.delete_credential_with_http_info(id, **kwargs) # noqa: E501
return data
def delete_credential_with_http_info(self, id, **kwargs): # noqa: E501
"""delete_credential # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_credential_with_http_info(id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str id: (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['id'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method delete_credential" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'id' is set
if ('id' not in params or
params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `delete_credential`") # noqa: E501
collection_formats = {}
path_params = {}
if 'id' in params:
path_params['id'] = params['id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/credentials/{id}', 'DELETE',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type=None, # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_credential(self, id, **kwargs): # noqa: E501
"""get_credential # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_credential(id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str id: (required)
:return: ApiCredential
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_credential_with_http_info(id, **kwargs) # noqa: E501
else:
(data) = self.get_credential_with_http_info(id, **kwargs) # noqa: E501
return data
def get_credential_with_http_info(self, id, **kwargs): # noqa: E501
"""get_credential # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_credential_with_http_info(id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str id: (required)
:return: ApiCredential
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['id'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_credential" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'id' is set
if ('id' not in params or
params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `get_credential`") # noqa: E501
collection_formats = {}
path_params = {}
if 'id' in params:
path_params['id'] = params['id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/credentials/{id}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ApiCredential', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def list_credentials(self, **kwargs): # noqa: E501
"""list_credentials # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.list_credentials(async_req=True)
>>> result = thread.get()
:param async_req bool
:param str page_token:
:param int page_size:
:param str sort_by: Can be format of \"field_name\", \"field_name asc\" or \"field_name desc\" Ascending by default.
:param str filter: A string-serialized JSON dictionary with key-value pairs that correspond to the Credential's attribute names and their respective values to be filtered for.
:return: ApiListCredentialsResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.list_credentials_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.list_credentials_with_http_info(**kwargs) # noqa: E501
return data
def list_credentials_with_http_info(self, **kwargs): # noqa: E501
"""list_credentials # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.list_credentials_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:param str page_token:
:param int page_size:
:param str sort_by: Can be format of \"field_name\", \"field_name asc\" or \"field_name desc\" Ascending by default.
:param str filter: A string-serialized JSON dictionary with key-value pairs that correspond to the Credential's attribute names and their respective values to be filtered for.
:return: ApiListCredentialsResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['page_token', 'page_size', 'sort_by', 'filter'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method list_credentials" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
if 'page_token' in params:
query_params.append(('page_token', params['page_token'])) # noqa: E501
if 'page_size' in params:
query_params.append(('page_size', params['page_size'])) # noqa: E501
if 'sort_by' in params:
query_params.append(('sort_by', params['sort_by'])) # noqa: E501
if 'filter' in params:
query_params.append(('filter', params['filter'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/credentials', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ApiListCredentialsResponse', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
| 36.79902 | 183 | 0.603304 | 1,713 | 15,014 | 5.047869 | 0.110333 | 0.046259 | 0.025905 | 0.033306 | 0.885856 | 0.869088 | 0.849428 | 0.831965 | 0.826414 | 0.81138 | 0 | 0.015928 | 0.305848 | 15,014 | 407 | 184 | 36.889435 | 0.813759 | 0.340749 | 0 | 0.728972 | 0 | 0 | 0.162572 | 0.032094 | 0 | 0 | 0 | 0 | 0 | 1 | 0.042056 | false | 0 | 0.018692 | 0 | 0.121495 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 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 | 7 |
131e10ec64b52de7a2b1892f018e34d624cb0722 | 1,218 | py | Python | DataStructuresI/arrays.py | aware91/CSPT13_DataStructures_gp | d6afee63d59beac3ef79fc5aec0ae7c5c046f1f7 | [
"MIT"
] | null | null | null | DataStructuresI/arrays.py | aware91/CSPT13_DataStructures_gp | d6afee63d59beac3ef79fc5aec0ae7c5c046f1f7 | [
"MIT"
] | null | null | null | DataStructuresI/arrays.py | aware91/CSPT13_DataStructures_gp | d6afee63d59beac3ef79fc5aec0ae7c5c046f1f7 | [
"MIT"
] | null | null | null | # Linear time iterate over all items
arr = [12, 23, 56, 87, 14] # n = 5
for num in arr: #O(n * 1) ==> O(n)
print(num) #O(1)
for num in arr: #O(n * 1) ==> O(n)
print(num) #O(1)
#O(n) + O(1) => O(n)
#O(n * 1) + O(n * 1) + O(1)
#O(2n) + O(1) => O(n) + O(1) => O(n)
# constant time lookup
print(arr[3]) # O(1)
# quadratic time nested iteration
for x in arr: #O(n)
for y in arr: #O(n) => O(n^2)
print(x, y) #O(1) => O(1 * n^2)
# O(n^2) + O(n) + O(1 * n^2)
# O(2n^2) + O(n) => O(n^2) + O(n^2)
# O(n^ 2)
# can we do better?
# Tom's Code
# Linear time iterate over all items
arr = [12, 23, 56, 87, 14] # n = 5
for num in arr: # O(n * 1) ==> O(n)
print(num) # O(1)
for num in arr: # O(n * 1) ==> O(n)
print(num) # O(1)
# O(n) + O(1) => O(n)
# O(n * 1) + O(n * 1) + O(1)
# O(2n) + O(1) => O(n) + O(1) => O(n)
# constant time lookup
print(arr[3]) # O(1)
# quadratic time nested iteration
for x in arr: # O(n)
for y in arr: # O(n) => O(n^2)
for z in arr: # O(n^3)
print(x, y, z) # O(1) => O(1 * n^2)
# O(n^2) + O(n) + O(1 * n^2)
# O(2n^2) + O(n) => O(n^2) + O(n^2)
# O(n^2) => O(n^3)
# 10 * 10 * 10 => 1000
# 100 * 100 * 100 => 1000000
# can we do better? | 22.145455 | 47 | 0.456486 | 276 | 1,218 | 2.014493 | 0.152174 | 0.143885 | 0.07554 | 0.113309 | 0.841727 | 0.841727 | 0.841727 | 0.841727 | 0.83813 | 0.83813 | 0 | 0.114811 | 0.284893 | 1,218 | 55 | 48 | 22.145455 | 0.523536 | 0.633826 | 0 | 0.842105 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0.421053 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 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 | 1 | 0 | 8 |
133c4365592ad87f5d0e05c8d438365089710996 | 103,570 | py | Python | src/abaqus/Part/Feature.py | Haiiliin/PyAbaqus | f20db6ebea19b73059fe875a53be370253381078 | [
"MIT"
] | 7 | 2022-01-21T09:15:45.000Z | 2022-02-15T09:31:58.000Z | src/abaqus/Part/Feature.py | Haiiliin/PyAbaqus | f20db6ebea19b73059fe875a53be370253381078 | [
"MIT"
] | null | null | null | src/abaqus/Part/Feature.py | Haiiliin/PyAbaqus | f20db6ebea19b73059fe875a53be370253381078 | [
"MIT"
] | null | null | null | from abaqusConstants import *
from ..BasicGeometry.Cell import Cell
from ..BasicGeometry.Edge import Edge
from ..BasicGeometry.Face import Face
from ..BasicGeometry.Vertex import Vertex
from ..Feature.Feature import Feature as BaseFeature
from ..Region.Region import Region
from ..Sketcher.ConstrainedSketch import ConstrainedSketch
class Feature(BaseFeature):
"""The following commands operate on Feature objects. For more information about the
Feature object, see Feature object.
Notes
-----
This object can be accessed by:
.. code-block:: python
import part
"""
def AutoRepair(self):
"""This method carries out a sequence of geometry repair operations if it contains invalid
entities. It is expected to improve the geometry, but it does not guarantee that the
number of invalid entities will decrease. In some cases, it can also increase the number
of invalid entities. Since a number of geometry repair operations and validity checks
are performed, it could be a slow operation depending on the complexity of the geometry.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Returns
-------
feature: Feature
A Feature object
"""
pass
def AddCells(self, faceList: tuple[Face], flipped: Boolean = OFF):
"""This method tries to convert a shell entity to a solid entity. The conversion is not
always successful.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
faceList
A sequence of Face objects specifying the faces bounding the cell to add.
flipped
A Boolean specifying the direction of feature creation. The possible values are True and
False. The default is True indicating that the direction is opposite to the face normal.
When multiple faces are selected, Abaqus attempts to create cells on both sides of the
selected faces and ignores the *flipped* argument.
Returns
-------
feature: Feature
A Feature object
"""
pass
def AnalyticRigidSurf2DPlanar(self, sketch: ConstrainedSketch):
"""This method creates a first Feature object for an analytical rigid surface by creating a
planar wire from the given ConstrainedSketch object.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
sketch
A ConstrainedSketch object specifying the planar wire.
Returns
-------
feature: Feature
A Feature object
"""
pass
def AnalyticRigidSurfExtrude(self, sketch: ConstrainedSketch, depth: float = 1):
"""This method creates a first Feature object for an analytical rigid surface by extruding
the given ConstrainedSketch object by the given depth, creating a surface.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
sketch
A ConstrainedSketch object specifying the planar wire.
depth
A Float specifying the extrusion depth. The default value is 1.0.
Returns
-------
feature: Feature
A Feature object
"""
pass
def AnalyticRigidSurfRevolve(self, sketch: ConstrainedSketch):
"""This method creates a first Feature object for an analytical rigid surface by revolving
the given ConstrainedSketch object by 360° about the *Y*-axis.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
sketch
A ConstrainedSketch object specifying the surface to be revolved.
Returns
-------
feature: Feature
A Feature object
"""
pass
def AssignMidsurfaceRegion(self, cellList: tuple[Cell]):
"""This method assign a mid-surface property to sequence of Cell objects. If a reference
representation of the part does not exist, it creates one. It also copies the *cells* to
the reference representation and deletes the *cells* from the active representation of
the part.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
cellList
A sequence of Cell objects specifying the regions that will be used for mid-surface
construction. These regions will be copied to the reference representation of the part.
Returns
-------
feature: Feature
A Feature object
"""
pass
def BaseSolidExtrude(self, sketch: ConstrainedSketch, depth: float, draftAngle: float = None, pitch: float = None):
"""This method creates a first Feature object by extruding the given ConstrainedSketch
object by the given depth, creating a solid. The ConstrainedSketch object must define a
closed profile.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
sketch
A ConstrainedSketch object specifying the plane shape to be extruded.
depth
A Float specifying the extrusion depth. Possible values are 10–5 ≤≤ *depth* ≤≤ 105.
draftAngle
A Float specifying the draft angle in degrees. Possible values are -90.0 ≤≤ *draftAngle*
≤≤ 90.0. By convention, for a positive draft angle an outer loop will draft outward and
an inner loop will draft inward. The opposite is true for a negative draft angle. The
default value, 0, implies a normal extrude. The arguments *draftAngle* and *pitch* are
mutually exclusive.
pitch
A Float specifying the pitch. The pitch is the distance traveled along the axial
direction by the sketch when the sketch has completed one full revolution about the
twist axis. Pitch can be specified as positive or negative to achieve right-handed or
left-handed twist about the twist axis, respectively. The default value, 0, implies a
normal extrude. Possible values are –105 ≤≤ *pitch* ≤≤ 105. The arguments *draftAngle*
and *pitch* are mutually exclusive.
Returns
-------
A Feature object.
"""
pass
def BaseSolidRevolve(self, sketch: ConstrainedSketch, angle: float, pitch: float = None,
flipRevolveDirection: Boolean = OFF, flipPitchDirection: Boolean = OFF,
moveSketchNormalToPath: Boolean = OFF):
"""This method creates a first Feature object by revolving the given ConstrainedSketch
object by the given angle, creating a solid. The ConstrainedSketch object must define a
closed profile and an axis of revolution. The axis is defined by a single construction
line.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
sketch
A ConstrainedSketch object specifying the shape to be revolved.
angle
A Float specifying the revolve angle in degrees. Possible values are 10–4 ≤≤ *angle* ≤≤
360.Note:If *pitch* >>0, there is no upper limit for *angle*.
pitch
A Float specifying the pitch. The pitch is the distance traveled along the axial
direction, measured between corresponding points on the sketch when it has completed one
full revolution about the axis of revolution. Possible values are 0 ≤≤ *pitch* ≤≤ 105.
The default value, 0, implies a normal revolve.
flipRevolveDirection
A Boolean specifying whether to override the direction of feature creation. If
*flipRevolveDirection*=OFF, the default direction of revolution is used. If
*flipRevolveDirection*=ON, the revolve direction is reversed. The default value is OFF.
flipPitchDirection
A Boolean specifying whether to override the direction of translation. If
*flipPitchDirection*=OFF, the direction of translation is given by the direction of the
revolve axis. If *flipPitchDirection*=ON, the translation direction is reversed. The
default value is OFF.
moveSketchNormalToPath
A Boolean specifying whether to rotate the sketch so that it is normal to the path of
revolution when using the *pitch* option. If *moveSketchNormalToPath*=OFF, the sketch
plane remains parallel to the revolve axis. If *moveSketchNormalToPath*=ON, the sketch
is moved to match the angle created by the *pitch* before being revolved. The default
value is OFF.
Returns
-------
A Feature object.
Raises
------
RangeError
"""
pass
def BaseSolidSweep(self, sketch: ConstrainedSketch, path: ConstrainedSketch):
"""This method creates a first Feature object by sweeping the given profile
ConstrainedSketch object along the path defined by the path ConstrainedSketch object,
creating a solid. The profile ConstrainedSketch object must define a closed profile. The
origin of the profile sketch is positioned at the start of the sweep path and swept
perpendicular to the path. No checks are made for self-intersection.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
sketch
A ConstrainedSketch object specifying the profile to be swept.
path
A ConstrainedSketch object specifying the path of the sweep.
Returns
-------
feature: Feature
A Feature object
"""
pass
def BaseShell(self, sketch: ConstrainedSketch):
"""This method creates a first Feature object by creating a planar shell from the given
ConstrainedSketch object. The ConstrainedSketch object must define a closed profile.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
sketch
A ConstrainedSketch object specifying the planar shell.
Returns
-------
feature: Feature
A Feature object
"""
pass
def BaseShellExtrude(self, sketch: ConstrainedSketch, depth: float, draftAngle: float = None, pitch: float = None):
"""This method creates a first Feature object by extruding the given ConstrainedSketch
object by the given depth, creating a shell. The ConstrainedSketch object can define
either an open or closed profile.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
sketch
A ConstrainedSketch object specifying the shape to be extruded.
depth
A Float specifying the extrusion depth. Possible values are Floats > 0.
draftAngle
A Float specifying the draft angle in degrees. Possible values are -90.0 ≤≤ *draftAngle*
≤≤ 90.0. By convention, for a positive draft angle an outer loop will draft outward and
an inner loop will draft inward. The opposite is true for a negative draft angle. The
default value, 0, implies a normal extrude. The arguments *draftAngle* and *pitch* are
mutually exclusive.
pitch
A Float specifying the pitch. The pitch is the distance traveled along the axial
direction by the sketch when the sketch has completed one full revolution about the
twist axis. Pitch can be specified as positive or negative to achieve right-handed or
left-handed twist about the twist axis, respectively. The default value, 0, implies a
normal extrude. Possible values are –105 ≤≤ *pitch* ≤≤ 105. The arguments *draftAngle*
and *pitch* are mutually exclusive.
Returns
-------
A Feature object.
Raises
------
RangeError
"""
pass
def BaseShellRevolve(self, sketch: ConstrainedSketch, angle: float, pitch: float = None,
flipRevolveDirection: Boolean = OFF, flipPitchDirection: Boolean = OFF,
moveSketchNormalToPath: Boolean = OFF):
"""This method creates a first Feature object by revolving the given ConstrainedSketch
object by the given angle, creating a shell. The ConstrainedSketch object can define
either an open or closed profile and an axis of revolution. The axis is defined by a
single construction line.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
sketch
A ConstrainedSketch object specifying the shape to be revolved.
angle
A Float specifying the revolve angle in degrees. Possible values are 0 ≤≤ *angle* ≤≤
360.Note:If *pitch* >> 0, there is no upper limit for *angle*.
pitch
A Float specifying the pitch. The pitch is the distance traveled along the axial
direction, measured between corresponding points on the sketch when it has completed one
full revolution about the axis of revolution. Possible values are 0 ≤≤ *pitch* ≤≤ 105.
The default value, 0, implies a normal revolve.
flipRevolveDirection
A Boolean specifying whether to override the direction of feature creation. If
*flipRevolveDirection*=OFF, the default direction of revolution is used. If
*flipRevolveDirection*=ON, the revolve direction is reversed. The default value is OFF.
flipPitchDirection
A Boolean specifying whether to override the direction of translation. If
*flipPitchDirection*=OFF, the direction of translation is given by the direction of the
revolve axis. If *flipPitchDirection*=ON, the translation direction is reversed. The
default value is OFF.
moveSketchNormalToPath
A Boolean specifying whether to rotate the sketch so that it is normal to the path of
revolution when using the *pitch* option. If *moveSketchNormalToPath*=OFF, the sketch
plane remains parallel to the revolve axis. If *moveSketchNormalToPath*=ON, the sketch
is moved to match the angle created by the *pitch* before being revolved. The default
value is OFF.
Returns
-------
feature: Feature
A Feature object
"""
pass
def BaseShellSweep(self, sketch: ConstrainedSketch, path: ConstrainedSketch):
"""This method creates a first Feature object by sweeping the given section
ConstrainedSketch object along the path defined by the path ConstrainedSketch object,
creating a shell. The ConstrainedSketch object can define either an open or closed
profile. The origin of the profile sketch is positioned at the start of the sweep path
and swept perpendicular to the path. No checks are made for self-intersection.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
sketch
A ConstrainedSketch object specifying the section to be swept.
path
A ConstrainedSketch object specifying the path of the sweep.
Returns
-------
feature: Feature
A Feature object
"""
pass
def BaseWire(self, sketch: ConstrainedSketch):
"""This method creates a first Feature object by creating a planar wire from the given
ConstrainedSketch object.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
sketch
A ConstrainedSketch object specifying the planar wire.
Returns
-------
feature: Feature
A Feature object
"""
pass
def BlendFaces(self, side1: tuple[Edge], side2: tuple, method: SymbolicConstant = None, path: Edge = Edge()):
"""This method creates a Feature object by creating new faces that blends two sets of
faces.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
side1
A sequence of Edge objects specifying one side of the blend. The edges must form a
continuous chain without branches.
side2
A sequence of Edge or Face objects specifying the second side of the blend. If *side2*
contains Edge objects then they must form a continuous chain without branches.
method
A SymbolicConstant indicating a method for creating blends. This argument is a required
argument if *side2* contains Edge object and it is ignored if *side2* contains
Faceobjects. It can have one of the following values:TANGENT: The blend is tangent to
the sides.SHORTEST_PATH: The blend connects the two sides based on linear interpolation
between the two sides.SPECIFY_PATH: The blend connects the two sides along a specified
path.
path
An Edge object that connects *side1* to *side2* and specifies the path for creating the
blend. This argument is required if *method*=SPECIFY_PATH; otherwise, it is ignored.
Returns
-------
feature: Feature
A Feature object
"""
pass
def Chamfer(self, length: float, edgeList: tuple[Edge]):
"""This method creates an additional Feature object by chamfering the given list of edges
with a given length.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
length
A Float specifying the length of the chamfer.
edgeList
A sequence of Edge objects specifying the edges to chamfer.
Returns
-------
feature: Feature
A Feature object
"""
pass
def Mirror(self, mirrorPlane: str, keepOriginal: Boolean, keepInternalBoundaries: Boolean = OFF):
"""This method mirrors existing part geometry across a plane to create new geometry.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
mirrorPlane
A Datum plane object or a planar Face object.
keepOriginal
A boolean specifying whether or not the original part geometry should be retained.
keepInternalBoundaries
A Boolean specifying whether internal boundaries will be retained. The default value is
OFF.
Returns
-------
feature: Feature
A Feature object
"""
pass
def ConvertToAnalytical(self):
"""This method attempts to change entities into a simpler form that will speed up
processing and make entities available during feature operations.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Returns
-------
feature: Feature
A Feature object
"""
pass
def ConvertToPrecise(self, method: SymbolicConstant = RECOMPUTE_GEOMETRY):
"""This method attempts to change imprecise entities so that the geometry becomes precise.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
method
A SymbolicConstant specifying the method to be used to convert the part to precise.
Possible values are RECOMPUTE_GEOMETRY and TIGHTEN_GAPS. The default value is
RECOMPUTE_GEOMETRY.
Returns
-------
feature: Feature
A Feature object
"""
pass
def CoverEdges(self, edgeList: tuple[Edge], tryAnalytical: Boolean = False):
"""This method generates a face using the given edges as the face's boundaries. The
CoverEdges method generates a face by creating the geometry consisting of the underlying
surface, associated edges, and vertices.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
edgeList
A sequence of Edge objects specifying the edges that bound the new face.
tryAnalytical
A Boolean specifying whether the newly created face should be analytical or not. The
default is False.
Returns
-------
A Feature object.
Raises
------
- If the given boundary is not a closed loop:
Parterror: Cannot find a closed loop
- If the given boundary contains a zero length component:
Parterror: Cannot find a closed loop
- If the underlying surface is too difficult to fit:
Parterror: Cannot construct face geometry
"""
pass
def Cut(self, sketchPlane: str, sketchPlaneSide: SymbolicConstant, sketchUpEdge: Edge,
sketch: ConstrainedSketch, sketchOrientation: SymbolicConstant = None):
"""This method creates an additional Feature object by cutting a hole using the given
ConstrainedSketch object.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
sketchPlane
A Datum plane object or a planar Face object.
sketchPlaneSide
A SymbolicConstant specifying the direction of feature creation. Possible values are
SIDE1 and SIDE2.
sketchUpEdge
An Edge object or a Datum axis object specifying the vertical (*Y*) direction of the
sketch.
sketch
A ConstrainedSketch object specifying the planar cut.
sketchOrientation
A SymbolicConstant specifying the orientation of *sketchUpEdge* on the sketch. Possible
values are RIGHT, LEFT, TOP, and BOTTOM.
Returns
-------
feature: Feature
A Feature object
"""
pass
def CutExtrude(self, sketchPlane: str, sketchPlaneSide: SymbolicConstant, sketchUpEdge: Edge,
sketchOrientation: SymbolicConstant, sketch: ConstrainedSketch, depth: float = None,
upToFace: str = '', draftAngle: float = None, pitch: float = None,
flipExtrudeDirection: Boolean = OFF):
"""This method creates an additional Feature object by extruding the given
ConstrainedSketch object by the given depth and cutting away material in the solid and
shell regions of the part. The ConstrainedSketch object must define a closed profile.
The CutExtrude method creates a blind cut (using *depth*), an up-to-face cut (using
*upToFace*), or a through-all cut (if *depth* and *upToFace* are not specified).
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
sketchPlane
A Datum plane object or a planar Face object.
sketchPlaneSide
A SymbolicConstant specifying the direction of feature creation. Possible values are
SIDE1 and SIDE2.
sketchUpEdge
An Edge object or a Datum axis object specifying the vertical (*Y*) direction of the
sketch.
sketchOrientation
A SymbolicConstant specifying the orientation of *sketchUpEdge* on the sketch. Possible
values are RIGHT, LEFT, TOP, and BOTTOM.
sketch
A ConstrainedSketch object specifying the planar sketch to be extruded.
depth
A Float specifying the extrusion depth. If *depth* is specified, the cut will be a blind
cut. The default is to not specify a depth.
upToFace
A Face specifying the face up to which to cut. If *upToFace* is specified, the cut will
be an up-to-face cut. The default is to not specify a face.Note:If neither *depth* nor
*upToFace* is specified, the cut will be a through-all cut.
draftAngle
A Float specifying the draft angle in degrees. Possible values are -90.0 ≤≤ *draftAngle*
≤≤ 90.0. By convention, for a positive draft angle an outer loop will draft outward and
an inner loop will draft inward. The opposite is true for a negative draft angle. The
default value, 0, implies a normal extrude. The arguments *draftAngle* and *pitch* are
mutually exclusive.
pitch
A Float specifying the pitch. The pitch is the distance traveled along the axial
direction by the sketch when the sketch has completed one full revolution about the
twist axis. Pitch can be specified as positive or negative to achieve right-handed or
left-handed twist about the twist axis, respectively. The default value, 0, implies a
normal extrude. Possible values are –105 ≤≤ *pitch* ≤≤ 105. The arguments *draftAngle*
and *pitch* are mutually exclusive.
flipExtrudeDirection
A Boolean specifying whether to override the direction of feature creation. If the value
is OFF, it means use the direction defined by the *sketchPlaneSide*; if the value is ON,
it means use the opposite direction to the one defined by *sketchPlaneSide*. The default
value is OFF.
Returns
-------
feature: Feature
A Feature object
"""
pass
def CutLoft(self, loftsections: tuple, startCondition: SymbolicConstant = None,
endCondition: SymbolicConstant = None, startTangent: float = None,
startMagnitude: float = None, endTangent: float = None, endMagnitude: float = None,
globalSmoothing: Boolean = OFF):
"""This method creates an additional Feature object by lofting between the given sections
and cutting away material from the part. You define the sections using a sequence of
edges from the part or an EdgeArray.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
loftsections
A sequence of sequences of edges specifying the cross-sections to be lofted. Each outer
sequence specifies a section through which the method will pass the loft feature. Each
outer sequence can be defined as a sequence of edges or as an EdgeArray. The edges
specifying a section must form a simple closed profile and must not contain multiple
loops.
startCondition
A SymbolicConstant specifying the tangent direction at the start section of the loft
feature. Possible values are NONE, NORMAL, RADIAL, and SPECIFIED. You can specify this
argument only if the start and end sections are planar. You cannot use this argument in
conjunction with the *path* argument. You must use the *startCondition* argument in
conjunction with the *endCondition* argument.
endCondition
A SymbolicConstant specifying the tangent direction at the end section of the loft
feature. Possible values are NONE, NORMAL, RADIAL, and SPECIFIED. You can specify this
argument only if the start and end sections are planar. You cannot use this argument in
conjunction with the *path* argument. You must use the *endCondition* argument in
conjunction with the *startCondition* argument.
startTangent
A Float specifying the angle in degrees of the tangent with respect to the plane in
which the start section lies. You must specify the *startTangent* argument if
*startCondition*=SPECIFIED. Possible values are 0.0 ≤≤ *startTangent* ≤≤ 180.0.
startMagnitude
A Float specifying the magnitude of the *startTangent*. You must specify the
*startMagnitude* argument if *startCondition*=SPECIFIED. Possible values are 0.0 <<
*startMagnitude* << 100.0.
endTangent
A Float specifying the angle in degrees of the tangent with respect to the plane in
which the end section lies. You must specify the *endTangent* argument if
*startCondition*=SPECIFIED. Possible values are 0.0 ≤≤ *endTangent* ≤≤ 180.0.
endMagnitude
A Float specifying the magnitude of the *endTangent*. This argument is to be used when
the *endCondition* argument has the value SPECIFIED. Possible values are 0.0 <<
*endMagnitude* << 100.0.
globalSmoothing
A Boolean specifying whether each path defined in the *paths* argument is applied
locally or globally.If the path is applied locally, its effect is felt only on faces
created from the edges on the *loftSections* through which the *paths* pass through.If
the path is applied globally, an averaging algorithm is applied over all the paths
defined and is distributed over all the faces created.The default value is ON
(globally).
Returns
-------
feature: Feature
A Feature object
"""
pass
def CutRevolve(self, sketchPlane: str, sketchPlaneSide: SymbolicConstant, sketchUpEdge: Edge,
sketchOrientation: SymbolicConstant, sketch: ConstrainedSketch, angle: float,
pitch: float = None, flipRevolveDirection: Boolean = OFF,
flipPitchDirection: Boolean = OFF, moveSketchNormalToPath: Boolean = OFF):
"""This method creates an additional Feature object by revolving the given
ConstrainedSketch object by the given angle and cutting away material from the part. The
ConstrainedSketch object must define a closed profile and an axis of revolution.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
sketchPlane
A Datum plane object or a planar Face object.
sketchPlaneSide
A SymbolicConstant specifying the direction of feature creation. Possible values are
SIDE1 and SIDE2.
sketchUpEdge
An Edge object or a Datum axis object specifying the vertical (*Y*) direction of the
sketch.
sketchOrientation
A SymbolicConstant specifying the orientation of *sketchUpEdge* on the sketch. Possible
values are RIGHT, LEFT, TOP, and BOTTOM.
sketch
A ConstrainedSketch object specifying the planar sketch to be revolved.
angle
A Float specifying the angle in degrees to be revolved.
pitch
A Float specifying the pitch. The pitch is the distance traveled along the axial
direction, measured between corresponding points on the sketch when it has completed one
full revolution about the axis of revolution. Possible values are 0 ≤≤ *pitch* ≤≤ 105.
The default value, 0, implies a normal revolve.
flipRevolveDirection
A Boolean specifying whether to override the direction of feature creation. If
*flipRevolveDirection*=OFF, the default direction of revolution is used. If
*flipRevolveDirection*=ON, the revolve direction is reversed. The default value is OFF.
flipPitchDirection
A Boolean specifying whether to override the direction of translation. If
*flipPitchDirection*=OFF, the direction of translation is given by the direction of the
revolve axis. If *flipPitchDirection*=ON, the translation direction is reversed. The
default value is OFF.
moveSketchNormalToPath
A Boolean specifying whether to rotate the sketch so that it is normal to the path of
revolution when using the *pitch* option. If *moveSketchNormalToPath*=OFF, the sketch
plane remains parallel to the revolve axis. If *moveSketchNormalToPath*=ON, the sketch
is moved to match the angle created by the *pitch* before being revolved. The default
value is OFF.
Returns
-------
feature: Feature
A Feature object
"""
pass
def CutSweep(self, path: str, profile: str, pathPlane: str = '', pathUpEdge: Edge = Edge(),
pathOrientation: SymbolicConstant = None, sketchPlane: str = '',
sketchUpEdge: Edge = Edge(), sketchOrientation: SymbolicConstant = None,
draftAngle: float = None, pitch: float = None, profileNormal: Boolean = OFF,
flipSweepDirection: Boolean = OFF):
"""This method creates an additional Feature object by sweeping the given ConstrainedSketch
object along a path which may be a ConstrainedSketch or a sequence of Edge objects and
cutting away material from the part. If the profile section is a ConstrainedSketch
object, it must define a closed profile. The section sketch can be created at the normal
plane at the start of the sweep path or it may be created on a Datum plane or a planar
Face. No checks are made for self-intersection.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
path
Path may either be a ConstrainedSketch object or a sequence of Edge objects specifying
the path of the sweep.
profile
Profile may either be a ConstrainedSketch object or a Face object specifying the section
to be swept.
pathPlane
A Datum plane object or a planar Face object. Only required when path is a
ConstrainedSketch object.
pathUpEdge
An Edge object or a Datum axis object specifying the vertical (*Y*) direction of the
path sketch. Only required when path is a ConstrainedSketch object.
pathOrientation
A SymbolicConstant specifying the orientation of *pathUpEdge* on the sketch. Possible
values are RIGHT, LEFT, TOP, and BOTTOM. Default value is RIGHT. Only required when path
is a ConstrainedSketch object.
sketchPlane
A Datum plane object or a planar Face object specifying the plane on which to sketch the
profile. Not required when profile is a Face object. When profile is chosen as a
ConstrainedSketch object, user may or may not give this as input. If user does not give
this as input, the normal plane at the start of the path will be the sketchPlane.
sketchUpEdge
An Edge object or a Datum axis object specifying the vertical (*Y*) direction of the
profile sketch. Only required when profile is a ConstrainedSketch object.
sketchOrientation
A SymbolicConstant specifying the orientation of *sketchUpEdge* on the sketch. Possible
values are RIGHT, LEFT, TOP, and BOTTOM. Default value is RIGHT. Only required when
profile is a ConstrainedSketch object.
draftAngle
A Float specifying the draft angle in degrees. Possible values are -90.0 ≤≤ *draftAngle*
≤≤ 90.0. By convention, for a positive draft angle an outer loop will draft outward and
an inner loop will draft inward. The opposite is true for a negative draft angle. The
default value, 0, implies a normal extrude. The arguments *draftAngle* and *pitch* are
mutually exclusive.
pitch
A Float specifying the pitch. The pitch is the distance traveled along the axial
direction by the sketch when the sketch has completed one full revolution about the
twist axis. Pitch can be specified as positive or negative to achieve right-handed or
left-handed twist about the twist axis, respectively. The default value, 0, implies a
normal extrude. Possible values are –105 ≤≤ *pitch* ≤≤ 105. The arguments *draftAngle*
and *pitch* are mutually exclusive.
profileNormal
A Boolean specifying whether to keep the profile normal same as original or varying
through out the sweep path. When *profileNormal*=OFF, the profile normal will vary
through out the sweep path. When *profileNormal*=ON, the profile normal will be same as
original through out the sweep path. The default value is OFF.
flipSweepDirection
A Boolean specifying whether to flip the direction in which sweep operation will be
performed. When *flipSweepDirection*=OFF, sweep operation will be performed in the
direction of path direction. When *flipSweepDirection*=ON, sweep operation will be
performed in the direction opposite to the path direction. The default value is OFF.
Returns
-------
feature: Feature
A Feature object
"""
pass
def ExtendFaces(self, faces: tuple[Face] = (), extendAlong: tuple[Edge] = (), distance: float = None,
upToFaces: tuple[Face] = (), trimToExtendedTargetSurfaces: Boolean = True,
upToReferenceRep: Boolean = OFF):
"""This method extends faces along its free edges by offsetting the external edges along
the surfaces. One of *distance*, *upToReferenceRep*, or *upToFaces* must be used to
specify how far the faces need to be extended.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
faces
A sequence of Face objects specifying the faces to be extended. The faces cannot belong
to the reference representation. The *faces* and *extendAlong* arguments are mutually
exclusive. One of them must be specified.
extendAlong
A sequence of Edge objects specifying the edges where to extend the faces. Only free
edges are considered. The interior edges will be ignored. The *faces* and *extendAlong*
arguments are mutually exclusive. One of them must be specified.
distance
A Float indicating the distance to extend the faces along the edges. Either *distance*,
*upToReferenceRep*, or *upToFaces* must be specified.
upToFaces
A sequence of Face objects specifying the faces that the selected faces should be
extended up to.
trimToExtendedTargetSurfaces
A Boolean indicating that the surfaces of up to target faces should be extended before
extending and trimming the selected faces. The default value is True.
upToReferenceRep
A Boolean indicating that the selected faces should be extended along the selected edges
and be trimmed along their intersection with the reference representation.
Returns
-------
feature: Feature
A Feature object
"""
pass
def FaceFromElementFaces(self, elementFaces: Region, stitch: Boolean = OFF, stitchTolerance: float = None,
analyticFitTolerance: float = None, associateFace: Boolean = OFF):
"""This method creates a geometry face from a collection of orphan element faces.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
elementFaces
A Region object specifying the collection of orphan element faces.
stitch
A Boolean specifying whether the created geometry face should be stitched with existing
geometry faces. Default value is TRUE.
stitchTolerance
A Float indicating the maximum gap to be stitched. The value should be smaller than the
minimum feature size and bigger than the maximum gap expected to be stitched in the
model. Otherwise this command may remove small (sliver) edges that are smaller than the
tolerance. If stitch tolerance is not provided then default value of 0.001 will be used
for stitching.
analyticFitTolerance
A Float indicating the analytical surface fitting tolerance. If analytical tolerance is
not provided then default value of 0.015 will be used for analytical surface fitting.
associateFace
A Boolean specifying whether the created geometry face should be associated with the
mesh. Default value is TRUE.
Returns
-------
feature: Feature
A Feature object
"""
pass
def HoleBlindFromEdges(self, plane: str, planeSide: SymbolicConstant, diameter: float, edge1: Edge,
distance1: float,
edge2: Edge, distance2: float, depth: float):
"""This method creates an additional Feature object by creating a circular blind hole of
the given diameter and depth and cutting away material in the solid and shell regions of
the part. The center of the hole is offset from two non-parallel straight edges by the
given distances.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
plane
A Datum plane object or a planar Face object.
planeSide
A SymbolicConstant specifying the direction of feature creation. Possible values are
SIDE1 and SIDE2.
diameter
A Float specifying the diameter of the hole.
edge1
An Edge object specifying the edge from which *distance1* is measured.
distance1
A Float specifying the offset from *edge1*.
edge2
An Edge object specifying the edge from which *distance2* is measured.
distance2
A Float specifying the offset from *edge2*.
depth
A Float specifying the depth of the hole.
Returns
-------
feature: Feature
A Feature object
"""
pass
def HoleFromEdges(self, diameter: float, edge1: Edge, distance1: float, edge2: Edge, distance2: float):
"""This method creates an additional Feature object by creating a circular hole of the
given diameter in a 2D planar part and cutting away material in the shell and wire
regions of the part. The center of the hole is offset from two non-parallel straight
edges by the given distances.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
diameter
A Float specifying the diameter of the hole.
edge1
An Edge object specifying the edge from which *distance1* is measured.
distance1
A Float specifying the offset from *edge1*.
edge2
An Edge object specifying the edge from which *distance2* is measured.
distance2
A Float specifying the offset from *edge2*.
Returns
-------
feature: Feature
A Feature object
"""
pass
def HoleThruAllFromEdges(self, plane: str, planeSide: SymbolicConstant, diameter: float, edge1: Edge,
distance1: float,
edge2: Edge, distance2: float):
"""This method creates an additional Feature object by creating a circular through hole of
the given diameter and cutting away material in the solid and shell regions of the part.
The center of the hole is offset from two non-parallel straight edges by the given
distances.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
plane
A Datum plane object or a planar Face object.
planeSide
A SymbolicConstant specifying the direction of feature creation. Possible values are
SIDE1 and SIDE2.
diameter
A Float specifying the diameter of the hole.
edge1
An Edge object specifying the edge from which *distance1* is measured.
distance1
A Float specifying the offset from *edge1*.
edge2
An Edge object specifying the edge from which *distance2* is measured.
distance2
A Float specifying the offset from *edge2*.
Returns
-------
feature: Feature
A Feature object
"""
pass
def MergeEdges(self, edgeList: tuple[Edge] = (), extendSelection: Boolean = OFF):
"""This method merges edges either by extending the user selection or using only the
selected edges.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
edgeList
A sequence of Edge objects specifying the edges to be merged.
extendSelection
A Boolean specifying whether the user selection needs to be extended to include edges
till branching occurs. Branching is said to occur when the vertex of an edge is shared
by more than two edges.
Returns
-------
feature: Feature
A Feature object
"""
pass
def OffsetFaces(self, faceList: tuple[Face], distance: float = None, targetFaces: tuple[Face] = (),
targetFacesMethod: SymbolicConstant = None, fractionDistance: float = None,
trimToReferenceRep: Boolean = OFF):
"""This method creates new faces by offsetting existing faces.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
faceList
A sequence of Face objects specifying the faces that will be offset. The faces may
belong to the part or to the reference representation associated with the part.
distance
A Float indicating the distance to offset the faces. Either *distance* or *targetFaces*
must be specified.
targetFaces
A sequence of Face objects whose distance to the faces argument together with the
*targetFacesMethod* determines the distance to offset the faces. Either *distance* or
*targetFaces* must be specified.
targetFacesMethod
A SymbolicConstant indicating how to calculate the distance to offset. It can have one
of the following values:HALF_OF_AVERAGE: Offset the faces by a distance equals to half
the average distance to target faces.CLOSEST_POINT_FRACTION: Offset the faces by a
distance equals to the fraction of the distance to the approximate closest point on the
selected target faces.FARTHEST_POINT_FRACTION: Offset the faces by a distance equals to
the fraction of the distance to the approximate farthest point on the selected target
faces.
fractionDistance
A Float indicating the fraction of the distance to the closest or the farthest point on
the target faces. Its default value is 0.5.
trimToReferenceRep
A Boolean indicating whether to extend the offset faces and trim them along their
intersection with the reference representation.
Returns
-------
feature: Feature
A Feature object
"""
pass
def RemoveCells(self, cellList: tuple[Cell]):
"""This method converts a solid entity to a shell entity.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
cellList
A sequence of Cell objects specifying the cells to remove.
Returns
-------
A Boolean value.
Raises
------
- If the intended volume to be turned into a shell entity is not three-dimensional.
Parterror: ConstrainedSketchGeometry that is not 3-dimensional does not contain cells.
"""
pass
def RemoveFaces(self, faceList: tuple[Face], deleteCells: Boolean = False):
"""This method removes faces from a solid entity or from a shell entity.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
faceList
A sequence of Face objects specifying the faces to remove.
deleteCells
A Boolean specifying whether all cells are to be deleted when the faces are removed. The
default value is False.
Returns
-------
feature: Feature
A Feature object
"""
pass
def RemoveFacesAndStitch(self, faceList: tuple[Face]):
"""This method removes faces from a solid entity and attempts to close the resulting gap by
extending the neighboring faces of the solid.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
faceList
A sequence of Face objects specifying the faces to remove.
Returns
-------
feature: Feature
A Feature object
"""
pass
def RemoveRedundantEntities(self, vertexList: tuple[Vertex] = (), edgeList: tuple[Edge] = (),
removeEdgeVertices: Boolean = True):
"""This method removes redundant edges and vertices from a solid or a shell entity. One of
the two arguments is required.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
vertexList
A sequence of ConstrainedSketchVertex objects specifying the vertices to be removed.
edgeList
A sequence of Edge objects specifying the edges to be removed.
removeEdgeVertices
A Boolean specifying whether the vertices of the redundant edges need to be removed. The
default is True.
Returns
-------
A Feature object.
Raises
------
- If the selected entity is not a redundant entity.
Parterror: None of the selected entities are redundant.
"""
pass
def RepairFaceNormals(self, faceList: tuple[Face] = ()):
"""This method works on the entire part or a sequence of shell faces. When the entire part
is selected, it aligns all the shell face normals, and inverts all of the solid faces'
normals if the solid was originally inside out. When a few shell faces are selected, it
inverts the normals of the selected faces.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
faceList
A sequence of Face objects.
Returns
-------
feature: Feature
A Feature object
"""
pass
def RepairInvalidEdges(self, edgeList: tuple[Edge]):
"""This method repairs invalid edges. It will always attempt to improve edges even if none
of selected edges are initially invalid and may leave behind invalid edges that could
not be repaired.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
edgeList
A sequence of Edge objects.
Returns
-------
feature: Feature
A Feature object
"""
pass
def RepairSliver(self, face: Face, point1: int, point2: int, toleranceChecks: Boolean = True):
"""This method repairs the selected sliver from the selected face. The sliver area is
specified using two points. A face partition is carried out at the specified points and
the smaller of the two faces is removed.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
face
A Face object specifying the face on which the sliver is located.
point1
A point specifying the location for partition creation. It can be a ConstrainedSketchVertex object, an
Interesting Point or three coordinates specifying the point on an edge of the *face*.
point2
A point specifying the location for partition creation. It can be a ConstrainedSketchVertex object, an
Interesting Point or three coordinates specifying the point on an edge of the *face*.
toleranceChecks
A Boolean specifying whether to use internal tolerance checks to restrict the size of
the sliver face being removed. The default is True.
Returns
-------
feature: Feature
A Feature object
"""
pass
def RepairSmallEdges(self, edgeList: tuple[Edge], toleranceChecks: Boolean = True):
"""This method repairs small edges. This method will attempt to replace selected small
edges with vertices and extend the adjacent faces and edges. This method might leave
behind some small edges that cannot be removed.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
edgeList
A sequence of Edge objects.
toleranceChecks
A Boolean specifying whether to use internal tolerance checks to restrict the size of
the edges being removed. The default is True.
Returns
-------
feature: Feature
A Feature object
"""
pass
def RepairSmallFaces(self, faceList: tuple[Face], toleranceChecks: Boolean = True):
"""This method repairs small faces. It will attempt to replace the selected small faces
with edges or vertices and extend the adjacent faces. This method might leave behind
some small faces that cannot be removed.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
faceList
A sequence of Face objects.
toleranceChecks
A Boolean specifying whether to use internal tolerance checks to restrict the size of
the faces being removed. The default is True.
Returns
-------
feature: Feature
A Feature object
"""
pass
def ReplaceFaces(self, faceList: tuple[Face], stitch: Boolean = True):
"""This method replaces the selected faces with a single face. If one single face is
selected, that alone is replaced with a new face.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
faceList
A sequence of Face objects to be replaced.
stitch
A Boolean specifying whether the newly created face needs to be stitched to the existing
geometry. The default is True.
Returns
-------
feature: Feature
A Feature object
"""
pass
def Round(self, radius: float, edgeList: tuple[Edge], vertexList: tuple[Vertex]):
"""This method creates an additional Feature object by rounding (filleting) the given list
of entities with the given radius.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
radius
A Float specifying the radius of the fillets.
edgeList
A sequence of Edge objects. Solid and Shell edges of a part can be rounded. The
operation will fail for non-manifold edges. The *edgeList* and *vertexList* arguments
are mutually exclusive. One of them must be specified.
vertexList
A sequence of ConstrainedSketchVertex objects. Vertices that are connected to two wire edges can be
rounded. The operation will fail for a vertex connected to a face. The *edgeList* and
*vertexList* arguments are mutually exclusive. One of them must be specified.
Returns
-------
feature: Feature
A Feature object
"""
pass
def Shell(self, sketchPlane: str, sketchPlaneSide: SymbolicConstant, sketchUpEdge: Edge,
sketch: ConstrainedSketch, sketchOrientation: SymbolicConstant = RIGHT):
"""This method creates an additional Feature object by creating a planar shell from the
given ConstrainedSketch object. The ConstrainedSketch object must define a closed
profile.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
sketchPlane
A Datum plane object or a planar Face object.
sketchPlaneSide
A SymbolicConstant specifying the direction of feature creation. Possible values are
SIDE1 and SIDE2.
sketchUpEdge
An Edge object or a Datum axis object specifying the vertical (*Y*) direction of the
sketch.
sketch
A ConstrainedSketch object specifying the planar shell.
sketchOrientation
A SymbolicConstant specifying the orientation of *sketchUpEdge* on the sketch. Possible
values are RIGHT, LEFT, TOP, and BOTTOM. The default value is RIGHT.
Returns
-------
feature: Feature
A Feature object
"""
pass
def ShellExtrude(self, sketchPlane: str, sketchPlaneSide: SymbolicConstant, sketchUpEdge: Edge,
sketch: ConstrainedSketch, depth: float = None, upToFace: str = '',
sketchOrientation: SymbolicConstant = RIGHT, draftAngle: float = None,
pitch: float = None, flipExtrudeDirection: Boolean = OFF,
keepInternalBoundaries: Boolean = OFF):
"""This method creates an additional Feature object by extruding the given
ConstrainedSketch object by the given depth, creating a shell protrusion. The
ConstrainedSketch object can define either an open or closed profile.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
sketchPlane
A Datum plane object or a planar Face object.
sketchPlaneSide
A SymbolicConstant specifying the direction of feature creation. Possible values are
SIDE1 and SIDE2.
sketchUpEdge
An Edge object or a Datum axis object specifying the vertical (*Y*) direction of the
sketch.
sketch
A ConstrainedSketch object specifying the planar sketch to be extruded.
depth
A Float specifying the extrusion depth. The default is to not specify a depth. Either
*depth* or *upToFace* must be used to define the extrusion depth.
upToFace
A Face specifying the face up to which to extrude. If *upToFace* is specified, the
extrusion will be an up-to-face extrusion. The default is to not specify a face. Either
*depth* or *upToFace* must be used to define the extrusion depth.
sketchOrientation
A SymbolicConstant specifying the orientation of *sketchUpEdge* on the sketch. Possible
values are RIGHT, LEFT, TOP, and BOTTOM. The default value is RIGHT.
draftAngle
A Float specifying the draft angle in degrees. Possible values are -90.0 ≤≤ *draftAngle*
≤≤ 90.0. By convention, for a positive draft angle an outer loop will draft outward and
an inner loop will draft inward. The opposite is true for a negative draft angle. The
default value, 0, implies a normal extrude. The arguments *draftAngle* and *pitch* are
mutually exclusive.
pitch
A Float specifying the pitch. The pitch is the distance traveled along the axial
direction by the sketch when the sketch has completed one full revolution about the
twist axis. Pitch can be specified as positive or negative to achieve right-handed or
left-handed twist about the twist axis, respectively. The default value, 0, implies a
normal extrude. Possible values are –105 ≤≤ *pitch* ≤≤ 105. The arguments *draftAngle*
and *pitch* are mutually exclusive.
flipExtrudeDirection
A Boolean specifying whether to override the direction of feature creation. If the value
is OFF, it means use the direction defined by the *sketchPlaneSide*; if the value is ON,
it means use the opposite direction to the one defined by *sketchPlaneSide*. The default
value is OFF.
keepInternalBoundaries
A Boolean specifying whether internal boundaries will be retained. The default value is
OFF.
Returns
-------
feature: Feature
A Feature object
"""
pass
def ShellLoft(self, loftsections: tuple, startCondition: SymbolicConstant = None,
endCondition: SymbolicConstant = None, startTangent: float = None,
startMagnitude: float = None, endTangent: float = None, endMagnitude: float = None,
paths: tuple = (), globalSmoothing: Boolean = OFF,
keepInternalBoundaries: Boolean = OFF):
"""This method creates an additional Feature object by lofting between the given sections
and adding shell faces to the part. You define the sections using a sequence of edges
from the part or an EdgeArray.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
loftsections
A sequence of sequences of edges specifying the cross-sections to be lofted. Each outer
sequence specifies a section through which the method will pass the loft feature. Each
outer sequence can be defined as a sequence of edges or as an EdgeArray. The edges
specifying a section must form a simple closed profile and must not contain multiple
loops.
startCondition
A SymbolicConstant specifying the tangent direction at the start section of the loft
feature. Possible values are NONE, NORMAL, RADIAL and SPECIFIED. You can specify this
argument only if the start and end sections are planar. You cannot use this argument in
conjunction with the *path* argument. You must use the *startCondition* argument in
conjunction with the *endCondition* argument.
endCondition
A SymbolicConstant specifying the tangent direction at the end section of the loft
feature. Possible values are NONE, NORMAL, RADIAL and SPECIFIED. You can specify this
argument only if the start and end sections are planar. You cannot use this argument in
conjunction with the *path* argument. You must use the *endCondition* argument in
conjunction with the *startCondition* argument.
startTangent
A Float specifying the angle in degrees of the tangent with respect to the plane in
which the start section lies. You must specify the *startTangent* argument if
*startCondition*=SPECIFIED. Possible values are 0.0 ≤≤ *startTangent* ≤≤ 180.0.
startMagnitude
A Float specifying the magnitude of the *startTangent*. You must specify the
*startMagnitude* argument if *startCondition*=SPECIFIED. Possible values are 0.0 <<
*startMagnitude* << 100.0.
endTangent
A Float specifying the angle in degrees of the tangent with respect to the plane in
which the end section lies. You must specify the *endTangent* argument if
*startCondition*=SPECIFIED. Possible values are 0.0 ≤≤ *endTangent* ≤≤ 180.0.
endMagnitude
A Float specifying the magnitude of the *endTangent*. This argument is to be used when
the *endCondition* argument has the value SPECIFIED. Possible values are 0.0 <<
*endMagnitude* << 100.0.
paths
A sequence of sequences of edges that pass through each section in the loft feature.
Each sequence specifies a path followed by the face or an edge created by a loft
feature. Each path must start at the first section, end at the last section, and pass
through each section. In addition, the order of the sequences must be the same as the
order of the sections in the *loftsections* argument. Each path must not self-intersect
and must be tangent continuous. In addition, the paths must not intersect each other.
You cannot use the *paths* argument in conjunction with the *startCondition* and
*endCondition* arguments.
globalSmoothing
A Boolean specifying whether each path defined in the *paths* argument is applied
locally or globally.If the path is applied locally, its effect is felt only on faces
created from the edges on the *loftsections* through which the *paths* pass through.If
the path is applied globally, an averaging algorithm is applied over all the paths
defined and is distributed over all the faces created.The default value is ON
(globally).
keepInternalBoundaries
A Boolean specifying whether internal boundaries will be retained. The default value is
OFF.
Returns
-------
feature: Feature
A Feature object
"""
pass
def ShellRevolve(self, sketchPlane: str, sketchPlaneSide: SymbolicConstant, sketchUpEdge: Edge,
sketch: ConstrainedSketch, angle: float, sketchOrientation: SymbolicConstant = RIGHT,
pitch: float = None, flipRevolveDirection: Boolean = OFF,
flipPitchDirection: Boolean = OFF, moveSketchNormalToPath: Boolean = OFF,
keepInternalBoundaries: Boolean = OFF):
"""This method creates an additional Feature object by revolving the given
ConstrainedSketch object by the given angle, creating a shell protrusion. The
ConstrainedSketch object can define either an open or closed profile and an axis of
revolution. The axis is defined by a single construction line. For a description of the
plane positioning arguments, see SolidExtrude.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
sketchPlane
A Datum plane object or a planar Face object.
sketchPlaneSide
A SymbolicConstant specifying the direction of feature creation. Possible values are
SIDE1 and SIDE2.
sketchUpEdge
An Edge object or a Datum axis object specifying the vertical (*Y*) direction of the
sketch.
sketch
A ConstrainedSketch object specifying the planar sketch to be revolved.
angle
A Float specifying the angle in degrees to be revolved.
sketchOrientation
A SymbolicConstant specifying the orientation of *sketchUpEdge* on the sketch. Possible
values are RIGHT, LEFT, TOP, and BOTTOM. The default value is RIGHT.
pitch
A Float specifying the pitch. The pitch is the distance traveled along the axial
direction, measured between corresponding points on the sketch when it has completed one
full revolution about the axis of revolution. Possible values are 0 ≤≤ *pitch* ≤≤ 105.
The default value, 0, implies a normal revolve.
flipRevolveDirection
A Boolean specifying whether to override the direction of feature creation. If
*flipRevolveDirection*=OFF, the default direction of revolution is used. If
*flipRevolveDirection*=ON, the revolve direction is reversed. The default value is OFF.
flipPitchDirection
A Boolean specifying whether to override the direction of translation. If
*flipPitchDirection*=OFF, the direction of translation is given by the direction of the
revolve axis. If *flipPitchDirection*=ON, the translation direction is reversed. The
default value is OFF.
moveSketchNormalToPath
A Boolean specifying whether to rotate the sketch so that it is normal to the path of
revolution when using the *pitch* option. If *moveSketchNormalToPath*=OFF, the sketch
plane remains parallel to the revolve axis. If *moveSketchNormalToPath*=ON, the sketch
is moved to match the angle created by the *pitch* before being revolved. The default
value is OFF.
keepInternalBoundaries
A Boolean specifying whether internal boundaries will be retained. The default value is
OFF.
Returns
-------
feature: Feature
A Feature object
"""
pass
def ShellSweep(self, path: str, profile: str, pathPlane: str = '', pathUpEdge: Edge = Edge(),
pathOrientation: SymbolicConstant = None, sketchPlane: str = '',
sketchUpEdge: Edge = Edge(), sketchOrientation: SymbolicConstant = None,
draftAngle: float = None, pitch: float = None, profileNormal: Boolean = OFF,
flipSweepDirection: Boolean = OFF, keepInternalBoundaries: Boolean = OFF):
"""This method creates an additional Feature object by sweeping the given ConstrainedSketch
object or a sequence of Edge objects along a path which may be a ConstrainedSketch or a
sequence of Edge objects, creating a shell swept protrusion. The section can be an open
or a closed profile. The section sketch can be created at the normal plane at the start
of the sweep path or it may be created on a Datum plane or a planar Face. No checks are
made for self-intersection.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
path
Path may either be a ConstrainedSketch object or a sequence of Edge objects specifying
the path of the sweep.
profile
Profile may either be a ConstrainedSketch object or a sequence of Edge objects
specifying the section to be swept.
pathPlane
A Datum plane object or a planar Face object. Only required when path is a
ConstrainedSketch object.
pathUpEdge
An Edge object or a Datum axis object specifying the vertical (*Y*) direction of the
path sketch. Only required when path is a ConstrainedSketch object.
pathOrientation
A SymbolicConstant specifying the orientation of *pathUpEdge* on the sketch. Possible
values are RIGHT, LEFT, TOP, and BOTTOM. Default value is RIGHT. Only required when path
is a ConstrainedSketch object.
sketchPlane
A Datum plane object or a planar Face object specifying the plane on which to sketch the
profile. Not required when profile is a Face object. When profile is chosen as
ConstrainedSketch object, user may or may not give this as input. If user does not give
this as input, the normal plane at the start of the path will be the sketchPlane.
sketchUpEdge
An Edge object or a Datum axis object specifying the vertical (*Y*) direction of the
profile sketch. Only required when profile is a ConstrainedSketch object.
sketchOrientation
A SymbolicConstant specifying the orientation of *sketchUpEdge* on the sketch. Possible
values are RIGHT, LEFT, TOP, and BOTTOM. Default value is RIGHT. Only required when
profile is a ConstrainedSketch object.
draftAngle
A Float specifying the draft angle in degrees. Possible values are -90.0 ≤≤ *draftAngle*
≤≤ 90.0. By convention, for a positive draft angle an outer loop will draft outward and
an inner loop will draft inward. The opposite is true for a negative draft angle. The
default value, 0, implies a normal extrude. The arguments *draftAngle* and *pitch* are
mutually exclusive.
pitch
A Float specifying the pitch. The pitch is the distance traveled along the axial
direction by the sketch when the sketch has completed one full revolution about the
twist axis. Pitch can be specified as positive or negative to achieve right-handed or
left-handed twist about the twist axis, respectively. The default value, 0, implies a
normal extrude. Possible values are –105 ≤≤ *pitch* ≤≤ 105. The arguments *draftAngle*
and *pitch* are mutually exclusive.
profileNormal
A Boolean specifying whether to keep the profile normal same as original or varying
through out the sweep path. When *profileNormal*=OFF, the profile normal will vary
through out the sweep path. When *profileNormal*=ON, the profile normal will be same as
original through out the sweep path. The default value is OFF.
flipSweepDirection
A Boolean specifying whether to flip the direction in which sweep operation will be
performed. When *flipSweepDirection*=OFF, sweep operation will be performed in the
direction of path direction. When *flipSweepDirection*=ON, sweep operation will be
performed in the direction opposite to the path direction. The default value is OFF.
keepInternalBoundaries
A Boolean specifying whether internal boundaries will be retained. The default value is
OFF.
Returns
-------
feature: Feature
A Feature object
"""
pass
def SolidExtrude(self, sketchPlane: str, sketchPlaneSide: SymbolicConstant, sketchUpEdge: Edge,
sketch: ConstrainedSketch, depth: float = None, upToFace: str = '',
sketchOrientation: SymbolicConstant = RIGHT, draftAngle: float = None,
pitch: float = None, flipExtrudeDirection: Boolean = OFF,
keepInternalBoundaries: Boolean = OFF):
"""This method creates an additional Feature object by extruding the given
ConstrainedSketch object by the given depth, creating a solid protrusion. The
ConstrainedSketch object must define a closed profile.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
sketchPlane
A Datum plane object or a planar Face object.
sketchPlaneSide
A SymbolicConstant specifying the direction of feature creation. Possible values are
SIDE1 and SIDE2.
sketchUpEdge
An Edge object or a Datum axis object specifying the vertical (*Y*) direction of the
sketch.
sketch
A ConstrainedSketch object specifying the planar sketch to be extruded.
depth
A Float specifying the extrusion depth. The default is to not specify a depth. Either
*depth* or *upToFace* must be used to define the extrusion depth.
upToFace
A Face specifying the face up to which to extrude. If *upToFace* is specified, the
extrusion will be an up-to-face extrusion. The default is to not specify a face. Either
*depth* or *upToFace* must be used to define the extrusion depth.
sketchOrientation
A SymbolicConstant specifying the orientation of *sketchUpEdge* on the sketch. Possible
values are RIGHT, LEFT, TOP, and BOTTOM. The default value is RIGHT.
draftAngle
A Float specifying the draft angle in degrees. Possible values are -90.0 ≤≤ *draftAngle*
≤≤ 90.0. By convention, for a positive draft angle an outer loop will draft outward and
an inner loop will draft inward. The opposite is true for a negative draft angle. The
default value, 0, implies a normal extrude. The arguments *draftAngle* and *pitch* are
mutually exclusive.
pitch
A Float specifying the pitch. The pitch is the distance traveled along the axial
direction by the sketch when the sketch has completed one full revolution about the
twist axis. Pitch can be specified as positive or negative to achieve right-handed or
left-handed twist about the twist axis, respectively. The default value, 0, implies a
normal extrude. Possible values are –105 ≤≤ *pitch* ≤≤ 105. The arguments *draftAngle*
and *pitch* are mutually exclusive.
flipExtrudeDirection
A Boolean specifying whether to override the direction of feature creation. If the value
is OFF, it means use the direction defined by the *sketchPlaneSide*; if the value is ON,
it means use the opposite direction to the one defined by *sketchPlaneSide*. The default
value is OFF.
keepInternalBoundaries
A Boolean specifying whether internal boundaries will be retained. The default value is
OFF.
Returns
-------
feature: Feature
A Feature object
"""
pass
def SolidLoft(self, loftsections: tuple, startCondition: SymbolicConstant = None,
endCondition: SymbolicConstant = None, startTangent: float = None,
startMagnitude: float = None, endTangent: float = None, endMagnitude: float = None,
paths: tuple = (), globalSmoothing: Boolean = OFF,
keepInternalBoundaries: Boolean = OFF):
"""This method creates an additional Feature object by lofting between the given sections
and adding material to the part. You define the sections using a sequence of edges from
the part or an EdgeArray.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
loftsections
A sequence of sequences of edges specifying the cross-sections to be lofted. Each outer
sequence specifies a section through which Abaqus will pass the loft feature. Each outer
sequence can be defined as a sequence of edges or as an EdgeArray. The edges specifying
a section must form a simple closed profile and must not contain multiple loops.
startCondition
A SymbolicConstant specifying the tangent direction at the start section of the loft
feature. Possible values are NONE, NORMAL, RADIAL and SPECIFIED. You can specify this
argument only if the start and end sections are planar. You cannot use this argument in
conjunction with the *path* argument. You must use the *startCondition* argument in
conjunction with the *endCondition* argument.
endCondition
A SymbolicConstant specifying the tangent direction at the end section of the loft
feature. Possible values are NONE, NORMAL, RADIAL and SPECIFIED. You can specify this
argument only if the start and end sections are planar. You cannot use this argument in
conjunction with the *path* argument. You must use the *endCondition* argument in
conjunction with the *startCondition* argument.
startTangent
A Float specifying the angle in degrees of the tangent with respect to the plane in
which the start section lies. You must specify the *startTangent* argument if
*startCondition*=SPECIFIED. Possible values are 0.0 ≤≤ *startTangent* ≤≤ 180.0.
startMagnitude
A Float specifying the magnitude of the *startTangent*. You must specify the
*startMagnitude* argument if *startCondition*=SPECIFIED. Possible values are 0.0 <<
*startMagnitude* << 100.0.
endTangent
A Float specifying the angle in degrees of the tangent with respect to the plane in
which the end section lies. You must specify the *endTangent* argument if
*startCondition*=SPECIFIED. Possible values are 0.0 ≤≤ *endTangent* ≤≤ 180.0.
endMagnitude
A Float specifying the magnitude of the *endTangent*. This argument is to be used when
the *endCondition* argument has the value SPECIFIED. Possible values are 0.0 <<
*endMagnitude* << 100.0.
paths
A sequence of sequences of edges that pass through each section in the loft feature.
Each sequence specifies a path followed by the face or an edge created by a loft
feature. Each path must start at the first section, end at the last section, and pass
through each section. In addition, the order of the sequences must be the same as the
order of the sections in the *loftsections* argument. Each path must not self-intersect
and must be tangent continuous. In addition, the paths must not intersect each other.
You cannot use the *paths* argument in conjunction with the *startCondition* and
*endCondition* arguments.
globalSmoothing
A Boolean specifying whether each path defined in the *paths* argument is applied
locally or globally.If the path is applied locally, its effect is felt only on faces
created from the edges on the *loftsections* through which the *paths* pass through.If
the path is applied globally, an averaging algorithm is applied over all the paths
defined and is distributed over all the faces created.The default value is ON
(globally).
keepInternalBoundaries
A Boolean specifying whether internal boundaries will be retained. The default value is
OFF.
Returns
-------
feature: Feature
A Feature object
"""
pass
def SolidRevolve(self, sketchPlane: str, sketchPlaneSide: SymbolicConstant, sketchUpEdge: Edge,
sketch: ConstrainedSketch, angle: float, sketchOrientation: SymbolicConstant = RIGHT,
pitch: float = None, flipRevolveDirection: Boolean = OFF,
flipPitchDirection: Boolean = OFF, moveSketchNormalToPath: Boolean = OFF,
keepInternalBoundaries: Boolean = OFF):
"""This method creates an additional Feature object by revolving the given
ConstrainedSketch object by the given angle, creating a solid protrusion. The
ConstrainedSketch object must define a closed profile and an axis of revolution. The
axis is defined by a single construction line.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
sketchPlane
A Datum plane object or a planar Face object.
sketchPlaneSide
A SymbolicConstant specifying the direction of feature creation. Possible values are
SIDE1 and SIDE2.
sketchUpEdge
An Edge object or a Datum axis object specifying the vertical (*Y*) direction of the
sketch.
sketch
A ConstrainedSketch object specifying the planar sketch to be revolved.
angle
A Float specifying the angle in degrees to be revolved.
sketchOrientation
A SymbolicConstant specifying the orientation of *sketchUpEdge* on the sketch. Possible
values are RIGHT, LEFT, TOP, and BOTTOM. The default value is RIGHT.
pitch
A Float specifying the pitch. The pitch is the distance traveled along the axial
direction, measured between corresponding points on the sketch when it has completed one
full revolution about the axis of revolution. Possible values are 0 ≤≤ *pitch* ≤≤ 105.
The default value, 0, implies a normal revolve.
flipRevolveDirection
A Boolean specifying whether to override the direction of feature creation. If
*flipRevolveDirection*=OFF, the default direction of revolution is used. If
*flipRevolveDirection*=ON, the revolve direction is reversed. The default value is OFF.
flipPitchDirection
A Boolean specifying whether to override the direction of translation. If
*flipPitchDirection*=OFF, the direction of translation is given by the direction of the
revolve axis. If *flipPitchDirection*=ON, the translation direction is reversed. The
default value is OFF.
moveSketchNormalToPath
A Boolean specifying whether to rotate the sketch so that it is normal to the path of
revolution when using the *pitch* option. If *moveSketchNormalToPath*=OFF, the sketch
plane remains parallel to the revolve axis. If *moveSketchNormalToPath*=ON, the sketch
is moved to match the angle created by the *pitch* before being revolved. The default
value is OFF.
keepInternalBoundaries
A Boolean specifying whether internal boundaries will be retained. The default value is
OFF.
Returns
-------
feature: Feature
A Feature object
"""
pass
def SolidSweep(self, path: str, profile: str, pathPlane: str = '', pathUpEdge: Edge = Edge(),
pathOrientation: SymbolicConstant = None, sketchPlane: str = '',
sketchUpEdge: Edge = Edge(), sketchOrientation: SymbolicConstant = None,
draftAngle: float = None, pitch: float = None, profileNormal: Boolean = OFF,
flipSweepDirection: Boolean = OFF, keepInternalBoundaries: Boolean = OFF):
"""This method creates an additional Feature object by sweeping the given ConstrainedSketch
object or a Face object along a path which may be a ConstrainedSketch or a sequence of
Edge objects, creating a solid swept protrusion. If the profile section is a
ConstrainedSketch object, it must define a closed profile. The section sketch can be
created at the normal plane at the start of the sweep path or it may be created on a
Datum plane or a planar Face. No checks are made for self-intersection.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
path
Path may either be a ConstrainedSketch object or a sequence of Edge objects specifying
the path of the sweep.
profile
Profile may either be a ConstrainedSketch object or a Face object specifying the section
to be swept.
pathPlane
A Datum plane object or a planar Face object. Only required when path is a
ConstrainedSketch object.
pathUpEdge
An Edge object or a Datum axis object specifying the vertical (*Y*) direction of the
path sketch. Only required when path is a ConstrainedSketch object.
pathOrientation
A SymbolicConstant specifying the orientation of *pathUpEdge* on the sketch. Possible
values are RIGHT, LEFT, TOP, and BOTTOM. Default value is RIGHT. Only required when path
is a ConstrainedSketch object.
sketchPlane
A Datum plane object or a planar Face object specifying the plane on which to sketch the
profile. Not required when profile is a Face object. When profile is chosen as
ConstrainedSketch object, user may or may not give this as input. If user does not give
this as input, the normal plane at the start of the path will be the sketchPlane.
sketchUpEdge
An Edge object or a Datum axis object specifying the vertical (*Y*) direction of the
profile sketch. Only required when profile is a ConstrainedSketch object.
sketchOrientation
A SymbolicConstant specifying the orientation of *sketchUpEdge* on the sketch. Possible
values are RIGHT, LEFT, TOP, and BOTTOM. Default value is RIGHT. Only required when
profile is a ConstrainedSketch object.
draftAngle
A Float specifying the draft angle in degrees. Possible values are -90.0 ≤≤ *draftAngle*
≤≤ 90.0. By convention, for a positive draft angle an outer loop will draft outward and
an inner loop will draft inward. The opposite is true for a negative draft angle. The
default value, 0, implies a normal extrude. The arguments *draftAngle* and *pitch* are
mutually exclusive.
pitch
A Float specifying the pitch. The pitch is the distance traveled along the axial
direction by the sketch when the sketch has completed one full revolution about the
twist axis. Pitch can be specified as positive or negative to achieve right-handed or
left-handed twist about the twist axis, respectively. The default value, 0, implies a
normal extrude. Possible values are –105 ≤≤ *pitch* ≤≤ 105. The arguments *draftAngle*
and *pitch* are mutually exclusive.
profileNormal
A Boolean specifying whether to keep the profile normal same as original or varying
through out the sweep path. When *profileNormal*=OFF, the profile normal will vary
through out the sweep path. When *profileNormal*=ON, the profile normal will be same as
original through out the sweep path. The default value is OFF.
flipSweepDirection
A Boolean specifying whether to flip the direction in which sweep operation will be
performed. When *flipSweepDirection*=OFF, sweep operation will be performed in the
direction of path direction. When *flipSweepDirection*=ON, sweep operation will be
performed in the direction opposite to the path direction. The default value is OFF.
keepInternalBoundaries
A Boolean specifying whether internal boundaries will be retained. The default value is
OFF.
Returns
-------
feature: Feature
A Feature object
"""
pass
def Stitch(self, edgeList: tuple[Edge] = (), stitchTolerance: float = None):
"""This method attempts to create a valid part by binding together free and imprecise edges
of all the faces of a part. If *edgeList* is not given, a global stitch will be
performed. If *stitchTolerance* is not specified, a value of 1.0 will be used.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
edgeList
A sequence of Edge objects specifying the edges that need to be stitched.
stitchTolerance
A Float indicating the maximum gap to be stitched. The value should be smaller than the
minimum feature size and bigger than the maximum gap expected to be stitched in the
model. Otherwise this command may remove small (sliver) edges that are smaller than the
tolerance.
Returns
-------
feature: Feature
A Feature object
"""
pass
def Wire(self, sketchPlane: str, sketchPlaneSide: SymbolicConstant, sketchUpEdge: Edge,
sketch: ConstrainedSketch, sketchOrientation: SymbolicConstant = RIGHT):
"""This method creates an additional Feature object by creating a planar wire from the
given ConstrainedSketch object. The ConstrainedSketch object must define a closed
profile.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
sketchPlane
A Datum plane object or a planar Face object specifying the plane on which to sketch.
sketchPlaneSide
A SymbolicConstant specifying the direction of feature creation. Possible values are
SIDE1 and SIDE2.
sketchUpEdge
An Edge object or a Datum axis object specifying the vertical (*Y*) direction of the
sketch.
sketch
A ConstrainedSketch object specifying the planar sketch to be revolved.
sketchOrientation
A SymbolicConstant specifying the orientation of *sketchUpEdge* on the sketch. Possible
values are RIGHT, LEFT, TOP, and BOTTOM. The default value is RIGHT.
Returns
-------
feature: Feature
A Feature object
"""
pass
def WireSpline(self, points: tuple, mergeType: SymbolicConstant = IMPRINT, smoothClosedSpline: Boolean = OFF):
"""This method creates an additional Feature object by creating a spline wire that passes
through a sequence of given points. Each point can be a datum point, a vertex, an
interesting point, or a tuple.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
points
A sequence of ConstrainedSketchVertex, Datum point, or InterestingPoint objects specifying the points
through which the spline wire will pass. *points* can also be a sequence of tuples of
Floats. You must specify at least two values in the sequence.
mergeType
A SymbolicConstant specifying the merge behavior of the wire with existing geometry. If
*mergeType* is MERGE, Abaqus merges the wire into solid regions of the part if the wire
passes through them. If *mergeType* is IMPRINT, Abaqus imprints the spline wire on
existing geometry as edges. If *mergeType* is SEPARATE, Abaqus neither merges nor
imprints the spline wire with existing geometry. It creates the wire separately. The
default value is IMPRINT.
smoothClosedSpline
A Boolean specifying the behavior of Abaqus when the points defining a spline wire form
a closed loop (the start and end points are the same). If *smoothClosedSpline*=ON,
Abaqus creates a smooth spline wire where the tangencies at the end point meet smoothly.
If *smoothClosedSpline*=OFF, Abaqus does not automatically create a smooth end
condition. The default value in OFF.
Returns
-------
feature: Feature
A Feature object
"""
pass
def WirePolyLine(self, points: tuple, mergeType: SymbolicConstant = IMPRINT, meshable: Boolean = ON):
"""This method creates an additional Feature object by creating a polyline wire that passes
through a sequence of given points. Each point can be a datum point, a vertex, an
interesting point, or a tuple.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
points
A sequence of ConstrainedSketchVertex, Datum point, or InterestingPoint objects specifying the points
through which the polyline wire will pass. *points* can also be a sequence of tuples of
Floats. You must specify at least two values in the sequence.
mergeType
A SymbolicConstant specifying the merge behavior of the wire with existing geometry. If
*mergeType* is MERGE, Abaqus merges the wire into solid regions of the part if the wire
passes through them. If *mergeType* is IMPRINT, Abaqus imprints the wire on existing
geometry as edges. If *mergeType* is SEPARATE, Abaqus neither merges nor imprints the
spline wire with existing geometry. It creates the wire separately. The default value is
IMPRINT.
meshable
A Boolean specifying whether the wire should be available for selection in meshing
operations. If *meshable*=OFF, the wire can be used for connector section assignment.
The default value is ON.
Returns
-------
feature: Feature
A Feature object
"""
pass
def WireFromEdge(self, edgeList: str):
"""This method creates an additional Feature object by creating a Wire by selecting one or
more Edge objects of a Solid or Shell part.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].parts[*name*].AutoRepair
Parameters
----------
edgeList
A list of Edge objects specifying the edges from which the wire is to be created.
Returns
-------
feature: Feature
A Feature object
"""
pass
| 44.222886 | 119 | 0.627827 | 12,148 | 103,570 | 5.362364 | 0.0554 | 0.035323 | 0.018529 | 0.013355 | 0.858417 | 0.848592 | 0.835667 | 0.822772 | 0.800129 | 0.793405 | 0 | 0.004535 | 0.318741 | 103,570 | 2,341 | 120 | 44.241777 | 0.916849 | 0.745911 | 0 | 0.556757 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.308108 | false | 0.308108 | 0.043243 | 0 | 0.356757 | 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 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 8 |
1369bd24ab9837ce6ae01807019b46f73d1565cc | 1,186 | py | Python | src/penta_decathlon.py | Pav0l/CellularAutomata-EU1 | 2acb4a7e3ef5ff6949121c0cf1c527aee5c2a438 | [
"MIT"
] | null | null | null | src/penta_decathlon.py | Pav0l/CellularAutomata-EU1 | 2acb4a7e3ef5ff6949121c0cf1c527aee5c2a438 | [
"MIT"
] | null | null | null | src/penta_decathlon.py | Pav0l/CellularAutomata-EU1 | 2acb4a7e3ef5ff6949121c0cf1c527aee5c2a438 | [
"MIT"
] | 1 | 2019-07-18T14:08:27.000Z | 2019-07-18T14:08:27.000Z | # Create Penta-decathlon starting state
def penta_decathlon(initial_state, row, col):
# start point
start_row = row
start_col = col
initial_state[start_row][start_col] = 0
initial_state[start_row+1][start_col] = 1
initial_state[start_row+2][start_col] = 1
initial_state[start_row][start_col+1] = 1
initial_state[start_row][start_col+2] = 1
initial_state[start_row][start_col+3] = 1
initial_state[start_row+1][start_col+4] = 1
initial_state[start_row+2][start_col+4] = 1
initial_state[start_row+3][start_col+1] = 1
initial_state[start_row+3][start_col+2] = 1
initial_state[start_row+3][start_col+3] = 1
start_row = start_row + 8
initial_state[start_row+1][start_col] = 1
initial_state[start_row+2][start_col] = 1
initial_state[start_row][start_col+1] = 1
initial_state[start_row][start_col+2] = 1
initial_state[start_row][start_col+3] = 1
initial_state[start_row+1][start_col+4] = 1
initial_state[start_row+2][start_col+4] = 1
initial_state[start_row+3][start_col+1] = 1
initial_state[start_row+3][start_col+2] = 1
initial_state[start_row+3][start_col+3] = 1
return initial_state
| 37.0625 | 47 | 0.711636 | 205 | 1,186 | 3.77561 | 0.087805 | 0.248062 | 0.46124 | 0.542636 | 0.824289 | 0.824289 | 0.788114 | 0.788114 | 0.788114 | 0.788114 | 0 | 0.052472 | 0.164418 | 1,186 | 31 | 48 | 38.258065 | 0.728557 | 0.041315 | 0 | 0.769231 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.038462 | false | 0 | 0 | 0 | 0.076923 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 1 | 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 | 10 |
1370b37b2791641e9cb3126cf89bb3a5089fdc93 | 19,869 | py | Python | pysal/spreg/tests/test_error_sp_het_sparse.py | cubensys/pysal | 8d50990f6e6603ba79ae1a887a20a1e3a0734e51 | [
"MIT",
"BSD-3-Clause"
] | null | null | null | pysal/spreg/tests/test_error_sp_het_sparse.py | cubensys/pysal | 8d50990f6e6603ba79ae1a887a20a1e3a0734e51 | [
"MIT",
"BSD-3-Clause"
] | null | null | null | pysal/spreg/tests/test_error_sp_het_sparse.py | cubensys/pysal | 8d50990f6e6603ba79ae1a887a20a1e3a0734e51 | [
"MIT",
"BSD-3-Clause"
] | 1 | 2021-07-19T01:46:17.000Z | 2021-07-19T01:46:17.000Z | import unittest
import pysal
import numpy as np
from scipy import sparse
from pysal.spreg import error_sp_het as HET
from pysal.common import RTOL
class TestBaseGMErrorHet(unittest.TestCase):
def setUp(self):
db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
y = np.array(db.by_col("HOVAL"))
self.y = np.reshape(y, (49,1))
X = []
X.append(db.by_col("INC"))
X.append(db.by_col("CRIME"))
self.X = np.array(X).T
self.X = np.hstack((np.ones(self.y.shape),self.X))
self.X = sparse.csr_matrix(self.X)
self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
self.w.transform = 'r'
def test_model(self):
reg = HET.BaseGM_Error_Het(self.y, self.X, self.w.sparse, step1c=True)
betas = np.array([[ 47.99626638], [ 0.71048989], [ -0.55876126], [ 0.41178776]])
np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([ 27.38122697])
np.testing.assert_allclose(reg.u[0],u,RTOL)
ef = np.array([ 32.29765975])
np.testing.assert_allclose(reg.e_filtered[0],ef,RTOL)
predy = np.array([ 53.08577603])
np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
np.testing.assert_allclose(reg.n,n)
k = 3
np.testing.assert_allclose(reg.k,k)
y = np.array([ 80.467003])
np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([ 1. , 19.531 , 15.72598])
np.testing.assert_allclose(reg.x[0].toarray()[0],x,RTOL)
i_s = 'Maximum number of iterations reached.'
np.testing.assert_string_equal(reg.iter_stop,i_s)
its = 1
np.testing.assert_allclose(reg.iteration,its,RTOL)
my = 38.436224469387746
np.testing.assert_allclose(reg.mean_y,my)
stdy = 18.466069465206047
np.testing.assert_allclose(reg.std_y,stdy)
vm = np.array([[ 1.31767529e+02, -3.58368748e+00, -1.65090647e+00,
0.00000000e+00],
[ -3.58368748e+00, 1.35513711e-01, 3.77539055e-02,
0.00000000e+00],
[ -1.65090647e+00, 3.77539055e-02, 2.61042702e-02,
0.00000000e+00],
[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
2.82398517e-02]])
np.testing.assert_allclose(reg.vm,vm,RTOL)
xtx = np.array([[ 4.90000000e+01, 7.04371999e+02, 1.72131237e+03],
[ 7.04371999e+02, 1.16866734e+04, 2.15575320e+04],
[ 1.72131237e+03, 2.15575320e+04, 7.39058986e+04]])
np.testing.assert_allclose(reg.xtx,xtx,RTOL)
class TestGMErrorHet(unittest.TestCase):
def setUp(self):
db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
y = np.array(db.by_col("HOVAL"))
self.y = np.reshape(y, (49,1))
X = []
X.append(db.by_col("INC"))
X.append(db.by_col("CRIME"))
self.X = np.array(X).T
self.X = sparse.csr_matrix(self.X)
self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
self.w.transform = 'r'
def test_model(self):
reg = HET.GM_Error_Het(self.y, self.X, self.w, step1c=True)
betas = np.array([[ 47.99626638], [ 0.71048989], [ -0.55876126], [ 0.41178776]])
np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([ 27.38122697])
np.testing.assert_allclose(reg.u[0],u,RTOL)
ef = np.array([ 32.29765975])
np.testing.assert_allclose(reg.e_filtered[0],ef,RTOL)
predy = np.array([ 53.08577603])
np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
np.testing.assert_allclose(reg.n,n)
k = 3
np.testing.assert_allclose(reg.k,k)
y = np.array([ 80.467003])
np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([ 1. , 19.531 , 15.72598])
np.testing.assert_allclose(reg.x[0].toarray()[0],x,RTOL)
i_s = 'Maximum number of iterations reached.'
np.testing.assert_string_equal(reg.iter_stop,i_s)
its = 1
np.testing.assert_allclose(reg.iteration,its,RTOL)
my = 38.436224469387746
np.testing.assert_allclose(reg.mean_y,my)
stdy = 18.466069465206047
np.testing.assert_allclose(reg.std_y,stdy)
vm = np.array([[ 1.31767529e+02, -3.58368748e+00, -1.65090647e+00,
0.00000000e+00],
[ -3.58368748e+00, 1.35513711e-01, 3.77539055e-02,
0.00000000e+00],
[ -1.65090647e+00, 3.77539055e-02, 2.61042702e-02,
0.00000000e+00],
[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
2.82398517e-02]])
np.testing.assert_allclose(reg.vm,vm,RTOL)
pr2 = 0.34951013222581306
np.testing.assert_allclose(reg.pr2,pr2)
stde = np.array([ 11.47900385, 0.36812187, 0.16156816, 0.16804717])
np.testing.assert_allclose(reg.std_err,stde,RTOL)
z_stat = np.array([[ 4.18122226e+00, 2.89946274e-05],
[ 1.93003988e+00, 5.36018970e-02],
[ -3.45836247e+00, 5.43469673e-04],
[ 2.45042960e+00, 1.42685863e-02]])
np.testing.assert_allclose(reg.z_stat,z_stat,RTOL)
xtx = np.array([[ 4.90000000e+01, 7.04371999e+02, 1.72131237e+03],
[ 7.04371999e+02, 1.16866734e+04, 2.15575320e+04],
[ 1.72131237e+03, 2.15575320e+04, 7.39058986e+04]])
np.testing.assert_allclose(reg.xtx,xtx,RTOL)
class TestBaseGMEndogErrorHet(unittest.TestCase):
def setUp(self):
db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
y = np.array(db.by_col("HOVAL"))
self.y = np.reshape(y, (49,1))
X = []
X.append(db.by_col("INC"))
self.X = np.array(X).T
self.X = np.hstack((np.ones(self.y.shape),self.X))
self.X = sparse.csr_matrix(self.X)
yd = []
yd.append(db.by_col("CRIME"))
self.yd = np.array(yd).T
q = []
q.append(db.by_col("DISCBD"))
self.q = np.array(q).T
self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
self.w.transform = 'r'
def test_model(self):
reg = HET.BaseGM_Endog_Error_Het(self.y, self.X, self.yd, self.q, self.w.sparse, step1c=True)
betas = np.array([[ 55.39707924], [ 0.46563046], [ -0.67038326], [ 0.41135023]])
np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([ 26.51812895])
np.testing.assert_allclose(reg.u[0],u,RTOL)
ef = np.array([ 31.46604707])
np.testing.assert_allclose(reg.e_filtered[0],ef,RTOL)
predy = np.array([ 53.94887405])
np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
np.testing.assert_allclose(reg.n,n)
k = 3
np.testing.assert_allclose(reg.k,k)
y = np.array([ 80.467003])
np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([ 1. , 19.531])
np.testing.assert_allclose(reg.x[0].toarray()[0],x,RTOL)
yend = np.array([ 15.72598])
np.testing.assert_allclose(reg.yend[0],yend,RTOL)
q = np.array([ 5.03])
np.testing.assert_allclose(reg.q[0],q,RTOL)
z = np.array([ 1. , 19.531 , 15.72598])
np.testing.assert_allclose(reg.z[0].toarray()[0],z,RTOL)
h = np.array([ 1. , 19.531, 5.03 ])
np.testing.assert_allclose(reg.h[0].toarray()[0],h,RTOL)
i_s = 'Maximum number of iterations reached.'
np.testing.assert_string_equal(reg.iter_stop,i_s)
its = 1
np.testing.assert_allclose(reg.iteration,its,RTOL)
my = 38.436224469387746
np.testing.assert_allclose(reg.mean_y,my)
stdy = 18.466069465206047
np.testing.assert_allclose(reg.std_y,stdy)
vm = np.array([[ 8.34637805e+02, -2.16932259e+01, -1.33327894e+01,
1.65840848e+00],
[ -2.16932259e+01, 5.97683070e-01, 3.39503523e-01,
-3.90111107e-02],
[ -1.33327894e+01, 3.39503523e-01, 2.19008080e-01,
-2.81929695e-02],
[ 1.65840848e+00, -3.90111107e-02, -2.81929695e-02,
3.15686105e-02]])
np.testing.assert_allclose(reg.vm,vm,RTOL)
hth = np.array([[ 49. , 704.371999 , 139.75 ],
[ 704.371999 , 11686.67338121, 2246.12800625],
[ 139.75 , 2246.12800625, 498.5851 ]])
np.testing.assert_allclose(reg.hth,hth,RTOL)
class TestGMEndogErrorHet(unittest.TestCase):
def setUp(self):
db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
y = np.array(db.by_col("HOVAL"))
self.y = np.reshape(y, (49,1))
X = []
X.append(db.by_col("INC"))
self.X = np.array(X).T
self.X = sparse.csr_matrix(self.X)
yd = []
yd.append(db.by_col("CRIME"))
self.yd = np.array(yd).T
q = []
q.append(db.by_col("DISCBD"))
self.q = np.array(q).T
self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
self.w.transform = 'r'
def test_model(self):
reg = HET.GM_Endog_Error_Het(self.y, self.X, self.yd, self.q, self.w, step1c=True)
betas = np.array([[ 55.39707924], [ 0.46563046], [ -0.67038326], [ 0.41135023]])
np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([ 26.51812895])
np.testing.assert_allclose(reg.u[0],u,RTOL)
predy = np.array([ 53.94887405])
np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
np.testing.assert_allclose(reg.n,n)
k = 3
np.testing.assert_allclose(reg.k,k)
y = np.array([ 80.467003])
np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([ 1. , 19.531])
np.testing.assert_allclose(reg.x[0].toarray()[0],x,RTOL)
yend = np.array([ 15.72598])
np.testing.assert_allclose(reg.yend[0],yend,RTOL)
q = np.array([ 5.03])
np.testing.assert_allclose(reg.q[0],q,RTOL)
z = np.array([ 1. , 19.531 , 15.72598])
np.testing.assert_allclose(reg.z[0].toarray()[0],z,RTOL)
h = np.array([ 1. , 19.531, 5.03 ])
np.testing.assert_allclose(reg.h[0].toarray()[0],h,RTOL)
i_s = 'Maximum number of iterations reached.'
np.testing.assert_string_equal(reg.iter_stop,i_s)
its = 1
np.testing.assert_allclose(reg.iteration,its,RTOL)
my = 38.436224469387746
np.testing.assert_allclose(reg.mean_y,my)
stdy = 18.466069465206047
np.testing.assert_allclose(reg.std_y,stdy)
vm = np.array([[ 8.34637805e+02, -2.16932259e+01, -1.33327894e+01,
1.65840848e+00],
[ -2.16932259e+01, 5.97683070e-01, 3.39503523e-01,
-3.90111107e-02],
[ -1.33327894e+01, 3.39503523e-01, 2.19008080e-01,
-2.81929695e-02],
[ 1.65840848e+00, -3.90111107e-02, -2.81929695e-02,
3.15686105e-02]])
np.testing.assert_allclose(reg.vm,vm,RTOL)
pr2 = 0.34648011338954804
np.testing.assert_allclose(reg.pr2,pr2,RTOL)
std_err = np.array([ 28.89009873, 0.77309965, 0.46798299,
0.17767558])
np.testing.assert_allclose(reg.std_err,std_err,RTOL)
z_stat = np.array([(1.9175109006819244, 0.055173057472126787), (0.60229035155742305, 0.54698088217644414), (-1.4324949211864271, 0.15200223057569454), (2.3151759776869496, 0.020603303355572443)])
np.testing.assert_allclose(reg.z_stat,z_stat,RTOL)
hth = np.array([[ 49. , 704.371999 , 139.75 ],
[ 704.371999 , 11686.67338121, 2246.12800625],
[ 139.75 , 2246.12800625, 498.5851 ]])
np.testing.assert_allclose(reg.hth,hth,RTOL)
class TestBaseGMComboHet(unittest.TestCase):
def setUp(self):
db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
y = np.array(db.by_col("HOVAL"))
self.y = np.reshape(y, (49,1))
X = []
X.append(db.by_col("INC"))
X.append(db.by_col("CRIME"))
self.X = np.array(X).T
self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
self.w.transform = 'r'
def test_model(self):
# Only spatial lag
yd2, q2 = pysal.spreg.utils.set_endog(self.y, self.X, self.w, None, None, 1, True)
self.X = np.hstack((np.ones(self.y.shape),self.X))
self.X = sparse.csr_matrix(self.X)
reg = HET.BaseGM_Combo_Het(self.y, self.X, yend=yd2, q=q2, w=self.w.sparse, step1c=True)
betas = np.array([[ 57.7778574 ], [ 0.73034922], [ -0.59257362], [ -0.2230231 ], [ 0.56636724]])
np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([ 25.65156033])
np.testing.assert_allclose(reg.u[0],u,RTOL)
ef = np.array([ 31.87664403])
np.testing.assert_allclose(reg.e_filtered[0],ef,RTOL)
predy = np.array([ 54.81544267])
np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
np.testing.assert_allclose(reg.n,n)
k = 4
np.testing.assert_allclose(reg.k,k)
y = np.array([ 80.467003])
np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([ 1. , 19.531 , 15.72598])
np.testing.assert_allclose(reg.x[0].toarray()[0],x,RTOL)
yend = np.array([ 35.4585005])
np.testing.assert_allclose(reg.yend[0],yend,RTOL)
q = np.array([ 18.594 , 24.7142675])
np.testing.assert_allclose(reg.q[0],q,RTOL)
z = np.array([ 1. , 19.531 , 15.72598 , 35.4585005])
np.testing.assert_allclose(reg.z[0].toarray()[0],z,RTOL)
i_s = 'Maximum number of iterations reached.'
np.testing.assert_string_equal(reg.iter_stop,i_s)
its = 1
np.testing.assert_allclose(reg.iteration,its,RTOL)
my = 38.436224469387746
np.testing.assert_allclose(reg.mean_y,my)
stdy = 18.466069465206047
np.testing.assert_allclose(reg.std_y,stdy,RTOL)
vm = np.array([[ 4.86218274e+02, -2.77268729e+00, -1.59987770e+00,
-1.01969471e+01, 2.74302006e+00],
[ -2.77268729e+00, 1.04680972e-01, 2.51172238e-02,
1.95136385e-03, 3.70052723e-03],
[ -1.59987770e+00, 2.51172238e-02, 2.15655720e-02,
7.65868344e-03, -7.30173070e-03],
[ -1.01969471e+01, 1.95136385e-03, 7.65868344e-03,
2.78273684e-01, -6.89402590e-02],
[ 2.74302006e+00, 3.70052723e-03, -7.30173070e-03,
-6.89402590e-02, 7.12034037e-02]])
np.testing.assert_allclose(reg.vm,vm,RTOL)
hth = np.array([[ 4.90000000e+01, 7.04371999e+02, 1.72131237e+03,
7.24743592e+02, 1.70735413e+03],
[ 7.04371999e+02, 1.16866734e+04, 2.15575320e+04,
1.10925200e+04, 2.23848036e+04],
[ 1.72131237e+03, 2.15575320e+04, 7.39058986e+04,
2.34796298e+04, 6.70145378e+04],
[ 7.24743592e+02, 1.10925200e+04, 2.34796298e+04,
1.16146226e+04, 2.30304624e+04],
[ 1.70735413e+03, 2.23848036e+04, 6.70145378e+04,
2.30304624e+04, 6.69879858e+04]])
np.testing.assert_allclose(reg.hth,hth,RTOL)
class TestGMComboHet(unittest.TestCase):
def setUp(self):
db=pysal.open(pysal.examples.get_path("columbus.dbf"),"r")
y = np.array(db.by_col("HOVAL"))
self.y = np.reshape(y, (49,1))
X = []
X.append(db.by_col("INC"))
X.append(db.by_col("CRIME"))
self.X = np.array(X).T
self.X = sparse.csr_matrix(self.X)
self.w = pysal.rook_from_shapefile(pysal.examples.get_path("columbus.shp"))
self.w.transform = 'r'
def test_model(self):
# Only spatial lag
reg = HET.GM_Combo_Het(self.y, self.X, w=self.w, step1c=True)
betas = np.array([[ 57.7778574 ], [ 0.73034922], [ -0.59257362], [ -0.2230231 ], [ 0.56636724]])
np.testing.assert_allclose(reg.betas,betas,RTOL)
u = np.array([ 25.65156033])
np.testing.assert_allclose(reg.u[0],u,RTOL)
ef = np.array([ 31.87664403])
np.testing.assert_allclose(reg.e_filtered[0],ef,RTOL)
ep = np.array([ 28.30648145])
np.testing.assert_allclose(reg.e_pred[0],ep,RTOL)
pe = np.array([ 52.16052155])
np.testing.assert_allclose(reg.predy_e[0],pe,RTOL)
predy = np.array([ 54.81544267])
np.testing.assert_allclose(reg.predy[0],predy,RTOL)
n = 49
np.testing.assert_allclose(reg.n,n)
k = 4
np.testing.assert_allclose(reg.k,k)
y = np.array([ 80.467003])
np.testing.assert_allclose(reg.y[0],y,RTOL)
x = np.array([ 1. , 19.531 , 15.72598])
np.testing.assert_allclose(reg.x[0].toarray()[0],x,RTOL)
yend = np.array([ 35.4585005])
np.testing.assert_allclose(reg.yend[0],yend,RTOL)
q = np.array([ 18.594 , 24.7142675])
np.testing.assert_allclose(reg.q[0].toarray()[0],q,RTOL)
z = np.array([ 1. , 19.531 , 15.72598 , 35.4585005])
np.testing.assert_allclose(reg.z[0].toarray()[0],z,RTOL)
i_s = 'Maximum number of iterations reached.'
np.testing.assert_string_equal(reg.iter_stop,i_s)
its = 1
np.testing.assert_allclose(reg.iteration,its,RTOL)
my = 38.436224469387746
np.testing.assert_allclose(reg.mean_y,my)
stdy = 18.466069465206047
np.testing.assert_allclose(reg.std_y,stdy)
vm = np.array([[ 4.86218274e+02, -2.77268729e+00, -1.59987770e+00,
-1.01969471e+01, 2.74302006e+00],
[ -2.77268729e+00, 1.04680972e-01, 2.51172238e-02,
1.95136385e-03, 3.70052723e-03],
[ -1.59987770e+00, 2.51172238e-02, 2.15655720e-02,
7.65868344e-03, -7.30173070e-03],
[ -1.01969471e+01, 1.95136385e-03, 7.65868344e-03,
2.78273684e-01, -6.89402590e-02],
[ 2.74302006e+00, 3.70052723e-03, -7.30173070e-03,
-6.89402590e-02, 7.12034037e-02]])
np.testing.assert_allclose(reg.vm,vm,RTOL)
pr2 = 0.3001582877472412
np.testing.assert_allclose(reg.pr2,pr2,RTOL)
pr2_e = 0.35613102283621967
np.testing.assert_allclose(reg.pr2_e,pr2_e,RTOL)
std_err = np.array([ 22.05035768, 0.32354439, 0.14685221, 0.52751653, 0.26683966])
np.testing.assert_allclose(reg.std_err,std_err,RTOL)
z_stat = np.array([(2.6202684885795335, 0.00878605635338265), (2.2573385444145524, 0.023986928627746887), (-4.0351698589183433, 5.456281036278686e-05), (-0.42277935292121521, 0.67245625315942159), (2.1225002455741895, 0.033795752094112265)])
np.testing.assert_allclose(reg.z_stat,z_stat,RTOL)
hth = np.array([[ 4.90000000e+01, 7.04371999e+02, 1.72131237e+03,
7.24743592e+02, 1.70735413e+03],
[ 7.04371999e+02, 1.16866734e+04, 2.15575320e+04,
1.10925200e+04, 2.23848036e+04],
[ 1.72131237e+03, 2.15575320e+04, 7.39058986e+04,
2.34796298e+04, 6.70145378e+04],
[ 7.24743592e+02, 1.10925200e+04, 2.34796298e+04,
1.16146226e+04, 2.30304624e+04],
[ 1.70735413e+03, 2.23848036e+04, 6.70145378e+04,
2.30304624e+04, 6.69879858e+04]])
np.testing.assert_allclose(reg.hth,hth,RTOL)
if __name__ == '__main__':
unittest.main()
| 47.762019 | 249 | 0.583119 | 2,837 | 19,869 | 3.995065 | 0.102221 | 0.086554 | 0.144256 | 0.209017 | 0.909388 | 0.905329 | 0.892977 | 0.886889 | 0.874449 | 0.874007 | 0 | 0.255855 | 0.258594 | 19,869 | 415 | 250 | 47.877108 | 0.513543 | 0.001661 | 0 | 0.8875 | 0 | 0 | 0.024 | 0 | 0 | 0 | 0 | 0 | 0.2725 | 1 | 0.03 | false | 0 | 0.015 | 0 | 0.06 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 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 | 9 |
13a9866b456ad0719246be223708ccedc380798f | 65,505 | py | Python | flare/kernels/three_body_mc_simple.py | aaronchen0316/flare | 47a2a89af635dfec6b41a873625ac2411da14ebb | [
"MIT"
] | 144 | 2019-04-03T21:23:31.000Z | 2022-03-27T09:09:24.000Z | flare/kernels/three_body_mc_simple.py | aaronchen0316/flare | 47a2a89af635dfec6b41a873625ac2411da14ebb | [
"MIT"
] | 217 | 2019-09-04T16:01:15.000Z | 2022-03-31T20:36:10.000Z | flare/kernels/three_body_mc_simple.py | aaronchen0316/flare | 47a2a89af635dfec6b41a873625ac2411da14ebb | [
"MIT"
] | 46 | 2019-04-26T03:19:29.000Z | 2022-03-22T08:14:58.000Z | import numpy as np
from flare.kernels.kernels import (
force_helper,
force_energy_helper,
grad_helper,
three_body_fe_perm,
three_body_ee_perm,
three_body_se_perm,
three_body_ff_perm,
three_body_sf_perm,
three_body_ss_perm,
three_body_grad_perm,
grad_constants,
)
from numba import njit
from flare.env import AtomicEnvironment
from typing import Callable
import flare.kernels.cutoffs as cf
from math import exp
class ThreeBodyKernel:
def __init__(
self,
hyperparameters: "ndarray",
cutoff: float,
cutoff_func: Callable = cf.quadratic_cutoff,
):
self.hyperparameters = hyperparameters
self.signal_variance = hyperparameters[0]
self.length_scale = hyperparameters[1]
self.cutoff = cutoff
self.cutoff_func = cutoff_func
def energy_energy(self, env1: AtomicEnvironment, env2: AtomicEnvironment):
args = self.get_args(env1, env2)
return energy_energy(*args)
def force_energy(self, env1: AtomicEnvironment, env2: AtomicEnvironment):
args = self.get_args(env1, env2)
return force_energy(*args)
def stress_energy(self, env1: AtomicEnvironment, env2: AtomicEnvironment):
args = self.get_args(env1, env2)
return stress_energy(*args)
def force_force(self, env1: AtomicEnvironment, env2: AtomicEnvironment):
args = self.get_args(env1, env2)
return force_force(*args)
def stress_force(self, env1: AtomicEnvironment, env2: AtomicEnvironment):
args = self.get_args(env1, env2)
return stress_force(*args)
def stress_stress(self, env1: AtomicEnvironment, env2: AtomicEnvironment):
args = self.get_args(env1, env2)
return stress_stress(*args)
def force_force_gradient(self, env1: AtomicEnvironment, env2: AtomicEnvironment):
args = self.get_args(env1, env2)
return force_force_gradient(*args)
def efs_energy(self, env1: AtomicEnvironment, env2: AtomicEnvironment):
args = self.get_args(env1, env2)
return efs_energy(*args)
def efs_force(self, env1: AtomicEnvironment, env2: AtomicEnvironment):
args = self.get_args(env1, env2)
return efs_force(*args)
def efs_self(self, env1: AtomicEnvironment):
return efs_self(
env1.bond_array_3,
env1.ctype,
env1.etypes,
env1.cross_bond_inds,
env1.cross_bond_dists,
env1.triplet_counts,
self.signal_variance,
self.length_scale,
self.cutoff,
self.cutoff_func,
)
def get_args(self, env1, env2):
return (
env1.bond_array_3,
env1.ctype,
env1.etypes,
env2.bond_array_3,
env2.ctype,
env2.etypes,
env1.cross_bond_inds,
env2.cross_bond_inds,
env1.cross_bond_dists,
env2.cross_bond_dists,
env1.triplet_counts,
env2.triplet_counts,
self.signal_variance,
self.length_scale,
self.cutoff,
self.cutoff_func,
)
@njit
def energy_energy(
bond_array_1,
c1,
etypes1,
bond_array_2,
c2,
etypes2,
cross_bond_inds_1,
cross_bond_inds_2,
cross_bond_dists_1,
cross_bond_dists_2,
triplets_1,
triplets_2,
sig,
ls,
r_cut,
cutoff_func,
):
"""3-body multi-element kernel between two local energies accelerated
with Numba.
Args:
bond_array_1 (np.ndarray): 3-body bond array of the first local
environment.
c1 (int): Species of the central atom of the first local environment.
etypes1 (np.ndarray): Species of atoms in the first local
environment.
bond_array_2 (np.ndarray): 3-body bond array of the second local
environment.
c2 (int): Species of the central atom of the second local environment.
etypes2 (np.ndarray): Species of atoms in the second local
environment.
cross_bond_inds_1 (np.ndarray): Two dimensional array whose row m
contains the indices of atoms n > m in the first local
environment that are within a distance r_cut of both atom n and
the central atom.
cross_bond_inds_2 (np.ndarray): Two dimensional array whose row m
contains the indices of atoms n > m in the second local
environment that are within a distance r_cut of both atom n and
the central atom.
cross_bond_dists_1 (np.ndarray): Two dimensional array whose row m
contains the distances from atom m of atoms n > m in the first
local environment that are within a distance r_cut of both atom
n and the central atom.
cross_bond_dists_2 (np.ndarray): Two dimensional array whose row m
contains the distances from atom m of atoms n > m in the second
local environment that are within a distance r_cut of both atom
n and the central atom.
triplets_1 (np.ndarray): One dimensional array of integers whose entry
m is the number of atoms in the first local environment that are
within a distance r_cut of atom m.
triplets_2 (np.ndarray): One dimensional array of integers whose entry
m is the number of atoms in the second local environment that are
within a distance r_cut of atom m.
sig (float): 3-body signal variance hyperparameter.
ls (float): 3-body length scale hyperparameter.
r_cut (float): 3-body cutoff radius.
cutoff_func (Callable): Cutoff function.
Returns:
float:
Value of the 3-body local energy kernel.
"""
kern = 0
sig2 = sig * sig
ls2 = 1 / (2 * ls * ls)
for m in range(bond_array_1.shape[0]):
ri1 = bond_array_1[m, 0]
fi1, _ = cutoff_func(r_cut, ri1, 0)
ei1 = etypes1[m]
for n in range(triplets_1[m]):
ind1 = cross_bond_inds_1[m, m + n + 1]
ri2 = bond_array_1[ind1, 0]
fi2, _ = cutoff_func(r_cut, ri2, 0)
ei2 = etypes1[ind1]
ri3 = cross_bond_dists_1[m, m + n + 1]
fi3, _ = cutoff_func(r_cut, ri3, 0)
fi = fi1 * fi2 * fi3
for p in range(bond_array_2.shape[0]):
rj1 = bond_array_2[p, 0]
fj1, _ = cutoff_func(r_cut, rj1, 0)
ej1 = etypes2[p]
for q in range(triplets_2[p]):
ind2 = cross_bond_inds_2[p, p + q + 1]
rj2 = bond_array_2[ind2, 0]
fj2, _ = cutoff_func(r_cut, rj2, 0)
ej2 = etypes2[ind2]
rj3 = cross_bond_dists_2[p, p + q + 1]
fj3, _ = cutoff_func(r_cut, rj3, 0)
fj = fj1 * fj2 * fj3
r11 = ri1 - rj1
r12 = ri1 - rj2
r13 = ri1 - rj3
r21 = ri2 - rj1
r22 = ri2 - rj2
r23 = ri2 - rj3
r31 = ri3 - rj1
r32 = ri3 - rj2
r33 = ri3 - rj3
kern += three_body_ee_perm(
r11,
r12,
r13,
r21,
r22,
r23,
r31,
r32,
r33,
c1,
c2,
ei1,
ei2,
ej1,
ej2,
fi,
fj,
ls2,
sig2,
)
return kern / 9
@njit
def force_energy(
bond_array_1,
c1,
etypes1,
bond_array_2,
c2,
etypes2,
cross_bond_inds_1,
cross_bond_inds_2,
cross_bond_dists_1,
cross_bond_dists_2,
triplets_1,
triplets_2,
sig,
ls,
r_cut,
cutoff_func,
):
"""3-body multi-element kernel between a force component and a local
energy accelerated with Numba.
Args:
bond_array_1 (np.ndarray): 3-body bond array of the first local
environment.
c1 (int): Species of the central atom of the first local environment.
etypes1 (np.ndarray): Species of atoms in the first local
environment.
bond_array_2 (np.ndarray): 3-body bond array of the second local
environment.
c2 (int): Species of the central atom of the second local environment.
etypes2 (np.ndarray): Species of atoms in the second local
environment.
cross_bond_inds_1 (np.ndarray): Two dimensional array whose row m
contains the indices of atoms n > m in the first local
environment that are within a distance r_cut of both atom n and
the central atom.
cross_bond_inds_2 (np.ndarray): Two dimensional array whose row m
contains the indices of atoms n > m in the second local
environment that are within a distance r_cut of both atom n and
the central atom.
cross_bond_dists_1 (np.ndarray): Two dimensional array whose row m
contains the distances from atom m of atoms n > m in the first
local environment that are within a distance r_cut of both atom
n and the central atom.
cross_bond_dists_2 (np.ndarray): Two dimensional array whose row m
contains the distances from atom m of atoms n > m in the second
local environment that are within a distance r_cut of both atom
n and the central atom.
triplets_1 (np.ndarray): One dimensional array of integers whose entry
m is the number of atoms in the first local environment that are
within a distance r_cut of atom m.
triplets_2 (np.ndarray): One dimensional array of integers whose entry
m is the number of atoms in the second local environment that are
within a distance r_cut of atom m.
sig (float): 3-body signal variance hyperparameter.
ls (float): 3-body length scale hyperparameter.
r_cut (float): 3-body cutoff radius.
cutoff_func (Callable): Cutoff function.
Returns:
float:
Value of the 3-body force/energy kernel.
"""
kern = np.zeros(3)
sig2 = sig * sig
ls1 = 1 / (2 * ls * ls)
ls2 = 1 / (ls * ls)
for m in range(bond_array_1.shape[0]):
ri1 = bond_array_1[m, 0]
ei1 = etypes1[m]
for n in range(triplets_1[m]):
ind1 = cross_bond_inds_1[m, m + n + 1]
ri2 = bond_array_1[ind1, 0]
ei2 = etypes1[ind1]
ri3 = cross_bond_dists_1[m, m + n + 1]
fi3, _ = cutoff_func(r_cut, ri3, 0)
for p in range(bond_array_2.shape[0]):
rj1 = bond_array_2[p, 0]
fj1, _ = cutoff_func(r_cut, rj1, 0)
ej1 = etypes2[p]
for q in range(triplets_2[p]):
ind2 = cross_bond_inds_2[p, p + q + 1]
rj2 = bond_array_2[ind2, 0]
fj2, _ = cutoff_func(r_cut, rj2, 0)
ej2 = etypes2[ind2]
rj3 = cross_bond_dists_2[p, p + q + 1]
fj3, _ = cutoff_func(r_cut, rj3, 0)
fj = fj1 * fj2 * fj3
r11 = ri1 - rj1
r12 = ri1 - rj2
r13 = ri1 - rj3
r21 = ri2 - rj1
r22 = ri2 - rj2
r23 = ri2 - rj3
r31 = ri3 - rj1
r32 = ri3 - rj2
r33 = ri3 - rj3
for d1 in range(3):
ci1 = bond_array_1[m, d1 + 1]
fi1, fdi1 = cutoff_func(r_cut, ri1, ci1)
ci2 = bond_array_1[ind1, d1 + 1]
fi2, fdi2 = cutoff_func(r_cut, ri2, ci2)
fi = fi1 * fi2 * fi3
fdi = fdi1 * fi2 * fi3 + fi1 * fdi2 * fi3
kern[d1] += three_body_fe_perm(
r11,
r12,
r13,
r21,
r22,
r23,
r31,
r32,
r33,
c1,
c2,
ci1,
ci2,
ei1,
ei2,
ej1,
ej2,
fi,
fj,
fdi,
ls1,
ls2,
sig2,
)
return kern / 3
@njit
def stress_energy(
bond_array_1,
c1,
etypes1,
bond_array_2,
c2,
etypes2,
cross_bond_inds_1,
cross_bond_inds_2,
cross_bond_dists_1,
cross_bond_dists_2,
triplets_1,
triplets_2,
sig,
ls,
r_cut,
cutoff_func,
):
"""3-body multi-element kernel between a force component and a local
energy accelerated with Numba.
Args:
bond_array_1 (np.ndarray): 3-body bond array of the first local
environment.
c1 (int): Species of the central atom of the first local environment.
etypes1 (np.ndarray): Species of atoms in the first local
environment.
bond_array_2 (np.ndarray): 3-body bond array of the second local
environment.
c2 (int): Species of the central atom of the second local environment.
etypes2 (np.ndarray): Species of atoms in the second local
environment.
cross_bond_inds_1 (np.ndarray): Two dimensional array whose row m
contains the indices of atoms n > m in the first local
environment that are within a distance r_cut of both atom n and
the central atom.
cross_bond_inds_2 (np.ndarray): Two dimensional array whose row m
contains the indices of atoms n > m in the second local
environment that are within a distance r_cut of both atom n and
the central atom.
cross_bond_dists_1 (np.ndarray): Two dimensional array whose row m
contains the distances from atom m of atoms n > m in the first
local environment that are within a distance r_cut of both atom
n and the central atom.
cross_bond_dists_2 (np.ndarray): Two dimensional array whose row m
contains the distances from atom m of atoms n > m in the second
local environment that are within a distance r_cut of both atom
n and the central atom.
triplets_1 (np.ndarray): One dimensional array of integers whose entry
m is the number of atoms in the first local environment that are
within a distance r_cut of atom m.
triplets_2 (np.ndarray): One dimensional array of integers whose entry
m is the number of atoms in the second local environment that are
within a distance r_cut of atom m.
sig (float): 3-body signal variance hyperparameter.
ls (float): 3-body length scale hyperparameter.
r_cut (float): 3-body cutoff radius.
cutoff_func (Callable): Cutoff function.
Returns:
float:
Value of the 3-body force/energy kernel.
"""
kern = np.zeros(6)
sig2 = sig * sig
ls1 = 1 / (2 * ls * ls)
ls2 = 1 / (ls * ls)
for m in range(bond_array_1.shape[0]):
ri1 = bond_array_1[m, 0]
fi1, _ = cutoff_func(r_cut, ri1, 0)
ei1 = etypes1[m]
for n in range(triplets_1[m]):
ind1 = cross_bond_inds_1[m, m + n + 1]
ri2 = bond_array_1[ind1, 0]
fi2, _ = cutoff_func(r_cut, ri2, 0)
ei2 = etypes1[ind1]
ri3 = cross_bond_dists_1[m, m + n + 1]
fi3, _ = cutoff_func(r_cut, ri3, 0)
fi = fi1 * fi2 * fi3
for p in range(bond_array_2.shape[0]):
rj1 = bond_array_2[p, 0]
fj1, _ = cutoff_func(r_cut, rj1, 0)
ej1 = etypes2[p]
for q in range(triplets_2[p]):
ind2 = cross_bond_inds_2[p, p + q + 1]
rj2 = bond_array_2[ind2, 0]
fj2, _ = cutoff_func(r_cut, rj2, 0)
ej2 = etypes2[ind2]
rj3 = cross_bond_dists_2[p, p + q + 1]
fj3, _ = cutoff_func(r_cut, rj3, 0)
fj = fj1 * fj2 * fj3
r11 = ri1 - rj1
r12 = ri1 - rj2
r13 = ri1 - rj3
r21 = ri2 - rj1
r22 = ri2 - rj2
r23 = ri2 - rj3
r31 = ri3 - rj1
r32 = ri3 - rj2
r33 = ri3 - rj3
stress_count = 0
for d1 in range(3):
ci1 = bond_array_1[m, d1 + 1]
fi1, fdi1 = cutoff_func(r_cut, ri1, ci1)
ci2 = bond_array_1[ind1, d1 + 1]
fi2, fdi2 = cutoff_func(r_cut, ri2, ci2)
fdi_p1 = fdi1 * fi2 * fi3
fdi_p2 = fi1 * fdi2 * fi3
fdi = fdi_p1 + fdi_p2
for d2 in range(d1, 3):
coord1 = bond_array_1[m, d2 + 1] * ri1
coord2 = bond_array_1[ind1, d2 + 1] * ri2
kern[stress_count] += three_body_se_perm(
r11,
r12,
r13,
r21,
r22,
r23,
r31,
r32,
r33,
c1,
c2,
ci1,
ci2,
ei1,
ei2,
ej1,
ej2,
fi,
fj,
fdi,
ls1,
ls2,
sig2,
coord1,
coord2,
fdi_p1,
fdi_p2,
)
stress_count += 1
return kern / 6
@njit
def force_force(
bond_array_1,
c1,
etypes1,
bond_array_2,
c2,
etypes2,
cross_bond_inds_1,
cross_bond_inds_2,
cross_bond_dists_1,
cross_bond_dists_2,
triplets_1,
triplets_2,
sig,
ls,
r_cut,
cutoff_func,
):
"""3-body multi-element kernel between two force components accelerated
with Numba.
Args:
bond_array_1 (np.ndarray): 3-body bond array of the first local
environment.
c1 (int): Species of the central atom of the first local environment.
etypes1 (np.ndarray): Species of atoms in the first local
environment.
bond_array_2 (np.ndarray): 3-body bond array of the second local
environment.
c2 (int): Species of the central atom of the second local environment.
etypes2 (np.ndarray): Species of atoms in the second local
environment.
cross_bond_inds_1 (np.ndarray): Two dimensional array whose row m
contains the indices of atoms n > m in the first local
environment that are within a distance r_cut of both atom n and
the central atom.
cross_bond_inds_2 (np.ndarray): Two dimensional array whose row m
contains the indices of atoms n > m in the second local
environment that are within a distance r_cut of both atom n and
the central atom.
cross_bond_dists_1 (np.ndarray): Two dimensional array whose row m
contains the distances from atom m of atoms n > m in the first
local environment that are within a distance r_cut of both atom
n and the central atom.
cross_bond_dists_2 (np.ndarray): Two dimensional array whose row m
contains the distances from atom m of atoms n > m in the second
local environment that are within a distance r_cut of both atom
n and the central atom.
triplets_1 (np.ndarray): One dimensional array of integers whose entry
m is the number of atoms in the first local environment that are
within a distance r_cut of atom m.
triplets_2 (np.ndarray): One dimensional array of integers whose entry
m is the number of atoms in the second local environment that are
within a distance r_cut of atom m.
sig (float): 3-body signal variance hyperparameter.
ls (float): 3-body length scale hyperparameter.
r_cut (float): 3-body cutoff radius.
cutoff_func (Callable): Cutoff function.
Return:
float: Value of the 3-body kernel.
"""
kern = np.zeros((3, 3))
# pre-compute constants that appear in the inner loop
sig2 = sig * sig
ls1 = 1 / (2 * ls * ls)
ls2 = 1 / (ls * ls)
ls3 = ls2 * ls2
# first loop over the first 3-body environment
for m in range(bond_array_1.shape[0]):
ri1 = bond_array_1[m, 0]
ei1 = etypes1[m]
# second loop over the first 3-body environment
for n in range(triplets_1[m]):
ind1 = cross_bond_inds_1[m, m + n + 1]
ri2 = bond_array_1[ind1, 0]
ei2 = etypes1[ind1]
ri3 = cross_bond_dists_1[m, m + n + 1]
fi3, _ = cutoff_func(r_cut, ri3, 0)
# first loop over the second 3-body environment
for p in range(bond_array_2.shape[0]):
rj1 = bond_array_2[p, 0]
ej1 = etypes2[p]
# second loop over the second 3-body environment
for q in range(triplets_2[p]):
ind2 = cross_bond_inds_2[p, p + 1 + q]
rj2 = bond_array_2[ind2, 0]
rj3 = cross_bond_dists_2[p, p + 1 + q]
fj3, _ = cutoff_func(r_cut, rj3, 0)
ej2 = etypes2[ind2]
r11 = ri1 - rj1
r12 = ri1 - rj2
r13 = ri1 - rj3
r21 = ri2 - rj1
r22 = ri2 - rj2
r23 = ri2 - rj3
r31 = ri3 - rj1
r32 = ri3 - rj2
r33 = ri3 - rj3
for d1 in range(3):
ci1 = bond_array_1[m, d1 + 1]
fi1, fdi1 = cutoff_func(r_cut, ri1, ci1)
ci2 = bond_array_1[ind1, d1 + 1]
fi2, fdi2 = cutoff_func(r_cut, ri2, ci2)
fi = fi1 * fi2 * fi3
fdi = fdi1 * fi2 * fi3 + fi1 * fdi2 * fi3
for d2 in range(3):
cj1 = bond_array_2[p, d2 + 1]
fj1, fdj1 = cutoff_func(r_cut, rj1, cj1)
cj2 = bond_array_2[ind2, d2 + 1]
fj2, fdj2 = cutoff_func(r_cut, rj2, cj2)
fj = fj1 * fj2 * fj3
fdj = fdj1 * fj2 * fj3 + fj1 * fdj2 * fj3
kern[d1, d2] += three_body_ff_perm(
r11,
r12,
r13,
r21,
r22,
r23,
r31,
r32,
r33,
c1,
c2,
ci1,
ci2,
cj1,
cj2,
ei1,
ei2,
ej1,
ej2,
fi,
fj,
fdi,
fdj,
ls1,
ls2,
ls3,
sig2,
)
return kern
@njit
def stress_force(
bond_array_1,
c1,
etypes1,
bond_array_2,
c2,
etypes2,
cross_bond_inds_1,
cross_bond_inds_2,
cross_bond_dists_1,
cross_bond_dists_2,
triplets_1,
triplets_2,
sig,
ls,
r_cut,
cutoff_func,
):
"""3-body multi-element kernel between two force components accelerated
with Numba.
Args:
bond_array_1 (np.ndarray): 3-body bond array of the first local
environment.
c1 (int): Species of the central atom of the first local environment.
etypes1 (np.ndarray): Species of atoms in the first local
environment.
bond_array_2 (np.ndarray): 3-body bond array of the second local
environment.
c2 (int): Species of the central atom of the second local environment.
etypes2 (np.ndarray): Species of atoms in the second local
environment.
cross_bond_inds_1 (np.ndarray): Two dimensional array whose row m
contains the indices of atoms n > m in the first local
environment that are within a distance r_cut of both atom n and
the central atom.
cross_bond_inds_2 (np.ndarray): Two dimensional array whose row m
contains the indices of atoms n > m in the second local
environment that are within a distance r_cut of both atom n and
the central atom.
cross_bond_dists_1 (np.ndarray): Two dimensional array whose row m
contains the distances from atom m of atoms n > m in the first
local environment that are within a distance r_cut of both atom
n and the central atom.
cross_bond_dists_2 (np.ndarray): Two dimensional array whose row m
contains the distances from atom m of atoms n > m in the second
local environment that are within a distance r_cut of both atom
n and the central atom.
triplets_1 (np.ndarray): One dimensional array of integers whose entry
m is the number of atoms in the first local environment that are
within a distance r_cut of atom m.
triplets_2 (np.ndarray): One dimensional array of integers whose entry
m is the number of atoms in the second local environment that are
within a distance r_cut of atom m.
sig (float): 3-body signal variance hyperparameter.
ls (float): 3-body length scale hyperparameter.
r_cut (float): 3-body cutoff radius.
cutoff_func (Callable): Cutoff function.
Return:
float: Value of the 3-body kernel.
"""
kern = np.zeros((6, 3))
# pre-compute constants that appear in the inner loop
sig2 = sig * sig
ls1 = 1 / (2 * ls * ls)
ls2 = 1 / (ls * ls)
ls3 = ls2 * ls2
# first loop over the first 3-body environment
for m in range(bond_array_1.shape[0]):
ri1 = bond_array_1[m, 0]
ei1 = etypes1[m]
# second loop over the first 3-body environment
for n in range(triplets_1[m]):
ind1 = cross_bond_inds_1[m, m + n + 1]
ri2 = bond_array_1[ind1, 0]
ei2 = etypes1[ind1]
ri3 = cross_bond_dists_1[m, m + n + 1]
fi3, _ = cutoff_func(r_cut, ri3, 0)
# first loop over the second 3-body environment
for p in range(bond_array_2.shape[0]):
rj1 = bond_array_2[p, 0]
ej1 = etypes2[p]
# second loop over the second 3-body environment
for q in range(triplets_2[p]):
ind2 = cross_bond_inds_2[p, p + 1 + q]
rj2 = bond_array_2[ind2, 0]
rj3 = cross_bond_dists_2[p, p + 1 + q]
fj3, _ = cutoff_func(r_cut, rj3, 0)
ej2 = etypes2[ind2]
r11 = ri1 - rj1
r12 = ri1 - rj2
r13 = ri1 - rj3
r21 = ri2 - rj1
r22 = ri2 - rj2
r23 = ri2 - rj3
r31 = ri3 - rj1
r32 = ri3 - rj2
r33 = ri3 - rj3
stress_count = 0
for d1 in range(3):
ci1 = bond_array_1[m, d1 + 1]
fi1, fdi1 = cutoff_func(r_cut, ri1, ci1)
ci2 = bond_array_1[ind1, d1 + 1]
fi2, fdi2 = cutoff_func(r_cut, ri2, ci2)
fi = fi1 * fi2 * fi3
fdi_p1 = fdi1 * fi2 * fi3
fdi_p2 = fi1 * fdi2 * fi3
fdi = fdi_p1 + fdi_p2
for d2 in range(d1, 3):
coord1 = bond_array_1[m, d2 + 1] * ri1
coord2 = bond_array_1[ind1, d2 + 1] * ri2
for d3 in range(3):
cj1 = bond_array_2[p, d3 + 1]
fj1, fdj1 = cutoff_func(r_cut, rj1, cj1)
cj2 = bond_array_2[ind2, d3 + 1]
fj2, fdj2 = cutoff_func(r_cut, rj2, cj2)
fj = fj1 * fj2 * fj3
fdj = fdj1 * fj2 * fj3 + fj1 * fdj2 * fj3
kern[stress_count, d3] += three_body_sf_perm(
r11,
r12,
r13,
r21,
r22,
r23,
r31,
r32,
r33,
c1,
c2,
ci1,
ci2,
cj1,
cj2,
ei1,
ei2,
ej1,
ej2,
fi,
fj,
fdi,
fdj,
ls1,
ls2,
ls3,
sig2,
coord1,
coord2,
fdi_p1,
fdi_p2,
)
stress_count += 1
return kern / 2
@njit
def stress_stress(
bond_array_1,
c1,
etypes1,
bond_array_2,
c2,
etypes2,
cross_bond_inds_1,
cross_bond_inds_2,
cross_bond_dists_1,
cross_bond_dists_2,
triplets_1,
triplets_2,
sig,
ls,
r_cut,
cutoff_func,
):
"""3-body multi-element kernel between two force components accelerated
with Numba.
Args:
bond_array_1 (np.ndarray): 3-body bond array of the first local
environment.
c1 (int): Species of the central atom of the first local environment.
etypes1 (np.ndarray): Species of atoms in the first local
environment.
bond_array_2 (np.ndarray): 3-body bond array of the second local
environment.
c2 (int): Species of the central atom of the second local environment.
etypes2 (np.ndarray): Species of atoms in the second local
environment.
cross_bond_inds_1 (np.ndarray): Two dimensional array whose row m
contains the indices of atoms n > m in the first local
environment that are within a distance r_cut of both atom n and
the central atom.
cross_bond_inds_2 (np.ndarray): Two dimensional array whose row m
contains the indices of atoms n > m in the second local
environment that are within a distance r_cut of both atom n and
the central atom.
cross_bond_dists_1 (np.ndarray): Two dimensional array whose row m
contains the distances from atom m of atoms n > m in the first
local environment that are within a distance r_cut of both atom
n and the central atom.
cross_bond_dists_2 (np.ndarray): Two dimensional array whose row m
contains the distances from atom m of atoms n > m in the second
local environment that are within a distance r_cut of both atom
n and the central atom.
triplets_1 (np.ndarray): One dimensional array of integers whose entry
m is the number of atoms in the first local environment that are
within a distance r_cut of atom m.
triplets_2 (np.ndarray): One dimensional array of integers whose entry
m is the number of atoms in the second local environment that are
within a distance r_cut of atom m.
sig (float): 3-body signal variance hyperparameter.
ls (float): 3-body length scale hyperparameter.
r_cut (float): 3-body cutoff radius.
cutoff_func (Callable): Cutoff function.
Return:
float: Value of the 3-body kernel.
"""
kern = np.zeros((6, 6))
# pre-compute constants that appear in the inner loop
sig2 = sig * sig
ls1 = 1 / (2 * ls * ls)
ls2 = 1 / (ls * ls)
ls3 = ls2 * ls2
# first loop over the first 3-body environment
for m in range(bond_array_1.shape[0]):
ri1 = bond_array_1[m, 0]
ei1 = etypes1[m]
# second loop over the first 3-body environment
for n in range(triplets_1[m]):
ind1 = cross_bond_inds_1[m, m + n + 1]
ri2 = bond_array_1[ind1, 0]
ei2 = etypes1[ind1]
ri3 = cross_bond_dists_1[m, m + n + 1]
fi3, _ = cutoff_func(r_cut, ri3, 0)
# first loop over the second 3-body environment
for p in range(bond_array_2.shape[0]):
rj1 = bond_array_2[p, 0]
ej1 = etypes2[p]
# second loop over the second 3-body environment
for q in range(triplets_2[p]):
ind2 = cross_bond_inds_2[p, p + 1 + q]
rj2 = bond_array_2[ind2, 0]
rj3 = cross_bond_dists_2[p, p + 1 + q]
fj3, _ = cutoff_func(r_cut, rj3, 0)
ej2 = etypes2[ind2]
r11 = ri1 - rj1
r12 = ri1 - rj2
r13 = ri1 - rj3
r21 = ri2 - rj1
r22 = ri2 - rj2
r23 = ri2 - rj3
r31 = ri3 - rj1
r32 = ri3 - rj2
r33 = ri3 - rj3
stress_count_1 = 0
for d1 in range(3):
ci1 = bond_array_1[m, d1 + 1]
fi1, fdi1 = cutoff_func(r_cut, ri1, ci1)
ci2 = bond_array_1[ind1, d1 + 1]
fi2, fdi2 = cutoff_func(r_cut, ri2, ci2)
fi = fi1 * fi2 * fi3
fdi_p1 = fdi1 * fi2 * fi3
fdi_p2 = fi1 * fdi2 * fi3
fdi = fdi_p1 + fdi_p2
for d2 in range(d1, 3):
coord1 = bond_array_1[m, d2 + 1] * ri1
coord2 = bond_array_1[ind1, d2 + 1] * ri2
stress_count_2 = 0
for d3 in range(3):
cj1 = bond_array_2[p, d3 + 1]
fj1, fdj1 = cutoff_func(r_cut, rj1, cj1)
cj2 = bond_array_2[ind2, d3 + 1]
fj2, fdj2 = cutoff_func(r_cut, rj2, cj2)
fj = fj1 * fj2 * fj3
fdj_p1 = fdj1 * fj2 * fj3
fdj_p2 = fj1 * fdj2 * fj3
fdj = fdj_p1 + fdj_p2
for d4 in range(d3, 3):
coord3 = bond_array_2[p, d4 + 1] * rj1
coord4 = bond_array_2[ind2, d4 + 1] * rj2
kern[
stress_count_1, stress_count_2
] += three_body_ss_perm(
r11,
r12,
r13,
r21,
r22,
r23,
r31,
r32,
r33,
c1,
c2,
ci1,
ci2,
cj1,
cj2,
ei1,
ei2,
ej1,
ej2,
fi,
fj,
fdi,
fdj,
ls1,
ls2,
ls3,
sig2,
coord1,
coord2,
coord3,
coord4,
fdi_p1,
fdi_p2,
fdj_p1,
fdj_p2,
)
stress_count_2 += 1
stress_count_1 += 1
return kern / 4
@njit
def force_force_gradient(
bond_array_1,
c1,
etypes1,
bond_array_2,
c2,
etypes2,
cross_bond_inds_1,
cross_bond_inds_2,
cross_bond_dists_1,
cross_bond_dists_2,
triplets_1,
triplets_2,
sig,
ls,
r_cut,
cutoff_func,
):
"""3-body multi-element kernel between two force components and its
gradient with respect to the hyperparameters.
Args:
bond_array_1 (np.ndarray): 3-body bond array of the first local
environment.
c1 (int): Species of the central atom of the first local environment.
etypes1 (np.ndarray): Species of atoms in the first local
environment.
bond_array_2 (np.ndarray): 3-body bond array of the second local
environment.
c2 (int): Species of the central atom of the second local environment.
etypes2 (np.ndarray): Species of atoms in the second local
environment.
cross_bond_inds_1 (np.ndarray): Two dimensional array whose row m
contains the indices of atoms n > m in the first local
environment that are within a distance r_cut of both atom n and
the central atom.
cross_bond_inds_2 (np.ndarray): Two dimensional array whose row m
contains the indices of atoms n > m in the second local
environment that are within a distance r_cut of both atom n and
the central atom.
cross_bond_dists_1 (np.ndarray): Two dimensional array whose row m
contains the distances from atom m of atoms n > m in the first
local environment that are within a distance r_cut of both atom
n and the central atom.
cross_bond_dists_2 (np.ndarray): Two dimensional array whose row m
contains the distances from atom m of atoms n > m in the second
local environment that are within a distance r_cut of both atom
n and the central atom.
triplets_1 (np.ndarray): One dimensional array of integers whose entry
m is the number of atoms in the first local environment that are
within a distance r_cut of atom m.
triplets_2 (np.ndarray): One dimensional array of integers whose entry
m is the number of atoms in the second local environment that are
within a distance r_cut of atom m.
sig (float): 3-body signal variance hyperparameter.
ls (float): 3-body length scale hyperparameter.
r_cut (float): 3-body cutoff radius.
cutoff_func (Callable): Cutoff function.
Returns:
(float, float):
Value of the 3-body kernel and its gradient with respect to the
hyperparameters.
"""
kernel_matrix = np.zeros((3, 3))
kernel_grad = np.zeros((2, 3, 3))
# pre-compute constants that appear in the inner loop
sig2, sig3, ls1, ls2, ls3, ls4, ls5, ls6 = grad_constants(sig, ls)
for m in range(bond_array_1.shape[0]):
ri1 = bond_array_1[m, 0]
ei1 = etypes1[m]
for n in range(triplets_1[m]):
ind1 = cross_bond_inds_1[m, m + n + 1]
ri3 = cross_bond_dists_1[m, m + n + 1]
ri2 = bond_array_1[ind1, 0]
ei2 = etypes1[ind1]
fi3, _ = cutoff_func(r_cut, ri3, 0)
for p in range(bond_array_2.shape[0]):
rj1 = bond_array_2[p, 0]
ej1 = etypes2[p]
for q in range(triplets_2[p]):
ind2 = cross_bond_inds_2[p, p + q + 1]
rj3 = cross_bond_dists_2[p, p + q + 1]
rj2 = bond_array_2[ind2, 0]
ej2 = etypes2[ind2]
fj3, _ = cutoff_func(r_cut, rj3, 0)
r11 = ri1 - rj1
r12 = ri1 - rj2
r13 = ri1 - rj3
r21 = ri2 - rj1
r22 = ri2 - rj2
r23 = ri2 - rj3
r31 = ri3 - rj1
r32 = ri3 - rj2
r33 = ri3 - rj3
for d1 in range(3):
ci1 = bond_array_1[m, d1 + 1]
fi1, fdi1 = cutoff_func(r_cut, ri1, ci1)
ci2 = bond_array_1[ind1, d1 + 1]
fi2, fdi2 = cutoff_func(r_cut, ri2, ci2)
fdi = fdi1 * fi2 * fi3 + fi1 * fdi2 * fi3
fi = fi1 * fi2 * fi3
for d2 in range(3):
cj1 = bond_array_2[p, d2 + 1]
fj1, fdj1 = cutoff_func(r_cut, rj1, cj1)
cj2 = bond_array_2[ind2, d2 + 1]
fj2, fdj2 = cutoff_func(r_cut, rj2, cj2)
fdj = fdj1 * fj2 * fj3 + fj1 * fdj2 * fj3
fj = fj1 * fj2 * fj3
kern_term, sig_term, ls_term = three_body_grad_perm(
r11,
r12,
r13,
r21,
r22,
r23,
r31,
r32,
r33,
c1,
c2,
ci1,
ci2,
cj1,
cj2,
ei1,
ei2,
ej1,
ej2,
fi,
fj,
fdi,
fdj,
ls1,
ls2,
ls3,
ls4,
ls5,
ls6,
sig2,
sig3,
)
kernel_matrix[d1, d2] += kern_term
kernel_grad[0, d1, d2] += sig_term
kernel_grad[1, d1, d2] += ls_term
return kernel_matrix, kernel_grad
@njit
def efs_energy(
bond_array_1,
c1,
etypes1,
bond_array_2,
c2,
etypes2,
cross_bond_inds_1,
cross_bond_inds_2,
cross_bond_dists_1,
cross_bond_dists_2,
triplets_1,
triplets_2,
sig,
ls,
r_cut,
cutoff_func,
):
energy_kernel = 0
force_kernels = np.zeros(3)
stress_kernels = np.zeros(6)
sig2 = sig * sig
ls1 = 1 / (2 * ls * ls)
ls2 = 1 / (ls * ls)
for m in range(bond_array_1.shape[0]):
ri1 = bond_array_1[m, 0]
fi1, _ = cutoff_func(r_cut, ri1, 0)
ei1 = etypes1[m]
for n in range(triplets_1[m]):
ind1 = cross_bond_inds_1[m, m + n + 1]
ri2 = bond_array_1[ind1, 0]
fi2, _ = cutoff_func(r_cut, ri2, 0)
ei2 = etypes1[ind1]
ri3 = cross_bond_dists_1[m, m + n + 1]
fi3, _ = cutoff_func(r_cut, ri3, 0)
fi = fi1 * fi2 * fi3
for p in range(bond_array_2.shape[0]):
rj1 = bond_array_2[p, 0]
fj1, _ = cutoff_func(r_cut, rj1, 0)
ej1 = etypes2[p]
for q in range(triplets_2[p]):
ind2 = cross_bond_inds_2[p, p + q + 1]
rj2 = bond_array_2[ind2, 0]
fj2, _ = cutoff_func(r_cut, rj2, 0)
ej2 = etypes2[ind2]
rj3 = cross_bond_dists_2[p, p + q + 1]
fj3, _ = cutoff_func(r_cut, rj3, 0)
fj = fj1 * fj2 * fj3
r11 = ri1 - rj1
r12 = ri1 - rj2
r13 = ri1 - rj3
r21 = ri2 - rj1
r22 = ri2 - rj2
r23 = ri2 - rj3
r31 = ri3 - rj1
r32 = ri3 - rj2
r33 = ri3 - rj3
energy_kernel += (
three_body_ee_perm(
r11,
r12,
r13,
r21,
r22,
r23,
r31,
r32,
r33,
c1,
c2,
ei1,
ei2,
ej1,
ej2,
fi,
fj,
ls1,
sig2,
)
/ 9
)
stress_count = 0
for d1 in range(3):
ci1 = bond_array_1[m, d1 + 1]
fi1, fdi1 = cutoff_func(r_cut, ri1, ci1)
ci2 = bond_array_1[ind1, d1 + 1]
fi2, fdi2 = cutoff_func(r_cut, ri2, ci2)
fi = fi1 * fi2 * fi3
fdi_p1 = fdi1 * fi2 * fi3
fdi_p2 = fi1 * fdi2 * fi3
fdi = fdi_p1 + fdi_p2
force_kernels[d1] += (
three_body_fe_perm(
r11,
r12,
r13,
r21,
r22,
r23,
r31,
r32,
r33,
c1,
c2,
ci1,
ci2,
ei1,
ei2,
ej1,
ej2,
fi,
fj,
fdi,
ls1,
ls2,
sig2,
)
/ 3
)
for d2 in range(d1, 3):
coord1 = bond_array_1[m, d2 + 1] * ri1
coord2 = bond_array_1[ind1, d2 + 1] * ri2
stress_kernels[stress_count] += (
three_body_se_perm(
r11,
r12,
r13,
r21,
r22,
r23,
r31,
r32,
r33,
c1,
c2,
ci1,
ci2,
ei1,
ei2,
ej1,
ej2,
fi,
fj,
fdi,
ls1,
ls2,
sig2,
coord1,
coord2,
fdi_p1,
fdi_p2,
)
/ 6
)
stress_count += 1
return energy_kernel, force_kernels, stress_kernels
@njit
def efs_force(
bond_array_1,
c1,
etypes1,
bond_array_2,
c2,
etypes2,
cross_bond_inds_1,
cross_bond_inds_2,
cross_bond_dists_1,
cross_bond_dists_2,
triplets_1,
triplets_2,
sig,
ls,
r_cut,
cutoff_func,
):
energy_kernels = np.zeros(3)
force_kernels = np.zeros((3, 3))
stress_kernels = np.zeros((6, 3))
# pre-compute constants that appear in the inner loop
sig2 = sig * sig
ls1 = 1 / (2 * ls * ls)
ls2 = 1 / (ls * ls)
ls3 = ls2 * ls2
# first loop over the first 3-body environment
for m in range(bond_array_1.shape[0]):
ri1 = bond_array_1[m, 0]
fi1, _ = cutoff_func(r_cut, ri1, 0)
ei1 = etypes1[m]
# second loop over the first 3-body environment
for n in range(triplets_1[m]):
ind1 = cross_bond_inds_1[m, m + n + 1]
ri2 = bond_array_1[ind1, 0]
fi2, _ = cutoff_func(r_cut, ri2, 0)
ei2 = etypes1[ind1]
ri3 = cross_bond_dists_1[m, m + n + 1]
fi3, _ = cutoff_func(r_cut, ri3, 0)
fi = fi1 * fi2 * fi3
# first loop over the second 3-body environment
for p in range(bond_array_2.shape[0]):
rj1 = bond_array_2[p, 0]
fj1, _ = cutoff_func(r_cut, rj1, 0)
ej1 = etypes2[p]
# second loop over the second 3-body environment
for q in range(triplets_2[p]):
ind2 = cross_bond_inds_2[p, p + 1 + q]
rj2 = bond_array_2[ind2, 0]
fj2, _ = cutoff_func(r_cut, rj2, 0)
rj3 = cross_bond_dists_2[p, p + 1 + q]
fj3, _ = cutoff_func(r_cut, rj3, 0)
ej2 = etypes2[ind2]
r11 = ri1 - rj1
r12 = ri1 - rj2
r13 = ri1 - rj3
r21 = ri2 - rj1
r22 = ri2 - rj2
r23 = ri2 - rj3
r31 = ri3 - rj1
r32 = ri3 - rj2
r33 = ri3 - rj3
for d3 in range(3):
cj1 = bond_array_2[p, d3 + 1]
fj1, fdj1 = cutoff_func(r_cut, rj1, cj1)
cj2 = bond_array_2[ind2, d3 + 1]
fj2, fdj2 = cutoff_func(r_cut, rj2, cj2)
fj = fj1 * fj2 * fj3
fdj = fdj1 * fj2 * fj3 + fj1 * fdj2 * fj3
energy_kernels[d3] += (
three_body_fe_perm(
r11,
r21,
r31,
r12,
r22,
r32,
r13,
r23,
r33,
c2,
c1,
-cj1,
-cj2,
ej1,
ej2,
ei1,
ei2,
fj,
fi,
fdj,
ls1,
ls2,
sig2,
)
/ 3
)
stress_count = 0
for d1 in range(3):
ci1 = bond_array_1[m, d1 + 1]
fi1, fdi1 = cutoff_func(r_cut, ri1, ci1)
ci2 = bond_array_1[ind1, d1 + 1]
fi2, fdi2 = cutoff_func(r_cut, ri2, ci2)
fi = fi1 * fi2 * fi3
fdi_p1 = fdi1 * fi2 * fi3
fdi_p2 = fi1 * fdi2 * fi3
fdi = fdi_p1 + fdi_p2
force_kernels[d1, d3] += three_body_ff_perm(
r11,
r12,
r13,
r21,
r22,
r23,
r31,
r32,
r33,
c1,
c2,
ci1,
ci2,
cj1,
cj2,
ei1,
ei2,
ej1,
ej2,
fi,
fj,
fdi,
fdj,
ls1,
ls2,
ls3,
sig2,
)
for d2 in range(d1, 3):
coord1 = bond_array_1[m, d2 + 1] * ri1
coord2 = bond_array_1[ind1, d2 + 1] * ri2
stress_kernels[stress_count, d3] += (
three_body_sf_perm(
r11,
r12,
r13,
r21,
r22,
r23,
r31,
r32,
r33,
c1,
c2,
ci1,
ci2,
cj1,
cj2,
ei1,
ei2,
ej1,
ej2,
fi,
fj,
fdi,
fdj,
ls1,
ls2,
ls3,
sig2,
coord1,
coord2,
fdi_p1,
fdi_p2,
)
/ 2
)
stress_count += 1
return energy_kernels, force_kernels, stress_kernels
@njit
def efs_self(
bond_array_1,
c1,
etypes1,
cross_bond_inds_1,
cross_bond_dists_1,
triplets_1,
sig,
ls,
r_cut,
cutoff_func,
):
energy_kernel = 0
force_kernels = np.zeros(3)
stress_kernels = np.zeros(6)
# pre-compute constants that appear in the inner loop
sig2 = sig * sig
ls1 = 1 / (2 * ls * ls)
ls2 = 1 / (ls * ls)
ls3 = ls2 * ls2
for m in range(bond_array_1.shape[0]):
ri1 = bond_array_1[m, 0]
fi1, _ = cutoff_func(r_cut, ri1, 0)
ei1 = etypes1[m]
for n in range(triplets_1[m]):
ind1 = cross_bond_inds_1[m, m + n + 1]
ri2 = bond_array_1[ind1, 0]
fi2, _ = cutoff_func(r_cut, ri2, 0)
ei2 = etypes1[ind1]
ri3 = cross_bond_dists_1[m, m + n + 1]
fi3, _ = cutoff_func(r_cut, ri3, 0)
fi = fi1 * fi2 * fi3
for p in range(bond_array_1.shape[0]):
rj1 = bond_array_1[p, 0]
fj1, _ = cutoff_func(r_cut, rj1, 0)
ej1 = etypes1[p]
for q in range(triplets_1[p]):
ind2 = cross_bond_inds_1[p, p + 1 + q]
rj2 = bond_array_1[ind2, 0]
fj2, _ = cutoff_func(r_cut, rj2, 0)
rj3 = cross_bond_dists_1[p, p + 1 + q]
fj3, _ = cutoff_func(r_cut, rj3, 0)
fj = fj1 * fj2 * fj3
ej2 = etypes1[ind2]
r11 = ri1 - rj1
r12 = ri1 - rj2
r13 = ri1 - rj3
r21 = ri2 - rj1
r22 = ri2 - rj2
r23 = ri2 - rj3
r31 = ri3 - rj1
r32 = ri3 - rj2
r33 = ri3 - rj3
energy_kernel += (
three_body_ee_perm(
r11,
r12,
r13,
r21,
r22,
r23,
r31,
r32,
r33,
c1,
c1,
ei1,
ei2,
ej1,
ej2,
fi,
fj,
ls1,
sig2,
)
/ 9
)
stress_count = 0
for d3 in range(3):
cj1 = bond_array_1[p, d3 + 1]
fj1, fdj1 = cutoff_func(r_cut, rj1, cj1)
cj2 = bond_array_1[ind2, d3 + 1]
fj2, fdj2 = cutoff_func(r_cut, rj2, cj2)
fdj_p1 = fdj1 * fj2 * fj3
fdj_p2 = fj1 * fdj2 * fj3
fdj = fdj_p1 + fdj_p2
ci1 = bond_array_1[m, d3 + 1]
fi1, fdi1 = cutoff_func(r_cut, ri1, ci1)
ci2 = bond_array_1[ind1, d3 + 1]
fi2, fdi2 = cutoff_func(r_cut, ri2, ci2)
fi = fi1 * fi2 * fi3
fdi_p1 = fdi1 * fi2 * fi3
fdi_p2 = fi1 * fdi2 * fi3
fdi = fdi_p1 + fdi_p2
force_kernels[d3] += three_body_ff_perm(
r11,
r12,
r13,
r21,
r22,
r23,
r31,
r32,
r33,
c1,
c1,
ci1,
ci2,
cj1,
cj2,
ei1,
ei2,
ej1,
ej2,
fi,
fj,
fdi,
fdj,
ls1,
ls2,
ls3,
sig2,
)
for d2 in range(d3, 3):
coord1 = bond_array_1[m, d2 + 1] * ri1
coord2 = bond_array_1[ind1, d2 + 1] * ri2
coord3 = bond_array_1[p, d2 + 1] * rj1
coord4 = bond_array_1[ind2, d2 + 1] * rj2
stress_kernels[stress_count] += (
three_body_ss_perm(
r11,
r12,
r13,
r21,
r22,
r23,
r31,
r32,
r33,
c1,
c1,
ci1,
ci2,
cj1,
cj2,
ei1,
ei2,
ej1,
ej2,
fi,
fj,
fdi,
fdj,
ls1,
ls2,
ls3,
sig2,
coord1,
coord2,
coord3,
coord4,
fdi_p1,
fdi_p2,
fdj_p1,
fdj_p2,
)
/ 4
)
stress_count += 1
return energy_kernel, force_kernels, stress_kernels
| 37.092299 | 85 | 0.404977 | 6,832 | 65,505 | 3.710041 | 0.029567 | 0.055391 | 0.03314 | 0.039768 | 0.936718 | 0.928788 | 0.923147 | 0.914586 | 0.908431 | 0.906182 | 0 | 0.081691 | 0.536738 | 65,505 | 1,765 | 86 | 37.113314 | 0.753575 | 0.250378 | 0 | 0.870283 | 0 | 0 | 0.000146 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.017296 | false | 0 | 0.005503 | 0.001572 | 0.040094 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 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 | 8 |
b917aa4d83028b6538940c70c6e2ac17b8188e92 | 7,413 | py | Python | test/test_registrar_db.py | THS-on/keylime | bb904fc98d9674832e630542d211e71102873b4d | [
"Apache-2.0"
] | 192 | 2019-05-08T14:43:50.000Z | 2022-03-28T20:21:28.000Z | test/test_registrar_db.py | THS-on/keylime | bb904fc98d9674832e630542d211e71102873b4d | [
"Apache-2.0"
] | 694 | 2019-04-18T14:08:36.000Z | 2022-03-31T13:55:37.000Z | test/test_registrar_db.py | THS-on/keylime | bb904fc98d9674832e630542d211e71102873b4d | [
"Apache-2.0"
] | 97 | 2019-04-17T19:04:00.000Z | 2022-03-20T18:19:28.000Z | '''
SPDX-License-Identifier: Apache-2.0
Copyright 2020 Luke Hinds (lhinds@redhat.com), Red Hat, Inc.
'''
import unittest
from sqlalchemy import create_engine
from keylime.db.registrar_db import RegistrarMain
from keylime.db.keylime_db import SessionManager
# BEGIN TEST DATA
test_data = {
'agent_id': 'd432fbb3-d2f1-4a97-9ef7-75bd81c00000',
'ek_tpm': """-----BEGIN PUBLIC KEY-----
MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEA0dLxdAABVJO6qxamjCMh
yhWZgiFHZHnPEe0tMFyK3fNVr/w8lX9r+QOLxLmkT0IdgsEYtGZGefbD+qQl4O1s
k25823Xzu5tEF8966rTdkfsv8CRrNaBLwWlnt/n+qjIoU3xZJMmR+mFfqTc3a6zV
mPOYJstFtM8r4b9HPCUq6Mte/J3Wx4FxI9R4UrCUyiAeH++0QapIxuEGsVIYs92n
GyvFQYBZFRU6cIt33iaqTrRCICJp+YblMnw54YJGAH2vTVQf6/fLAnQt5L1UfmTy
R/ZA6advx8soekSBOIAW7XmV8Xp9mSquIHZdSXMJlcn/B35PU3BdkUtIYm5JuGGt
PQIDAQAB
-----END PUBLIC KEY-----""",
'aik_tpm': """-----BEGIN PUBLIC KEY-----
MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEA0dLxdAABVJO6qxamjCMh
yhWZgiFHZHnPEe0tMFyK3fNVr/w8lX9r+QOLxLmkT0IdgsEYtGZGefbD+qQl4O1s
k25823Xzu5tEF8966rTdkfsv8CRrNaBLwWlnt/n+qjIoU3xZJMmR+mFfqTc3a6zV
mPOYJstFtM8r4b9HPCUq6Mte/J3Wx4FxI9R4UrCUyiAeH++0QapIxuEGsVIYs92n
GyvFQYBZFRU6cIt33iaqTrRCICJp+YblMnw54YJGAH2vTVQf6/fLAnQt5L1UfmTy
R/ZA6advx8soekSBOIAW7XmV8Xp9mSquIHZdSXMJlcn/B35PU3BdkUtIYm5JuGGt
PQIDAQAB
-----END PUBLIC KEY-----""",
'ekcert': """-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----""",
'virtual': 0,
'active': int(False),
'key': 'R0xKMU5maWVEaHVxQ0poZUZhWm9yRVkyRHZZUkVIMFA=',
'provider_keys': {},
'regcount': 1,
'ip': "127.0.0.1",
'port': 9002
}
agent_id = 'd432fbb3-d2f1-4a97-9ef7-75bd81c00000'
# END TEST DATA
class TestRegistrarDB(unittest.TestCase):
def setUp(self):
self.engine = create_engine('sqlite://')
RegistrarMain.metadata.create_all(self.engine, checkfirst=True)
self.session = SessionManager().make_session(self.engine)
self.populate_agent()
def populate_agent(self):
self.session.add(RegistrarMain(**test_data))
self.session.commit()
def test_add_agent(self):
agent = self.session.query(RegistrarMain).filter_by(
agent_id=agent_id).first()
self.assertEqual(
agent.ek_tpm, """-----BEGIN PUBLIC KEY-----
MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEA0dLxdAABVJO6qxamjCMh
yhWZgiFHZHnPEe0tMFyK3fNVr/w8lX9r+QOLxLmkT0IdgsEYtGZGefbD+qQl4O1s
k25823Xzu5tEF8966rTdkfsv8CRrNaBLwWlnt/n+qjIoU3xZJMmR+mFfqTc3a6zV
mPOYJstFtM8r4b9HPCUq6Mte/J3Wx4FxI9R4UrCUyiAeH++0QapIxuEGsVIYs92n
GyvFQYBZFRU6cIt33iaqTrRCICJp+YblMnw54YJGAH2vTVQf6/fLAnQt5L1UfmTy
R/ZA6advx8soekSBOIAW7XmV8Xp9mSquIHZdSXMJlcn/B35PU3BdkUtIYm5JuGGt
PQIDAQAB
-----END PUBLIC KEY-----""")
self.assertEqual(
agent.aik_tpm, """-----BEGIN PUBLIC KEY-----
MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEA0dLxdAABVJO6qxamjCMh
yhWZgiFHZHnPEe0tMFyK3fNVr/w8lX9r+QOLxLmkT0IdgsEYtGZGefbD+qQl4O1s
k25823Xzu5tEF8966rTdkfsv8CRrNaBLwWlnt/n+qjIoU3xZJMmR+mFfqTc3a6zV
mPOYJstFtM8r4b9HPCUq6Mte/J3Wx4FxI9R4UrCUyiAeH++0QapIxuEGsVIYs92n
GyvFQYBZFRU6cIt33iaqTrRCICJp+YblMnw54YJGAH2vTVQf6/fLAnQt5L1UfmTy
R/ZA6advx8soekSBOIAW7XmV8Xp9mSquIHZdSXMJlcn/B35PU3BdkUtIYm5JuGGt
PQIDAQAB
-----END PUBLIC KEY-----""")
self.assertEqual(
agent.ekcert, """-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----""")
self.assertEqual(
agent.virtual, 0)
self.assertEqual(
agent.active, int(False))
self.assertEqual(
agent.key, 'R0xKMU5maWVEaHVxQ0poZUZhWm9yRVkyRHZZUkVIMFA=')
self.assertEqual(
agent.provider_keys, {})
self.assertEqual(
agent.regcount, 1)
self.assertEqual(
agent.ip, "127.0.0.1")
self.assertEqual(
agent.port, 9002
)
def test_delete_agent(self):
agent = self.session.query(RegistrarMain).filter_by(
agent_id=agent_id).first()
self.session.query(RegistrarMain).filter_by(
agent_id=agent_id).delete()
self.session.commit()
agent = self.session.query(RegistrarMain).filter_by(
agent_id=agent_id).first()
self.assertIsNone(agent)
def tearDown(self):
self.session.close()
| 44.656627 | 71 | 0.837852 | 447 | 7,413 | 13.818792 | 0.331096 | 0.011332 | 0.032378 | 0.011009 | 0.846689 | 0.844099 | 0.844099 | 0.831472 | 0.831472 | 0.831472 | 0 | 0.098842 | 0.091056 | 7,413 | 165 | 72 | 44.927273 | 0.817898 | 0.017132 | 0 | 0.684932 | 0 | 0 | 0.739453 | 0.668407 | 0 | 1 | 0 | 0 | 0.075342 | 1 | 0.034247 | false | 0 | 0.027397 | 0 | 0.068493 | 0 | 0 | 0 | 1 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
b92fcf251dc0446ca613d9c05a62233e48318bb2 | 331 | py | Python | metalearn/metalearn/components/__init__.py | cosmicBboy/ml-research | 04fd31f68e7a44152caf6eaaf66ab59f136dd8f5 | [
"MIT"
] | 8 | 2018-03-04T21:14:27.000Z | 2021-11-16T11:50:12.000Z | metalearn/metalearn/components/__init__.py | cosmicBboy/ml-research | 04fd31f68e7a44152caf6eaaf66ab59f136dd8f5 | [
"MIT"
] | 24 | 2018-08-01T04:39:35.000Z | 2020-08-18T13:21:56.000Z | metalearn/metalearn/components/__init__.py | cosmicBboy/ml-research | 04fd31f68e7a44152caf6eaaf66ab59f136dd8f5 | [
"MIT"
] | 4 | 2020-01-22T04:21:44.000Z | 2021-06-25T10:11:22.000Z | from .. import ignore_warnings
from . import (
algorithm_component, classifiers, constants, data_preprocessors,
feature_preprocessors, hyperparameter, regressors)
__all__ = [
algorithm_component,
classifiers,
constants,
data_preprocessors,
feature_preprocessors,
hyperparameter,
regressors,
]
| 20.6875 | 68 | 0.743202 | 27 | 331 | 8.703704 | 0.518519 | 0.085106 | 0.246809 | 0.323404 | 0.842553 | 0.842553 | 0.842553 | 0.842553 | 0.842553 | 0.842553 | 0 | 0 | 0.193353 | 331 | 15 | 69 | 22.066667 | 0.88015 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.153846 | 0 | 0.153846 | 0 | 1 | 0 | 0 | null | 0 | 1 | 1 | 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 | 9 |
b93e7673185de09c9ad671470bc8e4b078eba9b0 | 14,454 | py | Python | onadata/apps/api/tests/viewsets/test_xform_list_api.py | sounay/flaming-octo-tribble | 21f21f0e7b2d7f745173f7957375a9d96c2a065e | [
"BSD-2-Clause"
] | 2 | 2017-11-30T17:43:48.000Z | 2018-10-26T23:44:32.000Z | onadata/apps/api/tests/viewsets/test_xform_list_api.py | sounay/flaming-octo-tribble | 21f21f0e7b2d7f745173f7957375a9d96c2a065e | [
"BSD-2-Clause"
] | null | null | null | onadata/apps/api/tests/viewsets/test_xform_list_api.py | sounay/flaming-octo-tribble | 21f21f0e7b2d7f745173f7957375a9d96c2a065e | [
"BSD-2-Clause"
] | 3 | 2016-04-17T06:02:51.000Z | 2018-08-23T21:29:03.000Z | import os
from django.conf import settings
from django.test import TransactionTestCase
from django_digest.test import DigestAuth
from onadata.apps.api.tests.viewsets.test_abstract_viewset import\
TestAbstractViewSet
from onadata.apps.api.viewsets.xform_list_api import XFormListApi
from onadata.libs.permissions import DataEntryRole
from onadata.libs.permissions import ReadOnlyRole
class TestXFormListApi(TestAbstractViewSet, TransactionTestCase):
def setUp(self):
super(self.__class__, self).setUp()
self.view = XFormListApi.as_view({
"get": "list"
})
self._publish_xls_form_to_project()
def test_get_xform_list(self):
request = self.factory.get('/')
response = self.view(request)
self.assertEqual(response.status_code, 401)
auth = DigestAuth('bob', 'bobbob')
request.META.update(auth(request.META, response))
response = self.view(request)
self.assertEqual(response.status_code, 200)
path = os.path.join(
os.path.dirname(__file__),
'..', 'fixtures', 'formList.xml')
with open(path) as f:
form_list_xml = f.read().strip()
data = {"hash": self.xform.hash, "pk": self.xform.pk}
content = response.render().content
self.assertEqual(content, form_list_xml % data)
self.assertTrue(response.has_header('X-OpenRosa-Version'))
self.assertTrue(
response.has_header('X-OpenRosa-Accept-Content-Length'))
self.assertTrue(response.has_header('Date'))
self.assertEqual(response['Content-Type'],
'text/xml; charset=utf-8')
def test_get_xform_list_inactive_form(self):
self.xform.downloadable = False
self.xform.save()
request = self.factory.get('/')
response = self.view(request)
self.assertEqual(response.status_code, 401)
auth = DigestAuth('bob', 'bobbob')
request.META.update(auth(request.META, response))
response = self.view(request)
self.assertEqual(response.status_code, 200)
xml = u'<?xml version="1.0" encoding="utf-8"?>\n<xforms '
xml += u'xmlns="http://openrosa.org/xforms/xformsList"></xforms>'
content = response.render().content
self.assertEqual(content, xml)
self.assertTrue(response.has_header('X-OpenRosa-Version'))
self.assertTrue(
response.has_header('X-OpenRosa-Accept-Content-Length'))
self.assertTrue(response.has_header('Date'))
self.assertEqual(response['Content-Type'],
'text/xml; charset=utf-8')
def test_get_xform_list_anonymous_user(self):
request = self.factory.get('/')
response = self.view(request)
self.assertEqual(response.status_code, 401)
response = self.view(request, username=self.user.username)
self.assertEqual(response.status_code, 200)
path = os.path.join(
os.path.dirname(__file__),
'..', 'fixtures', 'formList.xml')
with open(path) as f:
form_list_xml = f.read().strip()
data = {"hash": self.xform.hash, "pk": self.xform.pk}
content = response.render().content
self.assertEqual(content, form_list_xml % data)
self.assertTrue(response.has_header('X-OpenRosa-Version'))
self.assertTrue(
response.has_header('X-OpenRosa-Accept-Content-Length'))
self.assertTrue(response.has_header('Date'))
self.assertEqual(response['Content-Type'],
'text/xml; charset=utf-8')
def test_get_xform_list_anonymous_user_require_auth(self):
self.user.profile.require_auth = True
self.user.profile.save()
request = self.factory.get('/')
response = self.view(request)
self.assertEqual(response.status_code, 401)
response = self.view(request, username=self.user.username)
self.assertEqual(response.status_code, 401)
def test_get_xform_list_other_user_with_no_role(self):
request = self.factory.get('/')
response = self.view(request)
alice_data = {'username': 'alice', 'email': 'alice@localhost.com'}
alice_profile = self._create_user_profile(alice_data)
self.assertFalse(
ReadOnlyRole.user_has_role(alice_profile.user, self.xform)
)
auth = DigestAuth('alice', 'bobbob')
request.META.update(auth(request.META, response))
response = self.view(request)
self.assertEqual(response.status_code, 200)
content = response.render().content
self.assertNotIn(self.xform.id_string, content)
self.assertEqual(
content, '<?xml version="1.0" encoding="utf-8"?>\n<xforms '
'xmlns="http://openrosa.org/xforms/xformsList"></xforms>')
self.assertTrue(response.has_header('X-OpenRosa-Version'))
self.assertTrue(
response.has_header('X-OpenRosa-Accept-Content-Length'))
self.assertTrue(response.has_header('Date'))
self.assertEqual(response['Content-Type'], 'text/xml; charset=utf-8')
def test_get_xform_list_other_user_with_readonly_role(self):
request = self.factory.get('/')
response = self.view(request)
alice_data = {'username': 'alice', 'email': 'alice@localhost.com'}
alice_profile = self._create_user_profile(alice_data)
ReadOnlyRole.add(alice_profile.user, self.xform)
self.assertTrue(
ReadOnlyRole.user_has_role(alice_profile.user, self.xform)
)
auth = DigestAuth('alice', 'bobbob')
request.META.update(auth(request.META, response))
response = self.view(request)
self.assertEqual(response.status_code, 200)
content = response.render().content
self.assertNotIn(self.xform.id_string, content)
self.assertEqual(
content, '<?xml version="1.0" encoding="utf-8"?>\n<xforms '
'xmlns="http://openrosa.org/xforms/xformsList"></xforms>')
self.assertTrue(response.has_header('X-OpenRosa-Version'))
self.assertTrue(
response.has_header('X-OpenRosa-Accept-Content-Length'))
self.assertTrue(response.has_header('Date'))
self.assertEqual(response['Content-Type'], 'text/xml; charset=utf-8')
def test_get_xform_list_other_user_with_dataentry_role(self):
request = self.factory.get('/')
response = self.view(request)
alice_data = {'username': 'alice', 'email': 'alice@localhost.com'}
alice_profile = self._create_user_profile(alice_data)
DataEntryRole.add(alice_profile.user, self.xform)
self.assertTrue(
DataEntryRole.user_has_role(alice_profile.user, self.xform)
)
auth = DigestAuth('alice', 'bobbob')
request.META.update(auth(request.META, response))
response = self.view(request)
self.assertEqual(response.status_code, 200)
path = os.path.join(
os.path.dirname(__file__),
'..', 'fixtures', 'formList.xml')
with open(path) as f:
form_list_xml = f.read().strip()
data = {"hash": self.xform.hash, "pk": self.xform.pk}
content = response.render().content
self.assertEqual(content, form_list_xml % data)
self.assertTrue(response.has_header('X-OpenRosa-Version'))
self.assertTrue(
response.has_header('X-OpenRosa-Accept-Content-Length'))
self.assertTrue(response.has_header('Date'))
self.assertEqual(response['Content-Type'],
'text/xml; charset=utf-8')
def test_retrieve_xform_xml(self):
self.view = XFormListApi.as_view({
"get": "retrieve"
})
request = self.factory.head('/')
response = self.view(request, pk=self.xform.pk)
auth = DigestAuth('bob', 'bobbob')
request = self.factory.get('/')
request.META.update(auth(request.META, response))
response = self.view(request, pk=self.xform.pk)
self.assertEqual(response.status_code, 200)
path = os.path.join(
os.path.dirname(__file__),
'..', 'fixtures', 'Transportation Form.xml')
with open(path) as f:
form_xml = f.read().strip()
data = {"form_uuid": self.xform.uuid}
content = response.render().content.strip()
content = content.replace(
self.xform.version, u"20141112071722")
self.assertEqual(content, form_xml % data)
self.assertTrue(response.has_header('X-OpenRosa-Version'))
self.assertTrue(
response.has_header('X-OpenRosa-Accept-Content-Length'))
self.assertTrue(response.has_header('Date'))
self.assertEqual(response['Content-Type'],
'text/xml; charset=utf-8')
def _load_metadata(self, xform=None):
data_value = "screenshot.png"
data_type = 'media'
fixture_dir = os.path.join(
settings.PROJECT_ROOT, "apps", "main", "tests", "fixtures",
"transportation"
)
path = os.path.join(fixture_dir, data_value)
xform = xform or self.xform
self._add_form_metadata(xform, data_type, data_value, path)
def test_retrieve_xform_manifest(self):
self._load_metadata(self.xform)
self.view = XFormListApi.as_view({
"get": "manifest"
})
request = self.factory.head('/')
response = self.view(request, pk=self.xform.pk)
auth = DigestAuth('bob', 'bobbob')
request = self.factory.get('/')
request.META.update(auth(request.META, response))
response = self.view(request, pk=self.xform.pk)
self.assertEqual(response.status_code, 200)
manifest_xml = """<?xml version="1.0" encoding="utf-8"?>
<manifest xmlns="http://openrosa.org/xforms/xformsManifest"><mediaFile><filename>screenshot.png</filename><hash>%(hash)s</hash><downloadUrl>http://testserver/bob/xformsMedia/%(xform)s/%(pk)s.png</downloadUrl></mediaFile></manifest>""" # noqa
data = {"hash": self.metadata.hash, "pk": self.metadata.pk,
"xform": self.xform.pk}
content = response.render().content.strip()
self.assertEqual(content, manifest_xml % data)
self.assertTrue(response.has_header('X-OpenRosa-Version'))
self.assertTrue(
response.has_header('X-OpenRosa-Accept-Content-Length'))
self.assertTrue(response.has_header('Date'))
self.assertEqual(response['Content-Type'], 'text/xml; charset=utf-8')
def test_retrieve_xform_manifest_anonymous_user(self):
self._load_metadata(self.xform)
self.view = XFormListApi.as_view({
"get": "manifest"
})
request = self.factory.get('/')
response = self.view(request, pk=self.xform.pk)
self.assertEqual(response.status_code, 401)
response = self.view(request, pk=self.xform.pk,
username=self.user.username)
self.assertEqual(response.status_code, 200)
manifest_xml = """<?xml version="1.0" encoding="utf-8"?>
<manifest xmlns="http://openrosa.org/xforms/xformsManifest"><mediaFile><filename>screenshot.png</filename><hash>%(hash)s</hash><downloadUrl>http://testserver/bob/xformsMedia/%(xform)s/%(pk)s.png</downloadUrl></mediaFile></manifest>""" # noqa
data = {"hash": self.metadata.hash, "pk": self.metadata.pk,
"xform": self.xform.pk}
content = response.render().content.strip()
self.assertEqual(content, manifest_xml % data)
self.assertTrue(response.has_header('X-OpenRosa-Version'))
self.assertTrue(
response.has_header('X-OpenRosa-Accept-Content-Length'))
self.assertTrue(response.has_header('Date'))
self.assertEqual(response['Content-Type'], 'text/xml; charset=utf-8')
def test_retrieve_xform_manifest_anonymous_user_require_auth(self):
self.user.profile.require_auth = True
self.user.profile.save()
self._load_metadata(self.xform)
self.view = XFormListApi.as_view({
"get": "manifest"
})
request = self.factory.get('/')
response = self.view(request, pk=self.xform.pk)
self.assertEqual(response.status_code, 401)
response = self.view(request, pk=self.xform.pk,
username=self.user.username)
self.assertEqual(response.status_code, 401)
def test_retrieve_xform_media(self):
self._load_metadata(self.xform)
self.view = XFormListApi.as_view({
"get": "media"
})
request = self.factory.head('/')
response = self.view(request, pk=self.xform.pk,
metadata=self.metadata.pk, format='png')
auth = DigestAuth('bob', 'bobbob')
request = self.factory.get('/')
request.META.update(auth(request.META, response))
response = self.view(request, pk=self.xform.pk,
metadata=self.metadata.pk, format='png')
self.assertEqual(response.status_code, 200)
def test_retrieve_xform_media_anonymous_user(self):
self._load_metadata(self.xform)
self.view = XFormListApi.as_view({
"get": "media"
})
request = self.factory.get('/')
response = self.view(request, pk=self.xform.pk,
metadata=self.metadata.pk, format='png')
self.assertEqual(response.status_code, 401)
response = self.view(request, pk=self.xform.pk,
username=self.user.username,
metadata=self.metadata.pk, format='png')
self.assertEqual(response.status_code, 200)
def test_retrieve_xform_media_anonymous_user_require_auth(self):
self.user.profile.require_auth = True
self.user.profile.save()
self._load_metadata(self.xform)
self.view = XFormListApi.as_view({
"get": "media"
})
request = self.factory.get('/')
response = self.view(request, pk=self.xform.pk,
metadata=self.metadata.pk, format='png')
self.assertEqual(response.status_code, 401)
| 43.667674 | 242 | 0.622734 | 1,640 | 14,454 | 5.341463 | 0.089024 | 0.041096 | 0.078767 | 0.07089 | 0.89863 | 0.877854 | 0.871233 | 0.858219 | 0.845776 | 0.842237 | 0 | 0.00923 | 0.242978 | 14,454 | 330 | 243 | 43.8 | 0.791354 | 0.000623 | 0 | 0.800687 | 0 | 0.006873 | 0.149702 | 0.02555 | 0 | 0 | 0 | 0 | 0.243986 | 1 | 0.054983 | false | 0 | 0.027491 | 0 | 0.085911 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 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 | 7 |
b98668c6c03bd47e3eca7f941d0dab43670bebac | 65,542 | py | Python | ncsnv3/sampling.py | gunpowder78/google-research | d41bbaca1eb9bfd980ec2b3fd201c3ddb4d1f2e5 | [
"Apache-2.0"
] | 1 | 2022-03-19T04:26:12.000Z | 2022-03-19T04:26:12.000Z | ncsnv3/sampling.py | gunpowder78/google-research | d41bbaca1eb9bfd980ec2b3fd201c3ddb4d1f2e5 | [
"Apache-2.0"
] | null | null | null | ncsnv3/sampling.py | gunpowder78/google-research | d41bbaca1eb9bfd980ec2b3fd201c3ddb4d1f2e5 | [
"Apache-2.0"
] | 1 | 2022-03-30T07:20:29.000Z | 2022-03-30T07:20:29.000Z | # coding=utf-8
# Copyright 2022 The Google Research Authors.
#
# 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.
# pylint: skip-file
# pytype: skip-file
"""Various sampling methods."""
import functools
from absl import logging
import flax
import flax.deprecated.nn as nn
import flax.jax_utils as flax_utils
import jax
import jax.numpy as jnp
import numpy as np
from .models import utils as mutils
_SAMPLERS = {}
def register_sampler(func=None, *, name=None):
"""Decorator for registering sampler functions."""
def wrapper(func):
if name is None:
local_name = func.__name__
else:
local_name = name
if local_name in _SAMPLERS:
raise ValueError(f'Sampler {local_name} has already been registered!')
_SAMPLERS[local_name] = func
return func
if func is None:
return wrapper
else:
return wrapper(func)
def get_samples(rng, config, state, shape, scaler, inverse_scaler,
class_conditional=False, colab=False):
"""Generate samples. Assume state is unreplicated."""
continuous_sigmas = 'continuous' in config.training.loss
rng1, rng2 = jax.random.split(rng)
sampler_name = config.sampling.method
if sampler_name not in _SAMPLERS:
raise ValueError(
f'Sampler {sampler_name} not found. Supported samplers are {list(_SAMPLERS.keys())}'
)
else:
sampler = _SAMPLERS[sampler_name]
if class_conditional:
class_labels = jax.random.choice(rng1, config.data.num_classes,
shape=(shape[0], shape[1]))
rng1, _ = jax.random.split(rng1)
else:
class_labels = None
if sampler_name in ['diffusion_sampling', 'reverse_diffusion', 'gradient_flow']:
sigmas = mutils.get_sigmas(config)
init_sample = jax.random.normal(rng1, shape) * sigmas[0]
samples = sampler(
rng2,
init_sample,
state,
sigmas,
inverse_scaler,
class_labels=class_labels,
continuous_sigmas=continuous_sigmas,
final_only=config.sampling.final_only,
verbose=True,
colab=colab)
elif sampler_name in ['ddpm', 'ddpm_reproduce',
'ddpm_reverse_diffusion', 'ddpm_gradient_flow']:
ddpm_params = mutils.get_ddpm_params()
init_sample = jax.random.normal(rng1, shape)
samples = sampler(
rng2,
init_sample,
state,
ddpm_params,
inverse_scaler,
noise_removal=config.sampling.noise_removal,
final_only=config.sampling.final_only,
verbose=True,
colab=colab)
elif sampler_name in ['ddpm_ald_fix_snr_diffusion_sampling',
'ddpm_ald_fix_snr',
'ddpm_ald_fix_snr_reverse_diffusion',
'ddpm_ald_fix_snr_gradient_flow']:
ddpm_params = mutils.get_ddpm_params()
init_sample = jax.random.normal(rng1, shape)
samples = sampler(
rng2,
init_sample,
state,
ddpm_params,
inverse_scaler,
n_steps_each=config.sampling.n_steps_each,
target_snr=config.sampling.target_snr,
noise_removal=config.sampling.noise_removal,
final_only=config.sampling.final_only,
verbose=True,
colab=colab)
elif sampler_name == 'ald':
sigmas = mutils.get_sigmas(config)
init_sample = jax.random.uniform(rng1, shape)
init_sample = scaler(init_sample)
samples = sampler(
rng2,
init_sample,
state,
sigmas,
inverse_scaler,
n_steps_each=config.sampling.n_steps_each,
step_size=config.sampling.step_size,
class_labels=class_labels,
continuous_sigmas=continuous_sigmas,
noise_removal=config.sampling.noise_removal,
final_only=config.sampling.final_only,
verbose=True,
colab=colab)
elif sampler_name.startswith('ald_fix_snr'):
sigmas = mutils.get_sigmas(config)
init_sample = jax.random.uniform(rng1, shape)
init_sample = scaler(init_sample)
rng3, _ = jax.random.split(rng1)
init_sample = init_sample + jax.random.normal(rng3, shape) * sigmas[0]
samples = sampler(
rng2,
init_sample,
state,
sigmas,
inverse_scaler,
class_labels=class_labels,
continuous_sigmas=continuous_sigmas,
n_steps_each=config.sampling.n_steps_each,
target_snr=config.sampling.target_snr,
noise_removal=config.sampling.noise_removal,
final_only=config.sampling.final_only,
verbose=True,
colab=colab)
elif sampler_name == 'ddpm_ald_fix_snr':
init_sample = jax.random.normal(rng1, shape)
ddpm_params = mutils.get_ddpm_params()
samples = sampler(
rng2,
init_sample,
state,
ddpm_params,
inverse_scaler,
n_steps_each=config.sampling.n_steps_each,
target_snr=config.sampling.target_snr,
final_only=config.sampling.final_only,
verbose=True,
colab=colab
)
return samples
@register_sampler(name='ald')
def anneal_langevin_dynamics(rng,
init,
state,
sigmas,
inverse_scaler,
class_labels=None,
continuous_sigmas=False,
n_steps_each=200,
step_size=0.000008,
noise_removal=True,
final_only=False,
verbose=False,
colab=False):
"""The original annealed Langevin dynamics sampling used in NCSNv1/v2.
This function leverages `pmap` internally and shouldn't be pmapped itself.
sample with EMA.
Args:
rng: jax random state for Langevin dynamics sample generation.
init: the randomly initialized starting point for sampling.
state: the full state class. should be replicated.
sigmas: noise levels.
inverse_scaler: scale generated samples back to valid images.
class_labels: the target class labels for class-conditional generation.
continuous_sigmas: use a continuous distribution of sigmas.
n_steps_each: the number of Langevin steps for each noise level.
step_size: the step size for running Langevin dynamics
noise_removal: FID will increase by a large amount if set to False.
final_only: if True store only the last sample. Otherwise store the whole
sample history.
verbose: if True log running information.
colab: if True log to stdout.
Returns:
samples: list of image samples.
"""
images = []
x = init.copy()
model_ema = state.optimizer.target.replace(params=state.params_ema)
if class_labels is None:
@functools.partial(jax.pmap, axis_name='batch')
def score_eval(sample, noise_level):
labels = jnp.ones((sample.shape[0],), dtype=jnp.int32) * noise_level
with nn.stateful(state.model_state, mutable=False):
score = model_ema(sample, labels, train=False)
return score
else:
@functools.partial(jax.pmap, axis_name='batch')
def score_eval(sample, noise_level, class_label):
labels = jnp.ones((sample.shape[0],), dtype=jnp.int32) * noise_level
with nn.stateful(state.model_state, mutable=False):
score = model_ema(sample, labels, y=class_label, train=False)
return score
score_eval = functools.partial(score_eval, class_label=class_labels)
for c, sigma in enumerate(sigmas):
step = step_size * (sigma / sigmas[-1])**2
if continuous_sigmas:
noise_level = sigma
else:
noise_level = c
noise_level = flax.jax_utils.replicate(c)
for _ in range(n_steps_each):
grad = score_eval(x, noise_level)
rng, sample_rng = jax.random.split(rng)
noise = jax.random.normal(sample_rng, x.shape)
x = x + step * grad + noise * jnp.sqrt(step * 2)
if not final_only:
images.append(inverse_scaler(np.asarray(x)))
if verbose and jax.host_id() == 0:
grad_norm = jnp.linalg.norm(
grad.reshape((grad.shape[1] * grad.shape[0], -1)), axis=-1).mean()
noise_norm = jnp.linalg.norm(
noise.reshape((noise.shape[0] * noise.shape[1], -1)),
axis=-1).mean()
image_norm = jnp.linalg.norm(
x.reshape((x.shape[0] * x.shape[1], -1)), axis=-1).mean()
snr = jnp.sqrt(step / 2.) * grad_norm / noise_norm
grad_mean_norm = jnp.linalg.norm(grad.mean(axis=(0, 1)).reshape(
(-1,)))**2 * sigma**2
if colab:
print(
'level: %d, step_size: %.5e, grad_norm: %.5e, image_norm: %.5e, snr: %.5e, grad_mean_norm: %.5e'
% (c, step, grad_norm, image_norm, snr, grad_mean_norm))
else:
logging.info(
'level: %d, step_size: %.5e, grad_norm: %.5e, image_norm: %.5e, snr: %.5e, grad_mean_norm: %.5e',
c, step, grad_norm, image_norm, snr, grad_mean_norm)
if noise_removal:
if continuous_sigmas:
last_noise = flax.jax_utils.replicate(sigmas[-1])
else:
last_noise = flax.jax_utils.replicate(len(sigmas) - 1)
x = x + sigmas[-1]**2 * score_eval(x, last_noise)
logging.info('Finished noise removal!')
if final_only:
images.append(inverse_scaler(np.asarray(x)))
return images
@register_sampler
def diffusion_sampling(rng,
init,
state,
sigmas,
inverse_scaler,
class_labels=None,
continuous_sigmas=False,
final_only=False,
verbose=False,
colab=False):
"""Discrete diffusion sampling (the method in DDPM) applied to NCSNs.
This function leverages `pmap` internally and shouldn't be pmapped itself.
sample with EMA.
Args:
rng: jax random state for sample generation.
init: the randomly initialized starting point for sampling.
state: the full state class.
sigmas: noise levels.
inverse_scaler: scale generated samples back to valid images.
class_labels: the target class labels for class-conditional generation.
continuous_sigmas: use a continuous distribution of sigmas.
final_only: if True store only the last sample. Otherwise store the whole
sample history.
verbose: if True log running information.
colab: if True log to stdout.
Returns:
samples: list of image samples.
"""
images = []
x = init.copy()
model_ema = state.optimizer.target.replace(params=state.params_ema)
if class_labels is None:
@functools.partial(jax.pmap, axis_name='batch')
def score_eval(sample, noise_level):
labels = jnp.ones((sample.shape[0],), dtype=jnp.int32) * noise_level
with nn.stateful(state.model_state, mutable=False):
score = model_ema(sample, labels, train=False)
return score
else:
@functools.partial(jax.pmap, axis_name='batch')
def score_eval(sample, noise_level, class_label):
labels = jnp.ones((sample.shape[0],), dtype=jnp.int32) * noise_level
with nn.stateful(state.model_state, mutable=False):
score = model_ema(sample, labels, y=class_label, train=False)
return score
score_eval = functools.partial(score_eval, class_label=class_labels)
for T in range(len(sigmas)): # pylint: disable=invalid-name
if continuous_sigmas:
replicated_T = flax.jax_utils.replicate(sigmas[T])
else:
replicated_T = flax.jax_utils.replicate(T) # pylint: disable=invalid-name
grad = score_eval(x, replicated_T)
rng, sample_rng = jax.random.split(rng)
noise = jax.random.normal(sample_rng, x.shape)
x0 = x + sigmas[T]**2 * grad
if T == len(sigmas) - 1:
x = x0
else:
std = sigmas[T + 1] * jnp.sqrt(sigmas[T]**2 -
sigmas[T + 1]**2) / sigmas[T]
coeff = sigmas[T + 1]**2 / sigmas[T]**2
x = coeff * x + (1. - coeff) * x0 + std * noise
if not final_only:
images.append(inverse_scaler(np.asarray(x)))
if verbose and jax.host_id() == 0:
grad_norm = jnp.linalg.norm(
grad.reshape((grad.shape[1] * grad.shape[0], -1)), axis=-1).mean()
image_norm = jnp.linalg.norm(
x.reshape((x.shape[0] * x.shape[1], -1)), axis=-1).mean()
if colab:
print('level: %d, grad_norm: %.5e, image_norm: %.5e'
% (T, grad_norm, image_norm))
else:
logging.info('level: %d, grad_norm: %.5e, image_norm: %.5e', T,
grad_norm, image_norm)
if final_only:
images.append(inverse_scaler(np.asarray(x)))
return images
@register_sampler
def reverse_diffusion(rng,
init,
state,
sigmas,
inverse_scaler,
class_labels=None,
continuous_sigmas=False,
final_only=False,
verbose=False,
colab=False):
"""Reverse diffusion sampling for NCSNs.
This function leverages `pmap` internally and shouldn't be pmapped itself.
sample with EMA.
Args:
rng: jax random state for sample generation.
init: the randomly initialized starting point for sampling.
state: the full state class.
sigmas: noise levels.
inverse_scaler: scale generated samples back to valid images.
class_labels: the target class labels for class-conditional generation.
continuous_sigmas: use a continuous distribution of sigmas.
final_only: if True store only the last sample. Otherwise store the whole
sample history.
verbose: if True log running information.
colab: if True log to stdout.
Returns:
samples: list of image samples.
"""
images = []
x = init.copy()
model_ema = state.optimizer.target.replace(params=state.params_ema)
if class_labels is None:
@functools.partial(jax.pmap, axis_name='batch')
def score_eval(sample, noise_level):
labels = jnp.ones((sample.shape[0],), dtype=jnp.int32) * noise_level
with nn.stateful(state.model_state, mutable=False):
score = model_ema(sample, labels, train=False)
return score
else:
@functools.partial(jax.pmap, axis_name='batch')
def score_eval(sample, noise_level, class_label):
labels = jnp.ones((sample.shape[0],), dtype=jnp.int32) * noise_level
with nn.stateful(state.model_state, mutable=False):
score = model_ema(sample, labels, y=class_label, train=False)
return score
score_eval = functools.partial(score_eval, class_label=class_labels)
for T in range(len(sigmas)): # pylint: disable=invalid-name
if continuous_sigmas:
replicated_T = flax.jax_utils.replicate(sigmas[T])
else:
replicated_T = flax.jax_utils.replicate(T) # pylint: disable=invalid-name
grad = score_eval(x, replicated_T)
rng, sample_rng = jax.random.split(rng)
noise = jax.random.normal(sample_rng, x.shape)
variance = sigmas[T]**2 - sigmas[T+1]**2 if T < len(sigmas) - 1 else sigmas[T]**2
x0 = x + variance * grad
if T == len(sigmas) - 1:
x = x0
else:
std = jnp.sqrt(variance)
x = x0 + std * noise
if not final_only:
images.append(inverse_scaler(np.asarray(x)))
if verbose and jax.host_id() == 0:
grad_norm = jnp.linalg.norm(
grad.reshape((grad.shape[1] * grad.shape[0], -1)), axis=-1).mean()
image_norm = jnp.linalg.norm(
x.reshape((x.shape[0] * x.shape[1], -1)), axis=-1).mean()
if colab:
print('level: %d, grad_norm: %.5e, image_norm: %.5e'
% (T, grad_norm, image_norm))
else:
logging.info('level: %d, grad_norm: %.5e, image_norm: %.5e', T,
grad_norm, image_norm)
if final_only:
images.append(inverse_scaler(np.asarray(x)))
return images
@register_sampler
def gradient_flow(rng,
init,
state,
sigmas,
inverse_scaler,
class_labels=None,
continuous_sigmas=False,
final_only=False,
verbose=False,
colab=False):
"""Gradient flow sampling for NCSNs.
This function leverages `pmap` internally and shouldn't be pmapped itself.
sample with EMA.
Args:
rng: jax random state for sample generation.
init: the randomly initialized starting point for sampling.
state: the full state class.
sigmas: noise levels.
inverse_scaler: scale generated samples back to valid images.
class_labels: the target class labels for class-conditional generation.
continuous_sigmas: use a continuous distribution of sigmas.
final_only: if True store only the last sample. Otherwise store the whole
sample history.
verbose: if True log running information.
colab: if True log to stdout.
Returns:
samples: list of image samples.
"""
images = []
x = init.copy()
model_ema = state.optimizer.target.replace(params=state.params_ema)
if class_labels is None:
@functools.partial(jax.pmap, axis_name='batch')
def score_eval(sample, noise_level):
labels = jnp.ones((sample.shape[0],), dtype=jnp.int32) * noise_level
with nn.stateful(state.model_state, mutable=False):
score = model_ema(sample, labels, train=False)
return score
else:
@functools.partial(jax.pmap, axis_name='batch')
def score_eval(sample, noise_level, class_label):
labels = jnp.ones((sample.shape[0],), dtype=jnp.int32) * noise_level
with nn.stateful(state.model_state, mutable=False):
score = model_ema(sample, labels, y=class_label, train=False)
return score
score_eval = functools.partial(score_eval, class_label=class_labels)
for T in range(len(sigmas)): # pylint: disable=invalid-name
if continuous_sigmas:
replicated_T = flax.jax_utils.replicate(sigmas[T])
else:
replicated_T = flax.jax_utils.replicate(T) # pylint: disable=invalid-name
grad = score_eval(x, replicated_T)
rng, sample_rng = jax.random.split(rng)
noise = jax.random.normal(sample_rng, x.shape)
variance = sigmas[T]**2 - sigmas[T+1]**2 if T < len(sigmas) - 1 else sigmas[T]**2
x = x + variance * grad / 2.
if not final_only:
images.append(inverse_scaler(np.asarray(x)))
if verbose and jax.host_id() == 0:
grad_norm = jnp.linalg.norm(
grad.reshape((grad.shape[1] * grad.shape[0], -1)), axis=-1).mean()
image_norm = jnp.linalg.norm(
x.reshape((x.shape[0] * x.shape[1], -1)), axis=-1).mean()
if colab:
print('level: %d, grad_norm: %.5e, image_norm: %.5e'
% (T, grad_norm, image_norm))
else:
logging.info('level: %d, grad_norm: %.5e, image_norm: %.5e', T,
grad_norm, image_norm)
if final_only:
images.append(inverse_scaler(np.asarray(x)))
return images
@register_sampler(name='ald_fix_snr')
def anneal_langevin_dynamics_fix_snr(rng,
init,
state,
sigmas,
inverse_scaler,
class_labels=None,
continuous_sigmas=False,
n_steps_each=200,
target_snr=0.2,
noise_removal=True,
final_only=False,
verbose=False,
colab=False):
"""Annealed Langevin dynamics sampling for NCSNs in the form of fixed SNR.
Scales are estimated by the norm of score functions.
This function leverages `pmap` internally and shouldn't be pmapped itself.
sample with EMA.
Args:
rng: jax random state for Langevin dynamics sample generation.
init: the randomly initialized starting point for sampling.
state: the full state class.
sigmas: noise levels.
inverse_scaler: scale generated samples back to valid images.
class_labels: the target class labels for class-conditional generation.
continuous_sigmas: use a continuous distribution of sigmas.
n_steps_each: the number of Langevin steps for each noise level.
target_snr: the target signal to noise ratio.
noise_removal: FID will increase by a lot if set to False.
final_only: if True store only the last sample. Otherwise store the whole
sample history.
verbose: if True log running information.
colab: if True log to stdout.
Returns:
samples: list of image samples.
"""
images = []
x = init.copy()
model_ema = state.optimizer.target.replace(params=state.params_ema)
if class_labels is None:
@functools.partial(jax.pmap, axis_name='batch')
def score_eval(sample, noise_level):
labels = jnp.ones((sample.shape[0],), dtype=jnp.int32) * noise_level
with nn.stateful(state.model_state, mutable=False):
score = model_ema(sample, labels, train=False)
return score
else:
@functools.partial(jax.pmap, axis_name='batch')
def score_eval(sample, noise_level, class_label):
labels = jnp.ones((sample.shape[0],), dtype=jnp.int32) * noise_level
with nn.stateful(state.model_state, mutable=False):
score = model_ema(sample, labels, y=class_label, train=False)
return score
score_eval = functools.partial(score_eval, class_label=class_labels)
for c, sigma in enumerate(sigmas):
if continuous_sigmas:
noise_level = flax.jax_utils.replicate(sigma)
else:
noise_level = flax.jax_utils.replicate(c)
for _ in range(n_steps_each):
grad = score_eval(x, noise_level)
rng, sample_rng = jax.random.split(rng)
noise = jax.random.normal(sample_rng, x.shape)
grad_norm = jnp.linalg.norm(
grad.reshape((grad.shape[1] * grad.shape[0], -1)), axis=-1).mean()
noise_norm = jnp.linalg.norm(
noise.reshape((noise.shape[0] * noise.shape[1], -1)), axis=-1).mean()
step_size = (target_snr * noise_norm / grad_norm)**2 * 2.
x = x + step_size * grad + noise * jnp.sqrt(step_size * 2)
if not final_only:
images.append(inverse_scaler(np.asarray(x)))
if verbose and jax.host_id() == 0:
image_norm = jnp.linalg.norm(
x.reshape((x.shape[0] * x.shape[1], -1)), axis=-1).mean()
if colab:
print('level: %d, step_size: %.5e, image_norm: %.5e' %
(c, step_size, image_norm))
else:
logging.info('level: %d, step_size: %.5e, image_norm: %.5e', c,
step_size, image_norm)
if noise_removal:
if continuous_sigmas:
last_noise = sigmas[-1]
else:
last_noise = len(sigmas) - 1
x = x + sigmas[-1]**2 * score_eval(x, flax.jax_utils.replicate(last_noise))
logging.info('Finished noise removal!')
if final_only:
images.append(inverse_scaler(np.asarray(x)))
return images
@register_sampler(name='ddpm')
def ddpm_sampling(rng,
init,
state,
ddpm_params,
inverse_scaler,
noise_removal=False,
final_only=False,
verbose=False,
colab=False):
"""The sampling procedure of DDPMs as described in the DDPM paper.
This function leverages `pmap` internally and shouldn't be pmapped itself.
sample with EMA.
Args:
rng: jax random state for Langevin dynamics sample generation.
init: the randomly initialized starting point for sampling.
state: the full state class.
ddpm_params: a dictionary containing hyperparameters of the DDPM model.
noise_removal: whether to remove the noise at the final sampling step.
inverse_scaler: a function to scale generated inputs back to valid images.
final_only: if True store only the last sample. Otherwise store the whole
sample history.
verbose: if True log running information.
colab: if True log to stdout.
Returns:
samples: list of image samples.
"""
images = []
x = init.copy()
model_ema = state.optimizer.target.replace(params=state.params_ema)
@functools.partial(jax.pmap, axis_name='batch')
def score_eval(sample, noise_level):
labels = jnp.ones((sample.shape[0],), dtype=jnp.int32) * noise_level
with nn.stateful(state.model_state, mutable=False):
score = model_ema(sample, labels, train=False)
return score
betas = ddpm_params['betas']
alphas = ddpm_params['alphas']
sqrt_1m_alphas_cumprod = ddpm_params['sqrt_1m_alphas_cumprod']
for T in reversed(range(len(betas))): # pylint: disable=invalid-name
replicated_T = flax.jax_utils.replicate(T) # pylint: disable=invalid-name
grad = score_eval(x, replicated_T)
rng, sample_rng = jax.random.split(rng)
noise = jax.random.normal(sample_rng, x.shape)
x = 1. / np.sqrt(alphas[T, None, None, None, None]) * (
x -
(betas[T, None, None, None, None] /
sqrt_1m_alphas_cumprod[T, None, None, None, None]) * grad)
if not noise_removal or T > 0:
x = x + np.sqrt(betas[T, None, None, None, None]) * noise
if not final_only:
images.append(inverse_scaler(np.asarray(x)))
if verbose and jax.host_id() == 0:
grad_norm = jnp.linalg.norm(
grad.reshape((grad.shape[1] * grad.shape[0], -1)), axis=-1).mean()
image_norm = jnp.linalg.norm(
x.reshape((x.shape[0] * x.shape[1], -1)), axis=-1).mean()
if colab:
print('level: %d, grad_norm: %.5e, image_norm: %.5e' %
(T, grad_norm, image_norm))
else:
logging.info('level: %d, grad_norm: %.5e, image_norm: %.5e', T,
grad_norm, image_norm)
if final_only:
images.append(inverse_scaler(np.asarray(x)))
return images
@register_sampler(name='ddpm_reverse_diffusion')
def ddpm_reverse_diffusion(rng,
init,
state,
ddpm_params,
inverse_scaler,
noise_removal=False,
final_only=False,
verbose=False,
colab=False):
"""Reverse diffusion sampling for DDPMs.
This function leverages `pmap` internally and shouldn't be pmapped itself.
sample with EMA.
Args:
rng: jax random state for Langevin dynamics sample generation.
init: the randomly initialized starting point for sampling.
state: the full state class.
ddpm_params: a dictionary containing hyperparameters of the DDPM model.
noise_removal: whether to remove the noise at the final sampling step.
inverse_scaler: a function to scale generated inputs back to valid images.
final_only: if True store only the last sample. Otherwise store the whole
sample history.
verbose: if True log running information.
colab: if True log to stdout.
Returns:
samples: list of image samples.
"""
images = []
x = init.copy()
model_ema = state.optimizer.target.replace(params=state.params_ema)
@functools.partial(jax.pmap, axis_name='batch')
def score_eval(sample, noise_level):
labels = jnp.ones((sample.shape[0],), dtype=jnp.int32) * noise_level
with nn.stateful(state.model_state, mutable=False):
score = model_ema(sample, labels, train=False)
return score
betas = ddpm_params['betas']
alphas = ddpm_params['alphas']
sqrt_1m_alphas_cumprod = ddpm_params['sqrt_1m_alphas_cumprod']
for T in reversed(range(len(betas))): # pylint: disable=invalid-name
replicated_T = flax.jax_utils.replicate(T) # pylint: disable=invalid-name
grad = - score_eval(x, replicated_T) / sqrt_1m_alphas_cumprod[T]
rng, sample_rng = jax.random.split(rng)
noise = jax.random.normal(sample_rng, x.shape)
x = (2. - np.sqrt(1 - betas[T])) * x + grad * betas[T]
if not noise_removal or T > 0:
x = x + np.sqrt(betas[T, None, None, None, None]) * noise
if not final_only:
images.append(inverse_scaler(np.asarray(x)))
if verbose and jax.host_id() == 0:
grad_norm = jnp.linalg.norm(
grad.reshape((grad.shape[1] * grad.shape[0], -1)), axis=-1).mean()
image_norm = jnp.linalg.norm(
x.reshape((x.shape[0] * x.shape[1], -1)), axis=-1).mean()
if colab:
print('level: %d, grad_norm: %.5e, image_norm: %.5e' %
(T, grad_norm, image_norm))
else:
logging.info('level: %d, grad_norm: %.5e, image_norm: %.5e', T,
grad_norm, image_norm)
if final_only:
images.append(inverse_scaler(np.asarray(x)))
return images
@register_sampler
def ddpm_gradient_flow(rng,
init,
state,
ddpm_params,
inverse_scaler,
noise_removal=False,
final_only=False,
verbose=False,
colab=False):
"""Gradient flow sampling for DDPMs.
This function leverages `pmap` internally and shouldn't be pmapped itself.
sample with EMA.
Args:
rng: jax random state for Langevin dynamics sample generation.
init: the randomly initialized starting point for sampling.
state: the full state class.
ddpm_params: a dictionary containing hyperparameters of the DDPM model.
noise_removal: whether to remove the noise at the final sampling step.
inverse_scaler: a function to scale generated inputs back to valid images.
final_only: if True store only the last sample. Otherwise store the whole
sample history.
verbose: if True log running information.
colab: if True log to stdout.
Returns:
samples: list of image samples.
"""
images = []
x = init.copy()
model_ema = state.optimizer.target.replace(params=state.params_ema)
@functools.partial(jax.pmap, axis_name='batch')
def score_eval(sample, noise_level):
labels = jnp.ones((sample.shape[0],), dtype=jnp.int32) * noise_level
with nn.stateful(state.model_state, mutable=False):
score = model_ema(sample, labels, train=False)
return score
betas = ddpm_params['betas']
alphas = ddpm_params['alphas']
sqrt_1m_alphas_cumprod = ddpm_params['sqrt_1m_alphas_cumprod']
for T in reversed(range(len(betas))): # pylint: disable=invalid-name
replicated_T = flax.jax_utils.replicate(T) # pylint: disable=invalid-name
grad = - score_eval(x, replicated_T) / sqrt_1m_alphas_cumprod[T]
rng, sample_rng = jax.random.split(rng)
noise = jax.random.normal(sample_rng, x.shape)
x = (2. - np.sqrt(1 - betas[T])) * x + grad * betas[T] / 2.
if not final_only:
images.append(inverse_scaler(np.asarray(x)))
if verbose and jax.host_id() == 0:
grad_norm = jnp.linalg.norm(
grad.reshape((grad.shape[1] * grad.shape[0], -1)), axis=-1).mean()
image_norm = jnp.linalg.norm(
x.reshape((x.shape[0] * x.shape[1], -1)), axis=-1).mean()
if colab:
print('level: %d, grad_norm: %.5e, image_norm: %.5e' %
(T, grad_norm, image_norm))
else:
logging.info('level: %d, grad_norm: %.5e, image_norm: %.5e', T,
grad_norm, image_norm)
if final_only:
images.append(inverse_scaler(np.asarray(x)))
return images
@register_sampler
def ddpm_ald_fix_snr(rng,
init,
state,
ddpm_params,
inverse_scaler,
n_steps_each=2,
target_snr=0.15,
noise_removal=False,
final_only=False,
verbose=False,
colab=False):
"""Annealed Langevin dynamics sampling for DDPMs.
In the form of fixed SNR and estimated scales with norm of score functions.
This function leverages `pmap` internally and shouldn't be pmapped itself.
sample with EMA.
Args:
rng: jax random state for Langevin dynamics sample generation.
init: the randomly initialized starting point for sampling.
state: the full state class.
ddpm_params: a dictionary containing hyperparameters of the DDPM model.
noise_removal: whether to remove the noise at the final sampling step.
inverse_scaler: a function to scale generated inputs back to valid images.
final_only: if True store only the last sample. Otherwise store the whole
sample history.
verbose: if True log running information.
colab: if True log to stdout.
Returns:
samples: list of image samples.
"""
images = []
x = init.copy()
model_ema = state.optimizer.target.replace(params=state.params_ema)
@functools.partial(jax.pmap, axis_name='batch')
def score_eval(sample, noise_level):
labels = jnp.ones((sample.shape[0],), dtype=jnp.int32) * noise_level
with nn.stateful(state.model_state, mutable=False):
score = model_ema(sample, labels, train=False)
return score
betas = ddpm_params['betas']
alphas = ddpm_params['alphas']
alphas_cumprod = ddpm_params['alphas_cumprod']
sqrt_alphas_cumprod = jnp.sqrt(alphas_cumprod)
sqrt_1m_alphas_cumprod = ddpm_params['sqrt_1m_alphas_cumprod']
x = x / sqrt_alphas_cumprod[-1]
for T in reversed(range(len(betas))): # pylint: disable=invalid-name
replicated_T = flax.jax_utils.replicate(T) # pylint: disable=invalid-name
for _ in range(n_steps_each):
grad = -score_eval(
sqrt_alphas_cumprod[T] * x,
replicated_T) * sqrt_alphas_cumprod[T] / sqrt_1m_alphas_cumprod[T]
rng, sample_rng = jax.random.split(rng)
noise = jax.random.normal(sample_rng, x.shape)
grad_norm = jnp.linalg.norm(
grad.reshape((grad.shape[1] * grad.shape[0], -1)), axis=-1).mean()
noise_norm = jnp.linalg.norm(
noise.reshape((noise.shape[0] * noise.shape[1], -1)), axis=-1).mean()
step_size = (target_snr * noise_norm / grad_norm)**2 * 2.
x = x + step_size * grad + noise * jnp.sqrt(step_size * 2)
if not final_only:
images.append(inverse_scaler(np.asarray(x)))
if verbose and jax.host_id() == 0:
image_norm = jnp.linalg.norm(
x.reshape((x.shape[0] * x.shape[1], -1)), axis=-1).mean()
if colab:
print('level: %d, step_size: %.5e, image_norm: %.5e'
% (T, step_size, image_norm))
else:
logging.info('level: %d, step_size: %.5e, image_norm: %.5e', T,
step_size, image_norm)
if noise_removal:
x = x - sqrt_1m_alphas_cumprod[0] / sqrt_alphas_cumprod[0] * score_eval(
x * sqrt_alphas_cumprod[0], flax.jax_utils.replicate(0))
if final_only:
images.append(inverse_scaler(np.asarray(x)))
return images
@register_sampler(name='ddpm_reproduce')
def ddpm_sampling_reproduce(rng,
init,
state,
ddpm_params,
inverse_scaler,
noise_removal=False,
final_only=False,
verbose=False,
colab=False):
"""The sampling procedure of DDPMs. Same implementation as the DDPM codebase.
This function leverages `pmap` internally and shouldn't be pmapped itself.
sample with EMA.
Args:
rng: jax random state for Langevin dynamics sample generation.
init: the randomly initialized starting point for sampling.
state: the full state class.
ddpm_params: a dictionary containing hyperparameters of the DDPM model.
noise_removal: whether to remove the noise at the final sampling step.
inverse_scaler: a function to scale generated inputs back to valid images.
final_only: if True store only the last sample. Otherwise store the whole
sample history.
verbose: if True log running information.
colab: if True log to stdout.
Returns:
samples: list of image samples.
"""
images = []
x = init.copy()
model_ema = state.optimizer.target.replace(params=state.params_ema)
@functools.partial(jax.pmap, axis_name='batch')
def score_eval(sample, noise_level):
labels = jnp.ones((sample.shape[0],), dtype=jnp.int32) * noise_level
with nn.stateful(state.model_state, mutable=False):
score = model_ema(sample, labels, train=False)
return score
betas = ddpm_params['betas']
alphas = ddpm_params['alphas']
alphas_cumprod = ddpm_params['alphas_cumprod']
sqrt_1m_alphas_cumprod = ddpm_params['sqrt_1m_alphas_cumprod']
sqrt_recip_alphas_cumprod = np.sqrt(1. / alphas_cumprod)
sqrt_recipm1_alphas_cumprod = np.sqrt(1. / alphas_cumprod - 1)
alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
posterior_mean_coef1 = betas * np.sqrt(alphas_cumprod_prev) / (1. -
alphas_cumprod)
posterior_mean_coef2 = (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (
1. - alphas_cumprod)
for T in reversed(range(len(betas))): # pylint: disable=invalid-name
replicated_T = flax.jax_utils.replicate(T) # pylint: disable=invalid-name
grad = score_eval(x, replicated_T)
rng, sample_rng = jax.random.split(rng)
noise = jax.random.normal(sample_rng, x.shape)
x0 = sqrt_recip_alphas_cumprod[T, None, None, None,
None] * x - sqrt_recipm1_alphas_cumprod[
T, None, None, None, None] * grad
x0 = jnp.clip(x0, -1., 1.)
x = posterior_mean_coef1[T, None, None, None,
None] * x0 + posterior_mean_coef2[T, None, None,
None, None] * x
if not noise_removal or T > 0:
x = x + np.sqrt(betas[T, None, None, None, None]) * noise
if not final_only:
images.append(inverse_scaler(np.asarray(x)))
if verbose and jax.host_id() == 0:
grad_norm = jnp.linalg.norm(
grad.reshape((grad.shape[1] * grad.shape[0], -1)), axis=-1).mean()
image_norm = jnp.linalg.norm(
x.reshape((x.shape[0] * x.shape[1], -1)), axis=-1).mean()
if colab:
print('level: %d, grad_norm: %.5e, image_norm: %.5e'
% (T, grad_norm, image_norm))
else:
logging.info('level: %d, grad_norm: %.5e, image_norm: %.5e', T,
grad_norm, image_norm)
if final_only:
images.append(inverse_scaler(np.asarray(x)))
return images
@register_sampler(name='ddpm_ald_fix_snr_diffusion_sampling')
def ddpm_ald_fix_snr_diffusion_sampling(rng,
init,
state,
ddpm_params,
inverse_scaler,
n_steps_each=2,
target_snr=0.15,
noise_removal=False,
final_only=False,
verbose=False,
colab=False):
"""Discrete diffusion + annealed Langevin dynamics for sampling from DDPMs.
This function leverages `pmap` internally and shouldn't be pmapped itself.
sample with EMA.
Args:
rng: jax random state for Langevin dynamics sample generation.
init: the randomly initialized starting point for sampling.
state: the full state class.
ddpm_params: a dictionary containing hyperparameters of the DDPM model.
noise_removal: whether to remove the noise at the final sampling step.
inverse_scaler: a function to scale generated inputs back to valid images.
final_only: if True store only the last sample. Otherwise store the whole
sample history.
verbose: if True log running information.
colab: if True log to stdout.
Returns:
samples: list of image samples.
"""
images = []
x = init.copy()
model_ema = state.optimizer.target.replace(params=state.params_ema)
@functools.partial(jax.pmap, axis_name='batch')
def score_eval(sample, noise_level):
labels = jnp.ones((sample.shape[0],), dtype=jnp.int32) * noise_level
with nn.stateful(state.model_state, mutable=False):
score = model_ema(sample, labels, train=False)
return score
betas = ddpm_params['betas']
alphas = ddpm_params['alphas']
alphas_cumprod = ddpm_params['alphas_cumprod']
sqrt_alphas_cumprod = jnp.sqrt(alphas_cumprod)
sqrt_1m_alphas_cumprod = ddpm_params['sqrt_1m_alphas_cumprod']
sqrt_recip_alphas_cumprod = np.sqrt(1. / alphas_cumprod)
sqrt_recipm1_alphas_cumprod = np.sqrt(1. / alphas_cumprod - 1)
alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
posterior_mean_coef1 = betas * np.sqrt(alphas_cumprod_prev) / (1. -
alphas_cumprod)
posterior_mean_coef2 = (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (
1. - alphas_cumprod)
x = x / sqrt_alphas_cumprod[-1]
for T in reversed(range(len(betas))): # pylint: disable=invalid-name
replicated_T = flax.jax_utils.replicate(T) # pylint: disable=invalid-name
if T < len(betas) - 1:
y = sqrt_alphas_cumprod[T+1] * x
grad = score_eval(y, flax.jax_utils.replicate(T+1))
rng, sample_rng = jax.random.split(rng)
noise = jax.random.normal(sample_rng, x.shape)
y0 = sqrt_recip_alphas_cumprod[T+1, None, None, None,
None] * y - sqrt_recipm1_alphas_cumprod[
T+1, None, None, None, None] * grad
y0 = jnp.clip(y0, -1., 1.)
y = posterior_mean_coef1[T + 1, None, None, None,
None] * y0 + posterior_mean_coef2[
T + 1, None, None, None, None] * y
y = y + np.sqrt(betas[T + 1, None, None, None, None]) * noise
x = y / sqrt_alphas_cumprod[T]
for _ in range(n_steps_each):
grad = -score_eval(
sqrt_alphas_cumprod[T] * x,
replicated_T) * sqrt_alphas_cumprod[T] / sqrt_1m_alphas_cumprod[T]
rng, sample_rng = jax.random.split(rng)
noise = jax.random.normal(sample_rng, x.shape)
grad_norm = jnp.linalg.norm(
grad.reshape((grad.shape[1] * grad.shape[0], -1)), axis=-1).mean()
noise_norm = jnp.linalg.norm(
noise.reshape((noise.shape[0] * noise.shape[1], -1)), axis=-1).mean()
step_size = (target_snr * noise_norm / grad_norm)**2 * 2.
x = x + step_size * grad + noise * jnp.sqrt(step_size * 2)
if not final_only:
images.append(inverse_scaler(np.asarray(x)))
if verbose and jax.host_id() == 0:
image_norm = jnp.linalg.norm(
x.reshape((x.shape[0] * x.shape[1], -1)), axis=-1).mean()
if colab:
print('level: %d, step_size: %.5e, image_norm: %.5e'
% (T, step_size, image_norm))
else:
logging.info('level: %d, step_size: %.5e, image_norm: %.5e', T,
step_size, image_norm)
if noise_removal:
x = x - sqrt_1m_alphas_cumprod[0] / sqrt_alphas_cumprod[0] * score_eval(
x * sqrt_alphas_cumprod[0], flax.jax_utils.replicate(0))
if final_only:
images.append(inverse_scaler(np.asarray(x)))
return images
@register_sampler
def ddpm_ald_fix_snr_reverse_diffusion(rng,
init,
state,
ddpm_params,
inverse_scaler,
n_steps_each=2,
target_snr=0.15,
noise_removal=False,
final_only=False,
verbose=False,
colab=False):
"""Reverse diffusion + annealed Langevin dynamics for sampling from DDPMs.
This function leverages `pmap` internally and shouldn't be pmapped itself.
sample with EMA.
Args:
rng: jax random state for Langevin dynamics sample generation.
init: the randomly initialized starting point for sampling.
state: the full state class.
ddpm_params: a dictionary containing hyperparameters of the DDPM model.
noise_removal: whether to remove the noise at the final sampling step.
inverse_scaler: a function to scale generated inputs back to valid images.
final_only: if True store only the last sample. Otherwise store the whole
sample history.
verbose: if True log running information.
colab: if True log to stdout.
Returns:
samples: list of image samples.
"""
images = []
x = init.copy()
model_ema = state.optimizer.target.replace(params=state.params_ema)
@functools.partial(jax.pmap, axis_name='batch')
def score_eval(sample, noise_level):
labels = jnp.ones((sample.shape[0],), dtype=jnp.int32) * noise_level
with nn.stateful(state.model_state, mutable=False):
score = model_ema(sample, labels, train=False)
return score
betas = ddpm_params['betas']
alphas = ddpm_params['alphas']
alphas_cumprod = ddpm_params['alphas_cumprod']
sqrt_alphas_cumprod = jnp.sqrt(alphas_cumprod)
sqrt_1m_alphas_cumprod = ddpm_params['sqrt_1m_alphas_cumprod']
sqrt_recip_alphas_cumprod = np.sqrt(1. / alphas_cumprod)
sqrt_recipm1_alphas_cumprod = np.sqrt(1. / alphas_cumprod - 1)
alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
posterior_mean_coef1 = betas * np.sqrt(alphas_cumprod_prev) / (1. -
alphas_cumprod)
posterior_mean_coef2 = (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (
1. - alphas_cumprod)
x = x / sqrt_alphas_cumprod[-1]
for T in reversed(range(len(betas))): # pylint: disable=invalid-name
replicated_T = flax.jax_utils.replicate(T) # pylint: disable=invalid-name
if T < len(betas) - 1:
y = sqrt_alphas_cumprod[T+1] * x
grad = -score_eval(y, flax.jax_utils.replicate(T+1)) / sqrt_1m_alphas_cumprod[T+1]
rng, sample_rng = jax.random.split(rng)
noise = jax.random.normal(sample_rng, x.shape)
y = (2. - np.sqrt(1 - betas[T+1])) * y + grad * betas[T+1]
y = y + np.sqrt(betas[T+1]) * noise
x = y / sqrt_alphas_cumprod[T]
for _ in range(n_steps_each):
grad = -score_eval(
sqrt_alphas_cumprod[T] * x,
replicated_T) * sqrt_alphas_cumprod[T] / sqrt_1m_alphas_cumprod[T]
rng, sample_rng = jax.random.split(rng)
noise = jax.random.normal(sample_rng, x.shape)
grad_norm = jnp.linalg.norm(
grad.reshape((grad.shape[1] * grad.shape[0], -1)), axis=-1).mean()
noise_norm = jnp.linalg.norm(
noise.reshape((noise.shape[0] * noise.shape[1], -1)), axis=-1).mean()
step_size = (target_snr * noise_norm / grad_norm)**2 * 2.
x = x + step_size * grad + noise * jnp.sqrt(step_size * 2)
if not final_only:
images.append(inverse_scaler(np.asarray(x)))
if verbose and jax.host_id() == 0:
image_norm = jnp.linalg.norm(
x.reshape((x.shape[0] * x.shape[1], -1)), axis=-1).mean()
if colab:
print('level: %d, step_size: %.5e, image_norm: %.5e'
% (T, step_size, image_norm))
else:
logging.info('level: %d, step_size: %.5e, image_norm: %.5e', T,
step_size, image_norm)
if noise_removal:
x = x - sqrt_1m_alphas_cumprod[0] / sqrt_alphas_cumprod[0] * score_eval(
x * sqrt_alphas_cumprod[0], flax.jax_utils.replicate(0))
if final_only:
images.append(inverse_scaler(np.asarray(x)))
return images
@register_sampler
def ddpm_ald_fix_snr_gradient_flow(rng,
init,
state,
ddpm_params,
inverse_scaler,
n_steps_each=2,
target_snr=0.15,
noise_removal=False,
final_only=False,
verbose=False,
colab=False):
"""Gradient flow + annealed Langevin dynamics for sampling from DDPMs.
This function leverages `pmap` internally and shouldn't be pmapped itself.
sample with EMA.
Args:
rng: jax random state for Langevin dynamics sample generation.
init: the randomly initialized starting point for sampling.
state: the full state class.
ddpm_params: a dictionary containing hyperparameters of the DDPM model.
noise_removal: whether to remove the noise at the final sampling step.
inverse_scaler: a function to scale generated inputs back to valid images.
final_only: if True store only the last sample. Otherwise store the whole
sample history.
verbose: if True log running information.
colab: if True log to stdout.
Returns:
samples: list of image samples.
"""
images = []
x = init.copy()
model_ema = state.optimizer.target.replace(params=state.params_ema)
@functools.partial(jax.pmap, axis_name='batch')
def score_eval(sample, noise_level):
labels = jnp.ones((sample.shape[0],), dtype=jnp.int32) * noise_level
with nn.stateful(state.model_state, mutable=False):
score = model_ema(sample, labels, train=False)
return score
betas = ddpm_params['betas']
alphas = ddpm_params['alphas']
alphas_cumprod = ddpm_params['alphas_cumprod']
sqrt_alphas_cumprod = jnp.sqrt(alphas_cumprod)
sqrt_1m_alphas_cumprod = ddpm_params['sqrt_1m_alphas_cumprod']
sqrt_recip_alphas_cumprod = np.sqrt(1. / alphas_cumprod)
sqrt_recipm1_alphas_cumprod = np.sqrt(1. / alphas_cumprod - 1)
alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
posterior_mean_coef1 = betas * np.sqrt(alphas_cumprod_prev) / (1. -
alphas_cumprod)
posterior_mean_coef2 = (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (
1. - alphas_cumprod)
x = x / sqrt_alphas_cumprod[-1]
for T in reversed(range(len(betas))): # pylint: disable=invalid-name
replicated_T = flax.jax_utils.replicate(T) # pylint: disable=invalid-name
if T < len(betas) - 1:
y = sqrt_alphas_cumprod[T+1] * x
grad = -score_eval(y, flax.jax_utils.replicate(T+1)) / sqrt_1m_alphas_cumprod[T+1]
rng, sample_rng = jax.random.split(rng)
noise = jax.random.normal(sample_rng, x.shape)
y = (2. - np.sqrt(1 - betas[T+1])) * y + grad * betas[T+1] / 2.
x = y / sqrt_alphas_cumprod[T]
for _ in range(n_steps_each):
grad = -score_eval(
sqrt_alphas_cumprod[T] * x,
replicated_T) * sqrt_alphas_cumprod[T] / sqrt_1m_alphas_cumprod[T]
rng, sample_rng = jax.random.split(rng)
noise = jax.random.normal(sample_rng, x.shape)
grad_norm = jnp.linalg.norm(
grad.reshape((grad.shape[1] * grad.shape[0], -1)), axis=-1).mean()
noise_norm = jnp.linalg.norm(
noise.reshape((noise.shape[0] * noise.shape[1], -1)), axis=-1).mean()
step_size = (target_snr * noise_norm / grad_norm)**2 * 2.
x = x + step_size * grad + noise * jnp.sqrt(step_size * 2)
if not final_only:
images.append(inverse_scaler(np.asarray(x)))
if verbose and jax.host_id() == 0:
image_norm = jnp.linalg.norm(
x.reshape((x.shape[0] * x.shape[1], -1)), axis=-1).mean()
if colab:
print('level: %d, step_size: %.5e, image_norm: %.5e'
% (T, step_size, image_norm))
else:
logging.info('level: %d, step_size: %.5e, image_norm: %.5e', T,
step_size, image_norm)
if noise_removal:
x = x - sqrt_1m_alphas_cumprod[0] / sqrt_alphas_cumprod[0] * score_eval(
x * sqrt_alphas_cumprod[0], flax.jax_utils.replicate(0))
if final_only:
images.append(inverse_scaler(np.asarray(x)))
return images
@register_sampler
def ald_fix_snr_diffusion_sampling(rng,
init,
state,
sigmas,
inverse_scaler,
class_labels=None,
continuous_sigmas=False,
n_steps_each=200,
target_snr=0.2,
noise_removal=True,
final_only=False,
verbose=False,
colab=False):
"""Discrete diffusion sampling + annealed Langevin dynamics for NCSNs.
This function leverages `pmap` internally and shouldn't be pmapped itself.
sample with EMA.
Args:
rng: jax random state for Langevin dynamics sample generation.
init: the randomly initialized starting point for sampling.
state: the full state class.
sigmas: noise levels.
inverse_scaler: scale generated samples back to valid images.
class_labels: the target class labels for class-conditional generation.
continuous_sigmas: use a continuous distribution of sigmas.
n_steps_each: the number of Langevin steps for each noise level.
target_snr: the target signal to noise ratio.
noise_removal: FID will increase by a lot if set to False.
final_only: if True store only the last sample. Otherwise store the whole
sample history.
verbose: if True log running information.
colab: if True log to stdout.
Returns:
samples: list of image samples.
"""
images = []
x = init.copy()
model_ema = state.optimizer.target.replace(params=state.params_ema)
if class_labels is None:
@functools.partial(jax.pmap, axis_name='batch')
def score_eval(sample, noise_level):
labels = jnp.ones((sample.shape[0],), dtype=jnp.int32) * noise_level
with nn.stateful(state.model_state, mutable=False):
score = model_ema(sample, labels, train=False)
return score
else:
@functools.partial(jax.pmap, axis_name='batch')
def score_eval(sample, noise_level, class_label):
labels = jnp.ones((sample.shape[0],), dtype=jnp.int32) * noise_level
with nn.stateful(state.model_state, mutable=False):
score = model_ema(sample, labels, y=class_label, train=False)
return score
score_eval = functools.partial(score_eval, class_label=class_labels)
for c, sigma in enumerate(sigmas):
if continuous_sigmas:
noise_level = flax.jax_utils.replicate(sigma)
else:
noise_level = flax.jax_utils.replicate(c)
if c > 0:
prev_noise = sigmas[c - 1] if continuous_sigmas else c - 1
grad = score_eval(x, flax.jax_utils.replicate(prev_noise))
x0 = x + sigmas[c-1]**2 * grad
coeff = sigmas[c]**2 / sigmas[c - 1]**2
std = sigmas[c] / sigmas[c - 1] * jnp.sqrt(sigmas[c-1]**2 - sigmas[c]**2)
x = coeff * x + (1. - coeff) * x0
rng, noise_rng = jax.random.split(rng)
noise = jax.random.normal(noise_rng, x.shape)
x = x + std * noise
for step in range(n_steps_each):
grad = score_eval(x, noise_level)
rng, sample_rng = jax.random.split(rng)
noise = jax.random.normal(sample_rng, x.shape)
grad_norm = jnp.linalg.norm(
grad.reshape((grad.shape[1] * grad.shape[0], -1)), axis=-1).mean()
noise_norm = jnp.linalg.norm(
noise.reshape((noise.shape[0] * noise.shape[1], -1)), axis=-1).mean()
step_size = (target_snr * noise_norm / grad_norm)**2 * 2.
x = x + step_size * grad + noise * jnp.sqrt(step_size * 2)
if not final_only:
images.append(inverse_scaler(np.asarray(x)))
if verbose and jax.host_id() == 0:
image_norm = jnp.linalg.norm(
x.reshape((x.shape[0] * x.shape[1], -1)), axis=-1).mean()
if colab:
print('level: %d, step_size: %.5e, image_norm: %.5e' %
(c, step_size, image_norm))
else:
logging.info('level: %d, step_size: %.5e, image_norm: %.5e', c,
step_size, image_norm)
if noise_removal:
last_noise = sigmas[-1] if continuous_sigmas else len(sigmas) - 1
x = x + sigmas[-1]**2 * score_eval(x, flax.jax_utils.replicate(last_noise))
logging.info('Finished noise removal!')
if final_only:
images.append(inverse_scaler(np.asarray(x)))
return images
@register_sampler
def ald_fix_snr_reverse_diffusion(rng,
init,
state,
sigmas,
inverse_scaler,
class_labels=None,
continuous_sigmas=False,
n_steps_each=200,
target_snr=0.2,
noise_removal=True,
final_only=False,
verbose=False,
colab=False,
temperature=1.):
"""Reverse diffusion + annealed Langevin dynamics for NCSNs.
This function leverages `pmap` internally and shouldn't be pmapped itself.
sample with EMA.
Args:
rng: jax random state for Langevin dynamics sample generation.
init: the randomly initialized starting point for sampling.
state: the full state class.
sigmas: noise levels.
inverse_scaler: scale generated samples back to valid images.
class_labels: the target class labels for class-conditional generation.
continuous_sigmas: use a continuous distribution of sigmas.
n_steps_each: the number of Langevin steps for each noise level.
target_snr: the target signal to noise ratio.
noise_removal: FID will increase by a lot if set to False.
final_only: if True store only the last sample. Otherwise store the whole
sample history.
verbose: if True log running information.
colab: if True log to stdout.
Returns:
samples: list of image samples.
"""
images = []
x = init.copy()
model_ema = state.optimizer.target.replace(params=state.params_ema)
if class_labels is None:
@functools.partial(jax.pmap, axis_name='batch')
def score_eval(sample, noise_level):
labels = jnp.ones((sample.shape[0],), dtype=jnp.int32) * noise_level
with nn.stateful(state.model_state, mutable=False):
score = model_ema(sample, labels, train=False)
return score * 1. / temperature
else:
@functools.partial(jax.pmap, axis_name='batch')
def score_eval(sample, noise_level, class_label):
labels = jnp.ones((sample.shape[0],), dtype=jnp.int32) * noise_level
with nn.stateful(state.model_state, mutable=False):
score = model_ema(sample, labels, y=class_label, train=False)
return score * 1. / temperature
score_eval = functools.partial(score_eval, class_label=class_labels)
for c, sigma in enumerate(sigmas):
if c > 0:
prev_noise = sigmas[c - 1] if continuous_sigmas else c - 1
grad = score_eval(x, flax.jax_utils.replicate(prev_noise))
variance = (sigmas[c-1]**2 - sigma**2)
rng, sample_rng = jax.random.split(rng)
noise = jax.random.normal(sample_rng, x.shape)
x = x + variance * grad + jnp.sqrt(variance) * noise
noise_level = sigma if continuous_sigmas else c
noise_level = flax.jax_utils.replicate(noise_level)
for step in range(n_steps_each):
grad = score_eval(x, noise_level)
rng, sample_rng = jax.random.split(rng)
noise = jax.random.normal(sample_rng, x.shape)
grad_norm = jnp.linalg.norm(
grad.reshape((grad.shape[1] * grad.shape[0], -1)), axis=-1).mean()
noise_norm = jnp.linalg.norm(
noise.reshape((noise.shape[0] * noise.shape[1], -1)), axis=-1).mean()
step_size = (target_snr * noise_norm / grad_norm)**2 * 2.
x = x + step_size * grad + noise * jnp.sqrt(step_size * 2)
if not final_only:
images.append(inverse_scaler(np.asarray(x)))
if verbose and jax.host_id() == 0:
image_norm = jnp.linalg.norm(
x.reshape((x.shape[0] * x.shape[1], -1)), axis=-1).mean()
if colab:
print('level: %d, step_size: %.5e, image_norm: %.5e' %
(c, step_size, image_norm))
else:
logging.info('level: %d, step_size: %.5e, image_norm: %.5e', c,
step_size, image_norm)
if noise_removal:
last_noise = sigmas[-1] if continuous_sigmas else len(sigmas) - 1
x = x + sigmas[-1]**2 * score_eval(x, flax.jax_utils.replicate(last_noise))
logging.info('Finished noise removal!')
if final_only:
images.append(inverse_scaler(np.asarray(x)))
return images
@register_sampler
def ald_fix_snr_gradient_flow(rng,
init,
state,
sigmas,
inverse_scaler,
class_labels=None,
continuous_sigmas=False,
n_steps_each=200,
target_snr=0.2,
noise_removal=True,
final_only=False,
verbose=False,
colab=False,
temperature=1.):
"""Gradient flow + annealed Langevin dynamics for NCSNs.
This function leverages `pmap` internally and shouldn't be pmapped itself.
sample with EMA.
Args:
rng: jax random state for Langevin dynamics sample generation.
init: the randomly initialized starting point for sampling.
state: the full state class.
sigmas: noise levels.
inverse_scaler: scale generated samples back to valid images.
class_labels: the target class labels for class-conditional generation.
continuous_sigmas: use a continuous distribution of sigmas.
n_steps_each: the number of Langevin steps for each noise level.
target_snr: the target signal to noise ratio.
noise_removal: FID will increase by a lot if set to False.
final_only: if True store only the last sample. Otherwise store the whole
sample history.
verbose: if True log running information.
colab: if True log to stdout.
Returns:
samples: list of image samples.
"""
images = []
x = init.copy()
model_ema = state.optimizer.target.replace(params=state.params_ema)
if class_labels is None:
@functools.partial(jax.pmap, axis_name='batch')
def score_eval(sample, noise_level):
labels = jnp.ones((sample.shape[0],), dtype=jnp.int32) * noise_level
with nn.stateful(state.model_state, mutable=False):
score = model_ema(sample, labels, train=False)
return score * 1. / temperature
else:
@functools.partial(jax.pmap, axis_name='batch')
def score_eval(sample, noise_level, class_label):
labels = jnp.ones((sample.shape[0],), dtype=jnp.int32) * noise_level
with nn.stateful(state.model_state, mutable=False):
score = model_ema(sample, labels, y=class_label, train=False)
return score * 1. / temperature
score_eval = functools.partial(score_eval, class_label=class_labels)
for c, sigma in enumerate(sigmas):
if c > 0:
prev_noise = sigmas[c - 1] if continuous_sigmas else c - 1
grad = score_eval(x, flax.jax_utils.replicate(prev_noise))
variance = (sigmas[c-1]**2 - sigma**2)
rng, sample_rng = jax.random.split(rng)
noise = jax.random.normal(sample_rng, x.shape)
x = x + variance * grad / 2.
noise_level = sigma if continuous_sigmas else c
noise_level = flax.jax_utils.replicate(noise_level)
for step in range(n_steps_each):
grad = score_eval(x, noise_level)
rng, sample_rng = jax.random.split(rng)
noise = jax.random.normal(sample_rng, x.shape)
grad_norm = jnp.linalg.norm(
grad.reshape((grad.shape[1] * grad.shape[0], -1)), axis=-1).mean()
noise_norm = jnp.linalg.norm(
noise.reshape((noise.shape[0] * noise.shape[1], -1)), axis=-1).mean()
step_size = (target_snr * noise_norm / grad_norm)**2 * 2.
x = x + step_size * grad + noise * jnp.sqrt(step_size * 2)
if not final_only:
images.append(inverse_scaler(np.asarray(x)))
if verbose and jax.host_id() == 0:
image_norm = jnp.linalg.norm(
x.reshape((x.shape[0] * x.shape[1], -1)), axis=-1).mean()
if colab:
print('level: %d, step_size: %.5e, image_norm: %.5e' %
(c, step_size, image_norm))
else:
logging.info('level: %d, step_size: %.5e, image_norm: %.5e', c,
step_size, image_norm)
if noise_removal:
last_noise = sigmas[-1] if continuous_sigmas else len(sigmas) - 1
x = x + sigmas[-1]**2 * score_eval(x, flax.jax_utils.replicate(last_noise))
logging.info('Finished noise removal!')
if final_only:
images.append(inverse_scaler(np.asarray(x)))
return images
| 38.943553 | 111 | 0.625111 | 8,685 | 65,542 | 4.54715 | 0.035809 | 0.038514 | 0.013826 | 0.01808 | 0.938089 | 0.932341 | 0.921225 | 0.91611 | 0.91021 | 0.903525 | 0 | 0.013862 | 0.269171 | 65,542 | 1,682 | 112 | 38.966706 | 0.810605 | 0.220515 | 0 | 0.880829 | 0 | 0.001727 | 0.050756 | 0.007503 | 0 | 0 | 0 | 0 | 0 | 1 | 0.037133 | false | 0 | 0.007772 | 0 | 0.082902 | 0.013817 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 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 | 7 |
b997fda4dc6b144493fe08811857afd56ef78e74 | 120,212 | py | Python | webenmr/controllers/jobs.py | andreagia/WEBNMR | 512a8cc04cf69300796585feae722614501389a9 | [
"Apache-2.0"
] | null | null | null | webenmr/controllers/jobs.py | andreagia/WEBNMR | 512a8cc04cf69300796585feae722614501389a9 | [
"Apache-2.0"
] | null | null | null | webenmr/controllers/jobs.py | andreagia/WEBNMR | 512a8cc04cf69300796585feae722614501389a9 | [
"Apache-2.0"
] | null | null | null | import logging
import os, subprocess, glob
import re
import shutil
import tarfile
from pprint import pprint
import pickle
import zipfile
import md5
import copy
import commands
import simplejson as json
from lxml import etree
from pylons import request, response, session, tmpl_context as c
from pylons.controllers.util import abort, redirect
from pylons import config, app_globals
import pycurl
from dateutil import parser
from datetime import datetime
import time
from sqlalchemy.sql import and_
from sqlalchemy.sql import or_
from webenmr.lib.base import *
from webenmr.lib import return_values
from webenmr.lib.multi_input import multi_input
from webenmr.model import Projects, Users, Jobs
from webenmr.lib.return_values import S_OK, S_ERROR
from webenmr.lib.Subprocess import shellCall
from webenmr.lib.BaseSecurity import BaseSecurity
from webenmr.model import Calculations, Jobs, CalculationTipology, Users
from webenmr.lib import Certificates
from webenmr.lib import ssox
import math
log = logging.getLogger(__name__)
os.umask(0002)
os.environ['PATH'] += os.pathsep
class JobsProcessing(BaseSecurity):
def __init__(self):
BaseSecurity.__init__(self)
if session['PORTAL'] == 'amps-nmr':
attribute = "/enmr.eu/amber"
elif session['PORTAL'] == 'xplor-nih':
attribute = "/enmr.eu/xplornih"
else:
attribute = ""
if 'timesession' in session.keys():
before = session['timesession']
now = datetime.now()
hours = math.floor(((now - before).seconds) / 3600)
if hours > 1.0:
print "@@@@@@@ RINNOVO CERTIFICATO @@@@@@@@@@@"
ret = Certificates.proxy_initialize(attribute)
#ret = Certificates.proxy_initialize()
print "######VOMSPROXY INI #############"
if ret['OK']:
#print "VOMS_PROXY ", session['voms_proxy']
#print ret['Value']
print "OK"
else:
print 'voms-proxy-init problem: %s' % ret['Message']
print "@@@@@@@ CERTIFICATO BUONO @@@@@@@@@@@"
else:
print "@@@@@@@ PRIMO ACCESSO CERTIFICATO @@@@@@@@@@@"
session['timesession'] = datetime.now()
ret = Certificates.proxy_initialize(attribute)
#ret = Certificates.proxy_initialize()
print "######VOMSPROXY INI #############"
if ret['OK']:
#print "VOMS_PROXY ", session['voms_proxy']
#print ret['Value']
print "OK"
else:
print 'voms-proxy-init problem: %s' % ret['Message']
print "######VOMSPROXY END #############"
os.environ['X509_USER_PROXY'] = session['voms_proxy_file']
os.environ['X509_USER_CERT'] = session['voms_proxy_file']
os.environ['X509_USER_KEY'] = session['voms_proxy_file']
print "X509_USER_KEY ", session['voms_proxy_file']
print "X509_USER_KEY ", session['voms_proxy_file']
print "X509_USER_PROXY ", session['voms_proxy_file']
def submitclo(self, wdir, jdl):
'''Submit a job to the GRID
input:
wdir = working directory of the job
jdl = jdl file
return the result of the submission'''
cmd = """ssh webenmr@192.168.0.30 << EOF
cd %s
sync
curl -i -H "Content-Type: application/json" -X POST -d '{"application":"3","description":"amber test run", "output_files": [ {"name":"pro.tgz"}], "input_files": [ {"name":"run_amber.sh"},{"name":"in.tgz"} ]}' http://localhost:8888/v1.0/tasks?user=brunor
EOF
""" %(wdir)
print cmd
status, output, error =self.exec_cmd(cmd)
match = re.search('.*("id": "[0-9]+")',output)
idj=match.group(1).split()[1].replace('"',"")
print output
print idj
cmd1 = '''ssh webenmr@192.168.0.30 << EOF
cd %(wdir)s
sync
curl -i -F "file[]=@run_amber.sh" -F "file[]=@in.tgz" POST http://localhost:8888/v1.0/tasks/%(idj)s/input?user=brunor
EOF
''' %{'wdir':wdir,'idj':idj}
print cmd1
return self.exec_cmd(cmd1)
def submitgpu(self, wdir, jdl):
print "SUBMIT GPU "
'''Submit a job to the GRID
input:
wdir = working directory of the job
jdl = jdl file
return the result of the submission'''
certx = session['voms_proxy_file']
cmd = 'ssh webenmr@192.168.0.10 "cat %s > %s1 ; chmod 600 %s1; export X509_USER_PROXY=%s1 ;cd %s;/usr/bin/glite-ce-job-submit -a -r cegpu.cerm.unifi.it:8443/cream-pbs-batch %s"' %(certx, certx, certx, certx, wdir, jdl)
print cmd
return self.exec_cmd(cmd)
def submit(self, wdir, jdl):
'''Submit a job to the GRID
input:
wdir = working directory of the job
jdl = jdl file
return the result of the submission'''
certx = session['voms_proxy_file']
cmd = 'ssh webenmr@192.168.0.10 "cat %s > %s1 ; chmod 600 %s1; export X509_USER_PROXY=%s1 ;cd %s ;/usr/bin/glite-wms-job-submit -a %s"' %(certx, certx, certx, certx ,wdir, jdl)
print cmd
return self.exec_cmd(cmd)
def statusclo(self,guidf):
guid=guidf.split()[1]
cmd = 'ssh webenmr@192.168.0.30 " sync;curl -i -X GET http://localhost:8888/v1.0/tasks/%s?user=brunor"'%( guid)
print cmd
return self.exec_cmd(cmd)
def statusgpu(self, guid):
print "CHECK GPU"
'''Check the job status
input:
guid = the job identifier
return the status of the job'''
certx = session['voms_proxy_file']
#cmd = 'set;/usr/bin/glite-wms-job-status -c /opt/glite/etc/enmr.eu/glite_wms.conf %s' % str(guid)
cmd = 'ssh webenmr@192.168.0.10 "cat %s > %s1 ; chmod 600 %s1; export X509_USER_PROXY=%s1 ;glite-ce-job-status %s"' % (certx, certx, certx, certx,str(guid))
#cmd='voms-proxy-info'
#cmd = 'grid-cert-diagnostics -p'
print "***** jobs.py exec_cmd INI ******"
print cmd
print "***** jobs.py exec_cmd END ******"
#print "CMD1"
#cmd1=['/usr/bin/glite-wms-job-status','-c','/opt/glite/etc/enmr.eu/glite_wms.conf']
#cmd1.append(str(guid))
#print "CMD1"
#sep = {'SSH_ASKPASS': '/usr/libexec/openssh/gnome-ssh-askpass', 'VO_DTEAM_DEFAULT_SE': 'se-enmr.cerm.unifi.it', 'LCG_LOCATION': '/usr', 'GLOBUS_TCP_PORT_RANGE': '20000,25000', 'LESSOPEN': '|/usr/bin/lesspipe.sh %s', 'LCG_GFAL_INFOSYS': 'bdii-enmr.cerm.unifi.it:2170', 'LOGNAME': 'webenmr', 'USER': 'webenmr', 'INPUTRC': '/etc/inputrc', 'DPNS_HOST': 'se-enmr.cerm.unifi.it', 'PATH': '/usr/kerberos/bin:/bin:/usr/local/bin:/usr/bin:/home/webenmr/bin', 'GLITE_LOCATION_VAR': '/var', 'GLITE_SD_PLUGIN': 'file,bdii', 'LANG': 'en_US.UTF-8', 'TERM': 'xterm', 'SHELL': '/bin/bash', 'GLITE_LOCATION': '/usr', 'GRID_ENV_LOCATION': '/usr/libexec', 'VO_OPS_DEFAULT_SE': 'se-enmr.cerm.unifi.it', 'G_BROKEN_FILENAMES': '1', 'HISTSIZE': '1000', 'X509_USER_PROXY': '/home/webenmr/WebENMR/data/enmr_r1/user_2/.voms_cert', 'MANPATH': '/opt/glite/share/man::', 'VO_ENMR_EU_DEFAULT_SE': 'se-enmr.cerm.unifi.it', 'GLITE_SD_SERVICES_XML': '/opt/glite/etc/services.xml', 'HOME': '/home/webenmr', 'MYPROXY_SERVER': 'myproxy.cnaf.infn.it', 'PYTHONPATH': '/usr/lib64/python2.4/site-packages:/usr/lib64/python:/opt/fpconst/lib/python2.4/site-packages:/opt/ZSI/lib/python2.4/site-packages', 'GT_PROXY_MODE': 'old', 'GLITE_WMS_LOCATION': '/usr', '_': '/usr/bin/ipython', 'PERL5LIB': '/usr/lib64/perl5', 'DPM_HOST': 'se-enmr.cerm.unifi.it', 'VO_INFNGRID_DEFAULT_SE': 'se-enmr.cerm.unifi.it', 'HOSTNAME': 'py-enmr.cerm.unifi.it', 'SHLVL': '1', 'PWD': '/home/webenmr', 'CVS_RSH': 'ssh', 'MAIL': '/var/spool/mail/webenmr', 'LS_COLORS': 'no=00:fi=00:di=00;34:ln=00;36:pi=40;33:so=00;35:bd=40;33;01:cd=40;33;01:or=01;05;37;41:mi=01;05;37;41:ex=00;32:*.cmd=00;32:*.exe=00;32:*.com=00;32:*.btm=00;32:*.bat=00;32:*.sh=00;32:*.csh=00;32:*.tar=00;31:*.tgz=00;31:*.arj=00;31:*.taz=00;31:*.lzh=00;31:*.zip=00;31:*.z=00;31:*.Z=00;31:*.gz=00;31:*.bz2=00;31:*.bz=00;31:*.tz=00;31:*.rpm=00;31:*.cpio=00;31:*.jpg=00;35:*.gif=00;35:*.bmp=00;35:*.xbm=00;35:*.xpm=00;35:*.png=00;35:*.tif=00;35:'}
#result_out=commands.getstatusoutput(cmd)
#child = subprocess.Popen( cmd1,close_fds = True,stdout = subprocess.PIPE,stderr = subprocess.PIPE,shell= False,env=sep)
#child = subprocess.Popen(cmd,stdout = subprocess.PIPE,stderr = subprocess.PIPE,shell= True,env=sep)
#result_out = child.communicate()[0]
#result_out=os.popen(cmd).read()
prova = 0
while prova == 0:
status, output, error =self.exec_cmd(cmd)
#child = subprocess.Popen(cmd,stdout = subprocess.PIPE,stderr = subprocess.PIPE,shell= True,env=sep)
#result_out = child.communicate()[0]
print "PPPPPPPPPPPPPPPPPPPPPPPPPPP"
print output
print "------- error-------------"
print error
print "PPPPPPPPPPPPPPPPPPPPPPPPPPP"
if ("GSSAPI" in error) or ("GSSAPI" in output):
prova = 0
else:
prova =1
#print result_out
#return result_out
return status, output, error
def status(self, guid):
'''Check the job status
input:
guid = the job identifier
return the status of the job'''
certx = session['voms_proxy_file']
#cmd = 'set;/usr/bin/glite-wms-job-status -c /opt/glite/etc/enmr.eu/glite_wms.conf %s' % str(guid)
cmd = 'ssh webenmr@192.168.0.10 "cat %s > %s1 ; chmod 600 %s1; export X509_USER_PROXY=%s1 ;glite-wms-job-status %s"' %(certx, certx, certx, certx, str(guid))
#cmd='voms-proxy-info'
#cmd = 'grid-cert-diagnostics -p'
print "***** jobs.py exec_cmd INI ******"
print cmd
print "***** jobs.py exec_cmd END ******"
print "CMD1"
#cmd1=['/usr/bin/glite-wms-job-status','-c','/opt/glite/etc/enmr.eu/glite_wms.conf']
#cmd1.append(str(guid))
print "CMD1"
#sep = {'SSH_ASKPASS': '/usr/libexec/openssh/gnome-ssh-askpass', 'VO_DTEAM_DEFAULT_SE': 'se-enmr.cerm.unifi.it', 'LCG_LOCATION': '/usr', 'GLOBUS_TCP_PORT_RANGE': '20000,25000', 'LESSOPEN': '|/usr/bin/lesspipe.sh %s', 'LCG_GFAL_INFOSYS': 'bdii-enmr.cerm.unifi.it:2170', 'LOGNAME': 'webenmr', 'USER': 'webenmr', 'INPUTRC': '/etc/inputrc', 'DPNS_HOST': 'se-enmr.cerm.unifi.it', 'PATH': '/usr/kerberos/bin:/bin:/usr/local/bin:/usr/bin:/home/webenmr/bin', 'GLITE_LOCATION_VAR': '/var', 'GLITE_SD_PLUGIN': 'file,bdii', 'LANG': 'en_US.UTF-8', 'TERM': 'xterm', 'SHELL': '/bin/bash', 'GLITE_LOCATION': '/usr', 'GRID_ENV_LOCATION': '/usr/libexec', 'VO_OPS_DEFAULT_SE': 'se-enmr.cerm.unifi.it', 'G_BROKEN_FILENAMES': '1', 'HISTSIZE': '1000', 'X509_USER_PROXY': '/home/webenmr/WebENMR/data/enmr_r1/user_2/.voms_cert', 'MANPATH': '/opt/glite/share/man::', 'VO_ENMR_EU_DEFAULT_SE': 'se-enmr.cerm.unifi.it', 'GLITE_SD_SERVICES_XML': '/opt/glite/etc/services.xml', 'HOME': '/home/webenmr', 'MYPROXY_SERVER': 'myproxy.cnaf.infn.it', 'PYTHONPATH': '/usr/lib64/python2.4/site-packages:/usr/lib64/python:/opt/fpconst/lib/python2.4/site-packages:/opt/ZSI/lib/python2.4/site-packages', 'GT_PROXY_MODE': 'old', 'GLITE_WMS_LOCATION': '/usr', '_': '/usr/bin/ipython', 'PERL5LIB': '/usr/lib64/perl5', 'DPM_HOST': 'se-enmr.cerm.unifi.it', 'VO_INFNGRID_DEFAULT_SE': 'se-enmr.cerm.unifi.it', 'HOSTNAME': 'py-enmr.cerm.unifi.it', 'SHLVL': '1', 'PWD': '/home/webenmr', 'CVS_RSH': 'ssh', 'MAIL': '/var/spool/mail/webenmr', 'LS_COLORS': 'no=00:fi=00:di=00;34:ln=00;36:pi=40;33:so=00;35:bd=40;33;01:cd=40;33;01:or=01;05;37;41:mi=01;05;37;41:ex=00;32:*.cmd=00;32:*.exe=00;32:*.com=00;32:*.btm=00;32:*.bat=00;32:*.sh=00;32:*.csh=00;32:*.tar=00;31:*.tgz=00;31:*.arj=00;31:*.taz=00;31:*.lzh=00;31:*.zip=00;31:*.z=00;31:*.Z=00;31:*.gz=00;31:*.bz2=00;31:*.bz=00;31:*.tz=00;31:*.rpm=00;31:*.cpio=00;31:*.jpg=00;35:*.gif=00;35:*.bmp=00;35:*.xbm=00;35:*.xpm=00;35:*.png=00;35:*.tif=00;35:'}
#result_out=commands.getstatusoutput(cmd)
#child = subprocess.Popen( cmd1,close_fds = True,stdout = subprocess.PIPE,stderr = subprocess.PIPE,shell= False,env=sep)
#child = subprocess.Popen(cmd,stdout = subprocess.PIPE,stderr = subprocess.PIPE,shell= True,env=sep)
#result_out = child.communicate()[0]
#result_out=os.popen(cmd).read()
prova = 0
while prova == 0:
status, output, error =self.exec_cmd(cmd)
#child = subprocess.Popen(cmd,stdout = subprocess.PIPE,stderr = subprocess.PIPE,shell= True,env=sep)
#result_out = child.communicate()[0]
print "PPPPPPPPPPPPPPPPPPPPPPPPPPP"
print output
print "------- error-------------"
print error
print "PPPPPPPPPPPPPPPPPPPPPPPPPPP"
if ("GSSAPI" in error) or ("GSSAPI" in output):
prova = 0
else:
prova =1
#print result_out
#return result_out
return status, output, error
#return self.exec_cmd(cmd)
def statusv3(self, guid):
'''Check the job status
input:
guid = the job identifier
return the status of the job'''
certx = session['voms_proxy_file']
diz_stat = {}
cmd = 'ssh webenmr@192.168.0.10 "export X509_USER_PROXY=%s1 ; /usr/bin/glite-wms-job-status -c /opt/glite/etc/enmr.eu/glite_wms.conf --verbosity 3 %s"' %(certx, guid)
print cmd
parse = self.exec_cmd(cmd)
read = False
for i in parse[1].split("\n"):
if "- Destination =" in i:
cmd ="/usr/bin/lcg-info --vo enmr.eu --list-ce --query 'CE=%s' --attrs 'CFP2000'" % i.split("=")[1]
print cmd
parse = self.exec_cmd(cmd)
for a1 in parse[1].split("\n"):
if "CFP2000" in a1:
diz_stat["CFP2000"] = a1.split()[2]
cmd ="/usr/bin/lcg-info --vo enmr.eu --list-ce --query 'CE=%s' --attrs 'CINT2000'" % i.split("=")[1]
print cmd
parse = self.exec_cmd(cmd)
for a1 in parse[1].split("\n"):
if "CINT2000" in a1:
diz_stat["CINT2000"] = a1.split()[2]
diz_stat["CE"] = i.split("=")[1]
if "- Stateentertimes =" in i:
read = True
if read and "Submitted" in i:
diz_stat["Submitted"] = i.split(":",1)[1]
if read and "Waiting" in i:
diz_stat["Waiting"] = i.split(":",1)[1]
if read and "Ready" in i:
diz_stat["Ready"] = i.split(":",1)[1]
if read and "Scheduled" in i:
diz_stat["Scheduled"] = i.split(":",1)[1]
if read and "Running" in i:
diz_stat["Running"] = i.split(":",1)[1]
if read and "Done" in i:
diz_stat["Done"] = i.split(":",1)[1]
if read and "Cleared" in i:
diz_stat["Cleared"] = i.split(":",1)[1]
if read and "Done" in i:
diz_stat["Done"] = i.split(":",1)[1]
if read and "Aborted" in i:
diz_stat["Aborted"] = i.split(":",1)[1]
if read and "Cancelled" in i:
diz_stat["Cancelled"] = i.split(":",1)[1]
if read and "Unknown" in i:
diz_stat["Unknown"] = i.split(":",1)[1]
return diz_stat
def kill(self, guid):
certx = session['voms_proxy_file']
cmd = 'ssh webenmr@192.168.0.10 "cat %s > %s1 ; chmod 600 %s1; export X509_USER_PROXY=%s1 ;/usr/bin/glite-wms-job-cancel --noint %s"' %(certx, certx, certx, certx, guid)
prova = 0
while prova == 0:
status, output, error =self.exec_cmd(cmd)
#child = subprocess.Popen(cmd,stdout = subprocess.PIPE,stderr = subprocess.PIPE,shell= True,env=sep)
#result_out = child.communicate()[0]
print "PPPPPPPPPPPPPPPPPPPPPPPPPPP"
print output
print "------- error-------------"
print error
print "PPPPPPPPPPPPPPPPPPPPPPPPPPP"
if ("GSS" in error) or ("GSS" in output):
prova = 0
else:
prova =1
return status, output, error
#return self.exec_cmd(cmd)
def retrieveclo(self, guid, outdir):
j1 = JobsProcessing()
cmd = 'ssh webenmr@192.168.0.30 " sync;curl -i -X GET http://localhost:8888/v1.0/tasks/%s?user=brunor"'%( guid)
print "check cloud job"
status, output, error =self.exec_cmd(cmd)
print cmd
# rimouvo la roba non json
print output
s=[]
conta = 0
for i in output.split("\n"):
conta = conta + i.count("{")
conta = conta - i.count("}")
if conta >0:
s.append(i)
s.append("}")
j = "".join(s)
print j
jsa = json.loads(j)
for i in jsa['output_files']:
if i["name"] == "pro.tgz":
fileo = i["url"]
print i["url"]
cmd = """ssh webenmr@192.168.0.30 << EOF
cd %s
wget "http://localhost:8888/v1.0/%s" -O ./pro.tgz
EOF
""" % (outdir,fileo)
print #########################"
print "Comando di retrive"
print cmd
print "########################"
status, output, error =self.exec_cmd(cmd)
return status, output, error
def retrievegpu(self, guid, outdir):
j1 = JobsProcessing()
certx = session['voms_proxy_file']
cmd = """ssh webenmr@192.168.0.10 << EOF
umask 002
cat %s > %s1
chmod 600 %s1
export X509_USER_PROXY=%s1
/usr/bin/glite-ce-job-output --noint --dir %s %s
cp -r %s/cegpu*/* %s
rm -r %s/cegpu*
cd %s
chown -R webenmr.apache *
chmod -R 660 *
whoami
ls -lh
EOF
""" % (certx, certx, certx, certx,outdir, guid,outdir,outdir,outdir,outdir)
print #########################"
# print "Comando di retrive"
# print cmd
print "########################"
prova = 0
while prova == 0:
status, output, error =self.exec_cmd(cmd)
print "PPPPPPPPPPPP RETRIVE GPU PPPPPPPPPPPPPPP"
print cmd
print output
print "---------Error----------------"
print type(error)
print error
print "PPPPPPPPPPPPPPPPPPPPPPPPPPP"
if ("UBERFTP ERROR" in error) or ("UBERFTP ERROR" in output):
prova = 0
# cmd1 = 'rm -rf %s/cegpu.cerm.unifi.it*;ls -lh %s' %(outdir,outdir)
# print "Rimuovo la directory ", cmd1
# cmd1s,cmd1o,cmd1e = j1.exec_cmd(cmd1)
# print cmd1o
else:
prova =1
return status, output, error
def retrieve(self, guid, outdir):
certx = session['voms_proxy_file']
cmd = """ssh webenmr@192.168.0.10 << EOF
umask 002
cat %s > %s1
chmod 600 %s1
export X509_USER_PROXY=%s1
/usr/bin/glite-wms-job-output --dir %s --nosubdir --noint %s
cd %s
chown -R webenmr.apache *
chmod -R 660 *
EOF
""" % (certx, certx, certx, certx, outdir, guid,outdir)
print #########################"
print "Coamando di retrive"
print cmd
print "########################"
prova = 0
while prova == 0:
status, output, error =self.exec_cmd(cmd)
print "PPPPPPPPPPPP RETRIVE PPPPPPPPPPPPPPP"
print output
print "---------Error----------------"
print type(error)
print error
print "PPPPPPPPPPPPPPPPPPPPPPPPPPP"
if "GSS" in error:
prova = 0
else:
prova =1
return status, output, error
def exec_cmd(self, cmd):
#cmdEnv = self._getExternalCmdEnvironment()
# metto il python path della versione 2.4
cmdEnv = {'SSH_ASKPASS': '/usr/libexec/openssh/gnome-ssh-askpass', 'VO_DTEAM_DEFAULT_SE': 'se-enmr.cerm.unifi.it', 'LCG_LOCATION': '/usr', 'GLOBUS_TCP_PORT_RANGE': '20000,25000', 'LESSOPEN': '|/usr/bin/lesspipe.sh %s', 'LCG_GFAL_INFOSYS': 'bdii-enmr.cerm.unifi.it:2170', 'LOGNAME': 'webenmr', 'USER': 'webenmr', 'INPUTRC': '/etc/inputrc', 'DPNS_HOST': 'se-enmr.cerm.unifi.it', 'PATH': '/usr/kerberos/bin:/bin:/usr/local/bin:/usr/bin:/home/webenmr/bin', 'GLITE_LOCATION_VAR': '/var', 'GLITE_SD_PLUGIN': 'file,bdii', 'LANG': 'en_US.UTF-8', 'TERM': 'xterm', 'SHELL': '/bin/bash', 'GLITE_LOCATION': '/usr', 'GRID_ENV_LOCATION': '/usr/libexec', 'VO_OPS_DEFAULT_SE': 'se-enmr.cerm.unifi.it', 'G_BROKEN_FILENAMES': '1', 'HISTSIZE': '1000', 'X509_USER_PROXY': '/home/webenmr/WebENMR/data/enmr_r1/user_2/.voms_cert', 'MANPATH': '/opt/glite/share/man::', 'VO_ENMR_EU_DEFAULT_SE': 'se-enmr.cerm.unifi.it', 'GLITE_SD_SERVICES_XML': '/opt/glite/etc/services.xml', 'HOME': '/home/webenmr', 'MYPROXY_SERVER': 'myproxy.cnaf.infn.it', 'PYTHONPATH': '/usr/lib64/python2.4/site-packages:/usr/lib64/python:/opt/fpconst/lib/python2.4/site-packages:/opt/ZSI/lib/python2.4/site-packages', 'GT_PROXY_MODE': 'old', 'GLITE_WMS_LOCATION': '/usr', '_': '/usr/bin/ipython', 'PERL5LIB': '/usr/lib64/perl5', 'DPM_HOST': 'se-enmr.cerm.unifi.it', 'VO_INFNGRID_DEFAULT_SE': 'se-enmr.cerm.unifi.it', 'OLDPWD': '/home/webenmr/WebENMR/webenmr', 'HOSTNAME': 'py-enmr.cerm.unifi.it', 'SHLVL': '1', 'PWD': '/home/webenmr', 'CVS_RSH': 'ssh', 'MAIL': '/var/spool/mail/webenmr', 'LS_COLORS': 'no=00:fi=00:di=00;34:ln=00;36:pi=40;33:so=00;35:bd=40;33;01:cd=40;33;01:or=01;05;37;41:mi=01;05;37;41:ex=00;32:*.cmd=00;32:*.exe=00;32:*.com=00;32:*.btm=00;32:*.bat=00;32:*.sh=00;32:*.csh=00;32:*.tar=00;31:*.tgz=00;31:*.arj=00;31:*.taz=00;31:*.lzh=00;31:*.zip=00;31:*.z=00;31:*.Z=00;31:*.gz=00;31:*.bz2=00;31:*.bz=00;31:*.tz=00;31:*.rpm=00;31:*.cpio=00;31:*.jpg=00;35:*.gif=00;35:*.bmp=00;35:*.xbm=00;35:*.xpm=00;35:*.png=00;35:*.tif=00;35:'}
#cmdEnv["PYTHONPATH"]="/usr/lib64/python2.4/site-packages:/usr/lib64/python:/opt/fpconst/lib/python2.4/site-packages:/opt/ZSI/lib/python2.4/site-packages"
#print cmdEnv
result = shellCall( self._secCmdTimeout, cmd, env = cmdEnv )
return result['Value']
class JobsController(BaseController):
def __before__(self):
"""
This __before__ method calls the parent method, and then sets up the
tabs on the page.
"""
#BaseController.__before__(self)
c.page_base = u'Jobs'
if 'REMOTE_USER' in session:
c.current_user = Session.query(Users).get(session['REMOTE_USER'])
#c.main_menu = Session.query(Menu).filter(and_(Menu.parent_id==None, Menu.sibling_id==None)).order_by(Menu.weight.asc()).all()
c.page_title = u'Jobs Management'
c.STATUS = {'R': 'Running',
'S': 'Scheduled',
'F': 'Finished',
'C': 'Cancelled',
'A': 'Aborted',
'L': 'Cleared',
'E': 'Finished/Retrieved'
}
def job_remove(self):
status = request.POST.get('status')
projects = Session.query(Projects).filter(and_(Projects.owner_id==session['REMOTE_USER'], Projects.removed == False)).all()
for p in projects:
for c in p.calculation:
if c.removed == False:
for j in c.job:
if j:
if j.status == status:
job = Session.query(Jobs).get(j.id)
job.removed = True
Session.add(job)
Session.commit()
#print "ho rimosso tutti i job %s" %status
#def remove_and_redirect(self):
# status = request.POST.get('status')
# self.job_remove(status)
# h.redirect('/jobs/show/all')
def job_list(self, id=None):
'''Show a job list'''
if id:
(c.calc_id, c.prj_id, job_id) = id.split('_')
c.jobs = Session.query(Jobs).filter(and_(Jobs.calculation_id==c.calc_id, Jobs.removed == False)).order_by('start_date').all()
if c.jobs:
return render('/jobs/job_list.mako')
else:
h.flash.set_message('No jobs for that calculation', 'error')
h.redirect('/calculations/calculation_list/%d' % int(c.prj_id))
def job_submit(self, id=None):
'''Submit a job in GRID'''
if id:
if session['PORTAL'] == 'amps-nmr':
attribute = "/enmr.eu/amber"
elif session['PORTAL'] == 'xplor-nih':
attribute = "/enmr.eu/xplornih"
else:
attribute = ""
ret = Certificates.proxy_initialize(attribute)
#ret = Certificates.proxy_initialize()
print "######VOMSPROXY INI #############"
if ret['OK']:
#print "VOMS_PROXY ", session['voms_proxy']
#print ret['Value']
print "OK"
else:
print 'voms-proxy-init problem: %s' % ret['Message']
print "######VOMSPROXY END #############"
os.environ['X509_USER_PROXY'] = session['voms_proxy_file']
os.environ['X509_USER_CERT'] = session['voms_proxy_file']
os.environ['X509_USER_KEY'] = session['voms_proxy_file']
print "X509_USER_KEY ", session['voms_proxy_file']
print "X509_USER_KEY ", session['voms_proxy_file']
print "X509_USER_PROXY ", session['voms_proxy_file']
(calc_id, prj_id, job_id) = id.split('_')
project = Session.query(Projects).get(int(prj_id))
calc = Session.query(Calculations).filter(and_(Calculations.id==int(calc_id), Calculations.project_id==project.id, Calculations.removed == False)).first()
if calc.calc_type.tipology == 'amps-nmr':
ty = 'amber'
else:
ty = calc.calc_type.tipology
w_dir = os.path.join(config['app_conf']['working_dir'],
project.owner.home,
project.name,
ty,
calc.name)
print w_dir
jP = JobsProcessing()
jdl = 'amber.jdl'
for jb in range(calc.jobs_to_submit):
wdir = os.path.join(w_dir, 'input', 'input_%s' % (jb+1))
serror = True
count_retry = 0
print "##### submit JDL CPU GPU ####"
print wdir, jdl
while serror:
time.sleep(count_retry)
count_retry = count_retry + 1
#sottomissione gpu
if session["usegpu"] == True:
status, output, error = jP.submitgpu(wdir, jdl)
print "USO -------GPU----------- "
elif session["useclo"] == True:
print "CLOUD"
status, output, error = jP.submitclo(wdir, jdl)
else:
status, output, error = jP.submit(wdir, jdl)
print 'status ', status
print 'output ', output
print 'error ', error
if output:
output = '%s' % output.strip()
if (len(error) > 0) or ("glexec error" in output):
serror = True
print "######ERROR IN SUBMISSION --- RETRY #############"
print "NUMBER %d" %count_retry
print "##### ######"
if session["useclo"]:
serror = False
error = False
elif count_retry == 20:
serror = False
print "#### LIMIT of 20 resumittion #####"
else:
serror = False
print "## OK"
#status, output, error = jP.submit(wdir, jdl)
if output:
output = '%s' % output.strip()
#print 'status ', status
#print 'output ', output
#print 'error ', error
if error:
msg = 'Unable to start job %s' % (jb+1)
h.flash.set_message(msg, 'error')
h.redirect('/jobs/job_list/%s' % id)
try:
if session["usegpu"] == True:
print output
match = re.search('.*(https:\/\/\S+:8443\/[0-9A-Za-z_\.\-]+)', output)
print "********* ",match.group(1)," **************"
elif session["useclo"] == True:
match = re.search('.*("task": "[0-9]+")',output)
else:
match = re.search('.*(https:\/\/\S+:9000\/[0-9A-Za-z_\.\-]+)', output)
calc = Session.query(Calculations).get(int(calc_id))
j = Jobs()
j.calculation = calc
if session["useclo"] == True:
j.guid =u'futuregateway %s' %match.group(1).split()[1].replace('"',"")
else:
j.guid = u'%s' % match.group(1)
j.start_date = datetime.now()
j.dir_name = wdir
if session["usegpu"] == True:
j.type = "gpu"
elif session["useclo"] == True:
j.type = "clo"
else:
j.type = "cpu"
except AttributeError:
j.guid = u'No guid'
j.status = u'A'
j.log = u'%s' % error
msg = 'Job %s aborted' %(jb+1)
if match:
j.status = u'S'
msg = '%s Job(s) successfully started' %(jb+1)
h.flash.set_message(msg, 'success')
#h.redirect('/jobs/job_list/%s' % id)
else:
j.guid = u'No guid'
j.status = u'A'
j.log = u'%s' % error
msg = 'Job %s aborted' %(jb+1)
h.flash.set_message(msg, 'error')
Session.add(j)
Session.commit()
h.redirect('/jobs/show/all')
def checkalljobs(self):
owner = Session.query(Users).get(session['REMOTE_USER'])
projects = Session.query(Projects).filter(and_(Projects.owner_id == session['REMOTE_USER'], Projects.removed == False)).all()
if projects:
c_id = Session.query(CalculationTipology).filter(CalculationTipology.tipology == session['PORTAL']).all()
if c_id[0].id == 5:
c2_id = 6
else:
c2_id = c_id[0].id
for p in projects:
pid = p.id
for c in p.calculation:
if (c.calc_type_id == c_id[0].id or c.calc_type_id == c2_id) and c.removed == False:
cid = c.id
for j in c.job:
if j:
jid =j.id
if j.status == 'R' or j.status == 'S':
s = "%d_%d_%d" % (cid, pid, jid)
self.job_status(s)
def job_status(self, id=None):
'''Keep a job status'''
def isFloat(s):
try:
float(s)
except ValueError:
return False
return True
if id:
(calc_id, prj_id, job_id) = id.split('_')
job = Session.query(Jobs).get(int(job_id))
if session['PORTAL'] == 'amps-nmr' or session['PORTAL'] == 'amber' or session['PORTAL'] == 'xplor-nih':
job = Session.query(Jobs).get(int(job_id))
j = JobsProcessing()
usegpu = False
useclo = False
if re.search('.*(https:\/\/\S+:8443\/[0-9A-Za-z_\.\-]+)',job.guid ):
usegpu = True
elif re.search('.*(futuregateway [0-9]+)', job.guid):
useclo = True
else:
usegpu = False
if usegpu:
status, output, error = j.statusgpu(job.guid)
elif useclo:
status, output, error = j.statusclo(job.guid)
else:
status, output, error = j.status(job.guid)
print 'status ', status
print 'output ', output
print 'error ', error
reason = ''
if output:
#ssox
print "SSOX INI"
userLid = session['REMOTE_USER']
member = Session.query(Users).filter(and_(Users.id == userLid, Users.removed==False)).first()
#ip = request.environ['REMOTE_ADDR']
ip = "150.217.163.184"
if member:
user_ssox = member.ssoxs_uid
print ip
print job_id
print user_ssox
if session['PORTAL'] == 'amps-nmr':
ssox.ssox_amber(user_ssox, ip, job_id, "0")
elif session['PORTAL'] == 'xplor-nih':
ssox.ssox_xplor(user_ssox, ip, job_id, "0")
print "SSOX _END"
output = '%s' % output.strip()
try:
if usegpu:
if "REALLY-RUNNING" in output:
status = 'Running'
elif "IDLE" in output:
status = 'Scheduled'
elif "DONE-OK" in output:
status = 'Success'
else:
status = 'Aborted'
elif useclo:
print "-----FACCIO IL CHECK DEL JOB CLOUD----"
print output
if "RUNNING" in output:
status ='Running'
print "----- ho messo status Running "
elif "DONE" in output:
status ='Success'
print "----- ho messo status Succes "
else:
status ='Scheduled'
else:
status = re.search('.*(Current Status:\ *[a-zA-Z].*)', output).group(1).split(':')[1].strip()
if status == 'Ready':
job.status = u'R'
if member:
user_ssox = member.ssoxs_uid
print ip
print job_id
print user_ssox
if session['PORTAL'] == 'amps-nmr':
ssox.ssox_amber(user_ssox, ip, job_id, "2")
elif session['PORTAL'] == 'xplor-nih':
ssox.ssox_xplor(user_ssox, ip, job_id, "2")
# else:
# reason = re.search('.*(Status Reason:\ *[a-zA-Z].*)', output).group(1).split(':')[1].strip()
elif status == 'Running':
job.status = u'R'
if member:
user_ssox = member.ssoxs_uid
print ip
print job_id
print user_ssox
if session['PORTAL'] == 'amps-nmr':
ssox.ssox_amber(user_ssox, ip, job_id, "4")
elif session['PORTAL'] == 'xplor-nih':
ssox.ssox_xplor(user_ssox, ip, job_id, "4")
elif status == 'Scheduled':
job.status = u'S'
if member:
user_ssox = member.ssoxs_uid
print ip
print job_id
print user_ssox
if session['PORTAL'] == 'amps-nmr':
ssox.ssox_amber(user_ssox, ip, job_id, "3")
elif session['PORTAL'] == 'xplor-nih':
ssox.ssox_xplor(user_ssox, ip, job_id, "3")
elif 'Success' in status or '(Exit Code !=0)' in status:
job.status = u'F'
if member:
user_ssox = member.ssoxs_uid
print ip
print job_id
print user_ssox
if session['PORTAL'] == 'amps-nmr':
ssox.ssox_amber(user_ssox, ip, job_id, "5")
elif session['PORTAL'] == 'xplor-nih':
ssox.ssox_xplor(user_ssox, ip, job_id, "5")
if usegpu:
print " --------RETRIEVE GPU---------"
res = self.job_autoretrievegpu(job_id)
elif useclo:
print "-------- RETRIVE CLOUD ---------"
res = self.job_autoretrieveclo(job_id)
else:
print "-------RETRIVE CPU ---------"
res = self.job_autoretrieve(job_id)
print "####RES"
print res
if res:
job.status = u'E'
reason = 'Job successfully completed. Now, you can view/download the results.'
# if not (usegpu or useclo):
# info_time = j.statusv3(job.guid)
# # for read the time use dateutil.parser.parse('Tue Apr 23 17:07:25 2013 CEST')
# # the format of glite-wms-job-status are in ctime
# print "#######INFOTIME#########"
# print info_time
# if len(info_time["Submitted"] ) > 10:
# job.submitted_date = parser.parse(info_time["Submitted"])
# print parser.parse(info_time["Submitted"])
# if len(info_time["Waiting"]) > 10:
# job.waiting_date = parser.parse(info_time["Waiting"])
# print parser.parse(info_time["Waiting"])
# if len(info_time["Ready"]):
# job.ready_date = parser.parse(info_time["Ready"])
# if len(info_time["Scheduled"]) > 10:
# job.scheduled_date = parser.parse(info_time["Scheduled"])
# if len(info_time["Running"]) > 10:
# job.running_date = parser.parse(info_time["Running"])
# if len(info_time["Done"]) > 10:
# job.done_date = parser.parse(info_time["Done"])
# if len(info_time["Aborted"]) > 10:
# job.aborted_date = parser.parse(info_time["Aborted"])
# if len (info_time["Cancelled"]) > 10:
# job.cancelled_date = parser.parse(info_time["Cancelled"])
# if len(info_time["Unknown"]) > 10:
# job.unknown_date = parser.parse(info_time["Unknown"])
# if "CFP2000" in info_time.keys():
# if isFloat(info_time["CFP2000"]):
# job.cfp2000 = float(info_time["CFP2000"])
# if "CINT2000" in info_time.keys():
# if isFloat(info_time["CINT2000"]):
# job.cint2000 = float(info_time["CINT2000"])
# if "CE" in info_time.keys():
# job.ce = info_time["CE"]
#elif '(Exit Code !=0)' in status:
# job.status = u'F'
# res = self.job_autoretrieve(job_id)
# if res:
# job.status = u'E'
# reason = 'Job successfully completed. Now, you can view/download the results.'
elif 'Cancelled' in status:
job.status = u'C'
elif 'Aborted' in status:
job.status = u'A'
elif 'Cleared' in status:
job.status = u'L'
elif 'Ready' in status:
job.status = u'R'
if reason:
job.log = u'%s' % reason
Session.add(job)
Session.commit()
except AttributeError:
status = "Aborted"
## modifica da rimettere
#job.status = u"A"
#job.log = "WMS submission failure."
#Session.add(job)
#Session.commit()
##h.redirect('/jobs/job_list/%s' % id)
##h.redirect('/jobs/show/run')
elif session['PORTAL'] == 'maxocc':
jj = JobsProcessing()
proj = Session.query(Projects).get(int(prj_id))
calc = Session.query(Calculations).get(int(calc_id))
tip = Session.query(CalculationTipology).get(int(calc.calc_type_id))
if calc.calc_type_id == 6:
print "ranch calculation"
status, output, error = jj.status(job.guid)
print 'status ', status
print 'output ', output
print 'error ', error
reason = ''
if output:
output = '%s' % output.strip()
try:
status = re.search('.*(Current Status:\ *[a-zA-Z].*)', output).group(1).split(':')[1].strip()
print "lo status: %s" %status
if status == 'Ready':
job.status = u'R'
else:
reason = re.search('.*(Status Reason:\ *[a-zA-Z].*)', output).group(1).split(':')[1].strip()
print "la reason: %s" % reason
if status == 'Running':
job.status = u'R'
elif status == 'Scheduled':
job.status = u'S'
elif 'Success' in status:
print "SUCCESSO"
job.status = u'F'
res = self.job_autoretrieve(job_id)
if res:
job.status = u'E'
reason = 'Job successfully completed. Now, you can view/download the results.'
elif '(Exit Code !=0)' in status:
job.status = u'F'
res = self.job_autoretrieve(job_id)
if res:
job.status = u'E'
reason = 'Job successfully completed. Now, you can view/download the results.'
elif 'Cancelled' in status:
job.status = u'C'
elif 'Aborted' in status:
job.status = u'A'
elif 'Cleared' in status:
job.status = u'L'
elif 'Ready' in status:
job.status = u'R'
if reason:
job.log = u'%s' % reason
Session.add(job)
Session.commit()
except AttributeError:
status = "Aborted"
job.status = "A"
job.log = "WMS submission failure."
Session.add(job)
Session.commit()
else:
conto = calc.name
print "##########CHECK RISULTATI MAXOCC ANALISYS ############"
print "nome calcolo: %s" %conto
percentuale = 80.0
doc = etree.ElementTree(file=os.path.join(config['app_conf']['maxocc_data'], "STATUS.xml"))
#doc = etree.ElementTree(file="/opt/jobcontrol/STATUS.xml")
output = []
tot_jobs = 0
submitted = False
totOre = 0
for a in doc.getiterator():
if a.tag == "jobs":
if a.get("Project_name") == conto:
submitted = True
totOre = (time.mktime(time.strptime(time.asctime()))- time.mktime(time.strptime(a.get("Start_date"))))/3600
if a.get("Status") == "E":
output.append(a.get("output"))
tot_jobs = a.get("num_job_project")
print "TOTALE ORE %d" %totOre
print 'calcolo %s - job terminati: %d su %s' %(conto, len(output), tot_jobs)
if ((len(output) >= int(round((percentuale*int(tot_jobs))/100))) and submitted):
print "calcoli maxocc finiti!"
job.status = u'E'
job.log = u'Job completed. Use the Project-->Manage menu to download data'
Session.add(job)
Session.commit()
#lanciare l'analisi
path_proj = ''
path_proj = path_proj + "/".join(output[0].split("/")[:-2])
print 'project path: %s' %path_proj
print "Lancio i subprocess per l'analisi"
#cmd_status = os.system("/home/webenmr/WebENMR/data/maxocc/templates/ana_script.sh")
#subprocess.Popen('cd %s; /bin/sh /home/webenmr/WebENMR/data/maxocc/templates/ana_script.sh' %path_proj, shell=True).wait()
cmd = 'cd %s; /bin/sh %s/ana_script.sh' %(path_proj,config['app_conf']['maxocc_templ'])
print cmd
#subprocess.Popen('cd %s; /bin/sh /home/webenmr/WebENMR/data/maxocc/templates/ana_script.sh' %path_proj, shell=True).wait()
print jj.exec_cmd(cmd)
#os.system('cd %s; sh /home/webenmr/WebENMR/data/maxocc/templates/ana_script.sh&' %path_proj)
#controllare size di 0.mo SE contiene righe run altro script
#subprocess.Popen('cd %s; python /home/webenmr/WebENMR/data/maxocc/templates/num3.py' %path_proj, shell=True).wait()
if os.path.exists(os.path.join(path_proj, '0.crv')):
ftemp = open(os.path.join(path_proj, '0.mo'))
str = ftemp.readline()
ftemp.close()
print str
print len(str)
if len(str) or os.path.exists(os.path.join(path_proj, "artificial.mo")):
cmd = 'cd %s; /usr/bin/python %s/num5.py' %(path_proj,config['app_conf']['maxocc_templ'])
#subprocess.Popen('cd %s; /usr/bin/python /home/webenmr/WebENMR/data/maxocc/templates/num4.py', shell=True).wait()
print jj.exec_cmd(cmd)
#os.system('cd %s; /usr/bin/python /home/webenmr/WebENMR/data/maxocc/templates/num3.py&' %path_proj)
#copiare i risultati, distinguere il caso 0.mo vuoto
if os.path.exists(os.path.join(path_proj, 'mo.png')):
pdir = os.path.join(config['app_conf']['working_dir'], proj.owner.home, proj.name, tip.tipology, calc.name, "output")
if not os.path.exists(pdir):
os.makedirs(pdir)
else:
shutil.rmtree(pdir)
os.makedirs(pdir)
shutil.copy(os.path.join(path_proj, 'mo.png'), pdir)
shutil.copy(os.path.join(path_proj, 'mo-usr.crv'), pdir)
shutil.copy(os.path.join(path_proj, 'mo.val'), pdir)
shutil.copy(os.path.join(path_proj, 'mo.log'), pdir)
shutil.copy(os.path.join(path_proj, 'mo-det.png'), pdir)
else:
print 'non esiste mo.png'
pdir = os.path.join(config['app_conf']['working_dir'], proj.owner.home, proj.name, tip.tipology, calc.name, 'output')
cmd = 'cd %s; /bin/sh %s/anaResultMaxocc.bash' %(path_proj,config['app_conf']['maxocc_templ'])
print "sto creando results.tgz"
#subprocess.Popen(cmd, shell=True).wait()
jj.exec_cmd(cmd)
if os.path.exists(os.path.join(path_proj, "results.tgz")):
print "ho creato results.tgz"
if not os.path.exists(pdir):
os.makedirs(pdir)
shutil.copy(os.path.join(path_proj, 'results.tgz'), pdir)
print "finito di copiare nella dir del progetto"
else:
print os.path.join(path_proj, "results.tgz")
print 'non ho trovato results.tgz'
#job.status = u'R'
#job.log = u'Job is running. %d on %s completed jobs.' %(len(output), tot_jobs)
#Session.add(job)
#Session.commit()
elif not submitted:
job.status = u'S'
job.log = u'Scheduled job. The server is submitting your job in Grid.'
Session.add(job)
Session.commit()
elif totOre > 72:
job.status = u'A'
job.log = u'Aborted job. Try to re-submit your job.'
Session.add(job)
Session.commit()
else:
job.status = u'R'
job.log = u'Calculation is running, with %d on %s completed jobs.' %(len(output), tot_jobs)
Session.add(job)
Session.commit()
def job_kill(self, id=None):
'''Kill a running job'''
if id:
(calc_id, prj_id, job_id) = id.split('_')
job = Session.query(Jobs).get(int(job_id))
if session['PORTAL'] == 'amps-nmr' or session['PORTAL'] == 'amber' or session['PORTAL'] == 'xplor-nih':
j = JobsProcessing()
status, output, error = j.kill(job.guid)
print status, output, error
if output:
output = '%s' % output.strip()
match = re.search('.*(glite-wms-job-cancel Success)', output)
if match:
print match.group(1)
job.status = u'C'
job.log = u''
Session.add(job)
Session.commit()
h.flash.set_message('Job(s) successfully killed.', 'success')
#h.redirect('/jobs/job_list/%s' % id)
h.redirect('/jobs/show/all')
job.log = u'%s' % error
Session.add(job)
Session.commit()
h.flash.set_message('Unable to kill the job.', 'error')
#h.redirect('/jobs/job_list/%s' % id)
h.redirect('/jobs/show/all')
elif session['PORTAL'] == 'maxocc':
job_list = Session.query(Jobs).filter(Jobs.calculation_id==int(calc_id))
job_item = job_list[0]
job_item.removed = True
Session.add(job_item)
calc = Session.query(Calculations).get(job.calculation_id)
calc.removed = True
Session.add(calc)
Session.commit()
h.flash.set_message('Job(s) successfully killed.', 'success')
#h.redirect('/jobs/job_list/%s' % id)
h.redirect('/jobs/show/all')
# python 2.4's tarfile doesn't have extractall.
def extractall(self, tf, path="."):
for tarinfo in tf:
if tarinfo.isdir():
# Extract directories with a safe mode.
tarinfo = copy.copy(tarinfo)
tarinfo.mode = 0700
tf.extract(tarinfo, path)
def job_autoretrieveclo(self, id):
'''Auto-Retrieve a terminated job'''
if id:
j = JobsProcessing()
owner = Session.query(Users).get(session['REMOTE_USER'])
job = Session.query(Jobs).get(int(id))
c = Session.query(Calculations).filter(Calculations.id == job.calculation_id)
calc = c[0]
proj = Session.query(Projects).filter(Projects.id == calc.project_id)
project = proj[0]
output_num = job.dir_name
if output_num != None:
output_num = os.path.basename(job.dir_name).split("_")[-1]
else:
job_md5 = md5.new("%s" %job)
output_num = "output_%s" % job_md5.hexdigest()[0:10]
if calc.calc_type.tipology == 'ranch':
tip = 'maxocc'
else:
tip = calc.calc_type.tipology
print calc.name, tip
if not os.path.exists(os.path.join(config['app_conf']['working_dir'],
owner.home,
project.name, tip, calc.name)):
tip = 'amber'
if not os.path.exists(os.path.join(config['app_conf']['working_dir'],
owner.home,
project.name,
tip,
calc.name, 'output')):
os.mkdir(os.path.join(config['app_conf']['working_dir'],
owner.home,
project.name,
tip,
calc.name, 'output'))
#else:
# os.mkdir(os.path.join(config['app_conf']['working_dir'],
# owner.home,
# project.name,
# calc.calc_type.tipology,
# calc.name, 'output'))
# tip = calc.calc_type.tipology
wdir = os.path.join(config['app_conf']['working_dir'],
owner.home,
project.name)
outdir = os.path.join(config['app_conf']['working_dir'],
owner.home,
project.name,
tip,
calc.name, 'output',
'output_%s' % output_num )
if not os.path.exists(outdir):
os.mkdir(outdir)
print outdir
jobidn = job.guid.split()[1]
status, output, error = j.retrieveclo(jobidn, outdir)
print 'status ', status
print 'output ', output
print 'error ', error
ret = 0
if output:
untar_job = os.path.join(outdir, "pro.tgz")
if os.path.getsize(untar_job) > 0:
ret=1
#subprocess.Popen('cd %s; /bin/tar xvfz %s' %(outdir, untar_job), shell=True).wait()
cmd = 'cd %s;/bin/gunzip %s;/bin/tar xvf %s' %(outdir, "pro.tgz", "pro.tar")
print j.exec_cmd(cmd)
#tar_job = tarfile.open(untar_job, "r:gz")
#self.extractall(tar_job, outdir)
#tar_job.close()
os.remove(os.path.join(outdir, "pro.tar"))
#print "Ho finito di fare lo SSSTARRR di %s in %s" %(untar_job, outdir)
#ret = 1
h.flash.set_message('Job succesfully retrieved.', 'success')
#h.redirect('/jobs/job_list/%s' % id)
job.log = u'%s' % error[:254]
Session.add(job)
Session.commit()
#h.flash.set_message('Unable to retrieve the job.', 'error')
#h.redirect('/jobs/job_list/%s' % id)
return ret
def job_autoretrievegpu(self, id):
'''Auto-Retrieve a terminated job'''
if id:
j = JobsProcessing()
owner = Session.query(Users).get(session['REMOTE_USER'])
job = Session.query(Jobs).get(int(id))
c = Session.query(Calculations).filter(Calculations.id == job.calculation_id)
calc = c[0]
proj = Session.query(Projects).filter(Projects.id == calc.project_id)
project = proj[0]
output_num = job.dir_name
if output_num != None:
output_num = os.path.basename(job.dir_name).split("_")[-1]
else:
job_md5 = md5.new("%s" %job)
output_num = "output_%s" % job_md5.hexdigest()[0:10]
if calc.calc_type.tipology == 'ranch':
tip = 'maxocc'
else:
tip = calc.calc_type.tipology
print calc.name, tip
if not os.path.exists(os.path.join(config['app_conf']['working_dir'],
owner.home,
project.name, tip, calc.name)):
tip = 'amber'
if not os.path.exists(os.path.join(config['app_conf']['working_dir'],
owner.home,
project.name,
tip,
calc.name, 'output')):
os.mkdir(os.path.join(config['app_conf']['working_dir'],
owner.home,
project.name,
tip,
calc.name, 'output'))
#else:
# os.mkdir(os.path.join(config['app_conf']['working_dir'],
# owner.home,
# project.name,
# calc.calc_type.tipology,
# calc.name, 'output'))
# tip = calc.calc_type.tipology
wdir = os.path.join(config['app_conf']['working_dir'],
owner.home,
project.name)
outdir = os.path.join(config['app_conf']['working_dir'],
owner.home,
project.name,
tip,
calc.name, 'output',
'output_%s' % output_num )
if not os.path.exists(outdir):
os.mkdir(outdir)
print outdir
status, output, error = j.retrievegpu(job.guid, outdir)
print 'status ', status
print 'output ', output
print 'error ', error
ret = 0
if output:
ret = 1
output = '%s' % output.strip()
match = re.search('.*(output will be stored in the dir)', output)
if match:
print "IF MATCH"
#regex = '.*(%s[a-zA-Z/].*)' % wdir
#s = re.compile(regex)
#where = os.path.basename(re.search(s, output).group(1))
#job.status = u'E'
#job.log = u'Retrieved in: output/%s' % outdir
#Session.add(job)
#Session.commit()
#if there is a ranch+calcall job
untar_job = os.path.join(outdir, "pro.tgz")
#subprocess.Popen('cd %s; /bin/tar xvfz %s' %(outdir, untar_job), shell=True).wait()
cmd = 'cd %s;/bin/gunzip %s;/bin/tar xvf %s' %(outdir, "pro.tgz", "pro.tar")
print j.exec_cmd(cmd)
#tar_job = tarfile.open(untar_job, "r:gz")
#self.extractall(tar_job, outdir)
#tar_job.close()
os.remove(os.path.join(outdir, "pro.tar"))
#print "Ho finito di fare lo SSSTARRR di %s in %s" %(untar_job, outdir)
#ret = 1
h.flash.set_message('Job succesfully retrieved.', 'success')
#h.redirect('/jobs/job_list/%s' % id)
job.log = u'%s' % error[:254]
Session.add(job)
Session.commit()
#h.flash.set_message('Unable to retrieve the job.', 'error')
#h.redirect('/jobs/job_list/%s' % id)
return ret
def job_autoretrieve(self, id):
'''Auto-Retrieve a terminated job'''
if id:
j = JobsProcessing()
owner = Session.query(Users).get(session['REMOTE_USER'])
job = Session.query(Jobs).get(int(id))
c = Session.query(Calculations).filter(Calculations.id == job.calculation_id)
calc = c[0]
proj = Session.query(Projects).filter(Projects.id == calc.project_id)
project = proj[0]
output_num = job.dir_name
if output_num != None:
output_num = os.path.basename(job.dir_name).split("_")[-1]
else:
job_md5 = md5.new("%s" %job)
output_num = "output_%s" % job_md5.hexdigest()[0:10]
if calc.calc_type.tipology == 'ranch':
tip = 'maxocc'
else:
tip = calc.calc_type.tipology
print calc.name, tip
if not os.path.exists(os.path.join(config['app_conf']['working_dir'],
owner.home,
project.name, tip, calc.name)):
tip = 'amber'
if not os.path.exists(os.path.join(config['app_conf']['working_dir'],
owner.home,
project.name,
tip,
calc.name, 'output')):
os.mkdir(os.path.join(config['app_conf']['working_dir'],
owner.home,
project.name,
tip,
calc.name, 'output'))
#else:
# os.mkdir(os.path.join(config['app_conf']['working_dir'],
# owner.home,
# project.name,
# calc.calc_type.tipology,
# calc.name, 'output'))
# tip = calc.calc_type.tipology
wdir = os.path.join(config['app_conf']['working_dir'],
owner.home,
project.name)
outdir = os.path.join(config['app_conf']['working_dir'],
owner.home,
project.name,
tip,
calc.name, 'output',
'output_%s' % output_num )
if not os.path.exists(outdir):
os.mkdir(outdir)
print outdir
status, output, error = j.retrieve(job.guid, outdir)
print 'status ', status
print 'output ', output
print 'error ', error
ret = 0
if output:
ret = 1
output = '%s' % output.strip()
match = re.search('.*(successfully retrieved)', output)
if match:
print "IF MATCH"
#regex = '.*(%s[a-zA-Z/].*)' % wdir
#s = re.compile(regex)
#where = os.path.basename(re.search(s, output).group(1))
#job.status = u'E'
#job.log = u'Retrieved in: output/%s' % outdir
#Session.add(job)
#Session.commit()
#if there is a ranch+calcall job
untar_job = os.path.join(outdir, "pro.tgz")
#subprocess.Popen('cd %s; /bin/tar xvfz %s' %(outdir, untar_job), shell=True).wait()
cmd = 'cd %s; /bin/gunzip %s;/bin/tar xvf %s' %(outdir, "pro.tgz", "pro.tar")
print j.exec_cmd(cmd)
#tar_job = tarfile.open(untar_job, "r:gz")
#self.extractall(tar_job, outdir)
#tar_job.close()
os.remove(os.path.join(outdir, "pro.tar"))
#print "Ho finito di fare lo SSSTARRR di %s in %s" %(untar_job, outdir)
#ret = 1
h.flash.set_message('Job succesfully retrieved.', 'success')
#h.redirect('/jobs/job_list/%s' % id)
job.log = u'%s' % error[:254]
Session.add(job)
Session.commit()
#h.flash.set_message('Unable to retrieve the job.', 'error')
#h.redirect('/jobs/job_list/%s' % id)
return ret
def job_retrieve(self, id=None):
'''Retrieve a terminated job'''
if id:
(calc_id, prj_id, job_id) = id.split('_')
j = JobsProcessing()
owner = Session.query(Users).get(session['REMOTE_USER'])
job = Session.query(Jobs).get(int(job_id))
project = Session.query(Projects).get(int(prj_id))
calc = Session.query(Calculations).get(int(calc_id))
output_num = job.dir_name
if output_num != None:
output_num = os.path.basename(job.dir_name).split("_")[-1]
else:
job_md5 = md5.new("%s" %job)
output_num = "output_%s" % job_md5.hexdigest()[0:10]
if not os.path.isdir(os.path.join(config['app_conf']['working_dir'],
owner.home,
project.name,
calc.calc_type.tipology,
calc.name, 'output')):
os.mkdir(os.path.join(config['app_conf']['working_dir'],
owner.home,
project.name,
calc.calc_type.tipology,
calc.name, 'output'))
wdir = os.path.join(config['app_conf']['working_dir'],
owner.home,
project.name)
outdir = os.path.join(config['app_conf']['working_dir'],
owner.home,
project.name,
calc.calc_type.tipology,
calc.name, 'output',
'output_%s' % output_num )
if not os.path.isdir(outdir):
os.mkdir(outdir)
print outdir
status, output, error = j.retrieve(job.guid, outdir)
print 'status ', status
print 'output ', output
print 'error ', error
if output:
output = '%s' % output.strip()
match = re.search('.*(successfully retrieved)', output)
if match:
#regex = '.*(%s[a-zA-Z/].*)' % wdir
#s = re.compile(regex)
#where = os.path.basename(re.search(s, output).group(1))
job.status = u'E'
job.log = u'Retrieved in: output/%s' % outdir
Session.add(job)
Session.commit()
#decompress pro.tgz
untar_job = os.path.join(outdir, "pro.tgz")
tar_job = tarfile.open(untar_job, "r:gz")
for member in tar_job.getmembers():
print "Member ", member.name
#print "Where " , where
print "Outdir " , outdir
if not os.path.isfile( os.path.join(outdir, member.name)):
file_tar = tar_job.extractfile(member).read()
fout = open(os.path.join(outdir, member.name), "w")
fout.write(file_tar)
fout.close()
tar_job.close()
#remove tar file
if os.path.isfile(untar_job):
os.remove(untar_job)
ret = 1
h.flash.set_message('Job succesfully retrieved.', 'success')
h.redirect('/jobs/job_list/%s' % id)
job.log = u'%s' % error
Session.add(job)
Session.commit()
h.flash.set_message('Unable to retrieve the job.', 'error')
h.redirect('/jobs/job_list/%s' % id)
def checkGRID(self):
dd = """( other.GlueCEUniqueID == "pbs-enmr.cerm.unifi.it:8443/cream-pbs-verylong");"""
# dd = """( other.GlueCEUniqueID == "gazon.nikhef.nl:8443/cream-pbs-medium" || other.GlueCEUniqueID == "pbs-enmr.cerm.unifi.it:8443/cream-pbs-verylong");"""
if os.path.exists("/opt/checkGRID/pickle.pck"):
file_pickle = open("/opt/checkGRID/pickle.pck", "r") # read mode
job = pickle.load(file_pickle)
else:
print "NO JOBS INFORMATIONS "
return dd
#pprint(job)
queue_time = 1441
ce_sub = []
for check_ce in job.keys():
if job[check_ce].has_key("status"):
if job[check_ce]["status"] == "OK":
if job[check_ce].has_key("VOMaxCPUTime"):
time = job[check_ce]["VOMaxCPUTime"]
if time.isdigit():
if int(job[check_ce]["VOMaxCPUTime"]) > queue_time and int(job[check_ce]["VOMaxCPUTime"]) < 9999999999 :
ce_sub.append(check_ce)
print ce_sub
if len(ce_sub) > 0:
list_cet = "("
for i in ce_sub:
list_cet = list_cet + ' other.GlueCEUniqueID == "%s" ||' %i
list_ce = list_cet[:-2] + ")"
else:
list_ce = dd
# MODIFICA PER LANCIARE CONTI SOLO SU PBS
# return list_ce
return dd
def job_killall(self, id=None):
'''Kill all calculation running jobs'''
if id:
(calc_id, prj_id, job_id) = id.split('_')
if session["PORTAL"] == 'amps-nmr':
jobs = Session.query(Jobs).filter(and_(Jobs.calculation_id==int(calc_id), Jobs.status=='R')).all()
jp = JobsProcessing()
with_error = False
for j in jobs:
status, output, error = jp.kill(j.guid)
job = Session.query(Jobs).get(int(j.id))
if output:
output = '%s' % output.strip()
match = re.search('.*(glite-wms-job-cancel Success)', output)
if match:
print match.group(1)
job.status = u'C'
job.log = u''
Session.add(job)
Session.commit()
else:
job.log = u'%s' % error
Session.add(job)
Session.commit()
with_error = True
if with_error:
h.flash.set_message('Job(s) killed with errors.', 'error')
h.redirect('/jobs/job_list/%s' % id)
else:
h.flash.set_message('Job(s) successfully killed.', 'success')
h.redirect('/jobs/job_list/%s' % id)
elif session['PORTAL'] == 'maxocc':
job_list = Session.query(Jobs).filter(Jobs.calculation_id==int(calc_id, Jobs.removed == False))
if job_list:
job_item = job_list[0]
job_item.removed = True
Session.add(job_item)
calc = Session.query(Calculations).get(job_item.calculation_id)
calc.removed = True
Session.add(calc)
Session.commit()
h.flash.set_message('Job(s) successfully killed.', 'success')
h.redirect('/jobs/job_list/%s' % id)
else:
h.flash.set_message('Unable to kill job.', 'error')
h.redirect('/jobs/job_list/%s' % id)
@check_access('Run Jobs')
def show(self, id=None):
'''Show jobs informations'''
if id:
c.show = id
c.projects = Session.query(Projects).filter(and_(Projects.owner_id == session['REMOTE_USER'], Projects.removed == False)).all()
if session['PORTAL'] == 'maxocc':
ranch = 'ranch'
calctype = Session.query(CalculationTipology).filter(or_(CalculationTipology.tipology == session['PORTAL'], CalculationTipology.tipology == ranch))
c.calctype2_id = 6
else:
calctype = Session.query(CalculationTipology).filter((CalculationTipology.tipology == session['PORTAL']))
c.calctype2_id = calctype[0].id
c.calctype_id = calctype[0].id
return render('/jobs/showall.mako')
@check_access('Run Jobs')
def show_calc(self):
path = request.GET.get("path")
pathList = path.split("::")
proj = pathList[0]
calc = pathList[1]
prj = Session.query(Projects).filter(and_(Projects.name == proj, Projects.removed == False, Projects.owner_id == session['REMOTE_USER'])).all()
if prj:
calculation = Session.query(Calculations).filter(and_(Calculations.name == calc, Calculations.removed == False, Calculations.project_id == prj[0].id)).all();
if calculation:
prj[0].calculation = calculation
c.projects = prj
c.show = 'all'
c.projects = prj
c.calctype_id = calculation[0].calc_type_id
return render('/jobs/showall.mako')
else:
h.flash.set_message('No calculation', 'error')
h.redirect('/filemanager/')
else:
h.flash.set_message('No project', 'error')
h.redirect('/filemanager/')
@check_access('Run Jobs')
def job_prepare(self):
'''Prepare a job for submission
steps:
a) check for the calculation existance
b) directory creation
c) database new calculation entry creation
d) put data into new directory
e) start job
'''
#grid_req = """( other.GlueCEUniqueID == "pbs-enmr.cerm.unifi.it:8443/cream-pbs-long" || other.GlueCEUniqueID == "ce01.dur.scotgrid.ac.uk:2119/jobmanager-lcgpbs-q6h" );"""
#grid_req = """other.GlueCEPolicyMaxCPUTime > 2000 && (other.GlueCEInfoHostName == "deimos.htc.biggrid.nl" || other.GlueCEInfoHostName == "trekker.nikhef.nl" || other.GlueCEInfoHostName == "pbs-enmr.cerm.unifi.it" || other.GlueCEInfoHostName == "gazon.nikhef.nl" || other.GlueCEInfoHostName == "ce-enmr.chemie.uni-frankfurt.de" || other.GlueCEInfoHostName == "ce-enmr.chem.uu.nl" );"""
#grid_req = """other.GlueCEPolicyMaxCPUTime > 2000 && (other.GlueCEInfoHostName == "deimos.htc.biggrid.nl" || other.GlueCEInfoHostName == "trekker.nikhef.nl" || other.GlueCEInfoHostName == "gazon.nikhef.nl" || other.GlueCEInfoHostName == "ce-enmr.chemie.uni-frankfurt.de" || other.GlueCEInfoHostName == "ce-enmr.chem.uu.nl" );"""
print "REQUEST POST INI"
for key in request.POST:
print(key)
value = request.POST[key]
print(value)
print "REQUEST POST END"
prj_id = request.POST.get('prj_id')
calc_name = request.POST.get('calc_name')
if session['PORTAL'] == 'amps-nmr':
tipology = 'amber'
else:
tipology = session['PORTAL']
#tipology = request.POST.get('tipology')
multij = request.POST.get('multij')
if multij == "on":
multi_job = True
else:
multi_job = False
#usegpu = False
#usaclo = False
calccpugpu = request.POST.get('calccpugpu')
print "********* GPU **********"
print calccpugpu
print "********* GPU **********"
if calccpugpu == "calcgpu":
session["usegpu"] = True
session["useclo"] = False
elif calccpugpu == "calclo":
session["useclo"] = True
session["usegpu"] = False
else:
session["useclo"] = False
session["usegpu"] = False
session.save()
numStep = request.POST.get('step')
print "step %s" %numStep
list_input_sander = list()
for i in range(0, int(numStep)):
list_input_sander.append("sander"+str(i)+".in")
#print "###############List of Amber .in files###############"
#print list_input_sander
#print "numStep"
#print numStep
#print "#####################################################"
#
calc_name = calc_name.replace(' ', '_')
owner = Session.query(Users).get(session['REMOTE_USER'])
project = Session.query(Projects).get(int(prj_id))
calc_type = Session.query(CalculationTipology).filter(CalculationTipology.tipology == session['PORTAL']).first()
#cname = Session.query(Calculations).filter(and_(Calculations.name==calc_name, Calculations.project_id==int(prj_id))).first()
#
## Check the existance of the calculation
#if cname:
# h.flash.set_message('Calculation name already exists', 'Error')
# h.redirect('/calculations/%s' % tipology)
# Directory creation
if os.path.isfile(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"multijobs.tgz")):
#multi_job = True
if os.path.isfile(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"multijobs.tgz")):
multi = tarfile.open(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"multijobs.tgz"),"r:gz")
#else:
# multi_job = False
print "######### EVALUATE MULTI_JOB ##################"
if multi_job:
print "################# MULTI_JOB TRUE ##########################"
list_directory = []
pdir = os.path.join(config['app_conf']['working_dir'], owner.home, project.name, tipology, calc_name)
print pdir
try:
os.makedirs(pdir)
except IOError, (errno, strerror):
h.flash.set_message('An error occurred during calculation creation.', 'Error')
h.redirect('/calculations/%s' % tipology)
num_m = 0
pdbo = os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'), "amber_in.pdb")
pdb_Am = os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'), "out_leap.pdb")
n_crd = os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'), "prmcrd")
ll_multi = multi_input(pdbo, pdb_Am, n_crd)
print "############MEMBERSNAME###############"
for member in multi.getmembers():
print member.name
for member in multi.getmembers():
if member.name[-4:] == ".pdb":
if "pdb_" in member.name and member.name.split("pdb_")[1][:-4].isdigit():
num_m = member.name.split("pdb_")[1][:-4]
else:
num_m = member.name[:-4]
#num_m += 1
f = multi.extractfile(member)
content = f.readlines()
m_dir = os.path.join(config['app_conf']['working_dir'], owner.home, project.name, tipology, calc_name, "input", "input_%s" %num_m)
print "********************MULTIJOB DIR**************************"
print m_dir
list_directory.append(m_dir)
try:
os.makedirs(m_dir)
except IOError, (errno, strerror):
h.flash.set_message('An error occurred during calculation creation.', 'Error')
h.redirect('/calculations/%s' % tipology)
new_ppdb = ll_multi.add(content)
#print new_ppdb
open(pdbo,"w").writelines(new_ppdb)
open(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'), "amber_in_pre_leap_%s.pdb"%num_m),"w").writelines(new_ppdb)
os.environ["AMBER_HOME"] = "/prog/amber10"
amber_h_exe = "/prog/amber10/exe/"
leap_in = os.path.join(config['app_conf']['amber_data'], session.get('DIR_CACHE'), "leap.in")
#leap_in = "leap1.in"
cmd = "%s/tleap -O -f %s "%(amber_h_exe, leap_in)
out_leap_s = os.popen(cmd).read()
if os.path.isfile(os.path.join(config['app_conf']['amber_data'], session.get('DIR_CACHE'), "pdb_solvent.pdb")):
shutil.copy(os.path.join(config['app_conf']['amber_data'], session.get('DIR_CACHE'), "pdb_solvent.pdb"),os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'), "amber_in_after_leap_%s.pdb"%num_m))
cmd = "%s/ambpdb -p %s < %s > %s"%(amber_h_exe, os.path.join(config['app_conf']['amber_data'], session.get('DIR_CACHE'),"prmtop"), os.path.join(config['app_conf']['amber_data'], session.get('DIR_CACHE'),"prmcrd"), os.path.join(config['app_conf']['amber_data'], session.get('DIR_CACHE'),"amber_in_after_leap_crd_%s.pdb"%num_m))
os.popen(cmd).read()
print out_leap_s
#print pdb2leap_ter
#ce_sub_list = app_globals.grid_req
ce_sub_list = self.checkGRID()
#GPU
if session["usegpu"]:
jdl = """[
executable = "run_amber.sh";
stdoutput = "std.out";
stderror = "std.err";
outputsandboxbasedesturi = "gsiftp://localhost";
inputsandbox = {"%s/in.tgz","%s/run_amber.sh"};
outputsandbox = {"std.out", "std.err","pro.tgz"};
]""" %(m_dir, m_dir)
#CPU
else:
jdl = """
Executable = "run_amber.sh";
StdOutput = "std.out";
StdError = "std.err";
VirtualOrganisation="enmr.eu";
InputSandbox = {"%s/in.tgz","%s/run_amber.sh"};
OutputSandbox = {"std.out", "std.err","pro.tgz"};
Requirements = %s
""" %(m_dir, m_dir, ce_sub_list)
abs_fullpath = os.path.join(m_dir, "amber.jdl")
open(abs_fullpath,"w").write(jdl)
if session["usegpu"]:
run_amber = """
#!/bin/bash
export CUDA_HOME=/usr/local/cuda-5.5
export LD_LIBRARY_PATH=${CUDA_HOME}/lib64
export AMBERHOME=/nfs_export/gpucluster/NMR_GPU/bin/amber14GPU_NOE/
PATH=/usr/lib64/openmpi/bin:${CUDA_HOME}/bin:${PATH}
export PATH
#cd /nfs_export/gpucluster/GPU_Validation_Test
#How many GPUs in node
echo $AMBERHOME
nvidia-smi
DIRAE=$AMBERHOME/bin/
tar xvfz in.tgz
#AMBER_COMMAND
#$DIRAE/sander -O -i sander.in -o sander.out -p prmtop -c prmcrd -r prm_out.crd
#$DIRAE/ambpdb -p prmtop < prm_out.crd > amber_final.pdb
tar cvfz pro.tgz ./* --exclude in.tgz --exclude run_amber.sh --exclude gettensor.pl
"""
elif session["useclo"]:
run_amber = """
#!/bin/bash
#
# amber_run.sh - This script is meant to run on Grid or Container
#
if ["$VO_ENMR_EU_SW_DIR" != "" ]; then
echo "Running on Grid"
/bin/uname -a | /bin/grep 'x86_64' > /dev/null && ARCH='64' || ARCH='32'
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/amber11/lib
AMBERHOME=$VO_ENMR_EU_SW_DIR/CIRMMP/amber/11/$ARCH
DIRAE=$AMBERHOME/exe/
else
echo "Running on Container"
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/amber14/lib
AMBERHOME=/usr/local/amber14
DIRAE=$AMBERHOME/bin/
fi
tar xvfz in.tgz
#AMBER_COMMAND
#$DIRAE/sander -O -i sander.in -o sander.out -p prmtop -c prmcrd -r prm_out.crd
#$DIRAE/ambpdb -p prmtop < prm_out.crd > amber_final.pdb
tar cvfz pro.tgz ./* --exclude in.tgz --exclude run_amber.sh --exclude gettensor.pl
"""
else:
run_amber = """
#!/bin/bash
/bin/uname -a | /bin/grep 'x86_64' > /dev/null && ARCH='64' || ARCH='32'
if [ -d "$VO_ENMR_EU_SW_DIR/CIRMMP/amber/12/$ARCH" ]; then
AMBERHOME=$VO_ENMR_EU_SW_DIR/CIRMMP/amber/12/$ARCH
DIRAE=$AMBERHOME/bin/
else
AMBERHOME=$VO_ENMR_EU_SW_DIR/CIRMMP/amber/11/$ARCH
DIRAE=$AMBERHOME/exe/
fi
tar xvfz in.tgz
#AMBER_COMMAND
#$DIRAE/sander -O -i sander.in -o sander.out -p prmtop -c prmcrd -r prm_out.crd
#$DIRAE/ambpdb -p prmtop < prm_out.crd > amber_final.pdb
tar cvfz pro.tgz ./* --exclude in.tgz --exclude run_amber.sh --exclude gettensor.pl
# Notify end
echo "Done"
"""
abs_fullpath = os.path.join(m_dir, "run_amber.sh")
run_amber_post = []
for a in run_amber.split("\n"):
run_amber_post.append(a + "\n")
if "#AMBER_COMMAND" in a:
ct = 0
for i in list_input_sander:
ct = ct + 1
if ct == 1:
if session["usegpu"]:
run_amber_post.append("$DIRAE/pmemd.cuda -O -i %s -o %s -p prmtop -c prmcrd -r %s -ref prmcrd \n" %(i, "sander" + str(ct-1) + ".out", i[:-4] + str(ct-1) + ".crd" ))
else:
run_amber_post.append("$DIRAE/sander -O -i %s -o %s -p prmtop -c prmcrd -r %s -ref prmcrd \n" %(i, "sander" + str(ct-1) + ".out", i[:-4] + str(ct-1) + ".crd" ))
run_amber_post.append("$DIRAE/ambpdb -p prmtop < %s > %s \n" %(i[:-4] + str(ct-1) + ".crd", "amber_final" + str(ct-1) + ".pdb"))
run_amber_post.append("perl gettensor.pl sander0.out \n")
else:
if session["usegpu"]:
run_amber_post.append("$DIRAE/pmemd.cuda -O -i %s -o %s -p prmtop -c %s -r %s -ref %s \n" %(i, "sander" + str(ct-1) + ".out", i[:-4] + str(ct-2) + ".crd", i[:-4] + str(ct-1) + ".crd",i[:-4] + str(ct-2) + ".crd" ))
else:
run_amber_post.append("$DIRAE/sander -O -i %s -o %s -p prmtop -c %s -r %s -ref %s \n" %(i, "sander" + str(ct-1) + ".out", i[:-4] + str(ct-2) + ".crd", i[:-4] + str(ct-1) + ".crd",i[:-4] + str(ct-2) + ".crd" ))
run_amber_post.append("$DIRAE/ambpdb -p prmtop < %s > %s \n" %(i[:-4] + str(ct-1) + ".crd", "amber_final" + str(ct-1) + ".pdb"))
open(abs_fullpath,"w").writelines(run_amber_post)
tar = tarfile.open(os.path.join(m_dir,"in.tgz"),"w:gz")
tar.add(os.path.join(config['app_conf']['amber_data'],"gettensor.pl"), arcname="gettensor.pl")
print os.path.join(config['app_conf']['amber_data'],"gettensor.pl")
tar.add(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"prmcrd"), arcname="prmcrd")
tar.add(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"prmtop"), arcname="prmtop")
if os.path.isfile(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"allNOE_allDIH.in")):
tar.add(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"allNOE_allDIH.in"),arcname="allNOE_allDIH.in")
if os.path.isfile(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"PCS.in")):
tar.add(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"PCS.in"),arcname="PCS.in")
if os.path.isfile(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"allRDC.in")):
tar.add(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"allRDC.in"),arcname="allRDC.in")
for i in list_input_sander:
tar.add(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'), i), arcname=i)
tar.close()
#ELSE MULTIJOB
else:
num_m = 1
pdir = os.path.join(config['app_conf']['working_dir'], owner.home, project.name, tipology, calc_name, "input", 'input_1')
print pdir
try:
os.makedirs(pdir)
except IOError, (errno, strerror):
h.flash.set_message('An error occurred during calculation creation.', 'Error')
h.redirect('/calculations/%s' % tipology)
#ce_sub_list = app_globals.grid_req
ce_sub_list = self.checkGRID()
if session["usegpu"]:
jdl = """[
executable = "run_amber.sh";
stdoutput = "std.out";
stderror = "std.err";
outputsandboxbasedesturi = "gsiftp://localhost";
inputsandbox = {"%s/in.tgz","%s/run_amber.sh"};
outputsandbox = {"std.out", "std.err","pro.tgz"};
]""" %(pdir, pdir)
#CPU
else:
jdl = """
Executable = "run_amber.sh";
StdOutput = "std.out";
StdError = "std.err";
VirtualOrganisation="enmr.eu";
InputSandbox = {"%s/in.tgz","%s/run_amber.sh"};
OutputSandbox = {"std.out", "std.err","pro.tgz"};
Requirements = other.GlueCEUniqueID == "pbs-enmr.cerm.unifi.it:8443/cream-pbs-short";
#Requirements = %s
""" %(pdir, pdir, ce_sub_list)
abs_fullpath = os.path.join(pdir, "amber.jdl")
open(abs_fullpath,"w").write(jdl)
if session["usegpu"]:
run_amber = """
#!/bin/bash
export CUDA_HOME=/usr/local/cuda-5.5
export LD_LIBRARY_PATH=${CUDA_HOME}/lib64
export AMBERHOME=/nfs_export/gpucluster/NMR_GPU/bin/amber14GPU_NOE/
PATH=/usr/lib64/openmpi/bin:${CUDA_HOME}/bin:${PATH}
export PATH
#cd /nfs_export/gpucluster/GPU_Validation_Test
#How many GPUs in node
echo $AMBERHOME
nvidia-smi
DIRAE=$AMBERHOME/bin/
tar xvfz in.tgz
#AMBER_COMMAND
#$DIRAE/sander -O -i sander.in -o sander.out -p prmtop -c prmcrd -r prm_out.crd
#$DIRAE/ambpdb -p prmtop < prm_out.crd > amber_final.pdb
tar cvfz pro.tgz ./* --exclude in.tgz --exclude run_amber.sh --exclude gettensor.pl
"""
elif session["useclo"]:
run_amber = """
#!/bin/bash
#
# amber_run.sh - This script is meant to run on Grid or Container
#
if ["$VO_ENMR_EU_SW_DIR" != "" ]; then
echo "Running on Grid"
/bin/uname -a | /bin/grep 'x86_64' > /dev/null && ARCH='64' || ARCH='32'
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/amber11/lib
AMBERHOME=$VO_ENMR_EU_SW_DIR/CIRMMP/amber/11/$ARCH
DIRAE=$AMBERHOME/exe/
else
echo "Running on Container"
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/amber14/lib
AMBERHOME=/usr/local/amber14
DIRAE=$AMBERHOME/bin/
fi
tar xvfz in.tgz
#AMBER_COMMAND
#$DIRAE/sander -O -i sander.in -o sander.out -p prmtop -c prmcrd -r prm_out.crd
#$DIRAE/ambpdb -p prmtop < prm_out.crd > amber_final.pdb
tar cvfz pro.tgz ./* --exclude in.tgz --exclude run_amber.sh --exclude gettensor.pl
# Notify end
echo "Done"
"""
else:
run_amber = """
#!/bin/bash
/bin/uname -a | /bin/grep 'x86_64' > /dev/null && ARCH='64' || ARCH='32'
AMBERHOME=$VO_ENMR_EU_SW_DIR/CIRMMP/amber/PCS/$ARCH
DIRAE=$AMBERHOME/bin/
tar xvfz in.tgz
#AMBER_COMMAND
#$DIRAE/sander -O -i sander.in -o sander.out -p prmtop -c prmcrd -r prm_out.crd
#$DIRAE/ambpdb -p prmtop < prm_out.crd > amber_final.pdb
tar cvfz pro.tgz ./* --exclude in.tgz --exclude run_amber.sh --exclude gettensor.pl
"""
abs_fullpath = os.path.join(pdir, "run_amber.sh")
run_amber_post = []
for a in run_amber.split("\n"):
run_amber_post.append(a + "\n")
if "#AMBER_COMMAND" in a:
ct = 0
for i in list_input_sander:
ct = ct + 1
if ct == 1:
if session["usegpu"]:
run_amber_post.append("$DIRAE/pmemd.cuda -O -i %s -o %s -p prmtop -c prmcrd -r %s -ref prmcrd \n" %(i, "sander" + str(ct-1) + ".out", i[:-4] + str(ct-1) + ".crd" ))
else:
run_amber_post.append("$DIRAE/sander -O -i %s -o %s -p prmtop -c prmcrd -r %s -ref prmcrd \n" %(i, "sander" + str(ct-1) + ".out", i[:-4] + str(ct-1) + ".crd" ))
run_amber_post.append("$DIRAE/ambpdb -p prmtop < %s > %s \n" %(i[:-4] + str(ct-1) + ".crd", "amber_final" + str(ct-1) + ".pdb"))
run_amber_post.append("perl gettensor.pl sander0.out \n")
else:
if session["usegpu"]:
run_amber_post.append("$DIRAE/pmemd.cuda -O -i %s -o %s -p prmtop -c %s -r %s -ref %s \n" %(i, "sander" + str(ct-1) + ".out", i[:-4] + str(ct-2) + ".crd", i[:-4] + str(ct-1) + ".crd",i[:-4] + str(ct-2) + ".crd" ))
else:
run_amber_post.append("$DIRAE/sander -O -i %s -o %s -p prmtop -c %s -r %s -ref %s \n" %(i, "sander" + str(ct-1) + ".out", i[:-4] + str(ct-2) + ".crd", i[:-4] + str(ct-1) + ".crd",i[:-4] + str(ct-2) + ".crd" ))
run_amber_post.append("$DIRAE/ambpdb -p prmtop < %s > %s \n" %(i[:-4] + str(ct-1) + ".crd", "amber_final" + str(ct-1) + ".pdb"))
open(abs_fullpath,"w").writelines(run_amber_post)
tar = tarfile.open(os.path.join(pdir,"in.tgz"),"w:gz")
tar.add(os.path.join(config['app_conf']['amber_data'],"gettensor.pl"), arcname="gettensor.pl")
print os.path.join(config['app_conf']['amber_data'],"gettensor.pl")
tar.add(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"prmcrd"), arcname="prmcrd")
tar.add(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"prmtop"), arcname="prmtop")
if os.path.isfile(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"allNOE_allDIH.in")):
tar.add(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"allNOE_allDIH.in"),arcname="allNOE_allDIH.in")
if os.path.isfile(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"PCS.in")):
tar.add(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"PCS.in"),arcname="PCS.in")
if os.path.isfile(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"allRDC.in")):
tar.add(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"allRDC.in"),arcname="allRDC.in")
for i in list_input_sander:
tar.add(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'), i), arcname=i)
tar.close()
# New database entry creation
new_calc = Calculations()
new_calc.name = calc_name
new_calc.project = project
new_calc.calc_type = calc_type
new_calc.creation_date = datetime.now()
new_calc.jobs_to_submit = num_m
Session.add(new_calc)
Session.commit()
if os.path.isfile(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"multijobs.tar")):
os.remove(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"multijobs.tar"))
if os.path.isfile(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"multijobs.zip")):
os.remove(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"multijobs.zip"))
# Submit job
id = '%s_%s_0' % (new_calc.id, prj_id)
h.redirect('/jobs/job_submit/%s' % id)
@check_access('Run Jobs')
def job_prepareXA(self):
'''Prepare a job for submission
steps:
a) check for the calculation existance
b) directory creation
c) database new calculation entry creation
d) put data into new directory
e) start job
'''
def findxml3XA(xml, tag, attri):
#print tag, attri, strs, getattri
#print etree.tostring(xml, pretty_print=True)
res = []
for i in xml.getiterator():
if i.tag == "XA":
for a in i.getiterator():
if a.tag == tag:
res.append(a.get(attri))
#remove None values
return [x for x in res if x is not None]
#grid_req = """( other.GlueCEUniqueID == "pbs-enmr.cerm.unifi.it:8443/cream-pbs-long" || other.GlueCEUniqueID == "ce01.dur.scotgrid.ac.uk:2119/jobmanager-lcgpbs-q6h" );"""
#grid_req = """other.GlueCEPolicyMaxCPUTime > 2000 && (other.GlueCEInfoHostName == "deimos.htc.biggrid.nl" || other.GlueCEInfoHostName == "trekker.nikhef.nl" || other.GlueCEInfoHostName == "pbs-enmr.cerm.unifi.it" || other.GlueCEInfoHostName == "gazon.nikhef.nl" || other.GlueCEInfoHostName == "ce-enmr.chemie.uni-frankfurt.de" || other.GlueCEInfoHostName == "ce-enmr.chem.uu.nl" );"""
#grid_req = """other.GlueCEPolicyMaxCPUTime > 2000 && (other.GlueCEInfoHostName == "deimos.htc.biggrid.nl" || other.GlueCEInfoHostName == "trekker.nikhef.nl" || other.GlueCEInfoHostName == "gazon.nikhef.nl" || other.GlueCEInfoHostName == "ce-enmr.chemie.uni-frankfurt.de" || other.GlueCEInfoHostName == "ce-enmr.chem.uu.nl" );"""
#prj_id = request.POST.get('prj_id')
#prj_id = rrr #prendere da xml nome progetto
calc_name = request.POST.get('name')
#if session['PORTAL'] == 'amps-nmr':
tipology = 'amber'
#else:
# tipology = session['PORTAL']
#tipology = request.POST.get('tipology')
#multij = request.POST.get('multij')
#if multij == "on":
multi_job = True
#else:
# multi_job = False
xml_XA = etree.parse(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"XA.xml"))
xml_XA_r = xml_XA.getroot()
project_name = xml_XA_r.xpath("//info/xplor-calculation/project")[0].get("name")
#numStep = request.POST.get('step')
#numStep = ee # prendere da xml in dirache
#print "step %s" %numStep
#list_input_sander = list()
#for i in range(0, int(numStep)):
# list_input_sander.append("sander"+str(i)+".in")
#print "###############List of Amber .in files###############"
#print list_input_sander
#print "numStep"
#print numStep
#print "#####################################################"
#
calc_name = calc_name.replace(' ', '_')
owner = Session.query(Users).get(session['REMOTE_USER'])
project = Session.query(Projects).filter(Projects.name == project_name).first()
calc_type = Session.query(CalculationTipology).filter(CalculationTipology.tipology == "amps-nmr").first()
prj_id = project.id
#cname = Session.query(Calculations).filter(and_(Calculations.name==calc_name, Calculations.project_id==int(prj_id))).first()
#
## Check the existance of the calculation
#if cname:
# h.flash.set_message('Calculation name already exists', 'Error')
# h.redirect('/calculations/%s' % tipology)
# Directory creation
if os.path.isfile(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"multijobs.tgz")):
#multi_job = True
if os.path.isfile(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"multijobs.tgz")):
multi = tarfile.open(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"multijobs.tgz"),"r:gz")
#else:
# multi_job = False
print "######### EVALUATE MULTI_JOB ##################"
if multi_job:
print "################# MULTI_JOB TRUE ##########################"
list_directory = []
pdir = os.path.join(config['app_conf']['working_dir'], owner.home, project.name, tipology, calc_name)
print pdir
try:
os.makedirs(pdir)
except IOError, (errno, strerror):
h.flash.set_message('An error occurred during calculation creation.', 'Error')
h.redirect('/calculations/%s' % tipology)
num_m = 0
pdbo = os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'), "amber_in.pdb")
pdb_Am = os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'), "out_leap.pdb")
n_crd = os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'), "prmcrd")
ll_multi = multi_input(pdbo, pdb_Am, n_crd)
print "############MEMBERSNAME###############"
for member in multi.getmembers():
print member.name
for member in multi.getmembers():
if member.name[-4:] == ".pdb":
if "pdb_" in member.name and member.name.split("pdb_")[1][:-4].isdigit():
num_m = member.name.split("pdb_")[1][:-4]
else:
num_m = member.name[:-4]
#num_m += 1
f = multi.extractfile(member)
content = f.readlines()
m_dir = os.path.join(config['app_conf']['working_dir'], owner.home, project.name, tipology, calc_name, "input", "input_%s" %num_m)
print "********************MULTIJOB DIR**************************"
print m_dir
list_directory.append(m_dir)
try:
os.makedirs(m_dir)
except IOError, (errno, strerror):
h.flash.set_message('An error occurred during calculation creation.', 'Error')
h.redirect('/calculations/%s' % tipology)
new_ppdb = ll_multi.add(content)
#print new_ppdb
open(pdbo,"w").writelines(new_ppdb)
open(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'), "amber_in_pre_leap_%s.pdb"%num_m),"w").writelines(new_ppdb)
os.environ["AMBER_HOME"] = "/prog/amber10"
amber_h_exe = "/prog/amber10/exe/"
current_path = os.getcwd()
print current_path
leap_in = os.path.join(config['app_conf']['amber_data'], session.get('DIR_CACHE'), "leap.in")
#leap_in = "leap1.in"
os.chdir(os.path.join(config['app_conf']['amber_data'], session.get('DIR_CACHE')))
cmd = "%s/tleap -O -f %s "%(amber_h_exe, leap_in)
out_leap_s = os.popen(cmd).read()
os.chdir(current_path)
if os.path.isfile(os.path.join(config['app_conf']['amber_data'], session.get('DIR_CACHE'), "pdb_solvent.pdb")):
shutil.copy(os.path.join(config['app_conf']['amber_data'], session.get('DIR_CACHE'), "pdb_solvent.pdb"),os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'), "amber_in_after_leap_%s.pdb"%num_m))
cmd = "%s/ambpdb -p %s < %s > %s"%(amber_h_exe, os.path.join(config['app_conf']['amber_data'], session.get('DIR_CACHE'),"prmtop"), os.path.join(config['app_conf']['amber_data'], session.get('DIR_CACHE'),"prmcrd"), os.path.join(config['app_conf']['amber_data'], session.get('DIR_CACHE'),"amber_in_after_leap_crd_%s.pdb"%num_m))
os.popen(cmd).read()
print out_leap_s
#print pdb2leap_ter
#change tensor v
#ce_sub_list = app_globals.grid_req
ce_sub_list = self.checkGRID()
if session["usegpu"]:
jdl = """[
executable = "run_amber.sh";
stdoutput = "std.out";
stderror = "std.err";
outputsandboxbasedesturi = "gsiftp://localhost";
inputsandbox = {"%s/in.tgz","%s/run_amber.sh"};
outputsandbox = {"std.out", "std.err","pro.tgz"};
]""" %(m_dir, m_dir)
#CPU
else:
jdl = """
Executable = "run_amber.sh";
StdOutput = "std.out";
StdError = "std.err";
VirtualOrganisation="enmr.eu";
InputSandbox = {"%s/in.tgz","%s/run_amber.sh"};
OutputSandbox = {"std.out", "std.err","pro.tgz"};
Requirements = other.GlueCEUniqueID == "pbs-enmr.cerm.unifi.it:8443/cream-pbs-short";
#Requirements = %s
""" %(m_dir, m_dir, ce_sub_list)
abs_fullpath = os.path.join(m_dir, "amber.jdl")
open(abs_fullpath,"w").write(jdl)
# run_amber = """
##!/bin/bash
#/bin/uname -a | /bin/grep 'x86_64' > /dev/null && ARCH='64' || ARCH='32'
#
#AMBERHOME=$VO_ENMR_EU_SW_DIR/CIRMMP/amber/11/$ARCH
#
#DIRAE=$AMBERHOME/exe/
#
#tar xvfz in.tgz
#
#
##AMBER_COMMAND
#
#
##$DIRAE/sander -O -i sander.in -o sander.out -p prmtop -c prmcrd -r prm_out.crd
##$DIRAE/ambpdb -p prmtop < prm_out.crd > amber_final.pdb
#
#tar cvfz pro.tgz ./* --exclude in.tgz --exclude run_amber.sh --exclude gettensor.pl
#
#"""
# abs_fullpath = os.path.join(m_dir, "run_amber.sh")
# run_amber_post = []
# for a in run_amber.split("\n"):
# run_amber_post.append(a + "\n")
# if "#AMBER_COMMAND" in a:
# ct = 0
# for i in list_input_sander:
# ct = ct + 1
# if ct == 1:
# run_amber_post.append("$DIRAE/sander -O -i %s -o %s -p prmtop -c prmcrd -r %s -ref prmcrd \n" %(i, "sander" + str(ct-1) + ".out", i[:-4] + str(ct-1) + ".crd" ))
# run_amber_post.append("$DIRAE/ambpdb -p prmtop < %s > %s \n" %(i[:-4] + str(ct-1) + ".crd", "amber_final" + str(ct-1) + ".pdb"))
# run_amber_post.append("perl gettensor.pl sander0.out \n")
# else:
# run_amber_post.append("$DIRAE/sander -O -i %s -o %s -p prmtop -c %s -r %s -ref %s \n" %(i, "sander" + str(ct-1) + ".out", i[:-4] + str(ct-2) + ".crd", i[:-4] + str(ct-1) + ".crd",i[:-4] + str(ct-2) + ".crd" ))
# run_amber_post.append("$DIRAE/ambpdb -p prmtop < %s > %s \n" %(i[:-4] + str(ct-1) + ".crd", "amber_final" + str(ct-1) + ".pdb"))
#
# open(abs_fullpath,"w").writelines(run_amber_post)
shutil.copy(os.path.join(config['app_conf']['amber_data'], session.get('DIR_CACHE'), "run_amber.sh"),os.path.join(m_dir, "run_amber.sh"))
tar = tarfile.open(os.path.join(m_dir,"in.tgz"),"w:gz")
tar.add(os.path.join(config['app_conf']['amber_data'],"gettensor.pl"), arcname="gettensor.pl")
print os.path.join(config['app_conf']['amber_data'],"gettensor.pl")
tar.add(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"prmcrd"), arcname="prmcrd")
tar.add(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"prmtop"), arcname="prmtop")
if os.path.isfile(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"allNOE_allDIH.in")):
tar.add(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"allNOE_allDIH.in"),arcname="allNOE_allDIH.in")
if os.path.isfile(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"PCS.in")):
pcs_file_r = open(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"PCS.in"),"r").readlines()
#change any pcs values to fantall fitting
new_pcs_file =[]
diz_pcs = {}
print ""
for iz in xml_XA_r.xpath("//pcsinfo/pcsfile"):
pcsnum = iz.get("PCSnumber")
for i in xml_XA_r.getiterator():
if i.tag == "pdbfile":
if i.attrib["model"] == num_m:
diz_pcs[pcsnum] = {}
diz_pcs[pcsnum]["theta"] = i.attrib["theta"]
diz_pcs[pcsnum]["phi"] = i.attrib["phi"]
diz_pcs[pcsnum]["omega"] = i.attrib["omega"]
diz_pcs[pcsnum]["a1"] = i.attrib["a1"]
diz_pcs[pcsnum]["a2"] = i.attrib["a2"]
for z in pcs_file_r:
if "opttet" in z:
new_pcs_file.append(z.split("=")[0]+"=%s, \n" %diz_pcs[z.split(")")[0][-1]]["theta"])
elif "optomg" in z:
new_pcs_file.append(z.split("=")[0]+"=%s, \n" %diz_pcs[z.split(")")[0][-1]]["omega"])
elif "optphi" in z:
new_pcs_file.append(z.split("=")[0]+"=%s, \n" %diz_pcs[z.split(")")[0][-1]]["phi"])
elif "opta1" in z:
new_pcs_file.append(z.split("=")[0]+"=%s, \n" %diz_pcs[z.split(")")[0][-1]]["a1"])
elif "opta2" in z:
new_pcs_file.append(z.split("=")[0]+"=%s, \n" %diz_pcs[z.split(")")[0][-1]]["a2"])
else:
new_pcs_file.append(z)
open(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"PCS.in"),"w").writelines(new_pcs_file)
tar.add(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"PCS.in"),arcname="PCS.in")
if os.path.isfile(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"allRDC.in")):
tar.add(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"allRDC.in"),arcname="allRDC.in")
for i in glob.glob(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"sander*.in")):
tar.add(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'), i), arcname=os.path.basename(i))
tar.close()
#ELSE MULTIJOB
# else:
#
# num_m = 1
# pdir = os.path.join(config['app_conf']['working_dir'], owner.home, project.name, tipology, calc_name, "input", 'input_1')
# print pdir
# try:
# os.makedirs(pdir)
# except IOError, (errno, strerror):
# h.flash.set_message('An error occurred during calculation creation.', 'Error')
# h.redirect('/calculations/%s' % tipology)
#
# #ce_sub_list = app_globals.grid_req
# ce_sub_list = self.checkGRID()
# jdl = """
#Executable = "run_amber.sh";
#StdOutput = "std.out";
#StdError = "std.err";
#VirtualOrganisation="enmr.eu";
#InputSandbox = {"%s/in.tgz","%s/run_amber.sh"};
#OutputSandbox = {"std.out", "std.err","pro.tgz"};
#Requirements = %s
# """ %(pdir, pdir, ce_sub_list)
#
# abs_fullpath = os.path.join(pdir, "amber.jdl")
# open(abs_fullpath,"w").write(jdl)
#
# run_amber = """
##!/bin/bash
#/bin/uname -a | /bin/grep 'x86_64' > /dev/null && ARCH='64' || ARCH='32'
#
#AMBERHOME=$VO_ENMR_EU_SW_DIR/CIRMMP/amber/11/$ARCH
#
#DIRAE=$AMBERHOME/exe/
#
#tar xvfz in.tgz
#
#
##AMBER_COMMAND
#
#
##$DIRAE/sander -O -i sander.in -o sander.out -p prmtop -c prmcrd -r prm_out.crd
##$DIRAE/ambpdb -p prmtop < prm_out.crd > amber_final.pdb
#
#tar cvfz pro.tgz ./* --exclude in.tgz --exclude run_amber.sh --exclude gettensor.pl
#
#"""
# abs_fullpath = os.path.join(pdir, "run_amber.sh")
# run_amber_post = []
# for a in run_amber.split("\n"):
# run_amber_post.append(a + "\n")
# if "#AMBER_COMMAND" in a:
# ct = 0
# for i in list_input_sander:
# ct = ct + 1
# if ct == 1:
# run_amber_post.append("$DIRAE/sander -O -i %s -o %s -p prmtop -c prmcrd -r %s -ref prmcrd \n" %(i, "sander" + str(ct-1) + ".out", i[:-4] + str(ct-1) + ".crd" ))
# run_amber_post.append("$DIRAE/ambpdb -p prmtop < %s > %s \n" %(i[:-4] + str(ct-1) + ".crd", "amber_final" + str(ct-1) + ".pdb"))
# run_amber_post.append("perl gettensor.pl sander0.out \n")
# else:
# run_amber_post.append("$DIRAE/sander -O -i %s -o %s -p prmtop -c %s -r %s -ref %s \n" %(i, "sander" + str(ct-1) + ".out", i[:-4] + str(ct-2) + ".crd", i[:-4] + str(ct-1) + ".crd",i[:-4] + str(ct-2) + ".crd" ))
# run_amber_post.append("$DIRAE/ambpdb -p prmtop < %s > %s \n" %(i[:-4] + str(ct-1) + ".crd", "amber_final" + str(ct-1) + ".pdb"))
#
#
# open(abs_fullpath,"w").writelines(run_amber_post)
#
# tar = tarfile.open(os.path.join(pdir,"in.tgz"),"w:gz")
# tar.add(os.path.join(config['app_conf']['amber_data'],"gettensor.pl"), arcname="gettensor.pl")
# print os.path.join(config['app_conf']['amber_data'],"gettensor.pl")
# tar.add(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"prmcrd"), arcname="prmcrd")
# tar.add(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"prmtop"), arcname="prmtop")
# if os.path.isfile(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"allNOE_allDIH.in")):
# tar.add(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"allNOE_allDIH.in"),arcname="allNOE_allDIH.in")
# if os.path.isfile(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"PCS.in")):
# tar.add(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"PCS.in"),arcname="PCS.in")
# if os.path.isfile(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"allRDC.in")):
# tar.add(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"allRDC.in"),arcname="allRDC.in")
# for i in list_input_sander:
# tar.add(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'), i), arcname=i)
# tar.close()
# New database entry creation
new_calc = Calculations()
new_calc.name = calc_name
new_calc.project = project
new_calc.calc_type = calc_type
new_calc.creation_date = datetime.now()
new_calc.jobs_to_submit = num_m
Session.add(new_calc)
Session.commit()
if os.path.isfile(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"multijobs.tar")):
os.remove(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"multijobs.tar"))
if os.path.isfile(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"multijobs.zip")):
os.remove(os.path.join(config['app_conf']['amber_data'],session.get('DIR_CACHE'),"multijobs.zip"))
# Submit job
id = '%s_%s_0' % (new_calc.id, prj_id)
h.redirect('/jobs/job_submit/%s' % id)
| 48.787338 | 2,000 | 0.492838 | 13,909 | 120,212 | 4.133439 | 0.066647 | 0.02129 | 0.026612 | 0.032839 | 0.80519 | 0.786388 | 0.770994 | 0.75654 | 0.736085 | 0.721909 | 0 | 0.020531 | 0.35578 | 120,212 | 2,463 | 2,001 | 48.807146 | 0.721847 | 0.188093 | 0 | 0.673913 | 0 | 0.025264 | 0.232862 | 0.047079 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0.000588 | 0.019389 | null | null | 0.125734 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
b9ac2ae2d53a99128e7d5cf81614f0d3dd107ff9 | 28,821 | py | Python | rcnn/symbol/symbol_resnet.py | tonysy/mx-rcnn-flow | b78c3c964c802bb874d673170d7452e7a573a998 | [
"Apache-2.0"
] | 2 | 2018-01-31T02:47:42.000Z | 2019-07-05T03:48:54.000Z | rcnn/symbol/symbol_resnet.py | tonysy/mx-rcnn-flow | b78c3c964c802bb874d673170d7452e7a573a998 | [
"Apache-2.0"
] | null | null | null | rcnn/symbol/symbol_resnet.py | tonysy/mx-rcnn-flow | b78c3c964c802bb874d673170d7452e7a573a998 | [
"Apache-2.0"
] | null | null | null | import mxnet as mx
import proposal
import proposal_target
from rcnn.config import config
eps = 2e-5
use_global_stats = True
workspace = 512
res_deps = {'50': (3, 4, 6, 3), '101': (3, 4, 23, 3), '152': (3, 8, 36, 3), '200': (3, 24, 36, 3)}
units = res_deps['101']
filter_list = [256, 512, 1024, 2048]
def residual_unit(data, num_filter, stride, dim_match, name):
bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=eps, use_global_stats=use_global_stats, name=name + '_bn1')
act1 = mx.sym.Activation(data=bn1, act_type='relu', name=name + '_relu1')
conv1 = mx.sym.Convolution(data=act1, num_filter=int(num_filter * 0.25), kernel=(1, 1), stride=(1, 1), pad=(0, 0),
no_bias=True, workspace=workspace, name=name + '_conv1')
bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=eps, use_global_stats=use_global_stats, name=name + '_bn2')
act2 = mx.sym.Activation(data=bn2, act_type='relu', name=name + '_relu2')
conv2 = mx.sym.Convolution(data=act2, num_filter=int(num_filter * 0.25), kernel=(3, 3), stride=stride, pad=(1, 1),
no_bias=True, workspace=workspace, name=name + '_conv2')
bn3 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=eps, use_global_stats=use_global_stats, name=name + '_bn3')
act3 = mx.sym.Activation(data=bn3, act_type='relu', name=name + '_relu3')
conv3 = mx.sym.Convolution(data=act3, num_filter=num_filter, kernel=(1, 1), stride=(1, 1), pad=(0, 0), no_bias=True,
workspace=workspace, name=name + '_conv3')
if dim_match:
shortcut = data
else:
shortcut = mx.sym.Convolution(data=act1, num_filter=num_filter, kernel=(1, 1), stride=stride, no_bias=True,
workspace=workspace, name=name + '_sc')
sum = mx.sym.ElementWiseSum(*[conv3, shortcut], name=name + '_plus')
return sum
def get_resnet_conv(data):
# res1
data_bn = mx.sym.BatchNorm(data=data, fix_gamma=True, eps=eps, use_global_stats=use_global_stats, name='bn_data')
conv0 = mx.sym.Convolution(data=data_bn, num_filter=64, kernel=(7, 7), stride=(2, 2), pad=(3, 3),
no_bias=True, name="conv0", workspace=workspace)
bn0 = mx.sym.BatchNorm(data=conv0, fix_gamma=False, eps=eps, use_global_stats=use_global_stats, name='bn0')
relu0 = mx.sym.Activation(data=bn0, act_type='relu', name='relu0')
pool0 = mx.symbol.Pooling(data=relu0, kernel=(3, 3), stride=(2, 2), pad=(1, 1), pool_type='max', name='pool0')
# res2
unit = residual_unit(data=pool0, num_filter=filter_list[0], stride=(1, 1), dim_match=False, name='stage1_unit1')
for i in range(2, units[0] + 1):
unit = residual_unit(data=unit, num_filter=filter_list[0], stride=(1, 1), dim_match=True, name='stage1_unit%s' % i)
# res3
unit = residual_unit(data=unit, num_filter=filter_list[1], stride=(2, 2), dim_match=False, name='stage2_unit1')
for i in range(2, units[1] + 1):
unit = residual_unit(data=unit, num_filter=filter_list[1], stride=(1, 1), dim_match=True, name='stage2_unit%s' % i)
# res4
unit = residual_unit(data=unit, num_filter=filter_list[2], stride=(2, 2), dim_match=False, name='stage3_unit1')
for i in range(2, units[2] + 1):
unit = residual_unit(data=unit, num_filter=filter_list[2], stride=(1, 1), dim_match=True, name='stage3_unit%s' % i)
return unit
def get_resnet_train(num_classes=config.NUM_CLASSES, num_anchors=config.NUM_ANCHORS):
data = mx.symbol.Variable(name="data")
im_info = mx.symbol.Variable(name="im_info")
gt_boxes = mx.symbol.Variable(name="gt_boxes")
rpn_label = mx.symbol.Variable(name='label')
rpn_bbox_target = mx.symbol.Variable(name='bbox_target')
rpn_bbox_weight = mx.symbol.Variable(name='bbox_weight')
# shared convolutional layers
conv_feat = get_resnet_conv(data)
# RPN layers
rpn_conv = mx.symbol.Convolution(
data=conv_feat, kernel=(3, 3), pad=(1, 1), num_filter=512, name="rpn_conv_3x3")
rpn_relu = mx.symbol.Activation(data=rpn_conv, act_type="relu", name="rpn_relu")
rpn_cls_score = mx.symbol.Convolution(
data=rpn_relu, kernel=(1, 1), pad=(0, 0), num_filter=2 * num_anchors, name="rpn_cls_score")
rpn_bbox_pred = mx.symbol.Convolution(
data=rpn_relu, kernel=(1, 1), pad=(0, 0), num_filter=4 * num_anchors, name="rpn_bbox_pred")
# prepare rpn data
rpn_cls_score_reshape = mx.symbol.Reshape(
data=rpn_cls_score, shape=(0, 2, -1, 0), name="rpn_cls_score_reshape")
# classification
rpn_cls_prob = mx.symbol.SoftmaxOutput(data=rpn_cls_score_reshape, label=rpn_label, multi_output=True,
normalization='valid', use_ignore=True, ignore_label=-1,
is_hidden_layer=config.TRAIN.RPN_OHEM, name="rpn_cls_prob")
# bounding box regression
if config.RPN_IOU_LOSS:
rpn_bbox_loss_ = mx.symbol.Custom(data=rpn_bbox_pred, bbox_target=rpn_bbox_target, bbox_weight=rpn_bbox_weight,
op_type='rpn_iou_loss', name='rpn_bbox_loss_',
feature_stride=config.RPN_FEAT_STRIDE, scales=tuple(config.ANCHOR_SCALES),
ratios=tuple(config.ANCHOR_RATIOS))
else:
rpn_bbox_loss_ = rpn_bbox_weight * mx.symbol.smooth_l1(name='rpn_bbox_loss_', scalar=3.0, data=(rpn_bbox_pred - rpn_bbox_target))
# rpn output
if config.TRAIN.RPN_OHEM:
group = mx.symbol.Custom(
cls_prob=rpn_cls_prob, bbox_loss=rpn_bbox_loss_, label=rpn_label, bbox_pred=rpn_bbox_pred,
name='rpn_ohem', op_type='sample_anchors',
feature_stride=config.RPN_FEAT_STRIDE, scales=tuple(config.ANCHOR_SCALES), ratios=tuple(config.ANCHOR_RATIOS),
rpn_pre_nms_top_n=config.TRAIN.RPN_OHEM_ANCHORS, rpn_batch_size=config.TRAIN.RPN_BATCH_SIZE,
nms_threshold=config.TRAIN.RPN_OHEM_NMS, iou_loss=config.RPN_IOU_LOSS,
transform=config.TRAIN.RPN_OHEM_TRANSFORM, ignore=config.TRAIN.RPN_OHEM_IGNORE, np_ratio=config.TRAIN.RPN_OHEM_NP_RATIO)
rpn_group = [group[0], group[1], group[2], group[3]]
else:
rpn_bbox_loss = mx.sym.MakeLoss(name='rpn_bbox_loss', data=rpn_bbox_loss_, grad_scale=1.0 / config.TRAIN.RPN_BATCH_SIZE)
rpn_group = [rpn_cls_prob, rpn_bbox_loss]
# ROI proposal
rpn_cls_act = mx.symbol.SoftmaxActivation(
data=rpn_cls_score_reshape, mode="channel", name="rpn_cls_act")
rpn_cls_act_reshape = mx.symbol.Reshape(
data=rpn_cls_act, shape=(0, 2 * num_anchors, -1, 0), name='rpn_cls_act_reshape')
if config.TRAIN.CXX_PROPOSAL:
rois = mx.symbol.Proposal(
cls_prob=rpn_cls_act_reshape, bbox_pred=rpn_bbox_pred, im_info=im_info, name='rois',
feature_stride=config.RPN_FEAT_STRIDE, scales=tuple(config.ANCHOR_SCALES), ratios=tuple(config.ANCHOR_RATIOS),
rpn_pre_nms_top_n=config.TRAIN.RPN_PRE_NMS_TOP_N, rpn_post_nms_top_n=config.TRAIN.RPN_POST_NMS_TOP_N,
threshold=config.TRAIN.RPN_NMS_THRESH, rpn_min_size=config.TRAIN.RPN_MIN_SIZE, iou_loss=config.RPN_IOU_LOSS)
else:
rois = mx.symbol.Custom(
cls_prob=rpn_cls_act_reshape, bbox_pred=rpn_bbox_pred, im_info=im_info, name='rois',
op_type='proposal', feat_stride=config.RPN_FEAT_STRIDE,
scales=tuple(config.ANCHOR_SCALES), ratios=tuple(config.ANCHOR_RATIOS),
rpn_pre_nms_top_n=config.TRAIN.RPN_PRE_NMS_TOP_N, rpn_post_nms_top_n=config.TRAIN.RPN_POST_NMS_TOP_N,
threshold=config.TRAIN.RPN_NMS_THRESH, rpn_min_size=config.TRAIN.RPN_MIN_SIZE, iou_loss=config.RPN_IOU_LOSS)
# ROI proposal target
gt_boxes_reshape = mx.symbol.Reshape(data=gt_boxes, shape=(-1, 5), name='gt_boxes_reshape')
if config.TRAIN.RCNN_OHEM:
group = mx.symbol.Custom(rois=rois, gt_boxes=gt_boxes_reshape, op_type='proposal_target',
num_classes=num_classes, batch_images=config.TRAIN.BATCH_IMAGES,
batch_rois=config.TRAIN.RCNN_OHEM_ROIS, ohem=config.TRAIN.RCNN_OHEM)
else:
group = mx.symbol.Custom(rois=rois, gt_boxes=gt_boxes_reshape, op_type='proposal_target',
num_classes=num_classes, batch_images=config.TRAIN.BATCH_IMAGES,
batch_rois=config.TRAIN.BATCH_ROIS, fg_fraction=config.TRAIN.FG_FRACTION)
rois = group[0]
label = group[1]
bbox_target = group[2]
bbox_weight = group[3]
# Fast R-CNN
roi_pool = mx.symbol.ROIPooling(
name='roi_pool', data=conv_feat, rois=rois, pooled_size=(14, 14), spatial_scale=1.0 / config.RCNN_FEAT_STRIDE)
if config.RCNN_CTX_WINDOW:
roi_pool_ctx = mx.symbol.ROIPooling(
name='roi_pool_ctx', data=conv_feat, rois=rois, pooled_size=(14, 14),
spatial_scale=1.0 / config.RCNN_FEAT_STRIDE, pad=0.25)
roi_pool_concat = mx.symbol.Concat(roi_pool, roi_pool_ctx, name='roi_pool_concat')
roi_pool_red = mx.symbol.Convolution(
data=roi_pool_concat, num_filter=1024, kernel=(1, 1), stride=(1, 1), name='roi_pool_ctx_red')
roi_pool = mx.symbol.Activation(data=roi_pool_red, act_type='relu', name='roi_pool_relu')
# res5
unit = residual_unit(data=roi_pool, num_filter=filter_list[3], stride=(2, 2), dim_match=False, name='stage4_unit1')
for i in range(2, units[3] + 1):
unit = residual_unit(data=unit, num_filter=filter_list[3], stride=(1, 1), dim_match=True, name='stage4_unit%s' % i)
bn1 = mx.sym.BatchNorm(data=unit, fix_gamma=False, eps=eps, use_global_stats=use_global_stats, name='bn1')
relu1 = mx.sym.Activation(data=bn1, act_type='relu', name='relu1')
pool1 = mx.symbol.Pooling(data=relu1, global_pool=True, kernel=(7, 7), pool_type='avg', name='pool1')
# classification
cls_score = mx.symbol.FullyConnected(name='cls_score', data=pool1, num_hidden=num_classes)
cls_prob = mx.symbol.SoftmaxOutput(name='cls_prob', data=cls_score, label=label, normalization='batch',
is_hidden_layer=config.TRAIN.RCNN_OHEM)
# bounding box regression
bbox_pred = mx.symbol.FullyConnected(name='bbox_pred', data=pool1, num_hidden=num_classes * 4)
if config.RCNN_IOU_LOSS:
bbox_loss_ = mx.symbol.Custom(data=bbox_pred, bbox_target=bbox_target, bbox_weight=bbox_weight, rois=rois,
op_type='rcnn_iou_loss', name='bbox_loss_', num_classes=num_classes)
else:
bbox_loss_ = bbox_weight * mx.symbol.smooth_l1(name='bbox_loss_', scalar=1.0, data=(bbox_pred - bbox_target))
if config.TRAIN.RCNN_OHEM:
group = mx.symbol.Custom(
cls_prob=cls_prob, bbox_loss=bbox_loss_, label=label, rois=rois, bbox_pred=bbox_pred,
name='rcnn_ohem', op_type='sample_rois',
batch_images=config.TRAIN.BATCH_IMAGES, batch_size=config.TRAIN.BATCH_ROIS,
nms_threshold=config.TRAIN.RCNN_OHEM_NMS, iou_loss=config.RPN_IOU_LOSS,
transform=config.TRAIN.RCNN_OHEM_TRANSFORM, ignore=config.TRAIN.RCNN_OHEM_IGNORE)
rcnn_group = [group[0], group[1], group[2], group[3]]
for ind, name, last_shape in zip(range(len(rcnn_group)),
['cls_prob', 'bbox_loss', 'cls_mask', 'bbox_mask'],
[num_classes, num_classes * 4, num_classes, num_classes * 4]):
rcnn_group[ind] = mx.symbol.Reshape(data=rcnn_group[ind], shape=(config.TRAIN.BATCH_IMAGES, -1, last_shape),
name=name + '_reshape')
else:
bbox_loss = mx.sym.MakeLoss(name='bbox_loss', data=bbox_loss_, grad_scale=1.0 / config.TRAIN.BATCH_ROIS)
rcnn_group = [cls_prob, bbox_loss]
for ind, name, last_shape in zip(range(len(rcnn_group)),
['cls_prob', 'bbox_loss'],
[num_classes, num_classes * 4]):
rcnn_group[ind] = mx.symbol.Reshape(data=rcnn_group[ind], shape=(config.TRAIN.BATCH_IMAGES, -1, last_shape),
name=name + '_reshape')
# append label
label = mx.symbol.Reshape(data=label, shape=(config.TRAIN.BATCH_IMAGES, -1), name='label_reshape')
rcnn_group += [mx.symbol.BlockGrad(label, name='label_blockgrad')]
group = mx.symbol.Group(rpn_group + rcnn_group)
return group
def get_resnet_test(num_classes=config.NUM_CLASSES, num_anchors=config.NUM_ANCHORS):
data = mx.symbol.Variable(name="data")
im_info = mx.symbol.Variable(name="im_info")
# shared convolutional layers
conv_feat = get_resnet_conv(data)
# RPN
rpn_conv = mx.symbol.Convolution(
data=conv_feat, kernel=(3, 3), pad=(1, 1), num_filter=512, name="rpn_conv_3x3")
rpn_relu = mx.symbol.Activation(data=rpn_conv, act_type="relu", name="rpn_relu")
rpn_cls_score = mx.symbol.Convolution(
data=rpn_relu, kernel=(1, 1), pad=(0, 0), num_filter=2 * num_anchors, name="rpn_cls_score")
rpn_bbox_pred = mx.symbol.Convolution(
data=rpn_relu, kernel=(1, 1), pad=(0, 0), num_filter=4 * num_anchors, name="rpn_bbox_pred")
# ROI Proposal
rpn_cls_score_reshape = mx.symbol.Reshape(
data=rpn_cls_score, shape=(0, 2, -1, 0), name="rpn_cls_score_reshape")
rpn_cls_prob = mx.symbol.SoftmaxActivation(
data=rpn_cls_score_reshape, mode="channel", name="rpn_cls_prob")
rpn_cls_prob_reshape = mx.symbol.Reshape(
data=rpn_cls_prob, shape=(0, 2 * num_anchors, -1, 0), name='rpn_cls_prob_reshape')
if config.TEST.CXX_PROPOSAL:
rois = mx.symbol.Proposal(
cls_prob=rpn_cls_prob_reshape, bbox_pred=rpn_bbox_pred, im_info=im_info, name='rois',
feature_stride=config.RPN_FEAT_STRIDE, scales=tuple(config.ANCHOR_SCALES), ratios=tuple(config.ANCHOR_RATIOS),
rpn_pre_nms_top_n=config.TEST.RPN_PRE_NMS_TOP_N, rpn_post_nms_top_n=config.TEST.RPN_POST_NMS_TOP_N,
threshold=config.TEST.RPN_NMS_THRESH, rpn_min_size=config.TEST.RPN_MIN_SIZE, iou_loss=config.RPN_IOU_LOSS)
else:
rois = mx.symbol.Custom(
cls_prob=rpn_cls_prob_reshape, bbox_pred=rpn_bbox_pred, im_info=im_info, name='rois',
op_type='proposal', feat_stride=config.RPN_FEAT_STRIDE,
scales=tuple(config.ANCHOR_SCALES), ratios=tuple(config.ANCHOR_RATIOS),
rpn_pre_nms_top_n=config.TEST.RPN_PRE_NMS_TOP_N, rpn_post_nms_top_n=config.TEST.RPN_POST_NMS_TOP_N,
threshold=config.TEST.RPN_NMS_THRESH, rpn_min_size=config.TEST.RPN_MIN_SIZE, iou_loss=config.RPN_IOU_LOSS)
# Fast R-CNN
roi_pool = mx.symbol.ROIPooling(
name='roi_pool', data=conv_feat, rois=rois, pooled_size=(14, 14), spatial_scale=1.0 / config.RCNN_FEAT_STRIDE)
if config.RCNN_CTX_WINDOW:
roi_pool_ctx = mx.symbol.ROIPooling(
name='roi_pool_ctx', data=conv_feat, rois=rois, pooled_size=(7, 7),
spatial_scale=1.0 / config.RCNN_FEAT_STRIDE, pad=0.25)
roi_pool_concat = mx.symbol.Concat(roi_pool, roi_pool_ctx, name='roi_pool_concat')
roi_pool_red = mx.symbol.Convolution(
data=roi_pool_concat, num_filter=512, kernel=(1, 1), stride=(1, 1), name='roi_pool_ctx_red')
roi_pool = mx.symbol.Activation(data=roi_pool_red, act_type='relu', name='roi_pool_relu')
# res5
unit = residual_unit(data=roi_pool, num_filter=filter_list[3], stride=(2, 2), dim_match=False, name='stage4_unit1')
for i in range(2, units[3] + 1):
unit = residual_unit(data=unit, num_filter=filter_list[3], stride=(1, 1), dim_match=True, name='stage4_unit%s' % i)
bn1 = mx.sym.BatchNorm(data=unit, fix_gamma=False, eps=eps, use_global_stats=use_global_stats, name='bn1')
relu1 = mx.sym.Activation(data=bn1, act_type='relu', name='relu1')
pool1 = mx.symbol.Pooling(data=relu1, global_pool=True, kernel=(7, 7), pool_type='avg', name='pool1')
# classification
cls_score = mx.symbol.FullyConnected(name='cls_score', data=pool1, num_hidden=num_classes)
cls_prob = mx.symbol.SoftmaxOutput(name='cls_prob', data=cls_score)
# bounding box regression
bbox_pred = mx.symbol.FullyConnected(name='bbox_pred', data=pool1, num_hidden=num_classes * 4)
# reshape output
cls_prob = mx.symbol.Reshape(data=cls_prob, shape=(config.TEST.BATCH_IMAGES, -1, num_classes), name='cls_prob_reshape')
bbox_pred = mx.symbol.Reshape(data=bbox_pred, shape=(config.TEST.BATCH_IMAGES, -1, 4 * num_classes), name='bbox_pred_reshape')
# group output
group = mx.symbol.Group([rois, cls_prob, bbox_pred])
return group
def get_resnet_rpn(num_anchors=config.NUM_ANCHORS):
data = mx.symbol.Variable(name="data")
label = mx.symbol.Variable(name='label')
bbox_target = mx.symbol.Variable(name='bbox_target')
bbox_weight = mx.symbol.Variable(name='bbox_weight')
# shared convolutional layers
conv_feat = get_resnet_conv(data)
# RPN
rpn_conv = mx.symbol.Convolution(
data=conv_feat, kernel=(3, 3), pad=(1, 1), num_filter=512, name="rpn_conv_3x3")
rpn_relu = mx.symbol.Activation(data=rpn_conv, act_type="relu", name="rpn_relu")
rpn_cls_score = mx.symbol.Convolution(
data=rpn_relu, kernel=(1, 1), pad=(0, 0), num_filter=2 * num_anchors, name="rpn_cls_score")
rpn_bbox_pred = mx.symbol.Convolution(
data=rpn_relu, kernel=(1, 1), pad=(0, 0), num_filter=4 * num_anchors, name="rpn_bbox_pred")
# prepare rpn data
rpn_cls_score_reshape = mx.symbol.Reshape(
data=rpn_cls_score, shape=(0, 2, -1, 0), name="rpn_cls_score_reshape")
# classification
cls_prob = mx.symbol.SoftmaxOutput(data=rpn_cls_score_reshape, label=label, multi_output=True,
normalization='valid', use_ignore=True, ignore_label=-1,
is_hidden_layer=config.TRAIN.RPN_OHEM, name="cls_prob")
# bounding box regression
if config.RPN_IOU_LOSS:
bbox_loss_ = mx.symbol.Custom(data=rpn_bbox_pred, bbox_target=bbox_target, bbox_weight=bbox_weight,
op_type='rpn_iou_loss', name='bbox_loss_',
feature_stride=config.RPN_FEAT_STRIDE, scales=tuple(config.ANCHOR_SCALES),
ratios=tuple(config.ANCHOR_RATIOS))
else:
bbox_loss_ = bbox_weight * mx.symbol.smooth_l1(name='bbox_loss_', scalar=3.0, data=(rpn_bbox_pred - bbox_target))
if config.TRAIN.RPN_OHEM:
group = mx.symbol.Custom(
cls_prob=cls_prob, bbox_loss=bbox_loss_, label=label, bbox_pred=rpn_bbox_pred,
name='rpn_ohem', op_type='sample_anchors',
feature_stride=config.RPN_FEAT_STRIDE, scales=tuple(config.ANCHOR_SCALES), ratios=tuple(config.ANCHOR_RATIOS),
rpn_pre_nms_top_n=config.TRAIN.RPN_OHEM_ANCHORS, rpn_batch_size=config.TRAIN.RPN_BATCH_SIZE,
nms_threshold=config.TRAIN.RPN_OHEM_NMS, iou_loss=config.RPN_IOU_LOSS,
transform=config.TRAIN.RPN_OHEM_TRANSFORM, ignore=config.TRAIN.RPN_OHEM_IGNORE, np_ratio=config.TRAIN.RPN_OHEM_NP_RATIO)
rpn_group = [group[0], group[1], group[2], group[3]]
else:
bbox_loss = mx.sym.MakeLoss(name='rpn_bbox_loss', data=bbox_loss_, grad_scale=1.0 / config.TRAIN.RPN_BATCH_SIZE)
rpn_group = [cls_prob, bbox_loss]
# group output
group = mx.symbol.Group(rpn_group)
return group
def get_resnet_rpn_test(num_anchors=config.NUM_ANCHORS):
data = mx.symbol.Variable(name="data")
im_info = mx.symbol.Variable(name="im_info")
# shared convolutional layers
conv_feat = get_resnet_conv(data)
# RPN
rpn_conv = mx.symbol.Convolution(
data=conv_feat, kernel=(3, 3), pad=(1, 1), num_filter=512, name="rpn_conv_3x3")
rpn_relu = mx.symbol.Activation(data=rpn_conv, act_type="relu", name="rpn_relu")
rpn_cls_score = mx.symbol.Convolution(
data=rpn_relu, kernel=(1, 1), pad=(0, 0), num_filter=2 * num_anchors, name="rpn_cls_score")
rpn_bbox_pred = mx.symbol.Convolution(
data=rpn_relu, kernel=(1, 1), pad=(0, 0), num_filter=4 * num_anchors, name="rpn_bbox_pred")
# ROI Proposal
rpn_cls_score_reshape = mx.symbol.Reshape(
data=rpn_cls_score, shape=(0, 2, -1, 0), name="rpn_cls_score_reshape")
rpn_cls_prob = mx.symbol.SoftmaxActivation(
data=rpn_cls_score_reshape, mode="channel", name="rpn_cls_prob")
rpn_cls_prob_reshape = mx.symbol.Reshape(
data=rpn_cls_prob, shape=(0, 2 * num_anchors, -1, 0), name='rpn_cls_prob_reshape')
if config.TEST.CXX_PROPOSAL:
group = mx.symbol.Proposal(
cls_prob=rpn_cls_prob_reshape, bbox_pred=rpn_bbox_pred, im_info=im_info, name='rois', output_score=True,
feature_stride=config.RPN_FEAT_STRIDE, scales=tuple(config.ANCHOR_SCALES), ratios=tuple(config.ANCHOR_RATIOS),
rpn_pre_nms_top_n=config.TEST.PROPOSAL_PRE_NMS_TOP_N, rpn_post_nms_top_n=config.TEST.PROPOSAL_POST_NMS_TOP_N,
threshold=config.TEST.PROPOSAL_NMS_THRESH, rpn_min_size=config.TEST.PROPOSAL_MIN_SIZE, iou_loss=config.RPN_IOU_LOSS)
else:
group = mx.symbol.Custom(
cls_prob=rpn_cls_prob_reshape, bbox_pred=rpn_bbox_pred, im_info=im_info, name='rois', output_score=True,
op_type='proposal', feat_stride=config.RPN_FEAT_STRIDE,
scales=tuple(config.ANCHOR_SCALES), ratios=tuple(config.ANCHOR_RATIOS),
rpn_pre_nms_top_n=config.TEST.PROPOSAL_PRE_NMS_TOP_N, rpn_post_nms_top_n=config.TEST.PROPOSAL_POST_NMS_TOP_N,
threshold=config.TEST.PROPOSAL_NMS_THRESH, rpn_min_size=config.TEST.PROPOSAL_MIN_SIZE, iou_loss=config.RPN_IOU_LOSS)
# rois = group[0]
# score = group[1]
return group
def get_resnet_rcnn(num_classes=config.NUM_CLASSES):
data = mx.symbol.Variable(name="data")
rois = mx.symbol.Variable(name='rois')
label = mx.symbol.Variable(name='label')
bbox_target = mx.symbol.Variable(name='bbox_target')
bbox_weight = mx.symbol.Variable(name='bbox_weight')
# reshape input
rois = mx.symbol.Reshape(data=rois, shape=(-1, 5), name='rois_reshape')
label = mx.symbol.Reshape(data=label, shape=(-1, ), name='label_reshape')
bbox_target = mx.symbol.Reshape(data=bbox_target, shape=(-1, 4 * num_classes), name='bbox_target_reshape')
bbox_weight = mx.symbol.Reshape(data=bbox_weight, shape=(-1, 4 * num_classes), name='bbox_weight_reshape')
# shared convolutional layers
conv_feat = get_resnet_conv(data)
# Fast R-CNN
roi_pool = mx.symbol.ROIPooling(
name='roi_pool', data=conv_feat, rois=rois, pooled_size=(14, 14), spatial_scale=1.0 / config.RCNN_FEAT_STRIDE)
if config.RCNN_CTX_WINDOW:
roi_pool_ctx = mx.symbol.ROIPooling(
name='roi_pool_ctx', data=conv_feat, rois=rois, pooled_size=(14, 14),
spatial_scale=1.0 / config.RCNN_FEAT_STRIDE, pad=0.25)
roi_pool_concat = mx.symbol.Concat(roi_pool, roi_pool_ctx, name='roi_pool_concat')
roi_pool_red = mx.symbol.Convolution(
data=roi_pool_concat, num_filter=1024, kernel=(1, 1), stride=(1, 1), name='roi_pool_ctx_red')
roi_pool = mx.symbol.Activation(data=roi_pool_red, act_type='relu', name='roi_pool_relu')
# res5
unit = residual_unit(data=roi_pool, num_filter=filter_list[3], stride=(2, 2), dim_match=False, name='stage4_unit1')
for i in range(2, units[3] + 1):
unit = residual_unit(data=unit, num_filter=filter_list[3], stride=(1, 1), dim_match=True, name='stage4_unit%s' % i)
bn1 = mx.sym.BatchNorm(data=unit, fix_gamma=False, eps=eps, use_global_stats=use_global_stats, name='bn1')
relu1 = mx.sym.Activation(data=bn1, act_type='relu', name='relu1')
pool1 = mx.symbol.Pooling(data=relu1, global_pool=True, kernel=(7, 7), pool_type='avg', name='pool1')
# classification
cls_score = mx.symbol.FullyConnected(name='cls_score', data=pool1, num_hidden=num_classes)
cls_prob = mx.symbol.SoftmaxOutput(name='cls_prob', data=cls_score, label=label, normalization='batch',
is_hidden_layer=config.TRAIN.RCNN_OHEM)
# bounding box regression
bbox_pred = mx.symbol.FullyConnected(name='bbox_pred', data=pool1, num_hidden=num_classes * 4)
if config.RCNN_IOU_LOSS:
bbox_loss_ = mx.symbol.Custom(data=bbox_pred, bbox_target=bbox_target, bbox_weight=bbox_weight, rois=rois,
op_type='rcnn_iou_loss', name='bbox_loss_', num_classes=num_classes)
else:
bbox_loss_ = bbox_weight * mx.symbol.smooth_l1(name='bbox_loss_', scalar=1.0, data=(bbox_pred - bbox_target))
if config.TRAIN.RCNN_OHEM:
group = mx.symbol.Custom(
cls_prob=cls_prob, bbox_loss=bbox_loss_, label=label, rois=rois, bbox_pred=bbox_pred,
name='rcnn_ohem', op_type='sample_rois',
batch_images=config.TRAIN.BATCH_IMAGES, batch_size=config.TRAIN.BATCH_ROIS,
nms_threshold=config.TRAIN.RCNN_OHEM_NMS, iou_loss=config.RCNN_IOU_LOSS,
transform=config.TRAIN.RCNN_OHEM_TRANSFORM, ignore=config.TRAIN.RCNN_OHEM_IGNORE)
rcnn_group = [group[0], group[1], group[2], group[3]]
for ind, name, last_shape in zip(range(len(rcnn_group)),
['cls_prob', 'bbox_loss', 'cls_mask', 'bbox_mask'],
[num_classes, num_classes * 4, num_classes, num_classes * 4]):
rcnn_group[ind] = mx.symbol.Reshape(data=rcnn_group[ind], shape=(config.TRAIN.BATCH_IMAGES, -1, last_shape),
name=name + '_reshape')
else:
bbox_loss = mx.sym.MakeLoss(name='bbox_loss', data=bbox_loss_, grad_scale=1.0 / config.TRAIN.BATCH_ROIS)
rcnn_group = [cls_prob, bbox_loss]
for ind, name, last_shape in zip(range(len(rcnn_group)),
['cls_prob', 'bbox_loss'],
[num_classes, num_classes * 4]):
rcnn_group[ind] = mx.symbol.Reshape(data=rcnn_group[ind], shape=(config.TRAIN.BATCH_IMAGES, -1, last_shape),
name=name + '_reshape')
# group output
group = mx.symbol.Group(rcnn_group)
return group
def get_resnet_rcnn_test(num_classes=config.NUM_CLASSES):
data = mx.symbol.Variable(name="data")
rois = mx.symbol.Variable(name='rois')
# reshape rois
rois = mx.symbol.Reshape(data=rois, shape=(-1, 5), name='rois_reshape')
# shared convolutional layer
conv_feat = get_resnet_conv(data)
# Fast R-CNN
roi_pool = mx.symbol.ROIPooling(
name='roi_pool', data=conv_feat, rois=rois, pooled_size=(14, 14), spatial_scale=1.0 / config.RCNN_FEAT_STRIDE)
if config.RCNN_CTX_WINDOW:
roi_pool_ctx = mx.symbol.ROIPooling(
name='roi_pool_ctx', data=conv_feat, rois=rois, pooled_size=(14, 14),
spatial_scale=1.0 / config.RCNN_FEAT_STRIDE, pad=0.25)
roi_pool_concat = mx.symbol.Concat(roi_pool, roi_pool_ctx, name='roi_pool_concat')
roi_pool_red = mx.symbol.Convolution(
data=roi_pool_concat, num_filter=1024, kernel=(1, 1), stride=(1, 1), name='roi_pool_ctx_red')
roi_pool = mx.symbol.Activation(data=roi_pool_red, act_type='relu', name='roi_pool_relu')
# res5
unit = residual_unit(data=roi_pool, num_filter=filter_list[3], stride=(2, 2), dim_match=False, name='stage4_unit1')
for i in range(2, units[3] + 1):
unit = residual_unit(data=unit, num_filter=filter_list[3], stride=(1, 1), dim_match=True, name='stage4_unit%s' % i)
bn1 = mx.sym.BatchNorm(data=unit, fix_gamma=False, eps=eps, use_global_stats=use_global_stats, name='bn1')
relu1 = mx.sym.Activation(data=bn1, act_type='relu', name='relu1')
pool1 = mx.symbol.Pooling(data=relu1, global_pool=True, kernel=(7, 7), pool_type='avg', name='pool1')
# classification
cls_score = mx.symbol.FullyConnected(name='cls_score', data=pool1, num_hidden=num_classes)
cls_prob = mx.symbol.SoftmaxOutput(name='cls_prob', data=cls_score)
# bounding box regression
bbox_pred = mx.symbol.FullyConnected(name='bbox_pred', data=pool1, num_hidden=num_classes * 4)
# reshape output
cls_prob = mx.symbol.Reshape(data=cls_prob, shape=(config.TEST.BATCH_IMAGES, -1, num_classes), name='cls_prob_reshape')
bbox_pred = mx.symbol.Reshape(data=bbox_pred, shape=(config.TEST.BATCH_IMAGES, -1, 4 * num_classes), name='bbox_pred_reshape')
# group output
group = mx.symbol.Group([cls_prob, bbox_pred])
return group
| 58.106855 | 137 | 0.676763 | 4,301 | 28,821 | 4.227156 | 0.049756 | 0.055883 | 0.020021 | 0.022991 | 0.910126 | 0.902151 | 0.888785 | 0.859414 | 0.844673 | 0.834333 | 0 | 0.023292 | 0.195587 | 28,821 | 495 | 138 | 58.224242 | 0.760913 | 0.026578 | 0 | 0.736702 | 0 | 0 | 0.071031 | 0.003 | 0 | 0 | 0 | 0 | 0 | 1 | 0.021277 | false | 0 | 0.010638 | 0 | 0.053191 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 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 | 7 |
b9d9e50bccff31479e5fdebb85137be8ba081da7 | 51,285 | py | Python | src/the_tale/the_tale/game/actions/tests/test_action_move_simple.py | al-arz/the-tale | 542770257eb6ebd56a5ac44ea1ef93ff4ab19eb5 | [
"BSD-3-Clause"
] | null | null | null | src/the_tale/the_tale/game/actions/tests/test_action_move_simple.py | al-arz/the-tale | 542770257eb6ebd56a5ac44ea1ef93ff4ab19eb5 | [
"BSD-3-Clause"
] | null | null | null | src/the_tale/the_tale/game/actions/tests/test_action_move_simple.py | al-arz/the-tale | 542770257eb6ebd56a5ac44ea1ef93ff4ab19eb5 | [
"BSD-3-Clause"
] | null | null | null | import smart_imports
smart_imports.all()
def get_fast_transportation_points(clan_id, place_id):
resource_id = emissaries_logic.resource_id(clan_id=clan_id,
place_id=place_id)
return emissaries_tt_services.events_currencies.cmd_balance(resource_id,
currency=emissaries_relations.EVENT_CURRENCY.FAST_TRANSPORTATION)
class MoveSimpleTests(clans_helpers.ClansTestsMixin,
utils_testcase.TestCase):
def setUp(self):
super().setUp()
self.place_1, self.place_2, self.place_3 = game_logic.create_test_map()
self.account = self.accounts_factory.create_account()
self.storage = game_logic_storage.LogicStorage()
self.storage.load_account_data(self.account)
self.hero = self.storage.accounts_to_heroes[self.account.id]
self.action_idl = self.hero.actions.current_action
self.action_idl.state = self.action_idl.STATE.WAITING # skip first steps
self.hero.position.set_place(self.place_1)
self.path = navigation_path.simple_path(from_x=self.place_1.x, from_y=self.place_1.y,
to_x=self.place_2.x, to_y=self.place_2.y)
self.action_move = prototypes.ActionMoveSimplePrototype.create(hero=self.hero,
path=self.path,
destination=self.place_2,
break_at=None)
def test_create(self):
self.assertEqual(self.action_move.path, self.path)
self.assertEqual(self.action_move.destination, self.place_2)
self.assertEqual(self.action_move.break_at, None)
self.assertEqual(self.action_move.percents, 0)
def test_help_choices__teleport(self):
self.action_move.state = self.action_move.STATE.BATTLE
self.assertNotIn(abilities_relations.HELP_CHOICES.TELEPORT, self.action_move.HELP_CHOICES)
self.action_move.state = self.action_move.STATE.MOVING
self.assertIn(abilities_relations.HELP_CHOICES.TELEPORT, self.action_move.HELP_CHOICES)
def test_full_move(self):
self.assertEqual(self.hero.position.place_id, self.place_1.id)
visited_cells = set()
while len(self.hero.actions.actions_list) != 1:
self.storage.process_turn(continue_steps_if_needed=False)
visited_cells.add((self.hero.position.cell_x, self.hero.position.cell_y))
self.assertEqual(visited_cells, set(self.path._cells))
self.assertEqual(self.hero.position.place_id, self.place_2.id)
self.assertEqual(self.hero.actions.current_action, self.action_idl)
self.storage._test_save()
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
def test_modify_speed(self):
steps_count_1 = 0
steps_count_2 = 0
while len(self.hero.actions.actions_list) != 1:
steps_count_1 += 1
self.storage.process_turn(continue_steps_if_needed=False)
self.action_move = prototypes.ActionMoveSimplePrototype.create(hero=self.hero,
path=self.path,
destination=self.place_2,
break_at=None)
while len(self.hero.actions.actions_list) != 1:
steps_count_2 += 1
with mock.patch('the_tale.game.heroes.objects.Hero.modify_move_speed',
mock.Mock(return_value=self.hero.move_speed * 3)):
self.storage.process_turn(continue_steps_if_needed=False)
self.assertTrue(steps_count_2 * 2 < steps_count_1)
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
@mock.patch('the_tale.game.heroes.objects.Hero.can_picked_up_in_road', lambda self: True)
def test_picked_up(self):
self.assertEqual(self.action_move.percents, 0)
self.assertEqual(self.action_move.state, self.action_move.STATE.MOVING)
with self.check_increased(lambda: self.action_move.percents):
self.storage.process_turn(continue_steps_if_needed=False)
self.assertTrue(self.action_move.percents > 0)
self.assertTrue(self.hero.journal.messages[-1].key.is_ACTION_MOVE_SIMPLE_TO_PICKED_UP_IN_ROAD)
while len(self.hero.actions.actions_list) != 1:
self.storage.process_turn(continue_steps_if_needed=False)
self.assertEqual(self.hero.position.place_id, self.place_2.id)
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
@mock.patch('the_tale.game.heroes.objects.Hero.companion_fly_probability', 1.0)
def test_teleport_by_flying_companion(self):
companion_record = next(companions_storage.companions.enabled_companions())
self.hero.set_companion(companions_logic.create_companion(companion_record))
self.assertEqual(self.action_move.percents, 0)
self.assertEqual(self.action_move.state, self.action_move.STATE.MOVING)
with self.check_increased(lambda: self.action_move.percents):
self.storage.process_turn(continue_steps_if_needed=False)
self.assertTrue(self.action_move.percents > 0)
self.assertTrue(self.hero.journal.messages[-1].key.is_COMPANIONS_FLY)
while len(self.hero.actions.actions_list) != 1:
self.storage.process_turn(continue_steps_if_needed=False)
self.assertEqual(self.hero.position.place_id, self.place_2.id)
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
@mock.patch('the_tale.game.heroes.objects.Hero.companion_teleport_probability', 1.0)
def test_teleport_by_teleportator_companion__from_place(self):
companion_record = next(companions_storage.companions.enabled_companions())
self.hero.set_companion(companions_logic.create_companion(companion_record))
self.action_move.place_hero_in_current_place(create_action=False)
self.assertEqual(self.hero.position.place_id, self.place_1.id)
self.storage.process_turn(continue_steps_if_needed=False)
self.assertEqual(self.action_move.state, self.action_move.STATE.IN_CITY)
self.assertEqual(self.action_move.percents, 1)
self.assertEqual(self.hero.position.place_id, self.place_2.id)
self.assertTrue(any(message.key.is_COMPANIONS_TELEPORT
for message in self.hero.journal.messages
if message.key is not None))
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
@mock.patch('the_tale.game.heroes.objects.Hero.companion_teleport_probability', 0)
def test_teleport_by_teleportator_companion__from_place__no_teleportation(self):
companion_record = next(companions_storage.companions.enabled_companions())
self.hero.set_companion(companions_logic.create_companion(companion_record))
self.action_move.place_hero_in_current_place(create_action=False)
self.assertEqual(self.hero.position.place_id, self.place_1.id)
self.storage.process_turn(continue_steps_if_needed=False)
self.assertEqual(self.action_move.state, self.action_move.STATE.MOVING)
self.assertNotEqual(self.action_move.percents, 1)
self.assertEqual(self.hero.position.place_id, None)
self.assertFalse(any(message.key.is_COMPANIONS_TELEPORT
for message in self.hero.journal.messages
if message.key is not None))
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
@mock.patch('the_tale.game.heroes.objects.Hero.companion_teleport_probability', 1.0)
def test_teleport_by_teleportator_companion__from_place_from_moving_state(self):
companion_record = next(companions_storage.companions.enabled_companions())
self.hero.set_companion(companions_logic.create_companion(companion_record))
self.assertEqual(self.action_move.state, self.action_move.STATE.MOVING)
self.storage.process_turn(continue_steps_if_needed=False)
self.assertEqual(self.action_move.state, self.action_move.STATE.MOVING)
self.assertNotEqual(self.action_move.percents, 1)
self.assertEqual(self.hero.position.place_id, None)
self.assertFalse(any(message.key.is_COMPANIONS_TELEPORT
for message in self.hero.journal.messages
if message.key is not None))
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
@mock.patch('the_tale.game.heroes.objects.Hero.companion_teleport_probability', 1.0)
def test_teleport_by_teleportator_companion__from_start(self):
self.hero.actions.pop_action()
companion_record = next(companions_storage.companions.enabled_companions())
self.hero.set_companion(companions_logic.create_companion(companion_record))
self.action_move = prototypes.ActionMoveSimplePrototype.create(hero=self.hero,
path=self.path,
destination=self.place_2,
break_at=None)
self.assertEqual(self.action_move.state, self.action_move.STATE.IN_CITY)
self.assertEqual(self.action_move.percents, 1)
self.assertEqual(self.hero.position.place_id, self.place_2.id)
self.assertTrue(any(message.key.is_COMPANIONS_TELEPORT
for message in self.hero.journal.messages
if message.key is not None))
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
@mock.patch('the_tale.game.heroes.objects.Hero.companion_teleport_probability', 0.0)
def test_teleport_by_teleportator_companion__from_start_no_teleportation(self):
self.hero.actions.pop_action()
companion_record = next(companions_storage.companions.enabled_companions())
self.hero.set_companion(companions_logic.create_companion(companion_record))
self.action_move = prototypes.ActionMoveSimplePrototype.create(hero=self.hero,
path=self.path,
destination=self.place_2,
break_at=None)
self.storage.process_turn(continue_steps_if_needed=False)
self.assertEqual(self.action_move.state, self.action_move.STATE.MOVING)
self.assertNotEqual(self.action_move.percents, 1)
self.assertEqual(self.hero.position.place_id, None)
self.assertFalse(any(message.key.is_COMPANIONS_TELEPORT
for message in self.hero.journal.messages
if message.key is not None))
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
def test_teleport(self):
with self.check_increased(lambda: self.action_move.percents):
result = self.action_move.teleport(1, create_inplace_action=True)
self.assertTrue(result)
self.action_move.teleport(100500, create_inplace_action=True)
self.assertEqual(self.hero.position.place_id, self.place_2.id)
self.assertEqual(self.action_move.state, self.action_move.STATE.IN_CITY)
self.storage._test_save()
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
def test_teleport__zero_length_path(self):
self.action_move.path = navigation_path.Path(cells=[(self.place_1.x, self.place_1.y)])
self.assertEqual(self.hero.position.place_id, self.place_1.id)
with self.check_not_changed(lambda: self.action_move.percents):
result = self.action_move.teleport(1, create_inplace_action=True)
self.assertFalse(result)
self.assertEqual(self.hero.position.place_id, self.place_1.id)
self.assertEqual(self.action_move.state, self.action_move.STATE.MOVING)
self.storage._test_save()
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
def test_teleport__not_moving_state(self):
self.action_move.state = self.action_move.STATE.IN_CITY
with self.check_not_changed(lambda: self.action_move.percents):
self.action_move.teleport(1, create_inplace_action=True)
result = self.action_move.teleport(self.action_move.path.length, create_inplace_action=True)
self.assertFalse(result)
self.assertEqual(self.hero.position.place_id, self.place_1.id)
self.assertEqual(self.action_move.state, self.action_move.STATE.IN_CITY)
self.storage._test_save()
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
def test_teleport_to_place__from_place(self):
self.assertEqual(self.action_move.state, self.action_move.STATE.MOVING)
self.assertNotEqual(self.hero.position.place_id, None)
result = self.action_move.teleport_to_place(create_inplace_action=True)
self.assertTrue(result)
self.assertEqual(self.hero.position.place.id, self.place_2.id)
self.assertEqual(self.action_move.percents, 1)
self.assertFalse(self.action_move.leader)
self.assertEqual(self.hero.actions.current_action.TYPE, prototypes.ActionInPlacePrototype.TYPE)
self.storage._test_save()
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
def test_teleport_to_place__not_from_place(self):
self.storage.process_turn(continue_steps_if_needed=False)
self.assertEqual(self.action_move.state, self.action_move.STATE.MOVING)
self.assertEqual(self.hero.position.place_id, None)
result = self.action_move.teleport_to_place(create_inplace_action=True)
self.assertTrue(result)
self.assertEqual(self.hero.position.place.id, self.place_2.id)
self.assertEqual(self.action_move.percents, 1)
self.assertFalse(self.action_move.leader)
self.assertEqual(self.hero.actions.current_action.TYPE, prototypes.ActionInPlacePrototype.TYPE)
self.storage._test_save()
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
def test_teleport_to_place__not_moving_state(self):
self.action_move.state = self.action_move.STATE.IN_CITY
result = self.action_move.teleport_to_place(create_inplace_action=True)
self.assertFalse(result)
self.assertEqual(self.hero.position.place.id, self.place_1.id)
self.assertEqual(self.action_move.percents, 0)
self.storage._test_save()
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
def test_teleport_to_place__to_middle_place(self):
path_2 = navigation_path.simple_path(from_x=self.place_2.x, from_y=self.place_2.y,
to_x=self.place_3.x, to_y=self.place_3.y)
self.action_move.path.append(path_2)
self.assertEqual(self.action_move.state, self.action_move.STATE.MOVING)
self.assertNotEqual(self.hero.position.place_id, None)
self.action_move.teleport_to_place(create_inplace_action=True)
self.assertEqual(self.hero.position.place_id, self.place_2.id)
self.assertTrue(0 < self.action_move.percents < 1)
self.assertFalse(self.action_move.leader)
self.assertEqual(self.hero.actions.current_action.TYPE, prototypes.ActionInPlacePrototype.TYPE)
while self.action_move.state != self.action_move.STATE.MOVING:
self.storage.process_turn(continue_steps_if_needed=False)
self.action_move.teleport_to_place(create_inplace_action=True)
self.assertEqual(self.hero.position.place_id, self.place_3.id)
self.assertEqual(self.action_move.percents, 1)
self.assertFalse(self.action_move.leader)
self.assertEqual(self.hero.actions.current_action.TYPE, prototypes.ActionInPlacePrototype.TYPE)
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
def test_teleport_to_end(self):
path_2 = navigation_path.simple_path(from_x=self.place_2.x, from_y=self.place_2.y,
to_x=self.place_3.x, to_y=self.place_3.y)
self.action_move.path.append(path_2)
result = self.action_move.teleport_to_end()
self.assertTrue(result)
self.assertEqual(self.hero.position.place.id, self.place_3.id)
self.assertEqual(self.action_move.percents, 1)
self.assertFalse(self.action_move.leader)
self.assertEqual(self.hero.actions.current_action.TYPE, prototypes.ActionInPlacePrototype.TYPE)
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
def test_teleport_to_end__not_moving_state(self):
self.action_move.state = self.action_move.STATE.IN_CITY
result = self.action_move.teleport_to_end()
self.assertFalse(result)
self.assertEqual(self.hero.position.place_id, self.place_1.id)
self.assertEqual(self.action_move.percents, 0)
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
def test_teleport__length_is_0(self):
self.action_move.percents = 1
self.hero.position.set_place(self.place_2)
with self.check_not_changed(lambda: self.action_move.percents):
self.action_move.teleport(1, create_inplace_action=True)
self.assertEqual(self.action_move.state, self.action_move.STATE.IN_CITY)
self.storage._test_save()
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
def test_teleport__create_inplace_action(self):
path_2 = navigation_path.simple_path(from_x=self.place_2.x, from_y=self.place_2.y,
to_x=self.place_3.x, to_y=self.place_3.y)
self.action_move.path.append(path_2)
self.action_move.teleport(distance=100500, create_inplace_action=True)
self.assertEqual(self.hero.position.place_id, self.place_2.id)
self.assertTrue(0 < self.action_move.percents < 1)
self.assertFalse(self.action_move.leader)
self.assertEqual(self.hero.actions.current_action.TYPE, prototypes.ActionInPlacePrototype.TYPE)
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
def test_teleport__does_not_create_inplace_action(self):
path_2 = navigation_path.simple_path(from_x=self.place_2.x, from_y=self.place_2.y,
to_x=self.place_3.x, to_y=self.place_3.y)
self.action_move.path.append(path_2)
self.action_move.teleport(distance=100500, create_inplace_action=False)
self.assertEqual(self.hero.position.place_id, self.place_3.id)
self.assertEqual(self.action_move.percents, 1)
self.assertTrue(self.action_move.leader)
def test_teleport_by_clan(self):
self.prepair_forum_for_clans()
clan = self.create_clan(self.account, uid=1)
self.hero.clan_id = clan.id
self.place_1.attrs.fast_transportation.add(clan.id)
path_2 = navigation_path.simple_path(from_x=self.place_2.x, from_y=self.place_2.y,
to_x=self.place_3.x, to_y=self.place_3.y)
self.action_move.path.append(path_2)
self.assertTrue(self.action_move.teleport_with_clan())
self.assertEqual(self.action_move.state, self.action_move.STATE.IN_CITY)
self.assertEqual(self.hero.position.place_id, self.place_2.id)
self.assertTrue(self.hero.journal.messages[-1].key.is_ACTION_MOVE_SIMPLE_TO_TELEPORT_WITH_CLAN)
def test_teleport_by_clan__no_next_place(self):
self.prepair_forum_for_clans()
clan = self.create_clan(self.account, uid=1)
self.hero.clan_id = clan.id
self.place_1.attrs.fast_transportation.add(clan.id)
path_2 = navigation_path.simple_path(from_x=self.place_1.x, from_y=self.place_1.y,
to_x=self.place_1.x+1, to_y=self.place_1.y+1)
self.action_move.path = path_2
self.assertFalse(self.action_move.teleport_with_clan())
self.assertEqual(self.hero.position.place_id, self.place_1.id)
self.assertFalse(self.hero.journal.messages[-1].key.is_ACTION_MOVE_SIMPLE_TO_TELEPORT_WITH_CLAN)
def test_try_to_teleport_by_clan(self):
self.prepair_forum_for_clans()
clan = self.create_clan(self.account, uid=1)
self.hero.clan_id = clan.id
self.place_1.attrs.fast_transportation.add(clan.id)
self.hero.actions.pop_action()
path_2 = navigation_path.simple_path(from_x=self.place_2.x, from_y=self.place_2.y,
to_x=self.place_3.x, to_y=self.place_3.y)
self.path.append(path_2)
with self.check_delta(lambda: get_fast_transportation_points(clan_id=clan.id, place_id=self.place_1.id),
-tt_emissaries_constants.EVENT_CURRENCY_MULTIPLIER):
prototypes.ActionMoveSimplePrototype.create(hero=self.hero,
path=self.path,
destination=self.place_3,
break_at=None)
self.assertEqual(self.hero.position.place_id, self.place_2.id)
self.assertTrue(self.hero.journal.messages[-1].key.is_ACTION_MOVE_SIMPLE_TO_TELEPORT_WITH_CLAN)
def test_teleport_by_clan__not_from_place(self):
self.prepair_forum_for_clans()
clan = self.create_clan(self.account, uid=1)
self.hero.clan_id = clan.id
self.place_1.attrs.fast_transportation.add(clan.id)
self.hero.actions.pop_action()
self.hero.position.set_position(x=0.5, y=0.5)
path_2 = navigation_path.simple_path(from_x=0, from_y=0,
to_x=self.place_2.x, to_y=self.place_2.y)
with self.check_not_changed(lambda: get_fast_transportation_points(clan_id=clan.id, place_id=self.place_1.id)):
prototypes.ActionMoveSimplePrototype.create(hero=self.hero,
path=path_2,
destination=self.place_2,
break_at=None)
self.assertEqual(self.hero.position.x, 0.5)
self.assertEqual(self.hero.position.y, 0.5)
self.assertEqual(self.hero.position.place_id, None)
self.assertFalse(self.hero.journal.messages[-1].key.is_ACTION_MOVE_SIMPLE_TO_TELEPORT_WITH_CLAN)
def test_teleport_by_clan__wrong_clan(self):
self.prepair_forum_for_clans()
clan = self.create_clan(self.account, uid=1)
self.hero.clan_id = clan.id
self.place_1.attrs.fast_transportation.add(clan.id+1)
self.hero.actions.pop_action()
self.hero.position.set_position(x=0.5, y=0.5)
path_2 = navigation_path.simple_path(from_x=0, from_y=0,
to_x=self.place_2.x, to_y=self.place_2.y)
with self.check_not_changed(lambda: get_fast_transportation_points(clan_id=clan.id, place_id=self.place_1.id)):
prototypes.ActionMoveSimplePrototype.create(hero=self.hero,
path=path_2,
destination=self.place_2,
break_at=None)
self.assertEqual(self.hero.position.x, 0.5)
self.assertEqual(self.hero.position.y, 0.5)
self.assertEqual(self.hero.position.place_id, None)
self.assertFalse(self.hero.journal.messages[-1].key.is_ACTION_MOVE_SIMPLE_TO_TELEPORT_WITH_CLAN)
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: True)
def test_battle(self):
self.storage.process_turn(continue_steps_if_needed=False)
self.assertEqual(self.hero.actions.current_action.TYPE, prototypes.ActionBattlePvE1x1Prototype.TYPE)
self.storage._test_save()
def test_rest(self):
self.hero.health = 1
self.action_move.state = self.action_move.STATE.BATTLE
self.storage.process_turn(continue_steps_if_needed=False)
self.assertEqual(self.hero.actions.current_action.TYPE, prototypes.ActionRestPrototype.TYPE)
self.storage._test_save()
def test_regenerate_energy_on_move(self):
self.hero.preferences.set(heroes_relations.PREFERENCE_TYPE.ENERGY_REGENERATION_TYPE, heroes_relations.ENERGY_REGENERATION.PRAY)
self.hero.last_energy_regeneration_at_turn -= max(next(zip(*heroes_relations.ENERGY_REGENERATION.select('period'))))
self.action_move.state = self.action_move.STATE.MOVING
self.storage.process_turn(continue_steps_if_needed=False)
self.assertEqual(self.hero.actions.current_action.TYPE, prototypes.ActionRegenerateEnergyPrototype.TYPE)
self.storage._test_save()
def test_not_regenerate_energy_on_move_for_sacrifice(self):
self.hero.preferences.set(heroes_relations.PREFERENCE_TYPE.ENERGY_REGENERATION_TYPE, heroes_relations.ENERGY_REGENERATION.SACRIFICE)
self.hero.last_energy_regeneration_at_turn -= max(next(zip(*heroes_relations.ENERGY_REGENERATION.select('period'))))
self.action_move.state = self.action_move.STATE.MOVING
self.storage.process_turn(continue_steps_if_needed=False)
self.assertNotEqual(self.hero.actions.current_action.TYPE, prototypes.ActionRegenerateEnergyPrototype.TYPE)
self.storage._test_save()
def test_regenerate_energy_after_battle_for_sacrifice(self):
self.hero.preferences.set(heroes_relations.PREFERENCE_TYPE.ENERGY_REGENERATION_TYPE, heroes_relations.ENERGY_REGENERATION.SACRIFICE)
self.hero.last_energy_regeneration_at_turn -= max(next(zip(*heroes_relations.ENERGY_REGENERATION.select('period'))))
self.action_move.state = self.action_move.STATE.BATTLE
self.storage.process_turn(continue_steps_if_needed=False)
self.assertEqual(self.hero.actions.current_action.TYPE, prototypes.ActionRegenerateEnergyPrototype.TYPE)
self.storage._test_save()
def test_resurrect(self):
self.hero.kill()
self.action_move.state = self.action_move.STATE.BATTLE
self.storage.process_turn(continue_steps_if_needed=False)
self.assertEqual(self.hero.actions.current_action.TYPE, prototypes.ActionResurrectPrototype.TYPE)
self.storage._test_save()
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
def test_inplace(self):
self.action_move.percents = 0.99999
self.hero.position.set_position(*self.action_move.path.coordinates(self.action_move.percents))
self.storage.process_turn(continue_steps_if_needed=False)
self.assertEqual(self.hero.actions.current_action.TYPE, prototypes.ActionInPlacePrototype.TYPE)
self.storage._test_save()
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
def test_stop_when_quest_required_replane(self):
self.assertEqual(self.action_move.state, self.action_move.STATE.MOVING)
with mock.patch('the_tale.game.quests.container.QuestsContainer.has_quests', True):
self.storage.process_turn(continue_steps_if_needed=False)
self.assertFalse(self.action_move.replane_required)
self.assertEqual(self.action_move.state, self.action_move.STATE.MOVING)
self.action_move.replane_required = True
self.storage.process_turn(continue_steps_if_needed=False)
self.assertEqual(self.action_move.state, self.action_move.STATE.PROCESSED)
@mock.patch('the_tale.game.companions.objects.Companion.need_heal', True)
def test_hero_need_heal_companion(self):
companion_record = next(companions_storage.companions.enabled_companions())
self.hero.set_companion(companions_logic.create_companion(companion_record))
self.storage.process_turn(continue_steps_if_needed=False)
self.assertEqual(self.hero.actions.current_action.TYPE, prototypes.ActionHealCompanionPrototype.TYPE)
self.assertEqual(self.action_move.state, self.action_move.STATE.HEALING_COMPANION)
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
@mock.patch('the_tale.game.companions.objects.Companion.need_heal', True)
def test_hero_need_heal_companion__battle(self):
self.action_move.state = self.action_move.STATE.BATTLE
companion_record = next(companions_storage.companions.enabled_companions())
self.hero.set_companion(companions_logic.create_companion(companion_record))
self.storage.process_turn(continue_steps_if_needed=False)
self.assertEqual(self.hero.actions.current_action.TYPE, prototypes.ActionHealCompanionPrototype.TYPE)
self.assertEqual(self.action_move.state, self.action_move.STATE.HEALING_COMPANION)
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
def test_move_when_real_length_is_zero(self):
self.action_move.percents = 0
self.hero.position.set_place(self.action_move.destination)
self.action_move.path._cells = [self.action_move.path.destination_coordinates()]
self.action_move.path.length = 0
self.storage.process_turn(continue_steps_if_needed=False)
self.assertEqual(self.action_move.state, self.action_move.STATE.IN_CITY)
self.assertEqual(self.action_move.percents, 1)
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
@mock.patch('the_tale.game.heroes.objects.Hero.can_companion_say_wisdom', lambda hero: True)
@mock.patch('the_tale.game.balance.constants.COMPANIONS_EXP_PER_MOVE_PROBABILITY', 1.0)
def test_companion_say_wisdom(self):
companion_record = next(companions_storage.companions.enabled_companions())
self.hero.set_companion(companions_logic.create_companion(companion_record))
self.storage.process_turn(continue_steps_if_needed=False)
self.assertEqual(self.action_move.state, self.action_move.STATE.MOVING)
with self.check_delta(lambda: self.hero.experience, c.COMPANIONS_EXP_PER_MOVE_GET_EXP):
self.storage.process_turn(continue_steps_if_needed=False)
self.assertTrue(any(message.key.is_COMPANIONS_SAY_WISDOM for message in self.hero.journal.messages))
self.storage._test_save()
class MoveSimpleNoDestinationTests(clans_helpers.ClansTestsMixin,
utils_testcase.TestCase):
def setUp(self):
super().setUp()
self.place_1, self.place_2, self.place_3 = game_logic.create_test_map()
account = self.accounts_factory.create_account()
self.storage = game_logic_storage.LogicStorage()
self.storage.load_account_data(account)
self.hero = self.storage.accounts_to_heroes[account.id]
self.action_idl = self.hero.actions.current_action
self.action_idl.state = self.action_idl.STATE.WAITING # skip first steps
self.hero.position.set_place(self.place_1)
self.path = navigation_path.simple_path(from_x=self.place_1.x, from_y=self.place_1.y,
to_x=self.place_1.x+1, to_y=self.place_1.y+1)
self.action_move = prototypes.ActionMoveSimplePrototype.create(hero=self.hero,
path=self.path,
destination=None,
break_at=None)
def check_finish_position(self):
self.assertEqual(self.hero.position.place_id, None)
self.assertEqual(self.hero.position.x, self.place_1.x+1)
self.assertEqual(self.hero.position.y, self.place_1.y+1)
def test_create(self):
self.assertEqual(self.action_move.path, self.path)
self.assertEqual(self.action_move.destination, None)
self.assertEqual(self.action_move.break_at, None)
self.assertEqual(self.action_move.percents, 0)
def test_full_move(self):
self.assertEqual(self.hero.position.place_id, self.place_1.id)
visited_cells = set()
while len(self.hero.actions.actions_list) != 1:
self.storage.process_turn(continue_steps_if_needed=False)
visited_cells.add((self.hero.position.cell_x, self.hero.position.cell_y))
self.assertEqual(visited_cells, set(self.path._cells))
self.check_finish_position()
self.assertEqual(self.hero.actions.current_action, self.action_idl)
self.storage._test_save()
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
@mock.patch('the_tale.game.heroes.objects.Hero.can_picked_up_in_road', lambda self: True)
def test_picked_up(self):
self.assertEqual(self.action_move.percents, 0)
self.assertEqual(self.action_move.state, self.action_move.STATE.MOVING)
with self.check_increased(lambda: self.action_move.percents):
self.storage.process_turn(continue_steps_if_needed=False)
self.assertTrue(self.action_move.percents > 0)
while len(self.hero.actions.actions_list) != 1:
self.storage.process_turn(continue_steps_if_needed=False)
self.check_finish_position()
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
@mock.patch('the_tale.game.heroes.objects.Hero.companion_fly_probability', 1.0)
@mock.patch('the_tale.game.balance.constants.ANGEL_HELP_TELEPORT_DISTANCE', 0.5)
def test_teleport_by_flying_companion(self):
companion_record = next(companions_storage.companions.enabled_companions())
self.hero.set_companion(companions_logic.create_companion(companion_record))
self.assertEqual(self.action_move.percents, 0)
self.assertEqual(self.action_move.state, self.action_move.STATE.MOVING)
with self.check_increased(lambda: self.action_move.percents):
self.storage.process_turn(continue_steps_if_needed=False)
self.assertTrue(0 < self.action_move.percents < 1)
self.assertTrue(self.hero.journal.messages[-1].key.is_COMPANIONS_FLY)
while len(self.hero.actions.actions_list) != 1:
self.storage.process_turn(continue_steps_if_needed=False)
self.check_finish_position()
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
@mock.patch('the_tale.game.heroes.objects.Hero.companion_teleport_probability', 0.0)
def test_teleport_by_teleportator_companion__no_place_found(self):
companion_record = next(companions_storage.companions.enabled_companions())
self.hero.set_companion(companions_logic.create_companion(companion_record))
self.storage.process_turn(continue_steps_if_needed=False)
self.assertEqual(self.action_move.state, self.action_move.STATE.MOVING)
self.assertEqual(self.hero.position.place_id, None)
with mock.patch('the_tale.game.heroes.objects.Hero.companion_teleport_probability', 1.0):
self.storage.process_turn(continue_steps_if_needed=False)
self.assertEqual(self.action_move.state, self.action_move.STATE.MOVING)
self.assertTrue(0 < self.action_move.percents < 1)
self.assertEqual(self.hero.position.place_id, None)
self.assertFalse(any(message.key.is_COMPANIONS_TELEPORT
for message in self.hero.journal.messages
if message.key is not None))
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
def test_teleport(self):
with self.check_increased(lambda: self.action_move.percents):
self.action_move.teleport(1, create_inplace_action=True)
result = self.action_move.teleport(self.action_move.path.length, create_inplace_action=True)
self.assertTrue(result)
self.check_finish_position()
self.storage._test_save()
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
def test_teleport_to_place(self):
self.assertEqual(self.action_move.state, self.action_move.STATE.MOVING)
self.assertNotEqual(self.hero.position.place_id, None)
result = self.action_move.teleport_to_place(create_inplace_action=True)
self.assertFalse(result)
self.assertEqual(self.action_move.state, self.action_move.STATE.MOVING)
self.assertEqual(self.action_move.percents, 0)
self.storage._test_save()
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
def test_teleport_to_end(self):
result = self.action_move.teleport_to_end()
self.assertTrue(result)
self.check_finish_position()
self.assertEqual(self.action_move.percents, 1)
self.assertTrue(self.action_move.leader)
self.assertTrue(self.action_move.state, self.action_move.STATE.PROCESSED)
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
def test_move_when_real_length_is_zero(self):
self.action_move.percents = 0
self.hero.position.set_position(*self.path.destination_coordinates())
self.action_move.path._cells = [self.action_move.path.destination_coordinates()]
self.action_move.path.length = 0
self.storage.process_turn(continue_steps_if_needed=False)
self.assertEqual(self.action_move.state, self.action_move.STATE.PROCESSED)
self.assertEqual(self.action_move.percents, 1)
class MoveSimpleBreakAtTests(clans_helpers.ClansTestsMixin,
utils_testcase.TestCase):
def setUp(self):
super().setUp()
self.place_1, self.place_2, self.place_3 = game_logic.create_test_map()
self.account = self.accounts_factory.create_account()
self.storage = game_logic_storage.LogicStorage()
self.storage.load_account_data(self.account)
self.hero = self.storage.accounts_to_heroes[self.account.id]
self.action_idl = self.hero.actions.current_action
self.action_idl.state = self.action_idl.STATE.WAITING # skip first steps
self.hero.position.set_place(self.place_1)
self.path = navigation_path.simple_path(from_x=self.place_1.x, from_y=self.place_1.y,
to_x=self.place_2.x, to_y=self.place_2.y)
self.break_at = 0.75
self.real_destination = self.path.coordinates(self.break_at)
self.action_move = prototypes.ActionMoveSimplePrototype.create(hero=self.hero,
path=self.path,
destination=self.place_2,
break_at=self.break_at)
def test_create(self):
self.assertEqual(self.action_move.path, self.path)
self.assertEqual(self.action_move.destination, self.place_2)
self.assertEqual(self.action_move.break_at, self.break_at)
self.assertEqual(self.action_move.percents, 0)
def check_finish_position(self):
self.assertEqual(self.action_move.state, self.action_move.STATE.PROCESSED)
self.assertAlmostEqual(self.action_move.percents, self.break_at)
self.assertEqual(self.hero.position.place_id, None)
self.assertAlmostEqual(self.hero.position.x, self.real_destination[0])
self.assertAlmostEqual(self.hero.position.y, self.real_destination[1])
def test_full_move(self):
self.assertEqual(self.hero.position.place_id, self.place_1.id)
visited_cells = set()
while len(self.hero.actions.actions_list) != 1:
self.storage.process_turn(continue_steps_if_needed=False)
visited_cells.add((self.hero.position.cell_x, self.hero.position.cell_y))
self.check_finish_position()
self.assertEqual(self.hero.actions.current_action, self.action_idl)
self.storage._test_save()
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
@mock.patch('the_tale.game.heroes.objects.Hero.can_picked_up_in_road', lambda self: True)
def test_picked_up(self):
self.assertEqual(self.action_move.percents, 0)
self.assertEqual(self.action_move.state, self.action_move.STATE.MOVING)
with self.check_increased(lambda: self.action_move.percents):
self.storage.process_turn(continue_steps_if_needed=False)
self.assertTrue(self.action_move.percents > 0)
self.assertTrue(self.hero.journal.messages[-1].key.is_ACTION_MOVE_SIMPLE_TO_PICKED_UP_IN_ROAD)
while len(self.hero.actions.actions_list) != 1:
self.storage.process_turn(continue_steps_if_needed=False)
self.check_finish_position()
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
@mock.patch('the_tale.game.heroes.objects.Hero.companion_fly_probability', 1.0)
def test_teleport_by_flying_companion(self):
companion_record = next(companions_storage.companions.enabled_companions())
self.hero.set_companion(companions_logic.create_companion(companion_record))
self.assertEqual(self.action_move.percents, 0)
self.assertEqual(self.action_move.state, self.action_move.STATE.MOVING)
with self.check_increased(lambda: self.action_move.percents):
self.storage.process_turn(continue_steps_if_needed=False)
self.assertTrue(self.action_move.percents > 0)
self.assertTrue(self.hero.journal.messages[-1].key.is_COMPANIONS_FLY)
while len(self.hero.actions.actions_list) != 1:
self.storage.process_turn(continue_steps_if_needed=False)
self.check_finish_position()
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
@mock.patch('the_tale.game.heroes.objects.Hero.companion_teleport_probability', 1.0)
def test_teleport_by_teleportator_companion__from_place(self):
companion_record = next(companions_storage.companions.enabled_companions())
self.hero.set_companion(companions_logic.create_companion(companion_record))
self.action_move.place_hero_in_current_place(create_action=False)
self.storage.process_turn(continue_steps_if_needed=False)
self.check_finish_position()
self.assertTrue(any(message.key.is_COMPANIONS_TELEPORT
for message in self.hero.journal.messages
if message.key is not None))
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
@mock.patch('the_tale.game.heroes.objects.Hero.companion_teleport_probability', 1.0)
def test_teleport_by_teleportator_companion__from_start(self):
self.hero.actions.pop_action()
companion_record = next(companions_storage.companions.enabled_companions())
self.hero.set_companion(companions_logic.create_companion(companion_record))
self.action_move = prototypes.ActionMoveSimplePrototype.create(hero=self.hero,
path=self.path,
destination=self.place_2,
break_at=self.break_at)
self.check_finish_position()
self.assertTrue(any(message.key.is_COMPANIONS_TELEPORT
for message in self.hero.journal.messages
if message.key is not None))
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
def test_teleport(self):
with self.check_increased(lambda: self.action_move.percents):
result = self.action_move.teleport(0.1, create_inplace_action=True)
self.assertTrue(result)
self.action_move.teleport(100500, create_inplace_action=True)
self.check_finish_position()
self.storage._test_save()
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
def test_teleport_to_place__from_place(self):
self.assertEqual(self.action_move.state, self.action_move.STATE.MOVING)
self.assertNotEqual(self.hero.position.place_id, None)
result = self.action_move.teleport_to_place(create_inplace_action=True)
self.assertTrue(result)
self.check_finish_position()
self.storage._test_save()
@mock.patch('the_tale.game.heroes.objects.Hero.is_battle_start_needed', lambda self: False)
def test_teleport_to_end(self):
result = self.action_move.teleport_to_end()
self.assertTrue(result)
self.check_finish_position()
def test_teleport_by_clan(self):
self.prepair_forum_for_clans()
clan = self.create_clan(self.account, uid=1)
self.hero.clan_id = clan.id
self.place_1.attrs.fast_transportation.add(clan.id)
path_2 = navigation_path.simple_path(from_x=self.place_2.x, from_y=self.place_2.y,
to_x=self.place_3.x, to_y=self.place_3.y)
self.action_move.path.append(path_2)
self.assertTrue(self.action_move.teleport_with_clan())
self.assertEqual(self.action_move.state, self.action_move.STATE.IN_CITY)
self.assertEqual(self.hero.position.place_id, self.place_2.id)
self.assertTrue(self.hero.journal.messages[-1].key.is_ACTION_MOVE_SIMPLE_TO_TELEPORT_WITH_CLAN)
def test_teleport_by_clan__no_next_place(self):
self.prepair_forum_for_clans()
clan = self.create_clan(self.account, uid=1)
self.hero.clan_id = clan.id
self.place_1.attrs.fast_transportation.add(clan.id)
path_2 = navigation_path.simple_path(from_x=self.place_1.x, from_y=self.place_1.y,
to_x=self.place_1.x+1, to_y=self.place_1.y+1)
self.action_move.path = path_2
self.assertFalse(self.action_move.teleport_with_clan())
self.assertEqual(self.hero.position.place_id, self.place_1.id)
self.assertFalse(self.hero.journal.messages[-1].key.is_ACTION_MOVE_SIMPLE_TO_TELEPORT_WITH_CLAN)
def test_try_to_teleport_by_clan(self):
self.prepair_forum_for_clans()
clan = self.create_clan(self.account, uid=1)
self.hero.clan_id = clan.id
self.place_1.attrs.fast_transportation.add(clan.id)
self.hero.actions.pop_action()
path_2 = navigation_path.simple_path(from_x=self.place_2.x, from_y=self.place_2.y,
to_x=self.place_3.x, to_y=self.place_3.y)
self.path.append(path_2)
with self.check_delta(lambda: get_fast_transportation_points(clan_id=clan.id, place_id=self.place_1.id),
-tt_emissaries_constants.EVENT_CURRENCY_MULTIPLIER):
prototypes.ActionMoveSimplePrototype.create(hero=self.hero,
path=self.path,
destination=self.place_3,
break_at=self.break_at)
self.assertEqual(self.hero.position.place_id, self.place_2.id)
self.assertTrue(self.hero.journal.messages[-1].key.is_ACTION_MOVE_SIMPLE_TO_TELEPORT_WITH_CLAN)
def test_teleport_by_clan__not_from_place(self):
self.prepair_forum_for_clans()
clan = self.create_clan(self.account, uid=1)
self.hero.clan_id = clan.id
self.place_1.attrs.fast_transportation.add(clan.id)
self.hero.actions.pop_action()
self.hero.position.set_position(x=0.5, y=0.5)
path_2 = navigation_path.simple_path(from_x=0, from_y=0,
to_x=self.place_2.x, to_y=self.place_2.y)
with self.check_not_changed(lambda: get_fast_transportation_points(clan_id=clan.id, place_id=self.place_1.id)):
prototypes.ActionMoveSimplePrototype.create(hero=self.hero,
path=path_2,
destination=self.place_2,
break_at=self.break_at)
self.assertEqual(self.hero.position.x, 0.5)
self.assertEqual(self.hero.position.y, 0.5)
self.assertEqual(self.hero.position.place_id, None)
self.assertFalse(self.hero.journal.messages[-1].key.is_ACTION_MOVE_SIMPLE_TO_TELEPORT_WITH_CLAN)
def test_teleport_by_clan__wrong_clan(self):
self.prepair_forum_for_clans()
clan = self.create_clan(self.account, uid=1)
self.hero.clan_id = clan.id
self.place_1.attrs.fast_transportation.add(clan.id+1)
self.hero.actions.pop_action()
self.hero.position.set_position(x=0.5, y=0.5)
path_2 = navigation_path.simple_path(from_x=0, from_y=0,
to_x=self.place_2.x, to_y=self.place_2.y)
with self.check_not_changed(lambda: get_fast_transportation_points(clan_id=clan.id, place_id=self.place_1.id)):
prototypes.ActionMoveSimplePrototype.create(hero=self.hero,
path=path_2,
destination=self.place_2,
break_at=self.break_at)
self.assertEqual(self.hero.position.x, 0.5)
self.assertEqual(self.hero.position.y, 0.5)
self.assertEqual(self.hero.position.place_id, None)
self.assertFalse(self.hero.journal.messages[-1].key.is_ACTION_MOVE_SIMPLE_TO_TELEPORT_WITH_CLAN)
| 44.868766 | 140 | 0.686595 | 6,665 | 51,285 | 4.989497 | 0.033458 | 0.071267 | 0.094723 | 0.054849 | 0.963945 | 0.956217 | 0.949181 | 0.940671 | 0.93628 | 0.928672 | 0 | 0.009397 | 0.215677 | 51,285 | 1,142 | 141 | 44.908056 | 0.817348 | 0.000975 | 0 | 0.87947 | 0 | 0 | 0.069743 | 0.069392 | 0 | 0 | 0 | 0 | 0.27947 | 1 | 0.091391 | false | 0 | 0.002649 | 0 | 0.099338 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 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 | 7 |
b9fcb63ba90f9f9d47e3ee9126d497f1aaa44185 | 1,137 | py | Python | cyclops.py | JJFReibel/Cyclops | 19ec2be1c823170c12c1a2031db30f7e0fa10359 | [
"MIT"
] | null | null | null | cyclops.py | JJFReibel/Cyclops | 19ec2be1c823170c12c1a2031db30f7e0fa10359 | [
"MIT"
] | null | null | null | cyclops.py | JJFReibel/Cyclops | 19ec2be1c823170c12c1a2031db30f7e0fa10359 | [
"MIT"
] | null | null | null | # By Jean-Jacques F. Reibel
# Cyclops from X-Men
# I will not be held responsible for:
# any shenanigans
# ಠ_ಠ
# ¯¯\_(ツ)_/¯¯
import os
os.system("printf '\e[0;34;1;1m\n Cyclops\n'")
os.system("printf '\e[0;34;1;1m __\n'")
os.system("printf '\e[0;33;1;1m ('")
os.system("printf '\e[0;31;1;5mಠಠ'")
os.system("printf '\e[0;33;1;1m)'")
os.system("printf '\e[0;31;1;5m-------------------------------\n'")
os.system("printf '\e[0;35;1;1m \_|\n'")
os.system("printf '\e[0;34;1;1m _| |_\n'")
os.system("printf '\e[0;34;1;1m /| |\\'")
os.system("printf '\e[0;33;1;1m @\n'")
os.system("printf '\e[0;34;1;1m / | | \\/\n'")
os.system("printf '\e[0;34;1;1m / |___| \n'")
os.system("printf '\e[0;33;1;1m /` |___| \n'")
os.system("printf '\e[0;33;1;1m \___/\n'")
os.system("printf '\e[0;34;1;1m / \\\n'")
os.system("printf '\e[0;34;1;1m / \\\n'")
os.system("printf '\e[0;34;1;1m | |\n'")
os.system("printf '\e[0;34;1;1m | |\n'")
os.system("printf '\e[0;33;1;1m |__ |__\n'")
os.system("printf '\e[0;39;1;1m\n\n'")
| 37.9 | 67 | 0.489006 | 206 | 1,137 | 2.616505 | 0.179612 | 0.296846 | 0.519481 | 0.556586 | 0.788497 | 0.788497 | 0.756957 | 0.756957 | 0.756957 | 0.716141 | 0 | 0.110865 | 0.206684 | 1,137 | 29 | 68 | 39.206897 | 0.482262 | 0.098505 | 0 | 0.190476 | 0 | 0 | 0.706287 | 0.046169 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.047619 | 0 | 0.047619 | 0.952381 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 12 |
6a0200799b0ee0e4a62c8d481e191274d7c10f78 | 5,813 | py | Python | datasets/low_light.py | mingcv/Bread | 20dedfe2105b08ce8499b216c3c2bfd3699af17f | [
"Apache-2.0"
] | 24 | 2021-12-01T02:13:54.000Z | 2022-02-06T06:40:40.000Z | datasets/low_light.py | mingcv/Bread | 20dedfe2105b08ce8499b216c3c2bfd3699af17f | [
"Apache-2.0"
] | null | null | null | datasets/low_light.py | mingcv/Bread | 20dedfe2105b08ce8499b216c3c2bfd3699af17f | [
"Apache-2.0"
] | 6 | 2021-12-01T02:14:11.000Z | 2021-12-23T12:50:06.000Z | import os
import random
import torch
import torch.utils.data as data
import torchvision.transforms as T
from PIL import Image
class LowLightFDataset(data.Dataset):
def __init__(self, root, image_split='images_aug', targets_split='targets', training=True):
self.root = root
self.num_instances = 8
self.img_root = os.path.join(root, image_split)
self.target_root = os.path.join(root, targets_split)
self.training = training
print('----', image_split, targets_split, '----')
self.imgs = list(sorted(os.listdir(self.img_root)))
self.gts = list(sorted(os.listdir(self.target_root)))
names = [img_name.split('_')[0] + '.' + img_name.split('.')[-1] for img_name in self.imgs]
self.imgs = list(
filter(lambda img_name: img_name.split('_')[0] + '.' + img_name.split('.')[-1] in self.gts, self.imgs))
self.gts = list(filter(lambda gt: gt in names, self.gts))
print(len(self.imgs), len(self.gts))
self.preproc = T.Compose(
[T.ToTensor()]
)
self.preproc_gt = T.Compose(
[T.ToTensor()]
)
def __getitem__(self, idx):
fn, ext = self.gts[idx].split('.')
imgs = []
for i in range(self.num_instances):
img_path = os.path.join(self.img_root, f"{fn}_{i}.{ext}")
imgs += [self.preproc(Image.open(img_path).convert("RGB"))]
if self.training:
random.shuffle(imgs)
gt_path = os.path.join(self.target_root, self.gts[idx])
gt = Image.open(gt_path).convert("RGB")
gt = self.preproc_gt(gt)
# print(img_path, gt_path)
return torch.stack(imgs, dim=0), gt, fn
def __len__(self):
return len(self.gts)
class LowLightFDatasetEval(data.Dataset):
def __init__(self, root, targets_split='targets', training=True):
self.root = root
self.num_instances = 1
self.img_root = os.path.join(root, 'images')
self.target_root = os.path.join(root, targets_split)
self.training = training
self.imgs = list(sorted(os.listdir(self.img_root)))
self.gts = list(sorted(os.listdir(self.target_root)))
self.imgs = list(filter(lambda img_name: img_name in self.gts, self.imgs))
self.gts = list(filter(lambda gt: gt in self.imgs, self.gts))
print(len(self.imgs), len(self.gts))
self.preproc = T.Compose(
[T.ToTensor()]
)
self.preproc_gt = T.Compose(
[T.ToTensor()]
)
def __getitem__(self, idx):
fn, ext = self.gts[idx].split('.')
imgs = []
for i in range(self.num_instances):
img_path = os.path.join(self.img_root, f"{fn}.{ext}")
imgs += [self.preproc(Image.open(img_path).convert("RGB"))]
gt_path = os.path.join(self.target_root, self.gts[idx])
gt = Image.open(gt_path).convert("RGB")
gt = self.preproc_gt(gt)
# print(img_path, gt_path)
return torch.stack(imgs, dim=0), gt, fn
def __len__(self):
return len(self.gts)
class LowLightDataset(data.Dataset):
def __init__(self, root, targets_split='targets', color_tuning=False):
self.root = root
self.img_root = os.path.join(root, 'images')
self.target_root = os.path.join(root, targets_split)
self.color_tuning = color_tuning
self.imgs = list(sorted(os.listdir(self.img_root)))
self.gts = list(sorted(os.listdir(self.target_root)))
self.imgs = list(filter(lambda img_name: img_name in self.gts, self.imgs))
self.gts = list(filter(lambda gt: gt in self.imgs, self.gts))
print(len(self.imgs), len(self.gts))
self.preproc = T.Compose(
[T.ToTensor()]
)
self.preproc_gt = T.Compose(
[T.ToTensor()]
)
def __getitem__(self, idx):
fn, ext = self.gts[idx].split('.')
img_path = os.path.join(self.img_root, self.imgs[idx])
img = Image.open(img_path).convert("RGB")
img = self.preproc(img)
gt_path = os.path.join(self.target_root, self.gts[idx])
gt = Image.open(gt_path).convert("RGB")
gt = self.preproc_gt(gt)
if self.color_tuning:
return img, gt, 'a' + self.imgs[idx], 'a' + self.imgs[idx]
else:
return img, gt, fn
def __len__(self):
return len(self.imgs)
class LowLightDatasetReverse(data.Dataset):
def __init__(self, root, targets_split='targets', color_tuning=False):
self.root = root
self.img_root = os.path.join(root, 'images')
self.target_root = os.path.join(root, targets_split)
self.color_tuning = color_tuning
self.imgs = list(sorted(os.listdir(self.img_root)))
self.gts = list(sorted(os.listdir(self.target_root)))
self.imgs = list(filter(lambda img_name: img_name in self.gts, self.imgs))
self.gts = list(filter(lambda gt: gt in self.imgs, self.gts))
print(len(self.imgs), len(self.gts))
self.preproc = T.Compose(
[T.ToTensor()]
)
self.preproc_gt = T.Compose(
[T.ToTensor()]
)
def __getitem__(self, idx):
img_path = os.path.join(self.img_root, self.imgs[idx])
img = Image.open(img_path).convert("RGB")
img = self.preproc(img)
gt_path = os.path.join(self.target_root, self.gts[idx])
gt = Image.open(gt_path).convert("RGB")
gt = self.preproc_gt(gt)
if self.color_tuning:
return gt, img, 'a' + self.imgs[idx], 'a' + self.imgs[idx]
else:
fn, ext = os.path.splitext(self.imgs[idx])
return gt, img, '%03d' % int(fn) + ext
def __len__(self):
return len(self.imgs)
| 33.796512 | 115 | 0.592809 | 803 | 5,813 | 4.117061 | 0.097136 | 0.070175 | 0.048397 | 0.033878 | 0.88536 | 0.88536 | 0.877495 | 0.860557 | 0.838778 | 0.80248 | 0 | 0.002327 | 0.260795 | 5,813 | 171 | 116 | 33.994152 | 0.767047 | 0.008429 | 0 | 0.721805 | 0 | 0 | 0.022392 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.090226 | false | 0 | 0.045113 | 0.030075 | 0.240602 | 0.037594 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 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 | 7 |
6a03833c4a1e328042616520c617162485b3f881 | 17,355 | py | Python | sap2012/SAP_tables/temperature_reduction_when_heating_is_off_table_9b.py | building-energy/sap2012 | 4cb3a362be4662b0e96c56a3765771f0cba91422 | [
"MIT"
] | 7 | 2021-04-17T21:55:37.000Z | 2021-08-19T13:06:16.000Z | sap2012/SAP_tables/temperature_reduction_when_heating_is_off_table_9b.py | building-energy/sap2012 | 4cb3a362be4662b0e96c56a3765771f0cba91422 | [
"MIT"
] | null | null | null | sap2012/SAP_tables/temperature_reduction_when_heating_is_off_table_9b.py | building-energy/sap2012 | 4cb3a362be4662b0e96c56a3765771f0cba91422 | [
"MIT"
] | 2 | 2021-03-21T16:14:50.000Z | 2021-04-20T08:54:41.000Z | # -*- coding: utf-8 -*-
def temperature_reduction_when_heating_is_off_table_9b(
time_constant,
hours_heating_is_off_1_weekday_living_room,
hours_heating_is_off_2_weekday_living_room,
hours_heating_is_off_1_weekend_living_room,
hours_heating_is_off_2_weekend_living_room,
hours_heating_is_off_1_weekday_rest_of_dwelling,
hours_heating_is_off_2_weekday_rest_of_dwelling,
hours_heating_is_off_1_weekend_rest_of_dwelling,
hours_heating_is_off_2_weekend_rest_of_dwelling,
temperature_during_heating_living_room,
temperature_during_heating_rest_of_dwelling,
responsiveness_of_heating_system,
monthly_external_temperature_table_U1,
utilisation_factor_for_heating_living_room,
utilisation_factor_for_heating_rest_of_dwelling,
heat_transfer_coefficient,
total_internal_and_solar_gains
):
"""Calculates temperature reduction as given in Table 9b.
:param time_constant:
:type time_constant: list(float)
:param hours_heating_is_off_1_weekday_living_room:
:type hours_heating_is_off_1_weekday_living_room: float
:param hours_heating_is_off_2_weekday_living_room:
:type hours_heating_is_off_2_weekday_living_room: float
:param hours_heating_is_off_1_weekend_living_room:
:type hours_heating_is_off_1_weekend_living_room: float
:param hours_heating_is_off_2_weekend_living_room:
:type hours_heating_is_off_2_weekend_living_room: float
:param hours_heating_is_off_1_weekday_rest_of_dwelling:
:type hours_heating_is_off_1_weekday_rest_of_dwelling: float
:param hours_heating_is_off_2_weekday_rest_of_dwelling:
:type hours_heating_is_off_2_weekday_rest_of_dwelling: float
:param hours_heating_is_off_1_weekend_rest_of_dwelling:
:type hours_heating_is_off_1_weekend_rest_of_dwelling: float
:param hours_heating_is_off_2_weekend_rest_of_dwelling:
:type hours_heating_is_off_2_weekend_rest_of_dwelling: float
:param temperature_during_heating_living_room:
:type temperature_during_heating_living_room: float
:param temperature_during_heating_rest_of_dwelling:
:type temperature_during_heating_rest_of_dwelling: list(float)
:param responsiveness_of_heating_system:
:type responsiveness_of_heating_system: float
:param monthly_external_temperature_table_U1:
:type monthly_external_temperature_table_U1: list(float)
:param utilisation_factor_for_heating_living_room:
:type utilisation_factor_for_heating_living_room: list(float)
:param utilisation_factor_for_heating_rest_of_dwelling:
:type utilisation_factor_for_heating_rest_of_dwelling: list(float)
:param heat_transfer_coefficient: See (39), in W/K.
:type heat_transfer_coefficient: list(float)
:param total_internal_and_solar_gains: See (84) in W.
:type total_internal_and_solar_gains: list(float)
:returns: A dictionary with keys (
t_c,
internal_temperature_without_heating_living_room,
internal_temperature_without_heating_rest_of_dwelling,
temperature_reduction_when_heating_is_off_1_weekday_living_room,
temperature_reduction_when_heating_is_off_2_weekday_living_room,
temperature_reduction_when_heating_is_off_1_weekend_living_room,
temperature_reduction_when_heating_is_off_2_weekend_living_room,
temperature_reduction_when_heating_is_off_1_weekday_rest_of_dwelling,
temperature_reduction_when_heating_is_off_2_weekday_rest_of_dwelling,
temperature_reduction_when_heating_is_off_1_weekend_rest_of_dwelling,
temperature_reduction_when_heating_is_off_2_weekend_rest_of_dwelling
)
- **t_c** (`list` (`float`)) -
- **internal_temperature_without_heating_living_room** (`list` (`float`)) -
- **internal_temperature_without_heating_rest_of_dwelling** (`list` (`float`)) -
- **temperature_reduction_when_heating_is_off_1_weekday_living_room** (`list` (`float`)) -
- **temperature_reduction_when_heating_is_off_2_weekday_living_room** (`list` (`float`)) -
- **temperature_reduction_when_heating_is_off_1_weekend_living_room** (`list` (`float`)) -
- **temperature_reduction_when_heating_is_off_2_weekend_living_room** (`list` (`float`)) -
- **temperature_reduction_when_heating_is_off_1_weekday_rest_of_dwelling** (`list` (`float`)) -
- **temperature_reduction_when_heating_is_off_2_weekday_rest_of_dwelling** (`list` (`float`)) -
- **temperature_reduction_when_heating_is_off_1_weekend_rest_of_dwelling** (`list` (`float`)) -
- **temperature_reduction_when_heating_is_off_2_weekend_rest_of_dwelling** (`list` (`float`)) -
:rtype: dict
"""
t_c = []
for i in range(12):
t_c.append(4 + 0.25 * time_constant[i])
internal_temperature_without_heating_living_room = []
for i in range(12):
internal_temperature_without_heating_living_room.append((1 - responsiveness_of_heating_system) +
responsiveness_of_heating_system *
(monthly_external_temperature_table_U1[i] +
(utilisation_factor_for_heating_living_room[i] * total_internal_and_solar_gains[i] /
heat_transfer_coefficient[i])))
internal_temperature_without_heating_rest_of_dwelling = []
for i in range(12):
internal_temperature_without_heating_rest_of_dwelling.append((1 - responsiveness_of_heating_system) +
responsiveness_of_heating_system *
(monthly_external_temperature_table_U1[i] +
(utilisation_factor_for_heating_rest_of_dwelling[i] * total_internal_and_solar_gains[i] /
heat_transfer_coefficient[i])))
temperature_reduction_when_heating_is_off_1_weekday_living_room = []
for i in range(12):
if hours_heating_is_off_1_weekday_living_room > t_c[i]:
temperature_reduction_when_heating_is_off_1_weekday_living_room.append((temperature_during_heating_living_room -
internal_temperature_without_heating_living_room[i]) *
(hours_heating_is_off_1_weekday_living_room - 0.5 * t_c[i]) / 24)
else:
temperature_reduction_when_heating_is_off_1_weekday_living_room.append(0.5 * hours_heating_is_off_1_weekday_living_room**2 *
(temperature_during_heating_living_room -
internal_temperature_without_heating_living_room[i]) /
(24 * t_c[i]))
temperature_reduction_when_heating_is_off_2_weekday_living_room = []
for i in range(12):
if hours_heating_is_off_2_weekday_living_room > t_c[i]:
temperature_reduction_when_heating_is_off_2_weekday_living_room.append((temperature_during_heating_living_room -
internal_temperature_without_heating_living_room[i]) *
(hours_heating_is_off_2_weekday_living_room - 0.5 * t_c[i]) / 24)
else:
temperature_reduction_when_heating_is_off_2_weekday_living_room.append(0.5 * hours_heating_is_off_2_weekday_living_room**2 *
(temperature_during_heating_living_room -
internal_temperature_without_heating_living_room[i]) /
(24 * t_c[i]))
temperature_reduction_when_heating_is_off_1_weekend_living_room = []
for i in range(12):
if hours_heating_is_off_1_weekend_living_room > t_c[i]:
temperature_reduction_when_heating_is_off_1_weekend_living_room.append((temperature_during_heating_living_room -
internal_temperature_without_heating_living_room[i]) *
(hours_heating_is_off_1_weekend_living_room - 0.5 * t_c[i]) / 24)
else:
temperature_reduction_when_heating_is_off_1_weekend_living_room.append(0.5 * hours_heating_is_off_1_weekend_living_room**2 *
(temperature_during_heating_living_room -
internal_temperature_without_heating_living_room[i]) /
(24 * t_c[i]))
temperature_reduction_when_heating_is_off_2_weekend_living_room = []
for i in range(12):
if hours_heating_is_off_2_weekend_living_room > t_c[i]:
temperature_reduction_when_heating_is_off_2_weekend_living_room.append((temperature_during_heating_living_room -
internal_temperature_without_heating_living_room[i]) *
(hours_heating_is_off_2_weekend_living_room - 0.5 * t_c[i]) / 24)
else:
temperature_reduction_when_heating_is_off_2_weekend_living_room.append(0.5 * hours_heating_is_off_2_weekend_living_room**2 *
(temperature_during_heating_living_room -
internal_temperature_without_heating_living_room[i]) /
(24 * t_c[i]))
temperature_reduction_when_heating_is_off_1_weekday_rest_of_dwelling = []
for i in range(12):
if hours_heating_is_off_1_weekday_rest_of_dwelling > t_c[i]:
temperature_reduction_when_heating_is_off_1_weekday_rest_of_dwelling.append((temperature_during_heating_rest_of_dwelling[i] -
internal_temperature_without_heating_rest_of_dwelling[i]) *
(hours_heating_is_off_1_weekday_rest_of_dwelling - 0.5 * t_c[i]) / 24)
else:
temperature_reduction_when_heating_is_off_1_weekday_rest_of_dwelling.append(0.5 * hours_heating_is_off_1_weekday_rest_of_dwelling**2 *
(temperature_during_heating_rest_of_dwelling[i] -
internal_temperature_without_heating_rest_of_dwelling[i]) /
(24 * t_c[i]))
temperature_reduction_when_heating_is_off_2_weekday_rest_of_dwelling = []
for i in range(12):
if hours_heating_is_off_2_weekday_rest_of_dwelling > t_c[i]:
temperature_reduction_when_heating_is_off_2_weekday_rest_of_dwelling.append((temperature_during_heating_rest_of_dwelling[i] -
internal_temperature_without_heating_rest_of_dwelling[i]) *
(hours_heating_is_off_2_weekday_rest_of_dwelling - 0.5 * t_c[i]) / 24)
else:
temperature_reduction_when_heating_is_off_2_weekday_rest_of_dwelling.append(0.5 * hours_heating_is_off_2_weekday_rest_of_dwelling**2 *
(temperature_during_heating_rest_of_dwelling[i] -
internal_temperature_without_heating_rest_of_dwelling[i]) /
(24 * t_c[i]))
temperature_reduction_when_heating_is_off_1_weekend_rest_of_dwelling = []
for i in range(12):
if hours_heating_is_off_1_weekend_rest_of_dwelling > t_c[i]:
temperature_reduction_when_heating_is_off_1_weekend_rest_of_dwelling.append((temperature_during_heating_rest_of_dwelling[i] -
internal_temperature_without_heating_rest_of_dwelling[i]) *
(hours_heating_is_off_1_weekend_rest_of_dwelling - 0.5 * t_c[i]) / 24)
else:
temperature_reduction_when_heating_is_off_1_weekend_rest_of_dwelling.append(0.5 * hours_heating_is_off_1_weekend_rest_of_dwelling**2 *
(temperature_during_heating_rest_of_dwelling[i] -
internal_temperature_without_heating_rest_of_dwelling[i]) /
(24 * t_c[i]))
temperature_reduction_when_heating_is_off_2_weekend_rest_of_dwelling = []
for i in range(12):
if hours_heating_is_off_2_weekend_rest_of_dwelling > t_c[i]:
temperature_reduction_when_heating_is_off_2_weekend_rest_of_dwelling.append((temperature_during_heating_rest_of_dwelling[i] -
internal_temperature_without_heating_rest_of_dwelling[i]) *
(hours_heating_is_off_2_weekend_rest_of_dwelling - 0.5 * t_c[i]) / 24)
else:
temperature_reduction_when_heating_is_off_2_weekend_rest_of_dwelling.append(0.5 * hours_heating_is_off_2_weekend_rest_of_dwelling**2 *
(temperature_during_heating_rest_of_dwelling[i] -
internal_temperature_without_heating_rest_of_dwelling[i]) /
(24 * t_c[i]))
return dict(t_c=t_c,
internal_temperature_without_heating_living_room=internal_temperature_without_heating_living_room,
internal_temperature_without_heating_rest_of_dwelling=internal_temperature_without_heating_rest_of_dwelling,
temperature_reduction_when_heating_is_off_1_weekday_living_room=temperature_reduction_when_heating_is_off_1_weekday_living_room,
temperature_reduction_when_heating_is_off_2_weekday_living_room=temperature_reduction_when_heating_is_off_2_weekday_living_room,
temperature_reduction_when_heating_is_off_1_weekend_living_room=temperature_reduction_when_heating_is_off_1_weekend_living_room,
temperature_reduction_when_heating_is_off_2_weekend_living_room=temperature_reduction_when_heating_is_off_2_weekend_living_room,
temperature_reduction_when_heating_is_off_1_weekday_rest_of_dwelling=temperature_reduction_when_heating_is_off_1_weekday_rest_of_dwelling,
temperature_reduction_when_heating_is_off_2_weekday_rest_of_dwelling=temperature_reduction_when_heating_is_off_2_weekday_rest_of_dwelling,
temperature_reduction_when_heating_is_off_1_weekend_rest_of_dwelling=temperature_reduction_when_heating_is_off_1_weekend_rest_of_dwelling,
temperature_reduction_when_heating_is_off_2_weekend_rest_of_dwelling=temperature_reduction_when_heating_is_off_2_weekend_rest_of_dwelling
)
| 63.339416 | 154 | 0.591587 | 1,838 | 17,355 | 4.878128 | 0.041349 | 0.105398 | 0.140531 | 0.197078 | 0.95706 | 0.926835 | 0.888244 | 0.855008 | 0.813629 | 0.735333 | 0 | 0.019549 | 0.363354 | 17,355 | 273 | 155 | 63.571429 | 0.791927 | 0.210602 | 0 | 0.419118 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.007353 | false | 0 | 0 | 0 | 0.014706 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 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 | 7 |
6a234e4aafe60959db17236431b6bb32d54fe3ed | 32,338 | py | Python | lib/python2.7/site-packages/samba/tests/posixacl.py | abankalarm/pth-toolkit | bbd6bb0c7ba9c43c557c87e22fb2a72b20560f9f | [
"BSD-2-Clause"
] | 480 | 2015-02-03T11:59:43.000Z | 2022-03-21T13:23:29.000Z | lib/python2.7/site-packages/samba/tests/posixacl.py | brianwrf/pth-toolkit | 3641cdc76c0f52275315c9b18bf08b22521bd4d7 | [
"BSD-2-Clause"
] | 6 | 2015-02-03T14:06:12.000Z | 2021-05-11T12:07:02.000Z | lib/python2.7/site-packages/samba/tests/posixacl.py | brianwrf/pth-toolkit | 3641cdc76c0f52275315c9b18bf08b22521bd4d7 | [
"BSD-2-Clause"
] | 137 | 2015-02-05T13:31:57.000Z | 2022-02-23T09:44:18.000Z | # Unix SMB/CIFS implementation. Tests for NT and posix ACL manipulation
# Copyright (C) Matthieu Patou <mat@matws.net> 2009-2010
# Copyright (C) Andrew Bartlett 2012
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
"""Tests for the Samba3 NT -> posix ACL layer"""
from samba.ntacls import setntacl, getntacl, checkset_backend
from samba.dcerpc import xattr, security, smb_acl, idmap
from samba.param import LoadParm
from samba.tests import TestCaseInTempDir
from samba import provision
import random
import os
from samba.samba3 import smbd, passdb
from samba.samba3 import param as s3param
# To print a posix ACL use:
# for entry in posix_acl.acl:
# print "a_type: %d" % entry.a_type
# print "a_perm: %o" % entry.a_perm
# print "uid: %d" % entry.uid
# print "gid: %d" % entry.gid
class PosixAclMappingTests(TestCaseInTempDir):
def test_setntacl(self):
acl = "O:S-1-5-21-2212615479-2695158682-2101375467-512G:S-1-5-21-2212615479-2695158682-2101375467-513D:(A;OICI;0x001f01ff;;;S-1-5-21-2212615479-2695158682-2101375467-512)"
setntacl(self.lp, self.tempf, acl, "S-1-5-21-2212615479-2695158682-2101375467", use_ntvfs=False)
def test_setntacl_smbd_getntacl(self):
acl = "O:S-1-5-21-2212615479-2695158682-2101375467-512G:S-1-5-21-2212615479-2695158682-2101375467-513D:(A;OICI;0x001f01ff;;;S-1-5-21-2212615479-2695158682-2101375467-512)"
setntacl(self.lp, self.tempf, acl, "S-1-5-21-2212615479-2695158682-2101375467", use_ntvfs=True)
facl = getntacl(self.lp, self.tempf, direct_db_access=True)
anysid = security.dom_sid(security.SID_NT_SELF)
self.assertEquals(facl.as_sddl(anysid),acl)
def test_setntacl_smbd_setposixacl_getntacl(self):
acl = "O:S-1-5-21-2212615479-2695158682-2101375467-512G:S-1-5-21-2212615479-2695158682-2101375467-513D:(A;OICI;0x001f01ff;;;S-1-5-21-2212615479-2695158682-2101375467-512)"
setntacl(self.lp, self.tempf, acl, "S-1-5-21-2212615479-2695158682-2101375467", use_ntvfs=True)
# This will invalidate the ACL, as we have a hook!
smbd.set_simple_acl(self.tempf, 0640)
# However, this only asks the xattr
try:
facl = getntacl(self.lp, self.tempf, direct_db_access=True)
self.assertTrue(False)
except TypeError:
pass
def test_setntacl_invalidate_getntacl(self):
acl = "O:S-1-5-21-2212615479-2695158682-2101375467-512G:S-1-5-21-2212615479-2695158682-2101375467-513D:(A;OICI;0x001f01ff;;;S-1-5-21-2212615479-2695158682-2101375467-512)"
setntacl(self.lp, self.tempf, acl, "S-1-5-21-2212615479-2695158682-2101375467", use_ntvfs=True)
# This should invalidate the ACL, as we include the posix ACL in the hash
(backend_obj, dbname) = checkset_backend(self.lp, None, None)
backend_obj.wrap_setxattr(dbname,
self.tempf, "system.fake_access_acl", "")
#however, as this is direct DB access, we do not notice it
facl = getntacl(self.lp, self.tempf, direct_db_access=True)
anysid = security.dom_sid(security.SID_NT_SELF)
self.assertEquals(acl, facl.as_sddl(anysid))
def test_setntacl_invalidate_getntacl_smbd(self):
acl = "O:S-1-5-21-2212615479-2695158682-2101375467-512G:S-1-5-21-2212615479-2695158682-2101375467-513D:(A;OICI;0x001f01ff;;;S-1-5-21-2212615479-2695158682-2101375467-512)"
setntacl(self.lp, self.tempf, acl, "S-1-5-21-2212615479-2695158682-2101375467", use_ntvfs=False)
# This should invalidate the ACL, as we include the posix ACL in the hash
(backend_obj, dbname) = checkset_backend(self.lp, None, None)
backend_obj.wrap_setxattr(dbname,
self.tempf, "system.fake_access_acl", "")
#the hash would break, and we return an ACL based only on the mode, except we set the ACL using the 'ntvfs' mode that doesn't include a hash
facl = getntacl(self.lp, self.tempf)
anysid = security.dom_sid(security.SID_NT_SELF)
self.assertEquals(acl, facl.as_sddl(anysid))
def test_setntacl_smbd_invalidate_getntacl_smbd(self):
acl = "O:S-1-5-21-2212615479-2695158682-2101375467-512G:S-1-5-21-2212615479-2695158682-2101375467-513D:(A;OICI;0x001f01ff;;;S-1-5-21-2212615479-2695158682-2101375467-512)"
simple_acl_from_posix = "O:S-1-5-21-2212615479-2695158682-2101375467-512G:S-1-5-21-2212615479-2695158682-2101375467-513D:(A;;0x001f01ff;;;S-1-5-21-2212615479-2695158682-2101375467-512)(A;;0x001200a9;;;S-1-5-21-2212615479-2695158682-2101375467-513)(A;;;;;WD)"
os.chmod(self.tempf, 0750)
setntacl(self.lp, self.tempf, acl, "S-1-5-21-2212615479-2695158682-2101375467", use_ntvfs=False)
# This should invalidate the ACL, as we include the posix ACL in the hash
(backend_obj, dbname) = checkset_backend(self.lp, None, None)
backend_obj.wrap_setxattr(dbname,
self.tempf, "system.fake_access_acl", "")
#the hash will break, and we return an ACL based only on the mode
facl = getntacl(self.lp, self.tempf, direct_db_access=False)
anysid = security.dom_sid(security.SID_NT_SELF)
self.assertEquals(simple_acl_from_posix, facl.as_sddl(anysid))
def test_setntacl_getntacl_smbd(self):
acl = "O:S-1-5-21-2212615479-2695158682-2101375467-512G:S-1-5-21-2212615479-2695158682-2101375467-513D:(A;OICI;0x001f01ff;;;S-1-5-21-2212615479-2695158682-2101375467-512)"
setntacl(self.lp, self.tempf, acl, "S-1-5-21-2212615479-2695158682-2101375467", use_ntvfs=True)
facl = getntacl(self.lp, self.tempf, direct_db_access=False)
anysid = security.dom_sid(security.SID_NT_SELF)
self.assertEquals(facl.as_sddl(anysid),acl)
def test_setntacl_smbd_getntacl_smbd(self):
acl = "O:S-1-5-21-2212615479-2695158682-2101375467-512G:S-1-5-21-2212615479-2695158682-2101375467-513D:(A;OICI;0x001f01ff;;;S-1-5-21-2212615479-2695158682-2101375467-512)"
setntacl(self.lp, self.tempf, acl, "S-1-5-21-2212615479-2695158682-2101375467", use_ntvfs=False)
facl = getntacl(self.lp, self.tempf, direct_db_access=False)
anysid = security.dom_sid(security.SID_NT_SELF)
self.assertEquals(facl.as_sddl(anysid),acl)
def test_setntacl_smbd_setposixacl_getntacl_smbd(self):
acl = "O:S-1-5-21-2212615479-2695158682-2101375467-512G:S-1-5-21-2212615479-2695158682-2101375467-513D:(A;OICI;0x001f01ff;;;S-1-5-21-2212615479-2695158682-2101375467-512)"
simple_acl_from_posix = "O:S-1-5-21-2212615479-2695158682-2101375467-512G:S-1-5-21-2212615479-2695158682-2101375467-513D:(A;;0x001f019f;;;S-1-5-21-2212615479-2695158682-2101375467-512)(A;;0x00120089;;;S-1-5-21-2212615479-2695158682-2101375467-513)(A;;;;;WD)"
setntacl(self.lp, self.tempf, acl, "S-1-5-21-2212615479-2695158682-2101375467", use_ntvfs=False)
# This invalidates the hash of the NT acl just set because there is a hook in the posix ACL set code
smbd.set_simple_acl(self.tempf, 0640)
facl = getntacl(self.lp, self.tempf, direct_db_access=False)
anysid = security.dom_sid(security.SID_NT_SELF)
self.assertEquals(simple_acl_from_posix, facl.as_sddl(anysid))
def test_setntacl_smbd_setposixacl_group_getntacl_smbd(self):
acl = "O:S-1-5-21-2212615479-2695158682-2101375467-512G:S-1-5-21-2212615479-2695158682-2101375467-513D:(A;OICI;0x001f01ff;;;S-1-5-21-2212615479-2695158682-2101375467-512)"
BA_sid = security.dom_sid(security.SID_BUILTIN_ADMINISTRATORS)
simple_acl_from_posix = "O:S-1-5-21-2212615479-2695158682-2101375467-512G:S-1-5-21-2212615479-2695158682-2101375467-513D:(A;;0x001f019f;;;S-1-5-21-2212615479-2695158682-2101375467-512)(A;;0x00120089;;;BA)(A;;0x00120089;;;S-1-5-21-2212615479-2695158682-2101375467-513)(A;;;;;WD)"
setntacl(self.lp, self.tempf, acl, "S-1-5-21-2212615479-2695158682-2101375467", use_ntvfs=False)
# This invalidates the hash of the NT acl just set because there is a hook in the posix ACL set code
s4_passdb = passdb.PDB(self.lp.get("passdb backend"))
(BA_gid,BA_type) = s4_passdb.sid_to_id(BA_sid)
smbd.set_simple_acl(self.tempf, 0640, BA_gid)
# This should re-calculate an ACL based on the posix details
facl = getntacl(self.lp,self.tempf, direct_db_access=False)
anysid = security.dom_sid(security.SID_NT_SELF)
self.assertEquals(simple_acl_from_posix, facl.as_sddl(anysid))
def test_setntacl_smbd_getntacl_smbd_gpo(self):
acl = "O:DAG:DUD:P(A;OICI;0x001f01ff;;;DA)(A;OICI;0x001f01ff;;;EA)(A;OICIIO;0x001f01ff;;;CO)(A;OICI;0x001f01ff;;;DA)(A;OICI;0x001f01ff;;;SY)(A;OICI;0x001200a9;;;AU)(A;OICI;0x001200a9;;;ED)S:AI(OU;CIIDSA;WP;f30e3bbe-9ff0-11d1-b603-0000f80367c1;bf967aa5-0de6-11d0-a285-00aa003049e2;WD)(OU;CIIDSA;WP;f30e3bbf-9ff0-11d1-b603-0000f80367c1;bf967aa5-0de6-11d0-a285-00aa003049e2;WD)"
setntacl(self.lp, self.tempf, acl, "S-1-5-21-2212615479-2695158682-2101375467", use_ntvfs=False)
facl = getntacl(self.lp, self.tempf, direct_db_access=False)
domsid = security.dom_sid("S-1-5-21-2212615479-2695158682-2101375467")
self.assertEquals(facl.as_sddl(domsid),acl)
def test_setntacl_getposixacl(self):
acl = "O:S-1-5-21-2212615479-2695158682-2101375467-512G:S-1-5-21-2212615479-2695158682-2101375467-513D:(A;OICI;0x001f01ff;;;S-1-5-21-2212615479-2695158682-2101375467-512)"
setntacl(self.lp, self.tempf, acl, "S-1-5-21-2212615479-2695158682-2101375467", use_ntvfs=False)
facl = getntacl(self.lp, self.tempf)
anysid = security.dom_sid(security.SID_NT_SELF)
self.assertEquals(facl.as_sddl(anysid),acl)
posix_acl = smbd.get_sys_acl(self.tempf, smb_acl.SMB_ACL_TYPE_ACCESS)
def test_setposixacl_getposixacl(self):
smbd.set_simple_acl(self.tempf, 0640)
posix_acl = smbd.get_sys_acl(self.tempf, smb_acl.SMB_ACL_TYPE_ACCESS)
self.assertEquals(posix_acl.count, 4)
self.assertEquals(posix_acl.acl[0].a_type, smb_acl.SMB_ACL_USER_OBJ)
self.assertEquals(posix_acl.acl[0].a_perm, 6)
self.assertEquals(posix_acl.acl[1].a_type, smb_acl.SMB_ACL_GROUP_OBJ)
self.assertEquals(posix_acl.acl[1].a_perm, 4)
self.assertEquals(posix_acl.acl[2].a_type, smb_acl.SMB_ACL_OTHER)
self.assertEquals(posix_acl.acl[2].a_perm, 0)
self.assertEquals(posix_acl.acl[3].a_type, smb_acl.SMB_ACL_MASK)
self.assertEquals(posix_acl.acl[3].a_perm, 6)
def test_setposixacl_getntacl(self):
acl = ""
smbd.set_simple_acl(self.tempf, 0750)
try:
facl = getntacl(self.lp, self.tempf)
self.assertTrue(False)
except TypeError:
# We don't expect the xattr to be filled in in this case
pass
def test_setposixacl_getntacl_smbd(self):
s4_passdb = passdb.PDB(self.lp.get("passdb backend"))
group_SID = s4_passdb.gid_to_sid(os.stat(self.tempf).st_gid)
user_SID = s4_passdb.uid_to_sid(os.stat(self.tempf).st_uid)
smbd.set_simple_acl(self.tempf, 0640)
facl = getntacl(self.lp, self.tempf, direct_db_access=False)
acl = "O:%sG:%sD:(A;;0x001f019f;;;%s)(A;;0x00120089;;;%s)(A;;;;;WD)" % (user_SID, group_SID, user_SID, group_SID)
anysid = security.dom_sid(security.SID_NT_SELF)
self.assertEquals(acl, facl.as_sddl(anysid))
def test_setposixacl_dir_getntacl_smbd(self):
s4_passdb = passdb.PDB(self.lp.get("passdb backend"))
user_SID = s4_passdb.uid_to_sid(os.stat(self.tempdir).st_uid)
BA_sid = security.dom_sid(security.SID_BUILTIN_ADMINISTRATORS)
s4_passdb = passdb.PDB(self.lp.get("passdb backend"))
(BA_id,BA_type) = s4_passdb.sid_to_id(BA_sid)
self.assertEquals(BA_type, idmap.ID_TYPE_BOTH)
SO_sid = security.dom_sid(security.SID_BUILTIN_SERVER_OPERATORS)
(SO_id,SO_type) = s4_passdb.sid_to_id(SO_sid)
self.assertEquals(SO_type, idmap.ID_TYPE_BOTH)
smbd.chown(self.tempdir, BA_id, SO_id)
smbd.set_simple_acl(self.tempdir, 0750)
facl = getntacl(self.lp, self.tempdir, direct_db_access=False)
acl = "O:BAG:SOD:(A;;0x001f01ff;;;BA)(A;;0x001200a9;;;SO)(A;;;;;WD)(A;OICIIO;0x001f01ff;;;CO)(A;OICIIO;0x001200a9;;;CG)(A;OICIIO;0x001200a9;;;WD)"
anysid = security.dom_sid(security.SID_NT_SELF)
self.assertEquals(acl, facl.as_sddl(anysid))
def test_setposixacl_group_getntacl_smbd(self):
BA_sid = security.dom_sid(security.SID_BUILTIN_ADMINISTRATORS)
s4_passdb = passdb.PDB(self.lp.get("passdb backend"))
(BA_gid,BA_type) = s4_passdb.sid_to_id(BA_sid)
group_SID = s4_passdb.gid_to_sid(os.stat(self.tempf).st_gid)
user_SID = s4_passdb.uid_to_sid(os.stat(self.tempf).st_uid)
self.assertEquals(BA_type, idmap.ID_TYPE_BOTH)
smbd.set_simple_acl(self.tempf, 0640, BA_gid)
facl = getntacl(self.lp, self.tempf, direct_db_access=False)
domsid = passdb.get_global_sam_sid()
acl = "O:%sG:%sD:(A;;0x001f019f;;;%s)(A;;0x00120089;;;BA)(A;;0x00120089;;;%s)(A;;;;;WD)" % (user_SID, group_SID, user_SID, group_SID)
anysid = security.dom_sid(security.SID_NT_SELF)
self.assertEquals(acl, facl.as_sddl(anysid))
def test_setposixacl_getposixacl(self):
smbd.set_simple_acl(self.tempf, 0640)
posix_acl = smbd.get_sys_acl(self.tempf, smb_acl.SMB_ACL_TYPE_ACCESS)
self.assertEquals(posix_acl.count, 4)
self.assertEquals(posix_acl.acl[0].a_type, smb_acl.SMB_ACL_USER_OBJ)
self.assertEquals(posix_acl.acl[0].a_perm, 6)
self.assertEquals(posix_acl.acl[1].a_type, smb_acl.SMB_ACL_GROUP_OBJ)
self.assertEquals(posix_acl.acl[1].a_perm, 4)
self.assertEquals(posix_acl.acl[2].a_type, smb_acl.SMB_ACL_OTHER)
self.assertEquals(posix_acl.acl[2].a_perm, 0)
self.assertEquals(posix_acl.acl[3].a_type, smb_acl.SMB_ACL_MASK)
self.assertEquals(posix_acl.acl[3].a_perm, 7)
def test_setposixacl_dir_getposixacl(self):
smbd.set_simple_acl(self.tempdir, 0750)
posix_acl = smbd.get_sys_acl(self.tempdir, smb_acl.SMB_ACL_TYPE_ACCESS)
self.assertEquals(posix_acl.count, 4)
self.assertEquals(posix_acl.acl[0].a_type, smb_acl.SMB_ACL_USER_OBJ)
self.assertEquals(posix_acl.acl[0].a_perm, 7)
self.assertEquals(posix_acl.acl[1].a_type, smb_acl.SMB_ACL_GROUP_OBJ)
self.assertEquals(posix_acl.acl[1].a_perm, 5)
self.assertEquals(posix_acl.acl[2].a_type, smb_acl.SMB_ACL_OTHER)
self.assertEquals(posix_acl.acl[2].a_perm, 0)
self.assertEquals(posix_acl.acl[3].a_type, smb_acl.SMB_ACL_MASK)
self.assertEquals(posix_acl.acl[3].a_perm, 7)
def test_setposixacl_group_getposixacl(self):
BA_sid = security.dom_sid(security.SID_BUILTIN_ADMINISTRATORS)
s4_passdb = passdb.PDB(self.lp.get("passdb backend"))
(BA_gid,BA_type) = s4_passdb.sid_to_id(BA_sid)
self.assertEquals(BA_type, idmap.ID_TYPE_BOTH)
smbd.set_simple_acl(self.tempf, 0670, BA_gid)
posix_acl = smbd.get_sys_acl(self.tempf, smb_acl.SMB_ACL_TYPE_ACCESS)
self.assertEquals(posix_acl.count, 5)
self.assertEquals(posix_acl.acl[0].a_type, smb_acl.SMB_ACL_USER_OBJ)
self.assertEquals(posix_acl.acl[0].a_perm, 6)
self.assertEquals(posix_acl.acl[1].a_type, smb_acl.SMB_ACL_GROUP_OBJ)
self.assertEquals(posix_acl.acl[1].a_perm, 7)
self.assertEquals(posix_acl.acl[2].a_type, smb_acl.SMB_ACL_OTHER)
self.assertEquals(posix_acl.acl[2].a_perm, 0)
self.assertEquals(posix_acl.acl[3].a_type, smb_acl.SMB_ACL_GROUP)
self.assertEquals(posix_acl.acl[3].a_perm, 7)
self.assertEquals(posix_acl.acl[3].info.gid, BA_gid)
self.assertEquals(posix_acl.acl[4].a_type, smb_acl.SMB_ACL_MASK)
self.assertEquals(posix_acl.acl[4].a_perm, 7)
def test_setntacl_sysvol_check_getposixacl(self):
acl = provision.SYSVOL_ACL
domsid = passdb.get_global_sam_sid()
setntacl(self.lp, self.tempf,acl,str(domsid), use_ntvfs=False)
facl = getntacl(self.lp, self.tempf)
self.assertEquals(facl.as_sddl(domsid),acl)
posix_acl = smbd.get_sys_acl(self.tempf, smb_acl.SMB_ACL_TYPE_ACCESS)
LA_sid = security.dom_sid(str(domsid)+"-"+str(security.DOMAIN_RID_ADMINISTRATOR))
BA_sid = security.dom_sid(security.SID_BUILTIN_ADMINISTRATORS)
SO_sid = security.dom_sid(security.SID_BUILTIN_SERVER_OPERATORS)
SY_sid = security.dom_sid(security.SID_NT_SYSTEM)
AU_sid = security.dom_sid(security.SID_NT_AUTHENTICATED_USERS)
s4_passdb = passdb.PDB(self.lp.get("passdb backend"))
# These assertions correct for current plugin_s4_dc selftest
# configuration. When other environments have a broad range of
# groups mapped via passdb, we can relax some of these checks
(LA_uid,LA_type) = s4_passdb.sid_to_id(LA_sid)
self.assertEquals(LA_type, idmap.ID_TYPE_UID)
(BA_gid,BA_type) = s4_passdb.sid_to_id(BA_sid)
self.assertEquals(BA_type, idmap.ID_TYPE_BOTH)
(SO_gid,SO_type) = s4_passdb.sid_to_id(SO_sid)
self.assertEquals(SO_type, idmap.ID_TYPE_BOTH)
(SY_gid,SY_type) = s4_passdb.sid_to_id(SY_sid)
self.assertEquals(SO_type, idmap.ID_TYPE_BOTH)
(AU_gid,AU_type) = s4_passdb.sid_to_id(AU_sid)
self.assertEquals(AU_type, idmap.ID_TYPE_BOTH)
self.assertEquals(posix_acl.count, 9)
self.assertEquals(posix_acl.acl[0].a_type, smb_acl.SMB_ACL_GROUP)
self.assertEquals(posix_acl.acl[0].a_perm, 7)
self.assertEquals(posix_acl.acl[0].info.gid, BA_gid)
self.assertEquals(posix_acl.acl[1].a_type, smb_acl.SMB_ACL_USER)
self.assertEquals(posix_acl.acl[1].a_perm, 6)
self.assertEquals(posix_acl.acl[1].info.uid, LA_uid)
self.assertEquals(posix_acl.acl[2].a_type, smb_acl.SMB_ACL_OTHER)
self.assertEquals(posix_acl.acl[2].a_perm, 0)
self.assertEquals(posix_acl.acl[3].a_type, smb_acl.SMB_ACL_USER_OBJ)
self.assertEquals(posix_acl.acl[3].a_perm, 6)
self.assertEquals(posix_acl.acl[4].a_type, smb_acl.SMB_ACL_GROUP_OBJ)
self.assertEquals(posix_acl.acl[4].a_perm, 7)
self.assertEquals(posix_acl.acl[5].a_type, smb_acl.SMB_ACL_GROUP)
self.assertEquals(posix_acl.acl[5].a_perm, 5)
self.assertEquals(posix_acl.acl[5].info.gid, SO_gid)
self.assertEquals(posix_acl.acl[6].a_type, smb_acl.SMB_ACL_GROUP)
self.assertEquals(posix_acl.acl[6].a_perm, 7)
self.assertEquals(posix_acl.acl[6].info.gid, SY_gid)
self.assertEquals(posix_acl.acl[7].a_type, smb_acl.SMB_ACL_GROUP)
self.assertEquals(posix_acl.acl[7].a_perm, 5)
self.assertEquals(posix_acl.acl[7].info.gid, AU_gid)
self.assertEquals(posix_acl.acl[8].a_type, smb_acl.SMB_ACL_MASK)
self.assertEquals(posix_acl.acl[8].a_perm, 7)
# check that it matches:
# user::rwx
# user:root:rwx (selftest user actually)
# group::rwx
# group:Local Admins:rwx
# group:3000000:r-x
# group:3000001:rwx
# group:3000002:r-x
# mask::rwx
# other::---
#
# This is in this order in the NDR smb_acl (not re-orderded for display)
# a_type: GROUP
# a_perm: 7
# uid: -1
# gid: 10
# a_type: USER
# a_perm: 6
# uid: 0 (selftest user actually)
# gid: -1
# a_type: OTHER
# a_perm: 0
# uid: -1
# gid: -1
# a_type: USER_OBJ
# a_perm: 6
# uid: -1
# gid: -1
# a_type: GROUP_OBJ
# a_perm: 7
# uid: -1
# gid: -1
# a_type: GROUP
# a_perm: 5
# uid: -1
# gid: 3000020
# a_type: GROUP
# a_perm: 7
# uid: -1
# gid: 3000000
# a_type: GROUP
# a_perm: 5
# uid: -1
# gid: 3000001
# a_type: MASK
# a_perm: 7
# uid: -1
# gid: -1
#
def test_setntacl_sysvol_dir_check_getposixacl(self):
acl = provision.SYSVOL_ACL
domsid = passdb.get_global_sam_sid()
setntacl(self.lp, self.tempdir,acl,str(domsid), use_ntvfs=False)
facl = getntacl(self.lp, self.tempdir)
self.assertEquals(facl.as_sddl(domsid),acl)
posix_acl = smbd.get_sys_acl(self.tempdir, smb_acl.SMB_ACL_TYPE_ACCESS)
LA_sid = security.dom_sid(str(domsid)+"-"+str(security.DOMAIN_RID_ADMINISTRATOR))
BA_sid = security.dom_sid(security.SID_BUILTIN_ADMINISTRATORS)
SO_sid = security.dom_sid(security.SID_BUILTIN_SERVER_OPERATORS)
SY_sid = security.dom_sid(security.SID_NT_SYSTEM)
AU_sid = security.dom_sid(security.SID_NT_AUTHENTICATED_USERS)
s4_passdb = passdb.PDB(self.lp.get("passdb backend"))
# These assertions correct for current plugin_s4_dc selftest
# configuration. When other environments have a broad range of
# groups mapped via passdb, we can relax some of these checks
(LA_uid,LA_type) = s4_passdb.sid_to_id(LA_sid)
self.assertEquals(LA_type, idmap.ID_TYPE_UID)
(BA_gid,BA_type) = s4_passdb.sid_to_id(BA_sid)
self.assertEquals(BA_type, idmap.ID_TYPE_BOTH)
(SO_gid,SO_type) = s4_passdb.sid_to_id(SO_sid)
self.assertEquals(SO_type, idmap.ID_TYPE_BOTH)
(SY_gid,SY_type) = s4_passdb.sid_to_id(SY_sid)
self.assertEquals(SO_type, idmap.ID_TYPE_BOTH)
(AU_gid,AU_type) = s4_passdb.sid_to_id(AU_sid)
self.assertEquals(AU_type, idmap.ID_TYPE_BOTH)
self.assertEquals(posix_acl.count, 9)
self.assertEquals(posix_acl.acl[0].a_type, smb_acl.SMB_ACL_GROUP)
self.assertEquals(posix_acl.acl[0].a_perm, 7)
self.assertEquals(posix_acl.acl[0].info.gid, BA_gid)
self.assertEquals(posix_acl.acl[1].a_type, smb_acl.SMB_ACL_USER)
self.assertEquals(posix_acl.acl[1].a_perm, 7)
self.assertEquals(posix_acl.acl[1].info.uid, LA_uid)
self.assertEquals(posix_acl.acl[2].a_type, smb_acl.SMB_ACL_OTHER)
self.assertEquals(posix_acl.acl[2].a_perm, 0)
self.assertEquals(posix_acl.acl[3].a_type, smb_acl.SMB_ACL_USER_OBJ)
self.assertEquals(posix_acl.acl[3].a_perm, 7)
self.assertEquals(posix_acl.acl[4].a_type, smb_acl.SMB_ACL_GROUP_OBJ)
self.assertEquals(posix_acl.acl[4].a_perm, 7)
self.assertEquals(posix_acl.acl[5].a_type, smb_acl.SMB_ACL_GROUP)
self.assertEquals(posix_acl.acl[5].a_perm, 5)
self.assertEquals(posix_acl.acl[5].info.gid, SO_gid)
self.assertEquals(posix_acl.acl[6].a_type, smb_acl.SMB_ACL_GROUP)
self.assertEquals(posix_acl.acl[6].a_perm, 7)
self.assertEquals(posix_acl.acl[6].info.gid, SY_gid)
self.assertEquals(posix_acl.acl[7].a_type, smb_acl.SMB_ACL_GROUP)
self.assertEquals(posix_acl.acl[7].a_perm, 5)
self.assertEquals(posix_acl.acl[7].info.gid, AU_gid)
self.assertEquals(posix_acl.acl[8].a_type, smb_acl.SMB_ACL_MASK)
self.assertEquals(posix_acl.acl[8].a_perm, 7)
# check that it matches:
# user::rwx
# user:root:rwx (selftest user actually)
# group::rwx
# group:3000000:rwx
# group:3000001:r-x
# group:3000002:rwx
# group:3000003:r-x
# mask::rwx
# other::---
def test_setntacl_policies_dir_check_getposixacl(self):
acl = provision.POLICIES_ACL
domsid = passdb.get_global_sam_sid()
setntacl(self.lp, self.tempdir,acl,str(domsid), use_ntvfs=False)
facl = getntacl(self.lp, self.tempdir)
self.assertEquals(facl.as_sddl(domsid),acl)
posix_acl = smbd.get_sys_acl(self.tempdir, smb_acl.SMB_ACL_TYPE_ACCESS)
LA_sid = security.dom_sid(str(domsid)+"-"+str(security.DOMAIN_RID_ADMINISTRATOR))
BA_sid = security.dom_sid(security.SID_BUILTIN_ADMINISTRATORS)
SO_sid = security.dom_sid(security.SID_BUILTIN_SERVER_OPERATORS)
SY_sid = security.dom_sid(security.SID_NT_SYSTEM)
AU_sid = security.dom_sid(security.SID_NT_AUTHENTICATED_USERS)
PA_sid = security.dom_sid(str(domsid)+"-"+str(security.DOMAIN_RID_POLICY_ADMINS))
s4_passdb = passdb.PDB(self.lp.get("passdb backend"))
# These assertions correct for current plugin_s4_dc selftest
# configuration. When other environments have a broad range of
# groups mapped via passdb, we can relax some of these checks
(LA_uid,LA_type) = s4_passdb.sid_to_id(LA_sid)
self.assertEquals(LA_type, idmap.ID_TYPE_UID)
(BA_gid,BA_type) = s4_passdb.sid_to_id(BA_sid)
self.assertEquals(BA_type, idmap.ID_TYPE_BOTH)
(SO_gid,SO_type) = s4_passdb.sid_to_id(SO_sid)
self.assertEquals(SO_type, idmap.ID_TYPE_BOTH)
(SY_gid,SY_type) = s4_passdb.sid_to_id(SY_sid)
self.assertEquals(SO_type, idmap.ID_TYPE_BOTH)
(AU_gid,AU_type) = s4_passdb.sid_to_id(AU_sid)
self.assertEquals(AU_type, idmap.ID_TYPE_BOTH)
(PA_gid,PA_type) = s4_passdb.sid_to_id(PA_sid)
self.assertEquals(PA_type, idmap.ID_TYPE_BOTH)
self.assertEquals(posix_acl.count, 10)
self.assertEquals(posix_acl.acl[0].a_type, smb_acl.SMB_ACL_GROUP)
self.assertEquals(posix_acl.acl[0].a_perm, 7)
self.assertEquals(posix_acl.acl[0].info.gid, BA_gid)
self.assertEquals(posix_acl.acl[1].a_type, smb_acl.SMB_ACL_USER)
self.assertEquals(posix_acl.acl[1].a_perm, 7)
self.assertEquals(posix_acl.acl[1].info.uid, LA_uid)
self.assertEquals(posix_acl.acl[2].a_type, smb_acl.SMB_ACL_OTHER)
self.assertEquals(posix_acl.acl[2].a_perm, 0)
self.assertEquals(posix_acl.acl[3].a_type, smb_acl.SMB_ACL_USER_OBJ)
self.assertEquals(posix_acl.acl[3].a_perm, 7)
self.assertEquals(posix_acl.acl[4].a_type, smb_acl.SMB_ACL_GROUP_OBJ)
self.assertEquals(posix_acl.acl[4].a_perm, 7)
self.assertEquals(posix_acl.acl[5].a_type, smb_acl.SMB_ACL_GROUP)
self.assertEquals(posix_acl.acl[5].a_perm, 5)
self.assertEquals(posix_acl.acl[5].info.gid, SO_gid)
self.assertEquals(posix_acl.acl[6].a_type, smb_acl.SMB_ACL_GROUP)
self.assertEquals(posix_acl.acl[6].a_perm, 7)
self.assertEquals(posix_acl.acl[6].info.gid, SY_gid)
self.assertEquals(posix_acl.acl[7].a_type, smb_acl.SMB_ACL_GROUP)
self.assertEquals(posix_acl.acl[7].a_perm, 5)
self.assertEquals(posix_acl.acl[7].info.gid, AU_gid)
self.assertEquals(posix_acl.acl[8].a_type, smb_acl.SMB_ACL_GROUP)
self.assertEquals(posix_acl.acl[8].a_perm, 7)
self.assertEquals(posix_acl.acl[8].info.gid, PA_gid)
self.assertEquals(posix_acl.acl[9].a_type, smb_acl.SMB_ACL_MASK)
self.assertEquals(posix_acl.acl[9].a_perm, 7)
# check that it matches:
# user::rwx
# user:root:rwx (selftest user actually)
# group::rwx
# group:3000000:rwx
# group:3000001:r-x
# group:3000002:rwx
# group:3000003:r-x
# group:3000004:rwx
# mask::rwx
# other::---
def test_setntacl_policies_check_getposixacl(self):
acl = provision.POLICIES_ACL
domsid = passdb.get_global_sam_sid()
setntacl(self.lp, self.tempf, acl, str(domsid), use_ntvfs=False)
facl = getntacl(self.lp, self.tempf)
self.assertEquals(facl.as_sddl(domsid),acl)
posix_acl = smbd.get_sys_acl(self.tempf, smb_acl.SMB_ACL_TYPE_ACCESS)
LA_sid = security.dom_sid(str(domsid)+"-"+str(security.DOMAIN_RID_ADMINISTRATOR))
BA_sid = security.dom_sid(security.SID_BUILTIN_ADMINISTRATORS)
SO_sid = security.dom_sid(security.SID_BUILTIN_SERVER_OPERATORS)
SY_sid = security.dom_sid(security.SID_NT_SYSTEM)
AU_sid = security.dom_sid(security.SID_NT_AUTHENTICATED_USERS)
PA_sid = security.dom_sid(str(domsid)+"-"+str(security.DOMAIN_RID_POLICY_ADMINS))
s4_passdb = passdb.PDB(self.lp.get("passdb backend"))
# These assertions correct for current plugin_s4_dc selftest
# configuration. When other environments have a broad range of
# groups mapped via passdb, we can relax some of these checks
(LA_uid,LA_type) = s4_passdb.sid_to_id(LA_sid)
self.assertEquals(LA_type, idmap.ID_TYPE_UID)
(BA_gid,BA_type) = s4_passdb.sid_to_id(BA_sid)
self.assertEquals(BA_type, idmap.ID_TYPE_BOTH)
(SO_gid,SO_type) = s4_passdb.sid_to_id(SO_sid)
self.assertEquals(SO_type, idmap.ID_TYPE_BOTH)
(SY_gid,SY_type) = s4_passdb.sid_to_id(SY_sid)
self.assertEquals(SO_type, idmap.ID_TYPE_BOTH)
(AU_gid,AU_type) = s4_passdb.sid_to_id(AU_sid)
self.assertEquals(AU_type, idmap.ID_TYPE_BOTH)
(PA_gid,PA_type) = s4_passdb.sid_to_id(PA_sid)
self.assertEquals(PA_type, idmap.ID_TYPE_BOTH)
self.assertEquals(posix_acl.count, 10)
self.assertEquals(posix_acl.acl[0].a_type, smb_acl.SMB_ACL_GROUP)
self.assertEquals(posix_acl.acl[0].a_perm, 7)
self.assertEquals(posix_acl.acl[0].info.gid, BA_gid)
self.assertEquals(posix_acl.acl[1].a_type, smb_acl.SMB_ACL_USER)
self.assertEquals(posix_acl.acl[1].a_perm, 6)
self.assertEquals(posix_acl.acl[1].info.uid, LA_uid)
self.assertEquals(posix_acl.acl[2].a_type, smb_acl.SMB_ACL_OTHER)
self.assertEquals(posix_acl.acl[2].a_perm, 0)
self.assertEquals(posix_acl.acl[3].a_type, smb_acl.SMB_ACL_USER_OBJ)
self.assertEquals(posix_acl.acl[3].a_perm, 6)
self.assertEquals(posix_acl.acl[4].a_type, smb_acl.SMB_ACL_GROUP_OBJ)
self.assertEquals(posix_acl.acl[4].a_perm, 7)
self.assertEquals(posix_acl.acl[5].a_type, smb_acl.SMB_ACL_GROUP)
self.assertEquals(posix_acl.acl[5].a_perm, 5)
self.assertEquals(posix_acl.acl[5].info.gid, SO_gid)
self.assertEquals(posix_acl.acl[6].a_type, smb_acl.SMB_ACL_GROUP)
self.assertEquals(posix_acl.acl[6].a_perm, 7)
self.assertEquals(posix_acl.acl[6].info.gid, SY_gid)
self.assertEquals(posix_acl.acl[7].a_type, smb_acl.SMB_ACL_GROUP)
self.assertEquals(posix_acl.acl[7].a_perm, 5)
self.assertEquals(posix_acl.acl[7].info.gid, AU_gid)
self.assertEquals(posix_acl.acl[8].a_type, smb_acl.SMB_ACL_GROUP)
self.assertEquals(posix_acl.acl[8].a_perm, 7)
self.assertEquals(posix_acl.acl[8].info.gid, PA_gid)
self.assertEquals(posix_acl.acl[9].a_type, smb_acl.SMB_ACL_MASK)
self.assertEquals(posix_acl.acl[9].a_perm, 7)
# check that it matches:
# user::rwx
# user:root:rwx (selftest user actually)
# group::rwx
# group:Local Admins:rwx
# group:3000000:r-x
# group:3000001:rwx
# group:3000002:r-x
# group:3000003:rwx
# mask::rwx
# other::---
#
# This is in this order in the NDR smb_acl (not re-orderded for display)
# a_type: GROUP
# a_perm: 7
# uid: -1
# gid: 10
# a_type: USER
# a_perm: 6
# uid: 0 (selftest user actually)
# gid: -1
# a_type: OTHER
# a_perm: 0
# uid: -1
# gid: -1
# a_type: USER_OBJ
# a_perm: 6
# uid: -1
# gid: -1
# a_type: GROUP_OBJ
# a_perm: 7
# uid: -1
# gid: -1
# a_type: GROUP
# a_perm: 5
# uid: -1
# gid: 3000020
# a_type: GROUP
# a_perm: 7
# uid: -1
# gid: 3000000
# a_type: GROUP
# a_perm: 5
# uid: -1
# gid: 3000001
# a_type: GROUP
# a_perm: 7
# uid: -1
# gid: 3000003
# a_type: MASK
# a_perm: 7
# uid: -1
# gid: -1
#
def setUp(self):
super(PosixAclMappingTests, self).setUp()
s3conf = s3param.get_context()
s3conf.load(self.get_loadparm().configfile)
s3conf.set("xattr_tdb:file", os.path.join(self.tempdir,"xattr.tdb"))
self.lp = s3conf
self.tempf = os.path.join(self.tempdir, "test")
open(self.tempf, 'w').write("empty")
def tearDown(self):
smbd.unlink(self.tempf)
os.unlink(os.path.join(self.tempdir,"xattr.tdb"))
super(PosixAclMappingTests, self).tearDown()
| 45.164804 | 383 | 0.702146 | 5,098 | 32,338 | 4.209886 | 0.065712 | 0.137173 | 0.137965 | 0.157674 | 0.909608 | 0.897773 | 0.889293 | 0.870422 | 0.870189 | 0.859379 | 0 | 0.110642 | 0.17215 | 32,338 | 715 | 384 | 45.227972 | 0.691046 | 0.134177 | 0 | 0.84058 | 0 | 0.043478 | 0.14187 | 0.135102 | 0 | 0 | 0.012599 | 0 | 0.449275 | 0 | null | null | 0.120773 | 0.021739 | null | null | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 10 |
6a46c7c0d09609bc945f64b6638656de4e76f9b1 | 9,495 | py | Python | tests/unit_tests/mountcontrol/test_dome.py | mworion/MountWizzard4 | 4e06b29ec2ef70be40e114b911b7bdf2f858a4b1 | [
"Apache-2.0"
] | 16 | 2020-01-11T22:32:26.000Z | 2022-03-31T15:18:14.000Z | tests/unit_tests/mountcontrol/test_dome.py | mworion/MountWizzard4 | 4e06b29ec2ef70be40e114b911b7bdf2f858a4b1 | [
"Apache-2.0"
] | 196 | 2020-01-16T13:56:01.000Z | 2022-03-29T02:06:51.000Z | tests/unit_tests/mountcontrol/test_dome.py | mworion/MountWizzard4 | 4e06b29ec2ef70be40e114b911b7bdf2f858a4b1 | [
"Apache-2.0"
] | 6 | 2019-12-01T19:39:33.000Z | 2021-05-27T13:14:20.000Z | ############################################################
# -*- coding: utf-8 -*-
#
# # # # # # #
# ## ## # ## # #
# # # # # # # # # # #
# # ## # ## ## ######
# # # # # # #
#
# Python-based Tool for interaction with the 10micron mounts
# GUI with PyQT5 for python
#
# written in python3, (c) 2019-2021 by mworion
# Licence APL2.0
#
###########################################################
# standard libraries
import unittest
import unittest.mock as mock
# external packages
from skyfield.api import Angle
# local imports
from mountcontrol.dome import Dome
class TestConfigData(unittest.TestCase):
def setUp(self):
pass
def test_property_1(self):
dome = Dome()
dome.shutterState = '1'
dome.flapState = '1'
dome.slew = '1'
dome.azimuth = '1800'
self.assertEqual(dome.shutterState, 1)
self.assertEqual(dome.flapState, 1)
self.assertEqual(dome.slew, True)
self.assertEqual(dome.azimuth, 180)
def test_property_2(self):
dome = Dome()
dome.shutterState = '-1'
dome.flapState = '-1'
dome.slew = '-1'
dome.azimuth = '5400'
self.assertEqual(dome.shutterState, None)
self.assertEqual(dome.flapState, None)
self.assertEqual(dome.slew, True)
self.assertEqual(dome.azimuth, 180)
def test_property_3(self):
dome = Dome()
dome.shutterState = '5'
dome.flapState = '5'
dome.slew = 'e'
dome.azimuth = 'e'
self.assertEqual(dome.shutterState, None)
self.assertEqual(dome.flapState, None)
self.assertEqual(dome.slew, None)
self.assertEqual(dome.azimuth, None)
def test_property_4(self):
dome = Dome()
dome.shutterState = 'e'
dome.flapState = 'e'
self.assertEqual(dome.shutterState, None)
self.assertEqual(dome.flapState, None)
def test_Firmware_parse_1(self):
dome = Dome()
response = ['0', '0', '0', '1800']
suc = dome.parse(response, 4)
self.assertEqual(True, suc)
self.assertEqual(dome.shutterState, 0)
self.assertEqual(dome.flapState, 0)
self.assertEqual(dome.slew, False)
self.assertEqual(dome.azimuth, 180)
def test_Firmware_parse_2(self):
dome = Dome()
response = ['1', '2', '1', '5400']
suc = dome.parse(response, 4)
self.assertEqual(True, suc)
self.assertEqual(dome.shutterState, 1)
self.assertEqual(dome.flapState, 2)
self.assertEqual(dome.slew, True)
self.assertEqual(dome.azimuth, 180)
def test_Firmware_parse_3(self):
dome = Dome()
response = ['1', '2', '1']
suc = dome.parse(response, 4)
self.assertEqual(False, suc)
def test_Firmware_parse_4(self):
dome = Dome()
response = ['e', 'e', 'e', 'e']
suc = dome.parse(response, 4)
self.assertEqual(True, suc)
self.assertEqual(dome.shutterState, 1)
self.assertEqual(dome.flapState, 2)
self.assertEqual(dome.slew, True)
self.assertEqual(dome.azimuth, 180)
def test_Firmware_parse_4(self):
dome = Dome()
response = ['5', '-1', '1', '5400']
suc = dome.parse(response, 4)
self.assertEqual(True, suc)
self.assertEqual(dome.shutterState, None)
self.assertEqual(dome.flapState, None)
self.assertEqual(dome.slew, True)
def test_Firmware_poll_1(self):
dome = Dome()
response = ['0', '0', '0', '1800']
with mock.patch('mountcontrol.dome.Connection') as mConn:
mConn.return_value.communicate.return_value = False, response, 4
suc = dome.poll()
self.assertEqual(False, suc)
def test_Firmware_poll_2(self):
dome = Dome()
response = ['0', '0', '0', '1800']
with mock.patch('mountcontrol.dome.Connection') as mConn:
mConn.return_value.communicate.return_value = True, response, 4
suc = dome.poll()
self.assertEqual(True, suc)
def test_openShutter_1(self):
dome = Dome()
response = ['1']
with mock.patch('mountcontrol.dome.Connection') as mConn:
mConn.return_value.communicate.return_value = False, response, 0
suc = dome.openShutter()
self.assertEqual(False, suc)
def test_openShutter_2(self):
dome = Dome()
response = ['0']
with mock.patch('mountcontrol.dome.Connection') as mConn:
mConn.return_value.communicate.return_value = True, response, 0
suc = dome.openShutter()
self.assertEqual(False, suc)
def test_openShutter_3(self):
dome = Dome()
response = ['1']
with mock.patch('mountcontrol.dome.Connection') as mConn:
mConn.return_value.communicate.return_value = True, response, 0
suc = dome.openShutter()
self.assertEqual(True, suc)
def test_closeShutter_1(self):
dome = Dome()
response = ['1']
with mock.patch('mountcontrol.dome.Connection') as mConn:
mConn.return_value.communicate.return_value = False, response, 0
suc = dome.closeShutter()
self.assertEqual(False, suc)
def test_closeShutter_2(self):
dome = Dome()
response = ['0']
with mock.patch('mountcontrol.dome.Connection') as mConn:
mConn.return_value.communicate.return_value = True, response, 0
suc = dome.closeShutter()
self.assertEqual(False, suc)
def test_closeShutter_3(self):
dome = Dome()
response = ['1']
with mock.patch('mountcontrol.dome.Connection') as mConn:
mConn.return_value.communicate.return_value = True, response, 0
suc = dome.closeShutter()
self.assertEqual(True, suc)
def test_openFlap_1(self):
dome = Dome()
response = ['1']
with mock.patch('mountcontrol.dome.Connection') as mConn:
mConn.return_value.communicate.return_value = False, response, 0
suc = dome.openFlap()
self.assertEqual(False, suc)
def test_openFlap_2(self):
dome = Dome()
response = ['0']
with mock.patch('mountcontrol.dome.Connection') as mConn:
mConn.return_value.communicate.return_value = True, response, 0
suc = dome.openFlap()
self.assertEqual(False, suc)
def test_openFlap_3(self):
dome = Dome()
response = ['1']
with mock.patch('mountcontrol.dome.Connection') as mConn:
mConn.return_value.communicate.return_value = True, response, 0
suc = dome.openFlap()
self.assertEqual(True, suc)
def test_closeFlap_1(self):
dome = Dome()
response = ['1']
with mock.patch('mountcontrol.dome.Connection') as mConn:
mConn.return_value.communicate.return_value = False, response, 0
suc = dome.closeFlap()
self.assertEqual(False, suc)
def test_closeFlap_2(self):
dome = Dome()
response = ['0']
with mock.patch('mountcontrol.dome.Connection') as mConn:
mConn.return_value.communicate.return_value = True, response, 0
suc = dome.closeFlap()
self.assertEqual(False, suc)
def test_closeFlap_3(self):
dome = Dome()
response = ['1']
with mock.patch('mountcontrol.dome.Connection') as mConn:
mConn.return_value.communicate.return_value = True, response, 0
suc = dome.closeFlap()
self.assertEqual(True, suc)
def test_slewDome_1(self):
dome = Dome()
suc = dome.slewDome()
self.assertEqual(False, suc)
def test_slewDome_2(self):
dome = Dome()
response = ['1']
with mock.patch('mountcontrol.dome.Connection') as mConn:
mConn.return_value.communicate.return_value = False, response, 1
suc = dome.slewDome(azimuth=Angle(degrees=100))
self.assertEqual(False, suc)
def test_slewDome_3(self):
dome = Dome()
response = ['0']
with mock.patch('mountcontrol.dome.Connection') as mConn:
mConn.return_value.communicate.return_value = True, response, 1
suc = dome.slewDome(azimuth=100)
self.assertEqual(False, suc)
def test_slewDome_4(self):
dome = Dome()
response = ['1']
with mock.patch('mountcontrol.dome.Connection') as mConn:
mConn.return_value.communicate.return_value = True, response, 1
suc = dome.slewDome(azimuth=100)
self.assertEqual(True, suc)
def test_enableInternalDomeControl_1(self):
dome = Dome()
response = ['1']
with mock.patch('mountcontrol.dome.Connection') as mConn:
mConn.return_value.communicate.return_value = False, response, 0
suc = dome.enableInternalDomeControl()
self.assertEqual(False, suc)
def test_enableInternalDomeControl_2(self):
dome = Dome()
response = ['0']
with mock.patch('mountcontrol.dome.Connection') as mConn:
mConn.return_value.communicate.return_value = True, response, 0
suc = dome.enableInternalDomeControl()
self.assertEqual(True, suc)
| 30.928339 | 76 | 0.585045 | 1,040 | 9,495 | 5.242308 | 0.082692 | 0.148569 | 0.06383 | 0.088041 | 0.872524 | 0.861886 | 0.838775 | 0.786684 | 0.774945 | 0.738261 | 0 | 0.025162 | 0.284255 | 9,495 | 306 | 77 | 31.029412 | 0.777075 | 0.032965 | 0 | 0.713636 | 0 | 0 | 0.068333 | 0.059111 | 0 | 0 | 0 | 0 | 0.245455 | 1 | 0.136364 | false | 0.004545 | 0.018182 | 0 | 0.159091 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 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 | 7 |
e00e0b3abf0d8c09e7061e61ae1f17d5aae9e7bb | 16,105 | py | Python | src/manipulation.py | chungpuonn/UTAR-mobile-manipulator | 56219141c3553484de140cb70eb9687033de7768 | [
"MIT"
] | 1 | 2022-02-20T03:06:14.000Z | 2022-02-20T03:06:14.000Z | src/manipulation.py | chungpuonn/UTAR-mobile-manipulator | 56219141c3553484de140cb70eb9687033de7768 | [
"MIT"
] | null | null | null | src/manipulation.py | chungpuonn/UTAR-mobile-manipulator | 56219141c3553484de140cb70eb9687033de7768 | [
"MIT"
] | null | null | null | #! /usr/bin/env python
import sys
import copy
import rospy
import moveit_commander
import moveit_msgs.msg
import geometry_msgs.msg
from std_msgs.msg import String
from std_msgs.msg import Header
from moveit_commander.conversions import pose_to_list
from moveit_msgs.msg import RobotState, Constraints, OrientationConstraint
from add_collision_object_v0 import add_coll_obj
from attach_obj import attach_dettach
import tf
moveit_commander.roscpp_initialize(sys.argv)
rospy.init_node('manipulation', anonymous=True)
robot = moveit_commander.RobotCommander()
scene = moveit_commander.PlanningSceneInterface()
group = moveit_commander.MoveGroupCommander("arm")
group_h = moveit_commander.MoveGroupCommander("hand")
group.set_planner_id("PRMkConfigDefault")
#"PRMkConfigDefault"
group.set_planning_time(10)
group_name = "arm"
move_group = moveit_commander.MoveGroupCommander(group_name)
eef_link = move_group.get_end_effector_link()
display_trajectory_publisher = rospy.Publisher('/move_group/display_planned_path', moveit_msgs.msg.DisplayTrajectory, queue_size=1)
co = add_coll_obj()
att_dett = attach_dettach()
# print "============ Press `Enter` to detect the potential obstacles that are not able to be detected by 2D laser scan..."
# raw_input()
# group.set_named_target("RIGHT")
# plan1 = group.plan()
# rospy.sleep(2)
# group.go(wait=True)
# rospy.sleep(2)
# print "============ ..."
# raw_input()
# group.set_named_target("LEFT")
# plan1 = group.plan()
# rospy.sleep(2)
# group.go(wait=True)
# rospy.sleep(2)
# print "============ ..."
# raw_input()
# group.set_named_target("HOME")
# plan1 = group.plan()
# rospy.sleep(2)
# group.go(wait=True)
# rospy.sleep(2)
#(Set Pre-defined Pose)
print "============ Press `Enter` to prepare sensing for manipulation..."
raw_input()
group.set_named_target("SENSE")
plan1 = group.plan()
rospy.sleep(3)
group.go(wait=True)
rospy.sleep(3)
#FK (Set Joint Value)
print "============ Press `Enter` to rotate depth camera around to perceive a full environment of station..."
raw_input()
group_variable_values = group.get_current_joint_values()
group.set_named_target("SENSE")
plan1 = group.plan()
rospy.sleep(2)
group.go(wait=True)
rospy.sleep(2)
group_variable_values = group.get_current_joint_values()
group_variable_values[0] = -1.4 #Rotate CW 90 degrees
group.set_joint_value_target(group_variable_values)
plan1 = group.plan()
rospy.sleep(2)
group.go(wait=True)
rospy.sleep(2)
#FK (Set Joint Value)
group_variable_values = group.get_current_joint_values()
group_variable_values[0] = 1.4 #Rotate CW 90 degrees
group.set_joint_value_target(group_variable_values)
plan1 = group.plan()
rospy.sleep(2)
group.go(wait=True)
rospy.sleep(2)
#FK (Set Joint Value)
group_variable_values = group.get_current_joint_values()
group_variable_values[0] = 0 #Back to 0 degrees
group.set_joint_value_target(group_variable_values)
plan1 = group.plan()
rospy.sleep(2)
group.go(wait=True)
rospy.sleep(2)
group.set_named_target("SENSE")
plan1 = group.plan()
rospy.sleep(2)
group.go(wait=True)
rospy.sleep(2)
# Orientation constraint
group.goal_c = Constraints()
header = Header()
header.frame_id = "link_1"
orientation_c = OrientationConstraint()
orientation_c.header = header
orientation_c.link_name = "eff_link"
orientation_c.orientation.w = 1.0
orientation_c.absolute_x_axis_tolerance = 0.05
orientation_c.absolute_y_axis_tolerance = 0.05
orientation_c.absolute_z_axis_tolerance = 0.05
orientation_c.weight = 1.0
group.goal_c.orientation_constraints.append(orientation_c)
# group.set_path_constraints(group.goal_c)
# group.set_path_constraints(None)
# robot.hand.pick("Box_0")
# Subscribe to tf frame pose
listener = tf.TransformListener()
rect_flag = 0
cyl_flag = 0
sphe_flag = 0
while(rect_flag == 0 ):
try:
rect_flag = listener.frameExists("object_28")
except (tf.LookupException, tf.ConnectivityException, tf.ExtrapolationException):
print('Cant get frame of wooden block, retrying...')
print('Found wooden block!')
co.add_coll_obj()
listener.waitForTransform("/base_footprint", "/object_28", rospy.Time(), rospy.Duration(4.0))
(trans, rot) = listener.lookupTransform('base_footprint', 'object_28', rospy.Time())
goal_coord = [trans[0], trans[1], trans[2]] # x, y, z
#FK (Set Joint Value) - Rotate CW 20 degrees
group_variable_values = group.get_current_joint_values()
group_variable_values[0] = -0.3491
# group_variable_values[1] = 0.8028
# group_variable_values[2] = 0.4014
# group_variable_values[3] = -1.2043
group.set_joint_value_target(group_variable_values)
plan1 = group.plan()
rospy.sleep(1)
group.go(wait=True)
rospy.sleep(1)
while(cyl_flag == 0 ):
try:
cyl_flag = listener.frameExists("object_29")
except (tf.LookupException, tf.ConnectivityException, tf.ExtrapolationException):
print('Cant get frame of cylindrical shaft, retrying...')
print('Found cylindrical shaft!')
co.add_coll_obj()
listener.waitForTransform("/base_footprint", "/object_29", rospy.Time(), rospy.Duration(4.0))
(trans_2, rot_2) = listener.lookupTransform('base_footprint', 'object_29', rospy.Time())
goal_coord_2 = [trans_2[0], trans_2[1], trans_2[2]] # x, y, z
if rect_flag == 1:
print "============ Press `Enter` for arm repositioning..."
raw_input()
#(Set Pre-defined Pose)
group.set_named_target("BACK")
plan1 = group.plan()
rospy.sleep(2)
group.go(wait=True)
rospy.sleep(2)
# IK (Set Position) - Update end-effector XYZ coordinates to target object
group.set_path_constraints(group.goal_c)
print "============ Press `Enter` to move end-effector to target object by IK..."
raw_input()
target_position = [trans[0]-0.15, trans[1], trans[2]+0.065]
group.set_position_target(target_position)
plan1 = group.plan()
rospy.sleep(10)
group.go(wait=True)
rospy.sleep(10)
#FK (Set Joint Value) - Close the gripper
print "============ Press `Enter` to close the gripper and pick object..."
raw_input()
group_h_variable_values = group_h.get_current_joint_values()
group_h_variable_values[0] -= 0.0174533 # approx -1.0 degrees
plan2 = group_h.plan(group_h_variable_values)
fail_count = 0
rospy.sleep(1)
while plan2.joint_trajectory.points: # True if trajectory contains points
print "Close by decrementing step angle of left finger..."
group_h.go(wait=True)
rospy.sleep(1)
if group_h_variable_values[0] > -0.3142: #Within joint limit
group_h_variable_values[0] -= 0.0174533 # approx -1.0 degrees
else:
break
plan2 = group_h.plan(group_h_variable_values)
print "Left finger in contact with object!"
group_h_variable_values = group_h.get_current_joint_values()
rospy.sleep(2)
group_h_variable_values[1] += 0.0174533 # approx +1.0 degrees
plan2 = group_h.plan(group_h_variable_values)
rospy.sleep(1)
while plan2.joint_trajectory.points: # True if trajectory contains points
print "Close by decrementing step angle of right finger..."
group_h.go(wait=True)
rospy.sleep(1)
if group_h_variable_values[1] +0.0174533 < 0.3142: #Within joint limit
group_h_variable_values[1] += 0.0174533 # approx +1.0 degreess
else:
break
plan2 = group_h.plan(group_h_variable_values)
print "Right finger in contact with object!"
rospy.sleep(2)
group_h_variable_values = group_h.get_current_joint_values()
#(Set Pre-defined Pose) - Place at right pocket
att_dett.attach_box()
group.set_path_constraints(None)
print "============ Press `Enter` to place the target object to right pocket..."
raw_input()
group.set_named_target("TOP")
plan1 = group.plan()
rospy.sleep(2)
group.go(wait=True)
rospy.sleep(2)
print "============ ..."
raw_input()
#FK (Set Joint Value)
group_variable_values = group.get_current_joint_values()
group_variable_values[0] = -1.45 #Rotate CW 90 degrees
group.set_joint_value_target(group_variable_values)
plan1 = group.plan()
group.go(wait=True)
print "============ ..."
raw_input()
group.set_named_target("RIGHT")
plan1 = group.plan()
rospy.sleep(2)
group.go(wait=True)
rospy.sleep(2)
#(Set Pre-defined Pose) - Open the gripper
att_dett.dettach_box()
print "============ Press `Enter` to open the gripper and release object..."
raw_input()
group_h.set_named_target("OPEN")
plan2 = group_h.plan()
rospy.sleep(2)
group_h.go(wait=True)
rospy.sleep(2)
if cyl_flag == 1:
# #(Set Pre-defined Pose)
print "============ Press `Enter` to prepare next sensing for manipulation..."
raw_input()
group.set_named_target("BACK")
plan1 = group.plan()
rospy.sleep(2)
group.go(wait=True)
rospy.sleep(2)
# IK (Set Position) - Update end-effector XYZ coordinates to target object
group.set_path_constraints(group.goal_c)
print "============ Press `Enter` to move end-effector to target object by IK..."
raw_input()
target_position = [trans_2[0]-0.147, trans_2[1]+ 0.05, trans_2[2]+0.062]
group.set_position_target(target_position)
plan1 = group.plan()
rospy.sleep(10)
group.go(wait=True)
rospy.sleep(10)
#FK (Set Joint Value) - Close the gripper
print "============ Press `Enter` to close the gripper and pick object..."
raw_input()
group_h_variable_values = group_h.get_current_joint_values()
group_h_variable_values[0] -= 0.0174533 # approx -1.0 degrees
plan2 = group_h.plan(group_h_variable_values)
rospy.sleep(1)
while plan2.joint_trajectory.points: # True if trajectory contains points
print "Close by decrementing step angle of left finger..."
group_h.go(wait=True)
rospy.sleep(1)
if group_h_variable_values[0] -0.0174533 > -0.3142: #Within joint limit
group_h_variable_values[0] -= 0.0174533 # approx -1.0 degrees
else:
break
plan2 = group_h.plan(group_h_variable_values)
print "Left finger in contact with object!"
group_h_variable_values = group_h.get_current_joint_values()
rospy.sleep(2)
group_h_variable_values[1] += 0.0174533 # approx +1.0 degrees
plan2 = group_h.plan(group_h_variable_values)
rospy.sleep(1)
while plan2.joint_trajectory.points: # True if trajectory contains points
print "Close by decrementing step angle of right finger..."
group_h.go(wait=True)
rospy.sleep(1)
if group_h_variable_values[1] +0.0174533 < 0.3142: #Within joint limit
group_h_variable_values[1] += 0.0174533 # approx +1.0 degreess
else:
break
plan2 = group_h.plan(group_h_variable_values)
print "Right finger in contact with object!"
group_h_variable_values = group_h.get_current_joint_values()
rospy.sleep(2)
att_dett.attach_cylin()
#(Set Pre-defined Pose)
group.set_path_constraints(None)
print "============ Press `Enter` to adjust arm pose for navigation..."
raw_input()
group.set_named_target("BACK")
plan1 = group.plan()
rospy.sleep(2)
group.go(wait=True)
rospy.sleep(2)
#FK (Set Joint Value) - Extend the arm toward table at Station 2
group.set_path_constraints(group.goal_c)
print "============ Press `Enter` to extend the arm toward table at Station 2..."
raw_input()
group_variable_values = group.get_current_joint_values()
group_variable_values[0] = -0.2094 # 0 degrees
group_variable_values[1] = 0.4363 # 25 degrees
group_variable_values[2] = -0.4363 # -25 degrees
group_variable_values[3] = 0.0 # 0 degrees
group.set_joint_value_target(group_variable_values)
plan2 = group.plan()
group.go(wait=True)
rospy.sleep(2)
att_dett.dettach_cylin()
#(Set Pre-defined Pose) - Open the gripper
print "============ Press `Enter` to open the gripper and release object..."
raw_input()
group_h.set_named_target("OPEN")
plan2 = group_h.plan()
rospy.sleep(2)
group_h.go(wait=True)
rospy.sleep(2)
group.set_path_constraints(None)
#(Set Pre-defined Pose)
print "============ Press `Enter` to retract the arm..."
raw_input()
group.set_named_target("BACK")
plan1 = group.plan()
rospy.sleep(2)
group.go(wait=True)
rospy.sleep(2)
#(Set Pre-defined Pose)
print "============ Press `Enter` to pick-up object from right pocket..."
raw_input()
group.set_named_target("RIGHT")
plan1 = group.plan()
rospy.sleep(2)
group.go(wait=True)
rospy.sleep(2)
#FK (Set Joint Value) - Close the gripper
print "============ Press `Enter` to close the gripper..."
raw_input()
group_h_variable_values = group_h.get_current_joint_values()
group_h_variable_values[0] -= 0.0174533 # approx -1.0 degrees
plan2 = group_h.plan(group_h_variable_values)
fail_count = 0
rospy.sleep(1)
while plan2.joint_trajectory.points: # True if trajectory contains points
print "Close by decrementing step angle of left finger..."
group_h.go(wait=True)
rospy.sleep(1)
if group_h_variable_values[0] > -0.3142: #Within joint limit
group_h_variable_values[0] -= 0.0174533 # approx -1.0 degrees
else:
break
plan2 = group_h.plan(group_h_variable_values)
print "Left finger in contact with object!"
group_h_variable_values = group_h.get_current_joint_values()
rospy.sleep(2)
group_h_variable_values[1] += 0.0174533 # approx +1.0 degrees
plan2 = group_h.plan(group_h_variable_values)
rospy.sleep(1)
while plan2.joint_trajectory.points: # True if trajectory contains points
print "Close by decrementing step angle of right finger..."
group_h.go(wait=True)
rospy.sleep(1)
if group_h_variable_values[1] +0.0174533 < 0.3142: #Within joint limit
group_h_variable_values[1] += 0.0174533 # approx +1.0 degreess
else:
break
plan2 = group_h.plan(group_h_variable_values)
print "Right finger in contact with object!"
rospy.sleep(2)
group_h_variable_values = group_h.get_current_joint_values()
#(Set Pre-defined Pose) - Place at right pocket
att_dett.attach_box()
#(Set Pre-defined Pose)
group.set_path_constraints(None)
print "============ Press `Enter` to adjust arm pose for next manipulation..."
raw_input()
group.set_named_target("BACK")
plan1 = group.plan()
rospy.sleep(2)
group.go(wait=True)
rospy.sleep(2)
#FK (Set Joint Value) - Extend the arm toward table at Station 2
group.set_path_constraints(group.goal_c)
print "============ Press `Enter` to extend the arm toward table at Station 3..."
raw_input()
group_variable_values = group.get_current_joint_values()
group_variable_values[0] = -0.2094 # 0 degrees
group_variable_values[1] = 0.4363 # 25 degrees
group_variable_values[2] = -0.4363 # -25 degrees
group_variable_values[3] = 0.0 # 0 degrees
group.set_joint_value_target(group_variable_values)
plan2 = group.plan()
group.go(wait=True)
rospy.sleep(2)
att_dett.dettach_box()
#(Set Pre-defined Pose) - Open the gripper
print "============ Press `Enter` to open the gripper and release object..."
raw_input()
group_h.set_named_target("OPEN")
plan2 = group_h.plan()
rospy.sleep(2)
group_h.go(wait=True)
rospy.sleep(2)
#(Set Pre-defined Pose)
print "============ Press `Enter` to retract the arm..."
raw_input()
group.set_named_target("TOP")
plan1 = group.plan()
rospy.sleep(2)
group.go(wait=True)
rospy.sleep(2)
att_dett.remove_box()
#(Set Pre-defined Pose)
print "============ Press `Enter` to move arm back to the HOME pose..."
raw_input()
group.set_named_target("HOME")
plan1 = group.plan()
rospy.sleep(2)
group.go(wait=True)
rospy.sleep(2)
moveit_commander.roscpp_shutdown()
| 32.274549 | 131 | 0.688296 | 2,323 | 16,105 | 4.54972 | 0.105467 | 0.043145 | 0.052039 | 0.073801 | 0.813227 | 0.794115 | 0.777557 | 0.760337 | 0.754187 | 0.730627 | 0 | 0.038488 | 0.182055 | 16,105 | 498 | 132 | 32.339357 | 0.763835 | 0.1543 | 0 | 0.770492 | 0 | 0 | 0.173174 | 0.002366 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.035519 | null | null | 0.117486 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 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 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
e0364ba84cfdf879dd51e1f5b93d0395c4d7d8fc | 312 | py | Python | or_suite/agents/ambulance/__init__.py | JasmineSamadi/ORSuite | e2b2b0a5b497ea6566e794dcef1f176081fca4ce | [
"MIT"
] | 4 | 2021-12-01T10:56:17.000Z | 2022-02-06T17:07:43.000Z | or_suite/agents/ambulance/__init__.py | JasmineSamadi/ORSuite | e2b2b0a5b497ea6566e794dcef1f176081fca4ce | [
"MIT"
] | 2 | 2021-08-11T13:25:01.000Z | 2022-03-20T19:23:23.000Z | or_suite/agents/ambulance/__init__.py | JasmineSamadi/ORSuite | e2b2b0a5b497ea6566e794dcef1f176081fca4ce | [
"MIT"
] | 3 | 2021-04-02T20:24:25.000Z | 2021-04-10T23:53:28.000Z | from or_suite.agents.ambulance.median import *
from or_suite.agents.ambulance.median_graph import *
from or_suite.agents.ambulance.mode_graph import *
from or_suite.agents.ambulance.stable import *
from or_suite.agents.ambulance.median_sklearn import *
from or_suite.agents.ambulance.command_line_metric import * | 52 | 59 | 0.849359 | 47 | 312 | 5.404255 | 0.297872 | 0.141732 | 0.259843 | 0.401575 | 0.84252 | 0.84252 | 0.590551 | 0 | 0 | 0 | 0 | 0 | 0.073718 | 312 | 6 | 59 | 52 | 0.878893 | 0 | 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 | 1 | 1 | 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 | 8 |
e03fa84782ccaae5dbceeb1d5ca50c2184bbd703 | 66,655 | py | Python | src/ralph/discovery/migrations/0030_auto__add_field_device_logical_parent.py | deejay1/ralph | 26b7c66912590093e0087ba801e9108290ad0d63 | [
"Apache-2.0"
] | 1 | 2018-09-01T14:14:08.000Z | 2018-09-01T14:14:08.000Z | src/ralph/discovery/migrations/0030_auto__add_field_device_logical_parent.py | srikanth4372/sample | 127b5742ae464d42909a14d71e3c10c241ec3a23 | [
"Apache-2.0"
] | 1 | 2019-08-14T10:03:45.000Z | 2019-08-14T10:03:45.000Z | src/ralph/discovery/migrations/0030_auto__add_field_device_logical_parent.py | srikanth4372/sample | 127b5742ae464d42909a14d71e3c10c241ec3a23 | [
"Apache-2.0"
] | 1 | 2019-08-14T09:59:42.000Z | 2019-08-14T09:59:42.000Z | # -*- coding: utf-8 -*-
import datetime
from south.db import db
from south.v2 import SchemaMigration
from django.db import models
class Migration(SchemaMigration):
def forwards(self, orm):
# Adding field 'Device.logical_parent'
db.add_column('discovery_device', 'logical_parent',
self.gf('django.db.models.fields.related.ForeignKey')(related_name=u'logicalchild_set', on_delete=models.SET_NULL, default=None, to=orm['discovery.Device'], blank=True, null=True),
keep_default=False)
def backwards(self, orm):
# Deleting field 'Device.logical_parent'
db.delete_column('discovery_device', 'logical_parent_id')
models = {
'account.profile': {
'Meta': {'object_name': 'Profile'},
'activation_token': ('django.db.models.fields.CharField', [], {'default': "u''", 'max_length': '40', 'blank': 'True'}),
'birth_date': ('django.db.models.fields.DateField', [], {'null': 'True', 'blank': 'True'}),
'city': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}),
'company': ('django.db.models.fields.CharField', [], {'max_length': '64', 'blank': 'True'}),
'cost_center': ('django.db.models.fields.CharField', [], {'max_length': '1024', 'blank': 'True'}),
'country': ('django.db.models.fields.PositiveIntegerField', [], {'default': '153'}),
'department': ('django.db.models.fields.CharField', [], {'max_length': '64', 'blank': 'True'}),
'employee_id': ('django.db.models.fields.CharField', [], {'max_length': '64', 'blank': 'True'}),
'gender': ('django.db.models.fields.PositiveIntegerField', [], {'default': '2'}),
'home_page': (u'dj.choices.fields.ChoiceField', [], {'unique': 'False', 'primary_key': 'False', 'db_column': 'None', 'blank': 'False', u'default': '1', 'null': 'False', '_in_south': 'True', 'db_index': 'False'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'last_active': ('django.db.models.fields.DateTimeField', [], {'default': 'None', 'null': 'True', 'blank': 'True'}),
'location': ('django.db.models.fields.CharField', [], {'max_length': '128', 'blank': 'True'}),
'manager': ('django.db.models.fields.CharField', [], {'max_length': '1024', 'blank': 'True'}),
'nick': ('django.db.models.fields.CharField', [], {'default': "u''", 'max_length': '30', 'blank': 'True'}),
'profit_center': ('django.db.models.fields.CharField', [], {'max_length': '1024', 'blank': 'True'}),
'time_zone': ('django.db.models.fields.FloatField', [], {'default': '1.0'}),
'user': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['auth.User']", 'unique': 'True'})
},
'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'})
},
'business.businesssegment': {
'Meta': {'object_name': 'BusinessSegment'},
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '75', 'db_index': 'True'})
},
'business.department': {
'Meta': {'ordering': "(u'name',)", 'object_name': 'Department'},
'icon': (u'dj.choices.fields.ChoiceField', [], {'unique': 'False', 'primary_key': 'False', 'db_column': 'None', 'blank': 'True', u'default': 'None', 'null': 'True', '_in_south': 'True', 'db_index': 'False'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '75', 'db_index': 'True'})
},
'business.profitcenter': {
'Meta': {'object_name': 'ProfitCenter'},
'description': ('django.db.models.fields.TextField', [], {'default': 'None', 'null': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '75', 'db_index': 'True'})
},
'business.venture': {
'Meta': {'ordering': "(u'parent__symbol', u'symbol')", 'unique_together': "((u'parent', u'symbol'),)", 'object_name': 'Venture'},
'business_segment': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['business.BusinessSegment']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'cache_version': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'created': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'data_center': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['discovery.DataCenter']", 'null': 'True', 'blank': 'True'}),
'department': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['business.Department']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_infrastructure': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'margin_kind': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['discovery.MarginKind']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'modified': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '75', 'db_index': 'True'}),
'parent': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'related_name': "u'child_set'", 'null': 'True', 'blank': 'True', 'to': "orm['business.Venture']"}),
'path': ('django.db.models.fields.TextField', [], {'default': "u''", 'blank': 'True'}),
'preboot': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['deployment.Preboot']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'profit_center': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['business.ProfitCenter']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'show_in_ralph': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'symbol': ('django.db.models.fields.CharField', [], {'default': "u''", 'max_length': '32', 'blank': 'True'}),
'verified': ('django.db.models.fields.BooleanField', [], {'default': 'False'})
},
'business.ventureextracost': {
'Meta': {'ordering': "(u'type',)", 'unique_together': "((u'type', u'venture'),)", 'object_name': 'VentureExtraCost'},
'cache_version': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'cost': ('django.db.models.fields.FloatField', [], {'default': '0'}),
'created': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'expire': ('django.db.models.fields.DateField', [], {'default': 'None', 'null': 'True', 'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'modified': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'type': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['business.VentureExtraCostType']"}),
'venture': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['business.Venture']"})
},
'business.ventureextracosttype': {
'Meta': {'ordering': "(u'name',)", 'object_name': 'VentureExtraCostType'},
'cache_version': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'created': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'modified': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '75'})
},
'business.venturerole': {
'Meta': {'ordering': "(u'parent__name', u'name')", 'unique_together': "((u'name', u'venture'),)", 'object_name': 'VentureRole'},
'cache_version': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'created': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'modified': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '75'}),
'parent': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'related_name': "u'child_set'", 'null': 'True', 'blank': 'True', 'to': "orm['business.VentureRole']"}),
'path': ('django.db.models.fields.TextField', [], {'default': "u''", 'blank': 'True'}),
'preboot': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['deployment.Preboot']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'venture': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['business.Venture']"})
},
'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'})
},
'deployment.preboot': {
'Meta': {'ordering': "(u'name',)", 'object_name': 'Preboot'},
'cache_version': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'created': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'description': ('django.db.models.fields.TextField', [], {'default': "u''", 'blank': 'True'}),
'files': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'to': "orm['deployment.PrebootFile']", 'null': 'True', 'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'modified': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '75', 'db_index': 'True'})
},
'deployment.prebootfile': {
'Meta': {'object_name': 'PrebootFile'},
'description': ('django.db.models.fields.TextField', [], {'default': "u''", 'blank': 'True'}),
'file': ('django.db.models.fields.files.FileField', [], {'default': 'None', 'max_length': '100', 'null': 'True', 'blank': 'True'}),
'ftype': (u'dj.choices.fields.ChoiceField', [], {'unique': 'False', 'primary_key': 'False', 'db_column': 'None', 'blank': 'False', u'default': '101', 'null': 'False', '_in_south': 'True', 'db_index': 'False'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '75', 'db_index': 'True'}),
'raw_config': ('django.db.models.fields.TextField', [], {'blank': 'True'})
},
'discovery.componentmodel': {
'Meta': {'unique_together': "((u'speed', u'cores', u'size', u'type', u'family'),)", 'object_name': 'ComponentModel'},
'cache_version': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'cores': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0', 'blank': 'True'}),
'created': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'family': ('django.db.models.fields.CharField', [], {'default': "u''", 'max_length': '128', 'blank': 'True'}),
'group': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['discovery.ComponentModelGroup']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'max_save_priority': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'modified': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '255'}),
'save_priorities': ('django.db.models.fields.TextField', [], {'default': "u''"}),
'size': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0', 'blank': 'True'}),
'speed': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0', 'blank': 'True'}),
'type': ('django.db.models.fields.PositiveIntegerField', [], {'default': '8'})
},
'discovery.componentmodelgroup': {
'Meta': {'object_name': 'ComponentModelGroup'},
'cache_version': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'created': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'modified': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '75', 'db_index': 'True'}),
'per_size': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'price': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'blank': 'True'}),
'size_modifier': ('django.db.models.fields.PositiveIntegerField', [], {'default': '1'}),
'size_unit': ('django.db.models.fields.CharField', [], {'default': "u''", 'max_length': '50', 'blank': 'True'}),
'type': ('django.db.models.fields.PositiveIntegerField', [], {'default': '8'})
},
'discovery.datacenter': {
'Meta': {'ordering': "(u'name',)", 'object_name': 'DataCenter'},
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '75', 'db_index': 'True'})
},
'discovery.deprecationkind': {
'Meta': {'object_name': 'DeprecationKind'},
'cache_version': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'created': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'default': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'modified': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'months': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'blank': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '75', 'db_index': 'True'}),
'remarks': ('django.db.models.fields.TextField', [], {'default': "u''", 'blank': 'True'})
},
'discovery.device': {
'Meta': {'object_name': 'Device'},
'barcode': ('django.db.models.fields.CharField', [], {'default': 'None', 'max_length': '255', 'unique': 'True', 'null': 'True', 'blank': 'True'}),
'boot_firmware': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'cache_version': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'cached_cost': ('django.db.models.fields.FloatField', [], {'null': 'True', 'blank': 'True'}),
'cached_price': ('django.db.models.fields.FloatField', [], {'null': 'True', 'blank': 'True'}),
'chassis_position': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'blank': 'True'}),
'created': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'dc': ('django.db.models.fields.CharField', [], {'default': 'None', 'max_length': '32', 'null': 'True', 'blank': 'True'}),
'deleted': ('django.db.models.fields.BooleanField', [], {'default': 'False', 'db_index': 'True'}),
'deprecation_date': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'deprecation_kind': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['discovery.DeprecationKind']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'diag_firmware': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'hard_firmware': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'last_seen': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'logical_parent': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'logicalchild_set'", 'on_delete': 'models.SET_NULL', 'default': 'None', 'to': "orm['discovery.Device']", 'blank': 'True', 'null': 'True'}),
'management': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'managed_set'", 'on_delete': 'models.SET_NULL', 'default': 'None', 'to': "orm['discovery.IPAddress']", 'blank': 'True', 'null': 'True'}),
'margin_kind': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['discovery.MarginKind']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'max_save_priority': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'mgmt_firmware': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'model': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'device_set'", 'on_delete': 'models.SET_NULL', 'default': 'None', 'to': "orm['discovery.DeviceModel']", 'blank': 'True', 'null': 'True'}),
'modified': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '255'}),
'name2': ('django.db.models.fields.CharField', [], {'default': 'None', 'max_length': '255', 'null': 'True', 'blank': 'True'}),
'parent': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'child_set'", 'on_delete': 'models.SET_NULL', 'default': 'None', 'to': "orm['discovery.Device']", 'blank': 'True', 'null': 'True'}),
'position': ('django.db.models.fields.CharField', [], {'max_length': '16', 'null': 'True', 'blank': 'True'}),
'price': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'blank': 'True'}),
'purchase_date': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'rack': ('django.db.models.fields.CharField', [], {'default': 'None', 'max_length': '32', 'null': 'True', 'blank': 'True'}),
'remarks': ('django.db.models.fields.TextField', [], {'default': "u''", 'blank': 'True'}),
'role': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'save_priorities': ('django.db.models.fields.TextField', [], {'default': "u''"}),
'sn': ('django.db.models.fields.CharField', [], {'default': 'None', 'max_length': '255', 'unique': 'True', 'null': 'True', 'blank': 'True'}),
'support_expiration_date': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'support_kind': ('django.db.models.fields.CharField', [], {'default': 'None', 'max_length': '255', 'null': 'True', 'blank': 'True'}),
'uptime_seconds': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'uptime_timestamp': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'venture': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['business.Venture']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'venture_role': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['business.VentureRole']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'verified': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'warranty_expiration_date': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'})
},
'discovery.devicemodel': {
'Meta': {'object_name': 'DeviceModel'},
'cache_version': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'chassis_size': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'blank': 'True'}),
'created': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'group': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['discovery.DeviceModelGroup']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'max_save_priority': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'modified': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '255'}),
'save_priorities': ('django.db.models.fields.TextField', [], {'default': "u''"}),
'type': ('django.db.models.fields.PositiveIntegerField', [], {'default': '401'})
},
'discovery.devicemodelgroup': {
'Meta': {'object_name': 'DeviceModelGroup'},
'cache_version': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'created': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'modified': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '75', 'db_index': 'True'}),
'price': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'blank': 'True'}),
'slots': ('django.db.models.fields.FloatField', [], {'default': '0'}),
'type': ('django.db.models.fields.PositiveIntegerField', [], {'default': '401'})
},
'discovery.discoveryqueue': {
'Meta': {'ordering': "(u'name',)", 'object_name': 'DiscoveryQueue'},
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '75', 'db_index': 'True'})
},
'discovery.discoveryvalue': {
'Meta': {'object_name': 'DiscoveryValue'},
'date': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'ip': ('django.db.models.fields.IPAddressField', [], {'unique': 'True', 'max_length': '15'}),
'key': ('django.db.models.fields.TextField', [], {'default': "u''"}),
'plugin': ('django.db.models.fields.CharField', [], {'default': "u''", 'max_length': '64'}),
'value': ('django.db.models.fields.TextField', [], {'default': "u''"})
},
'discovery.discoverywarning': {
'Meta': {'object_name': 'DiscoveryWarning'},
'count': ('django.db.models.fields.IntegerField', [], {'default': '1'}),
'date': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'device': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['discovery.Device']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'ip': ('django.db.models.fields.IPAddressField', [], {'max_length': '15'}),
'message': ('django.db.models.fields.TextField', [], {'default': "u''", 'blank': 'True'}),
'plugin': ('django.db.models.fields.CharField', [], {'default': "u''", 'max_length': '64'})
},
'discovery.diskshare': {
'Meta': {'object_name': 'DiskShare'},
'cache_version': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'created': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'device': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['discovery.Device']"}),
'full': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'label': ('django.db.models.fields.CharField', [], {'default': 'None', 'max_length': '255', 'null': 'True', 'blank': 'True'}),
'max_save_priority': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'model': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['discovery.ComponentModel']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'modified': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'save_priorities': ('django.db.models.fields.TextField', [], {'default': "u''"}),
'share_id': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'blank': 'True'}),
'size': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'blank': 'True'}),
'snapshot_size': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'blank': 'True'}),
'wwn': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '33'})
},
'discovery.disksharemount': {
'Meta': {'unique_together': "((u'share', u'device'),)", 'object_name': 'DiskShareMount'},
'address': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['discovery.IPAddress']", 'null': 'True', 'blank': 'True'}),
'cache_version': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'created': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'device': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['discovery.Device']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_virtual': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'modified': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'server': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'related_name': "u'servermount_set'", 'null': 'True', 'blank': 'True', 'to': "orm['discovery.Device']"}),
'share': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['discovery.DiskShare']"}),
'size': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'blank': 'True'}),
'volume': ('django.db.models.fields.CharField', [], {'default': 'None', 'max_length': '255', 'null': 'True', 'blank': 'True'})
},
'discovery.environment': {
'Meta': {'ordering': "(u'name',)", 'object_name': 'Environment'},
'data_center': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['discovery.DataCenter']"}),
'domain': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'hosts_naming_template': ('django.db.models.fields.CharField', [], {'max_length': '30'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '75', 'db_index': 'True'}),
'next_server': ('django.db.models.fields.CharField', [], {'default': "u''", 'max_length': '32', 'blank': 'True'}),
'queue': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['discovery.DiscoveryQueue']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'})
},
'discovery.ethernet': {
'Meta': {'ordering': "(u'device', u'mac')", 'object_name': 'Ethernet'},
'cache_version': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'created': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'device': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['discovery.Device']"}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'label': ('django.db.models.fields.CharField', [], {'max_length': '255'}),
'mac': (u'lck.django.common.models.MACAddressField', [], {'unique': 'True', 'primary_key': 'False', 'db_column': 'None', 'blank': 'False', 'null': 'False', 'db_index': 'False'}),
'max_save_priority': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'model': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['discovery.ComponentModel']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'modified': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'save_priorities': ('django.db.models.fields.TextField', [], {'default': "u''"}),
'speed': ('django.db.models.fields.PositiveIntegerField', [], {'default': '11'})
},
'discovery.fibrechannel': {
'Meta': {'ordering': "(u'device', u'physical_id')", 'unique_together': "((u'device', u'physical_id'),)", 'object_name': 'FibreChannel'},
'cache_version': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'created': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'device': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['discovery.Device']"}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'label': ('django.db.models.fields.CharField', [], {'max_length': '255'}),
'max_save_priority': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'model': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['discovery.ComponentModel']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'modified': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'physical_id': ('django.db.models.fields.CharField', [], {'max_length': '32'}),
'save_priorities': ('django.db.models.fields.TextField', [], {'default': "u''"})
},
'discovery.genericcomponent': {
'Meta': {'object_name': 'GenericComponent'},
'boot_firmware': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'cache_version': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'created': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'device': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['discovery.Device']"}),
'diag_firmware': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'hard_firmware': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'label': ('django.db.models.fields.CharField', [], {'default': 'None', 'max_length': '255', 'null': 'True', 'blank': 'True'}),
'max_save_priority': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'mgmt_firmware': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'model': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['discovery.ComponentModel']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'modified': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'save_priorities': ('django.db.models.fields.TextField', [], {'default': "u''"}),
'sn': ('django.db.models.fields.CharField', [], {'default': 'None', 'max_length': '255', 'unique': 'True', 'null': 'True', 'blank': 'True'})
},
'discovery.historychange': {
'Meta': {'object_name': 'HistoryChange'},
'comment': ('django.db.models.fields.TextField', [], {'null': 'True'}),
'component': ('django.db.models.fields.CharField', [], {'default': "u''", 'max_length': '255'}),
'component_id': ('django.db.models.fields.IntegerField', [], {'default': 'None', 'null': 'True', 'blank': 'True'}),
'date': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'device': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['discovery.Device']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'field_name': ('django.db.models.fields.CharField', [], {'default': "u''", 'max_length': '64'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'new_value': ('django.db.models.fields.CharField', [], {'default': "u''", 'max_length': '255'}),
'old_value': ('django.db.models.fields.CharField', [], {'default': "u''", 'max_length': '255'}),
'plugin': ('django.db.models.fields.CharField', [], {'default': "u''", 'max_length': '64'}),
'user': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['auth.User']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'})
},
'discovery.historycost': {
'Meta': {'object_name': 'HistoryCost'},
'cores': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'daily_cost': ('django.db.models.fields.FloatField', [], {'default': '0'}),
'device': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['discovery.Device']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'end': ('django.db.models.fields.DateField', [], {'default': "u'2199-1-1'"}),
'extra': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['business.VentureExtraCost']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'start': ('django.db.models.fields.DateField', [], {'default': "u'0001-1-1'", 'null': 'True'}),
'venture': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['business.Venture']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'})
},
'discovery.historymodelchange': {
'Meta': {'object_name': 'HistoryModelChange'},
'component_model': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['discovery.ComponentModel']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'component_model_group': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['discovery.ComponentModelGroup']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'date': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'device_model': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['discovery.DeviceModel']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'device_model_group': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['discovery.DeviceModelGroup']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'field_name': ('django.db.models.fields.CharField', [], {'default': "u''", 'max_length': '64'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'new_value': ('django.db.models.fields.CharField', [], {'default': "u''", 'max_length': '255'}),
'old_value': ('django.db.models.fields.CharField', [], {'default': "u''", 'max_length': '255'}),
'user': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['auth.User']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'})
},
'discovery.ipaddress': {
'Meta': {'object_name': 'IPAddress'},
'address': ('django.db.models.fields.IPAddressField', [], {'default': 'None', 'max_length': '15', 'unique': 'True', 'null': 'True', 'blank': 'True'}),
'cache_version': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'created': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'dead_ping_count': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'device': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['discovery.Device']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'dns_info': ('django.db.models.fields.TextField', [], {'default': 'None', 'null': 'True', 'blank': 'True'}),
'hostname': ('django.db.models.fields.CharField', [], {'default': 'None', 'max_length': '255', 'null': 'True', 'blank': 'True'}),
'http_family': ('django.db.models.fields.TextField', [], {'default': 'None', 'max_length': '64', 'null': 'True', 'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_buried': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'is_management': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'is_public': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'last_plugins': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
'last_puppet': ('django.db.models.fields.DateTimeField', [], {'default': 'None', 'null': 'True', 'blank': 'True'}),
'last_seen': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'modified': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'network': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['discovery.Network']", 'null': 'True', 'blank': 'True'}),
'number': ('django.db.models.fields.BigIntegerField', [], {'unique': 'True'}),
'scan_summary': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['scan.ScanSummary']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'snmp_community': ('django.db.models.fields.CharField', [], {'default': 'None', 'max_length': '64', 'null': 'True', 'blank': 'True'}),
'snmp_name': ('django.db.models.fields.TextField', [], {'default': 'None', 'null': 'True', 'blank': 'True'}),
'snmp_version': ('django.db.models.fields.CharField', [], {'default': 'None', 'max_length': '5', 'null': 'True', 'blank': 'True'}),
'venture': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['business.Venture']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'})
},
'discovery.ipalias': {
'Meta': {'object_name': 'IPAlias'},
'address': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'+'", 'to': "orm['discovery.IPAddress']"}),
'cache_version': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'created': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'hostname': ('django.db.models.fields.CharField', [], {'max_length': '255'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'max_save_priority': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'modified': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'save_priorities': ('django.db.models.fields.TextField', [], {'default': "u''"})
},
'discovery.loadbalancermember': {
'Meta': {'unique_together': "((u'pool', u'address', u'port', u'device'),)", 'object_name': 'LoadBalancerMember'},
'address': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['discovery.IPAddress']"}),
'cache_version': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'created': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'device': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['discovery.Device']"}),
'enabled': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'max_save_priority': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'modified': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'pool': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['discovery.LoadBalancerPool']"}),
'port': ('django.db.models.fields.PositiveIntegerField', [], {}),
'save_priorities': ('django.db.models.fields.TextField', [], {'default': "u''"})
},
'discovery.loadbalancerpool': {
'Meta': {'object_name': 'LoadBalancerPool'},
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '75', 'db_index': 'True'})
},
'discovery.loadbalancervirtualserver': {
'Meta': {'object_name': 'LoadBalancerVirtualServer'},
'address': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['discovery.IPAddress']"}),
'cache_version': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'created': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'default_pool': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['discovery.LoadBalancerPool']"}),
'device': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['discovery.Device']"}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'max_save_priority': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'modified': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '255'}),
'port': ('django.db.models.fields.PositiveIntegerField', [], {}),
'save_priorities': ('django.db.models.fields.TextField', [], {'default': "u''"})
},
'discovery.marginkind': {
'Meta': {'object_name': 'MarginKind'},
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'margin': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'blank': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '75', 'db_index': 'True'}),
'remarks': ('django.db.models.fields.TextField', [], {'default': "u''", 'blank': 'True'})
},
'discovery.memory': {
'Meta': {'ordering': "(u'device', u'index')", 'unique_together': "((u'device', u'index'),)", 'object_name': 'Memory'},
'cache_version': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'created': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'device': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['discovery.Device']"}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'index': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'blank': 'True'}),
'label': ('django.db.models.fields.CharField', [], {'max_length': '255'}),
'max_save_priority': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'model': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['discovery.ComponentModel']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'modified': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'save_priorities': ('django.db.models.fields.TextField', [], {'default': "u''"}),
'size': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'blank': 'True'}),
'speed': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'blank': 'True'})
},
'discovery.network': {
'Meta': {'ordering': "(u'vlan',)", 'object_name': 'Network'},
'address': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '18'}),
'cache_version': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'created': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'custom_dns_servers': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'to': "orm['dnsedit.DNSServer']", 'null': 'True', 'blank': 'True'}),
'data_center': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['discovery.DataCenter']", 'null': 'True', 'blank': 'True'}),
'dhcp_broadcast': ('django.db.models.fields.BooleanField', [], {'default': 'False', 'db_index': 'True'}),
'dhcp_config': ('django.db.models.fields.TextField', [], {'default': "u''", 'blank': 'True'}),
'environment': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['discovery.Environment']", 'null': 'True', 'blank': 'True'}),
'gateway': ('django.db.models.fields.IPAddressField', [], {'default': 'None', 'max_length': '15', 'null': 'True', 'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'ignore_addresses': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'kind': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['discovery.NetworkKind']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'last_scan': ('django.db.models.fields.DateTimeField', [], {'default': 'None', 'null': 'True', 'blank': 'True'}),
'max_ip': ('django.db.models.fields.PositiveIntegerField', [], {'default': 'None', 'null': 'True', 'blank': 'True'}),
'min_ip': ('django.db.models.fields.PositiveIntegerField', [], {'default': 'None', 'null': 'True', 'blank': 'True'}),
'modified': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '75', 'db_index': 'True'}),
'racks': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['discovery.Device']", 'symmetrical': 'False'}),
'remarks': ('django.db.models.fields.TextField', [], {'default': "u''", 'blank': 'True'}),
'reserved': ('django.db.models.fields.PositiveIntegerField', [], {'default': '10'}),
'reserved_top_margin': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'terminators': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['discovery.NetworkTerminator']", 'symmetrical': 'False'}),
'vlan': ('django.db.models.fields.PositiveIntegerField', [], {'default': 'None', 'null': 'True', 'blank': 'True'})
},
'discovery.networkkind': {
'Meta': {'ordering': "(u'name',)", 'object_name': 'NetworkKind'},
'icon': ('django.db.models.fields.CharField', [], {'default': 'None', 'max_length': '32', 'null': 'True', 'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '75', 'db_index': 'True'})
},
'discovery.networkterminator': {
'Meta': {'ordering': "(u'name',)", 'object_name': 'NetworkTerminator'},
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '75', 'db_index': 'True'})
},
'discovery.operatingsystem': {
'Meta': {'ordering': "(u'label',)", 'unique_together': "((u'device',),)", 'object_name': 'OperatingSystem'},
'cache_version': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'cores_count': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'blank': 'True'}),
'created': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'device': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['discovery.Device']"}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'label': ('django.db.models.fields.CharField', [], {'max_length': '255'}),
'max_save_priority': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'memory': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'blank': 'True'}),
'model': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['discovery.ComponentModel']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'modified': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'save_priorities': ('django.db.models.fields.TextField', [], {'default': "u''"}),
'storage': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'blank': 'True'})
},
'discovery.pricingformula': {
'Meta': {'ordering': "(u'group', u'component_group')", 'unique_together': "((u'group', u'component_group'),)", 'object_name': 'PricingFormula'},
'component_group': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['discovery.ComponentModelGroup']"}),
'formula': ('django.db.models.fields.TextField', [], {}),
'group': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['discovery.PricingGroup']"}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'})
},
'discovery.pricinggroup': {
'Meta': {'ordering': "(u'name', u'date')", 'unique_together': "((u'name', u'date'),)", 'object_name': 'PricingGroup'},
'date': ('django.db.models.fields.DateField', [], {}),
'devices': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['discovery.Device']", 'symmetrical': 'False'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '64'})
},
'discovery.pricingvalue': {
'Meta': {'ordering': "(u'device', u'variable')", 'unique_together': "((u'device', u'variable'),)", 'object_name': 'PricingValue'},
'device': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['discovery.Device']"}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'value': ('django.db.models.fields.DecimalField', [], {'max_digits': '8', 'decimal_places': '2'}),
'variable': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['discovery.PricingVariable']"})
},
'discovery.pricingvariable': {
'Meta': {'ordering': "(u'group', u'name')", 'unique_together': "((u'group', u'name'),)", 'object_name': 'PricingVariable'},
'aggregate': ('django.db.models.fields.PositiveIntegerField', [], {'default': '1'}),
'group': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['discovery.PricingGroup']"}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '64'})
},
'discovery.processor': {
'Meta': {'ordering': "(u'device', u'index')", 'unique_together': "((u'device', u'index'),)", 'object_name': 'Processor'},
'cache_version': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'cores': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'blank': 'True'}),
'created': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'device': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['discovery.Device']"}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'index': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'blank': 'True'}),
'label': ('django.db.models.fields.CharField', [], {'max_length': '255'}),
'max_save_priority': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'model': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['discovery.ComponentModel']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'modified': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'save_priorities': ('django.db.models.fields.TextField', [], {'default': "u''"}),
'speed': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'blank': 'True'})
},
'discovery.software': {
'Meta': {'ordering': "(u'device', u'sn', u'path')", 'unique_together': "((u'device', u'path'),)", 'object_name': 'Software'},
'cache_version': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'created': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'device': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['discovery.Device']"}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'label': ('django.db.models.fields.CharField', [], {'max_length': '255'}),
'max_save_priority': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'model': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['discovery.ComponentModel']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'modified': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'path': ('django.db.models.fields.CharField', [], {'default': 'None', 'max_length': '255', 'null': 'True', 'blank': 'True'}),
'save_priorities': ('django.db.models.fields.TextField', [], {'default': "u''"}),
'sn': ('django.db.models.fields.CharField', [], {'default': 'None', 'max_length': '255', 'unique': 'True', 'null': 'True', 'blank': 'True'}),
'version': ('django.db.models.fields.CharField', [], {'default': 'None', 'max_length': '255', 'null': 'True', 'blank': 'True'})
},
'discovery.splunkusage': {
'Meta': {'ordering': "(u'device', u'day')", 'unique_together': "((u'device', u'day'),)", 'object_name': 'SplunkUsage'},
'cache_version': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'created': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'day': ('django.db.models.fields.DateField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'device': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['discovery.Device']"}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'max_save_priority': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'model': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['discovery.ComponentModel']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'modified': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'save_priorities': ('django.db.models.fields.TextField', [], {'default': "u''"}),
'size': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'blank': 'True'})
},
'discovery.storage': {
'Meta': {'ordering': "(u'device', u'sn', u'mount_point')", 'unique_together': "((u'device', u'mount_point'),)", 'object_name': 'Storage'},
'cache_version': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'created': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'device': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['discovery.Device']"}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'label': ('django.db.models.fields.CharField', [], {'max_length': '255'}),
'max_save_priority': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'model': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['discovery.ComponentModel']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'modified': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'mount_point': ('django.db.models.fields.CharField', [], {'default': 'None', 'max_length': '255', 'null': 'True', 'blank': 'True'}),
'save_priorities': ('django.db.models.fields.TextField', [], {'default': "u''"}),
'size': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'blank': 'True'}),
'sn': ('django.db.models.fields.CharField', [], {'default': 'None', 'max_length': '255', 'unique': 'True', 'null': 'True', 'blank': 'True'})
},
'dnsedit.dnsserver': {
'Meta': {'object_name': 'DNSServer'},
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'ip_address': ('django.db.models.fields.IPAddressField', [], {'unique': 'True', 'max_length': '15'}),
'is_default': ('django.db.models.fields.BooleanField', [], {'default': 'False', 'db_index': 'True'})
},
'scan.scansummary': {
'Meta': {'object_name': 'ScanSummary'},
'created': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'false_positive_checksum': ('django.db.models.fields.CharField', [], {'max_length': '32', 'null': 'True', 'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'job_id': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '36'}),
'modified': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'auto_now_add': 'True', 'blank': 'True'}),
'previous_checksum': ('django.db.models.fields.CharField', [], {'max_length': '32'})
},
'tags.tag': {
'Meta': {'object_name': 'Tag'},
'author': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['account.Profile']"}),
'cache_version': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'content_type': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'tags_tag_tags'", 'to': "orm['contenttypes.ContentType']"}),
'created': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'language': ('django.db.models.fields.PositiveIntegerField', [], {'default': '39'}),
'modified': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '75'}),
'object_id': ('django.db.models.fields.IntegerField', [], {'db_index': 'True'}),
'official': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'stem': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'related_tags'", 'null': 'True', 'to': "orm['tags.TagStem']"})
},
'tags.tagstem': {
'Meta': {'object_name': 'TagStem'},
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'language': ('django.db.models.fields.PositiveIntegerField', [], {'default': '39'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '75'}),
'tag_count': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'})
}
}
complete_apps = ['discovery'] | 95.906475 | 239 | 0.56935 | 6,642 | 66,655 | 5.619241 | 0.053598 | 0.107173 | 0.187177 | 0.267395 | 0.873429 | 0.851486 | 0.828524 | 0.796854 | 0.77258 | 0.710875 | 0 | 0.00697 | 0.175621 | 66,655 | 695 | 240 | 95.906475 | 0.67226 | 0.001455 | 0 | 0.44152 | 0 | 0.001462 | 0.614394 | 0.353722 | 0 | 0 | 0 | 0 | 0 | 1 | 0.002924 | false | 0.001462 | 0.005848 | 0 | 0.013158 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
e055584d21fafd5ab60ce0c9193653803c8b80ef | 10,089 | py | Python | generator_test.py | tatemunnich/Backgammon | 7a8ff4b80b419415230c89bb0af94304f095ae76 | [
"MIT"
] | null | null | null | generator_test.py | tatemunnich/Backgammon | 7a8ff4b80b419415230c89bb0af94304f095ae76 | [
"MIT"
] | null | null | null | generator_test.py | tatemunnich/Backgammon | 7a8ff4b80b419415230c89bb0af94304f095ae76 | [
"MIT"
] | null | null | null | import unittest
from board.Board import Board
from board.Dice import Dice
from move.Move import MoveNode
from move.MovementFactory import generate_moves
from players.MinimaxPlayer import alpha_beta, expectiminimax
class MyTestCase(unittest.TestCase):
def test_0(self):
b = Board()
d = Dice(5, 1)
m = generate_moves(b, "BLACK", d)
print(b)
print(m)
self.assertEqual(len(m), 8)
# self.assertEqual(set([str(move) for move in m]), {"17/22 19/20", "12/17 19/20", "1/7", "1/2 12/17", "17/23",
# "12/18", "1/2 17/22", "17/22 17/18"})
def test_1(self):
b = Board()
b.pointsContent = [0] * 26
b.pointsContent[1] = -2
b.pointsContent[12] = -5
b.pointsContent[17] = -3
b.pointsContent[19] = -5
b.pointsContent[2] = 14
b.pointsContent[7] = 1
b.whiteCheckers = {1, 12, 17, 19}
b.blackCheckers = {2, 7}
b.blackCheckersTaken = 0
b.whiteCheckersTaken = 0
d = Dice(5, 1)
m = generate_moves(b, "BLACK", d)
self.assertEqual(len(m), 1)
# self.assertEqual(m, {"7/2"})
def test_2(self):
b = Board()
b.pointsContent = [0] * 26
b.pointsContent[1] = -1
b.pointsContent[5] = -1
b.pointsContent[12] = -5
b.pointsContent[17] = -3
b.pointsContent[19] = -5
b.pointsContent[2] = 14
b.pointsContent[7] = 1
b.whiteCheckers = {1, 5, 12, 17, 19}
b.blackCheckers = {2, 7}
b.blackCheckersTaken = 0
b.whiteCheckersTaken = 0
d = Dice(1, 2)
m = generate_moves(b, "BLACK", d)
print(b)
print(m)
self.assertEqual(len(m), 4)
# self.assertEqual(m, {"7/5 2/1", "7/6 2/off", "7/5 5/4", "7/6 6/4"})
def test_3(self):
b = Board()
b.pointsContent = [0] * 26
b.pointsContent[1] = -1
b.pointsContent[12] = -6
b.pointsContent[17] = -3
b.pointsContent[19] = -5
b.pointsContent[2] = 14
b.pointsContent[7] = 1
b.whiteCheckers = {1, 12, 17, 19}
b.blackCheckers = {2, 7}
b.blackCheckersTaken = 0
b.whiteCheckersTaken = 0
d = Dice(1, 2)
m = generate_moves(b, "BLACK", d)
print(b)
print(m)
self.assertEqual(len(m), 3)
# self.assertEqual(m, {"7/5 2/1", "7/6 2/off", "7/5 5/4==7/6 6/4"})
def test_4(self):
b = Board()
b.pointsContent = [0] * 26
b.pointsContent[1] = -1
b.pointsContent[2] = 2
b.pointsContent[4] = 1
b.pointsContent[5] = 1
b.pointsContent[6] = 3
b.pointsContent[8] = 1
b.pointsContent[12] = -5
b.pointsContent[13] = 5
b.pointsContent[17] = -2
b.pointsContent[19] = -4
b.pointsContent[20] = -1
b.pointsContent[22] = -1
b.pointsContent[24] = 2
b.whiteCheckers = {1, 12, 17, 19, 20, 22}
b.blackCheckers = {24, 13, 8, 6, 5, 4, 2}
b.whiteCheckersTaken = 1
b.blackCheckersTaken = 0
d = Dice(4, 2)
m = generate_moves(b, "WHITE", d)
print(b)
print(m)
self.assertEqual(len(m), 5)
# self.assertEqual(m, {"bar/4 /1/3", "bar/4 12/14", "bar/4 17/19", "bar/4 19/21", "bar/4 20/22"})
def test_5(self):
b = Board()
b.pointsContent = [0] * 26
b.pointsContent[1] = -2
b.pointsContent[12] = -5
b.pointsContent[17] = -3
b.pointsContent[19] = -5
b.pointsContent[24] = 2
b.pointsContent[13] = 5
b.pointsContent[4] = 3
b.pointsContent[6] = 5
b.whiteCheckers = {12, 17, 19}
b.blackCheckers = {24, 13, 6, 4}
b.whiteCheckersTaken = 2
b.blackCheckersTaken = 0
d = Dice(4, 6)
m = generate_moves(b, "WHITE", d)
print(b)
print(m)
self.assertEqual(len(m), 1)
# self.assertEqual(m, {})
def test_6(self):
b = Board()
b.pointsContent = [0] * 26
b.pointsContent[7] = -2
b.pointsContent[12] = -5
b.pointsContent[17] = -3
b.pointsContent[19] = -5
b.pointsContent[4] = 1
b.pointsContent[3] = 3
b.pointsContent[1] = 10
b.pointsContent[0] = 1
b.whiteCheckers = {7, 12, 17, 19}
b.blackCheckers = {4, 3, 1}
b.blackCheckersTaken = 0
b.whiteCheckersTaken = 0
d = Dice(4, 2)
m = generate_moves(b, "BLACK", d)
print(b)
print(m)
self.assertEqual(len(m), 2)
# self.assertEqual(m, {"4/off 3/1", "4/2 3/off"})
def test_7(self):
b = Board()
b.pointsContent = [0] * 26
b.pointsContent[7] = -2
b.pointsContent[12] = -5
b.pointsContent[17] = -3
b.pointsContent[19] = -5
b.pointsContent[4] = 1
b.pointsContent[3] = 3
b.pointsContent[1] = 10
b.pointsContent[0] = 1
b.whiteCheckers = {7, 12, 17, 19}
b.blackCheckers = {4, 3, 1}
b.blackCheckersTaken = 0
b.whiteCheckersTaken = 0
d = Dice(2, 2)
m = generate_moves(b, "BLACK", d)
print(b)
print(m)
self.assertEqual(len(m), 2)
# self.assertEqual(m, {"4/2 3/1 3/1 2/off", "4/2 3/1 3/1 3/1"})
def test_8(self):
b = Board()
b.pointsContent = [0] * 26
b.pointsContent[7] = -2
b.pointsContent[12] = -5
b.pointsContent[17] = -3
b.pointsContent[19] = -5
b.pointsContent[8] = 1
b.pointsContent[3] = 3
b.pointsContent[1] = 10
b.pointsContent[0] = 1
b.whiteCheckers = {7, 12, 17, 19}
b.blackCheckers = {8, 3, 1}
b.blackCheckersTaken = 0
b.whiteCheckersTaken = 0
d = Dice(2, 2)
m = generate_moves(b, "BLACK", d)
print(b)
print(m)
self.assertEqual(len(m), 4)
# self.assertEqual(m, {"8/6 6/4 3/1 3/1", "8/6 6/4 4/2 2/off", "8/6 3/1 3/1 3/1", "8/6 6/4 4/2 3/1"})
def test_9(self):
b = Board()
b.pointsContent = [0] * 26
b.pointsContent[1] = -2
b.pointsContent[6] = 5
b.pointsContent[7] = 3
b.pointsContent[8] = 4
b.pointsContent[12] = -4
b.pointsContent[13] = -1
b.pointsContent[18] = 2
b.pointsContent[19] = -3
b.pointsContent[21] = -2
b.pointsContent[23] = -2
b.pointsContent[24] = -1
b.whiteCheckers = {1, 12, 13, 19, 21, 23, 24}
b.blackCheckers = {6, 7, 8, 18}
b.whiteCheckersTaken = 0
b.blackCheckersTaken = 1
d = Dice(4, 1)
m = generate_moves(b, "BLACK", d)
print(b)
print(m)
self.assertEqual(len(m), 5)
def test_10(self):
b = Board()
b.pointsContent = [0] * 26
b.pointsContent[1] = -1
b.pointsContent[2] = 1
b.pointsContent[4] = 1
b.pointsContent[5] = 2
b.pointsContent[6] = 3
b.pointsContent[8] = 1
b.pointsContent[13] = 5
b.pointsContent[21] = -13
b.pointsContent[24] = 2
b.whiteCheckers = {1, 21}
b.blackCheckers = {24, 13, 8, 6, 5, 4, 2}
b.whiteCheckersTaken = 1
b.blackCheckersTaken = 0
d = Dice(4, 2)
m = generate_moves(b, "WHITE", d)
print(b)
print(m)
self.assertEqual(len(m), 2)
# self.assertEqual(m, {"bar/4 1/3", "bar/4 21/23"})
def test_11(self):
b = Board()
b.pointsContent = [0] * 26
b.pointsContent[1] = -1
b.pointsContent[2] = 1
b.pointsContent[4] = 1
b.pointsContent[5] = 2
b.pointsContent[6] = 3
b.pointsContent[8] = 1
b.pointsContent[13] = 5
b.pointsContent[18] = -1
b.pointsContent[21] = -12
b.pointsContent[24] = 2
b.whiteCheckers = {1, 18, 21}
b.blackCheckers = {24, 13, 8, 6, 5, 4, 2}
b.whiteCheckersTaken = 1
b.blackCheckersTaken = 0
d = Dice(4, 2)
m = generate_moves(b, "WHITE", d)
print(b)
print(m)
self.assertEqual(len(m), 4)
# self.assertEqual(m, {"bar/4 1/3", "bar/4 21/23", "bar/2 18/20", "bar/4 18/22"})
def test_12(self):
b = Board()
b.pointsContent = [0] * 26
b.pointsContent[2] = 5
b.pointsContent[3] = -1
b.pointsContent[4] = 1
b.pointsContent[5] = 1
b.pointsContent[6] = 3
b.pointsContent[13] = 1
b.pointsContent[16] = -2
b.pointsContent[18] = -2
b.pointsContent[19] = -3
b.pointsContent[20] = -2
b.pointsContent[21] = -2
b.pointsContent[22] = -2
b.pointsContent[23] = -1
b.pointsContent[24] = 3
b.whiteCheckers = {3, 16, 18, 19, 20, 21, 22, 23}
b.blackCheckers = {24, 13, 6, 5, 4, 2}
b.whiteCheckersTaken = 0
b.blackCheckersTaken = 1
d = Dice(4, 1)
m = generate_moves(b, "BLACK", d)
print(b)
print(m)
self.assertEqual(len(m), 3)
class TestMinimax(unittest.TestCase):
def test_0(self):
b = Board()
b.pointsContent = [0] * 26
b.pointsContent[1] = -3
b.pointsContent[2] = 6
b.pointsContent[3] = 4
b.pointsContent[6] = 2
b.pointsContent[4] = 1
b.pointsContent[13] = -1
b.pointsContent[19] = -4
b.pointsContent[20] = 2
b.pointsContent[22] = -1
b.pointsContent[23] = -4
b.pointsContent[24] = -2
b.whiteCheckers = {1, 13, 19, 22, 23, 24}
b.blackCheckers = {2, 3, 4, 6, 20}
b.blackCheckersTaken = 0
b.whiteCheckersTaken = 0
d = Dice(4, 1)
print(b)
current = MoveNode("start", board_after=b, deep=0)
ab = alpha_beta(current, 2, "BLACK", dice=d)
mm = expectiminimax(current, 2, "BLACK", dice=d)
print(ab)
print(mm)
if __name__ == '__main__':
unittest.main()
| 28.5 | 118 | 0.508574 | 1,375 | 10,089 | 3.703273 | 0.054545 | 0.362922 | 0.088374 | 0.038295 | 0.823252 | 0.799686 | 0.753142 | 0.705027 | 0.683032 | 0.660644 | 0 | 0.117859 | 0.340668 | 10,089 | 353 | 119 | 28.580737 | 0.647625 | 0.078303 | 0 | 0.746575 | 1 | 0 | 0.009477 | 0 | 0 | 0 | 0 | 0 | 0.044521 | 1 | 0.047945 | false | 0 | 0.020548 | 0 | 0.075342 | 0.092466 | 0 | 0 | 0 | null | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
e0596d69dc3f01a418205042477184fd9d2a141b | 1,630 | py | Python | src/translate/baidu.py | GitHubShen/shaniuWeb | 17abfe165cd2db8e32819d0551e8637c6160704f | [
"Apache-2.0"
] | null | null | null | src/translate/baidu.py | GitHubShen/shaniuWeb | 17abfe165cd2db8e32819d0551e8637c6160704f | [
"Apache-2.0"
] | null | null | null | src/translate/baidu.py | GitHubShen/shaniuWeb | 17abfe165cd2db8e32819d0551e8637c6160704f | [
"Apache-2.0"
] | null | null | null | #/usr/bin/env python
#coding=utf8
import http.client
import hashlib
import urllib
import random
appid = '' #你的appid
secretKey = '' #你的密钥
httpClient = None
def engtoch(q):
fromLang = 'en'
toLang = 'zh'
salt = random.randint(32768, 65536)
sign = appid+q+str(salt)+secretKey
m1 = hashlib.md5()
m1.update(sign.encode("utf8"))
sign = m1.hexdigest()
myurl = '/api/trans/vip/translate'
myurl = myurl+'?appid='+appid+'&q='+urllib.parse.quote(q)+'&from='+fromLang+'&to='+toLang+'&salt='+str(salt)+'&sign='+sign
try:
httpClient = http.client.HTTPConnection('api.fanyi.baidu.com')
httpClient.request('GET', myurl)
#response是HTTPResponse对象
response = httpClient.getresponse()
return response.read()
except Exception as e:
print(e)
finally:
if httpClient:
httpClient.close()
def chtoeng(q):
fromLang = 'zh'
toLang = 'en'
salt = random.randint(32768, 65536)
sign = appid+q+str(salt)+secretKey
m1 = hashlib.md5()
m1.update(sign.encode("utf8"))
sign = m1.hexdigest()
myurl = '/api/trans/vip/translate'
myurl = myurl+'?appid='+appid+'&q='+urllib.parse.quote(q)+'&from='+fromLang+'&to='+toLang+'&salt='+str(salt)+'&sign='+sign
try:
httpClient = http.client.HTTPConnection('api.fanyi.baidu.com')
httpClient.request('GET', myurl)
#response是HTTPResponse对象
response = httpClient.getresponse()
return response.read()
except Exception as e:
print(e)
finally:
if httpClient:
httpClient.close()
| 25.873016 | 126 | 0.610429 | 187 | 1,630 | 5.320856 | 0.352941 | 0.024121 | 0.034171 | 0.044221 | 0.824121 | 0.824121 | 0.824121 | 0.824121 | 0.824121 | 0.824121 | 0 | 0.02482 | 0.233742 | 1,630 | 62 | 127 | 26.290323 | 0.771817 | 0.053374 | 0 | 0.723404 | 0 | 0 | 0.111906 | 0.03123 | 0 | 0 | 0 | 0 | 0 | 1 | 0.042553 | false | 0 | 0.085106 | 0 | 0.170213 | 0.042553 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 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 | 7 |
0eca0e371e5bbca11af18e7f3ff4416f61707675 | 19,285 | py | Python | quedadas/tests/tests_meetings.py | fevsea/meet-Run-Server | 48454a4665f55da019334271641c514df231f177 | [
"MIT"
] | null | null | null | quedadas/tests/tests_meetings.py | fevsea/meet-Run-Server | 48454a4665f55da019334271641c514df231f177 | [
"MIT"
] | null | null | null | quedadas/tests/tests_meetings.py | fevsea/meet-Run-Server | 48454a4665f55da019334271641c514df231f177 | [
"MIT"
] | null | null | null | from collections import OrderedDict
from django.contrib.auth.models import User
from django.urls import reverse
from rest_framework import status
from rest_framework.authtoken.models import Token
from rest_framework.test import APITestCase
from populateDB import create_basic_user
from populateDB import create_basic_user_meeting
class MeetingsTests(APITestCase):
def setUp(self):
pass
def test_create_valid_meeting(self):
self.valid_payload = {
"title": "Testing Meeting",
"description": "bla bla bla",
"public": False,
"level": 1,
"date": "2017-11-28T10:52:39",
"latitude": "41.388576",
"longitude": "2.11284",
"chat": None
}
create_basic_user()
self.user = User.objects.get(username='awaisI')
token = Token.objects.create(user=self.user)
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
response = self.client.post(
reverse('meeting_list'),
data=self.valid_payload,
format='json'
)
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
resp = {
'id': 1,
'title': 'Testing Meeting',
'description': 'bla bla bla',
'public': False,
'level': 1,
'date': '2017-11-28T10:52:39Z',
'latitude': '41.388576',
'longitude': '2.11284',
'owner': OrderedDict({
'id': 1,
'username': 'awaisI',
'first_name': 'Awais',
'last_name': 'Iqbal',
'postal_code': '08019',
'question': 'hola?',
'level': 1
}),
'chat': None
}
self.assertEqual(response.data, resp)
def test_create_empty_meeting(self):
self.valid_payload = {
"title": "",
"description": "",
"public": False,
"level": None,
"date": None,
"latitude": "",
"longitude": "",
"chat": None
}
create_basic_user()
self.user = User.objects.get(username='awaisI')
token = Token.objects.create(user=self.user)
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
response = self.client.post(
reverse('meeting_list'),
data=self.valid_payload,
format='json'
)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
resp = {
'title': [
"This field may not be blank."
],
'level': [
"This field may not be null."
],
'date': [
"This field may not be null."
],
'latitude': [
"This field may not be blank."
],
'longitude': [
"This field may not be blank."
]
}
self.assertEqual(response.data, resp)
def test_get_meeting(self):
create_basic_user_meeting()
self.user = User.objects.get(username='awaisI')
token = Token.objects.create(user=self.user)
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
response = self.client.get(
reverse('meeting_detail', kwargs={'pk': 1})
)
self.assertEqual(response.status_code, status.HTTP_200_OK)
resp = {
'id': 1,
'title': 'Testing Meeting',
'description': 'bla bla bla',
'public': False,
'level': 1,
'date': '2017-11-28T10:52:39Z',
'latitude': '41.388576',
'longitude': '2.11284',
'owner': OrderedDict({
'id': 1,
'username': 'awaisI',
'first_name': 'Awais',
'last_name': 'Iqbal',
'postal_code': '08019',
'question': 'hola?',
'level': 1
}),
'chat': None
}
self.assertEqual(response.data, resp)
def test_update_meeting_title(self):
create_basic_user_meeting()
self.user = User.objects.get(username='awaisI')
token = Token.objects.create(user=self.user)
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
self.valid_payload = {
"title": "awaisI2",
}
response = self.client.patch(
reverse('meeting_detail', kwargs={'pk': 1}),
data=self.valid_payload,
format='json'
)
self.assertEqual(response.status_code, status.HTTP_200_OK)
response = self.client.get(
reverse('meeting_detail', kwargs={'pk': 1})
)
self.assertEqual(response.status_code, status.HTTP_200_OK)
resp = {
'id': 1,
'title': 'awaisI2',
'description': 'bla bla bla',
'public': False,
'level': 1,
'date': '2017-11-28T10:52:39Z',
'latitude': '41.388576',
'longitude': '2.11284',
'owner': OrderedDict({
'id': 1,
'username': 'awaisI',
'first_name': 'Awais',
'last_name': 'Iqbal',
'postal_code': '08019',
'question': 'hola?',
'level': 1
}),
'chat': None
}
self.assertEqual(response.data, resp)
def test_update_meeting_description(self):
create_basic_user_meeting()
self.user = User.objects.get(username='awaisI')
token = Token.objects.create(user=self.user)
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
self.valid_payload = {
"description": "desc2",
}
response = self.client.patch(
reverse('meeting_detail', kwargs={'pk': 1}),
data=self.valid_payload,
format='json'
)
self.assertEqual(response.status_code, status.HTTP_200_OK)
response = self.client.get(
reverse('meeting_detail', kwargs={'pk': 1})
)
self.assertEqual(response.status_code, status.HTTP_200_OK)
resp = {
'id': 1,
'title': 'Testing Meeting',
'description': 'desc2',
'public': False,
'level': 1,
'date': '2017-11-28T10:52:39Z',
'latitude': '41.388576',
'longitude': '2.11284',
'owner': OrderedDict({
'id': 1,
'username': 'awaisI',
'first_name': 'Awais',
'last_name': 'Iqbal',
'postal_code': '08019',
'question': 'hola?',
'level': 1
}),
'chat': None
}
self.assertEqual(response.data, resp)
def test_update_meeting_public(self):
create_basic_user_meeting()
self.user = User.objects.get(username='awaisI')
token = Token.objects.create(user=self.user)
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
self.valid_payload = {
"public": True,
}
response = self.client.patch(
reverse('meeting_detail', kwargs={'pk': 1}),
data=self.valid_payload,
format='json'
)
self.assertEqual(response.status_code, status.HTTP_200_OK)
response = self.client.get(
reverse('meeting_detail', kwargs={'pk': 1})
)
self.assertEqual(response.status_code, status.HTTP_200_OK)
resp = {
'id': 1,
'title': 'Testing Meeting',
'description': 'bla bla bla',
'public': True,
'level': 1,
'date': '2017-11-28T10:52:39Z',
'latitude': '41.388576',
'longitude': '2.11284',
'owner': OrderedDict({
'id': 1,
'username': 'awaisI',
'first_name': 'Awais',
'last_name': 'Iqbal',
'postal_code': '08019',
'question': 'hola?',
'level': 1
}),
'chat': None
}
self.assertEqual(response.data, resp)
def test_update_meeting_level(self):
create_basic_user_meeting()
self.user = User.objects.get(username='awaisI')
token = Token.objects.create(user=self.user)
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
self.valid_payload = {
"level": 2,
}
response = self.client.patch(
reverse('meeting_detail', kwargs={'pk': 1}),
data=self.valid_payload,
format='json'
)
self.assertEqual(response.status_code, status.HTTP_200_OK)
response = self.client.get(
reverse('meeting_detail', kwargs={'pk': 1})
)
self.assertEqual(response.status_code, status.HTTP_200_OK)
resp = {
'id': 1,
'title': 'Testing Meeting',
'description': 'bla bla bla',
'public': False,
'level': 2,
'date': '2017-11-28T10:52:39Z',
'latitude': '41.388576',
'longitude': '2.11284',
'owner': OrderedDict({
'id': 1,
'username': 'awaisI',
'first_name': 'Awais',
'last_name': 'Iqbal',
'postal_code': '08019',
'question': 'hola?',
'level': 1
}),
'chat': None
}
self.assertEqual(response.data, resp)
def test_update_meeting_date(self):
create_basic_user_meeting()
self.user = User.objects.get(username='awaisI')
token = Token.objects.create(user=self.user)
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
self.valid_payload = {
"datet": "2017-12-28T10:52:39Z",
}
response = self.client.patch(
reverse('meeting_detail', kwargs={'pk': 1}),
data=self.valid_payload,
format='json'
)
self.assertEqual(response.status_code, status.HTTP_200_OK)
response = self.client.get(
reverse('meeting_detail', kwargs={'pk': 1})
)
self.assertEqual(response.status_code, status.HTTP_200_OK)
resp = {
'id': 1,
'title': 'Testing Meeting',
'description': 'bla bla bla',
'public': False,
'level': 1,
'date': '2017-11-28T10:52:39Z',
'latitude': '41.388576',
'longitude': '2.11284',
'owner': OrderedDict({
'id': 1,
'username': 'awaisI',
'first_name': 'Awais',
'last_name': 'Iqbal',
'postal_code': '08019',
'question': 'hola?',
'level': 1
}),
'chat': None
}
self.assertEqual(response.data, resp)
def test_update_meeting_lat(self):
create_basic_user_meeting()
self.user = User.objects.get(username='awaisI')
token = Token.objects.create(user=self.user)
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
self.valid_payload = {
"latitude": "42.388576",
}
response = self.client.patch(
reverse('meeting_detail', kwargs={'pk': 1}),
data=self.valid_payload,
format='json'
)
self.assertEqual(response.status_code, status.HTTP_200_OK)
response = self.client.get(
reverse('meeting_detail', kwargs={'pk': 1})
)
self.assertEqual(response.status_code, status.HTTP_200_OK)
resp = {
'id': 1,
'title': 'Testing Meeting',
'description': 'bla bla bla',
'public': False,
'level': 1,
'date': '2017-11-28T10:52:39Z',
'latitude': '42.388576',
'longitude': '2.11284',
'owner': OrderedDict({
'id': 1,
'username': 'awaisI',
'first_name': 'Awais',
'last_name': 'Iqbal',
'postal_code': '08019',
'question': 'hola?',
'level': 1
}),
'chat': None
}
self.assertEqual(response.data, resp)
def test_update_meeting_lon(self):
create_basic_user_meeting()
self.user = User.objects.get(username='awaisI')
token = Token.objects.create(user=self.user)
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
self.valid_payload = {
"longitude": "2.19984",
}
response = self.client.patch(
reverse('meeting_detail', kwargs={'pk': 1}),
data=self.valid_payload,
format='json'
)
self.assertEqual(response.status_code, status.HTTP_200_OK)
response = self.client.get(
reverse('meeting_detail', kwargs={'pk': 1})
)
self.assertEqual(response.status_code, status.HTTP_200_OK)
resp = {
'id': 1,
'title': 'Testing Meeting',
'description': 'bla bla bla',
'public': False,
'level': 1,
'date': '2017-11-28T10:52:39Z',
'latitude': '41.388576',
'longitude': '2.19984',
'owner': OrderedDict({
'id': 1,
'username': 'awaisI',
'first_name': 'Awais',
'last_name': 'Iqbal',
'postal_code': '08019',
'question': 'hola?',
'level': 1
}),
'chat': None
}
self.assertEqual(response.data, resp)
def test_delete_meeting(self):
create_basic_user_meeting()
self.user = User.objects.get(username='awaisI')
token = Token.objects.create(user=self.user)
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
response = self.client.delete(
reverse('meeting_detail', kwargs={'pk': 1}),
format='json'
)
self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)
response = self.client.get(
reverse('meeting_detail', kwargs={'pk': 1})
)
self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)
def test_get_meetings(self):
create_basic_user_meeting()
response = self.client.get(
reverse('meeting_list'),
format='json'
)
self.assertEqual(response.status_code, status.HTTP_200_OK)
resp = OrderedDict([
('count', 1),
('next', None),
('previous', None),
('results',
[ # array de usuarios
OrderedDict([ # cada usuario es un orderedDict
('id', 1),
('title', 'Testing Meeting'),
('description', 'bla bla bla'),
('public', False),
('level', 1),
('date', '2017-11-28T10:52:39Z'),
('latitude', '41.388576'),
('longitude', '2.11284'),
('owner',
OrderedDict([ # cada usuario es un orderedDict
('id', 1),
('username', 'awaisI'),
('first_name', 'Awais'),
('last_name', 'Iqbal'),
('postal_code', '08019'),
('question', 'hola?'),
('level', 1)
])
),
('chat', None)
])
]
)
])
self.assertEqual(response.data, resp)
def test_add_get_delete_meeting_tracking(self):
create_basic_user_meeting()
self.user = User.objects.get(username='awaisI')
token = Token.objects.create(user=self.user)
self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)
response = self.client.get(
reverse('meeting-track', kwargs={'user': 1, 'meeting': 1})
)
self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND) # comprobamos que no hay ningun tracking
self.valid_payload = {
"averagespeed": 19635.94,
"distance": 221159.58,
"steps": 0,
"totalTimeMillis": 11263,
"calories": 0.0,
"routePoints": [
{"latitude": 3.0, "longitude": 41.2000},
{"latitude": 5.0, "longitude": 41.2000}
]
}
response = self.client.post(
reverse('meeting-track', kwargs={'user': 1, 'meeting': 1}),
data=self.valid_payload,
format='json'
)
self.assertEqual(response.status_code, status.HTTP_201_CREATED) # comprobamos que se ha creado
resp = {
"user": 1,
"meeting": 1,
"averagespeed": 19635.94,
"distance": 221159.58,
"steps": 0,
"totalTimeMillis": 11263,
"calories": 0.0,
"routePoints": [
OrderedDict([
('latitude', 3.0),
('longitude', 41.2000)
]),
OrderedDict([
('latitude', 5.0),
('longitude', 41.2000)
])
]
}
self.assertEqual(response.data, resp) # check el contenido del tracking
response = self.client.get(
reverse('meeting-track', kwargs={'user': 1, 'meeting': 1})
)
self.assertEqual(response.status_code, status.HTTP_200_OK) # comprobamos que no hay ningun tracking
resp = {
"user": 1,
"meeting": 1,
"averagespeed": 19635.94,
"distance": 221159.58,
"steps": 0,
"totalTimeMillis": 11263,
"calories": 0.0,
"routePoints": [
OrderedDict([
('latitude', 3.0),
('longitude', 41.2000)
]),
OrderedDict([
('latitude', 5.0),
('longitude', 41.2000)
])
]
}
self.assertEqual(response.data, resp) # check el contenido del tracking
response = self.client.delete(
reverse('meeting-track', kwargs={'user': 1, 'meeting': 1}),
data=self.valid_payload,
format='json'
)
self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT) # comprobamos que se ha borrado
response = self.client.get(
reverse('meeting-track', kwargs={'user': 1, 'meeting': 1})
)
self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND) # comprobamos que no hay ningun tracking
| 33.952465 | 115 | 0.491833 | 1,804 | 19,285 | 5.120843 | 0.088137 | 0.061702 | 0.094609 | 0.07848 | 0.920762 | 0.917948 | 0.869344 | 0.865231 | 0.859602 | 0.859602 | 0 | 0.054404 | 0.374747 | 19,285 | 567 | 116 | 34.012346 | 0.711727 | 0.016541 | 0 | 0.75 | 0 | 0 | 0.177578 | 0 | 0 | 0 | 0 | 0 | 0.07197 | 1 | 0.026515 | false | 0.001894 | 0.015152 | 0 | 0.043561 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 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 | 7 |
0ef86a1599b29f94a7d1a96bb8b274da76057748 | 9,545 | py | Python | tests/gold_tests/thread_config/thread_config.test.py | cmcfarlen/trafficserver | 2aa1d3106398eb082e5a454212b0273c63d5f69d | [
"Apache-2.0"
] | 1,351 | 2015-01-03T08:25:40.000Z | 2022-03-31T09:14:08.000Z | tests/gold_tests/thread_config/thread_config.test.py | cmcfarlen/trafficserver | 2aa1d3106398eb082e5a454212b0273c63d5f69d | [
"Apache-2.0"
] | 7,009 | 2015-01-14T16:22:45.000Z | 2022-03-31T17:18:04.000Z | tests/gold_tests/thread_config/thread_config.test.py | cmcfarlen/trafficserver | 2aa1d3106398eb082e5a454212b0273c63d5f69d | [
"Apache-2.0"
] | 901 | 2015-01-11T19:21:08.000Z | 2022-03-18T18:21:33.000Z | '''
'''
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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 sys
Test.Summary = 'Test that Trafficserver starts with different thread configurations.'
Test.ContinueOnFail = True
ts = Test.MakeATSProcess('ts-1_exec-0_accept-1_task-1_aio')
ts.Disk.records_config.update({
'proxy.config.exec_thread.autoconfig': 0,
'proxy.config.exec_thread.autoconfig.scale': 1.5,
'proxy.config.exec_thread.limit': 1,
'proxy.config.accept_threads': 0,
'proxy.config.task_threads': 1,
'proxy.config.cache.threads_per_disk': 1,
'proxy.config.diags.debug.enabled': 1,
'proxy.config.diags.debug.tags': 'iocore_thread_start|iocore_net_accept_start'})
ts.Setup.CopyAs('check_threads.py', Test.RunDirectory)
tr = Test.AddTestRun()
TS_ROOT = ts.Env['TS_ROOT']
tr.Processes.Default.Command = f'{sys.executable} check_threads.py -p {TS_ROOT} -e 1 -a 0 -t 1 -c 1'
tr.Processes.Default.ReturnCode = 0
tr.Processes.Default.StartBefore(ts)
ts = Test.MakeATSProcess('ts-1_exec-1_accept-2_task-8_aio')
ts.Disk.records_config.update({
'proxy.config.exec_thread.autoconfig': 0,
'proxy.config.exec_thread.autoconfig.scale': 1.5,
'proxy.config.exec_thread.limit': 1,
'proxy.config.accept_threads': 1,
'proxy.config.task_threads': 2,
'proxy.config.cache.threads_per_disk': 8,
'proxy.config.diags.debug.enabled': 1,
'proxy.config.diags.debug.tags': 'iocore_thread_start|iocore_net_accept_start'})
tr = Test.AddTestRun()
TS_ROOT = ts.Env['TS_ROOT']
tr.Processes.Default.Command = f'{sys.executable} check_threads.py -p {TS_ROOT} -e 1 -a 1 -t 2 -c 8'
tr.Processes.Default.ReturnCode = 0
tr.Processes.Default.StartBefore(ts)
ts = Test.MakeATSProcess('ts-1_exec-10_accept-10_task-32_aio')
ts.Disk.records_config.update({
'proxy.config.exec_thread.autoconfig': 0,
'proxy.config.exec_thread.autoconfig.scale': 1.5,
'proxy.config.exec_thread.limit': 1,
'proxy.config.accept_threads': 10,
'proxy.config.task_threads': 10,
'proxy.config.cache.threads_per_disk': 32,
'proxy.config.diags.debug.enabled': 1,
'proxy.config.diags.debug.tags': 'iocore_thread_start|iocore_net_accept_start'})
tr = Test.AddTestRun()
TS_ROOT = ts.Env['TS_ROOT']
tr.Processes.Default.Command = f'{sys.executable} check_threads.py -p {TS_ROOT} -e 1 -a 10 -t 10 -c 32'
tr.Processes.Default.ReturnCode = 0
tr.Processes.Default.StartBefore(ts)
ts = Test.MakeATSProcess('ts-2_exec-0_accept-1_task-1_aio')
ts.Disk.records_config.update({
'proxy.config.exec_thread.autoconfig': 0,
'proxy.config.exec_thread.autoconfig.scale': 1.5,
'proxy.config.exec_thread.limit': 2,
'proxy.config.accept_threads': 0,
'proxy.config.task_threads': 1,
'proxy.config.cache.threads_per_disk': 1,
'proxy.config.diags.debug.enabled': 1,
'proxy.config.diags.debug.tags': 'iocore_thread_start|iocore_net_accept_start'})
tr = Test.AddTestRun()
TS_ROOT = ts.Env['TS_ROOT']
tr.Processes.Default.Command = f'{sys.executable} check_threads.py -p {TS_ROOT} -e 2 -a 0 -t 1 -c 1'
tr.Processes.Default.ReturnCode = 0
tr.Processes.Default.StartBefore(ts)
ts = Test.MakeATSProcess('ts-2_exec-1_accept-2_task-8_aio')
ts.Disk.records_config.update({
'proxy.config.exec_thread.autoconfig': 0,
'proxy.config.exec_thread.autoconfig.scale': 1.5,
'proxy.config.exec_thread.limit': 2,
'proxy.config.accept_threads': 1,
'proxy.config.task_threads': 2,
'proxy.config.cache.threads_per_disk': 8,
'proxy.config.diags.debug.enabled': 1,
'proxy.config.diags.debug.tags': 'iocore_thread_start|iocore_net_accept_start'})
tr = Test.AddTestRun()
TS_ROOT = ts.Env['TS_ROOT']
tr.Processes.Default.Command = f'{sys.executable} check_threads.py -p {TS_ROOT} -e 2 -a 1 -t 2 -c 8'
tr.Processes.Default.ReturnCode = 0
tr.Processes.Default.StartBefore(ts)
ts = Test.MakeATSProcess('ts-2_exec-10_accept-10_task-32_aio')
ts.Disk.records_config.update({
'proxy.config.exec_thread.autoconfig': 0,
'proxy.config.exec_thread.autoconfig.scale': 1.5,
'proxy.config.exec_thread.limit': 2,
'proxy.config.accept_threads': 10,
'proxy.config.task_threads': 10,
'proxy.config.cache.threads_per_disk': 32,
'proxy.config.diags.debug.enabled': 1,
'proxy.config.diags.debug.tags': 'iocore_thread_start|iocore_net_accept_start'})
tr = Test.AddTestRun()
TS_ROOT = ts.Env['TS_ROOT']
tr.Processes.Default.Command = f'{sys.executable} check_threads.py -p {TS_ROOT} -e 2 -a 10 -t 10 -c 32'
tr.Processes.Default.ReturnCode = 0
tr.Processes.Default.StartBefore(ts)
ts = Test.MakeATSProcess('ts-32_exec-0_accept-1_task-1_aio')
ts.Disk.records_config.update({
'proxy.config.exec_thread.autoconfig': 0,
'proxy.config.exec_thread.autoconfig.scale': 1.5,
'proxy.config.exec_thread.limit': 32,
'proxy.config.accept_threads': 0,
'proxy.config.task_threads': 1,
'proxy.config.cache.threads_per_disk': 1,
'proxy.config.diags.debug.enabled': 1,
'proxy.config.diags.debug.tags': 'iocore_thread_start|iocore_net_accept_start'})
tr = Test.AddTestRun()
TS_ROOT = ts.Env['TS_ROOT']
tr.Processes.Default.Command = f'{sys.executable} check_threads.py -p {TS_ROOT} -e 32 -a 0 -t 1 -c 1'
tr.Processes.Default.ReturnCode = 0
tr.Processes.Default.StartBefore(ts)
ts = Test.MakeATSProcess('ts-32_exec-1_accept-2_task-8_aio')
ts.Disk.records_config.update({
'proxy.config.exec_thread.autoconfig': 0,
'proxy.config.exec_thread.autoconfig.scale': 1.5,
'proxy.config.exec_thread.limit': 32,
'proxy.config.accept_threads': 1,
'proxy.config.task_threads': 2,
'proxy.config.cache.threads_per_disk': 8,
'proxy.config.diags.debug.enabled': 1,
'proxy.config.diags.debug.tags': 'iocore_thread_start|iocore_net_accept_start'})
tr = Test.AddTestRun()
TS_ROOT = ts.Env['TS_ROOT']
tr.Processes.Default.Command = f'{sys.executable} check_threads.py -p {TS_ROOT} -e 32 -a 1 -t 2 -c 8'
tr.Processes.Default.ReturnCode = 0
tr.Processes.Default.StartBefore(ts)
ts = Test.MakeATSProcess('ts-32_exec-10_accept-10_task-32_aio')
ts.Disk.records_config.update({
'proxy.config.exec_thread.autoconfig': 0,
'proxy.config.exec_thread.autoconfig.scale': 1.5,
'proxy.config.exec_thread.limit': 32,
'proxy.config.accept_threads': 10,
'proxy.config.task_threads': 10,
'proxy.config.cache.threads_per_disk': 32,
'proxy.config.diags.debug.enabled': 1,
'proxy.config.diags.debug.tags': 'iocore_thread_start|iocore_net_accept_start'})
tr = Test.AddTestRun()
TS_ROOT = ts.Env['TS_ROOT']
tr.Processes.Default.Command = f'{sys.executable} check_threads.py -p {TS_ROOT} -e 32 -a 10 -t 10 -c 32'
tr.Processes.Default.ReturnCode = 0
tr.Processes.Default.StartBefore(ts)
ts = Test.MakeATSProcess('ts-100_exec-0_accept-1_task-1_aio')
ts.Disk.records_config.update({
'proxy.config.exec_thread.autoconfig': 0,
'proxy.config.exec_thread.autoconfig.scale': 1.5,
'proxy.config.exec_thread.limit': 100,
'proxy.config.accept_threads': 0,
'proxy.config.task_threads': 1,
'proxy.config.cache.threads_per_disk': 1,
'proxy.config.diags.debug.enabled': 1,
'proxy.config.diags.debug.tags': 'iocore_thread_start|iocore_net_accept_start'})
tr = Test.AddTestRun()
TS_ROOT = ts.Env['TS_ROOT']
tr.Processes.Default.Command = f'{sys.executable} check_threads.py -p {TS_ROOT} -e 100 -a 0 -t 1 -c 1'
tr.Processes.Default.ReturnCode = 0
tr.Processes.Default.StartBefore(ts)
ts = Test.MakeATSProcess('ts-100_exec-1_accept-2_task-8_aio')
ts.Disk.records_config.update({
'proxy.config.exec_thread.autoconfig': 0,
'proxy.config.exec_thread.autoconfig.scale': 1.5,
'proxy.config.exec_thread.limit': 100,
'proxy.config.accept_threads': 1,
'proxy.config.task_threads': 2,
'proxy.config.cache.threads_per_disk': 8,
'proxy.config.diags.debug.enabled': 1,
'proxy.config.diags.debug.tags': 'iocore_thread_start|iocore_net_accept_start'})
tr = Test.AddTestRun()
TS_ROOT = ts.Env['TS_ROOT']
tr.Processes.Default.Command = f'{sys.executable} check_threads.py -p {TS_ROOT} -e 100 -a 1 -t 2 -c 8'
tr.Processes.Default.ReturnCode = 0
tr.Processes.Default.StartBefore(ts)
ts = Test.MakeATSProcess('ts-100_exec-10_accept-10_task-32_aio')
ts.Disk.records_config.update({
'proxy.config.exec_thread.autoconfig': 0,
'proxy.config.exec_thread.autoconfig.scale': 1.5,
'proxy.config.exec_thread.limit': 100,
'proxy.config.accept_threads': 10,
'proxy.config.task_threads': 10,
'proxy.config.cache.threads_per_disk': 32,
'proxy.config.diags.debug.enabled': 1,
'proxy.config.diags.debug.tags': 'iocore_thread_start|iocore_net_accept_start'})
tr = Test.AddTestRun()
TS_ROOT = ts.Env['TS_ROOT']
tr.Processes.Default.Command = f'{sys.executable} check_threads.py -p {TS_ROOT} -e 100 -a 10 -t 10 -c 32'
tr.Processes.Default.ReturnCode = 0
tr.Processes.Default.StartBefore(ts)
| 41.864035 | 105 | 0.735464 | 1,487 | 9,545 | 4.550773 | 0.090114 | 0.156051 | 0.079799 | 0.111719 | 0.888872 | 0.888872 | 0.885474 | 0.885474 | 0.885474 | 0.885474 | 0 | 0.032105 | 0.115663 | 9,545 | 227 | 106 | 42.048458 | 0.769577 | 0.080147 | 0 | 0.847826 | 0 | 0.065217 | 0.563956 | 0.451919 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.005435 | 0 | 0.005435 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
0efd4029f91eaadfa8f28dc83ada6061e30b95b7 | 13,734 | py | Python | tests/unit/modules/test_solarisips.py | thusoy/salt | 9bcafa652a4bf9d5fddce73253b191597b68fd31 | [
"Apache-2.0"
] | 1 | 2015-01-18T15:04:16.000Z | 2015-01-18T15:04:16.000Z | tests/unit/modules/test_solarisips.py | thusoy/salt | 9bcafa652a4bf9d5fddce73253b191597b68fd31 | [
"Apache-2.0"
] | null | null | null | tests/unit/modules/test_solarisips.py | thusoy/salt | 9bcafa652a4bf9d5fddce73253b191597b68fd31 | [
"Apache-2.0"
] | null | null | null | # -*- coding: utf-8 -*-
# Import Python Libs
from __future__ import absolute_import
# Import Salt Testing Libs
from tests.support.mixins import LoaderModuleMockMixin
from tests.support.unit import TestCase, skipIf
from tests.support.mock import (
MagicMock,
patch,
NO_MOCK,
NO_MOCK_REASON
)
# Import Salt libs
import salt.modules.solarisips as solarisips
import salt.modules.pkg_resource as pkg_resource
import salt.utils.data
@skipIf(NO_MOCK, NO_MOCK_REASON)
class IpsTestCase(TestCase, LoaderModuleMockMixin):
'''
Test cases for salt.modules.solarisips
'''
def setup_loader_modules(self):
self.opts = opts = salt.config.DEFAULT_MINION_OPTS
utils = salt.loader.utils(
opts,
whitelist=['pkg', 'path', 'platform'])
return {
pkg_resource: {
'__grains__': {
'osarch': 'sparcv9',
'os_family': 'Solaris',
'osmajorrelease': 11,
'kernelrelease': 5.11,
},
},
solarisips: {
'__opts__': opts,
'__utils__': utils,
}
}
def test_install_single_package(self):
'''
Test installing a single package
'''
pkg_list_pre = {
'pkg://solaris/compress/bzip2': '1.0.6,5.11-0.175.3.10.0.4.0:20160630T215500Z',
'pkg://solaris/compress/gzip': '1.5,5.11-0.175.3.0.0.30.0:20150821T161446Z',
'pkg://solaris/compress/p7zip': '16.2.3,5.11-0.175.3.34.0.2.0:20180614T204908Z',
}
pkg_list_post = {
'pkg://solaris/compress/bzip2': '1.0.6,5.11-0.175.3.10.0.4.0:20160630T215500Z',
'pkg://solaris/compress/gzip': '1.5,5.11-0.175.3.0.0.30.0:20150821T161446Z',
'pkg://solaris/compress/p7zip': '16.2.3,5.11-0.175.3.34.0.2.0:20180614T204908Z',
'pkg://solaris/text/less': '458,5.11-0.175.3.0.0.30.0:20150821T172730Z',
}
install_cmd = {
'pid': 1234,
'retcode': 0,
'stderr': '',
'stdout': '',
}
mock_install_cmd = MagicMock(return_value=install_cmd)
list_pkgs_responses = [pkg_list_pre, pkg_list_post]
with patch.object(solarisips, 'is_installed', return_value=False), \
patch.object(solarisips, 'list_pkgs', side_effect=list_pkgs_responses), \
patch.dict(solarisips.__salt__, {'cmd.run_all': mock_install_cmd}):
result = solarisips.install(name='less', refresh=False)
self.assertEqual(result, salt.utils.data.compare_dicts(pkg_list_pre, pkg_list_post))
def test_install_list_pkgs(self):
'''
Test installing a list of packages
'''
pkg_list_pre = {
'pkg://solaris/compress/bzip2': '1.0.6,5.11-0.175.3.10.0.4.0:20160630T215500Z',
'pkg://solaris/compress/gzip': '1.5,5.11-0.175.3.0.0.30.0:20150821T161446Z',
'pkg://solaris/compress/p7zip': '16.2.3,5.11-0.175.3.34.0.2.0:20180614T204908Z',
}
pkg_list_post = {
'pkg://solaris/compress/bzip2': '1.0.6,5.11-0.175.3.10.0.4.0:20160630T215500Z',
'pkg://solaris/compress/gzip': '1.5,5.11-0.175.3.0.0.30.0:20150821T161446Z',
'pkg://solaris/compress/p7zip': '16.2.3,5.11-0.175.3.34.0.2.0:20180614T204908Z',
'pkg://solaris/text/less': '458,5.11-0.175.3.0.0.30.0:20150821T172730Z',
'pkg://solaris/system/library/security/libsasl': '0.5.11,5.11-0.175.3.32.0.1.0:20180406T191209Z',
}
install_cmd = {
'pid': 1234,
'retcode': 0,
'stderr': '',
'stdout': '',
}
mock_install_cmd = MagicMock(return_value=install_cmd)
list_pkgs_responses = [pkg_list_pre, pkg_list_post]
with patch.object(solarisips, 'is_installed', return_value=False), \
patch.object(solarisips, 'list_pkgs', side_effect=list_pkgs_responses), \
patch.dict(solarisips.__salt__, {'cmd.run_all': mock_install_cmd}):
result = solarisips.install(pkgs=['less', 'libsasl'], refresh=False)
self.assertEqual(result, salt.utils.data.compare_dicts(pkg_list_pre, pkg_list_post))
def test_install_dict_pkgs_no_version(self):
'''
Test installing a list of packages
'''
pkg_list_pre = {
'pkg://solaris/compress/bzip2': '1.0.6,5.11-0.175.3.10.0.4.0:20160630T215500Z',
'pkg://solaris/compress/gzip': '1.5,5.11-0.175.3.0.0.30.0:20150821T161446Z',
'pkg://solaris/compress/p7zip': '16.2.3,5.11-0.175.3.34.0.2.0:20180614T204908Z',
}
pkg_list_post = {
'pkg://solaris/compress/bzip2': '1.0.6,5.11-0.175.3.10.0.4.0:20160630T215500Z',
'pkg://solaris/compress/gzip': '1.5,5.11-0.175.3.0.0.30.0:20150821T161446Z',
'pkg://solaris/compress/p7zip': '16.2.3,5.11-0.175.3.34.0.2.0:20180614T204908Z',
'pkg://solaris/text/less': '458,5.11-0.175.3.0.0.30.0:20150821T172730Z',
'pkg://solaris/system/library/security/libsasl': '0.5.11,5.11-0.175.3.32.0.1.0:20180406T191209Z',
}
install_cmd = {
'pid': 1234,
'retcode': 0,
'stderr': '',
'stdout': '',
}
mock_install_cmd = MagicMock(return_value=install_cmd)
list_pkgs_responses = [pkg_list_pre, pkg_list_post]
with patch.object(solarisips, 'is_installed', return_value=False), \
patch.object(solarisips, 'list_pkgs', side_effect=list_pkgs_responses), \
patch.dict(solarisips.__salt__, {'cmd.run_all': mock_install_cmd}):
result = solarisips.install(pkgs=[{'less': ''}, {'libsasl': ''}], refresh=False)
self.assertEqual(result, salt.utils.data.compare_dicts(pkg_list_pre, pkg_list_post))
def test_install_dict_pkgs_with_version(self):
'''
Test installing a list of packages
'''
pkg_list_pre = {
'pkg://solaris/compress/bzip2': '1.0.6,5.11-0.175.3.10.0.4.0:20160630T215500Z',
'pkg://solaris/compress/gzip': '1.5,5.11-0.175.3.0.0.30.0:20150821T161446Z',
'pkg://solaris/compress/p7zip': '16.2.3,5.11-0.175.3.34.0.2.0:20180614T204908Z',
}
pkg_list_post = {
'pkg://solaris/compress/bzip2': '1.0.6,5.11-0.175.3.10.0.4.0:20160630T215500Z',
'pkg://solaris/compress/gzip': '1.5,5.11-0.175.3.0.0.30.0:20150821T161446Z',
'pkg://solaris/compress/p7zip': '16.2.3,5.11-0.175.3.34.0.2.0:20180614T204908Z',
'pkg://solaris/text/less': '458,5.11-0.175.3.0.0.30.0:20150821T172730Z',
'pkg://solaris/system/library/security/libsasl': '0.5.11,5.11-0.175.3.32.0.1.0:20180406T191209Z',
}
install_cmd = {
'pid': 1234,
'retcode': 0,
'stderr': '',
'stdout': '',
}
mock_install_cmd = MagicMock(return_value=install_cmd)
list_pkgs_responses = [pkg_list_pre, pkg_list_post]
with patch.object(solarisips, 'is_installed', return_value=False), \
patch.object(solarisips, 'list_pkgs', side_effect=list_pkgs_responses), \
patch.dict(solarisips.__salt__, {'cmd.run_all': mock_install_cmd}):
result = solarisips.install(pkgs=[
{'less': '458,5.11-0.175.3.0.0.30.0:20150821T172730Z'},
{'libsasl': '0.5.11,5.11-0.175.3.32.0.1.0:20180406T191209Z'}], refresh=False)
self.assertEqual(result, salt.utils.data.compare_dicts(pkg_list_pre, pkg_list_post))
def test_install_already_installed_single_pkg(self):
'''
Test installing a package that is already installed
'''
result = None
expected_result = {}
with patch.object(solarisips, 'is_installed', return_value=True):
result = solarisips.install(name='less')
self.assertEqual(result, expected_result)
def test_install_dict_pkgs_with_version_validate_cmd(self):
'''
Test installing a list of packages
'''
def check_param(arg, **kwargs):
self.assertEqual(arg, [
'pkg',
'install',
'-v',
'--accept',
'less@458,5.11-0.175.3.0.0.30.0:20150821T172730Z',
'libsasl@0.5.11,5.11-0.175.3.32.0.1.0:20180406T191209Z'
])
return {
'pid': 1234,
'retcode': 0,
'stderr': '',
'stdout': '',
}
pkg_list_pre = {
'pkg://solaris/compress/bzip2': '1.0.6,5.11-0.175.3.10.0.4.0:20160630T215500Z',
'pkg://solaris/compress/gzip': '1.5,5.11-0.175.3.0.0.30.0:20150821T161446Z',
'pkg://solaris/compress/p7zip': '16.2.3,5.11-0.175.3.34.0.2.0:20180614T204908Z',
}
pkg_list_post = {
'pkg://solaris/compress/bzip2': '1.0.6,5.11-0.175.3.10.0.4.0:20160630T215500Z',
'pkg://solaris/compress/gzip': '1.5,5.11-0.175.3.0.0.30.0:20150821T161446Z',
'pkg://solaris/compress/p7zip': '16.2.3,5.11-0.175.3.34.0.2.0:20180614T204908Z',
'pkg://solaris/text/less': '458,5.11-0.175.3.0.0.30.0:20150821T172730Z',
'pkg://solaris/system/library/security/libsasl': '0.5.11,5.11-0.175.3.32.0.1.0:20180406T191209Z',
}
mock_install_cmd = MagicMock(side_effect=check_param)
list_pkgs_responses = [pkg_list_pre, pkg_list_post]
with patch.object(solarisips, 'is_installed', return_value=False), \
patch.object(solarisips, 'list_pkgs', side_effect=list_pkgs_responses):
with patch.dict(solarisips.__salt__, {'cmd.run_all': mock_install_cmd}):
result = solarisips.install(pkgs=[
{'less': '458,5.11-0.175.3.0.0.30.0:20150821T172730Z'},
{'libsasl': '0.5.11,5.11-0.175.3.32.0.1.0:20180406T191209Z'}], refresh=False)
def test_install_dict_pkgs_no_version_validate_cmd(self):
'''
Test installing a list of packages
'''
def check_param(arg, **kwargs):
self.assertEqual(arg, [
'pkg',
'install',
'-v',
'--accept',
'less',
'libsasl'
])
return {
'pid': 1234,
'retcode': 0,
'stderr': '',
'stdout': '',
}
pkg_list_pre = {
'pkg://solaris/compress/bzip2': '1.0.6,5.11-0.175.3.10.0.4.0:20160630T215500Z',
'pkg://solaris/compress/gzip': '1.5,5.11-0.175.3.0.0.30.0:20150821T161446Z',
'pkg://solaris/compress/p7zip': '16.2.3,5.11-0.175.3.34.0.2.0:20180614T204908Z',
}
pkg_list_post = {
'pkg://solaris/compress/bzip2': '1.0.6,5.11-0.175.3.10.0.4.0:20160630T215500Z',
'pkg://solaris/compress/gzip': '1.5,5.11-0.175.3.0.0.30.0:20150821T161446Z',
'pkg://solaris/compress/p7zip': '16.2.3,5.11-0.175.3.34.0.2.0:20180614T204908Z',
'pkg://solaris/text/less': '458,5.11-0.175.3.0.0.30.0:20150821T172730Z',
'pkg://solaris/system/library/security/libsasl': '0.5.11,5.11-0.175.3.32.0.1.0:20180406T191209Z',
}
mock_install_cmd = MagicMock(side_effect=check_param)
list_pkgs_responses = [pkg_list_pre, pkg_list_post]
with patch.object(solarisips, 'is_installed', return_value=False), \
patch.object(solarisips, 'list_pkgs', side_effect=list_pkgs_responses):
with patch.dict(solarisips.__salt__, {'cmd.run_all': mock_install_cmd}):
result = solarisips.install(pkgs=[
{'less': ''},
{'libsasl': ''}], refresh=False)
def test_install_list_pkgs_validate_cmd(self):
'''
Test installing a list of packages
'''
def check_param(arg, **kwargs):
self.assertEqual(arg, [
'pkg',
'install',
'-v',
'--accept',
'less',
'libsasl'
])
return {
'pid': 1234,
'retcode': 0,
'stderr': '',
'stdout': '',
}
pkg_list_pre = {
'pkg://solaris/compress/bzip2': '1.0.6,5.11-0.175.3.10.0.4.0:20160630T215500Z',
'pkg://solaris/compress/gzip': '1.5,5.11-0.175.3.0.0.30.0:20150821T161446Z',
'pkg://solaris/compress/p7zip': '16.2.3,5.11-0.175.3.34.0.2.0:20180614T204908Z',
}
pkg_list_post = {
'pkg://solaris/compress/bzip2': '1.0.6,5.11-0.175.3.10.0.4.0:20160630T215500Z',
'pkg://solaris/compress/gzip': '1.5,5.11-0.175.3.0.0.30.0:20150821T161446Z',
'pkg://solaris/compress/p7zip': '16.2.3,5.11-0.175.3.34.0.2.0:20180614T204908Z',
'pkg://solaris/text/less': '458,5.11-0.175.3.0.0.30.0:20150821T172730Z',
'pkg://solaris/system/library/security/libsasl': '0.5.11,5.11-0.175.3.32.0.1.0:20180406T191209Z',
}
mock_install_cmd = MagicMock(side_effect=check_param)
list_pkgs_responses = [pkg_list_pre, pkg_list_post]
with patch.object(solarisips, 'is_installed', return_value=False), \
patch.object(solarisips, 'list_pkgs', side_effect=list_pkgs_responses):
with patch.dict(solarisips.__salt__, {'cmd.run_all': mock_install_cmd}):
result = solarisips.install(pkgs=['less', 'libsasl'], refresh=False)
| 46.087248 | 109 | 0.566477 | 1,818 | 13,734 | 4.119362 | 0.073157 | 0.028442 | 0.032581 | 0.057017 | 0.890373 | 0.876352 | 0.876352 | 0.868874 | 0.862598 | 0.862598 | 0 | 0.189875 | 0.267948 | 13,734 | 297 | 110 | 46.242424 | 0.555003 | 0.030363 | 0 | 0.712 | 0 | 0.244 | 0.374333 | 0.325225 | 0 | 0 | 0 | 0 | 0.032 | 1 | 0.048 | false | 0 | 0.028 | 0 | 0.096 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
161992962e2a1106ebaa144b4c2bdedc2d8e3e7c | 2,658 | py | Python | src/genie/libs/parser/iosxe/tests/ShowPlatformPacketSumm/cli/equal/golden2_expected.py | nielsvanhooy/genieparser | 9a1955749697a6777ca614f0af4d5f3a2c254ccd | [
"Apache-2.0"
] | null | null | null | src/genie/libs/parser/iosxe/tests/ShowPlatformPacketSumm/cli/equal/golden2_expected.py | nielsvanhooy/genieparser | 9a1955749697a6777ca614f0af4d5f3a2c254ccd | [
"Apache-2.0"
] | null | null | null | src/genie/libs/parser/iosxe/tests/ShowPlatformPacketSumm/cli/equal/golden2_expected.py | nielsvanhooy/genieparser | 9a1955749697a6777ca614f0af4d5f3a2c254ccd | [
"Apache-2.0"
] | null | null | null | expected_output = {
'packets': {
0: {'input_intf': 'Vl120', 'output_intf': 'internal0/0/rp:0', 'state': 'PUNT',
'reason': {'code': 130, 'text': '(wls CAPWAP Packets to LFTS'}},
1: {'input_intf': 'Vl120', 'output_intf': 'internal0/0/rp:0', 'state': 'PUNT',
'reason': {'code': 97, 'text': '(Packets to LFTS)'}},
2: {'input_intf': 'Vl120', 'output_intf': 'internal0/0/rp:0', 'state': 'PUNT',
'reason': {'code': 129, 'text': '(wls 802.11 Packets to LFTS'}},
3: {'input_intf': 'Vl120', 'output_intf': 'internal0/0/rp:0', 'state': 'PUNT',
'reason': {'code': 129, 'text': '(wls 802.11 Packets to LFTS'}},
4: {'input_intf': 'Vl120', 'output_intf': 'internal0/0/rp:0', 'state': 'PUNT',
'reason': {'code': 97, 'text': '(Packets to LFTS)'}},
5: {'input_intf': 'Vl120', 'output_intf': 'internal0/0/rp:0', 'state': 'PUNT',
'reason': {'code': 129, 'text': '(wls 802.11 Packets to LFTS'}},
6: {'input_intf': 'Vl120', 'output_intf': 'internal0/0/rp:0', 'state': 'PUNT',
'reason': {'code': 129, 'text': '(wls 802.11 Packets to LFTS'}},
7: {'input_intf': 'Vl120', 'output_intf': 'internal0/0/rp:0', 'state': 'PUNT',
'reason': {'code': 129, 'text': '(wls 802.11 Packets to LFTS'}},
8: {'input_intf': 'Vl120', 'output_intf': 'internal0/0/rp:0', 'state': 'PUNT',
'reason': {'code': 129, 'text': '(wls 802.11 Packets to LFTS'}},
9: {'input_intf': 'Vl120', 'output_intf': 'internal0/0/rp:0', 'state': 'PUNT',
'reason': {'code': 129, 'text': '(wls 802.11 Packets to LFTS'}},
10: {'input_intf': 'Vl120', 'output_intf': 'internal0/0/rp:0', 'state': 'PUNT',
'reason': {'code': 129, 'text': '(wls 802.11 Packets to LFTS'}},
11: {'input_intf': 'Vl120', 'output_intf': 'internal0/0/rp:0', 'state': 'PUNT',
'reason': {'code': 129, 'text': '(wls 802.11 Packets to LFTS'}},
12: {'input_intf': 'Vl120', 'output_intf': 'internal0/0/rp:0', 'state': 'PUNT',
'reason': {'code': 129, 'text': '(wls 802.11 Packets to LFTS'}},
13: {'input_intf': 'Vl120', 'output_intf': 'internal0/0/rp:0', 'state': 'PUNT',
'reason': {'code': 129, 'text': '(wls 802.11 Packets to LFTS'}},
14: {'input_intf': 'Vl120', 'output_intf': 'internal0/0/rp:0', 'state': 'PUNT',
'reason': {'code': 129, 'text': '(wls 802.11 Packets to LFTS'}},
15: {'input_intf': 'Vl120', 'output_intf': 'internal0/0/rp:0', 'state': 'PUNT',
'reason': {'code': 129, 'text': '(wls 802.11 Packets to LFTS'}}
}
}
| 71.837838 | 87 | 0.52784 | 348 | 2,658 | 3.936782 | 0.106322 | 0.105109 | 0.163504 | 0.233577 | 0.947445 | 0.947445 | 0.947445 | 0.947445 | 0.947445 | 0.947445 | 0 | 0.110896 | 0.2231 | 2,658 | 36 | 88 | 73.833333 | 0.552542 | 0 | 0 | 0.388889 | 0 | 0 | 0.548909 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 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 | 7 |
1656e0872feded24de0a9fa56895444bd6e3f2b1 | 56,037 | py | Python | huaweicloud-sdk-ces/huaweicloudsdkces/v1/ces_client.py | huaweicloud/huaweicloud-sdk-python-v3 | 7a6270390fcbf192b3882bf763e7016e6026ef78 | [
"Apache-2.0"
] | 64 | 2020-06-12T07:05:07.000Z | 2022-03-30T03:32:50.000Z | huaweicloud-sdk-ces/huaweicloudsdkces/v1/ces_client.py | huaweicloud/huaweicloud-sdk-python-v3 | 7a6270390fcbf192b3882bf763e7016e6026ef78 | [
"Apache-2.0"
] | 11 | 2020-07-06T07:56:54.000Z | 2022-01-11T11:14:40.000Z | huaweicloud-sdk-ces/huaweicloudsdkces/v1/ces_client.py | huaweicloud/huaweicloud-sdk-python-v3 | 7a6270390fcbf192b3882bf763e7016e6026ef78 | [
"Apache-2.0"
] | 24 | 2020-06-08T11:42:13.000Z | 2022-03-04T06:44:08.000Z | # coding: utf-8
from __future__ import absolute_import
import datetime
import re
import importlib
import six
from huaweicloudsdkcore.client import Client, ClientBuilder
from huaweicloudsdkcore.exceptions import exceptions
from huaweicloudsdkcore.utils import http_utils
from huaweicloudsdkcore.sdk_stream_request import SdkStreamRequest
class CesClient(Client):
"""
:param configuration: .Configuration object for this client
:param pool_threads: The number of threads to use for async requests
to the API. More threads means more concurrent API requests.
"""
PRIMITIVE_TYPES = (float, bool, bytes, six.text_type) + six.integer_types
NATIVE_TYPES_MAPPING = {
'int': int,
'long': int if six.PY3 else long,
'float': float,
'str': str,
'bool': bool,
'date': datetime.date,
'datetime': datetime.datetime,
'object': object,
}
def __init__(self):
super(CesClient, self).__init__()
self.model_package = importlib.import_module("huaweicloudsdkces.v1.model")
self.preset_headers = {'User-Agent': 'HuaweiCloud-SDK-Python'}
@classmethod
def new_builder(cls, clazz=None):
if clazz is None:
return ClientBuilder(cls)
if clazz.__name__ != "CesClient":
raise TypeError("client type error, support client type is CesClient")
return ClientBuilder(clazz)
def batch_list_metric_data(self, request):
"""批量查询监控数据
批量查询指定时间范围内指定指标的指定粒度的监控数据，目前最多支持500指标的批量查询。 对于不同的period取值和查询的指标数量，默认的最大查询区间(to-from)不同。 规则为\"指标数量*(to-from)/监控周期<=3000\"，若超出阈值，会自动调整from以满足规则。
:param BatchListMetricDataRequest request
:return: BatchListMetricDataResponse
"""
return self.batch_list_metric_data_with_http_info(request)
def batch_list_metric_data_with_http_info(self, request):
"""批量查询监控数据
批量查询指定时间范围内指定指标的指定粒度的监控数据，目前最多支持500指标的批量查询。 对于不同的period取值和查询的指标数量，默认的最大查询区间(to-from)不同。 规则为\"指标数量*(to-from)/监控周期<=3000\"，若超出阈值，会自动调整from以满足规则。
:param BatchListMetricDataRequest request
:return: BatchListMetricDataResponse
"""
all_params = ['batch_list_metric_data_request_body']
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = {}
body_params = None
if 'body' in local_var_params:
body_params = local_var_params['body']
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/V1.0/{project_id}/batch-query-metric-data',
method='POST',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='BatchListMetricDataResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def create_alarm(self, request):
"""创建告警规则
创建一条告警规则。
:param CreateAlarmRequest request
:return: CreateAlarmResponse
"""
return self.create_alarm_with_http_info(request)
def create_alarm_with_http_info(self, request):
"""创建告警规则
创建一条告警规则。
:param CreateAlarmRequest request
:return: CreateAlarmResponse
"""
all_params = ['create_alarm_request_body']
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = {}
body_params = None
if 'body' in local_var_params:
body_params = local_var_params['body']
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/V1.0/{project_id}/alarms',
method='POST',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='CreateAlarmResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def create_alarm_template(self, request):
"""创建自定义告警模板
创建自定义告警模板。
:param CreateAlarmTemplateRequest request
:return: CreateAlarmTemplateResponse
"""
return self.create_alarm_template_with_http_info(request)
def create_alarm_template_with_http_info(self, request):
"""创建自定义告警模板
创建自定义告警模板。
:param CreateAlarmTemplateRequest request
:return: CreateAlarmTemplateResponse
"""
all_params = ['create_alarm_template_request_body']
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = {}
body_params = None
if 'body' in local_var_params:
body_params = local_var_params['body']
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/V1.0/{project_id}/alarm-template',
method='POST',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='CreateAlarmTemplateResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def create_events(self, request):
"""上报事件
上报自定义事件。
:param CreateEventsRequest request
:return: CreateEventsResponse
"""
return self.create_events_with_http_info(request)
def create_events_with_http_info(self, request):
"""上报事件
上报自定义事件。
:param CreateEventsRequest request
:return: CreateEventsResponse
"""
all_params = ['event_items']
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = {}
body_params = None
if 'body' in local_var_params:
body_params = local_var_params['body']
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/V1.0/{project_id}/events',
method='POST',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='CreateEventsResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def create_metric_data(self, request):
"""添加监控数据
添加一条或多条指标监控数据。
:param CreateMetricDataRequest request
:return: CreateMetricDataResponse
"""
return self.create_metric_data_with_http_info(request)
def create_metric_data_with_http_info(self, request):
"""添加监控数据
添加一条或多条指标监控数据。
:param CreateMetricDataRequest request
:return: CreateMetricDataResponse
"""
all_params = ['metric_data_item']
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = {}
body_params = None
if 'body' in local_var_params:
body_params = local_var_params['body']
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/V1.0/{project_id}/metric-data',
method='POST',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='CreateMetricDataResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def create_resource_group(self, request):
"""创建资源分组
创建资源分组，资源分组支持将各类资源按照业务集中进行分组管理，可以从分组角度查看监控与告警信息，以提升运维效率。
:param CreateResourceGroupRequest request
:return: CreateResourceGroupResponse
"""
return self.create_resource_group_with_http_info(request)
def create_resource_group_with_http_info(self, request):
"""创建资源分组
创建资源分组，资源分组支持将各类资源按照业务集中进行分组管理，可以从分组角度查看监控与告警信息，以提升运维效率。
:param CreateResourceGroupRequest request
:return: CreateResourceGroupResponse
"""
all_params = ['create_resource_group_request_body']
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = {}
body_params = None
if 'body' in local_var_params:
body_params = local_var_params['body']
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/V1.0/{project_id}/resource-groups',
method='POST',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='CreateResourceGroupResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def delete_alarm(self, request):
"""删除告警规则
删除一条告警规则。
:param DeleteAlarmRequest request
:return: DeleteAlarmResponse
"""
return self.delete_alarm_with_http_info(request)
def delete_alarm_with_http_info(self, request):
"""删除告警规则
删除一条告警规则。
:param DeleteAlarmRequest request
:return: DeleteAlarmResponse
"""
all_params = ['alarm_id']
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
if 'alarm_id' in local_var_params:
path_params['alarm_id'] = local_var_params['alarm_id']
query_params = []
header_params = {}
form_params = {}
body_params = None
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/V1.0/{project_id}/alarms/{alarm_id}',
method='DELETE',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='DeleteAlarmResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def delete_alarm_template(self, request):
"""删除自定义告警模板
根据ID删除自定义告警模板。
:param DeleteAlarmTemplateRequest request
:return: DeleteAlarmTemplateResponse
"""
return self.delete_alarm_template_with_http_info(request)
def delete_alarm_template_with_http_info(self, request):
"""删除自定义告警模板
根据ID删除自定义告警模板。
:param DeleteAlarmTemplateRequest request
:return: DeleteAlarmTemplateResponse
"""
all_params = ['template_id']
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
if 'template_id' in local_var_params:
path_params['template_id'] = local_var_params['template_id']
query_params = []
header_params = {}
form_params = {}
body_params = None
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/V1.0/{project_id}/alarm-template/{template_id}',
method='DELETE',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='DeleteAlarmTemplateResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def delete_resource_group(self, request):
"""删除资源分组
删除一条资源分组。
:param DeleteResourceGroupRequest request
:return: DeleteResourceGroupResponse
"""
return self.delete_resource_group_with_http_info(request)
def delete_resource_group_with_http_info(self, request):
"""删除资源分组
删除一条资源分组。
:param DeleteResourceGroupRequest request
:return: DeleteResourceGroupResponse
"""
all_params = ['group_id']
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
if 'group_id' in local_var_params:
path_params['group_id'] = local_var_params['group_id']
query_params = []
header_params = {}
form_params = {}
body_params = None
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/V1.0/{project_id}/resource-groups/{group_id}',
method='DELETE',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='DeleteResourceGroupResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def list_alarm_histories(self, request):
"""查询告警历史
查询告警历史列表。
:param ListAlarmHistoriesRequest request
:return: ListAlarmHistoriesResponse
"""
return self.list_alarm_histories_with_http_info(request)
def list_alarm_histories_with_http_info(self, request):
"""查询告警历史
查询告警历史列表。
:param ListAlarmHistoriesRequest request
:return: ListAlarmHistoriesResponse
"""
all_params = ['group_id', 'alarm_id', 'alarm_name', 'alarm_status', 'alarm_level', 'namespace', '_from', 'to', 'start', 'limit']
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
query_params = []
if 'group_id' in local_var_params:
query_params.append(('group_id', local_var_params['group_id']))
if 'alarm_id' in local_var_params:
query_params.append(('alarm_id', local_var_params['alarm_id']))
if 'alarm_name' in local_var_params:
query_params.append(('alarm_name', local_var_params['alarm_name']))
if 'alarm_status' in local_var_params:
query_params.append(('alarm_status', local_var_params['alarm_status']))
if 'alarm_level' in local_var_params:
query_params.append(('alarm_level', local_var_params['alarm_level']))
if 'namespace' in local_var_params:
query_params.append(('namespace', local_var_params['namespace']))
if '_from' in local_var_params:
query_params.append(('from', local_var_params['_from']))
if 'to' in local_var_params:
query_params.append(('to', local_var_params['to']))
if 'start' in local_var_params:
query_params.append(('start', local_var_params['start']))
if 'limit' in local_var_params:
query_params.append(('limit', local_var_params['limit']))
header_params = {}
form_params = {}
body_params = None
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/V1.0/{project_id}/alarm-histories',
method='GET',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='ListAlarmHistoriesResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def list_alarm_templates(self, request):
"""查询自定义告警模板列表
查询自定义告警模板列表
:param ListAlarmTemplatesRequest request
:return: ListAlarmTemplatesResponse
"""
return self.list_alarm_templates_with_http_info(request)
def list_alarm_templates_with_http_info(self, request):
"""查询自定义告警模板列表
查询自定义告警模板列表
:param ListAlarmTemplatesRequest request
:return: ListAlarmTemplatesResponse
"""
all_params = ['alarm_template_id', 'namespace', 'dname', 'start', 'limit']
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
query_params = []
if 'alarm_template_id' in local_var_params:
query_params.append(('alarmTemplateId', local_var_params['alarm_template_id']))
if 'namespace' in local_var_params:
query_params.append(('namespace', local_var_params['namespace']))
if 'dname' in local_var_params:
query_params.append(('dname', local_var_params['dname']))
if 'start' in local_var_params:
query_params.append(('start', local_var_params['start']))
if 'limit' in local_var_params:
query_params.append(('limit', local_var_params['limit']))
header_params = {}
form_params = {}
body_params = None
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/V1.0/{project_id}/alarm-template',
method='GET',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='ListAlarmTemplatesResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def list_alarms(self, request):
"""查询告警规则列表
查询告警规则列表，可以指定分页条件限制结果数量，可以指定排序规则。
:param ListAlarmsRequest request
:return: ListAlarmsResponse
"""
return self.list_alarms_with_http_info(request)
def list_alarms_with_http_info(self, request):
"""查询告警规则列表
查询告警规则列表，可以指定分页条件限制结果数量，可以指定排序规则。
:param ListAlarmsRequest request
:return: ListAlarmsResponse
"""
all_params = ['limit', 'order', 'start']
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
query_params = []
if 'limit' in local_var_params:
query_params.append(('limit', local_var_params['limit']))
if 'order' in local_var_params:
query_params.append(('order', local_var_params['order']))
if 'start' in local_var_params:
query_params.append(('start', local_var_params['start']))
header_params = {}
form_params = {}
body_params = None
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/V1.0/{project_id}/alarms',
method='GET',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='ListAlarmsResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def list_event_detail(self, request):
"""查询某一事件监控详情
根据事件监控名称，查询该事件发生的详细信息。
:param ListEventDetailRequest request
:return: ListEventDetailResponse
"""
return self.list_event_detail_with_http_info(request)
def list_event_detail_with_http_info(self, request):
"""查询某一事件监控详情
根据事件监控名称，查询该事件发生的详细信息。
:param ListEventDetailRequest request
:return: ListEventDetailResponse
"""
all_params = ['event_name', 'event_type', 'event_source', 'event_level', 'event_user', 'event_state', '_from', 'to', 'start', 'limit']
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
if 'event_name' in local_var_params:
path_params['event_name'] = local_var_params['event_name']
query_params = []
if 'event_type' in local_var_params:
query_params.append(('event_type', local_var_params['event_type']))
if 'event_source' in local_var_params:
query_params.append(('event_source', local_var_params['event_source']))
if 'event_level' in local_var_params:
query_params.append(('event_level', local_var_params['event_level']))
if 'event_user' in local_var_params:
query_params.append(('event_user', local_var_params['event_user']))
if 'event_state' in local_var_params:
query_params.append(('event_state', local_var_params['event_state']))
if '_from' in local_var_params:
query_params.append(('from', local_var_params['_from']))
if 'to' in local_var_params:
query_params.append(('to', local_var_params['to']))
if 'start' in local_var_params:
query_params.append(('start', local_var_params['start']))
if 'limit' in local_var_params:
query_params.append(('limit', local_var_params['limit']))
header_params = {}
form_params = {}
body_params = None
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/V1.0/{project_id}/event/{event_name}',
method='GET',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='ListEventDetailResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def list_events(self, request):
"""查询事件监控列表
查询事件监控列表，包括系统事件和自定义事件。
:param ListEventsRequest request
:return: ListEventsResponse
"""
return self.list_events_with_http_info(request)
def list_events_with_http_info(self, request):
"""查询事件监控列表
查询事件监控列表，包括系统事件和自定义事件。
:param ListEventsRequest request
:return: ListEventsResponse
"""
all_params = ['event_type', 'event_name', '_from', 'to', 'start', 'limit']
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
query_params = []
if 'event_type' in local_var_params:
query_params.append(('event_type', local_var_params['event_type']))
if 'event_name' in local_var_params:
query_params.append(('event_name', local_var_params['event_name']))
if '_from' in local_var_params:
query_params.append(('from', local_var_params['_from']))
if 'to' in local_var_params:
query_params.append(('to', local_var_params['to']))
if 'start' in local_var_params:
query_params.append(('start', local_var_params['start']))
if 'limit' in local_var_params:
query_params.append(('limit', local_var_params['limit']))
header_params = {}
form_params = {}
body_params = None
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/V1.0/{project_id}/events',
method='GET',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='ListEventsResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def list_metrics(self, request):
"""查询指标列表
查询系统当前可监控指标列表，可以指定指标命名空间、指标名称、维度、排序方式，起始记录和最大记录条数过滤查询结果。
:param ListMetricsRequest request
:return: ListMetricsResponse
"""
return self.list_metrics_with_http_info(request)
def list_metrics_with_http_info(self, request):
"""查询指标列表
查询系统当前可监控指标列表，可以指定指标命名空间、指标名称、维度、排序方式，起始记录和最大记录条数过滤查询结果。
:param ListMetricsRequest request
:return: ListMetricsResponse
"""
all_params = ['dim_0', 'dim_1', 'dim_2', 'limit', 'metric_name', 'namespace', 'order', 'start']
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
query_params = []
if 'dim_0' in local_var_params:
query_params.append(('dim.0', local_var_params['dim_0']))
if 'dim_1' in local_var_params:
query_params.append(('dim.1', local_var_params['dim_1']))
if 'dim_2' in local_var_params:
query_params.append(('dim.2', local_var_params['dim_2']))
if 'limit' in local_var_params:
query_params.append(('limit', local_var_params['limit']))
if 'metric_name' in local_var_params:
query_params.append(('metric_name', local_var_params['metric_name']))
if 'namespace' in local_var_params:
query_params.append(('namespace', local_var_params['namespace']))
if 'order' in local_var_params:
query_params.append(('order', local_var_params['order']))
if 'start' in local_var_params:
query_params.append(('start', local_var_params['start']))
header_params = {}
form_params = {}
body_params = None
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/V1.0/{project_id}/metrics',
method='GET',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='ListMetricsResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def list_resource_group(self, request):
"""查询所有资源分组
查询所创建的所有资源分组。
:param ListResourceGroupRequest request
:return: ListResourceGroupResponse
"""
return self.list_resource_group_with_http_info(request)
def list_resource_group_with_http_info(self, request):
"""查询所有资源分组
查询所创建的所有资源分组。
:param ListResourceGroupRequest request
:return: ListResourceGroupResponse
"""
all_params = ['group_name', 'group_id', 'status', 'start', 'limit']
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
query_params = []
if 'group_name' in local_var_params:
query_params.append(('group_name', local_var_params['group_name']))
if 'group_id' in local_var_params:
query_params.append(('group_id', local_var_params['group_id']))
if 'status' in local_var_params:
query_params.append(('status', local_var_params['status']))
if 'start' in local_var_params:
query_params.append(('start', local_var_params['start']))
if 'limit' in local_var_params:
query_params.append(('limit', local_var_params['limit']))
header_params = {}
form_params = {}
body_params = None
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/V1.0/{project_id}/resource-groups',
method='GET',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='ListResourceGroupResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def show_alarm(self, request):
"""查询单条告警规则信息
根据告警ID查询告警规则信息。
:param ShowAlarmRequest request
:return: ShowAlarmResponse
"""
return self.show_alarm_with_http_info(request)
def show_alarm_with_http_info(self, request):
"""查询单条告警规则信息
根据告警ID查询告警规则信息。
:param ShowAlarmRequest request
:return: ShowAlarmResponse
"""
all_params = ['alarm_id']
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
if 'alarm_id' in local_var_params:
path_params['alarm_id'] = local_var_params['alarm_id']
query_params = []
header_params = {}
form_params = {}
body_params = None
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/V1.0/{project_id}/alarms/{alarm_id}',
method='GET',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='ShowAlarmResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def show_event_data(self, request):
"""查询主机配置数据
查询指定时间范围指定事件类型的主机配置数据，可以通过参数指定需要查询的数据维度。注意：该接口提供给HANA场景下SAP Monitor查询主机配置数据，其他场景下查不到主机配置数据。
:param ShowEventDataRequest request
:return: ShowEventDataResponse
"""
return self.show_event_data_with_http_info(request)
def show_event_data_with_http_info(self, request):
"""查询主机配置数据
查询指定时间范围指定事件类型的主机配置数据，可以通过参数指定需要查询的数据维度。注意：该接口提供给HANA场景下SAP Monitor查询主机配置数据，其他场景下查不到主机配置数据。
:param ShowEventDataRequest request
:return: ShowEventDataResponse
"""
all_params = ['namespace', 'dim_0', 'type', '_from', 'to', 'dim_1', 'dim_2', 'dim_3']
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
query_params = []
if 'namespace' in local_var_params:
query_params.append(('namespace', local_var_params['namespace']))
if 'dim_0' in local_var_params:
query_params.append(('dim.0', local_var_params['dim_0']))
if 'dim_1' in local_var_params:
query_params.append(('dim.1', local_var_params['dim_1']))
if 'dim_2' in local_var_params:
query_params.append(('dim.2', local_var_params['dim_2']))
if 'dim_3' in local_var_params:
query_params.append(('dim.3', local_var_params['dim_3']))
if 'type' in local_var_params:
query_params.append(('type', local_var_params['type']))
if '_from' in local_var_params:
query_params.append(('from', local_var_params['_from']))
if 'to' in local_var_params:
query_params.append(('to', local_var_params['to']))
header_params = {}
form_params = {}
body_params = None
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/V1.0/{project_id}/event-data',
method='GET',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='ShowEventDataResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def show_metric_data(self, request):
"""查询监控数据
查询指定时间范围指定指标的指定粒度的监控数据，可以通过参数指定需要查询的数据维度。
:param ShowMetricDataRequest request
:return: ShowMetricDataResponse
"""
return self.show_metric_data_with_http_info(request)
def show_metric_data_with_http_info(self, request):
"""查询监控数据
查询指定时间范围指定指标的指定粒度的监控数据，可以通过参数指定需要查询的数据维度。
:param ShowMetricDataRequest request
:return: ShowMetricDataResponse
"""
all_params = ['namespace', 'metric_name', 'dim_0', 'filter', 'period', '_from', 'to', 'dim_1', 'dim_2', 'dim_3']
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
query_params = []
if 'namespace' in local_var_params:
query_params.append(('namespace', local_var_params['namespace']))
if 'metric_name' in local_var_params:
query_params.append(('metric_name', local_var_params['metric_name']))
if 'dim_0' in local_var_params:
query_params.append(('dim.0', local_var_params['dim_0']))
if 'dim_1' in local_var_params:
query_params.append(('dim.1', local_var_params['dim_1']))
if 'dim_2' in local_var_params:
query_params.append(('dim.2', local_var_params['dim_2']))
if 'dim_3' in local_var_params:
query_params.append(('dim.3', local_var_params['dim_3']))
if 'filter' in local_var_params:
query_params.append(('filter', local_var_params['filter']))
if 'period' in local_var_params:
query_params.append(('period', local_var_params['period']))
if '_from' in local_var_params:
query_params.append(('from', local_var_params['_from']))
if 'to' in local_var_params:
query_params.append(('to', local_var_params['to']))
header_params = {}
form_params = {}
body_params = None
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/V1.0/{project_id}/metric-data',
method='GET',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='ShowMetricDataResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def show_quotas(self, request):
"""查询配额
查询用户可以创建的资源配额总数及当前使用量，当前仅有告警规则一种资源类型。
:param ShowQuotasRequest request
:return: ShowQuotasResponse
"""
return self.show_quotas_with_http_info(request)
def show_quotas_with_http_info(self, request):
"""查询配额
查询用户可以创建的资源配额总数及当前使用量，当前仅有告警规则一种资源类型。
:param ShowQuotasRequest request
:return: ShowQuotasResponse
"""
all_params = []
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = {}
body_params = None
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/V1.0/{project_id}/quotas',
method='GET',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='ShowQuotasResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def show_resource_group(self, request):
"""查询资源分组下的资源
根据资源分组ID查询资源分组下的资源。
:param ShowResourceGroupRequest request
:return: ShowResourceGroupResponse
"""
return self.show_resource_group_with_http_info(request)
def show_resource_group_with_http_info(self, request):
"""查询资源分组下的资源
根据资源分组ID查询资源分组下的资源。
:param ShowResourceGroupRequest request
:return: ShowResourceGroupResponse
"""
all_params = ['group_id', 'status', 'namespace', 'dname', 'start', 'limit']
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
if 'group_id' in local_var_params:
path_params['group_id'] = local_var_params['group_id']
query_params = []
if 'status' in local_var_params:
query_params.append(('status', local_var_params['status']))
if 'namespace' in local_var_params:
query_params.append(('namespace', local_var_params['namespace']))
if 'dname' in local_var_params:
query_params.append(('dname', local_var_params['dname']))
if 'start' in local_var_params:
query_params.append(('start', local_var_params['start']))
if 'limit' in local_var_params:
query_params.append(('limit', local_var_params['limit']))
header_params = {}
form_params = {}
body_params = None
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/V1.0/{project_id}/resource-groups/{group_id}',
method='GET',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='ShowResourceGroupResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def update_alarm(self, request):
"""修改告警规则
修改告警规则。
:param UpdateAlarmRequest request
:return: UpdateAlarmResponse
"""
return self.update_alarm_with_http_info(request)
def update_alarm_with_http_info(self, request):
"""修改告警规则
修改告警规则。
:param UpdateAlarmRequest request
:return: UpdateAlarmResponse
"""
all_params = ['alarm_id', 'update_alarm_request_body']
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
if 'alarm_id' in local_var_params:
path_params['alarm_id'] = local_var_params['alarm_id']
query_params = []
header_params = {}
form_params = {}
body_params = None
if 'body' in local_var_params:
body_params = local_var_params['body']
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/V1.0/{project_id}/alarms/{alarm_id}',
method='PUT',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='UpdateAlarmResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def update_alarm_action(self, request):
"""启停告警规则
启动或停止一条告警规则。
:param UpdateAlarmActionRequest request
:return: UpdateAlarmActionResponse
"""
return self.update_alarm_action_with_http_info(request)
def update_alarm_action_with_http_info(self, request):
"""启停告警规则
启动或停止一条告警规则。
:param UpdateAlarmActionRequest request
:return: UpdateAlarmActionResponse
"""
all_params = ['alarm_id', 'modify_alarm_action_req']
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
if 'alarm_id' in local_var_params:
path_params['alarm_id'] = local_var_params['alarm_id']
query_params = []
header_params = {}
form_params = {}
body_params = None
if 'body' in local_var_params:
body_params = local_var_params['body']
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/V1.0/{project_id}/alarms/{alarm_id}/action',
method='PUT',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='UpdateAlarmActionResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def update_alarm_template(self, request):
"""更新自定义告警模板
更新自定义告警模板。
:param UpdateAlarmTemplateRequest request
:return: UpdateAlarmTemplateResponse
"""
return self.update_alarm_template_with_http_info(request)
def update_alarm_template_with_http_info(self, request):
"""更新自定义告警模板
更新自定义告警模板。
:param UpdateAlarmTemplateRequest request
:return: UpdateAlarmTemplateResponse
"""
all_params = ['template_id', 'update_alarm_template_request_body']
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
if 'template_id' in local_var_params:
path_params['template_id'] = local_var_params['template_id']
query_params = []
header_params = {}
form_params = {}
body_params = None
if 'body' in local_var_params:
body_params = local_var_params['body']
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/V1.0/{project_id}/alarm-template/{template_id}',
method='PUT',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='UpdateAlarmTemplateResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def update_resource_group(self, request):
"""更新资源分组
更新资源分组，资源分组支持将各类资源按照业务集中进行分组管理，可以从分组角度查看监控与告警信息，以提升运维效率。
:param UpdateResourceGroupRequest request
:return: UpdateResourceGroupResponse
"""
return self.update_resource_group_with_http_info(request)
def update_resource_group_with_http_info(self, request):
"""更新资源分组
更新资源分组，资源分组支持将各类资源按照业务集中进行分组管理，可以从分组角度查看监控与告警信息，以提升运维效率。
:param UpdateResourceGroupRequest request
:return: UpdateResourceGroupResponse
"""
all_params = ['group_id', 'update_resource_group_request_body']
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
if 'group_id' in local_var_params:
path_params['group_id'] = local_var_params['group_id']
query_params = []
header_params = {}
form_params = {}
body_params = None
if 'body' in local_var_params:
body_params = local_var_params['body']
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/V1.0/{project_id}/resource-groups/{group_id}',
method='PUT',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='UpdateResourceGroupResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def call_api(self, resource_path, method, path_params=None, query_params=None, header_params=None, body=None,
post_params=None, response_type=None, response_headers=None, auth_settings=None,
collection_formats=None, request_type=None):
"""Makes the HTTP request and returns deserialized data.
:param resource_path: Path to method endpoint.
:param method: Method to call.
:param path_params: Path parameters in the url.
:param query_params: Query parameters in the url.
:param header_params: Header parameters to be placed in the request header.
:param body: Request body.
:param post_params dict: Request post form parameters,
for `application/x-www-form-urlencoded`, `multipart/form-data`.
:param auth_settings list: Auth Settings names for the request.
:param response_type: Response data type.
:param response_headers: Header should be added to response data.
:param collection_formats: dict of collection formats for path, query,
header, and post parameters.
:param request_type: Request data type.
:return:
Return the response directly.
"""
return self.do_http_request(
method=method,
resource_path=resource_path,
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body,
post_params=post_params,
response_type=response_type,
response_headers=response_headers,
collection_formats=collection_formats,
request_type=request_type)
| 31.340604 | 150 | 0.622589 | 5,717 | 56,037 | 5.710163 | 0.053175 | 0.055874 | 0.097779 | 0.043621 | 0.879308 | 0.87361 | 0.854771 | 0.819972 | 0.805208 | 0.719865 | 0 | 0.002771 | 0.28508 | 56,037 | 1,787 | 151 | 31.358142 | 0.812091 | 0.117351 | 0 | 0.810476 | 0 | 0 | 0.103163 | 0.033023 | 0 | 0 | 0 | 0 | 0 | 1 | 0.050476 | false | 0 | 0.009524 | 0 | 0.113333 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 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 | 7 |
16839e646d07bee5dae7b1825c8751ece72502dd | 144 | py | Python | python/mleap/pyspark/__init__.py | neilsummers/mleap | 80fc0ea25e617e26316320478c8a75e79e20f8a6 | [
"Apache-2.0"
] | 2 | 2019-07-21T15:06:12.000Z | 2019-07-21T15:06:18.000Z | python/mleap/pyspark/__init__.py | neilsummers/mleap | 80fc0ea25e617e26316320478c8a75e79e20f8a6 | [
"Apache-2.0"
] | 1 | 2021-06-02T00:01:52.000Z | 2021-06-02T00:01:52.000Z | python/mleap/pyspark/__init__.py | yongsheng268/mleap | 8a914752b364d9385d086d6553c377d7af767f57 | [
"Apache-2.0"
] | 2 | 2019-10-12T15:07:52.000Z | 2020-10-10T23:10:45.000Z | import mleap.pyspark
import sys
sys.modules['pyspark.ml.mleap'] = mleap
sys.modules['pyspark.ml.mleap.pyspark'] = sys.modules['mleap.pyspark']
| 24 | 70 | 0.756944 | 21 | 144 | 5.190476 | 0.285714 | 0.330275 | 0.311927 | 0.348624 | 0.440367 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.076389 | 144 | 5 | 71 | 28.8 | 0.819549 | 0 | 0 | 0 | 0 | 0 | 0.368056 | 0.166667 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 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 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 7 |
168dd75a098788ba73ca9b6e67dad8fb062da8a2 | 34,675 | py | Python | tests/unit/test_jobs.py | TomasTomecek/packit-service | f0e5c0c04df80a600fdba33c1a8dbf9f81fdea08 | [
"MIT"
] | null | null | null | tests/unit/test_jobs.py | TomasTomecek/packit-service | f0e5c0c04df80a600fdba33c1a8dbf9f81fdea08 | [
"MIT"
] | 2 | 2020-09-02T08:14:27.000Z | 2020-09-03T03:16:27.000Z | tests/unit/test_jobs.py | shreyaspapi/packit-service | a64e7db9f354df9b3c346948e661a89236b22387 | [
"MIT"
] | null | null | null | # MIT License
#
# Copyright (c) 2018-2019 Red Hat, Inc.
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import pytest
from flexmock import flexmock
from packit.config import JobConfig, JobType, JobConfigTriggerType
from packit.config.job_config import JobMetadataConfig
from packit_service.service.events import TheJobTriggerType
from packit_service.worker.handlers import (
PullRequestCoprBuildHandler,
ProposeDownstreamHandler,
CoprBuildStartHandler,
CoprBuildEndHandler,
TestingFarmResultsHandler,
)
from packit_service.worker.handlers.fedmsg_handlers import KojiBuildReportHandler
from packit_service.worker.handlers.github_handlers import (
PullRequestGithubKojiBuildHandler,
PushGithubKojiBuildHandler,
PushCoprBuildHandler,
ReleaseGithubKojiBuildHandler,
GitHubPullRequestCommentCoprBuildHandler,
GitHubIssueCommentProposeUpdateHandler,
)
from packit_service.worker.jobs import (
get_handlers_for_event,
get_config_for_handler_kls,
)
@pytest.mark.parametrize(
"trigger,db_trigger,jobs,result",
[
pytest.param(
TheJobTriggerType.pull_request,
flexmock(job_config_trigger_type=JobConfigTriggerType.pull_request),
[],
set(),
id="nothing_configured",
),
pytest.param(
TheJobTriggerType.pull_request,
flexmock(job_config_trigger_type=JobConfigTriggerType.pull_request),
[
JobConfig(
type=JobType.copr_build, trigger=JobConfigTriggerType.pull_request,
)
],
{PullRequestCoprBuildHandler},
id="config=copr_build@trigger=pull_request",
),
pytest.param(
TheJobTriggerType.pull_request,
flexmock(job_config_trigger_type=JobConfigTriggerType.pull_request),
[JobConfig(type=JobType.build, trigger=JobConfigTriggerType.pull_request,)],
{PullRequestCoprBuildHandler},
id="config=build@trigger=pull_request",
),
pytest.param(
TheJobTriggerType.push,
flexmock(job_config_trigger_type=JobConfigTriggerType.commit),
[JobConfig(type=JobType.copr_build, trigger=JobConfigTriggerType.commit,)],
{PushCoprBuildHandler},
id="config=copr_build_on_push@trigger=push",
),
pytest.param(
TheJobTriggerType.commit,
flexmock(job_config_trigger_type=JobConfigTriggerType.commit),
[JobConfig(type=JobType.copr_build, trigger=JobConfigTriggerType.commit,)],
{PushCoprBuildHandler},
id="config=copr_build_on_push@trigger=commit",
),
pytest.param(
TheJobTriggerType.release,
flexmock(job_config_trigger_type=JobConfigTriggerType.release),
[
JobConfig(
type=JobType.propose_downstream,
trigger=JobConfigTriggerType.release,
)
],
{ProposeDownstreamHandler},
id="propose_downstream",
),
pytest.param(
TheJobTriggerType.copr_start,
flexmock(job_config_trigger_type=JobConfigTriggerType.pull_request),
[
JobConfig(
type=JobType.copr_build, trigger=JobConfigTriggerType.pull_request,
)
],
{CoprBuildStartHandler},
id="config=copr_build@trigger=copr_start",
),
pytest.param(
TheJobTriggerType.copr_end,
flexmock(job_config_trigger_type=JobConfigTriggerType.pull_request),
[
JobConfig(
type=JobType.copr_build, trigger=JobConfigTriggerType.pull_request,
)
],
{CoprBuildEndHandler},
id="config=copr_build@trigger=copr_end",
),
pytest.param(
TheJobTriggerType.pull_request,
flexmock(job_config_trigger_type=JobConfigTriggerType.pull_request),
[
JobConfig(
type=JobType.copr_build, trigger=JobConfigTriggerType.pull_request,
),
JobConfig(
type=JobType.copr_build, trigger=JobConfigTriggerType.release,
),
],
{PullRequestCoprBuildHandler},
id="config=copr_build_on_pull_request_and_release@trigger=pull_request",
),
pytest.param(
TheJobTriggerType.copr_end,
flexmock(job_config_trigger_type=JobConfigTriggerType.pull_request),
[
JobConfig(
type=JobType.copr_build, trigger=JobConfigTriggerType.pull_request,
),
JobConfig(
type=JobType.copr_build, trigger=JobConfigTriggerType.release,
),
],
{CoprBuildEndHandler},
id="config=copr_build_on_pull_request_and_release@trigger=copr_end",
),
pytest.param(
TheJobTriggerType.pull_request,
flexmock(job_config_trigger_type=JobConfigTriggerType.pull_request),
[JobConfig(type=JobType.tests, trigger=JobConfigTriggerType.pull_request,)],
{PullRequestCoprBuildHandler},
id="config=tests@trigger=pull_request",
),
pytest.param(
TheJobTriggerType.copr_start,
flexmock(job_config_trigger_type=JobConfigTriggerType.pull_request),
[JobConfig(type=JobType.tests, trigger=JobConfigTriggerType.pull_request,)],
{CoprBuildStartHandler},
id="config=tests@trigger=copr_start",
),
pytest.param(
TheJobTriggerType.copr_end,
flexmock(job_config_trigger_type=JobConfigTriggerType.pull_request),
[JobConfig(type=JobType.tests, trigger=JobConfigTriggerType.pull_request,)],
{CoprBuildEndHandler},
id="config=tests@trigger=copr_end",
),
pytest.param(
TheJobTriggerType.testing_farm_results,
flexmock(job_config_trigger_type=JobConfigTriggerType.pull_request),
[JobConfig(type=JobType.tests, trigger=JobConfigTriggerType.pull_request,)],
{TestingFarmResultsHandler},
id="config=tests@trigger=testing_farm_results",
),
pytest.param(
TheJobTriggerType.testing_farm_results,
flexmock(job_config_trigger_type=JobConfigTriggerType.pull_request),
[
JobConfig(
type=JobType.tests, trigger=JobConfigTriggerType.pull_request,
),
JobConfig(
type=JobType.copr_build, trigger=JobConfigTriggerType.pull_request,
),
],
{TestingFarmResultsHandler},
id="config=tests_and_copr_build@trigger=testing_farm_results",
),
pytest.param(
TheJobTriggerType.pull_request,
flexmock(job_config_trigger_type=JobConfigTriggerType.pull_request),
[
JobConfig(
type=JobType.tests, trigger=JobConfigTriggerType.pull_request,
),
JobConfig(
type=JobType.copr_build, trigger=JobConfigTriggerType.pull_request,
),
],
{PullRequestCoprBuildHandler},
id="config=tests_and_copr_build@trigger=pull_request",
),
pytest.param(
TheJobTriggerType.copr_start,
flexmock(job_config_trigger_type=JobConfigTriggerType.pull_request),
[
JobConfig(
type=JobType.tests, trigger=JobConfigTriggerType.pull_request,
),
JobConfig(
type=JobType.copr_build, trigger=JobConfigTriggerType.pull_request,
),
],
{CoprBuildStartHandler},
id="config=tests_and_copr_build@trigger=copr_start",
),
pytest.param(
TheJobTriggerType.copr_start,
flexmock(job_config_trigger_type=JobConfigTriggerType.pull_request),
[
JobConfig(
type=JobType.tests, trigger=JobConfigTriggerType.pull_request,
),
JobConfig(
type=JobType.copr_build, trigger=JobConfigTriggerType.pull_request,
),
JobConfig(
type=JobType.propose_downstream,
trigger=JobConfigTriggerType.release,
),
JobConfig(
type=JobType.sync_from_downstream,
trigger=JobConfigTriggerType.commit,
),
],
{CoprBuildStartHandler},
id="config=tests_and_copr_build_and_propose_and_sync@trigger=copr_start",
),
pytest.param(
TheJobTriggerType.pull_request,
flexmock(job_config_trigger_type=JobConfigTriggerType.pull_request),
[
JobConfig(
type=JobType.production_build,
trigger=JobConfigTriggerType.pull_request,
),
],
{PullRequestGithubKojiBuildHandler},
id="config=production_build@trigger=pull_request",
),
pytest.param(
TheJobTriggerType.push,
flexmock(job_config_trigger_type=JobConfigTriggerType.commit),
[
JobConfig(
type=JobType.production_build, trigger=JobConfigTriggerType.commit,
),
],
{PushGithubKojiBuildHandler},
id="config=production_build@trigger=push",
),
pytest.param(
TheJobTriggerType.commit,
flexmock(job_config_trigger_type=JobConfigTriggerType.commit),
[
JobConfig(
type=JobType.production_build, trigger=JobConfigTriggerType.commit,
),
],
{PushGithubKojiBuildHandler},
id="config=production_build@trigger=commit",
),
pytest.param(
TheJobTriggerType.release,
flexmock(job_config_trigger_type=JobConfigTriggerType.release),
[
JobConfig(
type=JobType.production_build, trigger=JobConfigTriggerType.release,
),
],
{ReleaseGithubKojiBuildHandler},
id="config=production_build@trigger=release",
),
pytest.param(
TheJobTriggerType.push,
flexmock(job_config_trigger_type=JobConfigTriggerType.commit),
[
JobConfig(
type=JobType.production_build, trigger=JobConfigTriggerType.commit,
),
JobConfig(
type=JobType.production_build,
trigger=JobConfigTriggerType.pull_request,
),
],
{PushGithubKojiBuildHandler},
id="config=production_build_on_pull_request_and_commit@trigger=commit",
),
pytest.param(
TheJobTriggerType.koji_results,
flexmock(job_config_trigger_type=JobConfigTriggerType.pull_request),
[
JobConfig(
type=JobType.production_build,
trigger=JobConfigTriggerType.pull_request,
),
],
{KojiBuildReportHandler},
id="config=production_build@trigger=koji_results",
),
pytest.param(
TheJobTriggerType.koji_results,
flexmock(job_config_trigger_type=JobConfigTriggerType.pull_request),
[
JobConfig(
type=JobType.copr_build, trigger=JobConfigTriggerType.pull_request,
),
],
set(),
id="config=copr_build@trigger=koji_results",
),
pytest.param(
TheJobTriggerType.koji_results,
flexmock(job_config_trigger_type=JobConfigTriggerType.pull_request),
[
JobConfig(
type=JobType.production_build,
trigger=JobConfigTriggerType.pull_request,
),
JobConfig(
type=JobType.copr_build, trigger=JobConfigTriggerType.pull_request,
),
],
{KojiBuildReportHandler},
id="config=production_build_and_copr_build@trigger=koji_results",
),
pytest.param(
TheJobTriggerType.issue_comment,
flexmock(job_config_trigger_type=None),
[
JobConfig(
type=JobType.propose_downstream,
trigger=JobConfigTriggerType.release,
),
],
{GitHubIssueCommentProposeUpdateHandler},
id="config=propose_downstream@trigger=issue_comment",
),
],
)
def test_get_handlers_for_event(trigger, db_trigger, jobs, result):
event_handlers = set(
get_handlers_for_event(
event=flexmock(trigger=trigger, db_trigger=db_trigger),
package_config=flexmock(jobs=jobs),
)
)
assert event_handlers == result
@pytest.mark.parametrize(
"handler_kls,event,jobs,result_job_config",
[
pytest.param(
PullRequestCoprBuildHandler,
flexmock(
trigger=TheJobTriggerType.pull_request,
db_trigger=flexmock(
job_config_trigger_type=JobConfigTriggerType.pull_request
),
),
[
JobConfig(
type=JobType.copr_build, trigger=JobConfigTriggerType.pull_request
)
],
[
JobConfig(
type=JobType.copr_build, trigger=JobConfigTriggerType.pull_request
)
],
id="copr_build_for_pr",
),
pytest.param(
PullRequestCoprBuildHandler,
flexmock(
trigger=TheJobTriggerType.pull_request,
db_trigger=flexmock(
job_config_trigger_type=JobConfigTriggerType.pull_request
),
),
[JobConfig(type=JobType.tests, trigger=JobConfigTriggerType.pull_request)],
[JobConfig(type=JobType.tests, trigger=JobConfigTriggerType.pull_request)],
id="copr_build_for_pr_when_test_defined",
),
pytest.param(
PullRequestCoprBuildHandler,
flexmock(
trigger=TheJobTriggerType.pull_request,
db_trigger=flexmock(
job_config_trigger_type=JobConfigTriggerType.pull_request
),
),
[
JobConfig(
type=JobType.tests, trigger=JobConfigTriggerType.pull_request
),
JobConfig(
type=JobType.copr_build, trigger=JobConfigTriggerType.pull_request
),
],
[
JobConfig(
type=JobType.copr_build, trigger=JobConfigTriggerType.pull_request
)
],
id="copr_build_for_pr_when_test_and_build_defined",
),
pytest.param(
PullRequestCoprBuildHandler,
flexmock(
trigger=TheJobTriggerType.pull_request,
db_trigger=flexmock(
job_config_trigger_type=JobConfigTriggerType.pull_request
),
),
[
JobConfig(
type=JobType.tests, trigger=JobConfigTriggerType.pull_request
),
JobConfig(
type=JobType.copr_build, trigger=JobConfigTriggerType.release
),
],
[JobConfig(type=JobType.tests, trigger=JobConfigTriggerType.pull_request)],
id="copr_build_for_pr_when_test_for_pr_and_build_for_release_are_defined",
),
pytest.param(
PullRequestCoprBuildHandler,
flexmock(
trigger=TheJobTriggerType.commit,
db_trigger=flexmock(
job_config_trigger_type=JobConfigTriggerType.commit
),
),
[
JobConfig(
type=JobType.tests, trigger=JobConfigTriggerType.pull_request
),
JobConfig(
type=JobType.copr_build, trigger=JobConfigTriggerType.pull_request
),
],
[],
id="copr_build_for_commit_when_test_and_build_are_defined_for_pr",
),
pytest.param(
PullRequestCoprBuildHandler,
flexmock(
trigger=TheJobTriggerType.commit,
db_trigger=flexmock(
job_config_trigger_type=JobConfigTriggerType.commit
),
),
[
JobConfig(
type=JobType.tests, trigger=JobConfigTriggerType.pull_request
),
JobConfig(
type=JobType.copr_build, trigger=JobConfigTriggerType.pull_request
),
JobConfig(type=JobType.copr_build, trigger=JobConfigTriggerType.commit),
],
[JobConfig(type=JobType.copr_build, trigger=JobConfigTriggerType.commit)],
id="copr_build_for_commit_when_test_and_build_are_defined_for_pr_and_build_for_push",
),
pytest.param(
PullRequestCoprBuildHandler,
flexmock(
trigger=TheJobTriggerType.commit,
db_trigger=flexmock(
job_config_trigger_type=JobConfigTriggerType.commit
),
),
[
JobConfig(
type=JobType.tests, trigger=JobConfigTriggerType.pull_request
),
JobConfig(
type=JobType.copr_build, trigger=JobConfigTriggerType.pull_request
),
JobConfig(
type=JobType.copr_build,
trigger=JobConfigTriggerType.commit,
metadata=JobMetadataConfig(branch="a"),
),
JobConfig(
type=JobType.copr_build,
trigger=JobConfigTriggerType.commit,
metadata=JobMetadataConfig(branch="b"),
),
],
[
JobConfig(
type=JobType.copr_build,
trigger=JobConfigTriggerType.commit,
metadata=JobMetadataConfig(branch="a"),
),
JobConfig(
type=JobType.copr_build,
trigger=JobConfigTriggerType.commit,
metadata=JobMetadataConfig(branch="b"),
),
],
id="copr_build_for_commit_when_test_and_build_are_defined_for_pr_and_build_for_push"
"@more_branches",
),
pytest.param(
PullRequestCoprBuildHandler,
flexmock(
trigger=TheJobTriggerType.pull_request,
db_trigger=flexmock(
job_config_trigger_type=JobConfigTriggerType.pull_request
),
),
[
JobConfig(
type=JobType.propose_downstream,
trigger=JobConfigTriggerType.release,
),
JobConfig(
type=JobType.copr_build, trigger=JobConfigTriggerType.pull_request
),
JobConfig(
type=JobType.production_build,
trigger=JobConfigTriggerType.pull_request,
),
],
[
JobConfig(
type=JobType.copr_build, trigger=JobConfigTriggerType.pull_request
)
],
id="copr_build_for_pr_when_test_build_and_koji_build_defined",
),
pytest.param(
PullRequestCoprBuildHandler,
flexmock(
trigger=TheJobTriggerType.pull_request,
db_trigger=flexmock(
job_config_trigger_type=JobConfigTriggerType.pull_request
),
),
[
JobConfig(
type=JobType.propose_downstream,
trigger=JobConfigTriggerType.release,
),
JobConfig(
type=JobType.copr_build, trigger=JobConfigTriggerType.pull_request
),
JobConfig(
type=JobType.build, trigger=JobConfigTriggerType.pull_request
),
JobConfig(
type=JobType.production_build,
trigger=JobConfigTriggerType.pull_request,
),
],
[
JobConfig(
type=JobType.copr_build, trigger=JobConfigTriggerType.pull_request
),
JobConfig(
type=JobType.build, trigger=JobConfigTriggerType.pull_request
),
],
id="copr_build_for_pr_multiple_defs",
),
pytest.param(
PullRequestGithubKojiBuildHandler,
flexmock(
trigger=TheJobTriggerType.pull_request,
db_trigger=flexmock(
job_config_trigger_type=JobConfigTriggerType.pull_request
),
),
[
JobConfig(
type=JobType.production_build,
trigger=JobConfigTriggerType.pull_request,
)
],
[
JobConfig(
type=JobType.production_build,
trigger=JobConfigTriggerType.pull_request,
)
],
id="koji_build_for_pr",
),
pytest.param(
PullRequestGithubKojiBuildHandler,
flexmock(
trigger=TheJobTriggerType.commit,
db_trigger=flexmock(
job_config_trigger_type=JobConfigTriggerType.commit
),
),
[
JobConfig(
type=JobType.production_build,
trigger=JobConfigTriggerType.commit,
metadata=JobMetadataConfig(branch="some-branch"),
),
JobConfig(
type=JobType.production_build,
trigger=JobConfigTriggerType.pull_request,
),
],
[
JobConfig(
type=JobType.production_build,
trigger=JobConfigTriggerType.commit,
metadata=JobMetadataConfig(branch="some-branch"),
),
],
id="koji_build_for_commit_when_pr_and_branch_configured",
),
pytest.param(
ProposeDownstreamHandler,
flexmock(
trigger=TheJobTriggerType.release,
db_trigger=flexmock(
job_config_trigger_type=JobConfigTriggerType.release
),
),
[
JobConfig(
type=JobType.propose_downstream,
trigger=JobConfigTriggerType.release,
)
],
[
JobConfig(
type=JobType.propose_downstream,
trigger=JobConfigTriggerType.release,
)
],
id="propose_downstream",
),
pytest.param(
ProposeDownstreamHandler,
flexmock(
trigger=TheJobTriggerType.release,
db_trigger=flexmock(
job_config_trigger_type=JobConfigTriggerType.release
),
),
[
JobConfig(
type=JobType.propose_downstream,
trigger=JobConfigTriggerType.release,
metadata=JobMetadataConfig(targets=["a"]),
),
JobConfig(
type=JobType.propose_downstream,
trigger=JobConfigTriggerType.release,
metadata=JobMetadataConfig(targets=["b"]),
),
],
[
JobConfig(
type=JobType.propose_downstream,
trigger=JobConfigTriggerType.release,
metadata=JobMetadataConfig(targets=["a"]),
),
JobConfig(
type=JobType.propose_downstream,
trigger=JobConfigTriggerType.release,
metadata=JobMetadataConfig(targets=["b"]),
),
],
id="propose_downstream_multiple",
),
pytest.param(
CoprBuildEndHandler,
flexmock(
trigger=TheJobTriggerType.pull_request,
db_trigger=flexmock(
job_config_trigger_type=JobConfigTriggerType.pull_request
),
),
[
JobConfig(
type=JobType.copr_build, trigger=JobConfigTriggerType.pull_request
)
],
[
JobConfig(
type=JobType.copr_build, trigger=JobConfigTriggerType.pull_request
)
],
id="copr_build_end",
),
pytest.param(
CoprBuildEndHandler,
flexmock(
trigger=TheJobTriggerType.pull_request,
db_trigger=flexmock(
job_config_trigger_type=JobConfigTriggerType.pull_request
),
),
[JobConfig(type=JobType.build, trigger=JobConfigTriggerType.pull_request)],
[JobConfig(type=JobType.build, trigger=JobConfigTriggerType.pull_request)],
id="copr_build_end_with_build_alias",
),
pytest.param(
KojiBuildReportHandler,
flexmock(
trigger=TheJobTriggerType.koji_results,
db_trigger=flexmock(
job_config_trigger_type=JobConfigTriggerType.pull_request
),
),
[
JobConfig(
type=JobType.production_build,
trigger=JobConfigTriggerType.pull_request,
)
],
[
JobConfig(
type=JobType.production_build,
trigger=JobConfigTriggerType.pull_request,
)
],
id="koji_results",
),
pytest.param(
KojiBuildReportHandler,
flexmock(
trigger=TheJobTriggerType.koji_results,
db_trigger=flexmock(
job_config_trigger_type=JobConfigTriggerType.pull_request
),
),
[
JobConfig(
type=JobType.copr_build, trigger=JobConfigTriggerType.pull_request
)
],
[],
id="koji_results_when_copr_build_defined",
),
pytest.param(
KojiBuildReportHandler,
flexmock(
trigger=TheJobTriggerType.koji_results,
db_trigger=flexmock(
job_config_trigger_type=JobConfigTriggerType.pull_request
),
),
[JobConfig(type=JobType.tests, trigger=JobConfigTriggerType.pull_request)],
[],
id="koji_results_when_test_defined",
),
pytest.param(
KojiBuildReportHandler,
flexmock(
trigger=TheJobTriggerType.koji_results,
db_trigger=flexmock(
job_config_trigger_type=JobConfigTriggerType.pull_request
),
),
[
JobConfig(
type=JobType.tests, trigger=JobConfigTriggerType.pull_request
),
JobConfig(
type=JobType.copr_build, trigger=JobConfigTriggerType.pull_request
),
JobConfig(
type=JobType.production_build,
trigger=JobConfigTriggerType.pull_request,
),
],
[
JobConfig(
type=JobType.production_build,
trigger=JobConfigTriggerType.pull_request,
)
],
id="koji_results_when_test_and_copr_build_defined",
),
pytest.param(
KojiBuildReportHandler,
flexmock(
trigger=TheJobTriggerType.koji_results,
db_trigger=flexmock(
job_config_trigger_type=JobConfigTriggerType.pull_request
),
),
[
JobConfig(
type=JobType.production_build,
trigger=JobConfigTriggerType.pull_request,
),
JobConfig(
type=JobType.production_build, trigger=JobConfigTriggerType.release,
),
JobConfig(
type=JobType.production_build, trigger=JobConfigTriggerType.commit,
),
],
[
JobConfig(
type=JobType.production_build,
trigger=JobConfigTriggerType.pull_request,
)
],
id="koji_results_when_multiple_triggers_defined",
),
pytest.param(
KojiBuildReportHandler,
flexmock(
trigger=TheJobTriggerType.koji_results,
db_trigger=flexmock(
job_config_trigger_type=JobConfigTriggerType.release
),
),
[
JobConfig(
type=JobType.production_build,
trigger=JobConfigTriggerType.pull_request,
),
JobConfig(
type=JobType.production_build, trigger=JobConfigTriggerType.release,
),
JobConfig(
type=JobType.production_build, trigger=JobConfigTriggerType.commit,
),
],
[
JobConfig(
type=JobType.production_build, trigger=JobConfigTriggerType.release,
)
],
id="koji_results_for_release_when_multiple_triggers_defined",
),
pytest.param(
KojiBuildReportHandler,
flexmock(
trigger=TheJobTriggerType.koji_results,
db_trigger=flexmock(
job_config_trigger_type=JobConfigTriggerType.commit
),
),
[
JobConfig(
type=JobType.production_build,
trigger=JobConfigTriggerType.pull_request,
),
JobConfig(
type=JobType.production_build, trigger=JobConfigTriggerType.release,
),
JobConfig(
type=JobType.production_build, trigger=JobConfigTriggerType.commit,
),
],
[
JobConfig(
type=JobType.production_build, trigger=JobConfigTriggerType.commit,
)
],
id="koji_results_for_commit_when_multiple_triggers_defined",
),
pytest.param(
GitHubPullRequestCommentCoprBuildHandler,
flexmock(
trigger=TheJobTriggerType.pr_comment,
db_trigger=flexmock(
job_config_trigger_type=JobConfigTriggerType.pull_request
),
),
[JobConfig(type=JobType.tests, trigger=JobConfigTriggerType.pull_request)],
[JobConfig(type=JobType.tests, trigger=JobConfigTriggerType.pull_request)],
id="pr_comment_when_test_defined",
),
pytest.param(
ProposeDownstreamHandler,
flexmock(
trigger=TheJobTriggerType.release,
db_trigger=flexmock(job_config_trigger_type=None),
),
[
JobConfig(
type=JobType.propose_downstream,
trigger=JobConfigTriggerType.release,
)
],
[
JobConfig(
type=JobType.propose_downstream,
trigger=JobConfigTriggerType.release,
)
],
id="issue_comment_when_propose_downstream_defined",
),
],
)
def test_get_config_for_handler_kls(handler_kls, event, jobs, result_job_config):
job_config = get_config_for_handler_kls(
handler_kls=handler_kls, event=event, package_config=flexmock(jobs=jobs),
)
assert job_config == result_job_config
| 37.486486 | 97 | 0.542725 | 2,468 | 34,675 | 7.348865 | 0.074554 | 0.077025 | 0.117991 | 0.152175 | 0.873959 | 0.848928 | 0.835971 | 0.818989 | 0.802227 | 0.797927 | 0 | 0.000377 | 0.387253 | 34,675 | 924 | 98 | 37.527056 | 0.85325 | 0.030858 | 0 | 0.799331 | 0 | 0 | 0.06652 | 0.061815 | 0 | 0 | 0 | 0 | 0.00223 | 1 | 0.00223 | false | 0 | 0.010033 | 0 | 0.012263 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 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 | 7 |
16aa8c258681fb116a9076a2f59a47935be5a5d2 | 170 | py | Python | transformer/plugins/__init__.py | jsabak/Transformer | c96ff1bed109f114f7c69143c85ee362e5f518d1 | [
"MIT"
] | null | null | null | transformer/plugins/__init__.py | jsabak/Transformer | c96ff1bed109f114f7c69143c85ee362e5f518d1 | [
"MIT"
] | null | null | null | transformer/plugins/__init__.py | jsabak/Transformer | c96ff1bed109f114f7c69143c85ee362e5f518d1 | [
"MIT"
] | null | null | null | from .resolve import resolve
from .contracts import plugin, Contract, apply, group_by_contract
__all__ = ["resolve", "plugin", "Contract", "apply", "group_by_contract"]
| 34 | 73 | 0.758824 | 21 | 170 | 5.761905 | 0.47619 | 0.231405 | 0.31405 | 0.396694 | 0.561983 | 0.561983 | 0 | 0 | 0 | 0 | 0 | 0 | 0.111765 | 170 | 4 | 74 | 42.5 | 0.801325 | 0 | 0 | 0 | 0 | 0 | 0.252941 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.666667 | 0 | 0.666667 | 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 | 1 | 0 | 0 | 7 |
bc86447f8a44dc47b1448d6d42523fd1b0028d62 | 5,654 | py | Python | cassie/misc/rewards/trajmatch_reward.py | WooQi57/cassie-run | 9aac12e3a69a011735540d9f5711b8f06da9af81 | [
"MIT"
] | 36 | 2019-10-01T22:50:12.000Z | 2022-02-09T06:17:16.000Z | cassie/misc/rewards/trajmatch_reward.py | WooQi57/cassie-run | 9aac12e3a69a011735540d9f5711b8f06da9af81 | [
"MIT"
] | 5 | 2019-11-26T02:35:39.000Z | 2020-11-29T23:20:48.000Z | cassie/misc/rewards/trajmatch_reward.py | WooQi57/cassie-run | 9aac12e3a69a011735540d9f5711b8f06da9af81 | [
"MIT"
] | 24 | 2019-09-23T19:26:48.000Z | 2022-02-14T14:04:18.000Z | import numpy as np
def trajmatch_reward(self):
qpos = np.copy(self.sim.qpos())
qvel = np.copy(self.sim.qvel())
phase_diff = self.phase - np.floor(self.phase)
ref_pos_prev, ref_vel_prev = self.get_ref_state(int(np.floor(self.phase)))
if phase_diff != 0:
ref_pos_next, ref_vel_next = self.get_ref_state(int(np.ceil(self.phase)))
ref_pos_diff = ref_pos_next - ref_pos_prev
ref_vel_diff = ref_vel_next - ref_vel_prev
ref_pos = ref_pos_prev + phase_diff*ref_pos_diff
ref_vel = ref_vel_prev + phase_diff*ref_vel_diff
else:
ref_pos = ref_pos_prev
ref_vel = ref_vel_prev
ref_pos, ref_vel = self.get_ref_state(self.phase)
# TODO: should be variable; where do these come from?
# TODO: see magnitude of state variables to gauge contribution to reward
weight = [0.15, 0.15, 0.1, 0.05, 0.05, 0.15, 0.15, 0.1, 0.05, 0.05]
joint_error = 0
com_error = 0
orientation_error = 0
spring_error = 0
# each joint pos
for i, j in enumerate(self.pos_idx):
target = ref_pos[j]
actual = qpos[j]
joint_error += 30 * weight[i] * (target - actual) ** 2
# center of mass: x, y, z
for j in [0, 1, 2]:
target = ref_pos[j]
actual = qpos[j]
# NOTE: in Xie et al y target is 0
com_error += (target - actual) ** 2
# COM orientation: qx, qy, qz
for j in [4, 5, 6]:
target = ref_pos[j] # NOTE: in Xie et al orientation target is 0
actual = qpos[j]
orientation_error += (target - actual) ** 2
# left and right shin springs
for i in [15, 29]:
target = ref_pos[i] # NOTE: in Xie et al spring target is 0
actual = qpos[i]
spring_error += 1000 * (target - actual) ** 2
reward = 0.5 * np.exp(-joint_error) + \
0.3 * np.exp(-com_error) + \
0.1 * np.exp(-orientation_error) + \
0.1 * np.exp(-spring_error)
# orientation error does not look informative
# maybe because it's comparing euclidean distance on quaternions
# print("reward: {8}\njoint:\t{0:.2f}, % = {1:.2f}\ncom:\t{2:.2f}, % = {3:.2f}\norient:\t{4:.2f}, % = {5:.2f}\nspring:\t{6:.2f}, % = {7:.2f}\n\n".format(
# 0.5 * np.exp(-joint_error), 0.5 * np.exp(-joint_error) / reward * 100,
# 0.3 * np.exp(-com_error), 0.3 * np.exp(-com_error) / reward * 100,
# 0.1 * np.exp(-orientation_error), 0.1 * np.exp(-orientation_error) / reward * 100,
# 0.1 * np.exp(-spring_error), 0.1 * np.exp(-spring_error) / reward * 100,
# reward
# )
# )
return reward
def trajmatch_footorient_hiprollvelact_reward(self):
qpos = np.copy(self.sim.qpos())
qvel = np.copy(self.sim.qvel())
phase_diff = self.phase - np.floor(self.phase)
ref_pos_prev, ref_vel_prev = self.get_ref_state(int(np.floor(self.phase)))
if phase_diff != 0:
ref_pos_next, ref_vel_next = self.get_ref_state(int(np.ceil(self.phase)))
ref_pos_diff = ref_pos_next - ref_pos_prev
ref_vel_diff = ref_vel_next - ref_vel_prev
ref_pos = ref_pos_prev + phase_diff*ref_pos_diff
ref_vel = ref_vel_prev + phase_diff*ref_vel_diff
else:
ref_pos = ref_pos_prev
ref_vel = ref_vel_prev
ref_pos, ref_vel = self.get_ref_state(self.phase)
# TODO: should be variable; where do these come from?
# TODO: see magnitude of state variables to gauge contribution to reward
weight = [0.15, 0.15, 0.1, 0.05, 0.05, 0.15, 0.15, 0.1, 0.05, 0.05]
joint_error = 0
com_error = 0
orientation_error = 0
spring_error = 0
# each joint pos
for i, j in enumerate(self.pos_idx):
target = ref_pos[j]
actual = qpos[j]
joint_error += 30 * weight[i] * (target - actual) ** 2
# center of mass: x, y, z
for j in [0, 1, 2]:
target = ref_pos[j]
actual = qpos[j]
# NOTE: in Xie et al y target is 0
com_error += (target - actual) ** 2
# COM orientation: qx, qy, qz
for j in [4, 5, 6]:
target = ref_pos[j] # NOTE: in Xie et al orientation target is 0
actual = qpos[j]
orientation_error += (target - actual) ** 2
# left and right shin springs
for i in [15, 29]:
target = ref_pos[i] # NOTE: in Xie et al spring target is 0
actual = qpos[i]
spring_error += 1000 * (target - actual) ** 2
reward = 0.3 * np.exp(-joint_error) + \
0.2 * np.exp(-com_error) + \
0.1 * np.exp(-orientation_error) + \
0.1 * np.exp(-spring_error) \
+ .075*np.exp(-self.l_foot_orient_cost) + .075*np.exp(-self.r_foot_orient_cost) \
+ .1*np.exp(-self.hiproll_cost) + 0.05*np.exp(-self.hiproll_act)
# orientation error does not look informative
# maybe because it's comparing euclidean distance on quaternions
# print("reward: {8}\njoint:\t{0:.2f}, % = {1:.2f}\ncom:\t{2:.2f}, % = {3:.2f}\norient:\t{4:.2f}, % = {5:.2f}\nspring:\t{6:.2f}, % = {7:.2f}\n\n".format(
# 0.5 * np.exp(-joint_error), 0.5 * np.exp(-joint_error) / reward * 100,
# 0.3 * np.exp(-com_error), 0.3 * np.exp(-com_error) / reward * 100,
# 0.1 * np.exp(-orientation_error), 0.1 * np.exp(-orientation_error) / reward * 100,
# 0.1 * np.exp(-spring_error), 0.1 * np.exp(-spring_error) / reward * 100,
# reward
# )
# )
return reward | 37.443709 | 157 | 0.567563 | 879 | 5,654 | 3.47099 | 0.141069 | 0.058997 | 0.025565 | 0.027532 | 0.94723 | 0.941986 | 0.941986 | 0.936087 | 0.936087 | 0.936087 | 0 | 0.057301 | 0.29625 | 5,654 | 151 | 158 | 37.443709 | 0.709475 | 0.348956 | 0 | 0.870588 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.006623 | 0 | 1 | 0.023529 | false | 0 | 0.011765 | 0 | 0.058824 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 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 | 7 |
bccde81bd34901917d2fe9b33a539d2651e48fec | 111,668 | py | Python | hl7apy/v2_6/messages.py | tmoat/hl7apy | e5ca5eef86c91e0e3f312b89e0a9a77651e21158 | [
"MIT"
] | null | null | null | hl7apy/v2_6/messages.py | tmoat/hl7apy | e5ca5eef86c91e0e3f312b89e0a9a77651e21158 | [
"MIT"
] | null | null | null | hl7apy/v2_6/messages.py | tmoat/hl7apy | e5ca5eef86c91e0e3f312b89e0a9a77651e21158 | [
"MIT"
] | null | null | null | from .groups import GROUPS
from .segments import SEGMENTS
MESSAGES = {
'ACK': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('MSA', SEGMENTS['MSA'], (1, 1), 'SEG'),
('ERR', SEGMENTS['ERR'], (0, -1), 'SEG'),)),
'ADR_A19': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('MSA', SEGMENTS['MSA'], (1, 1), 'SEG'),
('ERR', SEGMENTS['ERR'], (0, 1), 'SEG'),
('QAK', SEGMENTS['QAK'], (0, 1), 'SEG'),
('QRD', SEGMENTS['QRD'], (1, 1), 'SEG'),
('QRF', SEGMENTS['QRF'], (0, 1), 'SEG'),
('ADR_A19_QUERY_RESPONSE', GROUPS['ADR_A19_QUERY_RESPONSE'], (1, -1), 'GRP'),
('DSC', SEGMENTS['DSC'], (0, 1), 'SEG'),)),
'ADT_A01': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('EVN', SEGMENTS['EVN'], (1, 1), 'SEG'),
('PID', SEGMENTS['PID'], (1, 1), 'SEG'),
('PD1', SEGMENTS['PD1'], (0, 1), 'SEG'),
('ARV', SEGMENTS['ARV'], (0, -1), 'SEG'),
('ROL', SEGMENTS['ROL'], (0, -1), 'SEG'),
('NK1', SEGMENTS['NK1'], (0, -1), 'SEG'),
('PV1', SEGMENTS['PV1'], (1, 1), 'SEG'),
('PV2', SEGMENTS['PV2'], (0, 1), 'SEG'),
('ARV', SEGMENTS['ARV'], (0, -1), 'SEG'),
('ROL', SEGMENTS['ROL'], (0, -1), 'SEG'),
('DB1', SEGMENTS['DB1'], (0, -1), 'SEG'),
('OBX', SEGMENTS['OBX'], (0, -1), 'SEG'),
('AL1', SEGMENTS['AL1'], (0, -1), 'SEG'),
('DG1', SEGMENTS['DG1'], (0, -1), 'SEG'),
('DRG', SEGMENTS['DRG'], (0, 1), 'SEG'),
('ADT_A01_PROCEDURE', GROUPS['ADT_A01_PROCEDURE'], (0, -1), 'GRP'),
('GT1', SEGMENTS['GT1'], (0, -1), 'SEG'),
('ADT_A01_INSURANCE', GROUPS['ADT_A01_INSURANCE'], (0, -1), 'GRP'),
('ACC', SEGMENTS['ACC'], (0, 1), 'SEG'),
('UB1', SEGMENTS['UB1'], (0, 1), 'SEG'),
('UB2', SEGMENTS['UB2'], (0, 1), 'SEG'),
('PDA', SEGMENTS['PDA'], (0, 1), 'SEG'),)),
'ADT_A02': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('EVN', SEGMENTS['EVN'], (1, 1), 'SEG'),
('PID', SEGMENTS['PID'], (1, 1), 'SEG'),
('PD1', SEGMENTS['PD1'], (0, 1), 'SEG'),
('ARV', SEGMENTS['ARV'], (0, -1), 'SEG'),
('ROL', SEGMENTS['ROL'], (0, -1), 'SEG'),
('PV1', SEGMENTS['PV1'], (1, 1), 'SEG'),
('PV2', SEGMENTS['PV2'], (0, 1), 'SEG'),
('ARV', SEGMENTS['ARV'], (0, -1), 'SEG'),
('ROL', SEGMENTS['ROL'], (0, -1), 'SEG'),
('DB1', SEGMENTS['DB1'], (0, -1), 'SEG'),
('OBX', SEGMENTS['OBX'], (0, -1), 'SEG'),
('PDA', SEGMENTS['PDA'], (0, 1), 'SEG'),)),
'ADT_A03': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('EVN', SEGMENTS['EVN'], (1, 1), 'SEG'),
('PID', SEGMENTS['PID'], (1, 1), 'SEG'),
('PD1', SEGMENTS['PD1'], (0, 1), 'SEG'),
('ARV', SEGMENTS['ARV'], (0, -1), 'SEG'),
('ROL', SEGMENTS['ROL'], (0, -1), 'SEG'),
('NK1', SEGMENTS['NK1'], (0, -1), 'SEG'),
('PV1', SEGMENTS['PV1'], (1, 1), 'SEG'),
('PV2', SEGMENTS['PV2'], (0, 1), 'SEG'),
('ARV', SEGMENTS['ARV'], (0, -1), 'SEG'),
('ROL', SEGMENTS['ROL'], (0, -1), 'SEG'),
('DB1', SEGMENTS['DB1'], (0, -1), 'SEG'),
('AL1', SEGMENTS['AL1'], (0, -1), 'SEG'),
('DG1', SEGMENTS['DG1'], (0, -1), 'SEG'),
('DRG', SEGMENTS['DRG'], (0, 1), 'SEG'),
('ADT_A03_PROCEDURE', GROUPS['ADT_A03_PROCEDURE'], (0, -1), 'GRP'),
('OBX', SEGMENTS['OBX'], (0, -1), 'SEG'),
('GT1', SEGMENTS['GT1'], (0, -1), 'SEG'),
('ADT_A03_INSURANCE', GROUPS['ADT_A03_INSURANCE'], (0, -1), 'GRP'),
('ACC', SEGMENTS['ACC'], (0, 1), 'SEG'),
('PDA', SEGMENTS['PDA'], (0, 1), 'SEG'),)),
'ADT_A05': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('EVN', SEGMENTS['EVN'], (1, 1), 'SEG'),
('PID', SEGMENTS['PID'], (1, 1), 'SEG'),
('PD1', SEGMENTS['PD1'], (0, 1), 'SEG'),
('ARV', SEGMENTS['ARV'], (0, -1), 'SEG'),
('ROL', SEGMENTS['ROL'], (0, -1), 'SEG'),
('NK1', SEGMENTS['NK1'], (0, -1), 'SEG'),
('PV1', SEGMENTS['PV1'], (1, 1), 'SEG'),
('PV2', SEGMENTS['PV2'], (0, 1), 'SEG'),
('ARV', SEGMENTS['ARV'], (0, -1), 'SEG'),
('ROL', SEGMENTS['ROL'], (0, -1), 'SEG'),
('DB1', SEGMENTS['DB1'], (0, -1), 'SEG'),
('OBX', SEGMENTS['OBX'], (0, -1), 'SEG'),
('AL1', SEGMENTS['AL1'], (0, -1), 'SEG'),
('DG1', SEGMENTS['DG1'], (0, -1), 'SEG'),
('DRG', SEGMENTS['DRG'], (0, 1), 'SEG'),
('ADT_A05_PROCEDURE', GROUPS['ADT_A05_PROCEDURE'], (0, -1), 'GRP'),
('GT1', SEGMENTS['GT1'], (0, -1), 'SEG'),
('ADT_A05_INSURANCE', GROUPS['ADT_A05_INSURANCE'], (0, -1), 'GRP'),
('ACC', SEGMENTS['ACC'], (0, 1), 'SEG'),
('UB1', SEGMENTS['UB1'], (0, 1), 'SEG'),
('UB2', SEGMENTS['UB2'], (0, 1), 'SEG'),)),
'ADT_A06': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('EVN', SEGMENTS['EVN'], (1, 1), 'SEG'),
('PID', SEGMENTS['PID'], (1, 1), 'SEG'),
('PD1', SEGMENTS['PD1'], (0, 1), 'SEG'),
('ARV', SEGMENTS['ARV'], (0, -1), 'SEG'),
('ROL', SEGMENTS['ROL'], (0, -1), 'SEG'),
('MRG', SEGMENTS['MRG'], (0, 1), 'SEG'),
('NK1', SEGMENTS['NK1'], (0, -1), 'SEG'),
('PV1', SEGMENTS['PV1'], (1, 1), 'SEG'),
('PV2', SEGMENTS['PV2'], (0, 1), 'SEG'),
('ARV', SEGMENTS['ARV'], (0, -1), 'SEG'),
('ROL', SEGMENTS['ROL'], (0, -1), 'SEG'),
('DB1', SEGMENTS['DB1'], (0, -1), 'SEG'),
('OBX', SEGMENTS['OBX'], (0, -1), 'SEG'),
('AL1', SEGMENTS['AL1'], (0, -1), 'SEG'),
('DG1', SEGMENTS['DG1'], (0, -1), 'SEG'),
('DRG', SEGMENTS['DRG'], (0, 1), 'SEG'),
('ADT_A06_PROCEDURE', GROUPS['ADT_A06_PROCEDURE'], (0, -1), 'GRP'),
('GT1', SEGMENTS['GT1'], (0, -1), 'SEG'),
('ADT_A06_INSURANCE', GROUPS['ADT_A06_INSURANCE'], (0, -1), 'GRP'),
('ACC', SEGMENTS['ACC'], (0, 1), 'SEG'),
('UB1', SEGMENTS['UB1'], (0, 1), 'SEG'),
('UB2', SEGMENTS['UB2'], (0, 1), 'SEG'),)),
'ADT_A08': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('EVN', SEGMENTS['EVN'], (1, 1), 'SEG'),
('PID', SEGMENTS['PID'], (1, 1), 'SEG'),
('PD1', SEGMENTS['PD1'], (0, 1), 'SEG'),
('ARV', SEGMENTS['ARV'], (0, -1), 'SEG'),
('ROL', SEGMENTS['ROL'], (0, -1), 'SEG',),
('NK1', SEGMENTS['NK1'], (0, -1), 'SEG'),
('PV1', SEGMENTS['PV1'], (1, 1), 'SEG'),
('PV2', SEGMENTS['PV2'], (0, 1), 'SEG'),
('ARV', SEGMENTS['ARV'], (0, -1), 'SEG'),
('ROL', SEGMENTS['ROL'], (0, -1), 'SEG',),
('DB1', SEGMENTS['DB1'], (0, -1), 'SEG'),
('OBX', SEGMENTS['OBX'], (0, -1), 'SEG'),
('AL1', SEGMENTS['AL1'], (0, -1), 'SEG'),
('DG1', SEGMENTS['DG1'], (0, -1), 'SEG'),
('DRG', SEGMENTS['DRG'], (0, 1), 'SEG'),
('ADT_A08_PROCEDURE', GROUPS['ADT_A08_PROCEDURE'], (0, -1), 'GRP'),
('GT1', SEGMENTS['GT1'], (0, -1), 'SEG'),
('ADT_A08_INSURANCE', GROUPS['ADT_A08_INSURANCE'], (0, -1), 'GRP'),
('ACC', SEGMENTS['ACC'], (0, 1), 'SEG'),
('UB1', SEGMENTS['UB1'], (0, 1), 'SEG'),
('UB2', SEGMENTS['UB2'], (0, 1), 'SEG'),
('PDA', SEGMENTS['PDA'], (0, 1), 'SEG'),)),
'ADT_A09': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('EVN', SEGMENTS['EVN'], (1, 1), 'SEG'),
('PID', SEGMENTS['PID'], (1, 1), 'SEG'),
('PD1', SEGMENTS['PD1'], (0, 1), 'SEG'),
('PV1', SEGMENTS['PV1'], (1, 1), 'SEG'),
('PV2', SEGMENTS['PV2'], (0, 1), 'SEG'),
('DB1', SEGMENTS['DB1'], (0, -1), 'SEG'),
('OBX', SEGMENTS['OBX'], (0, -1), 'SEG'),
('DG1', SEGMENTS['DG1'], (0, -1), 'SEG'),)),
'ADT_A12': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('EVN', SEGMENTS['EVN'], (1, 1), 'SEG'),
('PID', SEGMENTS['PID'], (1, 1), 'SEG'),
('PD1', SEGMENTS['PD1'], (0, 1), 'SEG'),
('PV1', SEGMENTS['PV1'], (1, 1), 'SEG'),
('PV2', SEGMENTS['PV2'], (0, 1), 'SEG'),
('DB1', SEGMENTS['DB1'], (0, -1), 'SEG'),
('OBX', SEGMENTS['OBX'], (0, -1), 'SEG'),
('DG1', SEGMENTS['DG1'], (0, 1), 'SEG'),)),
'ADT_A15': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('EVN', SEGMENTS['EVN'], (1, 1), 'SEG'),
('PID', SEGMENTS['PID'], (1, 1), 'SEG'),
('PD1', SEGMENTS['PD1'], (0, 1), 'SEG'),
('ARV', SEGMENTS['ARV'], (0, -1), 'SEG'),
('ROL', SEGMENTS['ROL'], (0, -1), 'SEG'),
('PV1', SEGMENTS['PV1'], (1, 1), 'SEG'),
('PV2', SEGMENTS['PV2'], (0, 1), 'SEG'),
('ARV', SEGMENTS['ARV'], (0, -1), 'SEG'),
('ROL', SEGMENTS['ROL'], (0, -1), 'SEG'),
('DB1', SEGMENTS['DB1'], (0, -1), 'SEG'),
('OBX', SEGMENTS['OBX'], (0, -1), 'SEG'),
('DG1', SEGMENTS['DG1'], (0, -1), 'SEG'),)),
'ADT_A16': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('EVN', SEGMENTS['EVN'], (1, 1), 'SEG'),
('PID', SEGMENTS['PID'], (1, 1), 'SEG'),
('PD1', SEGMENTS['PD1'], (0, 1), 'SEG'),
('ARV', SEGMENTS['ARV'], (0, -1), 'SEG'),
('ROL', SEGMENTS['ROL'], (0, -1), 'SEG'),
('NK1', SEGMENTS['NK1'], (0, -1), 'SEG'),
('PV1', SEGMENTS['PV1'], (1, 1), 'SEG'),
('PV2', SEGMENTS['PV2'], (0, 1), 'SEG'),
('ARV', SEGMENTS['ARV'], (0, -1), 'SEG'),
('ROL', SEGMENTS['ROL'], (0, -1), 'SEG'),
('DB1', SEGMENTS['DB1'], (0, -1), 'SEG'),
('OBX', SEGMENTS['OBX'], (0, -1), 'SEG'),
('AL1', SEGMENTS['AL1'], (0, -1), 'SEG'),
('DG1', SEGMENTS['DG1'], (0, -1), 'SEG'),
('DRG', SEGMENTS['DRG'], (0, 1), 'SEG'),
('ADT_A16_PROCEDURE', GROUPS['ADT_A16_PROCEDURE'], (0, -1), 'GRP'),
('GT1', SEGMENTS['GT1'], (0, -1), 'SEG'),
('ADT_A16_INSURANCE', GROUPS['ADT_A16_INSURANCE'], (0, -1), 'GRP'),
('ACC', SEGMENTS['ACC'], (0, 1), 'SEG'),)),
'ADT_A17': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('EVN', SEGMENTS['EVN'], (1, 1), 'SEG'),
('PID', SEGMENTS['PID'], (1, 1), 'SEG'),
('PD1', SEGMENTS['PD1'], (0, 1), 'SEG'),
('PV1', SEGMENTS['PV1'], (1, 1), 'SEG'),
('PV2', SEGMENTS['PV2'], (0, 1), 'SEG'),
('DB1', SEGMENTS['DB1'], (0, -1), 'SEG'),
('OBX', SEGMENTS['OBX'], (0, -1), 'SEG'),
('PID', SEGMENTS['PID'], (1, 1), 'SEG'),
('PD1', SEGMENTS['PD1'], (0, 1), 'SEG'),
('PV1', SEGMENTS['PV1'], (1, 1), 'SEG'),
('PV2', SEGMENTS['PV2'], (0, 1), 'SEG'),
('DB1', SEGMENTS['DB1'], (0, -1), 'SEG'),
('OBX', SEGMENTS['OBX'], (0, -1), 'SEG'),)),
'ADT_A18': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('EVN', SEGMENTS['EVN'], (1, 1), 'SEG'),
('PID', SEGMENTS['PID'], (1, 1), 'SEG'),
('PD1', SEGMENTS['PD1'], (0, 1), 'SEG'),
('MRG', SEGMENTS['MRG'], (1, 1), 'SEG'),
('PV1', SEGMENTS['PV1'], (1, 1), 'SEG'),)),
'ADT_A20': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('EVN', SEGMENTS['EVN'], (1, 1), 'SEG'),
('NPU', SEGMENTS['NPU'], (1, 1), 'SEG'),)),
'ADT_A21': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('EVN', SEGMENTS['EVN'], (1, 1), 'SEG'),
('PID', SEGMENTS['PID'], (1, 1), 'SEG'),
('PD1', SEGMENTS['PD1'], (0, 1), 'SEG'),
('PV1', SEGMENTS['PV1'], (1, 1), 'SEG'),
('PV2', SEGMENTS['PV2'], (0, 1), 'SEG'),
('DB1', SEGMENTS['DB1'], (0, -1), 'SEG'),
('OBX', SEGMENTS['OBX'], (0, -1), 'SEG'),)),
'ADT_A24': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('EVN', SEGMENTS['EVN'], (1, 1), 'SEG'),
('PID', SEGMENTS['PID'], (1, 1), 'SEG'),
('PD1', SEGMENTS['PD1'], (0, 1), 'SEG'),
('PV1', SEGMENTS['PV1'], (0, 1), 'SEG'),
('DB1', SEGMENTS['DB1'], (0, -1), 'SEG'),
('PID', SEGMENTS['PID'], (1, 1), 'SEG'),
('PD1', SEGMENTS['PD1'], (0, 1), 'SEG'),
('PV1', SEGMENTS['PV1'], (0, 1), 'SEG'),
('DB1', SEGMENTS['DB1'], (0, -1), 'SEG'),)),
'ADT_A30': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('EVN', SEGMENTS['EVN'], (1, 1), 'SEG'),
('PID', SEGMENTS['PID'], (1, 1), 'SEG'),
('PD1', SEGMENTS['PD1'], (0, 1), 'SEG'),
('ARV', SEGMENTS['ARV'], (0, -1), 'SEG'),
('MRG', SEGMENTS['MRG'], (1, 1), 'SEG'),)),
'ADT_A37': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('EVN', SEGMENTS['EVN'], (1, 1), 'SEG'),
('PID', SEGMENTS['PID'], (1, 1), 'SEG'),
('PD1', SEGMENTS['PD1'], (0, 1), 'SEG'),
('PV1', SEGMENTS['PV1'], (0, 1), 'SEG'),
('DB1', SEGMENTS['DB1'], (0, -1), 'SEG'),
('PID', SEGMENTS['PID'], (1, 1), 'SEG'),
('PD1', SEGMENTS['PD1'], (0, 1), 'SEG'),
('PV1', SEGMENTS['PV1'], (0, 1), 'SEG'),
('DB1', SEGMENTS['DB1'], (0, -1), 'SEG'),)),
'ADT_A38': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('EVN', SEGMENTS['EVN'], (1, 1), 'SEG'),
('PID', SEGMENTS['PID'], (1, 1), 'SEG'),
('PD1', SEGMENTS['PD1'], (0, 1), 'SEG'),
('PV1', SEGMENTS['PV1'], (1, 1), 'SEG'),
('PV2', SEGMENTS['PV2'], (0, 1), 'SEG'),
('DB1', SEGMENTS['DB1'], (0, -1), 'SEG'),
('OBX', SEGMENTS['OBX'], (0, -1), 'SEG'),
('DG1', SEGMENTS['DG1'], (0, -1), 'SEG'),
('DRG', SEGMENTS['DRG'], (0, 1), 'SEG'),)),
'ADT_A39': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('EVN', SEGMENTS['EVN'], (1, 1), 'SEG'),
('ADT_A39_PATIENT', GROUPS['ADT_A39_PATIENT'], (1, -1), 'GRP'),)),
'ADT_A43': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('EVN', SEGMENTS['EVN'], (1, 1), 'SEG'),
('ADT_A43_PATIENT', GROUPS['ADT_A43_PATIENT'], (1, -1), 'GRP'),)),
'ADT_A45': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('EVN', SEGMENTS['EVN'], (1, 1), 'SEG'),
('PID', SEGMENTS['PID'], (1, 1), 'SEG'),
('PD1', SEGMENTS['PD1'], (0, 1), 'SEG'),
('ADT_A45_MERGE_INFO', GROUPS['ADT_A45_MERGE_INFO'], (1, -1), 'GRP'),)),
'ADT_A50': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
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'ADT_A52': ('sequence',
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'ADT_A54': ('sequence',
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'ADT_A60': ('sequence',
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'ADT_A61': ('sequence',
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'BAR_P01': ('sequence',
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'BAR_P02': ('sequence',
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'BAR_P05': ('sequence',
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('BAR_P05_VISIT', GROUPS['BAR_P05_VISIT'], (1, -1), 'GRP'),)),
'BAR_P06': ('sequence',
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('BAR_P06_PATIENT', GROUPS['BAR_P06_PATIENT'], (1, -1), 'GRP'),)),
'BAR_P10': ('sequence',
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'BAR_P12': ('sequence',
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'BPS_O29': ('sequence',
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('BPS_O29_ORDER', GROUPS['BPS_O29_ORDER'], (1, -1), 'GRP'),)),
'BRP_O30': ('sequence',
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'BRT_O32': ('sequence',
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('BRT_O32_RESPONSE', GROUPS['BRT_O32_RESPONSE'], (0, 1), 'GRP'),)),
'BTS_O31': ('sequence',
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('BTS_O31_ORDER', GROUPS['BTS_O31_ORDER'], (1, -1), 'GRP'),)),
'CRM_C01': ('sequence',
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('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('CRM_C01_PATIENT', GROUPS['CRM_C01_PATIENT'], (1, -1), 'GRP'),)),
'CSU_C09': ('sequence',
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('CSU_C09_PATIENT', GROUPS['CSU_C09_PATIENT'], (1, -1), 'GRP'),)),
'DFT_P03': ('sequence',
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('DFT_P03_FINANCIAL', GROUPS['DFT_P03_FINANCIAL'], (1, -1), 'GRP'),
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('DFT_P03_INSURANCE', GROUPS['DFT_P03_INSURANCE'], (0, -1), 'GRP'),
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'DFT_P11': ('sequence',
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('DFT_P11_FINANCIAL', GROUPS['DFT_P11_FINANCIAL'], (1, -1), 'GRP'),)),
'DOC_T12': ('sequence',
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('DOC_T12_RESULT', GROUPS['DOC_T12_RESULT'], (1, -1), 'GRP'),
('DSC', SEGMENTS['DSC'], (0, 1), 'SEG'),)),
'EAC_U07': ('sequence',
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('EAC_U07_COMMAND', GROUPS['EAC_U07_COMMAND'], (1, -1), 'GRP'),
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'EAN_U09': ('sequence',
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('EAN_U09_NOTIFICATION', GROUPS['EAN_U09_NOTIFICATION'], (1, -1), 'GRP'),
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'EAR_U08': ('sequence',
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('EQU', SEGMENTS['EQU'], (1, 1), 'SEG'),
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('ROL', SEGMENTS['ROL'], (0, 1), 'SEG'),)),
'EHC_E01': ('sequence',
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('UAC', SEGMENTS['UAC'], (0, -1), 'SEG'),
('EHC_E01_INVOICE_INFORMATION', GROUPS['EHC_E01_INVOICE_INFORMATION'], (1, 1), 'GRP'),)),
'EHC_E02': ('sequence',
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('EHC_E02_INVOICE_INFORMATION', GROUPS['EHC_E02_INVOICE_INFORMATION'], (1, 1), 'GRP'),)),
'EHC_E04': ('sequence',
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('UAC', SEGMENTS['UAC'], (0, -1), 'SEG'),
('EHC_E04_REASSESSMENT_REQUEST_INFO', GROUPS['EHC_E04_REASSESSMENT_REQUEST_INFO'], (1, 1), 'GRP'),)),
'EHC_E10': ('sequence',
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('MSA', SEGMENTS['MSA'], (1, 1), 'SEG'),
('ERR', SEGMENTS['ERR'], (0, -1), 'SEG'),
('EHC_E10_INVOICE_PROCESSING_RESULTS_INFO', GROUPS['EHC_E10_INVOICE_PROCESSING_RESULTS_INFO'], (1, -1),
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'EHC_E12': ('sequence',
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('CTD', SEGMENTS['CTD'], (0, -1), 'SEG'),
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('PSS', SEGMENTS['PSS'], (1, 1), 'SEG'),
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('PSL', SEGMENTS['PSL'], (0, -1), 'SEG'),
('EHC_E12_REQUEST', GROUPS['EHC_E12_REQUEST'], (1, -1), 'GRP'),)),
'EHC_E13': ('sequence',
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('PSL', SEGMENTS['PSL'], (0, 1), 'SEG'),
('EHC_E13_REQUEST', GROUPS['EHC_E13_REQUEST'], (1, -1), 'GRP'),)),
'EHC_E15': ('sequence',
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('EHC_E15_PAYMENT_REMITTANCE_HEADER_INFO', GROUPS['EHC_E15_PAYMENT_REMITTANCE_HEADER_INFO'], (1, 1),
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('EHC_E15_PAYMENT_REMITTANCE_DETAIL_INFO', GROUPS['EHC_E15_PAYMENT_REMITTANCE_DETAIL_INFO'], (0, -1),
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('EHC_E15_ADJUSTMENT_PAYEE', GROUPS['EHC_E15_ADJUSTMENT_PAYEE'], (0, -1), 'GRP'),)),
'EHC_E20': ('sequence',
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('EHC_E20_AUTHORIZATION_REQUEST', GROUPS['EHC_E20_AUTHORIZATION_REQUEST'], (1, 1), 'GRP'),)),
'EHC_E21': ('sequence',
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('UAC', SEGMENTS['UAC'], (0, -1), 'SEG'),
('EHC_E21_AUTHORIZATION_REQUEST', GROUPS['EHC_E21_AUTHORIZATION_REQUEST'], (1, 1), 'GRP'),)),
'EHC_E24': ('sequence',
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('MSA', SEGMENTS['MSA'], (1, 1), 'SEG'),
('ERR', SEGMENTS['ERR'], (0, -1), 'SEG'),
('EHC_E24_AUTHORIZATION_RESPONSE_INFO', GROUPS['EHC_E24_AUTHORIZATION_RESPONSE_INFO'], (1, 1),
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'ESR_U02': ('sequence',
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('EQU', SEGMENTS['EQU'], (1, 1), 'SEG'),
('ROL', SEGMENTS['ROL'], (0, 1), 'SEG'),)),
'ESU_U01': ('sequence',
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('ISD', SEGMENTS['ISD'], (0, -1), 'SEG'),
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'INR_U06': ('sequence',
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('INV', SEGMENTS['INV'], (1, -1), 'SEG'),
('ROL', SEGMENTS['ROL'], (0, 1), 'SEG'),)),
'INU_U05': ('sequence',
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('EQU', SEGMENTS['EQU'], (1, 1), 'SEG'),
('INV', SEGMENTS['INV'], (1, -1), 'SEG'),
('ROL', SEGMENTS['ROL'], (0, 1), 'SEG'),)),
'LSU_U12': ('sequence',
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('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
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('EQU', SEGMENTS['EQU'], (1, 1), 'SEG'),
('EQP', SEGMENTS['EQP'], (1, -1), 'SEG'),
('ROL', SEGMENTS['ROL'], (0, 1), 'SEG'),)),
'MDM_T01': ('sequence',
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('EVN', SEGMENTS['EVN'], (1, 1), 'SEG'),
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('PV1', SEGMENTS['PV1'], (1, 1), 'SEG'),
('MDM_T01_COMMON_ORDER', GROUPS['MDM_T01_COMMON_ORDER'], (0, -1), 'GRP'),
('TXA', SEGMENTS['TXA'], (1, 1), 'SEG'),)),
'MDM_T02': ('sequence',
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('PID', SEGMENTS['PID'], (1, 1), 'SEG'),
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('MDM_T02_COMMON_ORDER', GROUPS['MDM_T02_COMMON_ORDER'], (0, -1), 'GRP'),
('TXA', SEGMENTS['TXA'], (1, 1), 'SEG'),
('MDM_T02_OBSERVATION', GROUPS['MDM_T02_OBSERVATION'], (1, -1), 'GRP'),)),
'MFK_M01': ('sequence',
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('MSA', SEGMENTS['MSA'], (1, 1), 'SEG'),
('ERR', SEGMENTS['ERR'], (0, -1), 'SEG'),
('MFI', SEGMENTS['MFI'], (1, 1), 'SEG'),
('MFA', SEGMENTS['MFA'], (0, -1), 'SEG'),)),
'MFN_M01': ('sequence',
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('MFI', SEGMENTS['MFI'], (1, 1), 'SEG'),
('MFN_M01_MF', GROUPS['MFN_M01_MF'], (1, -1), 'GRP'),)),
'MFN_M02': ('sequence',
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('MFI', SEGMENTS['MFI'], (1, 1), 'SEG'),
('MFN_M02_MF_STAFF', GROUPS['MFN_M02_MF_STAFF'], (1, -1), 'GRP'),)),
'MFN_M03': ('sequence',
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('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('MFI', SEGMENTS['MFI'], (1, 1), 'SEG'),
('MFN_M03_MF_TEST', GROUPS['MFN_M03_MF_TEST'], (1, -1), 'GRP'),)),
'MFN_M04': ('sequence',
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'MFN_M17': ('sequence',
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'MFN_Znn': ('sequence',
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'OMG_O19': ('sequence',
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'OMS_O05': ('sequence',
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'ORB_O28': ('sequence',
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'ORP_O10': ('sequence',
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('ORP_O10_RESPONSE', GROUPS['ORP_O10_RESPONSE'], (0, 1), 'GRP'),)),
'ORR_O02': ('sequence',
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('ORR_O02_RESPONSE', GROUPS['ORR_O02_RESPONSE'], (0, 1), 'GRP'),)),
'ORS_O06': ('sequence',
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'ORU_R01': ('sequence',
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'ORU_R30': ('sequence',
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('ORU_R30_OBSERVATION', GROUPS['ORU_R30_OBSERVATION'], (1, -1), 'GRP'),)),
'OSQ_Q06': ('sequence',
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'OSR_Q06': ('sequence',
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('OSR_Q06_RESPONSE', GROUPS['OSR_Q06_RESPONSE'], (0, 1), 'GRP'),
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'OUL_R21': ('sequence',
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'OUL_R22': ('sequence',
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'OUL_R23': ('sequence',
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'OUL_R24': ('sequence',
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('OUL_R24_ORDER', GROUPS['OUL_R24_ORDER'], (1, -1), 'GRP'),
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'PEX_P07': ('sequence',
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('PEX_P07_EXPERIENCE', GROUPS['PEX_P07_EXPERIENCE'], (1, -1), 'GRP'),)),
'PGL_PC6': ('sequence',
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('PGL_PC6_GOAL', GROUPS['PGL_PC6_GOAL'], (1, -1), 'GRP'),)),
'PMU_B01': ('sequence',
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'PMU_B03': ('sequence',
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'PMU_B04': ('sequence',
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'PMU_B07': ('sequence',
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'PMU_B08': ('sequence',
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'PPG_PCG': ('sequence',
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('PPG_PCG_PATHWAY', GROUPS['PPG_PCG_PATHWAY'], (1, -1), 'GRP'),)),
'PPP_PCB': ('sequence',
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('PPP_PCB_PATHWAY', GROUPS['PPP_PCB_PATHWAY'], (1, -1), 'GRP'),)),
'PPR_PC1': ('sequence',
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('PPR_PC1_PROBLEM', GROUPS['PPR_PC1_PROBLEM'], (1, -1), 'GRP'),)),
'PPT_PCL': ('sequence',
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('PPT_PCL_PATIENT', GROUPS['PPT_PCL_PATIENT'], (1, -1), 'GRP'),)),
'PPV_PCA': ('sequence',
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('PPV_PCA_PATIENT', GROUPS['PPV_PCA_PATIENT'], (1, -1), 'GRP'),)),
'PRR_PC5': ('sequence',
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('QRD', SEGMENTS['QRD'], (1, 1), 'SEG'),
('PRR_PC5_PATIENT', GROUPS['PRR_PC5_PATIENT'], (1, -1), 'GRP'),)),
'PTR_PCF': ('sequence',
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('ERR', SEGMENTS['ERR'], (0, -1), 'SEG'),
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('QRD', SEGMENTS['QRD'], (1, 1), 'SEG'),
('PTR_PCF_PATIENT', GROUPS['PTR_PCF_PATIENT'], (1, -1), 'GRP'),)),
'QBP_E03': ('sequence',
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('UAC', SEGMENTS['UAC'], (0, -1), 'SEG'),
('QBP_E03_QUERY_INFORMATION', GROUPS['QBP_E03_QUERY_INFORMATION'], (1, 1), 'GRP'),)),
'QBP_E22': ('sequence',
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('UAC', SEGMENTS['UAC'], (0, -1), 'SEG'),
('QBP_E22_QUERY', GROUPS['QBP_E22_QUERY'], (1, 1), 'GRP'),)),
'QBP_Q11': ('sequence',
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('DSC', SEGMENTS['DSC'], (0, 1), 'SEG'),)),
'QBP_Q13': ('sequence',
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('RCP', SEGMENTS['RCP'], (1, 1), 'SEG'),
('DSC', SEGMENTS['DSC'], (0, 1), 'SEG'),)),
'QBP_Q15': ('sequence',
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('DSC', SEGMENTS['DSC'], (0, 1), 'SEG'),)),
'QBP_Q21': ('sequence',
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('DSC', SEGMENTS['DSC'], (0, 1), 'SEG'),)),
'QBP_Qnn': ('sequence',
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('ERR', SEGMENTS['ERR'], (0, 1), 'SEG'),
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('QPD', SEGMENTS['QPD'], (1, 1), 'SEG'),
('ANYHL7SEGMENT', SEGMENTS['ANYHL7SEGMENT'], (1, 1), 'SEG'),
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'QBP_Z73': ('sequence',
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('QPD', SEGMENTS['QPD'], (1, 1), 'SEG'),
('RCP', SEGMENTS['RCP'], (1, 1), 'SEG'),)),
'QCN_J01': ('sequence',
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('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
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('QID', SEGMENTS['QID'], (1, 1), 'SEG'),)),
'QRY_A19': ('sequence',
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('QRF', SEGMENTS['QRF'], (0, 1), 'SEG'),)),
'QRY_PC4': ('sequence',
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('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('QRD', SEGMENTS['QRD'], (1, 1), 'SEG'),
('QRF', SEGMENTS['QRF'], (0, 1), 'SEG'),)),
'QRY_Q01': ('sequence',
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('QRF', SEGMENTS['QRF'], (0, 1), 'SEG'),
('DSC', SEGMENTS['DSC'], (0, 1), 'SEG'),)),
'QRY_R02': ('sequence',
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('QRD', SEGMENTS['QRD'], (1, 1), 'SEG'),
('QRF', SEGMENTS['QRF'], (1, 1), 'SEG'),)),
'QRY_T12': ('sequence',
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('QRD', SEGMENTS['QRD'], (1, 1), 'SEG'),
('QRF', SEGMENTS['QRF'], (0, 1), 'SEG'),)),
'QSB_Q16': ('sequence',
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('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('QPD', SEGMENTS['QPD'], (1, 1), 'SEG'),
('RCP', SEGMENTS['RCP'], (1, 1), 'SEG'),
('DSC', SEGMENTS['DSC'], (0, 1), 'SEG'),)),
'QVR_Q17': ('sequence',
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('QPD', SEGMENTS['QPD'], (1, 1), 'SEG'),
('QVR_Q17_QBP', GROUPS['QVR_Q17_QBP'], (0, 1), 'GRP'),
('RCP', SEGMENTS['RCP'], (1, 1), 'SEG'),
('DSC', SEGMENTS['DSC'], (0, 1), 'SEG'),)),
'RAR_RAR': ('sequence',
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('MSA', SEGMENTS['MSA'], (1, 1), 'SEG'),
('ERR', SEGMENTS['ERR'], (0, -1), 'SEG'),
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('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('RAR_RAR_DEFINITION', GROUPS['RAR_RAR_DEFINITION'], (1, -1), 'GRP'),
('DSC', SEGMENTS['DSC'], (0, 1), 'SEG'),)),
'RAS_O17': ('sequence',
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('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('NTE', SEGMENTS['NTE'], (0, -1), 'SEG'),
('RAS_O17_PATIENT', GROUPS['RAS_O17_PATIENT'], (0, 1), 'GRP'),
('RAS_O17_ORDER', GROUPS['RAS_O17_ORDER'], (1, -1), 'GRP'),)),
'RCI_I05': ('sequence',
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('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
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('QRD', SEGMENTS['QRD'], (1, 1), 'SEG'),
('QRF', SEGMENTS['QRF'], (0, 1), 'SEG'),
('RCI_I05_PROVIDER', GROUPS['RCI_I05_PROVIDER'], (1, -1), 'GRP'),
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('DG1', SEGMENTS['DG1'], (0, -1), 'SEG'),
('DRG', SEGMENTS['DRG'], (0, -1), 'SEG'),
('AL1', SEGMENTS['AL1'], (0, -1), 'SEG'),
('RCI_I05_OBSERVATION', GROUPS['RCI_I05_OBSERVATION'], (0, -1), 'GRP'),
('NTE', SEGMENTS['NTE'], (0, -1), 'SEG'),)),
'RCL_I06': ('sequence',
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('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
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('DSP', SEGMENTS['DSP'], (0, -1), 'SEG'),
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'RDE_O11': ('sequence',
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('RDE_O11_PATIENT', GROUPS['RDE_O11_PATIENT'], (0, 1), 'GRP'),
('RDE_O11_ORDER', GROUPS['RDE_O11_ORDER'], (1, -1), 'GRP'),)),
'RDR_RDR': ('sequence',
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('RDR_RDR_DEFINITION', GROUPS['RDR_RDR_DEFINITION'], (1, -1), 'GRP'),
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'RDS_O13': ('sequence',
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('RDS_O13_PATIENT', GROUPS['RDS_O13_PATIENT'], (0, 1), 'GRP'),
('RDS_O13_ORDER', GROUPS['RDS_O13_ORDER'], (1, -1), 'GRP'),)),
'RDY_K15': ('sequence',
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'REF_I12': ('sequence',
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('REF_I12_AUTHORIZATION_CONTACT', GROUPS['REF_I12_AUTHORIZATION_CONTACT'], (0, 1), 'GRP'),
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'RER_RER': ('sequence',
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'RGR_RGR': ('sequence',
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('RGR_RGR_DEFINITION', GROUPS['RGR_RGR_DEFINITION'], (1, -1), 'GRP'),
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'RGV_O15': ('sequence',
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('RGV_O15_ORDER', GROUPS['RGV_O15_ORDER'], (1, -1), 'GRP'),)),
'ROR_ROR': ('sequence',
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'RPA_I08': ('sequence',
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'RPI_I01': ('sequence',
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'RPI_I04': ('sequence',
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'RPL_I02': ('sequence',
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'RPR_I03': ('sequence',
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'RQA_I08': ('sequence',
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('RQA_I08_PROCEDURE', GROUPS['RQA_I08_PROCEDURE'], (0, -1), 'GRP'),
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'RQC_I05': ('sequence',
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'RQI_I01': ('sequence',
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'RQP_I04': ('sequence',
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'RRA_O18': ('sequence',
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('RRA_O18_RESPONSE', GROUPS['RRA_O18_RESPONSE'], (0, 1), 'GRP'),)),
'RRD_O14': ('sequence',
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('RRD_O14_RESPONSE', GROUPS['RRD_O14_RESPONSE'], (0, 1), 'GRP'),)),
'RRE_O12': ('sequence',
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('RRE_O12_RESPONSE', GROUPS['RRE_O12_RESPONSE'], (0, 1), 'GRP'),)),
'RRG_O16': ('sequence',
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('RRG_O16_RESPONSE', GROUPS['RRG_O16_RESPONSE'], (0, 1), 'GRP'),)),
'RRI_I12': ('sequence',
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('RRI_I12_PROCEDURE', GROUPS['RRI_I12_PROCEDURE'], (0, -1), 'GRP'),
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'RSP_E03': ('sequence',
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('RSP_E03_QUERY_ACK', GROUPS['RSP_E03_QUERY_ACK'], (1, 1), 'GRP'),)),
'RSP_E22': ('sequence',
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('RSP_E22_QUERY_ACK', GROUPS['RSP_E22_QUERY_ACK'], (1, 1), 'GRP'),)),
'RSP_K11': ('sequence',
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('RSP_K11_ROW_DEFINITION', GROUPS['RSP_K11_ROW_DEFINITION'], (0, 1), 'GRP'),
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'RSP_K21': ('sequence',
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'RSP_K23': ('sequence',
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'RSP_K25': ('sequence',
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('RSP_K25_STAFF', GROUPS['RSP_K25_STAFF'], (1, -1), 'GRP'),
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'RSP_K31': ('sequence',
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('RSP_K31_RESPONSE', GROUPS['RSP_K31_RESPONSE'], (1, -1), 'GRP'),
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'RSP_Q11': ('sequence',
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'RSP_Z82': ('sequence',
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('RSP_Z82_QUERY_RESPONSE', GROUPS['RSP_Z82_QUERY_RESPONSE'], (1, -1), 'GRP'),
('DSC', SEGMENTS['DSC'], (0, 1), 'SEG'),)),
'RSP_Z86': ('sequence',
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('QAK', SEGMENTS['QAK'], (1, 1), 'SEG'),
('QPD', SEGMENTS['QPD'], (1, 1), 'SEG'),
('RSP_Z86_QUERY_RESPONSE', GROUPS['RSP_Z86_QUERY_RESPONSE'], (1, -1), 'GRP'),
('DSC', SEGMENTS['DSC'], (0, 1), 'SEG'),)),
'RSP_Z88': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('MSA', SEGMENTS['MSA'], (1, 1), 'SEG'),
('ERR', SEGMENTS['ERR'], (0, 1), 'SEG'),
('QAK', SEGMENTS['QAK'], (1, 1), 'SEG'),
('QPD', SEGMENTS['QPD'], (1, 1), 'SEG'),
('RCP', SEGMENTS['RCP'], (1, 1), 'SEG'),
('RSP_Z88_QUERY_RESPONSE', GROUPS['RSP_Z88_QUERY_RESPONSE'], (1, -1), 'GRP'),
('DSC', SEGMENTS['DSC'], (1, 1), 'SEG'),)),
'RSP_Z90': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('MSA', SEGMENTS['MSA'], (1, 1), 'SEG'),
('ERR', SEGMENTS['ERR'], (0, 1), 'SEG'),
('QAK', SEGMENTS['QAK'], (1, 1), 'SEG'),
('QPD', SEGMENTS['QPD'], (1, 1), 'SEG'),
('RCP', SEGMENTS['RCP'], (1, 1), 'SEG'),
('RSP_Z90_QUERY_RESPONSE', GROUPS['RSP_Z90_QUERY_RESPONSE'], (1, -1), 'GRP'),
('DSC', SEGMENTS['DSC'], (1, 1), 'SEG'),)),
'RTB_K13': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('MSA', SEGMENTS['MSA'], (1, 1), 'SEG'),
('ERR', SEGMENTS['ERR'], (0, 1), 'SEG'),
('QAK', SEGMENTS['QAK'], (1, 1), 'SEG'),
('QPD', SEGMENTS['QPD'], (1, 1), 'SEG'),
('RTB_K13_ROW_DEFINITION', GROUPS['RTB_K13_ROW_DEFINITION'], (0, 1), 'GRP'),)),
'RTB_Z74': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('MSA', SEGMENTS['MSA'], (1, 1), 'SEG'),
('ERR', SEGMENTS['ERR'], (0, -1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('QAK', SEGMENTS['QAK'], (1, 1), 'SEG'),
('QPD', SEGMENTS['QPD'], (1, 1), 'SEG'),
('RTB_Z74_ROW_DEFINITION', GROUPS['RTB_Z74_ROW_DEFINITION'], (0, 1), 'GRP'),
('DSC', SEGMENTS['DSC'], (0, 1), 'SEG'),)),
'SDR_S31': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('SDR_S31_ANTI_MICROBIAL_DEVICE_DATA', GROUPS['SDR_S31_ANTI_MICROBIAL_DEVICE_DATA'], (1, 1), 'GRP'),)),
'SDR_S32': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
(
'SDR_S32_ANTI_MICROBIAL_DEVICE_CYCLE_DATA', GROUPS['SDR_S32_ANTI_MICROBIAL_DEVICE_CYCLE_DATA'], (1, 1),
'GRP'),)),
'SIU_S12': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SCH', SEGMENTS['SCH'], (1, 1), 'SEG'),
('TQ1', SEGMENTS['TQ1'], (0, -1), 'SEG'),
('NTE', SEGMENTS['NTE'], (0, -1), 'SEG'),
('SIU_S12_PATIENT', GROUPS['SIU_S12_PATIENT'], (0, -1), 'GRP'),
('SIU_S12_RESOURCES', GROUPS['SIU_S12_RESOURCES'], (1, -1), 'GRP'),)),
'SLR_S28': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('SLT', SEGMENTS['SLT'], (1, -1), 'SEG'),)),
'SQM_S25': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('QRD', SEGMENTS['QRD'], (1, 1), 'SEG'),
('QRF', SEGMENTS['QRF'], (0, 1), 'SEG'),
('SQM_S25_REQUEST', GROUPS['SQM_S25_REQUEST'], (0, 1), 'GRP'),
('DSC', SEGMENTS['DSC'], (0, 1), 'SEG'),)),
'SQR_S25': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('MSA', SEGMENTS['MSA'], (1, 1), 'SEG'),
('ERR', SEGMENTS['ERR'], (0, -1), 'SEG'),
('QAK', SEGMENTS['QAK'], (1, 1), 'SEG'),
('SQR_S25_SCHEDULE', GROUPS['SQR_S25_SCHEDULE'], (0, -1), 'GRP'),
('DSC', SEGMENTS['DSC'], (0, 1), 'SEG'),)),
'SRM_S01': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('ARQ', SEGMENTS['ARQ'], (1, 1), 'SEG'),
('APR', SEGMENTS['APR'], (0, 1), 'SEG'),
('NTE', SEGMENTS['NTE'], (0, -1), 'SEG'),
('SRM_S01_PATIENT', GROUPS['SRM_S01_PATIENT'], (0, -1), 'GRP'),
('SRM_S01_RESOURCES', GROUPS['SRM_S01_RESOURCES'], (1, -1), 'GRP'),)),
'SRR_S01': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('MSA', SEGMENTS['MSA'], (1, 1), 'SEG'),
('ERR', SEGMENTS['ERR'], (0, -1), 'SEG'),
('SRR_S01_SCHEDULE', GROUPS['SRR_S01_SCHEDULE'], (0, 1), 'GRP'),)),
'SSR_U04': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('EQU', SEGMENTS['EQU'], (1, 1), 'SEG'),
('SSR_U04_SPECIMEN_CONTAINER', GROUPS['SSR_U04_SPECIMEN_CONTAINER'], (1, -1), 'GRP'),
('ROL', SEGMENTS['ROL'], (0, 1), 'SEG'),)),
'SSU_U03': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('EQU', SEGMENTS['EQU'], (1, 1), 'SEG'),
('SSU_U03_SPECIMEN_CONTAINER', GROUPS['SSU_U03_SPECIMEN_CONTAINER'], (1, -1), 'GRP'),
('ROL', SEGMENTS['ROL'], (0, 1), 'SEG'),)),
'STC_S33': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('SCP', SEGMENTS['SCP'], (1, -1), 'SEG'),)),
'SUR_P09': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SUR_P09_FACILITY', GROUPS['SUR_P09_FACILITY'], (1, -1), 'GRP'),)),
'TCU_U10': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('EQU', SEGMENTS['EQU'], (1, 1), 'SEG'),
('TCU_U10_TEST_CONFIGURATION', GROUPS['TCU_U10_TEST_CONFIGURATION'], (1, -1), 'GRP'),
('ROL', SEGMENTS['ROL'], (0, 1), 'SEG'),)),
'VXQ_V01': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('QRD', SEGMENTS['QRD'], (1, 1), 'SEG'),
('QRF', SEGMENTS['QRF'], (0, 1), 'SEG'),)),
'VXR_V03': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('MSA', SEGMENTS['MSA'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('QRD', SEGMENTS['QRD'], (1, 1), 'SEG'),
('QRF', SEGMENTS['QRF'], (0, 1), 'SEG'),
('PID', SEGMENTS['PID'], (1, 1), 'SEG'),
('PD1', SEGMENTS['PD1'], (0, 1), 'SEG'),
('NK1', SEGMENTS['NK1'], (0, -1), 'SEG'),
('VXR_V03_PATIENT_VISIT', GROUPS['VXR_V03_PATIENT_VISIT'], (0, 1), 'GRP'),
('GT1', SEGMENTS['GT1'], (0, -1), 'SEG'),
('VXR_V03_INSURANCE', GROUPS['VXR_V03_INSURANCE'], (0, -1), 'GRP'),
('VXR_V03_ORDER', GROUPS['VXR_V03_ORDER'], (0, -1), 'GRP'),)),
'VXU_V04': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('PID', SEGMENTS['PID'], (1, 1), 'SEG'),
('PD1', SEGMENTS['PD1'], (0, 1), 'SEG'),
('NK1', SEGMENTS['NK1'], (0, -1), 'SEG'),
('VXU_V04_PATIENT', GROUPS['VXU_V04_PATIENT'], (0, 1), 'GRP'),
('GT1', SEGMENTS['GT1'], (0, -1), 'SEG'),
('VXU_V04_INSURANCE', GROUPS['VXU_V04_INSURANCE'], (0, -1), 'GRP'),
('VXU_V04_ORDER', GROUPS['VXU_V04_ORDER'], (0, -1), 'GRP'),)),
'VXX_V02': ('sequence',
(('MSH', SEGMENTS['MSH'], (1, 1), 'SEG'),
('MSA', SEGMENTS['MSA'], (1, 1), 'SEG'),
('SFT', SEGMENTS['SFT'], (0, -1), 'SEG'),
('UAC', SEGMENTS['UAC'], (0, 1), 'SEG'),
('QRD', SEGMENTS['QRD'], (1, 1), 'SEG'),
('QRF', SEGMENTS['QRF'], (0, 1), 'SEG'),
('VXX_V02_PATIENT', GROUPS['VXX_V02_PATIENT'], (1, -1), 'GRP'),)),
}
| 58.618373 | 120 | 0.376106 | 11,896 | 111,668 | 3.413753 | 0.038416 | 0.142034 | 0.109825 | 0.078675 | 0.812411 | 0.765747 | 0.75245 | 0.746787 | 0.743093 | 0.726619 | 0 | 0.068105 | 0.341494 | 111,668 | 1,904 | 121 | 58.64916 | 0.484157 | 0 | 0 | 0.74041 | 0 | 0 | 0.229493 | 0.028871 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.001051 | 0 | 0.001051 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
bce13e4c34238fffdb582d266a2d195c22f5d0e1 | 17,665 | py | Python | webapp/tests/forms/steps/lotse/test_pauschbetrag.py | digitalservice4germany/steuerlotse | ef3e094e4d7d4768431a50ac4be60672cd03221d | [
"MIT"
] | 20 | 2021-07-02T07:49:08.000Z | 2022-03-18T22:26:10.000Z | webapp/tests/forms/steps/lotse/test_pauschbetrag.py | digitalservice4germany/steuerlotse | ef3e094e4d7d4768431a50ac4be60672cd03221d | [
"MIT"
] | 555 | 2021-06-28T15:35:15.000Z | 2022-03-31T11:51:55.000Z | webapp/tests/forms/steps/lotse/test_pauschbetrag.py | digitalservice4germany/steuerlotse | ef3e094e4d7d4768431a50ac4be60672cd03221d | [
"MIT"
] | 1 | 2021-07-04T20:34:12.000Z | 2021-07-04T20:34:12.000Z | from unittest.mock import patch, MagicMock
import pytest
from flask_babel import _
from pydantic import ValidationError
from werkzeug.datastructures import MultiDict, ImmutableMultiDict
from app.forms.flows.lotse_step_chooser import LotseStepChooser
from app.forms.steps.lotse.pauschbetrag import calculate_pauschbetrag, HasPauschbetragClaimPersonAPrecondition, \
HasPauschbetragClaimPersonBPrecondition, StepPauschbetragPersonA, StepPauschbetragPersonB
class TestCalculatePauschbetrag:
def test_if_no_merkzeichen_or_pflegegrad_set_then_return_correct_value_for_disability_degree(self):
input_output_pairs = [
(None, 0),
(20, 384),
(25, 384),
(30, 620),
(35, 620),
(40, 860),
(45, 860),
(50, 1140),
(55, 1140),
(60, 1440),
(65, 1440),
(70, 1780),
(75, 1780),
(80, 2120),
(85, 2120),
(90, 2460),
(95, 2460),
(100, 2840),
]
params = {
'has_pflegegrad': 'no',
'has_merkzeichen_bl': False,
'has_merkzeichen_tbl': False,
'has_merkzeichen_h': False,
}
for disability_degree, expected_result in input_output_pairs:
calculated_pauschbetrag = calculate_pauschbetrag(**params, disability_degree=disability_degree)
assert calculated_pauschbetrag == expected_result
def test_if_merkzeichen_ag_and_no_pflegegrad_set_then_return_correct_value_for_disability_degree(self):
input_output_pairs = [
(None, 0),
(20, 384),
(30, 620),
(40, 860),
(50, 1140),
(60, 1440),
(70, 1780),
(80, 2120),
(90, 2460),
(100, 2840),
]
params = {
'has_pflegegrad': 'no',
'has_merkzeichen_bl': False,
'has_merkzeichen_tbl': False,
'has_merkzeichen_h': False,
}
for disability_degree, expected_result in input_output_pairs:
calculated_pauschbetrag = calculate_pauschbetrag(**params, disability_degree=disability_degree)
assert calculated_pauschbetrag == expected_result
input_output_pairs = [
(None, 0),
(20, 384),
(30, 620),
(40, 860),
(50, 1140),
(60, 1440),
(70, 1780),
(80, 2120),
(90, 2460),
(100, 2840),
]
params = {
'has_pflegegrad': 'no',
'has_merkzeichen_bl': False,
'has_merkzeichen_tbl': False,
'has_merkzeichen_h': False,
}
for disability_degree, expected_result in input_output_pairs:
calculated_pauschbetrag = calculate_pauschbetrag(**params, disability_degree=disability_degree)
assert calculated_pauschbetrag == expected_result
def test_if_pflegegrad_set_and_no_merkzeichen_then_return_7400_for_all_disability_degree(self):
disability_degree_values = [None, 20, 30, 40, 50, 60, 70, 80, 90, 100]
params = {
'has_pflegegrad': 'yes',
'has_merkzeichen_bl': False,
'has_merkzeichen_tbl': False,
'has_merkzeichen_h': False,
}
for disability_degree in disability_degree_values:
calculated_pauschbetrag = calculate_pauschbetrag(**params, disability_degree=disability_degree)
assert calculated_pauschbetrag == 7400
def test_if_pflegegrad_set_and_merkzeichen_bl_then_return_7400_for_all_disability_degree(self):
disability_degree_values = [None, 20, 30, 40, 50, 60, 70, 80, 90, 100]
params = {
'has_pflegegrad': 'yes',
'has_merkzeichen_bl': True,
'has_merkzeichen_tbl': False,
'has_merkzeichen_h': False,
}
for disability_degree in disability_degree_values:
calculated_pauschbetrag = calculate_pauschbetrag(**params, disability_degree=disability_degree)
assert calculated_pauschbetrag == 7400
def test_if_pflegegrad_set_and_merkzeichen_tbl_then_return_7400_for_all_disability_degree(self):
disability_degree_values = [None, 20, 30, 40, 50, 60, 70, 80, 90, 100]
params = {
'has_pflegegrad': 'yes',
'has_merkzeichen_bl': False,
'has_merkzeichen_tbl': True,
'has_merkzeichen_h': False,
}
for disability_degree in disability_degree_values:
calculated_pauschbetrag = calculate_pauschbetrag(**params, disability_degree=disability_degree)
assert calculated_pauschbetrag == 7400
def test_if_pflegegrad_set_and_merkzeichen_h_then_return_7400_for_all_disability_degree(self):
disability_degree_values = [None, 20, 30, 40, 50, 60, 70, 80, 90, 100]
params = {
'has_pflegegrad': 'yes',
'has_merkzeichen_bl': False,
'has_merkzeichen_tbl': False,
'has_merkzeichen_h': True,
}
for disability_degree in disability_degree_values:
calculated_pauschbetrag = calculate_pauschbetrag(**params, disability_degree=disability_degree)
assert calculated_pauschbetrag == 7400
def test_if_merkzeichen_bl_and_no_pflegegrad_set_then_return_7400_for_all_disability_degree(self):
disability_degree_values = [None, 20, 30, 40, 50, 60, 70, 80, 90, 100]
params = {
'has_pflegegrad': 'no',
'has_merkzeichen_bl': True,
'has_merkzeichen_tbl': False,
'has_merkzeichen_h': False,
}
for disability_degree in disability_degree_values:
calculated_pauschbetrag = calculate_pauschbetrag(**params, disability_degree=disability_degree)
assert calculated_pauschbetrag == 7400
def test_if_merkzeichen_tbl_and_no_pflegegrad_set_then_return_7400_for_all_disability_degree(self):
disability_degree_values = [None, 20, 30, 40, 50, 60, 70, 80, 90, 100]
params = {
'has_pflegegrad': 'no',
'has_merkzeichen_bl': False,
'has_merkzeichen_tbl': True,
'has_merkzeichen_h': False,
}
for disability_degree in disability_degree_values:
calculated_pauschbetrag = calculate_pauschbetrag(**params, disability_degree=disability_degree)
assert calculated_pauschbetrag == 7400
def test_if_merkzeichen_h_and_no_pflegegrad_set_then_return_7400_for_all_disability_degree(self):
disability_degree_values = [None, 20, 30, 40, 50, 60, 70, 80, 90, 100]
params = {
'has_pflegegrad': 'no',
'has_merkzeichen_bl': False,
'has_merkzeichen_tbl': False,
'has_merkzeichen_h': True,
}
for disability_degree in disability_degree_values:
calculated_pauschbetrag = calculate_pauschbetrag(**params, disability_degree=disability_degree)
assert calculated_pauschbetrag == 7400
def test_if_no_parameters_set_and_disability_degree_under_20_then_zero_should_be_return(self):
calculated_pauschbetrag = calculate_pauschbetrag(disability_degree=19)
assert calculated_pauschbetrag == 0
def test_if_no_parameters_then_zero_should_be_return(self):
calculated_pauschbetrag = calculate_pauschbetrag()
assert calculated_pauschbetrag == 0
class TestHasPauschbetragClaimPersonAPrecondition:
def test_if_calculate_pauschbetrag_returns_zero_then_raise_validation_error(self):
with patch('app.forms.steps.lotse.pauschbetrag.calculate_pauschbetrag', MagicMock(return_value=0)):
with pytest.raises(ValidationError):
HasPauschbetragClaimPersonAPrecondition.parse_obj({})
def test_if_calculate_pauschbetrag_returns_number_other_than_zero_then_raise_no_error(self):
with patch('app.forms.steps.lotse.pauschbetrag.calculate_pauschbetrag', MagicMock(return_value=1)):
HasPauschbetragClaimPersonAPrecondition.parse_obj({})
class TestHasPauschbetragClaimPersonBPrecondition:
def test_if_calculate_pauschbetrag_returns_zero_then_raise_validation_error(self):
with patch('app.forms.steps.lotse.pauschbetrag.calculate_pauschbetrag', MagicMock(return_value=0)):
with pytest.raises(ValidationError):
HasPauschbetragClaimPersonBPrecondition.parse_obj({})
def test_if_calculate_pauschbetrag_returns_number_other_than_zero_then_raise_no_error(self):
with patch('app.forms.steps.lotse.pauschbetrag.calculate_pauschbetrag', MagicMock(return_value=1)):
HasPauschbetragClaimPersonBPrecondition.parse_obj({})
class TestPauschbetragPersonAGetPauschbetrag:
def test_if_merkzeichen_given_then_get_pauschbetrag_returns_result_of_calculate_pauschbetrag(self, new_test_request_context):
stored_data = MultiDict({
'person_a_has_disability':'yes',
'person_a_has_pflegegrad': True,
'person_a_disability_degree': 25,
'person_a_has_merkzeichen_bl': True,
'person_a_has_merkzeichen_tbl': True,
'person_a_has_merkzeichen_h': True
})
form_data = MultiDict({
'person_a_requests_pauschbetrag': 'yes',
})
with new_test_request_context(stored_data=stored_data, form_data=form_data, method='POST'):
step = LotseStepChooser().get_correct_step(
StepPauschbetragPersonA.name, True, ImmutableMultiDict(form_data))
pauschbetrag = step.get_pauschbetrag(stored_data)
expected_pauschbetrag = calculate_pauschbetrag(
has_pflegegrad=True,
disability_degree=25,
has_merkzeichen_bl=True,
has_merkzeichen_tbl=True,
has_merkzeichen_h=True)
assert pauschbetrag == expected_pauschbetrag
class TestPauschbetragPersonAGetOverviewValueRepresentation:
def test_if_merkzeichen_given_and_requests_pauschbetrag_yes_then_get_pauschbetrag_returns_result_of_calculate_pauschbetrag(self, new_test_request_context):
stored_data = {
'person_a_has_disability': 'yes',
'person_a_has_pflegegrad': 'yes',
}
value = 'yes'
pauschbetrag_result = "1"
with new_test_request_context(stored_data=stored_data):
with patch('app.forms.steps.lotse.pauschbetrag.StepPauschbetragPersonA.get_pauschbetrag', MagicMock(return_value=pauschbetrag_result)):
step = LotseStepChooser().get_correct_step(
StepPauschbetragPersonA.name, True, ImmutableMultiDict({}))
assert step.name == StepPauschbetragPersonA.name
overview_value = step.get_overview_value_representation(value)
assert str(pauschbetrag_result) in overview_value
def test_if_merkzeichen_given_and_requests_pauschbetrag_no_then_get_pauschbetrag_returns_no_request_label(self, new_test_request_context):
stored_data = {
'person_a_has_disability': 'yes',
'person_a_has_pflegegrad': 'yes',
}
value = 'no'
with new_test_request_context(stored_data=stored_data):
step = LotseStepChooser().get_correct_step(
StepPauschbetragPersonA.name, True, ImmutableMultiDict({}))
overview_value = step.get_overview_value_representation(value)
assert overview_value == _('form.lotse.summary.not-requested')
class TestPauschbetragPersonBValidation:
def test_if_person_b_has_disability_is_given_then_validation_should_be_success(self, new_test_request_context):
data = MultiDict({
'familienstand': 'married',
'familienstand_married_lived_separated': 'no',
'familienstand_confirm_zusammenveranlagung': True,
'person_a_has_disability': 'no',
'person_b_has_disability': 'yes',
'person_b_has_pflegegrad': 'yes',
'person_b_requests_pauschbetrag': 'yes',
})
with new_test_request_context(stored_data=data):
step = LotseStepChooser().get_correct_step(
StepPauschbetragPersonB.name, True, ImmutableMultiDict(data))
form = step.render_info.form
assert form.validate() is True
def test_if_person_b_requests_pauschbetrag_is_not_given_then_validation_should_be_false(self, new_test_request_context):
form_data = {}
stored_data = {
'familienstand': 'married',
'familienstand_married_lived_separated': 'no',
'familienstand_confirm_zusammenveranlagung': True,
'person_b_has_disability': 'yes',
'person_b_has_pflegegrad': 'yes',
}
with new_test_request_context(form_data=form_data, stored_data=stored_data, method='POST'):
step = LotseStepChooser().get_correct_step(
StepPauschbetragPersonB.name, True, ImmutableMultiDict(form_data))
form = step.render_info.form
assert form.validate() is False
class TestPauschbetragPersonBGetPauschbetrag:
def test_if_merkzeichen_given_then_get_pauschbetrag_returns_result_of_calculate_pauschbetrag(self, new_test_request_context):
stored_data = MultiDict({
'familienstand': 'married',
'familienstand_married_lived_separated': 'no',
'familienstand_confirm_zusammenveranlagung': True,
'person_b_has_disability':'yes',
'person_b_has_pflegegrad': True,
'person_b_disability_degree': 25,
'person_b_has_merkzeichen_bl': True,
'person_b_has_merkzeichen_tbl': True,
'person_b_has_merkzeichen_h': True
})
form_data = MultiDict({
'person_b_requests_pauschbetrag': 'yes',
})
with new_test_request_context(stored_data=stored_data, form_data=form_data, method='POST'):
step = LotseStepChooser().get_correct_step(
StepPauschbetragPersonB.name, True, ImmutableMultiDict(form_data))
pauschbetrag = step.get_pauschbetrag(stored_data)
expected_pauschbetrag = calculate_pauschbetrag(
has_pflegegrad=True,
disability_degree=25,
has_merkzeichen_bl=True,
has_merkzeichen_tbl=True,
has_merkzeichen_h=True)
assert pauschbetrag == expected_pauschbetrag
class TestPauschbetragPersonAValidation:
def test_if_person_a_requests_pauschbetrag_is_given_then_validation_should_be_success(self, new_test_request_context):
form_data = {'person_a_requests_pauschbetrag': 'no'}
stored_data = {'person_a_has_disability': 'yes', 'person_a_has_pflegegrad': 'yes'}
with new_test_request_context(form_data=form_data, stored_data=stored_data, method='POST'):
step = LotseStepChooser().get_correct_step(
StepPauschbetragPersonA.name, True, ImmutableMultiDict(form_data))
form = step.render_info.form
assert form.validate() is True
def test_if_person_a_requests_pauschbetrag_is_not_given_then_validation_should_be_false(self, new_test_request_context):
form_data = {}
stored_data = {'person_a_has_disability': 'yes', 'person_a_has_pflegegrad': 'yes'}
with new_test_request_context(form_data=form_data, stored_data=stored_data, method='POST'):
step = LotseStepChooser().get_correct_step(
StepPauschbetragPersonA.name, True, ImmutableMultiDict(form_data))
form = step.render_info.form
assert form.validate() is False
class TestPauschbetragPersonBGetOverviewValueRepresentation:
def test_if_merkzeichen_given_then_get_pauschbetrag_returns_result_of_calculate_pauschbetrag(self, new_test_request_context):
stored_data = {
'familienstand': 'married',
'familienstand_married_lived_separated': 'no',
'familienstand_confirm_zusammenveranlagung': True,
'person_b_has_disability': 'yes',
'person_b_has_pflegegrad': 'yes',
}
value = 'yes'
pauschbetrag_result = "1"
with new_test_request_context(stored_data=stored_data):
with patch('app.forms.steps.lotse.pauschbetrag.StepPauschbetragPersonB.get_pauschbetrag', MagicMock(return_value=pauschbetrag_result)):
step = LotseStepChooser().get_correct_step(
StepPauschbetragPersonB.name, True, ImmutableMultiDict({}))
overview_value = step.get_overview_value_representation(value)
assert str(pauschbetrag_result) in overview_value
def test_if_merkzeichen_given_and_requests_pauschbetrag_no_then_get_pauschbetrag_returns_no_request_label(self, new_test_request_context):
stored_data = {
'familienstand': 'married',
'familienstand_married_lived_separated': 'no',
'familienstand_confirm_zusammenveranlagung': True,
'person_b_has_disability': 'yes',
'person_b_has_pflegegrad': 'yes',
}
value = 'no'
with new_test_request_context(stored_data=stored_data):
step = LotseStepChooser().get_correct_step(
StepPauschbetragPersonB.name, True, ImmutableMultiDict({}))
overview_value = step.get_overview_value_representation(value)
assert overview_value == _('form.lotse.summary.not-requested') | 42.669082 | 159 | 0.672007 | 1,819 | 17,665 | 6.069269 | 0.085761 | 0.085507 | 0.02038 | 0.038043 | 0.873641 | 0.856431 | 0.854529 | 0.853804 | 0.83904 | 0.828623 | 0 | 0.031095 | 0.248118 | 17,665 | 414 | 160 | 42.669082 | 0.800105 | 0 | 0 | 0.761905 | 0 | 0 | 0.145024 | 0.093343 | 0 | 0 | 0 | 0 | 0.068452 | 1 | 0.074405 | false | 0 | 0.020833 | 0 | 0.122024 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 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 | 7 |
bced9bba713693160020d3bed4521ddf338b4f16 | 7,396 | py | Python | src/eventIsotropy/cylGen.py | caricesarotti/event_isotropy | cbaf9cf60ad63b74127341af282c7f921ea4a504 | [
"MIT"
] | 3 | 2020-05-21T14:39:12.000Z | 2020-11-19T15:15:56.000Z | src/eventIsotropy/cylGen.py | caricesarotti/event_isotropy | cbaf9cf60ad63b74127341af282c7f921ea4a504 | [
"MIT"
] | 1 | 2020-06-17T16:42:29.000Z | 2020-06-17T18:57:07.000Z | src/eventIsotropy/cylGen.py | caricesarotti/event_isotropy | cbaf9cf60ad63b74127341af282c7f921ea4a504 | [
"MIT"
] | 2 | 2020-05-07T10:56:34.000Z | 2020-06-17T07:22:47.000Z | #
# Created by C. Cesarotti (ccesarotti@g.harvard.edu) 05/2019
#
# Code to generate cylindrically or ring-like quasi isotropic
# events.
#
import sys
import warnings
import numpy as np
import math
import random
######################################
#
def eng(px,py,pz):
return np.sqrt(px**2+py**2+pz**2)
#
#
def eta(px, py, pz):
etaV = np.arctanh(pz/np.sqrt(px**2+py**2+pz**2))
return etaV
#
#
def pT(px, py):
return np.sqrt(px**2+py**2)
#
######################################
#
## Return the points of a uniformly grided cylinder in eta - phi space. Phi goes from 0 to 2 pi,
## Eta goes from -etaMax to etaMax
## piSeg is how many slices you want in the phi direction, must be integer
## Returns points on the cylinder with roughly equal gridding on the z axis
def cylinderGen(piSeg, etaMax):
flag = False
# Check that piSeg is a positive integer
if float(piSeg).is_integer():
if etaMax>0:
flag=True
if flag:
# First, calculate the fraction of the points that is along the phi direction
etaSeg = int(math.floor(etaMax*piSeg/np.pi))
phiVals = [2*np.pi*(j+0.5)/piSeg for j in range(piSeg)]
etaVals = [-1.0*etaMax + 2.0*etaMax*(j+0.5)/(etaSeg) for j in range(etaSeg)]
# Build set of points in (phi, eta)
cylPoints=[]
for j in range(len(phiVals)):
points = [[etaVals[i], phiVals[j]] for i in range(len(etaVals))]
cylPoints = cylPoints+points
# RETURNS
# Array of points of the cylinder configuration in (phi, eta) space.
return np.array(cylPoints)
else:
raise Exception('Error: first argument must be a positive integer, second argument must be positive')
###############################
# Randomly offsets angular position
def cylinderGenShift(piSeg, etaMax):
## Return the points of a uniformly grided cylinder in eta - phi space. Phi goes from 0 to 2 pi,
## Eta goes from -etaMax to etaMax
## piSeg is how many slices you want in the phi direction.
## Returns points on the cylinder with roughly equal gridding on the z axis
flag = False
# Check that piSeg is a positive integer
if float(piSeg).is_integer():
if etaMax>0:
flag=True
if flag:
###
randShift = random.uniform(0,2*np.pi/piSeg)
etaSeg = int(math.floor(etaMax*piSeg/np.pi))
phiVals = [2*np.pi*(j)/piSeg+randShift for j in range(piSeg)]
etaVals = [-1.0*etaMax + 2.0*etaMax*(j+0.5)/(etaSeg) for j in range(etaSeg)]
# Build set of points in (phi, eta)
cylPoints=[]
for j in range(len(phiVals)):
points = [[etaVals[i], phiVals[j]] for i in range(len(etaVals))]
cylPoints = cylPoints+points
# RETURNS # Array of points of the cylinder configuration in (phi, eta) space.
return np.array(cylPoints)
#################################
#
## Returns an array of the pT values for all the particles in the passed array
#
def pTFromVec(vecArray):
if len(vecArray.shape) != 2 or vecArray.shape[1] != 3:
raise Exception('cylGen Error: invalid format. Enter array of 3 vectors')
pTvals = [pT(vec[0], vec[1]) for vec in vecArray]
return np.array(pTvals)
## Returns an array of the eta values for all the particles in the passed array
#
def etaFromVec(vecArray):
if len(vecArray.shape) != 2 or vecArray.shape[1] != 3:
raise Exception('cylGen Error: invalid format. Enter array of 3 vectors')
etaVals = [eta(vec[0], vec[1], vec[2]) for vec in vecArray]
return np.array(etaVals)
#
#### Defines just a ring of particles
def ringGen(piSeg):
## Return the points of a uniformly grided ring
flag = False
# Check that piSeg is a positive integer
if float(piSeg).is_integer():
flag=True
if flag:
# First, calculate the fraction of the points that is along the phi direction
phiVals = [2*np.pi*(j+0.5)/piSeg for j in range(piSeg)]
return np.array(phiVals)
else:
raise Exception('Error: first argument must be a positive integer')
#################################
#### Defines just a ring of particles
## piSeg is an integer
def ringGenShift(piSeg):
## Return the points of a uniformly grided ring
flag = False
# Check that piSeg is a positive integer
if float(piSeg).is_integer():
flag=True
if flag:
# First, calculate the fraction of the points that is along the phi direction
randShift = random.uniform(0,2*np.pi/piSeg)
#randShift = 0. # Don't need random shift for collider events, already random. Just for testing.
phiVals = [2*np.pi*j/piSeg+randShift for j in range(piSeg)]
return np.array(phiVals)
else:
raise Exception('Error: first argument must be a positive integer')
#################################
| 50.657534 | 349 | 0.409951 | 715 | 7,396 | 4.234965 | 0.202797 | 0.025429 | 0.015852 | 0.029062 | 0.822985 | 0.810436 | 0.792602 | 0.765522 | 0.733818 | 0.733818 | 0 | 0.015587 | 0.49689 | 7,396 | 145 | 350 | 51.006897 | 0.798173 | 0.547458 | 0 | 0.652778 | 1 | 0 | 0.094141 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.125 | false | 0 | 0.069444 | 0.027778 | 0.319444 | 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 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
bcf8b8b25869b4e56b7efaf26740f641bdf15a53 | 130 | py | Python | ivy/array/random.py | saurbhc/ivy | 20b327b4fab543b26ad5a18acf4deddd6e3c804b | [
"Apache-2.0"
] | 161 | 2021-01-20T22:11:13.000Z | 2022-01-09T09:46:33.000Z | ivy/array/random.py | saurbhc/ivy | 20b327b4fab543b26ad5a18acf4deddd6e3c804b | [
"Apache-2.0"
] | 4 | 2021-11-10T17:04:36.000Z | 2021-11-26T06:40:43.000Z | ivy/array/random.py | saurbhc/ivy | 20b327b4fab543b26ad5a18acf4deddd6e3c804b | [
"Apache-2.0"
] | 8 | 2021-02-17T20:56:33.000Z | 2022-01-09T16:45:40.000Z | # global
import abc
# ToDo: implement all random methods here as public class methods
class ArrayWithRandom(abc.ABC):
pass
| 14.444444 | 65 | 0.753846 | 18 | 130 | 5.444444 | 0.777778 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.192308 | 130 | 8 | 66 | 16.25 | 0.933333 | 0.538462 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.125 | 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 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 7 |
4c1dc65bf601f7196c2b79cf30736b0f7ff20cfa | 49,454 | py | Python | micronet/compression/quantization/wqaq/iao/quantize.py | RiccardoRuggiero/micronet | bfdac2a50a5f0f8484a253b356c06a166bf7e6a0 | [
"MIT"
] | null | null | null | micronet/compression/quantization/wqaq/iao/quantize.py | RiccardoRuggiero/micronet | bfdac2a50a5f0f8484a253b356c06a166bf7e6a0 | [
"MIT"
] | null | null | null | micronet/compression/quantization/wqaq/iao/quantize.py | RiccardoRuggiero/micronet | bfdac2a50a5f0f8484a253b356c06a166bf7e6a0 | [
"MIT"
] | null | null | null | import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import distributed
from torch.nn import init
from torch.nn.parameter import Parameter
from torch.autograd import Function
# ********************* observers(统计min/max) *********************
class ObserverBase(nn.Module):
def __init__(self, q_level, device):
super(ObserverBase, self).__init__()
self.q_level = q_level
self.device = device
def update_range(self, min_val, max_val):
raise NotImplementedError
@torch.no_grad()
def forward(self, input):
if self.q_level == 'L': # layer级(activation/weight)
min_val = torch.min(input)
max_val = torch.max(input)
elif self.q_level == 'C': # channel级(conv_weight)
input = torch.flatten(input, start_dim=1)
min_val = torch.min(input, 1)[0]
max_val = torch.max(input, 1)[0]
elif self.q_level == 'FC': # channel级(fc_weight)
min_val = torch.min(input, 1, keepdim=True)[0]
max_val = torch.max(input, 1, keepdim=True)[0]
self.update_range(min_val, max_val)
class MinMaxObserver(ObserverBase):
def __init__(self, q_level, device, out_channels):
super(MinMaxObserver, self).__init__(q_level, device)
self.num_flag = 0
self.out_channels = out_channels
if self.q_level == 'L':
self.min_val = torch.zeros((1), dtype=torch.float32, device=self.device)
self.max_val = torch.zeros((1), dtype=torch.float32, device=self.device)
elif self.q_level == 'C':
self.min_val = torch.zeros((out_channels, 1, 1, 1), dtype=torch.float32, device=self.device)
self.max_val = torch.zeros((out_channels, 1, 1, 1), dtype=torch.float32, device=self.device)
elif self.q_level == 'FC':
self.min_val = torch.zeros((out_channels, 1), dtype=torch.float32, device=self.device)
self.max_val = torch.zeros((out_channels, 1), dtype=torch.float32, device=self.device)
def update_range(self, min_val_cur, max_val_cur):
if self.q_level == 'C':
min_val_cur.resize_(self.min_val.shape)
max_val_cur.resize_(self.max_val.shape)
if self.num_flag == 0:
self.num_flag += 1
min_val = min_val_cur
max_val = max_val_cur
else:
min_val = torch.min(min_val_cur, self.min_val)
max_val = torch.max(max_val_cur, self.max_val)
self.min_val.copy_(min_val)
self.max_val.copy_(max_val)
class MovingAverageMinMaxObserver(ObserverBase):
def __init__(self, q_level, device, out_channels, momentum=0.1):
super(MovingAverageMinMaxObserver, self).__init__(q_level, device)
self.momentum = momentum
self.num_flag = 0
self.out_channels = out_channels
if self.q_level == 'L':
self.min_val = torch.zeros((1), dtype=torch.float32, device=self.device)
self.max_val = torch.zeros((1), dtype=torch.float32, device=self.device)
elif self.q_level == 'C':
self.min_val = torch.zeros((out_channels, 1, 1, 1), dtype=torch.float32, device=self.device)
self.max_val = torch.zeros((out_channels, 1, 1, 1), dtype=torch.float32, device=self.device)
elif self.q_level == 'FC':
self.min_val = torch.zeros((out_channels, 1), dtype=torch.float32, device=self.device)
self.max_val = torch.zeros((out_channels, 1), dtype=torch.float32, device=self.device)
def update_range(self, min_val_cur, max_val_cur):
if self.q_level == 'C':
min_val_cur.resize_(self.min_val.shape)
max_val_cur.resize_(self.max_val.shape)
if self.num_flag == 0:
self.num_flag += 1
min_val = min_val_cur
max_val = max_val_cur
else:
min_val = (1 - self.momentum) * self.min_val + self.momentum * min_val_cur
max_val = (1 - self.momentum) * self.max_val + self.momentum * max_val_cur
self.min_val.copy_(min_val)
self.max_val.copy_(max_val)
# ********************* quantizers（量化器，量化） *********************
# 取整(ste)
class Round(Function):
@staticmethod
def forward(self, input):
output = torch.round(input)
return output
@staticmethod
def backward(self, grad_output):
grad_input = grad_output.clone()
return grad_input
class Quantizer(nn.Module):
def __init__(self, bits, observer, activation_weight_flag, qaft=False):
super(Quantizer, self).__init__()
self.bits = bits
self.observer = observer
self.activation_weight_flag = activation_weight_flag
self.qaft = qaft
# scale/zero_point/eps
if self.observer.q_level == 'L':
self.register_buffer('scale', torch.ones((1), dtype=torch.float32))
self.register_buffer('zero_point', torch.zeros((1), dtype=torch.float32))
elif self.observer.q_level == 'C':
self.register_buffer('scale', torch.ones((self.observer.out_channels, 1, 1, 1), dtype=torch.float32))
self.register_buffer('zero_point', torch.zeros((self.observer.out_channels, 1, 1, 1), dtype=torch.float32))
elif self.observer.q_level == 'FC':
self.register_buffer('scale', torch.ones((self.observer.out_channels, 1), dtype=torch.float32))
self.register_buffer('zero_point', torch.zeros((self.observer.out_channels, 1), dtype=torch.float32))
self.eps = torch.tensor((torch.finfo(torch.float32).eps), dtype=torch.float32, device=self.observer.device) # eps(1.1921e-07)
def update_qparams(self):
raise NotImplementedError
# 取整(ste)
def round(self, input):
output = Round.apply(input)
return output
def forward(self, input):
if self.bits == 32:
output = input
elif self.bits == 1:
print('！Binary quantization is not supported ！')
assert self.bits != 1
else:
#qat, update quant_para
if not self.qaft:
if self.training:
self.observer(input)
self.update_qparams() # update scale and zero_point
#qaft, freeze quant_para
# 量化/反量化
output = (torch.clamp(self.round(input / self.scale - self.zero_point),
self.quant_min_val, self.quant_max_val) + self.zero_point) * self.scale
return output
class SignedQuantizer(Quantizer):
def __init__(self, *args, **kwargs):
super(SignedQuantizer, self).__init__(*args, **kwargs)
if self.activation_weight_flag == 0: # weight
self.quant_min_val = torch.tensor((-((1 << (self.bits - 1)) - 1)), device=self.observer.device)
self.quant_max_val = torch.tensor(((1 << (self.bits - 1)) - 1), device=self.observer.device)
elif self.activation_weight_flag == 1: # activation
self.quant_min_val = torch.tensor((-(1 << (self.bits - 1))), device=self.observer.device)
self.quant_max_val = torch.tensor(((1 << (self.bits - 1)) - 1), device=self.observer.device)
else:
print('activation_weight_flag error')
class UnsignedQuantizer(Quantizer):
def __init__(self, *args, **kwargs):
super(UnsignedQuantizer, self).__init__(*args, **kwargs)
if self.activation_weight_flag == 0: # weight:
self.quant_min_val = torch.tensor((0), device=self.observer.device)
self.quant_max_val = torch.tensor(((1 << self.bits) - 2), device=self.observer.device)
elif self.activation_weight_flag == 1: # activation
self.quant_min_val = torch.tensor((0), device=self.observer.device)
self.quant_max_val = torch.tensor(((1 << self.bits) - 1), device=self.observer.device)
else:
print('activation_weight_flag error')
# 对称量化
class SymmetricQuantizer(SignedQuantizer):
def update_qparams(self):
quant_range = float(self.quant_max_val - self.quant_min_val) / 2 # quantized_range
float_range = torch.max(torch.abs(self.observer.min_val), torch.abs(self.observer.max_val)) # float_range
self.scale = float_range / quant_range # scale
self.scale = torch.max(self.scale, self.eps) # processing for very small scale
self.zero_point = torch.zeros_like(self.scale) # zero_point
# 非对称量化
class AsymmetricQuantizer(UnsignedQuantizer):
def update_qparams(self):
quant_range = float(self.quant_max_val - self.quant_min_val) # quantized_range
float_range = self.observer.max_val - self.observer.min_val # float_range
self.scale = float_range / quant_range # scale
self.scale = torch.max(self.scale, self.eps) # processing for very small scale
self.zero_point = torch.round(self.observer.min_val / self.scale) # zero_point
# ********************* 量化卷积（同时量化A/W，并做卷积） *********************
class QuantConv2d(nn.Conv2d):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True,
padding_mode='zeros',
a_bits=8,
w_bits=8,
q_type=0,
q_level=0,
device='cpu',
weight_observer=0,
quant_inference=False,
qaft=False):
super(QuantConv2d, self).__init__(in_channels, out_channels, kernel_size, stride, padding, dilation, groups,
bias, padding_mode)
self.quant_inference = quant_inference
if q_type == 0:
self.activation_quantizer = SymmetricQuantizer(bits=a_bits, observer=MovingAverageMinMaxObserver(
q_level='L', out_channels=None, device=device), activation_weight_flag=1, qaft=qaft)
if weight_observer == 0:
if q_level == 0:
self.weight_quantizer = SymmetricQuantizer(bits=w_bits, observer=MinMaxObserver(
q_level='C', out_channels=out_channels, device=device), activation_weight_flag=0, qaft=qaft)
else:
self.weight_quantizer = SymmetricQuantizer(bits=w_bits, observer=MinMaxObserver(
q_level='L', out_channels=None, device=device), activation_weight_flag=0, qaft=qaft)
else:
if q_level == 0:
self.weight_quantizer = SymmetricQuantizer(bits=w_bits, observer=MovingAverageMinMaxObserver(
q_level='C', out_channels=out_channels, device=device), activation_weight_flag=0, qaft=qaft)
else:
self.weight_quantizer = SymmetricQuantizer(bits=w_bits, observer=MovingAverageMinMaxObserver(
q_level='L', out_channels=None, device=device), activation_weight_flag=0, qaft=qaft)
else:
self.activation_quantizer = AsymmetricQuantizer(bits=a_bits, observer=MovingAverageMinMaxObserver(
q_level='L', out_channels=None, device=device), activation_weight_flag=1, qaft=qaft)
if weight_observer == 0:
if q_level == 0:
self.weight_quantizer = AsymmetricQuantizer(bits=w_bits, observer=MinMaxObserver(
q_level='C', out_channels=out_channels, device=device), activation_weight_flag=0, qaft=qaft)
else:
self.weight_quantizer = AsymmetricQuantizer(bits=w_bits, observer=MinMaxObserver(
q_level='L', out_channels=None, device=device), activation_weight_flag=0, qaft=qaft)
else:
if q_level == 0:
self.weight_quantizer = AsymmetricQuantizer(bits=w_bits, observer=MovingAverageMinMaxObserver(
q_level='C', out_channels=out_channels, device=device), activation_weight_flag=0, qaft=qaft)
else:
self.weight_quantizer = AsymmetricQuantizer(bits=w_bits, observer=MovingAverageMinMaxObserver(
q_level='L', out_channels=None, device=device), activation_weight_flag=0, qaft=qaft)
def forward(self, input):
quant_input = self.activation_quantizer(input)
if not self.quant_inference:
quant_weight = self.weight_quantizer(self.weight)
else:
quant_weight = self.weight
output = F.conv2d(quant_input, quant_weight, self.bias, self.stride, self.padding, self.dilation,
self.groups)
return output
class QuantConvTranspose2d(nn.ConvTranspose2d):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
output_padding=0,
groups=1,
bias=True,
dilation=1,
padding_mode='zeros',
a_bits=8,
w_bits=8,
q_type=0,
device='cpu',
weight_observer=0,
quant_inference=False,
qaft=False):
super(QuantConvTranspose2d, self).__init__(in_channels, out_channels, kernel_size, stride, padding, output_padding,
groups, bias, dilation, padding_mode)
self.quant_inference = quant_inference
if q_type == 0:
self.activation_quantizer = SymmetricQuantizer(bits=a_bits, observer=MovingAverageMinMaxObserver(
q_level='L', out_channels=None, device=device), activation_weight_flag=1, qaft=qaft)
if weight_observer == 0:
self.weight_quantizer = SymmetricQuantizer(bits=w_bits, observer=MinMaxObserver(
q_level='L', out_channels=None, device=device), activation_weight_flag=0, qaft=qaft)
else:
self.weight_quantizer = SymmetricQuantizer(bits=w_bits, observer=MovingAverageMinMaxObserver(
q_level='L', out_channels=None, device=device), activation_weight_flag=0, qaft=qaft)
else:
self.activation_quantizer = AsymmetricQuantizer(bits=a_bits, observer=MovingAverageMinMaxObserver(
q_level='L', out_channels=None, device=device), activation_weight_flag=1, qaft=qaft)
if weight_observer == 0:
self.weight_quantizer = AsymmetricQuantizer(bits=w_bits, observer=MinMaxObserver(
q_level='L', out_channels=None, device=device), activation_weight_flag=0, qaft=qaft)
else:
self.weight_quantizer = AsymmetricQuantizer(bits=w_bits, observer=MovingAverageMinMaxObserver(
q_level='L', out_channels=None, device=device), activation_weight_flag=0, qaft=qaft)
def forward(self, input):
quant_input = self.activation_quantizer(input)
if not self.quant_inference:
quant_weight = self.weight_quantizer(self.weight)
else:
quant_weight = self.weight
output = F.conv_transpose2d(quant_input, quant_weight, self.bias, self.stride, self.padding, self.output_padding,
self.groups, self.dilation)
return output
def reshape_to_activation(input):
return input.reshape(1, -1, 1, 1)
def reshape_to_weight(input):
return input.reshape(-1, 1, 1, 1)
def reshape_to_bias(input):
return input.reshape(-1)
# ********************* bn融合_量化卷积（bn融合后，同时量化A/W，并做卷积） *********************
class QuantBNFuseConv2d(QuantConv2d):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=False,
padding_mode='zeros',
eps=1e-5,
momentum=0.1,
a_bits=8,
w_bits=8,
q_type=0,
q_level=0,
device='cpu',
weight_observer=0,
pretrained_model=False,
qaft=False):
super(QuantBNFuseConv2d, self).__init__(in_channels, out_channels, kernel_size, stride, padding, dilation, groups,
bias, padding_mode)
self.num_flag = 0
self.pretrained_model = pretrained_model
self.qaft = qaft
self.eps = eps
self.momentum = momentum
self.gamma = Parameter(torch.Tensor(out_channels))
self.beta = Parameter(torch.Tensor(out_channels))
self.register_buffer('running_mean', torch.zeros((out_channels), dtype=torch.float32))
self.register_buffer('running_var', torch.ones((out_channels), dtype=torch.float32))
init.uniform_(self.gamma)
init.zeros_(self.beta)
if q_type == 0:
self.activation_quantizer = SymmetricQuantizer(bits=a_bits, observer=MovingAverageMinMaxObserver(
q_level='L', out_channels=None, device=device), activation_weight_flag=1, qaft=qaft)
if weight_observer == 0:
if q_level == 0:
self.weight_quantizer = SymmetricQuantizer(bits=w_bits, observer=MinMaxObserver(
q_level='C', out_channels=out_channels, device=device), activation_weight_flag=0, qaft=qaft)
else:
self.weight_quantizer = SymmetricQuantizer(bits=w_bits, observer=MinMaxObserver(
q_level='L', out_channels=None, device=device), activation_weight_flag=0, qaft=qaft)
else:
if q_level == 0:
self.weight_quantizer = SymmetricQuantizer(bits=w_bits, observer=MovingAverageMinMaxObserver(
q_level='C', out_channels=out_channels, device=device), activation_weight_flag=0, qaft=qaft)
else:
self.weight_quantizer = SymmetricQuantizer(bits=w_bits, observer=MovingAverageMinMaxObserver(
q_level='L', out_channels=None, device=device), activation_weight_flag=0, qaft=qaft)
else:
self.activation_quantizer = AsymmetricQuantizer(bits=a_bits, observer=MovingAverageMinMaxObserver(
q_level='L', out_channels=None, device=device), activation_weight_flag=1, qaft=qaft)
if weight_observer == 0:
if q_level == 0:
self.weight_quantizer = AsymmetricQuantizer(bits=w_bits, observer=MinMaxObserver(
q_level='C', out_channels=out_channels, device=device), activation_weight_flag=0, qaft=qaft)
else:
self.weight_quantizer = AsymmetricQuantizer(bits=w_bits, observer=MinMaxObserver(
q_level='L', out_channels=None, device=device), activation_weight_flag=0, qaft=qaft)
else:
if q_level == 0:
self.weight_quantizer = AsymmetricQuantizer(bits=w_bits, observer=MovingAverageMinMaxObserver(
q_level='C', out_channels=out_channels, device=device), activation_weight_flag=0, qaft=qaft)
else:
self.weight_quantizer = AsymmetricQuantizer(bits=w_bits, observer=MovingAverageMinMaxObserver(
q_level='L', out_channels=None, device=device), activation_weight_flag=0, qaft=qaft)
def forward(self, input):
if not self.qaft:
#qat, calibrate bn_statis_para
# 训练态
if self.training:
# 先做普通卷积得到A，以取得BN参数
output = F.conv2d(input, self.weight, self.bias, self.stride, self.padding, self.dilation,
self.groups)
# 更新BN统计参数（batch和running）
dims = [dim for dim in range(4) if dim != 1]
batch_mean = torch.mean(output, dim=dims)
batch_var = torch.var(output, dim=dims)
with torch.no_grad():
if not self.pretrained_model:
if self.num_flag == 0:
self.num_flag += 1
running_mean = batch_mean
running_var = batch_var
else:
running_mean = (1 - self.momentum) * self.running_mean + self.momentum * batch_mean
running_var = (1 - self.momentum) * self.running_var + self.momentum * batch_var
self.running_mean.copy_(running_mean)
self.running_var.copy_(running_var)
else:
running_mean = (1 - self.momentum) * self.running_mean + self.momentum * batch_mean
running_var = (1 - self.momentum) * self.running_var + self.momentum * batch_var
self.running_mean.copy_(running_mean)
self.running_var.copy_(running_var)
# BN融合
if self.bias is not None:
bias_fused = reshape_to_bias(self.beta + (self.bias - batch_mean) * (self.gamma / torch.sqrt(batch_var + self.eps)))
else:
bias_fused = reshape_to_bias(self.beta - batch_mean * (self.gamma / torch.sqrt(batch_var + self.eps))) # b融batch
weight_fused = self.weight * reshape_to_weight(self.gamma / torch.sqrt(self.running_var + self.eps)) # w融running
# 测试态
else:
# BN融合
if self.bias is not None:
bias_fused = reshape_to_bias(self.beta + (self.bias - self.running_mean) * (self.gamma / torch.sqrt(self.running_var + self.eps)))
else:
bias_fused = reshape_to_bias(self.beta - self.running_mean * (self.gamma / torch.sqrt(self.running_var + self.eps))) # b融running
weight_fused = self.weight * reshape_to_weight(self.gamma / torch.sqrt(self.running_var + self.eps)) # w融running
else:
#qaft, freeze bn_statis_para
# BN融合
if self.bias is not None:
bias_fused = reshape_to_bias(self.beta + (self.bias - self.running_mean) * (self.gamma / torch.sqrt(self.running_var + self.eps)))
else:
bias_fused = reshape_to_bias(self.beta - self.running_mean * (self.gamma / torch.sqrt(self.running_var + self.eps))) # b融running
weight_fused = self.weight * reshape_to_weight(self.gamma / torch.sqrt(self.running_var + self.eps)) # w融running
# 量化A和bn融合后的W
quant_input = self.activation_quantizer(input)
quant_weight = self.weight_quantizer(weight_fused)
if not self.qaft:
#qat, quant_bn_fuse_conv
# 量化卷积
if self.training: # 训练态
output = F.conv2d(quant_input, quant_weight, None, self.stride, self.padding, self.dilation,
self.groups) # 注意，这里不加bias（self.bias为None）
# （这里将训练态下，卷积中w融合running参数的效果转为融合batch参数的效果）running ——> batch
output *= reshape_to_activation(torch.sqrt(self.running_var + self.eps) / torch.sqrt(batch_var + self.eps))
output += reshape_to_activation(bias_fused)
else: # 测试态
output = F.conv2d(quant_input, quant_weight, bias_fused, self.stride, self.padding, self.dilation,
self.groups) # 注意，这里加bias，做完整的conv+bn
else:
#qaft, quant_bn_fuse_conv
output = F.conv2d(quant_input, quant_weight, bias_fused, self.stride, self.padding, self.dilation,
self.groups)
return output
class QuantLinear(nn.Linear):
def __init__(self,
in_features,
out_features,
bias=True,
a_bits=8,
w_bits=8,
q_type=0,
q_level=0,
device='cpu',
weight_observer=0,
quant_inference=False,
qaft=False):
super(QuantLinear, self).__init__(in_features, out_features, bias)
self.quant_inference = quant_inference
if q_type == 0:
self.activation_quantizer = SymmetricQuantizer(bits=a_bits, observer=MovingAverageMinMaxObserver(
q_level='L', out_channels=None, device=device), activation_weight_flag=1, qaft=qaft)
if weight_observer == 0:
if q_level == 0:
self.weight_quantizer = SymmetricQuantizer(bits=w_bits, observer=MinMaxObserver(
q_level='FC', out_channels=out_features, device=device), activation_weight_flag=0, qaft=qaft)
else:
self.weight_quantizer = SymmetricQuantizer(bits=w_bits, observer=MinMaxObserver(
q_level='L', out_channels=None, device=device), activation_weight_flag=0, qaft=qaft)
else:
if q_level == 0:
self.weight_quantizer = SymmetricQuantizer(bits=w_bits, observer=MovingAverageMinMaxObserver(
q_level='FC', out_channels=out_features, device=device), activation_weight_flag=0, qaft=qaft)
else:
self.weight_quantizer = SymmetricQuantizer(bits=w_bits, observer=MovingAverageMinMaxObserver(
q_level='L', out_channels=None, device=device), activation_weight_flag=0, qaft=qaft)
else:
self.activation_quantizer = AsymmetricQuantizer(bits=a_bits, observer=MovingAverageMinMaxObserver(
q_level='L', out_channels=None, device=device), activation_weight_flag=1, qaft=qaft)
if weight_observer == 0:
if q_level == 0:
self.weight_quantizer = AsymmetricQuantizer(bits=w_bits, observer=MinMaxObserver(
q_level='FC', out_channels=out_features, device=device), activation_weight_flag=0, qaft=qaft)
else:
self.weight_quantizer = AsymmetricQuantizer(bits=w_bits, observer=MinMaxObserver(
q_level='L', out_channels=None, device=device), activation_weight_flag=0, qaft=qaft)
else:
if q_level == 0:
self.weight_quantizer = AsymmetricQuantizer(bits=w_bits, observer=MovingAverageMinMaxObserver(
q_level='FC', out_channels=out_features, device=device), activation_weight_flag=0, qaft=qaft)
else:
self.weight_quantizer = AsymmetricQuantizer(bits=w_bits, observer=MovingAverageMinMaxObserver(
q_level='L', out_channels=None, device=device), activation_weight_flag=0, qaft=qaft)
def forward(self, input):
quant_input = self.activation_quantizer(input)
if not self.quant_inference:
quant_weight = self.weight_quantizer(self.weight)
else:
quant_weight = self.weight
output = F.linear(quant_input, quant_weight, self.bias)
return output
class QuantReLU(nn.ReLU):
def __init__(self, inplace=False, a_bits=8, q_type=0, device='cpu', qaft=False):
super(QuantReLU, self).__init__(inplace)
if q_type == 0:
self.activation_quantizer = SymmetricQuantizer(bits=a_bits, observer=MovingAverageMinMaxObserver(
q_level='L', out_channels=None, device=device), activation_weight_flag=1, qaft=qaft)
else:
self.activation_quantizer = AsymmetricQuantizer(bits=a_bits, observer=MovingAverageMinMaxObserver(
q_level='L', out_channels=None, device=device), activation_weight_flag=1, qaft=qaft)
def forward(self, input):
quant_input = self.activation_quantizer(input)
output = F.relu(quant_input, self.inplace)
return output
class QuantSigmoid(nn.Sigmoid):
def __init__(self, a_bits=8, q_type=0, device='cpu', qaft=False):
super(QuantSigmoid, self).__init__()
if q_type == 0:
self.activation_quantizer = SymmetricQuantizer(bits=a_bits, observer=MovingAverageMinMaxObserver(
q_level='L', out_channels=None, device=device), activation_weight_flag=1, qaft=qaft)
else:
self.activation_quantizer = AsymmetricQuantizer(bits=a_bits, observer=MovingAverageMinMaxObserver(
q_level='L', out_channels=None, device=device), activation_weight_flag=1, qaft=qaft)
def forward(self, input):
quant_input = self.activation_quantizer(input)
output = F.sigmoid(quant_input)
return output
class QuantMaxPool2d(nn.MaxPool2d):
def __init__(self, kernel_size, stride=None, padding=0, dilation=1,
return_indices=False, ceil_mode=False, a_bits=8, q_type=0, device='cpu', qaft=False):
super(QuantMaxPool2d, self).__init__(kernel_size, stride, padding, dilation,
return_indices, ceil_mode)
if q_type == 0:
self.activation_quantizer = SymmetricQuantizer(bits=a_bits, observer=MovingAverageMinMaxObserver(
q_level='L', out_channels=None, device=device), activation_weight_flag=1, qaft=qaft)
else:
self.activation_quantizer = AsymmetricQuantizer(bits=a_bits, observer=MovingAverageMinMaxObserver(
q_level='L', out_channels=None, device=device), activation_weight_flag=1, qaft=qaft)
def forward(self, input):
quant_input = self.activation_quantizer(input)
output = F.max_pool2d(quant_input, self.kernel_size, self.stride,
self.padding, self.dilation, self.return_indices, self.ceil_mode)
return output
class QuantAvgPool2d(nn.AvgPool2d):
def __init__(self, kernel_size, stride=None, padding=0, ceil_mode=False,
count_include_pad=True, divisor_override=None, a_bits=8, q_type=0, device='cpu', qaft=False):
super(QuantAvgPool2d, self).__init__(kernel_size, stride, padding, ceil_mode,
count_include_pad, divisor_override)
if q_type == 0:
self.activation_quantizer = SymmetricQuantizer(bits=a_bits, observer=MovingAverageMinMaxObserver(
q_level='L', out_channels=None, device=device), activation_weight_flag=1, qaft=qaft)
else:
self.activation_quantizer = AsymmetricQuantizer(bits=a_bits, observer=MovingAverageMinMaxObserver(
q_level='L', out_channels=None, device=device), activation_weight_flag=1, qaft=qaft)
def forward(self, input):
quant_input = self.activation_quantizer(input)
output = F.avg_pool2d(quant_input, self.kernel_size, self.stride, self.padding,
self.ceil_mode, self.count_include_pad, self.divisor_override)
return output
class QuantAdaptiveAvgPool2d(nn.AdaptiveAvgPool2d):
def __init__(self, output_size, a_bits=8, q_type=0, device='cpu', qaft=False):
super(QuantAdaptiveAvgPool2d, self).__init__(output_size)
if q_type == 0:
self.activation_quantizer = SymmetricQuantizer(bits=a_bits, observer=MovingAverageMinMaxObserver(
q_level='L', out_channels=None, device=device), activation_weight_flag=1, qaft=qaft)
else:
self.activation_quantizer = AsymmetricQuantizer(bits=a_bits, observer=MovingAverageMinMaxObserver(
q_level='L', out_channels=None, device=device), activation_weight_flag=1, qaft=qaft)
def forward(self, input):
quant_input = self.activation_quantizer(input)
output = F.adaptive_avg_pool2d(quant_input, self.output_size)
return output
def add_quant_op(module, a_bits=8, w_bits=8, q_type=0, q_level=0, device='cpu',
weight_observer=0, bn_fuse=0, quant_inference=False, pretrained_model=False, qaft=False):
for name, child in module.named_children():
if isinstance(child, nn.Conv2d):
if bn_fuse:
conv_name_temp = name
conv_child_temp = child
else:
if child.bias is not None:
quant_conv = QuantConv2d(child.in_channels, child.out_channels,
child.kernel_size, stride=child.stride,
padding=child.padding, dilation=child.dilation,
groups=child.groups, bias=True, padding_mode=child.padding_mode,
a_bits=a_bits, w_bits=w_bits, q_type=q_type,
q_level=q_level, device=device, weight_observer=weight_observer,
quant_inference=quant_inference, qaft=qaft)
quant_conv.bias.data = child.bias
else:
quant_conv = QuantConv2d(child.in_channels, child.out_channels,
child.kernel_size, stride=child.stride,
padding=child.padding, dilation=child.dilation,
groups=child.groups, bias=False, padding_mode=child.padding_mode,
a_bits=a_bits, w_bits=w_bits, q_type=q_type,
q_level=q_level, device=device, weight_observer=weight_observer,
quant_inference=quant_inference, qaft=qaft)
quant_conv.weight.data = child.weight
module._modules[name] = quant_conv
elif isinstance(child, nn.BatchNorm2d):
if bn_fuse:
if conv_child_temp.bias is not None:
quant_bn_fuse_conv = QuantBNFuseConv2d(conv_child_temp.in_channels,
conv_child_temp.out_channels,
conv_child_temp.kernel_size,
stride=conv_child_temp.stride,
padding=conv_child_temp.padding,
dilation=conv_child_temp.dilation,
groups=conv_child_temp.groups,
bias=True,
padding_mode=conv_child_temp.padding_mode,
eps=child.eps,
momentum=child.momentum,
a_bits=a_bits,
w_bits=w_bits,
q_type=q_type,
q_level=q_level,
device=device,
weight_observer=weight_observer,
pretrained_model=pretrained_model,
qaft=qaft)
quant_bn_fuse_conv.bias.data = conv_child_temp.bias
else:
quant_bn_fuse_conv = QuantBNFuseConv2d(conv_child_temp.in_channels,
conv_child_temp.out_channels,
conv_child_temp.kernel_size,
stride=conv_child_temp.stride,
padding=conv_child_temp.padding,
dilation=conv_child_temp.dilation,
groups=conv_child_temp.groups,
bias=False,
padding_mode=conv_child_temp.padding_mode,
eps=child.eps,
momentum=child.momentum,
a_bits=a_bits,
w_bits=w_bits,
q_type=q_type,
q_level=q_level,
device=device,
weight_observer=weight_observer,
pretrained_model=pretrained_model,
qaft=qaft)
quant_bn_fuse_conv.weight.data = conv_child_temp.weight
quant_bn_fuse_conv.gamma.data = child.weight
quant_bn_fuse_conv.beta.data = child.bias
quant_bn_fuse_conv.running_mean.copy_(child.running_mean)
quant_bn_fuse_conv.running_var.copy_(child.running_var)
module._modules[conv_name_temp] = quant_bn_fuse_conv
module._modules[name] = nn.Identity()
elif isinstance(child, nn.ConvTranspose2d):
if child.bias is not None:
quant_conv_transpose = QuantConvTranspose2d(child.in_channels,
child.out_channels,
child.kernel_size,
stride=child.stride,
padding=child.padding,
output_padding=child.output_padding,
groups=child.groups,
bias=True,
dilation=child.dilation,
padding_mode=child.padding_mode,
a_bits=a_bits,
w_bits=w_bits,
q_type=q_type,
device=device,
weight_observer=weight_observer,
quant_inference=quant_inference,
qaft=qaft)
quant_conv_transpose.bias.data = child.bias
else:
quant_conv_transpose = QuantConvTranspose2d(child.in_channels,
child.out_channels,
child.kernel_size,
stride=child.stride,
padding=child.padding,
output_padding=child.output_padding,
groups=child.groups,
bias=False,
dilation=child.dilation,
padding_mode=child.padding_mode,
a_bits=a_bits,
w_bits=w_bits,
q_type=q_type,
device=device,
weight_observer=weight_observer,
quant_inference=quant_inference,
qaft=qaft)
quant_conv_transpose.weight.data = child.weight
module._modules[name] = quant_conv_transpose
elif isinstance(child, nn.Linear):
if child.bias is not None:
quant_linear = QuantLinear(child.in_features, child.out_features,
bias=True, a_bits=a_bits, w_bits=w_bits,
q_type=q_type, q_level=q_level, device=device,
weight_observer=weight_observer,
quant_inference=quant_inference,
qaft=qaft)
quant_linear.bias.data = child.bias
else:
quant_linear = QuantLinear(child.in_features, child.out_features,
bias=False, a_bits=a_bits, w_bits=w_bits,
q_type=q_type, q_level=q_level, device=device,
weight_observer=weight_observer,
quant_inference=quant_inference,
qaft=qaft)
quant_linear.weight.data = child.weight
module._modules[name] = quant_linear
elif isinstance(child, nn.ReLU):
quant_relu = QuantReLU(inplace=child.inplace, a_bits=a_bits,
q_type=q_type, device=device, qaft=qaft)
module._modules[name] = quant_relu
elif isinstance(child, nn.Sigmoid):
quant_sigmoid = QuantSigmoid(a_bits=a_bits, q_type=q_type,
device=device, qaft=qaft)
module._modules[name] = quant_sigmoid
elif isinstance(child, nn.MaxPool2d):
quant_max_pool = QuantMaxPool2d(kernel_size=child.kernel_size,
stride=child.stride,
padding=child.padding,
a_bits=a_bits,
q_type=q_type,
device=device,
qaft=qaft)
module._modules[name] = quant_max_pool
elif isinstance(child, nn.AvgPool2d):
quant_avg_pool = QuantAvgPool2d(kernel_size=child.kernel_size,
stride=child.stride,
padding=child.padding,
a_bits=a_bits,
q_type=q_type,
device=device,
qaft=qaft)
module._modules[name] = quant_avg_pool
elif isinstance(child, nn.AdaptiveAvgPool2d):
quant_adaptive_avg_pool = QuantAdaptiveAvgPool2d(output_size=child.output_size,
a_bits=a_bits,
q_type=q_type,
device=device,
qaft=qaft)
module._modules[name] = quant_adaptive_avg_pool
else:
add_quant_op(child, a_bits=a_bits, w_bits=w_bits, q_type=q_type, q_level=q_level,
device=device, weight_observer=weight_observer, bn_fuse=bn_fuse,
quant_inference=quant_inference, pretrained_model=pretrained_model,
qaft=qaft)
def prepare(model, inplace=False, a_bits=8, w_bits=8, q_type=0, q_level=0,
device='cpu', weight_observer=0, bn_fuse=0, quant_inference=False,
pretrained_model=False, qaft=False):
if not inplace:
model = copy.deepcopy(model)
add_quant_op(model, a_bits=a_bits, w_bits=w_bits, q_type=q_type, q_level=q_level,
device=device, weight_observer=weight_observer, bn_fuse=bn_fuse,
quant_inference=quant_inference, pretrained_model=pretrained_model,
qaft=qaft)
return model
'''
# *** temp_dev ***
class QuantAdd(nn.Module):
def __init__(self, a_bits=8, q_type=0):
super(QuantAdd, self).__init__()
if q_type == 0:
self.activation_quantizer_0 = SymmetricQuantizer(bits=a_bits, observer=MovingAverageMinMaxObserver(q_level='L', out_channels=None, device=device), activation_weight_flag=1, device=device)
self.activation_quantizer_1 = SymmetricQuantizer(bits=a_bits, observer=MovingAverageMinMaxObserver(q_level='L', out_channels=None, device=device), activation_weight_flag=1, device=device)
else:
self.activation_quantizer_0 = AsymmetricQuantizer(bits=a_bits, observer=MovingAverageMinMaxObserver(q_level='L', out_channels=None, device=device), activation_weight_flag=2, device=device)
self.activation_quantizer_1 = AsymmetricQuantizer(bits=a_bits, observer=MovingAverageMinMaxObserver(q_level='L', out_channels=None, device=device), activation_weight_flag=2, device=device)
def forward(self, shortcut, input):
output = self.activation_quantizer_0(shortcut) + self.activation_quantizer_1(input)
return output
class QuantConcat(nn.Module):
def __init__(self, a_bits=8, q_type=0):
super(QuantConcat, self).__init__()
if q_type == 0:
self.activation_quantizer_0 = SymmetricQuantizer(bits=a_bits, observer=MovingAverageMinMaxObserver(q_level='L', out_channels=None, device=device), activation_weight_flag=1, device=device)
self.activation_quantizer_1 = SymmetricQuantizer(bits=a_bits, observer=MovingAverageMinMaxObserver(q_level='L', out_channels=None, device=device), activation_weight_flag=1, device=device)
else:
self.activation_quantizer_0 = AsymmetricQuantizer(bits=a_bits, observer=MovingAverageMinMaxObserver(q_level='L', out_channels=None, device=device), activation_weight_flag=2, device=device)
self.activation_quantizer_1 = AsymmetricQuantizer(bits=a_bits, observer=MovingAverageMinMaxObserver(q_level='L', out_channels=None, device=device), activation_weight_flag=2, device=device)
def forward(self, shortcut, input):
output = torch.cat((self.activation_quantizer_1(input), self.activation_quantizer_0(shortcut)), 1)
return output
'''
| 59.368547 | 200 | 0.533304 | 4,957 | 49,454 | 5.046803 | 0.048618 | 0.025902 | 0.050366 | 0.060439 | 0.817324 | 0.785066 | 0.7642 | 0.752768 | 0.74945 | 0.731902 | 0 | 0.011413 | 0.381668 | 49,454 | 832 | 201 | 59.439904 | 0.806632 | 0.020807 | 0 | 0.707928 | 0 | 0 | 0.006051 | 0.000954 | 0 | 0 | 0 | 0 | 0.001391 | 1 | 0.055633 | false | 0 | 0.011127 | 0.004172 | 0.115438 | 0.004172 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 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 | 7 |
4c4182091f1040c8dd300c2d25bddb401411ab71 | 8,001 | py | Python | lambda/python/DNS_setting_lambda.py | enrikiko/AWS | e8984e5fc5d015285ef2cc3f4295a273d1994e22 | [
"MIT"
] | null | null | null | lambda/python/DNS_setting_lambda.py | enrikiko/AWS | e8984e5fc5d015285ef2cc3f4295a273d1994e22 | [
"MIT"
] | null | null | null | lambda/python/DNS_setting_lambda.py | enrikiko/AWS | e8984e5fc5d015285ef2cc3f4295a273d1994e22 | [
"MIT"
] | null | null | null | import boto3, json
HOSTED_ZONE_ID = 'HOSTED_ZONE_ID'
def lambda_handler(event, context):
route53 = boto3.client('route53')
ip = event["ip"]
dns_changes = {
'Changes': [
{
'Action': 'UPSERT',
'ResourceRecordSet': {
'Name': "app.cortijodemazas.com.",
'Type': 'A',
'ResourceRecords': [
{
'Value': ip
}
],
'TTL': 300
}
},
{
'Action': 'UPSERT',
'ResourceRecordSet': {
'Name': "cortijodemazas.com.",
'Type': 'A',
'ResourceRecords': [
{
'Value': ip
}
],
'TTL': 300
}
},
{
'Action': 'UPSERT',
'ResourceRecordSet': {
'Name': "jenkins.cortijodemazas.com.",
'Type': 'A',
'ResourceRecords': [
{
'Value': ip
}
],
'TTL': 300
}
},
{
'Action': 'UPSERT',
'ResourceRecordSet': {
'Name': "public.cortijodemazas.com.",
'Type': 'A',
'ResourceRecords': [
{
'Value': ip
}
],
'TTL': 300
}
},
{
'Action': 'UPSERT',
'ResourceRecordSet': {
'Name': "back.app.cortijodemazas.com.",
'Type': 'A',
'ResourceRecords': [
{
'Value': ip
}
],
'TTL': 300
}
},
{
'Action': 'UPSERT',
'ResourceRecordSet': {
'Name': "camera1.cortijodemazas.com.",
'Type': 'A',
'ResourceRecords': [
{
'Value': ip
}
],
'TTL': 300
}
},
{
'Action': 'UPSERT',
'ResourceRecordSet': {
'Name': "camera2.cortijodemazas.com.",
'Type': 'A',
'ResourceRecords': [
{
'Value': ip
}
],
'TTL': 300
}
},
{
'Action': 'UPSERT',
'ResourceRecordSet': {
'Name': "camera.cortijodemazas.com.",
'Type': 'A',
'ResourceRecords': [
{
'Value': ip
}
],
'TTL': 300
}
},
{
'Action': 'UPSERT',
'ResourceRecordSet': {
'Name': "socket.cortijodemazas.com.",
'Type': 'A',
'ResourceRecords': [
{
'Value': ip
}
],
'TTL': 300
}
},
{
'Action': 'UPSERT',
'ResourceRecordSet': {
'Name': "file.cortijodemazas.com.",
'Type': 'A',
'ResourceRecords': [
{
'Value': ip
}
],
'TTL': 300
}
},
{
'Action': 'UPSERT',
'ResourceRecordSet': {
'Name': "vpn.cortijodemazas.com.",
'Type': 'A',
'ResourceRecords': [
{
'Value': ip
}
],
'TTL': 300
}
},
{
'Action': 'UPSERT',
'ResourceRecordSet': {
'Name': "dockerhub.cortijodemazas.com.",
'Type': 'A',
'ResourceRecords': [
{
'Value': ip
}
],
'TTL': 300
}
},
{
'Action': 'UPSERT',
'ResourceRecordSet': {
'Name': "couchbase.cortijodemazas.com.",
'Type': 'A',
'ResourceRecords': [
{
'Value': ip
}
],
'TTL': 300
}
},
{
'Action': 'UPSERT',
'ResourceRecordSet': {
'Name': "router.cortijodemazas.com.",
'Type': 'A',
'ResourceRecords': [
{
'Value': ip
}
],
'TTL': 300
}
},
{
'Action': 'UPSERT',
'ResourceRecordSet': {
'Name': "www.cortijodemazas.com.",
'Type': 'A',
'ResourceRecords': [
{
'Value': ip
}
],
'TTL': 300
}
},
{
'Action': 'UPSERT',
'ResourceRecordSet': {
'Name': "gitlab.cortijodemazas.com.",
'Type': 'A',
'ResourceRecords': [
{
'Value': ip
}
],
'TTL': 300
}
},
{
'Action': 'UPSERT',
'ResourceRecordSet': {
'Name': "octopi.cortijodemazas.com.",
'Type': 'A',
'ResourceRecords': [
{
'Value': ip
}
],
'TTL': 300
}
},
{
'Action': 'UPSERT',
'ResourceRecordSet': {
'Name': "ws.cortijodemazas.com.",
'Type': 'A',
'ResourceRecords': [
{
'Value': ip
}
],
'TTL': 300
}
},
{
'Action': 'UPSERT',
'ResourceRecordSet': {
'Name': "haproxy.cortijodemazas.com.",
'Type': 'A',
'ResourceRecords': [
{
'Value': ip
}
],
'TTL': 300
}
}
]
}
response = route53.change_resource_record_sets(
HostedZoneId=HOSTED_ZONE_ID,
ChangeBatch=dns_changes
)
return {
'statusCode': 201,
'body': ip
}
| 29.966292 | 60 | 0.23997 | 308 | 8,001 | 6.194805 | 0.185065 | 0.119497 | 0.288784 | 0.328616 | 0.812369 | 0.812369 | 0.812369 | 0.812369 | 0.812369 | 0.786164 | 0 | 0.025189 | 0.652668 | 8,001 | 266 | 61 | 30.078947 | 0.661389 | 0 | 0 | 0.503788 | 0 | 0 | 0.210849 | 0.058118 | 0 | 0 | 0 | 0 | 0 | 1 | 0.003788 | false | 0 | 0.003788 | 0 | 0.011364 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 1 | 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 | 9 |
4c44fcfff3c3ba9bf4e805ece8029040bae8016d | 114 | py | Python | good/listsAndTuplesNested.py | Alberto42/Interpreter | a56c4d905672572734a8470ef607b66727489f15 | [
"BSD-3-Clause"
] | null | null | null | good/listsAndTuplesNested.py | Alberto42/Interpreter | a56c4d905672572734a8470ef607b66727489f15 | [
"BSD-3-Clause"
] | null | null | null | good/listsAndTuplesNested.py | Alberto42/Interpreter | a56c4d905672572734a8470ef607b66727489f15 | [
"BSD-3-Clause"
] | null | null | null | l = [1,True,"str",[1],(1,2)]
print(l)
print( (l,(l,l)) )
print( [l,[l,l]] )
print( [l,(l,l)] )
print( (l,[l,l]) ) | 16.285714 | 28 | 0.438596 | 25 | 114 | 2 | 0.24 | 0.32 | 0.56 | 0.64 | 0.66 | 0.66 | 0.66 | 0.66 | 0.66 | 0.66 | 0 | 0.040816 | 0.140351 | 114 | 7 | 29 | 16.285714 | 0.469388 | 0 | 0 | 0 | 0 | 0 | 0.026087 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0.833333 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 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 | 1 | 0 | 8 |
4c518e94338e3a560b249b37c50c0c1176ca1edf | 11,579 | py | Python | django-rgd/rgd/datastore.py | Kitware/ResonantGeoData | 6d111cbe1d57df2cd230edcf4724f6e33471f5ff | [
"Apache-2.0"
] | 3 | 2020-03-10T14:47:07.000Z | 2020-05-05T16:55:27.000Z | django-rgd/rgd/datastore.py | Kitware/ResonantGeoData | 6d111cbe1d57df2cd230edcf4724f6e33471f5ff | [
"Apache-2.0"
] | 13 | 2020-04-14T14:36:06.000Z | 2020-05-07T15:03:42.000Z | django-rgd/rgd/datastore.py | Kitware/ResonantGeoData | 6d111cbe1d57df2cd230edcf4724f6e33471f5ff | [
"Apache-2.0"
] | 1 | 2020-03-03T15:47:52.000Z | 2020-03-03T15:47:52.000Z | import os
import pooch
registry = {
'20091021202517-01000100-VIS_0001.ntf': 'sha512:3d7e07f987e18fcaec81c13405b75bf285e6bf65ff7ed77d4d37fa3c1d43abbd1208eb849cbfbca77317096c8e9a2a6284c977662be19e9c840cc6397f0c31f1',
'aerial_rgba_000003.tiff': 'sha512:c680e44598728c7a95e98a4dc665873856b889bf186bbdc682beb43d3c4824a4c00adaa23613aeca497b9508934dad63bf4a00e0113ba5620b19d7b2bbb141d0',
'cclc_schu_100.tif': 'sha512:3435dc29da9f854da9b145058dfcacc65c9c78d1664af9a225f0ece07e16a950ae5da7eae1352cd167b5a330da532f58a1aa315be205132a7766650f2c2bffb2',
'landcover_sample_2000.tif': 'sha512:61d037022168eb640368f256851d9827d10cb69f46921d7063a62b632f95ec0b8a35b2e0521853e62522f16e91a98cecd0099bd0887995be66d42bf815c783e9',
'paris_france_10.tiff': 'sha512:16073b737ba055031918659aad3ec9f7daeea88c94d83b86d7de1026a09e5bd741fa03bd96f4fbd3438952d661e7cbe33937ceaec05771ed0f13f020f6865d1f',
'rgb_geotiff.tiff': 'sha512:2be5c8ab1b95a0dd835b278715093374020cb52b626345775d207c24d0b0c915dba587d62bbb186671fa5c64b7e9bc017c53e0b186ba744dd990892f91ee7a0f',
'RomanColosseum_WV2mulitband_10.tif': 'sha512:9fd95ba26bad88a4e10a53685c531134528008607155c2de69ef4598b73b69450fc1fa672345e62696cbf71dd84489f744407b3152815ed43fc20375d26c7bee',
'LC08_L1TP_034032_20200429_20200509_01_T1_sr_band1.tif': 'sha512:b0b52a537d79460afa63a4849c2c03cf686b6f32446a2c56320027f7e701965b0f2af31e0dd843471bc98eecd5f0dd1be67d8c38b4759a81a5a0aa707ae4fea6',
'LC08_L1TP_034032_20200429_20200509_01_T1_sr_band2.tif': 'sha512:492e30b6fdeebf67332d87ab07258c6bed1f9830c214492866bbd2f06f53fcba72b4984709cd4a486f49a4f1a5effaf482cda95f1456d6e3b7e693bee8d9c200',
'LC08_L1TP_034032_20200429_20200509_01_T1_sr_band3.tif': 'sha512:adf8114c240ad6d5462fcac0b480cabc827da3b095620741c4eb414da400c368a8f112a6f13470870e86cbefed6235efac6c3611bd3efd53dd6e12114006af56',
'Streams.zip': 'sha512:7b1469d6e039185183b31a8e0eed90940e1a4db63604673322160b3df2da813652596ddf961ff09a8433b18ce944ae1602b625d5572019ce18bf595c983bc358',
'Watershedt.zip': 'sha512:8a970437ca0b9b4df7b25c3346f2bd41a133dba2ef0e7d6a2361f982d54a9db2f57979d5ef66284017a77ce630beaede77f95af037749ed2e15604c91b5d8037',
'demo.kwcoco.json': 'sha512:04f915e2fe66aed5ec1aeb6b13734f9cb546308417656d1b74d870e54e737446f17be5c0996cadef2ce4e7c2778aa695cfe7cb7bc18b114c296b1fdf2a8afed0',
'demodata.zip': 'sha512:97afa8686ded070ecf68e71bfa2bbee91ac0a8a47d5c175ba940735a0a49ef22b9610aa9ab98464f395fcd6f51945fa41a96a2a331ac593feb4704ca7d98f899',
'demo_rle.kwcoco.json': 'sha512:f685da33ce8d965aa666cdd957e8b5f6880be3b90c1b8c16c885e0c46eebadb1c059df720e4575e59606dea8ac7b294df22ce5c6c0631bdf636af7229118bbdd',
'demo_rle.zip': 'sha512:bafa7951f6498ed7363bd28eedd3f14b7fb206abee6791c3dff2b2696066ac46e297f80b663692444d1a60c1d6aa5d4acb7c5109ead52007f037d4c6cf94e068',
'subset_metadata.klv': 'sha512:4f5dfa60119027b41351c81b9c74804afc7c3b768bdc7687300f779ec35443a81e5a0a8dfa2769de84d4abf240960688e674ade15981788de078579fe8dc9b5a',
'test_fmv.ts': 'sha512:f9ee5180adc0da3d213baff218e55ab5f5a0b2b75dd71d79048aecaf3ebc3b4611a5123e15bab571b5f9cb11f5dd140d585ef623ffb4de0a6ae8c5ca1a27d847',
'Elevation.tif': 'sha512:44587c3b00d349344bcec5cefd3bcda9fcef5e9bbdd0f1a2a4ce76016fa0bb68436ac206c96f54d6a828b45db212aa9d43410dbf40a61ba1d8eec934f7070250',
'MuniBounds.zip': 'sha512:506a6956a37eea9663abd4344535d0c6e44411ab4af97fedd1b7e678b47e8e43397538ae44575fd28d0f8cac760ce4fc7f538e32055c48115996043afabfd165',
'lm_cnty.zip': 'sha512:7518d92b6e84c8363182f637a9fa9d323abb5d6eab6e4f0d458c522f5e4098e4a38bd2aab5c9dc43c41356aa67f4ab7ed6179aa39da43bb00895641514793cd1',
'dlwatersan.zip': 'sha512:24bb7df972e01912742f3e3d3b9ba2d679709630f43c64c6fb0b35ad344f778837547ba9e1b54be4702fe31381ee20a6cb0466c9b5f1c53a320fa586626e3b81',
'dlschool.zip': 'sha512:3a691f1ec170a511fe3d3a856984dbd37016806eda6a73a5ee0e6ba9049e4efbf40b139569295c5efba865f4035249656b5d870ebbd5dfe3df9ed755f2467ac8',
'dlpark.zip': 'sha512:92719b8ca74f489320becd7f9c6eb42a40ff020dc05886edb3e505cf461efe84ec3bea59571a27c1562f3b7db81c91bf1c01ea8a0d860ddc2e77d2528ab23a09',
'dlmetro.zip': 'sha512:bc239503ac4b299c805dd0e550015aa97b984f432a25108528d9e1db162ea23302d154197c45e27afaa923cdaee3603b44653880048d2866dc29d93dc0b4aa4d',
'dllibrary.zip': 'sha512:55d6fbd275084b553cabba6b7fa2bd2ac9fa9305dbe10a68c8c1d93e29ac0b2f4541ca928dc24229df9bc58424f4d087362844711ab7250649eb2912192b642c',
'dlhospital.zip': 'sha512:9315629e36a3941c4d521741123d6a556647ba0c28ef15928a459d44390cf6e381008a4a3b304de30c0eb3fb5c9fb067fa971aed69656ce9e07632a980c593e1',
'dlfire.zip': 'sha512:c62c7af20ea356ed7e5b2d236074b82f73eae4313b8020495c306037814eeb7bd5dac086e6b35b66c7cfd08e61b22c43a272f7e1ecc218bf7e8139ecf27e23de',
'Solid_Mineral_lease_1.zip': 'sha512:a86e5389416f8d7378e337b39dc33cd85c159575e6f87ef5519bd583ce27c936b638e6f1636e5b3907a112618d416b5637f77c70f3272180ac11fd31cc27ae45',
'AG_lease.zip': 'sha512:3f65183ee7356cbfa72b727f3877d56ad199a18a7e2ae9d869ee2089174da3ffd65060c7a170314f77ceeaca0e8e77b3482209f497d303c8323d335a35f1b317',
'L1C_T13SCD_A019901_20201227T175922.tif': 'sha512:83d6f87e8ff245702a614357c416a2cda62ad385ac15abed1bd8becfb975b8773fb503b21a5e3d909812392f28bc16d0f55b8a2e8cc47abb8745895ec17b6783',
'L1C_T18TWN_A016525_20200505T155731.tif': 'sha512:2e4dafda12a7b9ae9098c8bb0cb2d9807a3252cacea649e6bff48932fc4bbb6522a91b2c2de8d3d29c472d964cd3db6ec9848fc47ba2fe5b74d8744ef7690aca',
'L1C_T18TWN_A018527_20200922T155115.tif': 'sha512:7e46f45e548b57d907df21a1993c93f9a272679aaf8b0afcbc53e35a2ba7e9a29e2ec3315ce348efe83d60f5f2ffec611da65d6fef71c7d9a05ea395dd79ec88',
'L1C_T18TWN_A025648_20200520T155359.tif': 'sha512:f1f65c924173610ea01e831ac8139994953d687612a5b4d0bb895db7837d8ba0d012f3ffa04919d21ff20c5d0181270a22b17df29df819bd7e248892a6bc28d1',
'L1C_T18TWN_A027793_20201017T155552.tif': 'sha512:61a59f37b0af8e3326fd8c189fa868d48460d5ba92b47b0bc8051b76b57e05cc51c76484fb2f4e260119e1870c3d46a6cd05a4da2a022455e251e45a7a9f29e9',
'L1C_T18TWN_A028079_20201106T160014.tif': 'sha512:68c3cce4033f2b18933ce60fd05bd3602a56c0b8f42d7e774f1af2c07581af485e76c03ce6b82d44eeb7443f60b7bb4fd14834e4bee2f74927b67b05313bf712',
'bpasg_emodis_week34_082320.tif': 'sha512:70f6ce20a7ff047a6c59e0aed70127a8a1674568108473f757aa80e652092b7cc4584f507d5c630db0b7e1da4f615dea987a4d8eda5bbd720131cd4903c09ca0',
'vegdri_diff_2020_34_gtif.tif': 'sha512:402dd8ce99ee2352dc6b1400bd9fae599675715c8b5b96d3d691cfdf7d5a04883cedec8e546481ae7d3a0ea1d500cef9b75045a3a9c8270b191ba1c3fe5cb4e9',
'vegdri_emodis_week34_082320.tif': 'sha512:fe8f9b1738c7e2bc4dc208701051b1f6ce7638305e567c1b088976740d3bb2fa176647460800bc03e3d7e39fd61b62318e7ce3f0752d7aac25700ebda711c17e',
'US_eMAH_NDVI.2020.350-356.1KM.VI_ACQI.006.2020359165956.tif': 'sha512:14ad91d12d8ea58352b6b2fc51d06ad9fea70488c28c85240c2b60c8fc7fcdd9866bf1d6a73817699acebe8a171a6639bc259a4bcdefbf4f52073a8e84b4b52a',
'US_eMAH_NDVI.2020.350-356.1KM.VI_NDVI.006.2020359165956.tif': 'sha512:abb5faef4217606356e9bf4ac7bf5927f6d6af4febf1ecb70d88f732b21f1895bfa7ef9dcc3529e0ed2ccebfaced2790f7dc0195abe7c5984a932bcf75180770',
'US_eMAH_NDVI.2020.350-356.1KM.VI_QUAL.006.2020359165956.tif': 'sha512:1aeb79fef9650d7b6b7993ced40615f82559e5c17da48893a801769a9e78c2bf1576a44340a251f369b62869983770c99b50ac3d38e6cae56ef6424437463f4e',
'elevation.tif': 'sha512:17ae33de3f4d779e02b878f0c828f1503609c956066ef0973f320983547ac98b8c0133b1c61ddfed4fbeb4a57f9203b573121d8b9eed876452423293e776312a',
'TC_NG_SFBay_US_Geo.tif': 'sha512:da2e66528f77a5e10af5de9e496074b77277c3da81dafc69790189510e5a7e18dba9e966329d36c979f1b547f0d36a82fbc4cfccc65ae9ef9e2747b5a9ee77b0',
'astro.png': 'sha512:de64fcb37e67d5b5946ee45eb659436b446a9a23ac5aefb6f3cce53e58a682a0828f5e8435cf7bd584358760d59915eb6e37a1b69ca34a78f3d511e6ebdad6fd',
'carl.png': 'sha512:12501af6b0e49567fc3df4c5316f9909931aa4a7794b967044e4bbe7b3dfec8331cdd741ef6c1e1f7915172128cabd340bf2372fa7017b5fa2628dada467fb2d',
'stars.png': 'sha512:e19e0c0c28c67441700cf272cb6ae20e5cc0baee24e5527e096e61e290ca823913224cdbabb884c5550e73587192428c0650921a00630c82f45c4eddf52c652f',
'landsat.csv': 'sha512:b5bfa4e5dc819f7f98579974de6ceacecf1e833dfe25255e76e7dabc1574bce89c6c5ca6941f7d050e7b3468268367190d81674a48182d71a2742906f4166a4a',
'sentinel.csv': 'sha512:847add5c6163bd810fead27b02268953354ba5693eb067efdc3357a3d0df9c803bc7b6d8fbf4bf47f65cad009ee28232314237db5473b9566d7360dfdbe28c65',
'landsat_texas.json': 'sha512:84cfced278442fc876c166ff70000f627e688061fdd1194da4d758080d7cb52caf9cc7b12db8af3fe01cb7b29ecfde78c0fbcf51f001b454b461b7fb0e6f87bb',
'topo.vtk': 'sha512:bddafeba2b20a2fd45f137c648e51867716a80786988126501534c2c857458e23d61f5cd8b73c710f5f1303fccf68c7e9556fd8489ee0f461e10ac1a2e7ba938',
'afie_1.jpg': 'sha512:88a2da3313df5a9422ab1cbc9a4b5715a7b5357923a938841f105de546c8d7380f689694cf3314621684ddd9216289764e6f488329ceb1f2ed0716213603b619',
'afie_2.jpg': 'sha512:b64261c8a9ea586a79aa87926ffd44499cd719ea21dd0ba82883d4108e3186faf9d77570a70f4bb88e40b66f3d341f1cf8a9524cb38b2038cad7031b037805d6',
'afie_3.jpg': 'sha512:fb1621f28ed6daee0d66a601c9b8a0ab9f7a75cf30bb487a558e4883b1ad9c6e891068be4f067f5046ac28c5ce418c39117165d20389b8eff9bb80b8e778acb4',
'afie.geojson': 'sha512:19965abd8eef84adc9b06be8626900c4646b41b900d666aee7105391d92a86eeb0c1d56f510cacfb86f500f1a89c4980c43d19358c754b806f177b66621cc2a7',
'LC08_L1TP_034032_20200429_20200509_01_T1_sr_band3.json': 'sha512:73a7a122a3f63135d06f16a7cc37bbd465a12ab0d1d79d5eb5eacf9099fde8e79830777f8ce90888c779b17223a6b6d058b71fa6f64a4c563eccd6b0403d7e92',
'envi_rgbsmall_bip.hdr': 'sha512:5dcd18462be5c569cf80fde5334bb9a47cb91f853cdd01c2c3b899dbce1db91ac27c266d879436497f7c3e64d00331022793f510b786118065083e79f1aba8a7',
'envi_rgbsmall_bip.img': 'sha512:eff9dcc3f5fdae898132ccb908ee0c13725f2b8178871b2c158ffe6f3306ba1408da59fde9228df6ed7ef9b1b720805a3fb5df4545eb80c640d221a7ee697a31',
'jacksonville-untextured.zip': 'sha512:00bb4aeeff6a5f9fd3f0777c0207d6daf6f71bbdf800a5d02d0c5964093b3443bb5b45d571abc2a216b15d343db19bcfa9cb026433c041970e3434385a28c168',
'jacksonville-textured.zip': 'sha512:0ddac53f5bac4fd67d6cf58b86dd1646ffe65a89b5c3f17442e1c4ef0f08f5f34d914e6999a64a4371361233c339f9746f46637073a5e76ffd535e621a615bf9',
'jacksonville-point-cloud-3d-tiles.zip': 'sha512:1994dddfdad026fafb3cdb91391b1404289aad1d99422babbae79874ba276bc58f4eae28fcb2d7bc2af36ff1d7ba7fc42f98b1ec4382be1041f2e1d96dafdbf1',
'dragon.zip': 'sha512:e8b88f5de6cb2d7a3d3cc649190b0230394094e91fabec54acb6dd97869d6fe311fca5806e082c76f085ee00e62630bd02b393668ac3c4e44d007e7374ae635c',
'IHTest_202009_Path3_Step5_BBXSWIR_12deg_DistStA.hdr': 'sha512:d8e11e23d81f397a895d8e302258805082fec9358d44baa63c971c56aecfc725fa0458ff86f2aa64c23c3461dd60d287c28af687393af853d2c1d2b4513163fa',
'IHTest_202009_Path3_Step5_BBXSWIR_12deg_DistStA.raw': 'sha512:8cb5a57c37c87e8a45d51a6d5e5b072a488b79ade82dcfc227473dd60a66cd5fb980a84b59e7541ed5cbfac52605d84ed0b8f055b8da7223d420d69faafe6abd',
}
class DKCPooch(pooch.Pooch):
def get_url(self, fname):
self._assert_file_in_registry(fname)
algo, hashvalue = self.registry[fname].split(':')
return self.base_url.format(algo=algo, hashvalue=hashvalue)
# path = pooch.cache_location(pooch.os_cache('geodata'), None, None)
datastore = DKCPooch(
path=pooch.utils.cache_location(
os.path.join(os.environ.get('TOX_WORK_DIR', pooch.utils.os_cache('pooch')), 'rgd_datastore')
),
base_url='https://data.kitware.com/api/v1/file/hashsum/{algo}/{hashvalue}/download',
registry=registry,
)
| 131.579545 | 205 | 0.903446 | 484 | 11,579 | 21.349174 | 0.506198 | 0.018291 | 0.00542 | 0.008516 | 0.035324 | 0.030098 | 0.030098 | 0.022162 | 0.007936 | 0 | 0 | 0.515001 | 0.041454 | 11,579 | 87 | 206 | 133.091954 | 0.415983 | 0.0057 | 0 | 0 | 0 | 0.0125 | 0.893928 | 0.846582 | 0 | 1 | 0 | 0 | 0.0125 | 1 | 0.0125 | false | 0 | 0.025 | 0 | 0.0625 | 0 | 0 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
4c53634507d1e02360d68bfadddaaadeff64af06 | 5,303 | py | Python | postcode/tests/test_uk.py | GregEremeev/postcode | 279d8a25a82fc43a56f163af86f853e3fcfbe007 | [
"BSD-3-Clause"
] | null | null | null | postcode/tests/test_uk.py | GregEremeev/postcode | 279d8a25a82fc43a56f163af86f853e3fcfbe007 | [
"BSD-3-Clause"
] | null | null | null | postcode/tests/test_uk.py | GregEremeev/postcode | 279d8a25a82fc43a56f163af86f853e3fcfbe007 | [
"BSD-3-Clause"
] | null | null | null | from postcode import uk
def test_validate_positive_cases():
validator = uk.PostcodeValidator('EC1A 1BB')
result = validator.validate()
assert result.is_valid
assert result.postcode == 'EC1A 1BB'
validator = uk.PostcodeValidator('w1a 0ax')
result = validator.validate()
assert result.is_valid
assert result.postcode == 'W1A 0AX'
validator = uk.PostcodeValidator('M1 1AE')
result = validator.validate()
assert result.is_valid
assert result.postcode == 'M1 1AE'
validator = uk.PostcodeValidator('B33 8TH')
result = validator.validate()
assert result.is_valid
assert result.postcode == 'B33 8TH'
validator = uk.PostcodeValidator('cr2 6xh')
result = validator.validate()
assert result.is_valid
assert result.postcode == 'CR2 6XH'
validator = uk.PostcodeValidator('DN55 1PT')
result = validator.validate()
assert result.is_valid
assert result.postcode == 'DN55 1PT'
def test_postcode_length():
"""postcode is between 5 to 7 excluding a space
"""
validator = uk.PostcodeValidator('EC1A')
validator._validate_postcode_length()
assert len(validator.processed_postcode.errors) == 1
validator = uk.PostcodeValidator('EC1AB 1AK')
validator._validate_postcode_length()
assert len(validator.processed_postcode.errors) == 1
def test_outward_code_length():
"""outward code - between 2 and 4 characters
"""
validator = uk.PostcodeValidator('EC1AA 1BB')
validator._validate_outward_code_length('EC1AA')
assert len(validator.processed_postcode.errors) == 1
validator = uk.PostcodeValidator('E 1BB')
validator._validate_outward_code_length('E')
assert len(validator.processed_postcode.errors) == 1
def test_area_length():
"""
area - from 1 to 2 characters and alphabetical
"""
validator = uk.PostcodeValidator('121A 1BB')
validator._validate_outward_code()
assert len(validator.processed_postcode.errors) == 1
assert 'patterns' in validator.processed_postcode.errors[0]
validator = uk.PostcodeValidator('ECEC1A 1BB')
validator._validate_outward_code()
assert len(validator.processed_postcode.errors) == 2
def test_district():
"""
district - 1 digit or 2 digits or a digit followed by a letter
"""
validator = uk.PostcodeValidator('ECA 1BB')
validator._validate_outward_code()
assert len(validator.processed_postcode.errors) == 1
assert 'patterns' in validator.processed_postcode.errors[0]
validator = uk.PostcodeValidator('ECAA 1BB')
validator._validate_outward_code()
assert len(validator.processed_postcode.errors) == 1
assert 'patterns' in validator.processed_postcode.errors[0]
validator = uk.PostcodeValidator('ECB1 1BB')
validator._validate_outward_code()
assert len(validator.processed_postcode.errors) == 1
assert 'patterns' in validator.processed_postcode.errors[0]
def test_sector():
"""
sector - 1 digit
"""
validator = uk.PostcodeValidator('EC1A CBB')
validator._validate_inward_code()
assert len(validator.processed_postcode.errors) == 1
assert 'sector' in validator.processed_postcode.errors[0]
def test_unit():
"""
unit - 2 characters
"""
validator = uk.PostcodeValidator('EC1A 178')
validator._validate_inward_code()
assert len(validator.processed_postcode.errors) == 1
assert 'unit' in validator.processed_postcode.errors[0]
def test_regexp_outword_code():
postcode = 'EC1( 1BB'
validator = uk.PostcodeValidator(postcode)
result = validator.validate()
assert result.is_valid == False
assert len(validator.processed_postcode.errors) == 1
assert 'pattern' in validator.processed_postcode.errors[0]
postcode = ')EC1 1BB'
validator = uk.PostcodeValidator(postcode)
result = validator.validate()
assert result.is_valid == False
assert len(validator.processed_postcode.errors) == 1
assert 'pattern' in validator.processed_postcode.errors[0]
postcode = 'E1Q( 1BB'
validator = uk.PostcodeValidator(postcode)
result = validator.validate()
assert result.is_valid == False
assert len(validator.processed_postcode.errors) == 1
assert 'pattern' in validator.processed_postcode.errors[0]
postcode = ')E1Q 1BB'
validator = uk.PostcodeValidator(postcode)
result = validator.validate()
assert result.is_valid == False
assert len(validator.processed_postcode.errors) == 1
assert 'pattern' in validator.processed_postcode.errors[0]
postcode = 'GIR? 1BB'
validator = uk.PostcodeValidator(postcode)
result = validator.validate()
assert result.is_valid == False
assert len(validator.processed_postcode.errors) == 1
assert 'pattern' in validator.processed_postcode.errors[0]
postcode = '!GIR 1BB'
validator = uk.PostcodeValidator(postcode)
result = validator.validate()
assert result.is_valid == False
assert len(validator.processed_postcode.errors) == 1
assert 'pattern' in validator.processed_postcode.errors[0]
def test_exceptions():
postcode_exc = 'GIR 0AA'
validator = uk.PostcodeValidator(postcode_exc)
postcode = validator.validate()
assert postcode.is_valid
assert postcode.postcode == postcode_exc
| 30.653179 | 66 | 0.716764 | 612 | 5,303 | 6.052288 | 0.132353 | 0.140929 | 0.203564 | 0.25054 | 0.74514 | 0.74514 | 0.725162 | 0.725162 | 0.698704 | 0.683585 | 0 | 0.023004 | 0.180275 | 5,303 | 172 | 67 | 30.831395 | 0.829078 | 0.045823 | 0 | 0.570175 | 0 | 0 | 0.063152 | 0 | 0 | 0 | 0 | 0 | 0.429825 | 1 | 0.078947 | false | 0 | 0.008772 | 0 | 0.087719 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 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 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
4c69f7cda8353d94a29a1f30481782e793442657 | 180 | py | Python | tests/utils/without_number_not_continuous_class.py | ontherivt/versionary | f9ae71b5614d42f1f9c587f23259c09670f154b1 | [
"MIT"
] | 6 | 2017-07-03T18:42:13.000Z | 2021-09-22T18:28:44.000Z | tests/utils/without_number_not_continuous_class.py | ontherivt/versionary | f9ae71b5614d42f1f9c587f23259c09670f154b1 | [
"MIT"
] | 1 | 2017-09-22T15:39:41.000Z | 2017-09-22T15:39:41.000Z | tests/utils/without_number_not_continuous_class.py | ontherivt/versionary | f9ae71b5614d42f1f9c587f23259c09670f154b1 | [
"MIT"
] | 1 | 2021-11-13T08:47:10.000Z | 2021-11-13T08:47:10.000Z | from versionary.decorators import versioned
@versioned()
class MyClass():
def hello():
return 1
@versioned()
class MyClassV3():
def hello():
return 3
| 11.25 | 43 | 0.627778 | 19 | 180 | 5.947368 | 0.684211 | 0.247788 | 0.247788 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.022727 | 0.266667 | 180 | 15 | 44 | 12 | 0.833333 | 0 | 0 | 0.444444 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.222222 | true | 0 | 0.111111 | 0.222222 | 0.777778 | 0 | 1 | 0 | 0 | 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 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 7 |
d5b4b0f105987b0ad15946e8a28a731efea7d3aa | 174 | py | Python | snips_nlu/intent_parser/__init__.py | ddorian/snips-nlu | 0934d386bb138ebb34764446416856cfac664e65 | [
"Apache-2.0"
] | 1 | 2021-01-01T15:03:22.000Z | 2021-01-01T15:03:22.000Z | snips_nlu/intent_parser/__init__.py | ddorian/snips-nlu | 0934d386bb138ebb34764446416856cfac664e65 | [
"Apache-2.0"
] | null | null | null | snips_nlu/intent_parser/__init__.py | ddorian/snips-nlu | 0934d386bb138ebb34764446416856cfac664e65 | [
"Apache-2.0"
] | null | null | null | from .deterministic_intent_parser import DeterministicIntentParser
from .intent_parser import IntentParser
from .probabilistic_intent_parser import ProbabilisticIntentParser
| 43.5 | 66 | 0.913793 | 17 | 174 | 9.058824 | 0.529412 | 0.233766 | 0.350649 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.068966 | 174 | 3 | 67 | 58 | 0.950617 | 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 | 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 | 7 |
d5dc9f1961c5ec20a1560dafb284b8f428b625df | 2,451 | py | Python | REDSI_1160929_1161573/boost_1_67_0/tools/build/test/toolset-mock/src/ar.py | Wultyc/ISEP_1718_2A2S_REDSI_TrabalhoGrupo | eb0f7ef64e188fe871f47c2ef9cdef36d8a66bc8 | [
"MIT"
] | null | null | null | REDSI_1160929_1161573/boost_1_67_0/tools/build/test/toolset-mock/src/ar.py | Wultyc/ISEP_1718_2A2S_REDSI_TrabalhoGrupo | eb0f7ef64e188fe871f47c2ef9cdef36d8a66bc8 | [
"MIT"
] | null | null | null | REDSI_1160929_1161573/boost_1_67_0/tools/build/test/toolset-mock/src/ar.py | Wultyc/ISEP_1718_2A2S_REDSI_TrabalhoGrupo | eb0f7ef64e188fe871f47c2ef9cdef36d8a66bc8 | [
"MIT"
] | null | null | null | #!/usr/bin/python
#
# Copyright 2017-2018 Steven Watanabe
#
# Distributed under the Boost Software License, Version 1.0.
# (See accompanying file LICENSE_1_0.txt or copy at
# http://www.boost.org/LICENSE_1_0.txt)
from MockProgram import *
command('ar', 'rc', output_file('bin/gcc-gnu-4.8.3/debug/link-static/libl1.a'), input_file('bin/gcc-gnu-4.8.3/debug/link-static/lib.o'))
command('ar', 'rc', output_file('bin/gcc-gnu-4.8.3/debug/link-static/runtime-link-static/libl1.a'), input_file('bin/gcc-gnu-4.8.3/debug/link-static/runtime-link-static/lib.o'))
command('ar', 'rc', output_file('bin/gcc-darwin-4.2.1/debug/link-static/target-os-darwin/libl1.a'), input_file('bin/gcc-darwin-4.2.1/debug/link-static/target-os-darwin/lib.o'))
command('ar', 'rc', output_file('bin/gcc-darwin-4.2.1/debug/link-static/runtime-link-static/target-os-darwin/libl1.a'), input_file('bin/gcc-darwin-4.2.1/debug/link-static/runtime-link-static/target-os-darwin/lib.o'))
command('ar', 'rc', output_file('bin/clang-darwin-3.9.0/debug/link-static/target-os-darwin/libl1.a'), input_file('bin/clang-darwin-3.9.0/debug/link-static/target-os-darwin/lib.o'))
command('ar', 'rc', output_file('bin/clang-darwin-3.9.0/debug/link-static/runtime-link-static/target-os-darwin/libl1.a'), input_file('bin/clang-darwin-3.9.0/debug/link-static/runtime-link-static/target-os-darwin/lib.o'))
command('ar', 'rc', output_file('bin/intel-darwin-10.2/debug/link-static/target-os-darwin/libl1.a'), input_file('bin/intel-darwin-10.2/debug/link-static/target-os-darwin/lib.o'))
command('ar', 'rc', output_file('bin/intel-darwin-10.2/debug/link-static/runtime-link-static/target-os-darwin/libl1.a'), input_file('bin/intel-darwin-10.2/debug/link-static/runtime-link-static/target-os-darwin/lib.o'))
command('ar', 'rc', output_file('bin/clang-linux-3.9.0/debug/link-static/libl1.a'), input_file('bin/clang-linux-3.9.0/debug/link-static/lib.o'))
command('ar', 'rc', output_file('bin/clang-linux-3.9.0/debug/link-static/runtime-link-static/libl1.a'), input_file('bin/clang-linux-3.9.0/debug/link-static/runtime-link-static/lib.o'))
command('ar', 'rcu', output_file('bin/clang-vxworks-4.0.1/debug/link-static/libl1.a'), input_file('bin/clang-vxworks-4.0.1/debug/link-static/lib.o'))
command('ar', 'rcu', output_file('bin/clang-vxworks-4.0.1/debug/link-static/runtime-link-static/libl1.a'), input_file('bin/clang-vxworks-4.0.1/debug/link-static/runtime-link-static/lib.o'))
main()
| 98.04 | 221 | 0.736026 | 455 | 2,451 | 3.903297 | 0.131868 | 0.202703 | 0.202703 | 0.101351 | 0.897523 | 0.897523 | 0.897523 | 0.897523 | 0.89527 | 0.89527 | 0 | 0.041809 | 0.043656 | 2,451 | 24 | 222 | 102.125 | 0.71587 | 0.081191 | 0 | 0 | 0 | 1.714286 | 0.715894 | 0.693381 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.071429 | 0 | 0.071429 | 0 | 0 | 0 | 0 | null | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 13 |
913a57374bcbbca6812c1e67f6459bdd75f4d92d | 141 | py | Python | octopod/ensemble/__init__.py | nathancooperjones/octopod | 1ab17f98405390a73f7995b781d292f44eb3f7e4 | [
"BSD-3-Clause"
] | null | null | null | octopod/ensemble/__init__.py | nathancooperjones/octopod | 1ab17f98405390a73f7995b781d292f44eb3f7e4 | [
"BSD-3-Clause"
] | null | null | null | octopod/ensemble/__init__.py | nathancooperjones/octopod | 1ab17f98405390a73f7995b781d292f44eb3f7e4 | [
"BSD-3-Clause"
] | null | null | null | from octopod.ensemble.dataset import OctopodEnsembleDataset
from octopod.ensemble.models import BertResnetEnsembleForMultiTaskClassification
| 47 | 80 | 0.914894 | 12 | 141 | 10.75 | 0.666667 | 0.170543 | 0.294574 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.056738 | 141 | 2 | 81 | 70.5 | 0.969925 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 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 | 7 |
9140eb40295d7cbffb359d8d80a7cbb0fba57714 | 592 | py | Python | src/testcase/GN_Y201S/input_case/GN_Y201S_Timer_Fish.py | maiyajj/AutoTest_script-Appium_Connect | f9c2c42c281a9e2f984acb4a72dda0694b053f22 | [
"Apache-2.0"
] | 28 | 2017-11-10T00:19:16.000Z | 2022-02-19T16:42:05.000Z | src/testcase/GN_Y201S/input_case/GN_Y201S_Timer_Fish.py | maiyajj/AutoTest_script-Appium_Connect | f9c2c42c281a9e2f984acb4a72dda0694b053f22 | [
"Apache-2.0"
] | null | null | null | src/testcase/GN_Y201S/input_case/GN_Y201S_Timer_Fish.py | maiyajj/AutoTest_script-Appium_Connect | f9c2c42c281a9e2f984acb4a72dda0694b053f22 | [
"Apache-2.0"
] | 23 | 2017-08-22T06:12:19.000Z | 2021-09-18T05:45:41.000Z | # coding=utf-8
try:
from src.testcase.GN_Y201S.case.GN_Y201S_TIMER_FISH.GN_Y201S_TIMER_FISH_001 import *
from src.testcase.GN_Y201S.case.GN_Y201S_TIMER_FISH.GN_Y201S_TIMER_FISH_002 import *
from src.testcase.GN_Y201S.case.GN_Y201S_TIMER_FISH.GN_Y201S_TIMER_FISH_003 import *
from src.testcase.GN_Y201S.case.GN_Y201S_TIMER_FISH.GN_Y201S_TIMER_FISH_004 import *
from src.testcase.GN_Y201S.case.GN_Y201S_TIMER_FISH.GN_Y201S_TIMER_FISH_005 import *
from src.testcase.GN_Y201S.case.GN_Y201S_TIMER_FISH.GN_Y201S_TIMER_FISH_006 import *
except ImportError as e:
print(e)
| 53.818182 | 88 | 0.826014 | 106 | 592 | 4.160377 | 0.226415 | 0.285714 | 0.326531 | 0.435374 | 0.857143 | 0.857143 | 0.857143 | 0.857143 | 0.857143 | 0.857143 | 0 | 0.137218 | 0.101351 | 592 | 10 | 89 | 59.2 | 0.691729 | 0.02027 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.777778 | 0 | 0.777778 | 0.111111 | 0 | 0 | 0 | null | 1 | 1 | 1 | 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 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 11 |
9142b3d3e4a0025c2df4a86c23210b73c4708ff2 | 8,183 | py | Python | test/testsTriggersExpressions/testLowerThan.py | mouton5000/DiscreteEventApplicationEditor | 4a4272fd9b0a7f3f228fee1e9e7b351e4a21cd33 | [
"MIT"
] | null | null | null | test/testsTriggersExpressions/testLowerThan.py | mouton5000/DiscreteEventApplicationEditor | 4a4272fd9b0a7f3f228fee1e9e7b351e4a21cd33 | [
"MIT"
] | null | null | null | test/testsTriggersExpressions/testLowerThan.py | mouton5000/DiscreteEventApplicationEditor | 4a4272fd9b0a7f3f228fee1e9e7b351e4a21cd33 | [
"MIT"
] | null | null | null | __author__ = 'mouton'
from unittest import TestCase
from triggerExpressions import Evaluation, LowerThan
from arithmeticExpressions import ALitteral, UndefinedLitteral, Division
from database import Variable
from test.testsTriggersExpressions import simpleTests
from math import pi, sqrt
class TestLowerOrEqualsThan(TestCase):
@classmethod
def setUpClass(cls):
import grammar.grammars
grammar.grammars.compileGrammars()
def setUp(self):
self.eval1 = Evaluation()
self.eval2 = Evaluation()
self.eval2[Variable('X')] = 1
self.eval2[Variable('T')] = 'abc'
self.eval2[Variable('Z')] = 12.0
def test_eval_true_not_same_integer_integer_with_empty_previous_evaluation(self):
expr1 = ALitteral(1)
expr2 = ALitteral(2)
self.eval_lower_than(self.eval1, expr1, expr2)
def test_eval_true_not_same_float_float_with_empty_previous_evaluation(self):
expr1 = ALitteral(sqrt(2))
expr2 = ALitteral(pi)
self.eval_lower_than(self.eval1, expr1, expr2)
def test_eval_true_not_same_string_string_with_empty_previous_evaluation(self):
expr1 = ALitteral('abc')
expr2 = ALitteral('def')
self.eval_lower_than(self.eval1, expr1, expr2)
def test_eval_true_not_same_integer_float_with_empty_previous_evaluation(self):
expr1 = ALitteral(1)
expr2 = ALitteral(2.0)
self.eval_lower_than(self.eval1, expr1, expr2)
def test_eval_true_not_same_float_integer_with_empty_previous_evaluation(self):
expr1 = ALitteral(1.0)
expr2 = ALitteral(2)
self.eval_lower_than(self.eval1, expr1, expr2)
def test_eval_true_not_same_integer_string_with_empty_previous_evaluation(self):
expr1 = ALitteral(1)
expr2 = ALitteral('abc')
self.eval_lower_than(self.eval1, expr1, expr2)
def test_eval_true_not_same_float_string_with_empty_previous_evaluation(self):
expr1 = ALitteral(1.0)
expr2 = ALitteral('abc')
self.eval_lower_than(self.eval1, expr1, expr2)
def eval_lower_than(self, previousEvaluation, expr1, expr2):
token = None
trig = LowerThan(expr1, expr2)
simpleTests.test_evaluation(self, trig, previousEvaluation, token, previousEvaluation)
def test_eval_false_not_same_integer_integer_with_empty_previous_evaluation(self):
expr1 = ALitteral(2)
expr2 = ALitteral(1)
self.eval_not_lower_than(self.eval1, expr1, expr2)
def test_eval_false_not_same_float_float_with_empty_previous_evaluation(self):
expr1 = ALitteral(pi)
expr2 = ALitteral(sqrt(2))
self.eval_not_lower_than(self.eval1, expr1, expr2)
def test_eval_false_not_same_string_string_with_empty_previous_evaluation(self):
expr1 = ALitteral('def')
expr2 = ALitteral('abc')
self.eval_not_lower_than(self.eval1, expr1, expr2)
def test_eval_false_not_same_float_integer_with_empty_previous_evaluation(self):
expr1 = ALitteral(2)
expr2 = ALitteral(1.0)
self.eval_not_lower_than(self.eval1, expr1, expr2)
def test_eval_false_not_same_integer_float_with_empty_previous_evaluation(self):
expr1 = ALitteral(2.0)
expr2 = ALitteral(1)
self.eval_not_lower_than(self.eval1, expr1, expr2)
def test_eval_false_not_same_integer_string_with_empty_previous_evaluation(self):
expr1 = ALitteral('abc')
expr2 = ALitteral(1)
self.eval_not_lower_than(self.eval1, expr1, expr2)
def test_eval_false_not_same_float_string_with_empty_previous_evaluation(self):
expr1 = ALitteral('abc')
expr2 = ALitteral(1.0)
self.eval_not_lower_than(self.eval1, expr1, expr2)
def test_eval_false_same_integer_integer_with_empty_previous_evaluation(self):
expr1 = ALitteral(1)
expr2 = ALitteral(1)
self.eval_not_lower_than(self.eval1, expr1, expr2)
def test_eval_false_same_integer_float_with_empty_previous_evaluation(self):
expr1 = ALitteral(1)
expr2 = ALitteral(1.0)
self.eval_not_lower_than(self.eval1, expr1, expr2)
def test_eval_false_same_float_integer_with_empty_previous_evaluation(self):
expr1 = ALitteral(1.0)
expr2 = ALitteral(1)
self.eval_not_lower_than(self.eval1, expr1, expr2)
def test_eval_false_same_float_float_with_empty_previous_evaluation(self):
expr1 = ALitteral(pi)
expr2 = ALitteral(pi)
self.eval_not_lower_than(self.eval1, expr1, expr2)
def test_eval_false_same_string_string_with_empty_previous_evaluation(self):
expr1 = ALitteral('abc')
expr2 = ALitteral('abc')
self.eval_not_lower_than(self.eval1, expr1, expr2)
def test_eval_ValueError_integer(self):
expr1 = ALitteral(Variable('Y'))
expr2 = ALitteral(1)
self.eval_not_lower_than(self.eval2, expr1, expr2)
def test_eval_ValueError_float(self):
expr1 = ALitteral(Variable('Y'))
expr2 = ALitteral(12.0)
self.eval_not_lower_than(self.eval2, expr1, expr2)
def test_eval_ValueError_string(self):
expr1 = ALitteral(Variable('Y'))
expr2 = ALitteral('abc')
self.eval_not_lower_than(self.eval2, expr1, expr2)
def test_eval_integer_ValueError(self):
expr1 = ALitteral(1)
expr2 = ALitteral(Variable('Y'))
self.eval_not_lower_than(self.eval2, expr1, expr2)
def test_eval_float_ValueError(self):
expr1 = ALitteral(12.0)
expr2 = ALitteral(Variable('Y'))
self.eval_not_lower_than(self.eval2, expr1, expr2)
def test_eval_string_ValueError(self):
expr1 = ALitteral('abc')
expr2 = ALitteral(Variable('Y'))
self.eval_not_lower_than(self.eval2, expr1, expr2)
def test_eval_TypeError_integer(self):
expr1 = UndefinedLitteral()
expr2 = ALitteral(1)
self.eval_not_lower_than(self.eval2, expr1, expr2)
def test_eval_TypeError_float(self):
expr1 = UndefinedLitteral()
expr2 = ALitteral(12.0)
self.eval_not_lower_than(self.eval2, expr1, expr2)
def test_eval_TypeError_string(self):
expr1 = UndefinedLitteral()
expr2 = ALitteral('abc')
self.eval_not_lower_than(self.eval2, expr1, expr2)
def test_eval_integer_TypeError(self):
expr1 = ALitteral(1)
expr2 = UndefinedLitteral()
self.eval_not_lower_than(self.eval2, expr1, expr2)
def test_eval_float_TypeError(self):
expr1 = ALitteral(12.0)
expr2 = UndefinedLitteral()
self.eval_not_lower_than(self.eval2, expr1, expr2)
def test_eval_string_TypeError(self):
expr1 = ALitteral('abc')
expr2 = UndefinedLitteral()
self.eval_not_lower_than(self.eval2, expr1, expr2)
def test_eval_ArithmeticError_integer(self):
expr1 = Division(ALitteral(1), ALitteral(0))
expr2 = ALitteral(1)
self.eval_not_lower_than(self.eval2, expr1, expr2)
def test_eval_ArithmeticError_float(self):
expr1 = Division(ALitteral(1), ALitteral(0))
expr2 = ALitteral(12.0)
self.eval_not_lower_than(self.eval2, expr1, expr2)
def test_eval_ArithmeticError_string(self):
expr1 = Division(ALitteral(1), ALitteral(0))
expr2 = ALitteral('abc')
self.eval_not_lower_than(self.eval2, expr1, expr2)
def test_eval_integer_ArithmeticError(self):
expr1 = ALitteral(1)
expr2 = Division(ALitteral(1), ALitteral(0))
self.eval_not_lower_than(self.eval2, expr1, expr2)
def test_eval_float_ArithmeticError(self):
expr1 = ALitteral(12.0)
expr2 = Division(ALitteral(1), ALitteral(0))
self.eval_not_lower_than(self.eval2, expr1, expr2)
def test_eval_string_ArithmeticError(self):
expr1 = ALitteral('abc')
expr2 = Division(ALitteral(1), ALitteral(0))
self.eval_not_lower_than(self.eval2, expr1, expr2)
def eval_not_lower_than(self, previousEvaluation, expr1, expr2):
token = None
trig = LowerThan(expr1, expr2)
simpleTests.test_evaluation(self, trig, previousEvaluation, token) | 37.027149 | 94 | 0.699377 | 1,045 | 8,183 | 5.137799 | 0.062201 | 0.076364 | 0.094431 | 0.110821 | 0.876513 | 0.842429 | 0.821382 | 0.800522 | 0.800335 | 0.779847 | 0 | 0.041699 | 0.208725 | 8,183 | 221 | 95 | 37.027149 | 0.78749 | 0 | 0 | 0.626437 | 0 | 0 | 0.008065 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.235632 | false | 0 | 0.04023 | 0 | 0.281609 | 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 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
e6bd96b1d6e66f2eb10b7c386845e1c2213c6764 | 3,018 | py | Python | tienda/models.py | FreyderUrbano/mypets | 8dffc425979ca2b5b97eaa004afb8b057e3a2a43 | [
"MIT"
] | null | null | null | tienda/models.py | FreyderUrbano/mypets | 8dffc425979ca2b5b97eaa004afb8b057e3a2a43 | [
"MIT"
] | null | null | null | tienda/models.py | FreyderUrbano/mypets | 8dffc425979ca2b5b97eaa004afb8b057e3a2a43 | [
"MIT"
] | null | null | null | from django.db import models
from django.db.models.fields import BooleanField, DateTimeField
# Create your models here.
class user(models.Model):
id = models.IntegerField.primary_key = True
first_name = models.CharField(max_length=200)
last_name = models.CharField(max_length=128)
id_identification_type = models.IntegerField
id_city = models.IntegerField
email = models.CharField(max_length = 200)
password = models.CharField(max_length = 200)
status = models.BooleanField
created_at = models.DateTimeField
updated_at = models.DateTimeField
deleted_at = models.DateTimeField
class Session(models.Model):
id = models.IntegerField.primary_key = True
id_user = models.IntegerField
ip = models.CharField(max_length = 200)
status = models.BooleanField
created_at = models.DateTimeField
updated_at = models.DateTimeField
deleted_at = models.DateTimeField
class Identification_type(models.Model):
id = models.IntegerField(20).primary_key = True
type = models.CharField(max_length = 150)
abrow = models.CharField(max_length = 4)
created_at = models.DateTimeField
updated_at = models.DateTimeField
deleted_at = models.DateTimeField
class City(models.Model):
id = models.IntegerField
code = models.CharField(max_length = 10)
name = models.CharField(max_length = 150)
abrev = models.CharField(max_length = 4)
id_country = models.IntegerField
status = models.BooleanField
created_at = models.DateTimeField
updated_at = models.DateTimeField
deleted_at = models.DateTimeField
class Country(models.Model):
id = models.IntegerField
code = models.CharField(max_length = 10)
name = models.CharField(max_length = 150)
abrev = models.CharField(max_length = 4)
status = models.BooleanField
created_at = models.DateTimeField
updated_at = models.DateTimeField
deleted_at = models.DateTimeField
class Pet(models.Model):
id = models.IntegerField
code = models.CharField(max_length = 10)
name = models.CharField(max_length = 150)
status = models.BooleanField
id_user = models.IntegerField
id_type = models.IntegerField
id_race = models.IntegerField
created_at = models.DateTimeField
updated_at = models.DateTimeField
deleted_at = models.DateTimeField
class Type(models.Model):
id = models.IntegerField
code = models.CharField(max_length = 100)
name = models.CharField(max_length = 150)
abrev = models.CharField(max_length = 4)
status = models.BooleanField
created_at = models.DateTimeField
updated_at = models.DateTimeField
deleted_at = models.DateTimeField
class Race(models.Model):
id = models.IntegerField
code = models.CharField(max_length = 10)
name = models.CharField(max_length = 150)
abrev = models.CharField(max_length = 4)
status = models.BooleanField
created_at = models.DateTimeField
updated_at = models.DateTimeField
deleted_at = models.DateTimeField
| 33.533333 | 63 | 0.735586 | 358 | 3,018 | 6.036313 | 0.139665 | 0.088848 | 0.233225 | 0.233225 | 0.80981 | 0.745951 | 0.727904 | 0.727904 | 0.686256 | 0.686256 | 0 | 0.020698 | 0.183565 | 3,018 | 89 | 64 | 33.910112 | 0.856331 | 0.007952 | 0 | 0.688312 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0.012987 | 0.025974 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 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 | 8 |
e6ea9b18dc3e09d9a61a777c01b85f087e409424 | 37,561 | py | Python | Lib/site-packages/PySide/examples/painting/svgviewer/svgviewer_rc.py | heylenz/python27 | bee49fa9d65b8ab7d591146a5b6cd47aeb41d940 | [
"bzip2-1.0.6",
"MIT"
] | null | null | null | Lib/site-packages/PySide/examples/painting/svgviewer/svgviewer_rc.py | heylenz/python27 | bee49fa9d65b8ab7d591146a5b6cd47aeb41d940 | [
"bzip2-1.0.6",
"MIT"
] | null | null | null | Lib/site-packages/PySide/examples/painting/svgviewer/svgviewer_rc.py | heylenz/python27 | bee49fa9d65b8ab7d591146a5b6cd47aeb41d940 | [
"bzip2-1.0.6",
"MIT"
] | null | null | null | # Resource object code
#
# Created: Tue Jan 10 14:08:21 2006
# by: The Resource Compiler for PyQt (Qt v4.1.0)
#
# WARNING! All changes made in this file will be lost!
from PySide import QtCore
qt_resource_data = "\
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qt_resource_name = "\
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qt_resource_struct = "\
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def qInitResources():
QtCore.qRegisterResourceData(0x01, qt_resource_struct, qt_resource_name, qt_resource_data)
def qCleanupResources():
QtCore.qUnregisterResourceData(0x01, qt_resource_struct, qt_resource_name, qt_resource_data)
qInitResources()
| 62.706177 | 96 | 0.727217 | 9,043 | 37,561 | 3.018578 | 0.014155 | 0.116496 | 0.127596 | 0.142433 | 0.946038 | 0.939884 | 0.928124 | 0.914936 | 0.898011 | 0.884383 | 0 | 0.424673 | 0.017438 | 37,561 | 598 | 97 | 62.811037 | 0.314962 | 0.004233 | 0 | 0.353846 | 0 | 0.953846 | 0 | 0 | 0 | 1 | 0.000214 | 0 | 0 | 1 | 0.003419 | false | 0 | 0.001709 | 0 | 0.005128 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
fc20e8cfdbbf0ef492dc5af10e7cc079f120522c | 104 | py | Python | scripts/common/parse_spark_logs/stage.py | SobhanOmranian/spark-dca | cd0c5ddbe433a1772442456549e37bf8838f75e3 | [
"BSD-3-Clause-Open-MPI",
"PSF-2.0",
"Apache-2.0",
"BSD-2-Clause",
"MIT",
"MIT-0",
"BSD-3-Clause-No-Nuclear-License-2014",
"BSD-3-Clause-Clear",
"PostgreSQL",
"BSD-3-Clause"
] | 2 | 2020-02-07T12:09:14.000Z | 2020-03-09T21:31:40.000Z | scripts/common/parse_spark_logs/stage.py | SobhanOmranian/spark-dca | cd0c5ddbe433a1772442456549e37bf8838f75e3 | [
"BSD-3-Clause-Open-MPI",
"PSF-2.0",
"Apache-2.0",
"BSD-2-Clause",
"MIT",
"MIT-0",
"BSD-3-Clause-No-Nuclear-License-2014",
"BSD-3-Clause-Clear",
"PostgreSQL",
"BSD-3-Clause"
] | 2 | 2020-05-15T21:45:35.000Z | 2021-01-21T00:21:21.000Z | scripts/common/parse_spark_logs/stage.py | SobhanOmranian/spark-dca | cd0c5ddbe433a1772442456549e37bf8838f75e3 | [
"BSD-3-Clause-Open-MPI",
"PSF-2.0",
"Apache-2.0",
"BSD-2-Clause",
"MIT",
"MIT-0",
"BSD-3-Clause-No-Nuclear-License-2014",
"BSD-3-Clause-Clear",
"PostgreSQL",
"BSD-3-Clause"
] | 3 | 2019-09-18T22:05:11.000Z | 2021-07-10T16:29:09.000Z | #!/usr/bin/env python3
import api as api
def getAllStages(appId):
return api.getAllStages(appId)
| 14.857143 | 35 | 0.730769 | 15 | 104 | 5.066667 | 0.733333 | 0.447368 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.011494 | 0.163462 | 104 | 6 | 36 | 17.333333 | 0.862069 | 0.201923 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0.333333 | 0.333333 | 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 | 1 | 1 | 1 | 0 | 0 | 7 |
fca91537c55aa5b4089b764a6e280d297ef007d7 | 6,869 | py | Python | indy_node/test/upgrade/test_migration_util.py | Rob-S/indy-node | 0aefbda62c5a7412d7e03b2fb9795c500ea67e9f | [
"Apache-2.0"
] | 627 | 2017-07-06T12:38:08.000Z | 2022-03-30T13:18:43.000Z | indy_node/test/upgrade/test_migration_util.py | Rob-S/indy-node | 0aefbda62c5a7412d7e03b2fb9795c500ea67e9f | [
"Apache-2.0"
] | 580 | 2017-06-29T17:59:57.000Z | 2022-03-29T21:37:52.000Z | indy_node/test/upgrade/test_migration_util.py | Rob-S/indy-node | 0aefbda62c5a7412d7e03b2fb9795c500ea67e9f | [
"Apache-2.0"
] | 704 | 2017-06-29T17:45:34.000Z | 2022-03-30T07:08:58.000Z | from indy_node.utils.migration_tool import _get_relevant_migrations
def comparator_relevant_migration_script(
migration_scripts,
current_version,
new_version,
expected_migration_scripts):
assert expected_migration_scripts == _get_relevant_migrations(
migration_scripts, current_version, new_version)
def test_relevant_migration_script_positive():
comparator_relevant_migration_script(['1_0_96_to_1_0_97'],
'1.0.95',
'1.0.98',
['1_0_96_to_1_0_97'])
comparator_relevant_migration_script(['1_0_96_to_1_0_97'],
'1.0.96',
'1.0.97',
['1_0_96_to_1_0_97'])
comparator_relevant_migration_script(['1_0_96_to_1_0_97'],
'0.0.56',
'1.0.97',
['1_0_96_to_1_0_97'])
comparator_relevant_migration_script(['1_0_96_to_1_0_97'],
'0.0.56',
'2.0.97',
['1_0_96_to_1_0_97'])
comparator_relevant_migration_script(['1_0_96_to_1_0_97'],
'1.0.96',
'2.0.1',
['1_0_96_to_1_0_97'])
comparator_relevant_migration_script(['1_0_96_to_1_0_97'],
'1.0.86',
'1.0.100',
['1_0_96_to_1_0_97'])
def test_relevant_migration_script_current_version_higher():
comparator_relevant_migration_script(['1_0_96_to_1_0_97'],
'1.0.97',
'1.0.98',
[])
comparator_relevant_migration_script(['1_0_96_to_1_0_97'],
'1.0.97',
'1.0.97',
[])
comparator_relevant_migration_script(['1_0_96_to_1_0_97'],
'1.0.100',
'1.0.102',
[])
def test_relevant_migration_script_downgrade():
comparator_relevant_migration_script(['1_0_96_to_1_0_97'],
'1.0.97',
'1.0.96',
[])
comparator_relevant_migration_script(['1_0_97_to_1_0_96'],
'1.0.97',
'1.0.96',
['1_0_97_to_1_0_96'])
comparator_relevant_migration_script(['1_0_96_to_1_0_97', '1_0_97_to_1_0_96'],
'1.0.84',
'1.0.102',
['1_0_96_to_1_0_97'])
comparator_relevant_migration_script(['1_0_96_to_1_0_97', '1_0_97_to_1_0_96'],
'1.0.102',
'1.0.84',
['1_0_97_to_1_0_96'])
def test_relevant_migration_script_new_version_lower():
comparator_relevant_migration_script(['1_0_96_to_1_0_97'],
'1.0.87',
'1.0.96',
[])
comparator_relevant_migration_script(['1_0_96_to_1_0_97'],
'0.0.57',
'0.0.197',
[])
def test_relevant_migration_script_multiple_scripts():
comparator_relevant_migration_script(['1_0_96_to_1_0_97', '1_0_100_to_1_0_102'],
'1.0.87',
'1.0.97',
['1_0_96_to_1_0_97'])
comparator_relevant_migration_script(
[
'1_0_96_to_1_0_97', '1_0_100_to_1_0_102'], '1.0.87', '1.0.102', [
'1_0_96_to_1_0_97', '1_0_100_to_1_0_102'])
comparator_relevant_migration_script(
[
'1_0_96_to_1_0_97', '1_0_100_to_1_0_102'], '1.0.96', '1.0.102', [
'1_0_96_to_1_0_97', '1_0_100_to_1_0_102'])
comparator_relevant_migration_script(['1_0_96_to_1_0_97', '1_0_100_to_1_0_102'],
'1.0.98',
'1.0.102',
['1_0_100_to_1_0_102'])
comparator_relevant_migration_script(
['1_0_96_to_1_0_97', '1_0_100_to_1_0_102'], '1.0.103', '1.0.104', [])
comparator_relevant_migration_script(
['1_0_96_to_1_0_97', '1_0_100_to_1_0_102'], '1.0.103', '1.0.104', [])
comparator_relevant_migration_script(
[
'1_0_96_to_1_0_97', '1_0_96_to_1_0_102'], '1.0.87', '1.0.102', [
'1_0_96_to_1_0_97', '1_0_96_to_1_0_102'])
comparator_relevant_migration_script(
[
'1_0_96_to_1_0_97', '1_0_96_to_1_0_102', '1_0_98_to_1_0_102'], '1.0.87', '1.0.102', [
'1_0_96_to_1_0_97', '1_0_96_to_1_0_102', '1_0_98_to_1_0_102'])
comparator_relevant_migration_script(['1_0_96_to_1_0_97',
'1_0_97_to_1_0_102',
'1_0_102_to_1_0_104'],
'1.0.87',
'1.0.104',
['1_0_96_to_1_0_97',
'1_0_97_to_1_0_102',
'1_0_102_to_1_0_104'])
def test_relevant_migration_reinstall():
comparator_relevant_migration_script(['1_0_96_to_1_0_97'],
'1.0.96',
'1.0.96',
[])
comparator_relevant_migration_script(['1_0_96_to_1_0_97'],
'1.0.97',
'1.0.97',
[])
comparator_relevant_migration_script(['1_0_96_to_1_0_97'],
'1.0.100',
'1.0.100',
[])
comparator_relevant_migration_script(['1_0_96_to_1_0_97'],
'1.0.95',
'1.0.95',
[])
| 48.716312 | 97 | 0.414908 | 805 | 6,869 | 2.898137 | 0.054658 | 0.152593 | 0.10973 | 0.113159 | 0.875268 | 0.7994 | 0.765109 | 0.754822 | 0.751822 | 0.72739 | 0 | 0.223331 | 0.489591 | 6,869 | 140 | 98 | 49.064286 | 0.442099 | 0 | 0 | 0.714286 | 0 | 0 | 0.204833 | 0 | 0 | 0 | 0 | 0 | 0.007937 | 1 | 0.055556 | false | 0 | 0.007937 | 0 | 0.063492 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
5d7fe33b9a6ed06d291a6a0dcae5cc65d88cbf5d | 5,747 | py | Python | regreg/smooth/tests/test_mglm.py | vishalbelsare/regreg | d1b62cc43cdd83331f2b0817b0ae099d5ef97966 | [
"BSD-2-Clause"
] | 11 | 2016-02-25T01:53:03.000Z | 2020-11-30T00:59:46.000Z | regreg/smooth/tests/test_mglm.py | vishalbelsare/regreg | d1b62cc43cdd83331f2b0817b0ae099d5ef97966 | [
"BSD-2-Clause"
] | 21 | 2015-09-17T19:18:09.000Z | 2021-04-28T06:15:02.000Z | regreg/smooth/tests/test_mglm.py | vishalbelsare/regreg | d1b62cc43cdd83331f2b0817b0ae099d5ef97966 | [
"BSD-2-Clause"
] | 8 | 2016-03-24T00:03:03.000Z | 2019-08-25T23:40:42.000Z | from copy import copy
import nose.tools as nt
import numpy as np
from regreg.smooth import mglm, glm
from ...tests.decorators import set_seed_for_test
@set_seed_for_test()
def test_gaussian_common():
X = np.random.standard_normal((10,5))
Y = np.random.standard_normal((10,3))
for case_weights in [np.ones(10), None]:
sat = mglm.stacked_common_loglike.gaussian(Y.T)
L = mglm.mglm(X, sat, case_weights=case_weights)
L.smooth_objective(np.zeros(L.shape), 'both')
L_sub = L.subsample(np.arange(5))
Xs = X[np.arange(5)]
Ys = Y[np.arange(5)]
beta = np.ones(L.shape)
value_sub = 0.5 * np.linalg.norm(Ys - Xs.dot(beta))**2
grad_sub = Xs.T.dot(Xs.dot(beta) - Ys)
f, g = L_sub.smooth_objective(beta, 'both')
np.testing.assert_allclose(value_sub, f)
np.testing.assert_allclose(grad_sub, g)
np.testing.assert_allclose(L.gradient(np.zeros(L.shape)),
-X.T.dot(Y))
Lcp = copy(L)
L.objective(np.zeros(L.shape))
L.latexify()
L.saturated_loss.data
L.data
# check that subsample is getting correct answer
Xsub = X[np.arange(5)]
Ysub = Y[np.arange(5)]
loss_sub = mglm.stacked_common_loglike.gaussian(Ysub.T)
beta = np.ones(L.shape)
if case_weights is not None:
Lsub2 = mglm.mglm(Xsub, loss_sub, case_weights=case_weights[np.arange(5)])
Lsub3 = mglm.mglm(Xsub, loss_sub, case_weights=case_weights[np.arange(5)])
else:
Lsub2 = mglm.mglm(Xsub, loss_sub)
Lsub3 = mglm.mglm(Xsub, loss_sub)
Lsub3.coef *= 2.
f2, g2 = Lsub2.smooth_objective(beta, 'both')
f3, g3 = Lsub3.smooth_objective(beta, 'both')
np.testing.assert_allclose(f3, 2*f2)
np.testing.assert_allclose(g3, 2*g2)
beta = np.ones(L.shape)
np.testing.assert_allclose(L_sub.gradient(beta),
Lsub2.gradient(beta))
@set_seed_for_test()
def test_multinomial():
"""
Test that multinomial regression with two categories is the same as logistic regression
"""
n = 500
p = 10
J = 4
X = np.random.standard_normal(n*p).reshape((n,p))
counts = np.random.randint(0,10,n*J).reshape((n,J)) + 2
for case_weights in [np.ones(n), None]:
sat = mglm.multinomial_loglike(counts.shape, counts)
L = mglm.mglm(X,
sat,
case_weights=case_weights)
L.smooth_objective(np.zeros(L.shape), 'both')
L_sub = L.subsample(np.arange(100))
np.testing.assert_allclose(L.gradient(np.zeros(L.shape)),
-X.T.dot(counts - counts.mean(1)[:,None]))
Lcp = copy(L)
L.objective(np.zeros(L.shape))
L.latexify()
L.saturated_loss.data
L.data
# check that subsample is getting correct answer
Xsub = X[np.arange(100)]
counts_sub = counts[np.arange(100)]
loss_sub = mglm.multinomial_loglike(counts_sub.shape, counts_sub)
beta = np.ones(L.shape)
if case_weights is not None:
Lsub2 = mglm.mglm(Xsub, loss_sub, case_weights=case_weights[np.arange(100)])
Lsub3 = mglm.mglm(Xsub, loss_sub, case_weights=case_weights[np.arange(100)])
else:
Lsub2 = mglm.mglm(Xsub, loss_sub)
Lsub3 = mglm.mglm(Xsub, loss_sub)
Lsub3.coef *= 2.
f2, g2 = Lsub2.smooth_objective(beta, 'both')
f3, g3 = Lsub3.smooth_objective(beta, 'both')
np.testing.assert_allclose(f3, 2*f2)
np.testing.assert_allclose(g3, 2*g2)
beta = np.ones(L.shape)
np.testing.assert_allclose(L_sub.gradient(beta),
Lsub2.gradient(beta))
@set_seed_for_test()
def test_multinomial_baseline():
"""
Test that multinomial regression with two categories is the same as logistic regression
"""
n = 500
p = 10
J = 4
X = np.random.standard_normal(n*p).reshape((n,p))
counts = np.random.randint(0,10,n*J).reshape((n,J)) + 2
for case_weights in [np.ones(n), None]:
sat = mglm.multinomial_baseline_loglike((n, J-1), counts)
L = mglm.mglm(X,
sat,
case_weights=case_weights)
L.smooth_objective(np.zeros(L.shape), 'both')
L_sub = L.subsample(np.arange(100))
np.testing.assert_allclose(L.gradient(np.zeros(L.shape)),
-X.T.dot(counts - counts.mean(1)[:,None])[:,:(J-1)])
Lcp = copy(L)
L.objective(np.zeros(L.shape))
L.latexify()
L.saturated_loss.data
L.data
# check that subsample is getting correct answer
Xsub = X[np.arange(100)]
counts_sub = counts[np.arange(100)]
loss_sub = mglm.multinomial_baseline_loglike((counts_sub.shape[0], J-1), counts_sub)
beta = np.ones(L.shape)
if case_weights is not None:
Lsub2 = mglm.mglm(Xsub, loss_sub, case_weights=case_weights[np.arange(100)])
Lsub3 = mglm.mglm(Xsub, loss_sub, case_weights=case_weights[np.arange(100)])
else:
Lsub2 = mglm.mglm(Xsub, loss_sub)
Lsub3 = mglm.mglm(Xsub, loss_sub)
Lsub3.coef *= 2.
f2, g2 = Lsub2.smooth_objective(beta, 'both')
f3, g3 = Lsub3.smooth_objective(beta, 'both')
np.testing.assert_allclose(f3, 2*f2)
np.testing.assert_allclose(g3, 2*g2)
beta = np.ones(L.shape)
np.testing.assert_allclose(L_sub.gradient(beta),
Lsub2.gradient(beta))
| 29.025253 | 92 | 0.584653 | 804 | 5,747 | 4.037313 | 0.131841 | 0.081331 | 0.064695 | 0.099199 | 0.875539 | 0.835798 | 0.822551 | 0.822551 | 0.80838 | 0.80838 | 0 | 0.031975 | 0.287106 | 5,747 | 197 | 93 | 29.172589 | 0.760312 | 0.055159 | 0 | 0.736 | 0 | 0 | 0.007416 | 0 | 0 | 0 | 0 | 0 | 0.112 | 1 | 0.024 | false | 0 | 0.04 | 0 | 0.064 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 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 | 7 |
5dafa29a87511dc4ac730f9a8987e1c8853849b7 | 5,356 | py | Python | research/models/BERTModels.py | takshakpdesai/research | 499d0c2db4cca807b296af579e592596f2a9a199 | [
"Apache-2.0"
] | 11 | 2019-09-16T22:27:59.000Z | 2022-01-05T22:16:31.000Z | research/models/BERTModels.py | takshakpdesai/research | 499d0c2db4cca807b296af579e592596f2a9a199 | [
"Apache-2.0"
] | 1 | 2020-04-10T09:06:33.000Z | 2020-04-10T17:00:33.000Z | research/models/BERTModels.py | takshakpdesai/research | 499d0c2db4cca807b296af579e592596f2a9a199 | [
"Apache-2.0"
] | 4 | 2021-04-10T17:21:35.000Z | 2021-07-16T18:30:40.000Z | import torch
import torch.nn as nn
from research.libnlp.SemanticRelation import SemanticRelation
def custom_loss_fct(loss_fct, predicted, labels, odd_one=SemanticRelation.NO_DIRECTION):
true, pred = list(), list()
for i, label in enumerate(labels):
if label != odd_one:
true.append(label)
pred.append(predicted[i])
try:
return loss_fct(torch.stack(pred), torch.stack(true))
except RuntimeError: # if either list is empty
return 0
class BERTForRelationClassification(nn.Module):
def __init__(self, lm, num_relations, logger, num_directions=2):
super(BERTForRelationClassification, self).__init__()
self.language_model = lm
self.relation_classifier = nn.Linear(770, num_relations) # TODO: get from config, for small or large
self.direction_classifier = nn.Linear(770, num_directions)
self.num_relations = num_relations
self.num_directions = num_directions
self.logger = logger
def forward(self, input_ids, input_segments, input_masks, position_vector1 = None, position_vector2 = None, relation_labels=None, direction_labels=None):
pooled_output = self.language_model(input_ids, token_type_ids = input_segments, attention_mask = input_masks)[0]
if position_vector1 is not None:
pooled_output = torch.cat((pooled_output, position_vector1.float().unsqueeze(-1), position_vector2.float().unsqueeze(-1)), -1)
pooled_output = pooled_output[:,-1]
predicted_relations = self.relation_classifier(pooled_output)
predicted_directions = self.direction_classifier(pooled_output)
if relation_labels is not None:
loss_fct = nn.CrossEntropyLoss()
relation_loss = loss_fct(predicted_relations.view(-1, self.num_relations), relation_labels.view(-1))
direction_loss = custom_loss_fct(loss_fct, predicted_directions, direction_labels)
return relation_loss + direction_loss
else:
return predicted_relations, predicted_directions
class BERTForRoleLabeling(nn.Module):
def __init__(self, lm, num_relations, logger):
super(BERTForRoleLabeling, self).__init__()
self.language_model = lm
self.relation_classifier = nn.Linear(769, num_relations) # TODO: get from config, for small or large
self.num_relations = num_relations
self.logger = logger
def forward(self, input_ids, input_segments, input_masks, position_vector = None, relation_labels=None):
pooled_output = self.language_model(input_ids, token_type_ids = input_segments, attention_mask = input_masks)[0]
if position_vector is not None:
pooled_output = torch.cat((pooled_output, position_vector.float().unsqueeze(-1)), -1)
predicted_relations = self.relation_classifier(pooled_output)
if relation_labels is not None:
loss_fct = nn.CrossEntropyLoss()
relation_loss = loss_fct(predicted_relations.view(-1, self.num_relations), relation_labels.view(-1))
return relation_loss
else:
return predicted_relations
class BERTForMultiTaskRelationClassification(nn.Module):
def __init__(self, lm, num_orig_relations, num_pw_relations, logger, num_directions=2):
super(BERTForMultiTaskRelationClassification, self).__init__()
self.language_model = lm
self.relation_classifier = nn.Linear(770, num_orig_relations) # TODO: get from config, for small or large
self.pw_classifier = nn.Linear(770, num_pw_relations)
self.direction_classifier = nn.Linear(770, num_directions)
self.num_orig_relations = num_orig_relations
self.num_pw_relations = num_pw_relations
self.num_directions = num_directions
self.logger = logger
def forward(self, input_ids, input_segments, input_masks, position_vector1 = None, position_vector2 = None, relation_labels=None, direction_labels=None, flag = False):
pooled_output = self.language_model(input_ids, token_type_ids = input_segments, attention_mask = input_masks)[0]
if position_vector1 is not None:
pooled_output = torch.cat((pooled_output, position_vector1.float().unsqueeze(-1), position_vector2.float().unsqueeze(-1)), -1)
pooled_output = pooled_output[:,-1]
if flag:
predicted_relations = self.pw_classifier(pooled_output)
predicted_directions = None
else:
predicted_relations = self.relation_classifier(pooled_output)
predicted_directions = self.direction_classifier(pooled_output)
if relation_labels is not None:
loss_fct = nn.CrossEntropyLoss()
if flag:
relation_loss = loss_fct(predicted_relations.view(-1, self.num_pw_relations), relation_labels.view(-1))
return relation_loss
else:
relation_loss = loss_fct(predicted_relations.view(-1, self.num_orig_relations), relation_labels.view(-1))
direction_loss = custom_loss_fct(loss_fct, predicted_directions, direction_labels)
return relation_loss + direction_loss
else:
return predicted_relations, predicted_directions | 55.216495 | 172 | 0.694175 | 631 | 5,356 | 5.570523 | 0.1458 | 0.064865 | 0.031863 | 0.029872 | 0.803698 | 0.778378 | 0.744808 | 0.73798 | 0.729161 | 0.686486 | 0 | 0.012527 | 0.224981 | 5,356 | 97 | 173 | 55.216495 | 0.834257 | 0.027819 | 0 | 0.563218 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.010309 | 0 | 1 | 0.08046 | false | 0 | 0.034483 | 0 | 0.252874 | 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 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
5de024b124c15382f9ccb81dbd3e6fad9db8c193 | 27,389 | py | Python | ptsemseg/models/unet.py | htt1994/recurrent-unet-drive | d260e39541a8224deebfd1bd47e7fefb1666cf05 | [
"MIT"
] | 3 | 2021-04-06T19:49:03.000Z | 2021-09-09T14:16:44.000Z | ptsemseg/models/unet.py | htt1994/recurrent-unet-drive | d260e39541a8224deebfd1bd47e7fefb1666cf05 | [
"MIT"
] | null | null | null | ptsemseg/models/unet.py | htt1994/recurrent-unet-drive | d260e39541a8224deebfd1bd47e7fefb1666cf05 | [
"MIT"
] | null | null | null | import torch.nn as nn
import torch.nn.functional as F
from ptsemseg.models.utils import *
class UnetDecoder(nn.Module):
def __init__(self, filters, feature_scale, feature_level, out_channels, is_deconv,
is_norm=True, is_groupnorm=True, **kwargs):
super(UnetDecoder, self).__init__()
self.filters = filters
self.feature_scale = feature_scale
self.feature_level = feature_level
self.is_norm = is_norm
self.is_groupnorm = is_groupnorm
self.is_deconv = is_deconv
self.out_channels = out_channels
# upsampling
self.up_concat = []
for i in range(feature_level):
b = feature_level - i - 1
self.up_concat.append(unetUp(filters[b+2], filters[b+1], self.is_deconv))
self.add_module('up_concat{}'.format(b), self.up_concat[i])
# final conv (without any concat)
self.final = nn.Conv2d(filters[1], out_channels, 1)
self.add_module('final', self.final)
# if out_channels % 8 == 0:
# group = int(out_channels/8)
# else:
# group = out_channels
#
# # print('out_channels is {}, group no is {}'.format(out_channels, group))
#
# print('Group No is ', group)
# self.final_gn = nn.GroupNorm(group, out_channels) \
# if self.is_groupnorm else nn.BatchNorm2d(out_channels)
# # self.final_gn = nn.GroupNorm(min(8, out_channels), out_channels) \
# # if self.is_groupnorm else nn.BatchNorm2d(out_channels)
# self.add_module('final_gn', self.final_gn)
def forward(self, inputs):
"""
take the inputs from the decoder.
:param inputs: conv_outputs, center from decoder.
:return:
"""
conv_outputs = inputs
up_output = [center]
for i in range(self.feature_level):
b = self.feature_level - i - 1
x = self.up_concat[i](conv_outputs[b], up_output[i])
up_output.append(x)
final = self.final(up_output[-1])
# print(final.device)
# print(self.final_gn.weight.device)
# print(self.final_gn.bias.device)
# final_gn = self.final_gn(final)
# print(final_gn.device)
# return final_gn
return final
@property
def display_filter(self):
return [self.filters[self.feature_level - i - 1] for i in range(self.feature_level)]
def __repr__(self):
a = super(UnetDecoder, self).__repr__()
return "UNet-Decoder: \n" \
"\t feature_level: {} \n \t aux_head_input: {}\n \t final_output: {} \n".format(
self.feature_level,
self.display_filter,
self.out_channels)
# return f"UNet-Decoder: \n" \
# f"\t feature_level: {self.feature_level} \n" \
# f"\t aux_head_input: {self.display_filter}\n" \
# f"\t final_output: {self.out_channels} \n"
# # f"\t model_arch: {a} \n" \
class UnetEncoder(nn.Module):
def __init__(self, in_channels, filters, feature_scale, feature_level,
is_norm=True, is_groupnorm=True,**kwargs):
super(UnetEncoder, self).__init__(**kwargs)
self.is_norm = is_norm
self.is_groupnorm = is_groupnorm
self.in_channels = in_channels
self.filters = filters
self.feature_scale = feature_scale
self.feature_level = feature_level
self.convs = []
self.poolings = []
assert self.in_channels == filters[0], \
"UnetEncoder filter 1 {}must match the input_channels {} ".format(filters[0], in_channels)
# f"UnetEncoder filter 1 {filters[0]}must match the input_channels {in_channels} "
for i in range(feature_level):
self.convs.append(unetConv2(filters[i], filters[i+1], self.is_norm, self.is_groupnorm))
self.add_module('conv_{}'.format(i), self.convs[i])
self.poolings.append(nn.MaxPool2d(kernel_size=2))
self.add_module('maxpool_{}'.format(i), self.poolings[i])
def forward(self, inputs):
conv_outputs = []
pools_outputs = [inputs, ]
for i in range(self.feature_level):
# Conv take the output of previous pool.
# Pool takes the output of previous conv.
x = self.convs[i](pools_outputs[i])
p = self.poolings[i](x)
conv_outputs.append(x)
pools_outputs.append(p)
return conv_outputs, pools_outputs
def __repr__(self):
"""
Override this representation.
:return:
"""
a = super(UnetEncoder, self).__repr__()
return "UNet-Encoder: \n" \
"\t feature_level: {} \n \t aux_head_output: {}\n".format(
self.feature_level,
self.filters[1: self.feature_level])
# f"\t model_arch: {a} \n" \
# return f"UNet-Encoder: \n" \
# f"\t feature_level: {self.feature_level} \n" \
# f"\t aux_head_output: {self.filters[1: self.feature_level]}\n"
# # f"\t model_arch: {a} \n" \
class unet(nn.Module):
def __init__(
self,
feature_scale=4,
n_classes=19,
is_deconv=True,
in_channels=3,
is_batchnorm=True,
):
super(unet, self).__init__()
self.is_deconv = is_deconv
self.in_channels = in_channels
self.is_batchnorm = is_batchnorm
self.feature_scale = feature_scale
filters = [32, 64, 128, 256, 512] # [8, 16, 32, 64, 128] [64, 128, 256, 512, 1024]
filters = [int(x / self.feature_scale) for x in filters]
# downsampling
self.conv1 = unetConv2(self.in_channels, filters[0], self.is_batchnorm)
self.maxpool1 = nn.MaxPool2d(kernel_size=2)
self.conv2 = unetConv2(filters[0], filters[1], self.is_batchnorm)
self.maxpool2 = nn.MaxPool2d(kernel_size=2)
self.conv3 = unetConv2(filters[1], filters[2], self.is_batchnorm)
self.maxpool3 = nn.MaxPool2d(kernel_size=2)
self.conv4 = unetConv2(filters[2], filters[3], self.is_batchnorm)
self.maxpool4 = nn.MaxPool2d(kernel_size=2)
self.center = unetConv2(filters[3], filters[4], self.is_batchnorm)
# upsampling
self.up_concat4 = unetUp(filters[4], filters[3], self.is_deconv)
self.up_concat3 = unetUp(filters[3], filters[2], self.is_deconv)
self.up_concat2 = unetUp(filters[2], filters[1], self.is_deconv)
self.up_concat1 = unetUp(filters[1], filters[0], self.is_deconv)
# final conv (without any concat)
self.final = nn.Conv2d(filters[0], n_classes-1, 1)
# self.final_gn = nn.GroupNorm(min(8, n_classes), n_classes)
def forward(self, inputs):
conv1 = self.conv1(inputs)
maxpool1 = self.maxpool1(conv1)
conv2 = self.conv2(maxpool1)
maxpool2 = self.maxpool2(conv2)
conv3 = self.conv3(maxpool2)
maxpool3 = self.maxpool3(conv3)
conv4 = self.conv4(maxpool3)
maxpool4 = self.maxpool4(conv4)
center = self.center(maxpool4)
up4 = self.up_concat4(conv4, center)
up3 = self.up_concat3(conv3, up4)
up2 = self.up_concat2(conv2, up3)
up1 = self.up_concat1(conv1, up2)
final = self.final(up1)
# final = self.final_gn(final)
final = F.sigmoid(final)
return final
class unet_expand(nn.Module):
"""
Only Expand Unetconv2d
"""
def __init__(
self,
feature_scale=1,
n_classes=19,
is_deconv=True,
in_channels=3,
is_batchnorm=True,
):
super(unet_expand, self).__init__()
self.is_deconv = is_deconv
self.in_channels = in_channels
self.is_batchnorm = is_batchnorm
self.feature_scale = feature_scale
filters = [32, 64, 128, 256, 512] # [8, 16, 32, 64, 128] [64, 128, 256, 512, 1024]
filters = [int(x / self.feature_scale) for x in filters]
# downsampling
self.conv1 = unetConv2_expand(self.in_channels, filters[0], self.is_batchnorm)
self.maxpool1 = nn.MaxPool2d(kernel_size=2)
self.conv2 = unetConv2_expand(filters[0], filters[1], self.is_batchnorm)
self.maxpool2 = nn.MaxPool2d(kernel_size=2)
self.conv3 = unetConv2_expand(filters[1], filters[2], self.is_batchnorm)
self.maxpool3 = nn.MaxPool2d(kernel_size=2)
self.conv4 = unetConv2_expand(filters[2], filters[3], self.is_batchnorm)
self.maxpool4 = nn.MaxPool2d(kernel_size=2)
self.center = unetConv2_expand(filters[3], filters[4], self.is_batchnorm)
# upsampling
self.up_concat4 = unetUp(filters[4], filters[3], self.is_deconv)
self.up_concat3 = unetUp(filters[3], filters[2], self.is_deconv)
self.up_concat2 = unetUp(filters[2], filters[1], self.is_deconv)
self.up_concat1 = unetUp(filters[1], filters[0], self.is_deconv)
# final conv (without any concat)
self.final = nn.Conv2d(filters[0], n_classes, 1)
# self.final_gn = nn.GroupNorm(min(8, n_classes), n_classes)
def forward(self, inputs):
conv1 = self.conv1(inputs)
maxpool1 = self.maxpool1(conv1)
conv2 = self.conv2(maxpool1)
maxpool2 = self.maxpool2(conv2)
conv3 = self.conv3(maxpool2)
maxpool3 = self.maxpool3(conv3)
conv4 = self.conv4(maxpool3)
maxpool4 = self.maxpool4(conv4)
center = self.center(maxpool4)
up4 = self.up_concat4(conv4, center)
up3 = self.up_concat3(conv3, up4)
up2 = self.up_concat2(conv2, up3)
up1 = self.up_concat1(conv1, up2)
final = self.final(up1)
# final = self.final_gn(final)
return final
class unet_expand_all(nn.Module):
"""
Expand Unetconv2d and UnetUP
"""
def __init__(
self,
feature_scale=1,
n_classes=19,
is_deconv=True,
in_channels=3,
is_batchnorm=True,
):
super(unet_expand_all, self).__init__()
self.is_deconv = is_deconv
self.in_channels = in_channels
self.is_batchnorm = is_batchnorm
self.feature_scale = feature_scale
filters = [32, 64, 128, 256, 512] # [8, 16, 32, 64, 128] [64, 128, 256, 512, 1024]
filters = [int(x / self.feature_scale) for x in filters]
# downsampling
self.conv1 = unetConv2_expand(self.in_channels, filters[0], self.is_batchnorm)
self.maxpool1 = nn.MaxPool2d(kernel_size=2)
self.conv2 = unetConv2_expand(filters[0], filters[1], self.is_batchnorm)
self.maxpool2 = nn.MaxPool2d(kernel_size=2)
self.conv3 = unetConv2_expand(filters[1], filters[2], self.is_batchnorm)
self.maxpool3 = nn.MaxPool2d(kernel_size=2)
self.conv4 = unetConv2_expand(filters[2], filters[3], self.is_batchnorm)
self.maxpool4 = nn.MaxPool2d(kernel_size=2)
self.center = unetConv2_expand(filters[3], filters[4], self.is_batchnorm)
# upsampling
self.up_concat4 = unetUp_expand(filters[4], filters[3], self.is_deconv)
self.up_concat3 = unetUp_expand(filters[3], filters[2], self.is_deconv)
self.up_concat2 = unetUp_expand(filters[2], filters[1], self.is_deconv)
self.up_concat1 = unetUp_expand(filters[1], filters[0], self.is_deconv)
# final conv (without any concat)
self.final = nn.Conv2d(filters[0], n_classes, 1)
# self.final_gn = nn.GroupNorm(min(8, n_classes), n_classes)
def forward(self, inputs):
conv1 = self.conv1(inputs)
maxpool1 = self.maxpool1(conv1)
conv2 = self.conv2(maxpool1)
maxpool2 = self.maxpool2(conv2)
conv3 = self.conv3(maxpool2)
maxpool3 = self.maxpool3(conv3)
conv4 = self.conv4(maxpool3)
maxpool4 = self.maxpool4(conv4)
center = self.center(maxpool4)
up4 = self.up_concat4(conv4, center)
up3 = self.up_concat3(conv3, up4)
up2 = self.up_concat2(conv2, up3)
up1 = self.up_concat1(conv1, up2)
final = self.final(up1)
# final = self.final_gn(final)
return final
class unet_deep_as_dru(nn.Module):
def __init__(
self,
feature_scale=4,
n_classes=21,
is_deconv=True,
in_channels=3,
is_batchnorm=True,
):
super(unet_deep_as_dru, self).__init__()
self.is_deconv = is_deconv
self.in_channels = in_channels
self.is_batchnorm = is_batchnorm
self.feature_scale = feature_scale
filters = [32, 64, 128, 256, 512] # [8, 16, 32, 64, 128] [64, 128, 256, 512, 1024]
filters = [int(x / self.feature_scale) for x in filters]
deep_filters = [128, 192, 128, 128]
# downsampling
self.conv1 = unetConv2(self.in_channels, filters[0], self.is_batchnorm)
self.maxpool1 = nn.MaxPool2d(kernel_size=2)
self.conv2 = unetConv2(filters[0], filters[1], self.is_batchnorm)
self.maxpool2 = nn.MaxPool2d(kernel_size=2)
self.conv3 = unetConv2(filters[1], filters[2], self.is_batchnorm)
self.maxpool3 = nn.MaxPool2d(kernel_size=2)
self.conv4 = unetConv2(filters[2], filters[3], self.is_batchnorm)
self.maxpool4 = nn.MaxPool2d(kernel_size=2)
self.center = unetConv2(filters[3], filters[4], self.is_batchnorm)
self.center_conv1 = nn.Sequential(
nn.Conv2d(deep_filters[0], deep_filters[1], 3, 1, 1),
nn.GroupNorm(min(8, deep_filters[1]), deep_filters[1]),
nn.ReLU(),
)
self.center_conv2 = nn.Sequential(
nn.Conv2d(deep_filters[1], deep_filters[2], 3, 1, 1),
nn.GroupNorm(min(8, deep_filters[2]), deep_filters[2]),
nn.ReLU(),
)
self.center_conv3 = nn.Sequential(
nn.Conv2d(deep_filters[2], deep_filters[3], 3, 1, 1),
nn.GroupNorm(min(8, deep_filters[3]), deep_filters[3]),
nn.ReLU(),
)
# upsampling
self.up_concat4 = unetUp(filters[4], filters[3], self.is_deconv)
self.up_concat3 = unetUp(filters[3], filters[2], self.is_deconv)
self.up_concat2 = unetUp(filters[2], filters[1], self.is_deconv)
self.up_concat1 = unetUp(filters[1], filters[0], self.is_deconv)
# final conv (without any concat)
self.final = nn.Conv2d(filters[0], n_classes, 1)
# self.final_gn = nn.GroupNorm(min(8, n_classes), n_classes)
def forward(self, inputs):
conv1 = self.conv1(inputs)
maxpool1 = self.maxpool1(conv1)
conv2 = self.conv2(maxpool1)
maxpool2 = self.maxpool2(conv2)
conv3 = self.conv3(maxpool2)
maxpool3 = self.maxpool3(conv3)
conv4 = self.conv4(maxpool3)
maxpool4 = self.maxpool4(conv4)
center = self.center(maxpool4)
center1 = self.center_conv1(center)
center2 = self.center_conv2(center1)
center3 = self.center_conv3(center2)
up4 = self.up_concat4(conv4, center3)
up3 = self.up_concat3(conv3, up4)
up2 = self.up_concat2(conv2, up3)
up1 = self.up_concat1(conv1, up2)
final = self.final(up1)
# final = self.final_gn(final)
return final
class unet_bn(nn.Module):
def __init__(
self,
feature_scale=4,
n_classes=21,
is_deconv=True,
in_channels=3,
is_batchnorm=True,
):
super(unet_bn, self).__init__()
self.is_deconv = is_deconv
self.in_channels = in_channels
self.is_batchnorm = is_batchnorm
self.feature_scale = feature_scale
filters = [32, 64, 128, 256, 512] # [8, 16, 32, 64, 128] [64, 128, 256, 512, 1024]
filters = [int(x / self.feature_scale) for x in filters]
# downsampling
self.conv1 = unetConv2(self.in_channels, filters[0], self.is_batchnorm, is_groupnorm=False)
self.maxpool1 = nn.MaxPool2d(kernel_size=2)
self.conv2 = unetConv2(filters[0], filters[1], self.is_batchnorm, is_groupnorm=False)
self.maxpool2 = nn.MaxPool2d(kernel_size=2)
self.conv3 = unetConv2(filters[1], filters[2], self.is_batchnorm, is_groupnorm=False)
self.maxpool3 = nn.MaxPool2d(kernel_size=2)
self.conv4 = unetConv2(filters[2], filters[3], self.is_batchnorm, is_groupnorm=False)
self.maxpool4 = nn.MaxPool2d(kernel_size=2)
self.center = unetConv2(filters[3], filters[4], self.is_batchnorm, is_groupnorm=False)
# upsampling
self.up_concat4 = unetUp(filters[4], filters[3], self.is_deconv)
self.up_concat3 = unetUp(filters[3], filters[2], self.is_deconv)
self.up_concat2 = unetUp(filters[2], filters[1], self.is_deconv)
self.up_concat1 = unetUp(filters[1], filters[0], self.is_deconv)
# final conv (without any concat)
self.final = nn.Conv2d(filters[0], n_classes, 1)
self.final_gn = nn.BatchNorm2d(n_classes)
# self.final_gn = nn.GroupNorm(min(8, n_classes), n_classes)
def forward(self, inputs):
conv1 = self.conv1(inputs)
maxpool1 = self.maxpool1(conv1)
conv2 = self.conv2(maxpool1)
maxpool2 = self.maxpool2(conv2)
conv3 = self.conv3(maxpool2)
maxpool3 = self.maxpool3(conv3)
conv4 = self.conv4(maxpool3)
maxpool4 = self.maxpool4(conv4)
center = self.center(maxpool4)
up4 = self.up_concat4(conv4, center)
up3 = self.up_concat3(conv3, up4)
up2 = self.up_concat2(conv2, up3)
up1 = self.up_concat1(conv1, up2)
final = self.final(up1)
final = self.final_gn(final)
return final
class GeneralUNet(nn.Module):
"""
Develop for the ConvGRU, those update gates.
About the filters, needs quite a bit tuning?
or just use the structure like other unet.
Quite interesting to see, if start late, what it will look like.
"""
def __init__(
self,
feature_scale=4,
out_channels=4,
is_deconv=True,
in_channels=4,
is_batchnorm=True,
feature_level=4,
filters=None
):
"""
:param feature_scale:
:param out_channels:
:param is_deconv:
:param in_channels:
:param is_batchnorm:
:param feature_level:
:param filters: should be with length shorter than
"""
super(GeneralUNet, self).__init__()
self.is_deconv = is_deconv
self.in_channels = in_channels
self.is_batchnorm = is_batchnorm
self.feature_scale = feature_scale
self.feature_level = feature_level
if filters is None:
filters = [32, 64, 128, 256, 512] # [8, 16, 32, 64, 128] [64, 128, 256, 512, 1024]
filters = [int(x / self.feature_scale) for x in filters]
# assert out_channels == filters[0], 'oh no ! out_channels should be {}'.format(filters[0])
# process this feature maps.
assert feature_level + 1 <= len(filters)
filters = filters[:feature_level + 1]
self.convs = []
self.poolings = []
filters = [self.in_channels, ] + filters
for i in range(feature_level):
self.convs.append(unetConv2(filters[i], filters[i+1], self.is_batchnorm))
self.add_module('conv_{}'.format(i),self.convs[i])
self.poolings.append(nn.MaxPool2d(kernel_size=2))
self.add_module('maxpool_{}'.format(i), self.poolings[i])
self.encoder = nn.ModuleList(self.convs + self.poolings)
self.center = unetConv2(filters[-2], filters[-1], self.is_batchnorm)
self.add_module('center', self.center)
self.up_concat = []
for i in range(feature_level):
b = feature_level - i - 1
self.up_concat.append(unetUp(filters[b+2], filters[b+1], self.is_deconv))
self.add_module('up_concat{}', format(b), self.up_concat[i])
# final conv (without any concat)
self.final = nn.Conv2d(filters[1], out_channels, 1)
self.add_module('final', self.final)
self.final_gn = nn.GroupNorm(min(8, out_channels), out_channels)
self.add_module('final_gn', self.final_gn)
self.decoder = nn.ModuleList(self.up_concat + [self.final, self.final_gn])
def forward(self, inputs):
conv_outputs = []
pools_outputs = [inputs,]
for i in range(self.feature_level):
# Conv take the output of previous pool.
# Pool takes the output of previous conv.
x = self.convs[i](pools_outputs[i])
p = self.poolings[i](x)
conv_outputs.append(x)
pools_outputs.append(p)
center = self.center(pools_outputs[-1])
up_output = [center]
for i in range(self.feature_level):
b = self.feature_level - i - 1
x = self.up_concat[i](conv_outputs[b], up_output[i])
up_output.append(x)
final = self.final(up_output[-1])
final_gn = self.final_gn(final)
return final_gn
# return up1
class GeneralUNet_v2(nn.Module):
"""
Develop for the ConvGRU, those update gates.
About the filters, needs quite a bit tuning?
or just use the structure like other unet.
Quite interesting to see, if start late, what it will look like.
"""
def __init__(
self,
feature_scale=1,
out_channels=4,
is_deconv=True,
in_channels=4,
is_norm=True,
is_groupnorm=True,
feature_level=4,
filters=None
):
"""
:param feature_scale:
:param out_channels:
:param is_deconv:
:param in_channels:
:param is_norm:
:param feature_level:
:param filters: level + 1.
[:feature_level] is for encoder/decoder
[feature_level] is for center node.
"""
super(GeneralUNet_v2, self).__init__()
self.is_deconv = is_deconv
self.in_channels = in_channels
self.out_channels = out_channels
self.is_norm = is_norm
self.is_groupnorm = is_groupnorm
self.feature_scale = feature_scale
self.feature_level = feature_level
if filters is None:
filters = [32, 64, 128, 256, 512] # [8, 16, 32, 64, 128] [64, 128, 256, 512, 1024]
filters = [int(x / self.feature_scale) for x in filters]
# assert out_channels == filters[0], 'oh no ! out_channels should be {}'.format(filters[0])
# process this feature maps.
if feature_level + 1 <= len(filters):
pass
elif feature_level == len(filters):
filters += [filters[-1]*2]
# filters = [filters[0]//2] + filters
else:
raise ValueError("GeneralUNet_v2: filter {} must match the following requirement:".format(filters) +
"length {} >= feature_level + 1 = {}. ".format(len(filters), feature_level + 1) +
"The following is for Encoder and decoder {} ".format(filters[:feature_level]) +
"and this is for center {}".format(filters[feature_level]))
filters = filters[:feature_level + 1]
filters = [self.in_channels, ] + filters
self.filters = filters
self.encoder = UnetEncoder(
in_channels=in_channels,
filters=filters,
feature_scale=feature_scale,
feature_level=feature_level,
is_norm=is_norm,
is_groupnorm=is_groupnorm
)
self.decoder = UnetDecoder(
filters=filters,
feature_scale=feature_scale,
feature_level=feature_level,
out_channels=out_channels,
is_deconv=is_deconv,
is_norm=is_norm,
is_groupnorm=is_groupnorm
)
self.center = unetConv2(filters[-2], filters[-1], is_norm, is_groupnorm)
def forward(self, inputs):
# print(inputs.device)
# print(self.encoder)
conv_outputs, pools_outputs = self.encoder(inputs)
# print(len(pools_outputs))
# print(pools_outputs[-1].device)
# print(self.center)
center = self.center(pools_outputs[-1])
# print(center.device)
# print(self.decoder)
final = self.decoder([conv_outputs, center])
# print('final', final.device)
return final
def __repr__(self):
res = "GeneralUNet_v2: \n" + \
"\t input_dim: {} \n".format(self.in_channels) + \
"\t output_dim: {} \n".format(self.out_channels) + \
"details:\n" + "\t Encoder filters: {} \n".format(self.filters[1:self.feature_level+1]) + \
"\t Center filters: {} \n".format(self.filters[-2:]) + \
"\t Decoder filters: {} \n".format(self.decoder.display_filter)
# res = f"GeneralUNet_v2: \n" \
# f"\t input_dim: {self.in_channels} \n" \
# f"\t output_dim: {self.out_channels} \n" \
# f"details:\n" \
# f"\t Encoder filters: {self.filters[1:self.feature_level+1]} \n" \
# f"\t Center filters: {self.filters[-2:]} \n" \
# f"\t Decoder filters: {self.decoder.display_filter} \n"
return res
class UNetGN(GeneralUNet_v2):
def __init__(self, n_classes, **kwargs):
filters = [32, 64, 128, 256, 512]
# filters = [4 * i for i in [32, 64, 128, 256, 512]]
super(UNetGN, self).__init__(
feature_scale=1,
is_norm=True,
is_groupnorm=True,
filters=filters,
in_channels=3,
out_channels=n_classes,
)
class UNetBN(GeneralUNet_v2):
def __init__(self, n_classes, **kwargs):
filters = [32, 64, 128, 256, 512]
super(UNetBN, self).__init__(
feature_scale=4,
is_norm=True,
is_groupnorm=False,
filters=filters,
in_channels=3,
out_channels=n_classes,
)
class UNetGN_R(GeneralUNet_v2):
def __init__(self, n_classes, **kwargs):
filters = [32, 64, 128, 256, 512]
super(UNetGN_R, self).__init__(
feature_scale=4,
is_norm=True,
is_groupnorm=True,
filters=filters,
in_channels=3,
out_channels=n_classes,
)
class UNetBN_R(GeneralUNet_v2):
def __init__(self, n_classes, **kwargs):
filters = [32, 64, 128, 256, 512]
super(UNetBN_R, self).__init__(
feature_scale=4,
is_norm=True,
is_groupnorm=False,
filters=filters,
in_channels=3,
out_channels=n_classes,
)
if __name__ == '__main__':
# Test general Unet
unet_test = GeneralUNet(feature_scale=4, out_channels=2, is_deconv=True,
in_channels=256, is_batchnorm=True, feature_level=1)
unet_2 = GeneralUNet_v2(feature_scale=4, out_channels=2, is_deconv=True,
in_channels=256, is_norm=True, feature_level=1)
inp = torch.ones([2, 256, 5, 5])
out = unet_test(inp)
out2 = unet_2(inp)
out.size() | 35.249678 | 112 | 0.598854 | 3,478 | 27,389 | 4.50575 | 0.061242 | 0.02795 | 0.031587 | 0.029481 | 0.827005 | 0.778763 | 0.75528 | 0.729883 | 0.719929 | 0.709527 | 0 | 0.048602 | 0.283325 | 27,389 | 777 | 113 | 35.249678 | 0.749758 | 0.155683 | 0 | 0.744554 | 0 | 0 | 0.026636 | 0 | 0 | 0 | 0 | 0 | 0.00396 | 1 | 0.051485 | false | 0.00198 | 0.005941 | 0.00198 | 0.108911 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 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 | 7 |
5d0761a2fb337c6ce53ac69b38f4d87dd3bcec87 | 685,700 | py | Python | gradio/validation_data.py | Chetan8000/gradio | 0af5b96e3011e63f98c2d5a2213814616e34ac33 | [
"Apache-2.0"
] | null | null | null | gradio/validation_data.py | Chetan8000/gradio | 0af5b96e3011e63f98c2d5a2213814616e34ac33 | [
"Apache-2.0"
] | null | null | null | gradio/validation_data.py | Chetan8000/gradio | 0af5b96e3011e63f98c2d5a2213814616e34ac33 | [
"Apache-2.0"
] | null | null | null | ENGLISH_TEXTS = [
"hello",
"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Vestibulum est tellus, euismod eu sollicitudin sed, rutrum eget eros. Cras elementum quam sed orci luctus, id commodo quam varius. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Quisque vitae mi tortor. Pellentesque porttitor pretium elit. Donec faucibus tellus enim, eget auctor quam commodo sed. Etiam commodo neque dui, sit amet dictum mauris molestie non.",
"Two wrongs don't make a right",
"Call ME ISHMAEL!!!!!!!!!!!",
"Test text test text",
]
BASE64_COLOR_IMAGES = [
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",
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",
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",
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",
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",
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",
]
| 40,335.294118 | 471,809 | 0.969157 | 20,223 | 685,700 | 32.861 | 0.956337 | 0.000081 | 0.000078 | 0.000114 | 0.000951 | 0.000859 | 0.000822 | 0.000822 | 0 | 0 | 0 | 0.157025 | 0.000198 | 685,700 | 16 | 471,810 | 42,856.25 | 0.812319 | 0 | 0 | 0 | 0 | 0.466667 | 0.999803 | 0.999067 | 0 | 1 | 0.000004 | 0 | 0 | 1 | 0 | false | 0.066667 | 0 | 0 | 0 | 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 | 1 | 1 | 1 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 8 |
5d0a5b5c7340c4952b57ec2219978604742be34a | 300 | py | Python | webscrapingapi_scrapy_sdk/__init__.py | WebScrapingAPI/scrapy-sdk | 66061d11ea0e5e9939002a878d3210551d3ae108 | [
"MIT"
] | null | null | null | webscrapingapi_scrapy_sdk/__init__.py | WebScrapingAPI/scrapy-sdk | 66061d11ea0e5e9939002a878d3210551d3ae108 | [
"MIT"
] | null | null | null | webscrapingapi_scrapy_sdk/__init__.py | WebScrapingAPI/scrapy-sdk | 66061d11ea0e5e9939002a878d3210551d3ae108 | [
"MIT"
] | null | null | null | __all__ = ['WebScrapingApiRequest', 'WebScrapingApiMiddleware', 'WebScrapingApiSpider']
from webscrapingapi_scrapy_sdk.request import WebScrapingApiRequest
from webscrapingapi_scrapy_sdk.middleware import WebScrapingApiMiddleware
from webscrapingapi_scrapy_sdk.spider import WebScrapingApiSpider | 60 | 88 | 0.883333 | 25 | 300 | 10.2 | 0.48 | 0.211765 | 0.282353 | 0.317647 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.07 | 300 | 5 | 89 | 60 | 0.913978 | 0 | 0 | 0 | 0 | 0 | 0.218855 | 0.151515 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.75 | 0 | 0.75 | 0 | 1 | 0 | 1 | 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 | 8 |
5d18f8d67958e8f5c1f41657fe4e707e761c87a8 | 11,085 | py | Python | ivy/functional/backends/mxnet/elementwise.py | thatguuyG/ivy | 09447a9670d440a309b62cfb468c1036e3a4f5ed | [
"Apache-2.0"
] | null | null | null | ivy/functional/backends/mxnet/elementwise.py | thatguuyG/ivy | 09447a9670d440a309b62cfb468c1036e3a4f5ed | [
"Apache-2.0"
] | null | null | null | ivy/functional/backends/mxnet/elementwise.py | thatguuyG/ivy | 09447a9670d440a309b62cfb468c1036e3a4f5ed | [
"Apache-2.0"
] | null | null | null | # global
import mxnet as mx
import math
from typing import Optional
# local
import ivy
from ivy.functional.backends.mxnet import (
_handle_flat_arrays_in_out,
_scalar_or_flat_array_to_scalar,
)
@_handle_flat_arrays_in_out
def add(
x1: mx.ndarray.ndarray.NDArray,
x2: mx.ndarray.ndarray.NDArray,
out: Optional[mx.ndarray.ndarray.NDArray] = None,
) -> mx.ndarray.ndarray.NDArray:
ret = mx.nd.add(x1, x2)
if ivy.exists(out):
return ivy.inplace_update(out, ret)
return ret
@_handle_flat_arrays_in_out
def bitwise_and(
x1: mx.ndarray.ndarray.NDArray,
x2: mx.ndarray.ndarray.NDArray,
out: Optional[mx.ndarray.ndarray.NDArray] = None,
) -> mx.nd.ndarray.NDArray:
ret = mx.numpy.bitwise_and(x1, x2)
if ivy.exists(out):
return ivy.inplace_update(out, ret)
return ret
@_handle_flat_arrays_in_out
def ceil(
x: mx.ndarray.ndarray.NDArray, out: Optional[mx.ndarray.ndarray.NDArray] = None
) -> mx.ndarray.ndarray.NDArray:
ret = mx.nd.ceil(x)
if ivy.exists(out):
return ivy.inplace_update(out, ret)
return ret
@_handle_flat_arrays_in_out
def floor(
x: mx.ndarray.ndarray.NDArray, out: Optional[mx.ndarray.ndarray.NDArray] = None
) -> mx.ndarray.ndarray.NDArray:
ret = mx.nd.floor(x)
if ivy.exists(out):
return ivy.inplace_update(out, ret)
return ret
@_handle_flat_arrays_in_out
def divide(
x1: mx.ndarray.ndarray.NDArray,
x2: mx.ndarray.ndarray.NDArray,
out: Optional[mx.ndarray.ndarray.NDArray] = None,
) -> mx.ndarray.ndarray.NDArray:
ret = mx.nd.divide(x1, x2)
if ivy.exists(out):
return ivy.inplace_update(out, ret)
return ret
@_handle_flat_arrays_in_out
def greater(
x1: mx.ndarray.ndarray.NDArray,
x2: mx.ndarray.ndarray.NDArray,
out: Optional[mx.ndarray.ndarray.NDArray] = None,
) -> mx.ndarray.ndarray.NDArray:
ret = mx.nd.greater(x1, x2)
if ivy.exists(out):
return ivy.inplace_update(out, ret)
return ret
@_handle_flat_arrays_in_out
def greater_equal(
x1: mx.ndarray.ndarray.NDArray,
x2: mx.ndarray.ndarray.NDArray,
out: Optional[mx.ndarray.ndarray.NDArray] = None,
) -> mx.ndarray.ndarray.NDArray:
ret = mx.nd.greater_equal(x1, x2)
if ivy.exists(out):
return ivy.inplace_update(out, ret)
return ret
@_handle_flat_arrays_in_out
def isfinite(
x: mx.ndarray.ndarray.NDArray, out: Optional[mx.ndarray.ndarray.NDArray] = None
) -> mx.ndarray.ndarray.NDArray:
# ToDo: remove float32 conversion once int8 and uint8 work correctly. Currently 0 returns 0 for these types.
ret = mx.nd.contrib.isfinite(x.astype("float32")).astype("bool")
if ivy.exists(out):
return ivy.inplace_update(out, ret)
return ret
@_handle_flat_arrays_in_out
def isinf(
x: mx.ndarray.ndarray.NDArray, out: Optional[mx.ndarray.ndarray.NDArray] = None
) -> mx.ndarray.ndarray.NDArray:
ret = mx.nd.contrib.isinf(x.astype("float32")).astype("bool")
if ivy.exists(out):
return ivy.inplace_update(out, ret)
return ret
def sqrt(
x: mx.ndarray.ndarray.NDArray, out: Optional[mx.ndarray.ndarray.NDArray] = None
) -> mx.ndarray.ndarray.NDArray:
if isinstance(x, float):
ret = math.sqrt(x)
else:
ret = mx.nd.sqrt(x)
if ivy.exists(out):
return ivy.inplace_update(out, ret)
return ret
@_handle_flat_arrays_in_out
def isnan(
x: mx.ndarray.ndarray.NDArray, out: Optional[mx.ndarray.ndarray.NDArray] = None
) -> mx.ndarray.ndarray.NDArray:
ret = mx.nd.contrib.isnan(x).astype("bool")
if ivy.exists(out):
return ivy.inplace_update(out, ret)
return ret
@_handle_flat_arrays_in_out
def less(
x1: mx.ndarray.ndarray.NDArray,
x2: mx.ndarray.ndarray.NDArray,
out: Optional[mx.ndarray.ndarray.NDArray] = None,
) -> mx.ndarray.ndarray.NDArray:
ret = mx.nd.lesser(x1, x2).astype("bool")
if ivy.exists(out):
return ivy.inplace_update(out, ret)
return ret
@_handle_flat_arrays_in_out
def logical_xor(
x1: mx.ndarray.ndarray.NDArray,
x2: mx.ndarray.ndarray.NDArray,
dtype: ["bool"],
out: Optional[mx.ndarray.ndarray.NDArray] = None,
) -> mx.ndarray.ndarray.NDArray:
ret = mx.nd.logical_xor(x1, x2, dtype).astype("bool")
if ivy.exists(out):
return ivy.inplace_update(out, ret)
return ret
@_handle_flat_arrays_in_out
def logical_not(
x: mx.ndarray.ndarray.NDArray, out: Optional[mx.ndarray.ndarray.NDArray] = None
) -> mx.ndarray.ndarray.NDArray:
ret = mx.nd.logical_not(x)
if ivy.exists(out):
return ivy.inplace_update(out, ret)
return ret
@_handle_flat_arrays_in_out
def acos(
x: mx.ndarray.ndarray.NDArray, out: Optional[mx.ndarray.ndarray.NDArray] = None
) -> mx.ndarray.ndarray.NDArray:
if isinstance(x, float):
ret = math.acos(x)
else:
ret = mx.nd.arccos(x)
if ivy.exists(out):
return ivy.inplace_update(out, ret)
return ret
@_handle_flat_arrays_in_out
def logical_and(
x1: mx.ndarray.ndarray.NDArray,
x2: mx.ndarray.ndarray.NDArray,
dtype: ["bool"],
out: Optional[mx.ndarray.ndarray.NDArray] = None,
) -> mx.ndarray.ndarray.NDArray:
ret = mx.nd.logical_and(x1, x2, dtype).astype("bool")
if ivy.exists(out):
return ivy.inplace_update(out, ret)
return ret
@_handle_flat_arrays_in_out
def logical_or(
x1: mx.ndarray.ndarray.NDArray,
x2: mx.ndarray.ndarray.NDArray,
dtype: ["bool"],
out: Optional[mx.ndarray.ndarray.NDArray] = None,
) -> mx.ndarray.ndarray.NDArray:
ret = mx.nd.logical_or(x1, x2, dtype).astype("bool")
if ivy.exists(out):
return ivy.inplace_update(out, ret)
return ret
@_handle_flat_arrays_in_out
def multiply(
x1: mx.ndarray.ndarray.NDArray,
x2: mx.ndarray.ndarray.NDArray,
out: Optional[mx.ndarray.ndarray.NDArray] = None,
) -> mx.ndarray.ndarray.NDArray:
ret = mx.nd.multiply(x1, x2)
if ivy.exists(out):
return ivy.inplace_update(out, ret)
return ret
@_handle_flat_arrays_in_out
def acosh(
x: mx.ndarray.ndarray.NDArray, out: Optional[mx.ndarray.ndarray.NDArray] = None
) -> mx.ndarray.ndarray.NDArray:
if isinstance(x, float):
ret = math.acosh(x)
else:
ret = mx.nd.arccosh(x)
if ivy.exists(out):
return ivy.inplace_update(out, ret)
return ret
@_handle_flat_arrays_in_out
def sin(
x: mx.ndarray.ndarray.NDArray, out: Optional[mx.ndarray.ndarray.NDArray] = None
) -> mx.ndarray.ndarray.NDArray:
if isinstance(x, float):
ret = math.sin(x)
else:
ret = mx.nd.sin(x)
if ivy.exists(out):
return ivy.inplace_update(out, ret)
return ret
@_handle_flat_arrays_in_out
def negative(
x: mx.ndarray.ndarray.NDArray, out: Optional[mx.ndarray.ndarray.NDArray] = None
) -> mx.ndarray.ndarray.NDArray:
ret = mx.np.negative(x)
if ivy.exists(out):
return ivy.inplace_update(out, ret)
return ret
@_handle_flat_arrays_in_out
def tanh(
x: mx.ndarray.ndarray.NDArray, out: Optional[mx.ndarray.ndarray.NDArray] = None
) -> mx.ndarray.ndarray.NDArray:
if isinstance(x, float):
ret = math.tanh(x)
else:
ret = mx.nd.tanh(x)
if ivy.exists(out):
return ivy.inplace_update(out, ret)
return ret
@_handle_flat_arrays_in_out
def bitwise_or(
x1: mx.ndarray.ndarray.NDArray,
x2: mx.ndarray.ndarray.NDArray,
out: Optional[mx.ndarray.ndarray.NDArray] = None,
) -> mx.nd.ndarray.NDArray:
ret = mx.numpy.bitwise_or(x1, x2)
if ivy.exists(out):
return ivy.inplace_update(out, ret)
return ret
@_handle_flat_arrays_in_out
def sinh(
x: mx.ndarray.ndarray.NDArray, out: Optional[mx.ndarray.ndarray.NDArray] = None
) -> mx.ndarray.ndarray.NDArray:
if isinstance(x, float):
ret = math.sinh(x)
else:
ret = mx.nd.sinh(x)
if ivy.exists(out):
return ivy.inplace_update(out, ret)
return ret
@_handle_flat_arrays_in_out
def square(
x: mx.ndarray.ndarray.NDArray, out: Optional[mx.ndarray.ndarray.NDArray] = None
) -> mx.ndarray.ndarray.NDArray:
ret = mx.nd.square(x)
if ivy.exists(out):
return ivy.inplace_update(out, ret)
return ret
@_handle_flat_arrays_in_out
def round(
x: mx.ndarray.ndarray.NDArray, out: Optional[mx.ndarray.ndarray.NDArray] = None
) -> mx.ndarray.ndarray.NDArray:
ret = mx.nd.round(x)
if ivy.exists(out):
return ivy.inplace_update(out, ret)
return ret
@_handle_flat_arrays_in_out
def trunc(
x: mx.ndarray.ndarray.NDArray, out: Optional[mx.ndarray.ndarray.NDArray] = None
) -> mx.nd.ndarray.NDArray:
ret = mx.np.trunc(x)
if ivy.exists(out):
return ivy.inplace_update(out, ret)
return ret
@_handle_flat_arrays_in_out
def subtract(
x1: mx.ndarray.ndarray.NDArray,
x2: mx.ndarray.ndarray.NDArray,
out: Optional[mx.ndarray.ndarray.NDArray] = None,
) -> mx.ndarray.ndarray.NDArray:
ret = mx.nd.subtract(x1, x2)
if ivy.exists(out):
return ivy.inplace_update(out, ret)
return ret
# noinspection PyShadowingBuiltins
@_handle_flat_arrays_in_out
def abs(
x: mx.ndarray.ndarray.NDArray, out: Optional[mx.ndarray.ndarray.NDArray] = None
) -> mx.nd.ndarray.NDArray:
ret = mx.nd.abs(x)
if ivy.exists(out):
return ivy.inplace_update(out, ret)
return ret
def cos(x: mx.ndarray.ndarray.NDArray,
out: Optional[mx.ndarray.ndarray.NDArray] = None)\
-> mx.ndarray.ndarray.NDArray:
if isinstance(x, float):
ret = math.cos(x)
else:
ret = mx.nd.cos(x)
if ivy.exists(out):
return ivy.inplace_update(out, ret)
return ret
def exp(x: mx.ndarray.ndarray.NDArray,
out: Optional[mx.ndarray.ndarray.NDArray] = None)\
-> mx.ndarray.ndarray.NDArray:
if isinstance(x, float):
ret = math.exp(x)
else:
ret = mx.nd.exp(x)
if ivy.exists(out):
return ivy.inplace_update(out, ret)
return ret
tan = lambda x: math.tan(x) if isinstance(x, float) else mx.nd.tan(x)
asin = lambda x: math.asin(x) if isinstance(x, float) else mx.nd.arcsin(x)
atan = lambda x: math.atan(x) if isinstance(x, float) else mx.nd.arctan(x)
atan2 = (
lambda x, y: math.atan2(x, y)
if isinstance(x, float)
else mx.np.arctan2(x.as_np_ndarray(), y.as_np_ndarray()).as_nd_ndarray()
)
cosh = lambda x: math.cosh(x) if isinstance(x, float) else mx.nd.cosh(x)
asinh = lambda x: math.asinh(x) if isinstance(x, float) else mx.nd.arcsinh(x)
atanh = lambda x: math.atanh(x) if isinstance(x, float) else mx.nd.arctanh(x)
log = lambda x: math.log(x) if isinstance(x, float) else mx.nd.log(x)
equal = lambda x1, x2: x1 == x2
equal.__name__ = "equal"
# Extra #
# ------#
minimum = lambda x, y: mx.nd.array(
mx.nd.minimum(
_scalar_or_flat_array_to_scalar(x), _scalar_or_flat_array_to_scalar(y)
)
)
maximum = lambda x, y: mx.nd.array(
mx.nd.maximum(
_scalar_or_flat_array_to_scalar(x), _scalar_or_flat_array_to_scalar(y)
)
)
erf = lambda x: math.erf(x) if isinstance(x, float) else mx.nd.erf(x)
| 27.643392 | 112 | 0.677492 | 1,668 | 11,085 | 4.364508 | 0.068345 | 0.396154 | 0.221978 | 0.319093 | 0.880907 | 0.864011 | 0.850687 | 0.850687 | 0.815247 | 0.815247 | 0 | 0.007242 | 0.190347 | 11,085 | 400 | 113 | 27.7125 | 0.8039 | 0.014975 | 0 | 0.693009 | 0 | 0 | 0.005408 | 0 | 0 | 0 | 0 | 0.0025 | 0 | 1 | 0.094225 | false | 0 | 0.015198 | 0 | 0.297872 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 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 | 9 |
5d271a052bce7345c5ff5a2f2f78d04f63a621e1 | 49 | py | Python | auto_red_test/__init__.py | qjjayy/red_test | ba718630be9e60d72e719cb6b100efec212a1164 | [
"MIT"
] | 2 | 2019-04-16T06:19:05.000Z | 2020-04-07T15:05:15.000Z | auto_red_test/__init__.py | qjjayy/red_test | ba718630be9e60d72e719cb6b100efec212a1164 | [
"MIT"
] | null | null | null | auto_red_test/__init__.py | qjjayy/red_test | ba718630be9e60d72e719cb6b100efec212a1164 | [
"MIT"
] | null | null | null | from generate_test_case import generate_red_test
| 24.5 | 48 | 0.918367 | 8 | 49 | 5.125 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.081633 | 49 | 1 | 49 | 49 | 0.911111 | 0 | 0 | 0 | 1 | 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 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
5d2abc27f5a260ea79422f747b8f36f18171c06e | 130 | py | Python | discord/raw_models.py | kuzaku-developers/disnake | 61cc1ad4c2bafd39726a1447c85f7e469e41af10 | [
"MIT"
] | null | null | null | discord/raw_models.py | kuzaku-developers/disnake | 61cc1ad4c2bafd39726a1447c85f7e469e41af10 | [
"MIT"
] | null | null | null | discord/raw_models.py | kuzaku-developers/disnake | 61cc1ad4c2bafd39726a1447c85f7e469e41af10 | [
"MIT"
] | null | null | null | from disnake.raw_models import *
from disnake.raw_models import __dict__ as __original_dict__
locals().update(__original_dict__)
| 26 | 60 | 0.846154 | 18 | 130 | 5.222222 | 0.555556 | 0.234043 | 0.297872 | 0.425532 | 0.553191 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.092308 | 130 | 4 | 61 | 32.5 | 0.79661 | 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 | 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 | 7 |
5d3501185f7cf26c8cac0524e6bb1e0c3d54216d | 4,373 | py | Python | tests/contrib/aiohttp/test_aiohttp_jinja2.py | discord/dd-trace-py | 3f6bca078e751bf7459fd02b7aff7f96eff0eeb6 | [
"Apache-2.0",
"BSD-3-Clause"
] | null | null | null | tests/contrib/aiohttp/test_aiohttp_jinja2.py | discord/dd-trace-py | 3f6bca078e751bf7459fd02b7aff7f96eff0eeb6 | [
"Apache-2.0",
"BSD-3-Clause"
] | null | null | null | tests/contrib/aiohttp/test_aiohttp_jinja2.py | discord/dd-trace-py | 3f6bca078e751bf7459fd02b7aff7f96eff0eeb6 | [
"Apache-2.0",
"BSD-3-Clause"
] | null | null | null | import sys
import aiohttp_jinja2
import pytest
from ddtrace import Pin
from ddtrace import tracer
from .app.web import set_filesystem_loader
from .app.web import set_package_loader
async def test_template_rendering(untraced_app_tracer, aiohttp_client):
app, tracer = untraced_app_tracer
client = await aiohttp_client(app)
# it should trace a template rendering
request = await client.request("GET", "/template/")
assert 200 == request.status
text = await request.text()
assert "OK" == text
# the trace is created
traces = tracer.pop_traces()
assert 1 == len(traces)
assert 1 == len(traces[0])
span = traces[0][0]
# with the right fields
assert "aiohttp.template" == span.name
assert "template" == span.span_type
assert "/template.jinja2" == span.get_tag("aiohttp.template")
assert 0 == span.error
async def test_template_rendering_snapshot(untraced_app_tracer, aiohttp_client, snapshot_context):
app, _ = untraced_app_tracer
Pin.override(aiohttp_jinja2, tracer=tracer)
with snapshot_context():
client = await aiohttp_client(app)
# it should trace a template rendering
request = await client.request("GET", "/template/")
assert 200 == request.status
async def test_template_rendering_filesystem(untraced_app_tracer, aiohttp_client, loop):
app, tracer = untraced_app_tracer
client = await aiohttp_client(app)
# it should trace a template rendering with a FileSystemLoader
set_filesystem_loader(app)
request = await client.request("GET", "/template/")
assert 200 == request.status
text = await request.text()
assert "OK" == text
# the trace is created
traces = tracer.pop_traces()
assert 1 == len(traces)
assert 1 == len(traces[0])
span = traces[0][0]
# with the right fields
assert "aiohttp.template" == span.name
assert "template" == span.span_type
assert "/template.jinja2" == span.get_tag("aiohttp.template")
assert 0 == span.error
@pytest.mark.skipif(sys.version_info < (3, 6), reason="Not compatible with Python 3.5")
async def test_template_rendering_package(untraced_app_tracer, aiohttp_client, loop):
app, tracer = untraced_app_tracer
client = await aiohttp_client(app)
# it should trace a template rendering with a PackageLoader
set_package_loader(app)
request = await client.request("GET", "/template/")
assert 200 == request.status
text = await request.text()
assert "OK" == text
# the trace is created
traces = tracer.pop_traces()
assert 1 == len(traces)
assert 1 == len(traces[0])
span = traces[0][0]
# with the right fields
assert "aiohttp.template" == span.name
assert "template" == span.span_type
assert "templates/template.jinja2" == span.get_tag("aiohttp.template")
assert 0 == span.error
async def test_template_decorator(untraced_app_tracer, aiohttp_client, loop):
app, tracer = untraced_app_tracer
client = await aiohttp_client(app)
# it should trace a template rendering
request = await client.request("GET", "/template_decorator/")
assert 200 == request.status
text = await request.text()
assert "OK" == text
# the trace is created
traces = tracer.pop_traces()
assert 1 == len(traces)
assert 1 == len(traces[0])
span = traces[0][0]
# with the right fields
assert "aiohttp.template" == span.name
assert "template" == span.span_type
assert "/template.jinja2" == span.get_tag("aiohttp.template")
assert 0 == span.error
async def test_template_error(untraced_app_tracer, aiohttp_client, loop):
app, tracer = untraced_app_tracer
client = await aiohttp_client(app)
# it should trace a template rendering
request = await client.request("GET", "/template_error/")
assert 500 == request.status
await request.text()
# the trace is created
traces = tracer.pop_traces()
assert 1 == len(traces)
assert 1 == len(traces[0])
span = traces[0][0]
# with the right fields
assert "aiohttp.template" == span.name
assert "template" == span.span_type
assert "/error.jinja2" == span.get_tag("aiohttp.template")
assert 1 == span.error
assert "division by zero" == span.get_tag("error.msg")
assert "ZeroDivisionError: division by zero" in span.get_tag("error.stack")
| 34.984 | 98 | 0.692888 | 580 | 4,373 | 5.07931 | 0.132759 | 0.051935 | 0.069246 | 0.054311 | 0.821792 | 0.75594 | 0.75594 | 0.743381 | 0.743381 | 0.743381 | 0 | 0.016862 | 0.199863 | 4,373 | 124 | 99 | 35.266129 | 0.825093 | 0.109993 | 0 | 0.698925 | 0 | 0 | 0.126194 | 0.006452 | 0 | 0 | 0 | 0 | 0.451613 | 1 | 0 | false | 0 | 0.075269 | 0 | 0.075269 | 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 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
5d42df9ee8e5c67258d4ae25c2afd85fb8443897 | 1,825 | py | Python | electricity_api/data_access.py | gbakthavatchalam/electricity-api | 5f8e8c9a813ffc059db8fb39d4022fb9a00f216b | [
"Apache-2.0"
] | null | null | null | electricity_api/data_access.py | gbakthavatchalam/electricity-api | 5f8e8c9a813ffc059db8fb39d4022fb9a00f216b | [
"Apache-2.0"
] | null | null | null | electricity_api/data_access.py | gbakthavatchalam/electricity-api | 5f8e8c9a813ffc059db8fb39d4022fb9a00f216b | [
"Apache-2.0"
] | null | null | null | from django.db.models.functions import Cast, Substr
from django.db.models import DateField, Min, Max
from electricity_api.models import Days, Months
def get_data(user_id, entity, start_date, limit):
if entity == "days":
return (
Days.objects.annotate(
timestamp=Cast(Substr("timestamp_raw", 1, 10), DateField())
)
.filter(timestamp__gte=start_date, user_id=user_id)
.all()[:limit]
)
else:
return (
Months.objects.annotate(
timestamp=Cast(Substr("timestamp_raw", 1, 10), DateField())
)
.filter(timestamp__gte=start_date, user_id=user_id)
.all()[:limit]
)
def get_limits(user_id, entity):
if entity == "days":
return (
Days.objects.annotate(
max_timestamp=Max(Cast(Substr("timestamp_raw", 1, 10), DateField())),
min_timestamp=Min(Cast(Substr("timestamp_raw", 1, 10), DateField())),
max_consumption=Max("consumption"),
min_consumption=Min("consumption"),
max_temperature=Max("temperature"),
min_temperature=Min("temperature"),
)
.filter(user_id=user_id)
.first()
)
else:
return (
Months.objects.annotate(
max_timestamp=Max(Cast(Substr("timestamp_raw", 1, 10), DateField())),
min_timestamp=Min(Cast(Substr("timestamp_raw", 1, 10), DateField())),
max_consumption=Max("consumption"),
min_consumption=Min("consumption"),
max_temperature=Max("temperature"),
min_temperature=Min("temperature"),
)
.filter(user_id=user_id)
.first()
)
| 34.433962 | 85 | 0.549041 | 184 | 1,825 | 5.23913 | 0.217391 | 0.062241 | 0.118257 | 0.136929 | 0.811203 | 0.778008 | 0.778008 | 0.732365 | 0.732365 | 0.732365 | 0 | 0.014658 | 0.327123 | 1,825 | 52 | 86 | 35.096154 | 0.770358 | 0 | 0 | 0.723404 | 0 | 0 | 0.095342 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.042553 | false | 0 | 0.06383 | 0 | 0.191489 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 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 | 7 |
53c10f5326f624c389111d1888af70b73b33a4bc | 24,520 | py | Python | scripts/model.py | Anguse/salsa_fusion | fb820b2a6cb16e008e15af466ab438fea164f4a6 | [
"MIT"
] | 1 | 2021-07-08T12:00:06.000Z | 2021-07-08T12:00:06.000Z | scripts/model.py | Anguse/salsa_fusion | fb820b2a6cb16e008e15af466ab438fea164f4a6 | [
"MIT"
] | 6 | 2020-09-25T22:39:52.000Z | 2022-02-09T23:43:13.000Z | scripts/model.py | Anguse/salsa_fusion | fb820b2a6cb16e008e15af466ab438fea164f4a6 | [
"MIT"
] | 1 | 2021-07-08T12:00:07.000Z | 2021-07-08T12:00:07.000Z | # TF SHORTCUTS
import tensorflow as tf
import tensorflow.contrib as tc
conv2d_layer = tc.layers.conv2d
conv2d_trans_layer = tc.layers.conv2d_transpose
conv1d_layer = tf.nn.conv1d
conv1d_trans_layer = tc.nn.conv1d_transpose
concat = tf.keras.layers.Concatenate
leakyRelu = tf.nn.leaky_relu
maxpool_layer = tc.layers.max_pool2d
dropout_layer = tf.layers.dropout
batchnorm = tc.layers.batch_norm
def resBlock(input_layer, filter_nbr, dropout_rate, kernel_size=(3, 3), stride=1, layer_name="rb", training=True,
pooling=True, repetition=1):
with tf.variable_scope(layer_name):
resA = input_layer
for i in range(repetition):
shortcut = conv2d_layer(resA, filter_nbr, kernel_size=(1, 1), stride=stride, activation_fn=leakyRelu,
scope=layer_name + '_s_%d' % (i + 0))
resA = conv2d_layer(resA, filter_nbr, kernel_size, normalizer_fn=batchnorm,
activation_fn=leakyRelu,
normalizer_params={'is_training': training},
scope=layer_name + '_%d_conv1' % (i + 0))
resA = conv2d_layer(resA, filter_nbr, kernel_size, normalizer_fn=batchnorm,
activation_fn=leakyRelu,
normalizer_params={'is_training': training},
scope=layer_name + '_%d_conv2' % (i + 0))
resA = tf.add(resA, shortcut)
if pooling:
resB = dropout_layer(resA, rate=dropout_rate, name="dropout")
resB = maxpool_layer(resB, (2, 2), padding='same')
print(str(layer_name) + str(resB.shape.as_list()))
return resB, resA
else:
resB = dropout_layer(resA, rate=dropout_rate, name="dropout")
print(str(layer_name) + str(resB.shape.as_list()))
return resB
def resBlock1d(input_layer, filter_nbr, dropout_rate, kernel_size=(3, 3), stride=1, layer_name="rb", training=True,
pooling=True, repetition=1):
with tf.variable_scope(layer_name):
resA = input_layer
for i in range(repetition):
# scope=layer_name + '_s_%d' % (i + 0)
# kernel_size=(1, 1)
# activation_fn=leakyRelu
shortcut = conv1d_layer(
resA, filter_nbr, stride=stride, padding='SAME')
# scope=layer_name + '_%d_conv1' % (i + 0)
# kernel_size
# activation_fn=leakyRelu
# normalizer_fn=batchnorm
# normalizer_params={'is_training': training}
resA = conv1d_layer(
resA, filter_nbr, stride=stride, padding='VALID')
# scope=layer_name + '_%d_conv2' % (i + 0)
# kernel_size
# normalizer_fn=batchnorm
# activation_fn=leakyRelu
# normalizer_params={'is_training': training}
resA = conv1d_layer(
resA, filter_nbr, stride=stride, padding='VALID')
resA = tf.add(resA, shortcut)
if pooling:
resB = dropout_layer(resA, rate=dropout_rate, name="dropout")
resB = maxpool_layer(resB, (2, 2), padding='same')
print(str(layer_name) + str(resB.shape.as_list()))
return resB, resA
else:
resB = dropout_layer(resA, rate=dropout_rate, name="dropout")
print(str(layer_name) + str(resB.shape.as_list()))
return resB
def upBlock(input_layer, skip_layer, filter_nbr, dropout_rate, kernel_size=(3, 3), layer_name="dec", training=True):
with tf.variable_scope(layer_name + "_up"):
upA = conv2d_trans_layer(input_layer, filter_nbr, kernel_size, 2, normalizer_fn=batchnorm,
activation_fn=leakyRelu,
normalizer_params={'is_training': training}, scope="tconv")
upA = dropout_layer(upA, rate=dropout_rate, name="dropout")
with tf.variable_scope(layer_name + "_add"):
upB = tf.add(upA, skip_layer, name="add")
upB = dropout_layer(upB, rate=dropout_rate, name="dropout_add")
with tf.variable_scope(layer_name + "_conv"):
upE = conv2d_layer(upB, filter_nbr, kernel_size, normalizer_fn=batchnorm,
activation_fn=leakyRelu,
normalizer_params={'is_training': training}, scope="conv1")
upE = conv2d_layer(upE, filter_nbr, kernel_size, normalizer_fn=batchnorm,
activation_fn=leakyRelu,
normalizer_params={'is_training': training}, scope="conv2")
upE = conv2d_layer(upE, filter_nbr, kernel_size, normalizer_fn=batchnorm,
activation_fn=leakyRelu,
normalizer_params={'is_training': training}, scope="conv3")
upE = dropout_layer(upE, rate=dropout_rate, name="dropout_conv")
print(str(layer_name) + str(upE.shape.as_list()))
return upE
def upBlock1d(input_layer, skip_layer, filter_nbr, dropout_rate, kernel_size=(3, 3), layer_name="dec", training=True):
with tf.variable_scope(layer_name + "_up"):
upA = conv2d_trans_layer(input_layer, filter_nbr, kernel_size, 2, normalizer_fn=batchnorm,
activation_fn=leakyRelu,
normalizer_params={'is_training': training}, scope="tconv")
upA = dropout_layer(upA, rate=dropout_rate, name="dropout")
with tf.variable_scope(layer_name + "_add"):
upB = tf.add(upA, skip_layer, name="add")
upB = dropout_layer(upB, rate=dropout_rate, name="dropout_add")
with tf.variable_scope(layer_name + "_conv"):
upE = conv2d_layer(upB, filter_nbr, kernel_size, normalizer_fn=batchnorm,
activation_fn=leakyRelu,
normalizer_params={'is_training': training}, scope="conv1")
upE = conv2d_layer(upE, filter_nbr, kernel_size, normalizer_fn=batchnorm,
activation_fn=leakyRelu,
normalizer_params={'is_training': training}, scope="conv2")
upE = conv2d_layer(upE, filter_nbr, kernel_size, normalizer_fn=batchnorm,
activation_fn=leakyRelu,
normalizer_params={'is_training': training}, scope="conv3")
upE = dropout_layer(upE, rate=dropout_rate, name="dropout_conv")
print(str(layer_name) + str(upE.shape.as_list()))
return upE
def create_SalsaNet(input_img, num_classes=3, dropout_rate=0.5, is_training=False, kernel_number=32):
print("--------------- SalsaNet model --------------------")
print("input", input_img.shape.as_list())
down0c, down0b = resBlock(input_img, filter_nbr=kernel_number, dropout_rate=dropout_rate,
kernel_size=3, stride=1, layer_name="res0", training=is_training, repetition=1)
down1c, down1b = resBlock(down0c, filter_nbr=2 * kernel_number, dropout_rate=dropout_rate,
kernel_size=3, stride=1, layer_name="res1", training=is_training, repetition=1)
down2c, down2b = resBlock(down1c, filter_nbr=4 * kernel_number, dropout_rate=dropout_rate,
kernel_size=3, stride=1, layer_name="res2", training=is_training, repetition=1)
down3c, down3b = resBlock(down2c, filter_nbr=8 * kernel_number, dropout_rate=dropout_rate,
kernel_size=3, stride=1, layer_name="res3", training=is_training, repetition=1)
down4b = resBlock(down3c, filter_nbr=8 * kernel_number, dropout_rate=dropout_rate, kernel_size=3,
stride=1, layer_name="res4", training=is_training, pooling=False, repetition=1)
up3e = upBlock(down4b, down3b, filter_nbr=8 * kernel_number, dropout_rate=dropout_rate,
kernel_size=(3, 3), layer_name="up3", training=is_training)
up2e = upBlock(up3e, down2b, filter_nbr=4 * kernel_number, dropout_rate=dropout_rate,
kernel_size=(3, 3), layer_name="up2", training=is_training)
up1e = upBlock(up2e, down1b, filter_nbr=2 * kernel_number, dropout_rate=dropout_rate,
kernel_size=(3, 3), layer_name="up1", training=is_training)
up0e = upBlock(up1e, down0b, filter_nbr=kernel_number, dropout_rate=dropout_rate,
kernel_size=(3, 3), layer_name="up0", training=is_training)
with tf.variable_scope('logits'):
logits = conv2d_layer(up0e, num_classes, [1, 1], activation_fn=None)
print("logits", logits.shape.as_list())
return logits
def create_SalsaNet_laser(input_img, num_classes=3, dropout_rate=0.5, is_training=False, kernel_number=32):
print("--------------- SalsaNet_laser model --------------------")
print("input", input_img.shape.as_list())
with tf.variable_scope("laser_block"):
down0c, down0b = resBlock(input_img, filter_nbr=kernel_number, dropout_rate=dropout_rate,
kernel_size=3, stride=1, layer_name="res0", training=is_training, repetition=1)
down1c, down1b = resBlock(down0c, filter_nbr=2 * kernel_number, dropout_rate=dropout_rate,
kernel_size=3, stride=1, layer_name="res1", training=is_training, repetition=1)
down2b = resBlock(down1c, filter_nbr=2 * kernel_number, dropout_rate=dropout_rate, kernel_size=3,
stride=1, layer_name="res2", training=is_training, pooling=False, repetition=1)
up1e = upBlock(down2b, down1b, filter_nbr=2 * kernel_number, dropout_rate=dropout_rate,
kernel_size=(3, 3), layer_name="up1", training=is_training)
up0e = upBlock(up1e, down0b, filter_nbr=kernel_number, dropout_rate=dropout_rate,
kernel_size=(3, 3), layer_name="up0", training=is_training)
with tf.variable_scope('logits'):
logits = conv2d_layer(up0e, num_classes, [1, 1], activation_fn=None)
print("logits", logits.shape.as_list())
return logits
print("--------------------------------------------------")
def create_SalsaNet_decoder_fusion(input_laser, input_depth, num_classes=3, dropout_rate=0.5, is_training=False, kernel_number=32):
print("--------------- SalsaNet_decoder_fusion model --------------------")
print("input_laser", input_laser.shape.as_list())
print("input_depth", input_depth.shape.as_list())
#laser_img_shape = (None, 60, 80)
#depth_img_shape = (None, 240, 320)
is_training_laser = True
print("--- laser ---")
with tf.variable_scope("laser_block"):
# [ ,30, 40, 32]
down0c, down0b = resBlock(input_laser, filter_nbr=kernel_number, dropout_rate=dropout_rate,
kernel_size=3, stride=1, layer_name="res0", training=is_training_laser, repetition=1)
# [ ,15, 20, 64]
down1c, down1b = resBlock(down0c, filter_nbr=2 * kernel_number, dropout_rate=dropout_rate,
kernel_size=3, stride=1, layer_name="res1", training=is_training_laser, repetition=1)
# [ ,15, 20, 64]
down2b = resBlock(down1c, filter_nbr=2 * kernel_number, dropout_rate=dropout_rate, kernel_size=3,
stride=1, layer_name="res2", training=is_training_laser, pooling=False, repetition=1)
# [ ,30, 40, 32]
up1e = upBlock(down2b, down1b, filter_nbr=2 * kernel_number, dropout_rate=dropout_rate,
kernel_size=(3, 3), layer_name="up1", training=is_training_laser)
# [ ,60, 80, 32]
up0e = upBlock(up1e, down0b, filter_nbr=kernel_number, dropout_rate=dropout_rate,
kernel_size=(3, 3), layer_name="up0", training=is_training_laser)
print("--- depth ---")
with tf.variable_scope("depth_block"):
# [ ,120, 160, 32]
ddown0c, ddown0b = resBlock(input_depth, filter_nbr=kernel_number, dropout_rate=dropout_rate,
kernel_size=3, stride=1, layer_name="dres0", training=is_training, repetition=1)
# [ ,60, 80, 64]
ddown1c, ddown1b = resBlock(ddown0c, filter_nbr=2 * kernel_number, dropout_rate=dropout_rate,
kernel_size=3, stride=1, layer_name="dres1", training=is_training, repetition=1)
# [ ,30, 40, 128]
ddown2c, ddown2b = resBlock(ddown1c, filter_nbr=4 * kernel_number, dropout_rate=dropout_rate,
kernel_size=3, stride=1, layer_name="dres2", training=is_training, repetition=1)
# [ ,15, 20, 256]
ddown3c, ddown3b = resBlock(ddown2c, filter_nbr=8 * kernel_number, dropout_rate=dropout_rate,
kernel_size=3, stride=1, layer_name="dres3", training=is_training, repetition=1)
# [ ,15, 20, 256]
ddown4b = resBlock(ddown3c, filter_nbr=8 * kernel_number, dropout_rate=dropout_rate, kernel_size=3,
stride=1, layer_name="dres4", training=is_training, pooling=False, repetition=1)
# concatenation [ ,15, 20, 256+64]
ddown4b = tf.concat(axis=3, values=[down2b, ddown4b])
print("concat ddown4b", ddown4b.shape.as_list())
'''
# fusion 1
ddown4_0c, ddown4_0b = resBlock(ddown4b, filter_nbr=16 * kernel_number, dropout_rate=dropout_rate,
kernel_size=3, stride=1, layer_name="fusion_0a", training=is_training, repetition=1)
ddown4_1c = resBlock(ddown4_0c, filter_nbr=16 * kernel_number, dropout_rate=dropout_rate,
kernel_size=3, stride=1, layer_name="fusion_1a", training=is_training, pooling=False, repetition=1)
ddown4_2c = upBlock(ddown4_1c, ddown4_0b, filter_nbr=16 * kernel_number, dropout_rate=dropout_rate,
kernel_size=(3, 3), layer_name="fusion_2a", training=is_training)
exit()
'''
# [ ,30, 40, 256]
dup3e = upBlock(ddown4b, ddown3b, filter_nbr=8 * kernel_number, dropout_rate=dropout_rate,
kernel_size=(3, 3), layer_name="dup3", training=is_training)
# concatenation [ ,30, 40, 256+64]
dup3e = tf.concat(axis=3, values=[up1e, dup3e])
print("concat dup3", dup3e.shape.as_list())
# [ ,60, 80, 128]
dup2e = upBlock(dup3e, ddown2b, filter_nbr=4 * kernel_number, dropout_rate=dropout_rate,
kernel_size=(3, 3), layer_name="dup2", training=is_training)
# concatenation [ ,60, 80, 128+32]
dup2e = tf.concat(axis=3, values=[up0e, dup2e])
print("concat dup2", dup2e.shape.as_list())
# [ ,120, 160, 64]
dup1e = upBlock(dup2e, ddown1b, filter_nbr=2 * kernel_number, dropout_rate=dropout_rate,
kernel_size=(3, 3), layer_name="dup1", training=is_training)
# [ ,240, 320, 32]
dup0e = upBlock(dup1e, ddown0b, filter_nbr=kernel_number, dropout_rate=dropout_rate,
kernel_size=(3, 3), layer_name="dup0", training=is_training)
with tf.variable_scope('logits'):
# [ ,240, 320, 5]
logits = conv2d_layer(dup0e, num_classes, [1, 1], activation_fn=None)
print("logits", logits.shape.as_list())
return logits
print("--------------------------------------------------")
def create_SalsaNet_encoder_fusion(input_laser, input_depth, num_classes=3, dropout_rate=0.5, is_training=False, kernel_number=32):
print("--------------- SalsaNet_encoder_fusion model --------------------")
print("input_laser", input_laser.shape.as_list())
print("input_depth", input_depth.shape.as_list())
#laser_img_shape = (None, 60, 80)
#depth_img_shape = (None, 240, 320)
print("--- laser ---")
with tf.variable_scope("laser_block"):
# [ ,30, 40, 32]
down0c, down0b = resBlock(input_laser, filter_nbr=kernel_number, dropout_rate=dropout_rate,
kernel_size=3, stride=1, layer_name="res0", training=False, repetition=1)
# [ ,15, 20, 64]
down1c, down1b = resBlock(down0c, filter_nbr=2 * kernel_number, dropout_rate=dropout_rate,
kernel_size=3, stride=1, layer_name="res1", training=False, repetition=1)
# [ ,15, 20, 64]
down2b = resBlock(down1c, filter_nbr=2 * kernel_number, dropout_rate=dropout_rate, kernel_size=3,
stride=1, layer_name="res2", training=False, pooling=False, repetition=1)
# [ ,30, 40, 32]
up1e = upBlock(down2b, down1b, filter_nbr=2 * kernel_number, dropout_rate=dropout_rate,
kernel_size=(3, 3), layer_name="up1", training=False)
# [ ,60, 80, 32]
up0e = upBlock(up1e, down0b, filter_nbr=kernel_number, dropout_rate=dropout_rate,
kernel_size=(3, 3), layer_name="up0", training=False)
print("--- depth ---")
with tf.variable_scope("depth_block"):
# [ ,120, 160, 32]
ddown0c, ddown0b = resBlock(input_depth, filter_nbr=kernel_number, dropout_rate=dropout_rate,
kernel_size=3, stride=1, layer_name="dres0", training=is_training, repetition=1)
# [ ,60, 80, 64]
ddown1c, ddown1b = resBlock(ddown0c, filter_nbr=2 * kernel_number, dropout_rate=dropout_rate,
kernel_size=3, stride=1, layer_name="dres1", training=is_training, repetition=1)
# [ ,30, 40, 128]
ddown2c, ddown2b = resBlock(ddown1c, filter_nbr=4 * kernel_number, dropout_rate=dropout_rate,
kernel_size=3, stride=1, layer_name="dres2", training=is_training, repetition=1)
# concatenation [ ,30, 40, 128+32]
ddown2c = tf.concat(axis=3, values=[ddown2c, down0c])
print("concat dres2", ddown2c.shape.as_list())
# [ ,15, 20, 256]
ddown3c, ddown3b = resBlock(ddown2c, filter_nbr=8 * kernel_number, dropout_rate=dropout_rate,
kernel_size=3, stride=1, layer_name="dres3", training=is_training, repetition=1)
# concatenation [ ,15, 20, 256+64]
ddown3c = tf.concat(axis=3, values=[ddown3c, down1c])
print("concat dres3", ddown3c.shape.as_list())
# [ ,15, 20, 256]
ddown4b = resBlock(ddown3c, filter_nbr=8 * kernel_number, dropout_rate=dropout_rate, kernel_size=3,
stride=1, layer_name="dres4", training=is_training, pooling=False, repetition=1)
# concatenation [ ,15, 20, 256+64]
ddown4b = tf.concat(axis=3, values=[ddown4b, down2b])
print("concat dres4", ddown4b.shape.as_list())
# [ ,30, 40, 256]
dup3e = upBlock(ddown4b, ddown3b, filter_nbr=8 * kernel_number, dropout_rate=dropout_rate,
kernel_size=(3, 3), layer_name="dup3", training=is_training)
# [ ,60, 80, 128]
dup2e = upBlock(dup3e, ddown2b, filter_nbr=4 * kernel_number, dropout_rate=dropout_rate,
kernel_size=(3, 3), layer_name="dup2", training=is_training)
# [ ,120, 160, 64]
dup1e = upBlock(dup2e, ddown1b, filter_nbr=2 * kernel_number, dropout_rate=dropout_rate,
kernel_size=(3, 3), layer_name="dup1", training=is_training)
# [ ,240, 320, 32]
dup0e = upBlock(dup1e, ddown0b, filter_nbr=kernel_number, dropout_rate=dropout_rate,
kernel_size=(3, 3), layer_name="dup0", training=is_training)
with tf.variable_scope('logits'):
# [ ,240, 320, 5]
logits = conv2d_layer(dup0e, num_classes, [1, 1], activation_fn=None)
print("logits", logits.shape.as_list())
return logits
print("--------------------------------------------------")
def create_SalsaNet_encoder_decoder_fusion(input_laser, input_depth, num_classes=3, dropout_rate=0.5, is_training=False, kernel_number=32):
print("--------------- SalsaNet_encoder_fusion model --------------------")
print("input_laser", input_laser.shape.as_list())
print("input_depth", input_depth.shape.as_list())
#laser_img_shape = (None, 60, 80)
#depth_img_shape = (None, 240, 320)
print("--- laser ---")
with tf.variable_scope("laser_block"):
# [ ,30, 40, 32]
down0c, down0b = resBlock(input_laser, filter_nbr=kernel_number, dropout_rate=dropout_rate,
kernel_size=3, stride=1, layer_name="res0", training=is_training, repetition=1)
# [ ,15, 20, 64]
down1c, down1b = resBlock(down0c, filter_nbr=2 * kernel_number, dropout_rate=dropout_rate,
kernel_size=3, stride=1, layer_name="res1", training=is_training, repetition=1)
# [ ,15, 20, 64]
down2b = resBlock(down1c, filter_nbr=2 * kernel_number, dropout_rate=dropout_rate, kernel_size=3,
stride=1, layer_name="res2", training=is_training, pooling=False, repetition=1)
# [ ,30, 40, 32]
up1e = upBlock(down2b, down1b, filter_nbr=2 * kernel_number, dropout_rate=dropout_rate,
kernel_size=(3, 3), layer_name="up1", training=is_training)
# [ ,60, 80, 32]
up0e = upBlock(up1e, down0b, filter_nbr=kernel_number, dropout_rate=dropout_rate,
kernel_size=(3, 3), layer_name="up0", training=is_training)
print("--- depth ---")
with tf.variable_scope("depth_block"):
# [ ,120, 160, 32]
ddown0c, ddown0b = resBlock(input_depth, filter_nbr=kernel_number, dropout_rate=dropout_rate,
kernel_size=3, stride=1, layer_name="dres0", training=is_training, repetition=1)
# [ ,60, 80, 64]
ddown1c, ddown1b = resBlock(ddown0c, filter_nbr=2 * kernel_number, dropout_rate=dropout_rate,
kernel_size=3, stride=1, layer_name="dres1", training=is_training, repetition=1)
# [ ,30, 40, 128]
ddown2c, ddown2b = resBlock(ddown1c, filter_nbr=4 * kernel_number, dropout_rate=dropout_rate,
kernel_size=3, stride=1, layer_name="dres2", training=is_training, repetition=1)
# concatenation [ ,30, 40, 128+32]
ddown2c = tf.concat(axis=3, values=[ddown2c, down0c])
print("concat dres2", ddown2c.shape.as_list())
# [ ,15, 20, 256]
ddown3c, ddown3b = resBlock(ddown2c, filter_nbr=8 * kernel_number, dropout_rate=dropout_rate,
kernel_size=3, stride=1, layer_name="dres3", training=is_training, repetition=1)
# concatenation [ ,15, 20, 256+64]
ddown3c = tf.concat(axis=3, values=[ddown3c, down1c])
print("concat dres3", ddown3c.shape.as_list())
# [ ,15, 20, 256]
ddown4b = resBlock(ddown3c, filter_nbr=8 * kernel_number, dropout_rate=dropout_rate, kernel_size=3,
stride=1, layer_name="dres4", training=is_training, pooling=False, repetition=1)
# concatenation [ ,15, 20, 256+64]
ddown4b = tf.concat(axis=3, values=[down2b, ddown4b])
print("concat ddown4b", ddown4b.shape.as_list())
# [ ,30, 40, 256]
dup3e = upBlock(ddown4b, ddown3b, filter_nbr=8 * kernel_number, dropout_rate=dropout_rate,
kernel_size=(3, 3), layer_name="dup3", training=is_training)
# concatenation [ ,30, 40, 256+64]
dup3e = tf.concat(axis=3, values=[up1e, dup3e])
print("concat dup3", dup3e.shape.as_list())
# [ ,60, 80, 128]
dup2e = upBlock(dup3e, ddown2b, filter_nbr=4 * kernel_number, dropout_rate=dropout_rate,
kernel_size=(3, 3), layer_name="dup2", training=is_training)
# concatenation [ ,60, 80, 128+32]
dup2e = tf.concat(axis=3, values=[up0e, dup2e])
print("concat dup2", dup2e.shape.as_list())
# [ ,120, 160, 64]
dup1e = upBlock(dup2e, ddown1b, filter_nbr=2 * kernel_number, dropout_rate=dropout_rate,
kernel_size=(3, 3), layer_name="dup1", training=is_training)
# [ ,240, 320, 32]
dup0e = upBlock(dup1e, ddown0b, filter_nbr=kernel_number, dropout_rate=dropout_rate,
kernel_size=(3, 3), layer_name="dup0", training=is_training)
with tf.variable_scope('logits'):
# [ ,240, 320, 5]
logits = conv2d_layer(dup0e, num_classes, [1, 1], activation_fn=None)
print("logits", logits.shape.as_list())
return logits
print("--------------------------------------------------")
| 54.368071 | 139 | 0.613418 | 2,991 | 24,520 | 4.768639 | 0.059512 | 0.105658 | 0.072565 | 0.092757 | 0.936689 | 0.930449 | 0.928416 | 0.913272 | 0.90549 | 0.904789 | 0 | 0.056413 | 0.252488 | 24,520 | 450 | 140 | 54.488889 | 0.721752 | 0.067455 | 0 | 0.817869 | 0 | 0 | 0.06699 | 0.012184 | 0 | 0 | 0 | 0 | 0 | 1 | 0.030928 | false | 0 | 0.006873 | 0 | 0.075601 | 0.154639 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 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 | 7 |
54f14ae77d6003fdd379d5191cf59e792b9758cc | 10,686 | py | Python | climateeconomics/tests/l1_test_gradient_carbonemissions_discipline.py | os-climate/witness-core | 3ef9a44d86804c5ad57deec3c9916348cb3bfbb8 | [
"MIT",
"Apache-2.0",
"BSD-3-Clause"
] | 1 | 2022-01-14T06:37:42.000Z | 2022-01-14T06:37:42.000Z | climateeconomics/tests/l1_test_gradient_carbonemissions_discipline.py | os-climate/witness-core | 3ef9a44d86804c5ad57deec3c9916348cb3bfbb8 | [
"MIT",
"Apache-2.0",
"BSD-3-Clause"
] | null | null | null | climateeconomics/tests/l1_test_gradient_carbonemissions_discipline.py | os-climate/witness-core | 3ef9a44d86804c5ad57deec3c9916348cb3bfbb8 | [
"MIT",
"Apache-2.0",
"BSD-3-Clause"
] | null | null | null | '''
Copyright 2022 Airbus SAS
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 pandas as pd
from os.path import join, dirname
from pandas import DataFrame, read_csv
from sos_trades_core.execution_engine.execution_engine import ExecutionEngine
from sos_trades_core.tests.core.abstract_jacobian_unit_test import AbstractJacobianUnittest
from energy_models.core.stream_type.carbon_models.carbon_dioxyde import CO2
class CarbonEmissionsJacobianDiscTest(AbstractJacobianUnittest):
# AbstractJacobianUnittest.DUMP_JACOBIAN = True
# np.set_printoptions(threshold=np.inf)
def setUp(self):
self.name = 'Test'
self.ee = ExecutionEngine(self.name)
def analytic_grad_entry(self):
return [
self.test_carbon_emissions_analytic_grad,
self.test_co2_objective_limit_grad
]
def test_carbon_emissions_analytic_grad(self):
self.model_name = 'carbonemission'
ns_dict = {'ns_witness': f'{self.name}',
'ns_public': f'{self.name}',
'ns_energy_mix': f'{self.name}',
'ns_ref': f'{self.name}',
'ns_ccs': f'{self.name}',
'ns_energy': f'{self.name}'}
self.ee.ns_manager.add_ns_def(ns_dict)
mod_path = 'climateeconomics.sos_wrapping.sos_wrapping_witness.carbonemissions.carbonemissions_discipline.CarbonemissionsDiscipline'
builder = self.ee.factory.get_builder_from_module(
self.model_name, mod_path)
self.ee.factory.set_builders_to_coupling_builder(builder)
self.ee.configure()
self.ee.display_treeview_nodes()
data_dir = join(dirname(__file__), 'data')
economics_df_all = read_csv(
join(data_dir, 'economics_data_onestep.csv'))
energy_supply_df_all = read_csv(
join(data_dir, 'energy_supply_data_onestep.csv'))
year_start = 2020
economics_df_y = economics_df_all[economics_df_all['years'] >= year_start][[
'years', 'gross_output']]
energy_supply_df_y = energy_supply_df_all[energy_supply_df_all['years'] >= year_start][[
'years', 'total_CO2_emitted']]
energy_supply_df_y["years"] = energy_supply_df_all['years']
energy_supply_df_y = energy_supply_df_y.rename(
columns={'total_CO2_emitted': 'Total CO2 emissions'})
co2_emissions_ccus_Gt = pd.DataFrame()
co2_emissions_ccus_Gt['years'] = energy_supply_df_y["years"]
co2_emissions_ccus_Gt['carbon_storage Limited by capture (Gt)'] = 0.02
CO2_emissions_by_use_sources = pd.DataFrame()
CO2_emissions_by_use_sources['years'] = energy_supply_df_y["years"]
CO2_emissions_by_use_sources['CO2 from energy mix (Gt)'] = 0.0
CO2_emissions_by_use_sources['carbon_capture from energy mix (Gt)'] = 0.0
CO2_emissions_by_use_sources['Total CO2 by use (Gt)'] = 20.0
CO2_emissions_by_use_sources['Total CO2 from Flue Gas (Gt)'] = 3.2
CO2_emissions_by_use_sinks = pd.DataFrame()
CO2_emissions_by_use_sinks['years'] = energy_supply_df_y["years"]
CO2_emissions_by_use_sinks[f'{CO2.name} removed by energy mix (Gt)'] = 0.0
co2_emissions_needed_by_energy_mix = pd.DataFrame()
co2_emissions_needed_by_energy_mix['years'] = energy_supply_df_y["years"]
co2_emissions_needed_by_energy_mix[
'carbon_capture needed by energy mix (Gt)'] = 0.0
# put manually the index
years = np.arange(year_start, 2101)
economics_df_y.index = years
energy_supply_df_y.index = years
co2_emissions_ccus_Gt.index = years
CO2_emissions_by_use_sources.index = years
CO2_emissions_by_use_sinks.index = years
co2_emissions_needed_by_energy_mix.index = years
CO2_emitted_forest = pd.DataFrame()
emission_forest = np.linspace(0.01, 0.10, len(years))
cum_emission = np.cumsum(emission_forest) + 3.21
CO2_emitted_forest['years'] = years
CO2_emitted_forest['emitted_CO2_evol_cumulative'] = cum_emission
values_dict = {f'{self.name}.economics_df': economics_df_y,
f'{self.name}.co2_emissions_Gt': energy_supply_df_y,
f'{self.name}.CO2_land_emissions': CO2_emitted_forest,
f'{self.name}.co2_emissions_ccus_Gt': co2_emissions_ccus_Gt,
f'{self.name}.CO2_emissions_by_use_sources': CO2_emissions_by_use_sources,
f'{self.name}.CO2_emissions_by_use_sinks': CO2_emissions_by_use_sinks,
f'{self.name}.co2_emissions_needed_by_energy_mix': co2_emissions_needed_by_energy_mix}
self.ee.load_study_from_input_dict(values_dict)
disc_techno = self.ee.root_process.sos_disciplines[0]
self.check_jacobian(location=dirname(__file__), filename=f'jacobian_carbon_emission_discipline.pkl',
discipline=disc_techno, step=1e-15, derr_approx='complex_step',
inputs=[f'{self.name}.economics_df',
f'{self.name}.CO2_emissions_by_use_sources',
f'{self.name}.CO2_land_emissions',
f'{self.name}.CO2_emissions_by_use_sinks', f'{self.name}.co2_emissions_needed_by_energy_mix', f'{self.name}.co2_emissions_ccus_Gt'],
outputs=[f'{self.name}.CO2_emissions_df',
f'{self.name}.CO2_objective', f'{self.name}.co2_emissions_Gt'])
def test_co2_objective_limit_grad(self):
self.model_name = 'carbonemission'
ns_dict = {'ns_witness': f'{self.name}',
'ns_public': f'{self.name}',
'ns_energy_mix': f'{self.name}',
'ns_ref': f'{self.name}',
'ns_ccs': f'{self.name}',
'ns_energy': f'{self.name}'}
self.ee.ns_manager.add_ns_def(ns_dict)
mod_path = 'climateeconomics.sos_wrapping.sos_wrapping_witness.carbonemissions.carbonemissions_discipline.CarbonemissionsDiscipline'
builder = self.ee.factory.get_builder_from_module(
self.model_name, mod_path)
self.ee.factory.set_builders_to_coupling_builder(builder)
self.ee.configure()
self.ee.display_treeview_nodes()
data_dir = join(dirname(__file__), 'data')
economics_df_all = read_csv(
join(data_dir, 'economics_data_onestep.csv'))
energy_supply_df_all = read_csv(
join(data_dir, 'energy_supply_data_onestep.csv'))
economics_df_y = economics_df_all[economics_df_all['years'] >= 2020][[
'years', 'gross_output']]
energy_supply_df_y = energy_supply_df_all[energy_supply_df_all['years'] >= 2020][[
'years', 'total_CO2_emitted']]
energy_supply_df_y["years"] = energy_supply_df_all['years']
energy_supply_df_y = energy_supply_df_y.rename(
columns={'total_CO2_emitted': 'Total CO2 emissions'})
co2_emissions_ccus_Gt = pd.DataFrame()
co2_emissions_ccus_Gt['years'] = energy_supply_df_y["years"]
co2_emissions_ccus_Gt['carbon_storage Limited by capture (Gt)'] = 0.02
CO2_emissions_by_use_sources = pd.DataFrame()
CO2_emissions_by_use_sources['years'] = energy_supply_df_y["years"]
CO2_emissions_by_use_sources['CO2 from energy mix (Gt)'] = 0.0
CO2_emissions_by_use_sources['carbon_capture from energy mix (Gt)'] = 0.0
CO2_emissions_by_use_sources['Total CO2 by use (Gt)'] = 20.0
CO2_emissions_by_use_sources['Total CO2 from Flue Gas (Gt)'] = 3.2
CO2_emissions_by_use_sinks = pd.DataFrame()
CO2_emissions_by_use_sinks['years'] = energy_supply_df_y["years"]
CO2_emissions_by_use_sinks[f'{CO2.name} removed by energy mix (Gt)'] = 0.0
co2_emissions_needed_by_energy_mix = pd.DataFrame()
co2_emissions_needed_by_energy_mix['years'] = energy_supply_df_y["years"]
co2_emissions_needed_by_energy_mix[
'carbon_capture needed by energy mix (Gt)'] = 0.0
# put manually the index
years = np.arange(2020, 2101)
economics_df_y.index = years
energy_supply_df_y.index = years
energy_supply_df_y['Total CO2 emissions'] = np.linspace(
0, -3000, len(years))
CO2_emitted_forest = pd.DataFrame()
emission_forest = np.linspace(0.04, 0.04, len(years))
cum_emission = np.cumsum(emission_forest) + 3.21
CO2_emitted_forest['years'] = years
CO2_emitted_forest['emitted_CO2_evol_cumulative'] = cum_emission
values_dict = {f'{self.name}.economics_df': economics_df_y,
f'{self.name}.co2_emissions_Gt': energy_supply_df_y,
f'{self.name}.CO2_land_emissions': CO2_emitted_forest,
f'{self.name}.co2_emissions_ccus_Gt': co2_emissions_ccus_Gt,
f'{self.name}.CO2_emissions_by_use_sources': CO2_emissions_by_use_sources,
f'{self.name}.CO2_emissions_by_use_sinks': CO2_emissions_by_use_sinks,
f'{self.name}.co2_emissions_needed_by_energy_mix': co2_emissions_needed_by_energy_mix}
self.ee.load_study_from_input_dict(values_dict)
disc_techno = self.ee.root_process.sos_disciplines[0]
self.check_jacobian(location=dirname(__file__), filename=f'jacobian_co2_objective_limit.pkl',
discipline=disc_techno, step=1e-15, derr_approx='complex_step',
inputs=[f'{self.name}.economics_df',
f'{self.name}.CO2_emissions_by_use_sources',
f'{self.name}.CO2_land_emissions',
f'{self.name}.CO2_emissions_by_use_sinks', f'{self.name}.co2_emissions_needed_by_energy_mix', f'{self.name}.co2_emissions_ccus_Gt'],
outputs=[f'{self.name}.CO2_emissions_df',
f'{self.name}.CO2_objective', f'{self.name}.co2_emissions_Gt'])
| 49.24424 | 168 | 0.660678 | 1,415 | 10,686 | 4.580212 | 0.15265 | 0.124055 | 0.061102 | 0.083938 | 0.830273 | 0.819164 | 0.796636 | 0.79139 | 0.789076 | 0.789076 | 0 | 0.023856 | 0.239004 | 10,686 | 216 | 169 | 49.472222 | 0.773119 | 0.063728 | 0 | 0.775 | 0 | 0 | 0.258555 | 0.153792 | 0 | 0 | 0 | 0 | 0 | 1 | 0.025 | false | 0 | 0.05 | 0.00625 | 0.0875 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 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 | 7 |
070cc9f010826d07dc89cfef90980f4104ea19cd | 9,261 | py | Python | LeafNATS/data/summarization/load_single.py | haophancs/TREQS | 49e354ce2a08cf963ec139d99936020e0f80ced8 | [
"MIT"
] | 149 | 2019-08-09T17:18:18.000Z | 2022-03-28T01:18:56.000Z | LeafNATS/data/summarization/load_single.py | haophancs/TREQS | 49e354ce2a08cf963ec139d99936020e0f80ced8 | [
"MIT"
] | 5 | 2019-08-14T18:23:24.000Z | 2021-10-03T20:17:28.000Z | LeafNATS/data/summarization/load_single.py | haophancs/TREQS | 49e354ce2a08cf963ec139d99936020e0f80ced8 | [
"MIT"
] | 32 | 2019-08-10T02:09:44.000Z | 2022-03-09T07:59:46.000Z | '''
@author Tian Shi
Please contact tshi@vt.edu
Users need to rewrite this file based on their data format.
'''
import glob
import os
import random
import re
import shutil
import numpy as np
import torch
from torch.autograd import Variable
def process_minibatch(batch_id, path_,
fkey_, batch_size,
vocab2id, max_lens=[400, 100]):
'''
Process the minibatch.
summary<sec>article.
'''
file_ = os.path.join(path_, 'batch_{}_{}'.format(
fkey_, batch_size), str(batch_id))
fp = open(file_, 'r')
src_arr = []
trg_arr = []
src_lens = []
trg_lens = []
for line in fp:
arr = re.split('<sec>', line[:-1])
dabs = re.split(r'\s', arr[0])
dabs = list(filter(None, dabs)) + ['<stop>']
trg_lens.append(len(dabs))
dabs2id = [
vocab2id[wd] if wd in vocab2id
else vocab2id['<unk>']
for wd in dabs
]
trg_arr.append(dabs2id)
dart = re.split(r'\s', arr[1])
dart = list(filter(None, dart))
src_lens.append(len(dart))
dart2id = [
vocab2id[wd] if wd in vocab2id
else vocab2id['<unk>']
for wd in dart
]
src_arr.append(dart2id)
fp.close()
src_max_lens = max_lens[0]
trg_max_lens = max_lens[1]
src_arr = [itm[:src_max_lens] for itm in src_arr]
trg_arr = [itm[:trg_max_lens] for itm in trg_arr]
src_arr = [
itm + [vocab2id['<pad>']]*(src_max_lens-len(itm))
for itm in src_arr
]
trg_input_arr = [
itm[:-1] + [vocab2id['<pad>']]*(1+trg_max_lens-len(itm))
for itm in trg_arr
]
trg_output_arr = [
itm[1:] + [vocab2id['<pad>']]*(1+trg_max_lens-len(itm))
for itm in trg_arr
]
src_var = Variable(torch.LongTensor(src_arr))
trg_input_var = Variable(torch.LongTensor(trg_input_arr))
trg_output_var = Variable(torch.LongTensor(trg_output_arr))
return src_var, trg_input_var, trg_output_var
def process_minibatch_explicit(batch_id, path_,
fkey_, batch_size,
vocab2id, max_lens=[400, 100]):
'''
Process the minibatch.
OOV explicit, i.e., build extended vocabulary.
'''
file_ = os.path.join(path_, 'batch_{}_{}'.format(
fkey_, batch_size), str(batch_id))
# build extended vocabulary
fp = open(file_, 'r')
ext_vocab = {}
ext_id2oov = {}
for line in fp:
arr = re.split('<sec>', line[:-1])
dabs = re.split(r'\s', arr[0])
dabs = list(filter(None, dabs))
for wd in dabs:
if wd not in vocab2id:
ext_vocab[wd] = {}
dart = re.split(r'\s', arr[1])
dart = list(filter(None, dart))
for wd in dart:
if wd not in vocab2id:
ext_vocab[wd] = {}
cnt = len(vocab2id)
for wd in ext_vocab:
ext_vocab[wd] = cnt
ext_id2oov[cnt] = wd
cnt += 1
fp.close()
fp = open(file_, 'r')
src_arr = []
src_arr_ex = []
trg_arr = []
trg_arr_ex = []
src_lens = []
trg_lens = []
for line in fp:
# abstract
arr = re.split('<sec>', line[:-1])
dabs = re.split(r'\s', arr[0])
dabs = list(filter(None, dabs)) + ['<stop>']
trg_lens.append(len(dabs))
# UNK
dabs2id = [
vocab2id[wd] if wd in vocab2id
else vocab2id['<unk>']
for wd in dabs
]
trg_arr.append(dabs2id)
# extend vocab
dabs2id = [
vocab2id[wd] if wd in vocab2id
else ext_vocab[wd]
for wd in dabs
]
trg_arr_ex.append(dabs2id)
# article
dart = re.split(r'\s', arr[1])
dart = list(filter(None, dart))
src_lens.append(len(dart))
# UNK
dart2id = [
vocab2id[wd] if wd in vocab2id
else vocab2id['<unk>']
for wd in dart
]
src_arr.append(dart2id)
# extend vocab
dart2id = [
vocab2id[wd] if wd in vocab2id
else ext_vocab[wd]
for wd in dart
]
src_arr_ex.append(dart2id)
fp.close()
src_max_lens = max_lens[0]
trg_max_lens = max_lens[1]
src_arr = [itm[:src_max_lens] for itm in src_arr]
trg_arr = [itm[:trg_max_lens] for itm in trg_arr]
src_arr_ex = [itm[:src_max_lens] for itm in src_arr_ex]
trg_arr_ex = [itm[:trg_max_lens] for itm in trg_arr_ex]
src_arr = [
itm + [vocab2id['<pad>']]*(src_max_lens-len(itm))
for itm in src_arr
]
trg_input_arr = [
itm[:-1] + [vocab2id['<pad>']]*(1+trg_max_lens-len(itm))
for itm in trg_arr
]
# extend oov
src_arr_ex = [
itm + [vocab2id['<pad>']]*(src_max_lens-len(itm))
for itm in src_arr_ex
]
trg_output_arr_ex = [
itm[1:] + [vocab2id['<pad>']]*(1+trg_max_lens-len(itm))
for itm in trg_arr_ex
]
src_var = Variable(torch.LongTensor(src_arr))
trg_input_var = Variable(torch.LongTensor(trg_input_arr))
# extend oov
src_var_ex = Variable(torch.LongTensor(src_arr_ex))
trg_output_var_ex = Variable(torch.LongTensor(trg_output_arr_ex))
return ext_id2oov, src_var, trg_input_var, \
src_var_ex, trg_output_var_ex
def process_minibatch_test(batch_id, path_,
fkey_, batch_size,
vocab2id, src_lens):
'''
Process the minibatch test
'''
file_ = os.path.join(path_, 'batch_{}_{}'.format(
fkey_, batch_size), str(batch_id))
fp = open(file_, 'r')
src_arr = []
src_idx = []
src_wt = []
trg_arr = []
for line in fp:
arr = re.split('<sec>', line[:-1])
dabs = re.split(r'\s', arr[0])
dabs = list(filter(None, dabs))
dabs = ' '.join(dabs)
trg_arr.append(dabs)
dart = re.split(r'\s', arr[1])
dart = list(filter(None, dart))
src_arr.append(dart)
dart2id = [vocab2id[wd] if wd in vocab2id else vocab2id['<unk>']
for wd in dart]
src_idx.append(dart2id)
dart2wt = [0.0 if wd in vocab2id else 1.0 for wd in dart]
src_wt.append(dart2wt)
fp.close()
src_idx = [itm[:src_lens] for itm in src_idx]
src_idx = [itm + [vocab2id['<pad>']] *
(src_lens-len(itm)) for itm in src_idx]
src_var = Variable(torch.LongTensor(src_idx))
src_wt = [itm[:src_lens] for itm in src_wt]
src_wt = [itm + [0.0]*(src_lens-len(itm)) for itm in src_wt]
src_msk = Variable(torch.FloatTensor(src_wt))
src_arr = [itm[:src_lens] for itm in src_arr]
src_arr = [itm + ['<pad>']*(src_lens-len(itm)) for itm in src_arr]
return src_var, src_arr, src_msk, trg_arr
def process_minibatch_explicit_test(batch_id, path_,
fkey_, batch_size,
vocab2id, src_lens):
'''
Process the minibatch test.
OOV explicit.
'''
file_ = os.path.join(path_, 'batch_{}_{}'.format(
fkey_, batch_size), str(batch_id))
# build extended vocabulary
fp = open(file_, 'r')
ext_vocab = {}
ext_id2oov = {}
for line in fp:
arr = re.split('<sec>', line[:-1])
dart = re.split(r'\s', arr[1])
dart = list(filter(None, dart))
for wd in dart:
if wd not in vocab2id:
ext_vocab[wd] = {}
cnt = len(vocab2id)
for wd in ext_vocab:
ext_vocab[wd] = cnt
ext_id2oov[cnt] = wd
cnt += 1
fp.close()
fp = open(file_, 'r')
src_arr = []
src_idx = []
src_idx_ex = []
src_wt = []
trg_arr = []
for line in fp:
arr = re.split('<sec>', line[:-1])
dabs = re.split(r'\s', arr[0])
dabs = list(filter(None, dabs))
dabs = ' '.join(dabs)
trg_arr.append(dabs)
dart = re.split(r'\s', arr[1])
dart = list(filter(None, dart))
src_arr.append(dart)
dart2id = [vocab2id[wd] if wd in vocab2id else vocab2id['<unk>']
for wd in dart]
src_idx.append(dart2id)
dart2id = [vocab2id[wd] if wd in vocab2id else ext_vocab[wd]
for wd in dart]
src_idx_ex.append(dart2id)
dart2wt = [0.0 if wd in vocab2id else 1.0 for wd in dart]
src_wt.append(dart2wt)
fp.close()
src_idx = [itm[:src_lens] for itm in src_idx]
src_idx = [itm + [vocab2id['<pad>']] *
(src_lens-len(itm)) for itm in src_idx]
src_var = Variable(torch.LongTensor(src_idx))
src_idx_ex = [itm[:src_lens] for itm in src_idx_ex]
src_idx_ex = [itm + [vocab2id['<pad>']] *
(src_lens-len(itm)) for itm in src_idx_ex]
src_var_ex = Variable(torch.LongTensor(src_idx_ex))
src_wt = [itm[:src_lens] for itm in src_wt]
src_wt = [itm + [0.0]*(src_lens-len(itm)) for itm in src_wt]
src_msk = Variable(torch.FloatTensor(src_wt))
src_arr = [itm[:src_lens] for itm in src_arr]
src_arr = [itm + ['<pad>']*(src_lens-len(itm)) for itm in src_arr]
return ext_id2oov, src_var, src_var_ex, src_arr, src_msk, trg_arr
| 29.306962 | 72 | 0.552964 | 1,319 | 9,261 | 3.649735 | 0.081122 | 0.043623 | 0.044869 | 0.0457 | 0.890528 | 0.861238 | 0.835895 | 0.817408 | 0.797258 | 0.783548 | 0 | 0.02018 | 0.315085 | 9,261 | 315 | 73 | 29.4 | 0.738767 | 0.044812 | 0 | 0.789474 | 0 | 0 | 0.023524 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.016194 | false | 0 | 0.032389 | 0 | 0.064777 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 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 | 7 |
4acac59edad758eff70dead26f2f675e525f95b1 | 15,152 | py | Python | tests/api/v3_1_1/test_network_access_authorization_exception_rules.py | CiscoISE/ciscoisesdk | 860b0fc7cc15d0c2a39c64608195a7ab3d5f4885 | [
"MIT"
] | 36 | 2021-05-18T16:24:19.000Z | 2022-03-05T13:44:41.000Z | tests/api/v3_1_1/test_network_access_authorization_exception_rules.py | CiscoISE/ciscoisesdk | 860b0fc7cc15d0c2a39c64608195a7ab3d5f4885 | [
"MIT"
] | 15 | 2021-06-08T19:03:37.000Z | 2022-02-25T14:47:33.000Z | tests/api/v3_1_1/test_network_access_authorization_exception_rules.py | CiscoISE/ciscoisesdk | 860b0fc7cc15d0c2a39c64608195a7ab3d5f4885 | [
"MIT"
] | 6 | 2021-06-10T09:32:01.000Z | 2022-01-12T08:34:39.000Z | # -*- coding: utf-8 -*-
"""IdentityServicesEngineAPI network_access_authorization_exception_rules API fixtures and tests.
Copyright (c) 2021 Cisco and/or its affiliates.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
import pytest
from fastjsonschema.exceptions import JsonSchemaException
from ciscoisesdk.exceptions import MalformedRequest
from ciscoisesdk.exceptions import ciscoisesdkException
from tests.environment import IDENTITY_SERVICES_ENGINE_VERSION
pytestmark = pytest.mark.skipif(IDENTITY_SERVICES_ENGINE_VERSION != '3.1.1', reason='version does not match')
def is_valid_get_network_access_local_exception_rules(json_schema_validate, obj):
if not obj:
return False
assert hasattr(obj, 'headers')
assert hasattr(obj, 'content')
assert hasattr(obj, 'text')
assert hasattr(obj, 'response')
json_schema_validate('jsd_2249e23ac4c658f5b75f19d13d6f7189_v3_1_1').validate(obj.response)
return True
def get_network_access_local_exception_rules(api):
endpoint_result = api.network_access_authorization_exception_rules.get_network_access_local_exception_rules(
policy_id='string'
)
return endpoint_result
@pytest.mark.network_access_authorization_exception_rules
def test_get_network_access_local_exception_rules(api, validator):
try:
assert is_valid_get_network_access_local_exception_rules(
validator,
get_network_access_local_exception_rules(api)
)
except Exception as original_e:
with pytest.raises((JsonSchemaException, MalformedRequest)):
print("ERROR: {error}".format(error=original_e))
raise original_e
def get_network_access_local_exception_rules_default(api):
endpoint_result = api.network_access_authorization_exception_rules.get_network_access_local_exception_rules(
policy_id='string'
)
return endpoint_result
@pytest.mark.network_access_authorization_exception_rules
def test_get_network_access_local_exception_rules_default(api, validator):
try:
assert is_valid_get_network_access_local_exception_rules(
validator,
get_network_access_local_exception_rules_default(api)
)
except Exception as original_e:
with pytest.raises((JsonSchemaException, MalformedRequest, TypeError)):
raise original_e
def is_valid_create_network_access_local_exception_rule(json_schema_validate, obj):
if not obj:
return False
assert hasattr(obj, 'headers')
assert hasattr(obj, 'content')
assert hasattr(obj, 'text')
assert hasattr(obj, 'response')
json_schema_validate('jsd_5c475afd2a5e57e4bd0952f2c5349c6c_v3_1_1').validate(obj.response)
return True
def create_network_access_local_exception_rule(api):
endpoint_result = api.network_access_authorization_exception_rules.create_network_access_local_exception_rule(
active_validation=False,
link={'href': 'string', 'rel': 'string', 'type': 'string'},
payload=None,
policy_id='string',
profile=['string'],
rule={'condition': {'conditionType': 'string', 'isNegate': True, 'link': {'href': 'string', 'rel': 'string', 'type': 'string'}, 'description': 'string', 'id': 'string', 'name': 'string', 'attributeName': 'string', 'attributeValue': 'string', 'dictionaryName': 'string', 'dictionaryValue': 'string', 'operator': 'string', 'children': [{'conditionType': 'string', 'isNegate': True, 'link': {'href': 'string', 'rel': 'string', 'type': 'string'}}], 'datesRange': {'endDate': 'string', 'startDate': 'string'}, 'datesRangeException': {'endDate': 'string', 'startDate': 'string'}, 'hoursRange': {'endTime': 'string', 'startTime': 'string'}, 'hoursRangeException': {'endTime': 'string', 'startTime': 'string'}, 'weekDays': ['string'], 'weekDaysException': ['string']}, 'default': True, 'hitCounts': 0, 'id': 'string', 'name': 'string', 'rank': 0, 'state': 'string'},
security_group='string'
)
return endpoint_result
@pytest.mark.network_access_authorization_exception_rules
def test_create_network_access_local_exception_rule(api, validator):
try:
assert is_valid_create_network_access_local_exception_rule(
validator,
create_network_access_local_exception_rule(api)
)
except Exception as original_e:
with pytest.raises((JsonSchemaException, MalformedRequest)):
print("ERROR: {error}".format(error=original_e))
raise original_e
def create_network_access_local_exception_rule_default(api):
endpoint_result = api.network_access_authorization_exception_rules.create_network_access_local_exception_rule(
active_validation=False,
policy_id='string',
link=None,
payload=None,
profile=None,
rule=None,
security_group=None
)
return endpoint_result
@pytest.mark.network_access_authorization_exception_rules
def test_create_network_access_local_exception_rule_default(api, validator):
try:
assert is_valid_create_network_access_local_exception_rule(
validator,
create_network_access_local_exception_rule_default(api)
)
except Exception as original_e:
with pytest.raises((JsonSchemaException, MalformedRequest, TypeError)):
raise original_e
def is_valid_reset_hit_counts_network_access_local_exceptions(json_schema_validate, obj):
if not obj:
return False
assert hasattr(obj, 'headers')
assert hasattr(obj, 'content')
assert hasattr(obj, 'text')
assert hasattr(obj, 'response')
json_schema_validate('jsd_8fc04e49e2a959cd8c498858e46f72f2_v3_1_1').validate(obj.response)
return True
def reset_hit_counts_network_access_local_exceptions(api):
endpoint_result = api.network_access_authorization_exception_rules.reset_hit_counts_network_access_local_exceptions(
active_validation=False,
payload=None,
policy_id='string'
)
return endpoint_result
@pytest.mark.network_access_authorization_exception_rules
def test_reset_hit_counts_network_access_local_exceptions(api, validator):
try:
assert is_valid_reset_hit_counts_network_access_local_exceptions(
validator,
reset_hit_counts_network_access_local_exceptions(api)
)
except Exception as original_e:
with pytest.raises((JsonSchemaException, MalformedRequest)):
print("ERROR: {error}".format(error=original_e))
raise original_e
def reset_hit_counts_network_access_local_exceptions_default(api):
endpoint_result = api.network_access_authorization_exception_rules.reset_hit_counts_network_access_local_exceptions(
active_validation=False,
policy_id='string',
payload=None
)
return endpoint_result
@pytest.mark.network_access_authorization_exception_rules
def test_reset_hit_counts_network_access_local_exceptions_default(api, validator):
try:
assert is_valid_reset_hit_counts_network_access_local_exceptions(
validator,
reset_hit_counts_network_access_local_exceptions_default(api)
)
except Exception as original_e:
with pytest.raises((JsonSchemaException, MalformedRequest, TypeError)):
raise original_e
def is_valid_get_network_access_local_exception_rule_by_id(json_schema_validate, obj):
if not obj:
return False
assert hasattr(obj, 'headers')
assert hasattr(obj, 'content')
assert hasattr(obj, 'text')
assert hasattr(obj, 'response')
json_schema_validate('jsd_0b3fe0f3ea8a5368aea79a847288993e_v3_1_1').validate(obj.response)
return True
def get_network_access_local_exception_rule_by_id(api):
endpoint_result = api.network_access_authorization_exception_rules.get_network_access_local_exception_rule_by_id(
id='string',
policy_id='string'
)
return endpoint_result
@pytest.mark.network_access_authorization_exception_rules
def test_get_network_access_local_exception_rule_by_id(api, validator):
try:
assert is_valid_get_network_access_local_exception_rule_by_id(
validator,
get_network_access_local_exception_rule_by_id(api)
)
except Exception as original_e:
with pytest.raises((JsonSchemaException, MalformedRequest)):
print("ERROR: {error}".format(error=original_e))
raise original_e
def get_network_access_local_exception_rule_by_id_default(api):
endpoint_result = api.network_access_authorization_exception_rules.get_network_access_local_exception_rule_by_id(
id='string',
policy_id='string'
)
return endpoint_result
@pytest.mark.network_access_authorization_exception_rules
def test_get_network_access_local_exception_rule_by_id_default(api, validator):
try:
assert is_valid_get_network_access_local_exception_rule_by_id(
validator,
get_network_access_local_exception_rule_by_id_default(api)
)
except Exception as original_e:
with pytest.raises((JsonSchemaException, MalformedRequest, TypeError)):
raise original_e
def is_valid_update_network_access_local_exception_rule_by_id(json_schema_validate, obj):
if not obj:
return False
assert hasattr(obj, 'headers')
assert hasattr(obj, 'content')
assert hasattr(obj, 'text')
assert hasattr(obj, 'response')
json_schema_validate('jsd_a22b2304dcc855abb2a298de6ecddb65_v3_1_1').validate(obj.response)
return True
def update_network_access_local_exception_rule_by_id(api):
endpoint_result = api.network_access_authorization_exception_rules.update_network_access_local_exception_rule_by_id(
active_validation=False,
id='string',
link={'href': 'string', 'rel': 'string', 'type': 'string'},
payload=None,
policy_id='string',
profile=['string'],
rule={'condition': {'conditionType': 'string', 'isNegate': True, 'link': {'href': 'string', 'rel': 'string', 'type': 'string'}, 'description': 'string', 'id': 'string', 'name': 'string', 'attributeName': 'string', 'attributeValue': 'string', 'dictionaryName': 'string', 'dictionaryValue': 'string', 'operator': 'string', 'children': [{'conditionType': 'string', 'isNegate': True, 'link': {'href': 'string', 'rel': 'string', 'type': 'string'}}], 'datesRange': {'endDate': 'string', 'startDate': 'string'}, 'datesRangeException': {'endDate': 'string', 'startDate': 'string'}, 'hoursRange': {'endTime': 'string', 'startTime': 'string'}, 'hoursRangeException': {'endTime': 'string', 'startTime': 'string'}, 'weekDays': ['string'], 'weekDaysException': ['string']}, 'default': True, 'hitCounts': 0, 'id': 'string', 'name': 'string', 'rank': 0, 'state': 'string'},
security_group='string'
)
return endpoint_result
@pytest.mark.network_access_authorization_exception_rules
def test_update_network_access_local_exception_rule_by_id(api, validator):
try:
assert is_valid_update_network_access_local_exception_rule_by_id(
validator,
update_network_access_local_exception_rule_by_id(api)
)
except Exception as original_e:
with pytest.raises((JsonSchemaException, MalformedRequest)):
print("ERROR: {error}".format(error=original_e))
raise original_e
def update_network_access_local_exception_rule_by_id_default(api):
endpoint_result = api.network_access_authorization_exception_rules.update_network_access_local_exception_rule_by_id(
active_validation=False,
id='string',
policy_id='string',
link=None,
payload=None,
profile=None,
rule=None,
security_group=None
)
return endpoint_result
@pytest.mark.network_access_authorization_exception_rules
def test_update_network_access_local_exception_rule_by_id_default(api, validator):
try:
assert is_valid_update_network_access_local_exception_rule_by_id(
validator,
update_network_access_local_exception_rule_by_id_default(api)
)
except Exception as original_e:
with pytest.raises((JsonSchemaException, MalformedRequest, TypeError)):
raise original_e
def is_valid_delete_network_access_local_exception_rule_by_id(json_schema_validate, obj):
if not obj:
return False
assert hasattr(obj, 'headers')
assert hasattr(obj, 'content')
assert hasattr(obj, 'text')
assert hasattr(obj, 'response')
json_schema_validate('jsd_29c0ec3a56f65447ba863ae0cac5ef6a_v3_1_1').validate(obj.response)
return True
def delete_network_access_local_exception_rule_by_id(api):
endpoint_result = api.network_access_authorization_exception_rules.delete_network_access_local_exception_rule_by_id(
id='string',
policy_id='string'
)
return endpoint_result
@pytest.mark.network_access_authorization_exception_rules
def test_delete_network_access_local_exception_rule_by_id(api, validator):
try:
assert is_valid_delete_network_access_local_exception_rule_by_id(
validator,
delete_network_access_local_exception_rule_by_id(api)
)
except Exception as original_e:
with pytest.raises((JsonSchemaException, MalformedRequest)):
print("ERROR: {error}".format(error=original_e))
raise original_e
def delete_network_access_local_exception_rule_by_id_default(api):
endpoint_result = api.network_access_authorization_exception_rules.delete_network_access_local_exception_rule_by_id(
id='string',
policy_id='string'
)
return endpoint_result
@pytest.mark.network_access_authorization_exception_rules
def test_delete_network_access_local_exception_rule_by_id_default(api, validator):
try:
assert is_valid_delete_network_access_local_exception_rule_by_id(
validator,
delete_network_access_local_exception_rule_by_id_default(api)
)
except Exception as original_e:
with pytest.raises((JsonSchemaException, MalformedRequest, TypeError)):
raise original_e
| 41.512329 | 866 | 0.74175 | 1,800 | 15,152 | 5.853333 | 0.120556 | 0.112282 | 0.112756 | 0.140945 | 0.865509 | 0.860668 | 0.859339 | 0.855353 | 0.838933 | 0.81549 | 0 | 0.011529 | 0.169945 | 15,152 | 364 | 867 | 41.626374 | 0.826191 | 0.078537 | 0 | 0.717857 | 0 | 0 | 0.126048 | 0.018488 | 0 | 0 | 0 | 0 | 0.128571 | 1 | 0.107143 | false | 0 | 0.017857 | 0 | 0.210714 | 0.021429 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 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 | 7 |
4add0030047ecea7142ded848c86183ab3c08b41 | 170 | py | Python | simplecv/interface/__init__.py | Bobholamovic/SimpleCV | f4edacf088d0155725a469e227de847820bdfa53 | [
"MIT"
] | 44 | 2019-05-12T10:02:23.000Z | 2022-01-26T07:30:45.000Z | simplecv/interface/__init__.py | Z-Zheng/simplecv | 4fa67581441ad150e82b3aa2c394a921f74e4ecd | [
"MIT"
] | 6 | 2019-11-05T02:23:18.000Z | 2021-06-15T07:06:41.000Z | simplecv/interface/__init__.py | Bobholamovic/SimpleCV | f4edacf088d0155725a469e227de847820bdfa53 | [
"MIT"
] | 8 | 2019-07-07T08:58:20.000Z | 2022-03-19T08:57:33.000Z | from simplecv.interface.module import CVModule
from simplecv.interface.learning_rate import LearningRateBase
from simplecv.interface.configurable import ConfigurableMixin | 56.666667 | 61 | 0.9 | 19 | 170 | 8 | 0.578947 | 0.236842 | 0.414474 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.064706 | 170 | 3 | 62 | 56.666667 | 0.955975 | 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 | 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 | 7 |
4aeba48f7c0cfa1068322e92411c1756e61d9d9c | 45,165 | py | Python | rmgpy/kinetics/arrheniusTest.py | pw0908/RMG-Py | 3846fcce701f2a5fd12dbfa429687e9fcd647298 | [
"MIT"
] | 1 | 2022-01-24T05:08:32.000Z | 2022-01-24T05:08:32.000Z | rmgpy/kinetics/arrheniusTest.py | pw0908/RMG-Py | 3846fcce701f2a5fd12dbfa429687e9fcd647298 | [
"MIT"
] | 72 | 2016-06-06T18:18:49.000Z | 2019-11-17T03:21:10.000Z | rmgpy/kinetics/arrheniusTest.py | pw0908/RMG-Py | 3846fcce701f2a5fd12dbfa429687e9fcd647298 | [
"MIT"
] | 3 | 2017-09-22T15:47:37.000Z | 2021-12-30T23:51:47.000Z | #!/usr/bin/env python
# -*- coding: utf-8 -*-
###############################################################################
# #
# RMG - Reaction Mechanism Generator #
# #
# Copyright (c) 2002-2019 Prof. William H. Green (whgreen@mit.edu), #
# Prof. Richard H. West (r.west@neu.edu) and the RMG Team (rmg_dev@mit.edu) #
# #
# Permission is hereby granted, free of charge, to any person obtaining a #
# copy of this software and associated documentation files (the 'Software'), #
# to deal in the Software without restriction, including without limitation #
# the rights to use, copy, modify, merge, publish, distribute, sublicense, #
# and/or sell copies of the Software, and to permit persons to whom the #
# Software is furnished to do so, subject to the following conditions: #
# #
# The above copyright notice and this permission notice shall be included in #
# all copies or substantial portions of the Software. #
# #
# THE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND, EXPRESS OR #
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, #
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE #
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER #
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING #
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER #
# DEALINGS IN THE SOFTWARE. #
# #
###############################################################################
"""
This script contains unit tests of the :mod:`rmgpy.kinetics.arrhenius` module.
"""
import unittest
import math
import numpy
from rmgpy.kinetics.arrhenius import Arrhenius, ArrheniusEP, PDepArrhenius, MultiArrhenius, MultiPDepArrhenius
import rmgpy.constants as constants
################################################################################
class TestArrhenius(unittest.TestCase):
"""
Contains unit tests of the :class:`Arrhenius` class.
"""
def setUp(self):
"""
A function run before each unit test in this class.
"""
self.A = 1.0e12
self.n = 0.5
self.Ea = 41.84
self.T0 = 1.
self.Tmin = 300.
self.Tmax = 3000.
self.comment = 'C2H6'
self.arrhenius = Arrhenius(
A = (self.A,"cm^3/(mol*s)"),
n = self.n,
Ea = (self.Ea,"kJ/mol"),
T0 = (self.T0,"K"),
Tmin = (self.Tmin,"K"),
Tmax = (self.Tmax,"K"),
comment = self.comment,
)
def test_A(self):
"""
Test that the Arrhenius A property was properly set.
"""
self.assertAlmostEqual(self.arrhenius.A.value_si * 1e6, self.A, delta=1e0)
def test_n(self):
"""
Test that the Arrhenius n property was properly set.
"""
self.assertAlmostEqual(self.arrhenius.n.value_si, self.n, 6)
def test_Ea(self):
"""
Test that the Arrhenius Ea property was properly set.
"""
self.assertAlmostEqual(self.arrhenius.Ea.value_si * 0.001, self.Ea, 6)
def test_T0(self):
"""
Test that the Arrhenius T0 property was properly set.
"""
self.assertAlmostEqual(self.arrhenius.T0.value_si, self.T0, 6)
def test_Tmin(self):
"""
Test that the Arrhenius Tmin property was properly set.
"""
self.assertAlmostEqual(self.arrhenius.Tmin.value_si, self.Tmin, 6)
def test_Tmax(self):
"""
Test that the Arrhenius Tmax property was properly set.
"""
self.assertAlmostEqual(self.arrhenius.Tmax.value_si, self.Tmax, 6)
def test_comment(self):
"""
Test that the Arrhenius comment property was properly set.
"""
self.assertEqual(self.arrhenius.comment, self.comment)
def test_isTemperatureValid(self):
"""
Test the Arrhenius.isTemperatureValid() method.
"""
Tdata = numpy.array([200,400,600,800,1000,1200,1400,1600,1800,2000])
validdata = numpy.array([False,True,True,True,True,True,True,True,True,True], numpy.bool)
for T, valid in zip(Tdata, validdata):
valid0 = self.arrhenius.isTemperatureValid(T)
self.assertEqual(valid0, valid)
def test_getRateCoefficient(self):
"""
Test the Arrhenius.getRateCoefficient() method.
"""
Tlist = numpy.array([200,400,600,800,1000,1200,1400,1600,1800,2000])
kexplist = numpy.array([1.6721e-4, 6.8770e1, 5.5803e3, 5.2448e4, 2.0632e5, 5.2285e5, 1.0281e6, 1.7225e6, 2.5912e6, 3.6123e6])
for T, kexp in zip(Tlist, kexplist):
kact = self.arrhenius.getRateCoefficient(T)
self.assertAlmostEqual(kexp, kact, delta=1e-4*kexp)
def test_changeT0(self):
"""
Test the Arrhenius.changeT0() method.
"""
Tlist = numpy.array([300,400,500,600,700,800,900,1000,1100,1200,1300,1400,1500])
k0list = numpy.array([self.arrhenius.getRateCoefficient(T) for T in Tlist])
self.arrhenius.changeT0(300)
self.assertEqual(self.arrhenius.T0.value_si, 300)
for T, kexp in zip(Tlist, k0list):
kact = self.arrhenius.getRateCoefficient(T)
self.assertAlmostEqual(kexp, kact, delta=1e-6*kexp)
def test_fitToData(self):
"""
Test the Arrhenius.fitToData() method.
"""
Tdata = numpy.array([300,400,500,600,700,800,900,1000,1100,1200,1300,1400,1500])
kdata = numpy.array([self.arrhenius.getRateCoefficient(T) for T in Tdata])
arrhenius = Arrhenius().fitToData(Tdata, kdata, kunits="m^3/(mol*s)")
self.assertEqual(float(self.arrhenius.T0.value_si), 1)
for T, k in zip(Tdata, kdata):
self.assertAlmostEqual(k, arrhenius.getRateCoefficient(T), delta=1e-6*k)
self.assertAlmostEqual(arrhenius.A.value_si, self.arrhenius.A.value_si, delta=1e0)
self.assertAlmostEqual(arrhenius.n.value_si, self.arrhenius.n.value_si, 1, 4)
self.assertAlmostEqual(arrhenius.Ea.value_si, self.arrhenius.Ea.value_si, 2)
self.assertAlmostEqual(arrhenius.T0.value_si, self.arrhenius.T0.value_si, 4)
def test_pickle(self):
"""
Test that an Arrhenius object can be pickled and unpickled with no loss
of information.
"""
import cPickle
arrhenius = cPickle.loads(cPickle.dumps(self.arrhenius,-1))
self.assertAlmostEqual(self.arrhenius.A.value, arrhenius.A.value, delta=1e0)
self.assertEqual(self.arrhenius.A.units, arrhenius.A.units)
self.assertAlmostEqual(self.arrhenius.n.value, arrhenius.n.value, 4)
self.assertAlmostEqual(self.arrhenius.Ea.value, arrhenius.Ea.value, 4)
self.assertEqual(self.arrhenius.Ea.units, arrhenius.Ea.units)
self.assertAlmostEqual(self.arrhenius.T0.value, arrhenius.T0.value, 4)
self.assertEqual(self.arrhenius.T0.units, arrhenius.T0.units)
self.assertAlmostEqual(self.arrhenius.Tmin.value, arrhenius.Tmin.value, 4)
self.assertEqual(self.arrhenius.Tmin.units, arrhenius.Tmin.units)
self.assertAlmostEqual(self.arrhenius.Tmax.value, arrhenius.Tmax.value, 4)
self.assertEqual(self.arrhenius.Tmax.units, arrhenius.Tmax.units)
self.assertEqual(self.arrhenius.comment, arrhenius.comment)
def test_repr(self):
"""
Test that an Arrhenius object can be reconstructed from its repr()
output with no loss of information.
"""
arrhenius = None
exec('arrhenius = {0!r}'.format(self.arrhenius))
self.assertAlmostEqual(self.arrhenius.A.value, arrhenius.A.value, delta=1e0)
self.assertEqual(self.arrhenius.A.units, arrhenius.A.units)
self.assertAlmostEqual(self.arrhenius.n.value, arrhenius.n.value, 4)
self.assertAlmostEqual(self.arrhenius.Ea.value, arrhenius.Ea.value, 4)
self.assertEqual(self.arrhenius.Ea.units, arrhenius.Ea.units)
self.assertAlmostEqual(self.arrhenius.T0.value, arrhenius.T0.value, 4)
self.assertEqual(self.arrhenius.T0.units, arrhenius.T0.units)
self.assertAlmostEqual(self.arrhenius.Tmin.value, arrhenius.Tmin.value, 4)
self.assertEqual(self.arrhenius.Tmin.units, arrhenius.Tmin.units)
self.assertAlmostEqual(self.arrhenius.Tmax.value, arrhenius.Tmax.value, 4)
self.assertEqual(self.arrhenius.Tmax.units, arrhenius.Tmax.units)
self.assertEqual(self.arrhenius.comment, arrhenius.comment)
def test_changeRate(self):
"""
Test the Arrhenius.changeRate() method.
"""
Tlist = numpy.array([300,400,500,600,700,800,900,1000,1100,1200,1300,1400,1500])
k0list = numpy.array([self.arrhenius.getRateCoefficient(T) for T in Tlist])
self.arrhenius.changeRate(2)
for T, kexp in zip(Tlist, k0list):
kact = self.arrhenius.getRateCoefficient(T)
self.assertAlmostEqual(2*kexp, kact, delta=1e-6*kexp)
def test_toCanteraKinetics(self):
"""
Test that the Arrhenius cantera object can be set properly within
a cantera ElementaryReaction object
"""
ctArrhenius = self.arrhenius.toCanteraKinetics()
self.assertAlmostEqual(ctArrhenius.pre_exponential_factor, 1e9,6)
self.assertAlmostEqual(ctArrhenius.temperature_exponent, 0.5)
self.assertAlmostEqual(ctArrhenius.activation_energy, 41.84e6)
def test_toArrheniusEP(self):
"""
Tests that the Arrhenius object can be converted to ArrheniusEP
"""
arrRate = self.arrhenius.getRateCoefficient(500)
arrEP = self.arrhenius.toArrheniusEP()
arrEPRate = arrEP.getRateCoefficient(500,10) # the second number should not matter
self.assertAlmostEqual(arrRate,arrEPRate)
def test_toArrheniusEP_with_alpha_and_Hrxn(self):
"""
Tests that the Arrhenius object can be converted to ArrheniusEP given parameters
"""
hrxn = 5
arrRate = self.arrhenius.getRateCoefficient(500)
arrEP = self.arrhenius.toArrheniusEP(alpha=1, dHrxn=hrxn)
self.assertAlmostEqual(1.,arrEP.alpha.value_si)
arrEPRate = arrEP.getRateCoefficient(500,hrxn)
self.assertAlmostEqual(arrRate,arrEPRate)
def test_toArrheniusEP_throws_error_with_just_alpha(self):
with self.assertRaises(Exception):
self.arrhenius.toArrheniusEP(alpha=1)
################################################################################
class TestArrheniusEP(unittest.TestCase):
"""
Contains unit tests of the :class:`ArrheniusEP` class.
"""
def setUp(self):
"""
A function run before each unit test in this class.
"""
self.A = 1.0e12
self.n = 0.5
self.alpha = 0.5
self.E0 = 41.84
self.Tmin = 300.
self.Tmax = 3000.
self.comment = 'C2H6'
self.arrhenius = ArrheniusEP(
A = (self.A,"cm^3/(mol*s)"),
n = self.n,
alpha = self.alpha,
E0 = (self.E0,"kJ/mol"),
Tmin = (self.Tmin,"K"),
Tmax = (self.Tmax,"K"),
comment = self.comment,
)
def test_A(self):
"""
Test that the ArrheniusEP A property was properly set.
"""
self.assertAlmostEqual(self.arrhenius.A.value_si * 1e6, self.A, delta=1e0)
def test_n(self):
"""
Test that the ArrheniusEP n property was properly set.
"""
self.assertAlmostEqual(self.arrhenius.n.value_si, self.n, 6)
def test_alpha(self):
"""
Test that the ArrheniusEP alpha property was properly set.
"""
self.assertAlmostEqual(self.arrhenius.alpha.value_si, self.alpha, 6)
def test_E0(self):
"""
Test that the ArrheniusEP E0 property was properly set.
"""
self.assertAlmostEqual(self.arrhenius.E0.value_si * 0.001, self.E0, 6)
def test_Tmin(self):
"""
Test that the ArrheniusEP Tmin property was properly set.
"""
self.assertAlmostEqual(self.arrhenius.Tmin.value_si, self.Tmin, 6)
def test_Tmax(self):
"""
Test that the ArrheniusEP Tmax property was properly set.
"""
self.assertAlmostEqual(self.arrhenius.Tmax.value_si, self.Tmax, 6)
def test_comment(self):
"""
Test that the ArrheniusEP comment property was properly set.
"""
self.assertEqual(self.arrhenius.comment, self.comment)
def test_isTemperatureValid(self):
"""
Test the ArrheniusEP.isTemperatureValid() method.
"""
Tdata = numpy.array([200,400,600,800,1000,1200,1400,1600,1800,2000])
validdata = numpy.array([False,True,True,True,True,True,True,True,True,True], numpy.bool)
for T, valid in zip(Tdata, validdata):
valid0 = self.arrhenius.isTemperatureValid(T)
self.assertEqual(valid0, valid)
def test_getRateCoefficient(self):
"""
Test the ArrheniusEP.getRateCoefficient() method.
"""
Tlist = numpy.array([200,400,600,800,1000,1200,1400,1600,1800,2000])
kexplist = numpy.array([1.6721e-4, 6.8770e1, 5.5803e3, 5.2448e4, 2.0632e5, 5.2285e5, 1.0281e6, 1.7225e6, 2.5912e6, 3.6123e6])
for T, kexp in zip(Tlist, kexplist):
kact = self.arrhenius.getRateCoefficient(T, )
self.assertAlmostEqual(kexp, kact, delta=1e-4*kexp)
def test_pickle(self):
"""
Test that an ArrheniusEP object can be pickled and unpickled with no loss
of information.
"""
import cPickle
arrhenius = cPickle.loads(cPickle.dumps(self.arrhenius, -1))
self.assertAlmostEqual(self.arrhenius.A.value, arrhenius.A.value, delta=1e0)
self.assertEqual(self.arrhenius.A.units, arrhenius.A.units)
self.assertAlmostEqual(self.arrhenius.n.value, arrhenius.n.value, 4)
self.assertAlmostEqual(self.arrhenius.alpha.value, arrhenius.alpha.value, 4)
self.assertAlmostEqual(self.arrhenius.E0.value, arrhenius.E0.value, 4)
self.assertEqual(self.arrhenius.E0.units, arrhenius.E0.units)
self.assertAlmostEqual(self.arrhenius.Tmin.value, arrhenius.Tmin.value, 4)
self.assertEqual(self.arrhenius.Tmin.units, arrhenius.Tmin.units)
self.assertAlmostEqual(self.arrhenius.Tmax.value, arrhenius.Tmax.value, 4)
self.assertEqual(self.arrhenius.Tmax.units, arrhenius.Tmax.units)
self.assertEqual(self.arrhenius.comment, arrhenius.comment)
def test_repr(self):
"""
Test that an ArrheniusEP object can be reconstructed from its repr()
output with no loss of information.
"""
arrhenius = None
exec('arrhenius = {0!r}'.format(self.arrhenius))
self.assertAlmostEqual(self.arrhenius.A.value, arrhenius.A.value, delta=1e0)
self.assertEqual(self.arrhenius.A.units, arrhenius.A.units)
self.assertAlmostEqual(self.arrhenius.n.value, arrhenius.n.value, 4)
self.assertAlmostEqual(self.arrhenius.alpha.value, arrhenius.alpha.value, 4)
self.assertAlmostEqual(self.arrhenius.E0.value, arrhenius.E0.value, 4)
self.assertEqual(self.arrhenius.E0.units, arrhenius.E0.units)
self.assertAlmostEqual(self.arrhenius.Tmin.value, arrhenius.Tmin.value, 4)
self.assertEqual(self.arrhenius.Tmin.units, arrhenius.Tmin.units)
self.assertAlmostEqual(self.arrhenius.Tmax.value, arrhenius.Tmax.value, 4)
self.assertEqual(self.arrhenius.Tmax.units, arrhenius.Tmax.units)
self.assertEqual(self.arrhenius.comment, arrhenius.comment)
def test_changeRate(self):
"""
Test the ArrheniusEP.changeRate() method.
"""
Tlist = numpy.array([300,400,500,600,700,800,900,1000,1100,1200,1300,1400,1500])
k0list = numpy.array([self.arrhenius.getRateCoefficient(T) for T in Tlist])
self.arrhenius.changeRate(2)
for T, kexp in zip(Tlist, k0list):
kact = self.arrhenius.getRateCoefficient(T)
self.assertAlmostEqual(2*kexp, kact, delta=1e-6*kexp)
################################################################################
class TestPDepArrhenius(unittest.TestCase):
"""
Contains unit tests of the :class:`PDepArrhenius` class.
"""
def setUp(self):
"""
A function run before each unit test in this class.
"""
self.arrhenius0 = Arrhenius(
A = (1.0e6,"s^-1"),
n = 1.0,
Ea = (10.0,"kJ/mol"),
T0 = (300.0,"K"),
Tmin = (300.0,"K"),
Tmax = (2000.0,"K"),
comment = """This data is completely made up""",
)
self.arrhenius1 = Arrhenius(
A = (1.0e12,"s^-1"),
n = 1.0,
Ea = (20.0,"kJ/mol"),
T0 = (300.0,"K"),
Tmin = (300.0,"K"),
Tmax = (2000.0,"K"),
comment = """This data is completely made up""",
)
self.pressures = numpy.array([0.1, 10.0])
self.arrhenius = [self.arrhenius0, self.arrhenius1]
self.Tmin = 300.0
self.Tmax = 2000.0
self.Pmin = 0.1
self.Pmax = 10.0
self.comment = """This data is completely made up"""
self.kinetics = PDepArrhenius(
pressures = (self.pressures,"bar"),
arrhenius = self.arrhenius,
Tmin = (self.Tmin,"K"),
Tmax = (self.Tmax,"K"),
Pmin = (self.Pmin,"bar"),
Pmax = (self.Pmax,"bar"),
comment = self.comment,
)
def test_pressures(self):
"""
Test that the PDepArrhenius pressures property was properly set.
"""
self.assertEqual(len(self.kinetics.pressures.value_si), 2)
for i in range(2):
self.assertAlmostEqual(self.kinetics.pressures.value_si[i] * 1e-5, self.pressures[i], 4)
def test_arrhenius(self):
"""
Test that the PDepArrhenius arrhenius property was properly set.
"""
self.assertEqual(len(self.kinetics.arrhenius), 2)
for i in range(2):
self.assertAlmostEqual(self.kinetics.arrhenius[i].A.value, self.arrhenius[i].A.value, delta=1e0)
self.assertEqual(self.kinetics.arrhenius[i].A.units, self.arrhenius[i].A.units)
self.assertAlmostEqual(self.kinetics.arrhenius[i].n.value, self.arrhenius[i].n.value, 4)
self.assertAlmostEqual(self.kinetics.arrhenius[i].Ea.value, self.arrhenius[i].Ea.value, 4)
self.assertEqual(self.kinetics.arrhenius[i].Ea.units, self.arrhenius[i].Ea.units)
self.assertAlmostEqual(self.kinetics.arrhenius[i].T0.value, self.arrhenius[i].T0.value, 4)
self.assertEqual(self.kinetics.arrhenius[i].T0.units, self.arrhenius[i].T0.units)
self.assertAlmostEqual(self.kinetics.arrhenius[i].Tmin.value, self.arrhenius[i].Tmin.value, 4)
self.assertEqual(self.kinetics.arrhenius[i].Tmin.units, self.arrhenius[i].Tmin.units)
self.assertAlmostEqual(self.kinetics.arrhenius[i].Tmax.value, self.arrhenius[i].Tmax.value, 4)
self.assertEqual(self.kinetics.arrhenius[i].Tmax.units, self.arrhenius[i].Tmax.units)
self.assertEqual(self.kinetics.arrhenius[i].comment, self.arrhenius[i].comment)
def test_Tmin(self):
"""
Test that the PDepArrhenius Tmin property was properly set.
"""
self.assertAlmostEqual(self.kinetics.Tmin.value_si, self.Tmin, 6)
def test_Tmax(self):
"""
Test that the PDepArrhenius Tmax property was properly set.
"""
self.assertAlmostEqual(self.kinetics.Tmax.value_si, self.Tmax, 6)
def test_Pmin(self):
"""
Test that the PDepArrhenius Pmin property was properly set.
"""
self.assertAlmostEqual(self.kinetics.Pmin.value_si*1e-5, self.Pmin, 6)
def test_Pmax(self):
"""
Test that the PDepArrhenius Pmax property was properly set.
"""
self.assertAlmostEqual(self.kinetics.Pmax.value_si*1e-5, self.Pmax, 6)
def test_comment(self):
"""
Test that the PDepArrhenius comment property was properly set.
"""
self.assertEqual(self.kinetics.comment, self.comment)
def test_isPressureDependent(self):
"""
Test the PDepArrhenius.isPressureDependent() method.
"""
self.assertTrue(self.kinetics.isPressureDependent())
def test_getRateCoefficient(self):
"""
Test the PDepArrhenius.getRateCoefficient() method.
"""
P = 1e4
for T in [300,400,500,600,700,800,900,1000,1100,1200,1300,1400,1500]:
k0 = self.kinetics.getRateCoefficient(T, P)
k1 = self.arrhenius0.getRateCoefficient(T)
self.assertAlmostEqual(k0, k1, delta=1e-6*k1)
P = 1e6
for T in [300,400,500,600,700,800,900,1000,1100,1200,1300,1400,1500]:
k0 = self.kinetics.getRateCoefficient(T, P)
k1 = self.arrhenius1.getRateCoefficient(T)
self.assertAlmostEqual(k0, k1, delta=1e-6*k1)
P = 1e5
for T in [300,400,500,600,700,800,900,1000,1100,1200,1300,1400,1500]:
k0 = self.kinetics.getRateCoefficient(T, P)
k1 = math.sqrt(self.arrhenius0.getRateCoefficient(T) * self.arrhenius1.getRateCoefficient(T))
self.assertAlmostEqual(k0, k1, delta=1e-6*k1)
def test_fitToData(self):
"""
Test the PDepArrhenius.fitToData() method.
"""
Tdata = numpy.array([300,400,500,600,700,800,900,1000,1100,1200,1300,1400,1500], numpy.float)
Pdata = numpy.array([1e4,3e4,1e5,3e5,1e6], numpy.float)
kdata = numpy.zeros([len(Tdata),len(Pdata)], numpy.float)
for t in range(len(Tdata)):
for p in range(len(Pdata)):
kdata[t,p] = self.kinetics.getRateCoefficient(Tdata[t], Pdata[p])
kinetics = PDepArrhenius().fitToData(Tdata, Pdata, kdata, kunits="s^-1")
for t in range(len(Tdata)):
for p in range(len(Pdata)):
self.assertAlmostEqual(kinetics.getRateCoefficient(Tdata[t], Pdata[p]), kdata[t,p], delta=1e-6*kdata[t,p])
def test_pickle(self):
"""
Test that a PDepArrhenius object can be successfully pickled and
unpickled with no loss of information.
"""
import cPickle
kinetics = cPickle.loads(cPickle.dumps(self.kinetics,-1))
Narrh = 2
self.assertEqual(len(self.kinetics.pressures.value), Narrh)
self.assertEqual(len(kinetics.pressures.value), Narrh)
self.assertEqual(len(self.kinetics.arrhenius), Narrh)
self.assertEqual(len(kinetics.arrhenius), Narrh)
for i in range(Narrh):
self.assertAlmostEqual(self.kinetics.pressures.value[i], kinetics.pressures.value[i], 4)
self.assertAlmostEqual(self.kinetics.arrhenius[i].A.value, kinetics.arrhenius[i].A.value, delta=1e0)
self.assertEqual(self.kinetics.arrhenius[i].A.units, kinetics.arrhenius[i].A.units)
self.assertAlmostEqual(self.kinetics.arrhenius[i].n.value, kinetics.arrhenius[i].n.value)
self.assertAlmostEqual(self.kinetics.arrhenius[i].T0.value, kinetics.arrhenius[i].T0.value, 4)
self.assertEqual(self.kinetics.arrhenius[i].T0.units, kinetics.arrhenius[i].T0.units)
self.assertAlmostEqual(self.kinetics.arrhenius[i].Ea.value, kinetics.arrhenius[i].Ea.value, 4)
self.assertEqual(self.kinetics.arrhenius[i].Ea.units, kinetics.arrhenius[i].Ea.units)
self.assertAlmostEqual(self.kinetics.Tmin.value, kinetics.Tmin.value, 4)
self.assertEqual(self.kinetics.Tmin.units, kinetics.Tmin.units)
self.assertAlmostEqual(self.kinetics.Tmax.value, kinetics.Tmax.value, 4)
self.assertEqual(self.kinetics.Tmax.units, kinetics.Tmax.units)
self.assertAlmostEqual(self.kinetics.Pmin.value, kinetics.Pmin.value, 4)
self.assertEqual(self.kinetics.Pmin.units, kinetics.Pmin.units)
self.assertAlmostEqual(self.kinetics.Pmax.value, kinetics.Pmax.value, 4)
self.assertEqual(self.kinetics.Pmax.units, kinetics.Pmax.units)
self.assertEqual(self.kinetics.comment, kinetics.comment)
def test_repr(self):
"""
Test that a PDepArrhenius object can be successfully reconstructed
from its repr() output with no loss of information.
"""
kinetics = None
exec('kinetics = {0!r}'.format(self.kinetics))
Narrh = 2
self.assertEqual(len(self.kinetics.pressures.value), Narrh)
self.assertEqual(len(kinetics.pressures.value), Narrh)
self.assertEqual(len(self.kinetics.arrhenius), Narrh)
self.assertEqual(len(kinetics.arrhenius), Narrh)
for i in range(Narrh):
self.assertAlmostEqual(self.kinetics.pressures.value[i], kinetics.pressures.value[i], 4)
self.assertAlmostEqual(self.kinetics.arrhenius[i].A.value, kinetics.arrhenius[i].A.value, delta=1e0)
self.assertEqual(self.kinetics.arrhenius[i].A.units, kinetics.arrhenius[i].A.units)
self.assertAlmostEqual(self.kinetics.arrhenius[i].n.value, kinetics.arrhenius[i].n.value)
self.assertAlmostEqual(self.kinetics.arrhenius[i].T0.value, kinetics.arrhenius[i].T0.value, 4)
self.assertEqual(self.kinetics.arrhenius[i].T0.units, kinetics.arrhenius[i].T0.units)
self.assertAlmostEqual(self.kinetics.arrhenius[i].Ea.value, kinetics.arrhenius[i].Ea.value, 4)
self.assertEqual(self.kinetics.arrhenius[i].Ea.units, kinetics.arrhenius[i].Ea.units)
self.assertAlmostEqual(self.kinetics.Tmin.value, kinetics.Tmin.value, 4)
self.assertEqual(self.kinetics.Tmin.units, kinetics.Tmin.units)
self.assertAlmostEqual(self.kinetics.Tmax.value, kinetics.Tmax.value, 4)
self.assertEqual(self.kinetics.Tmax.units, kinetics.Tmax.units)
self.assertAlmostEqual(self.kinetics.Pmin.value, kinetics.Pmin.value, 4)
self.assertEqual(self.kinetics.Pmin.units, kinetics.Pmin.units)
self.assertAlmostEqual(self.kinetics.Pmax.value, kinetics.Pmax.value, 4)
self.assertEqual(self.kinetics.Pmax.units, kinetics.Pmax.units)
self.assertEqual(self.kinetics.comment, kinetics.comment)
def test_changeRate(self):
"""
Test the PDepArrhenius.changeRate() method.
"""
Tlist = numpy.array([300,400,500,600,700,800,900,1000,1100,1200,1300,1400,1500])
k0list = numpy.array([self.kinetics.getRateCoefficient(T, 1e5) for T in Tlist])
self.kinetics.changeRate(2)
for T, kexp in zip(Tlist, k0list):
kact = self.kinetics.getRateCoefficient(T, 1e5)
self.assertAlmostEqual(2*kexp, kact, delta=1e-6*kexp)
################################################################################
class TestMultiArrhenius(unittest.TestCase):
"""
Contains unit tests of the :class:`MultiArrhenius` class.
"""
def setUp(self):
"""
A function run before each unit test in this class.
"""
self.Tmin = 350.
self.Tmax = 1500.
self.comment = 'Comment'
self.arrhenius = [
Arrhenius(
A = (9.3e-14,"cm^3/(molecule*s)"),
n = 0.0,
Ea = (4740*constants.R*0.001,"kJ/mol"),
T0 = (1,"K"),
Tmin = (self.Tmin,"K"),
Tmax = (self.Tmax,"K"),
comment = self.comment,
),
Arrhenius(
A = (1.4e-9,"cm^3/(molecule*s)"),
n = 0.0,
Ea = (11200*constants.R*0.001,"kJ/mol"),
T0 = (1,"K"),
Tmin = (self.Tmin,"K"),
Tmax = (self.Tmax,"K"),
comment = self.comment,
),
]
self.kinetics = MultiArrhenius(
arrhenius = self.arrhenius,
Tmin = (self.Tmin,"K"),
Tmax = (self.Tmax,"K"),
comment = self.comment,
)
self.single_kinetics = MultiArrhenius(
arrhenius = self.arrhenius[:1],
Tmin = (self.Tmin,"K"),
Tmax = (self.Tmax,"K"),
comment = self.comment,
)
def test_arrhenius(self):
"""
Test that the MultiArrhenius A property was properly set.
"""
self.assertEqual(self.kinetics.arrhenius, self.arrhenius)
def test_Tmin(self):
"""
Test that the MultiArrhenius Tmin property was properly set.
"""
self.assertAlmostEqual(self.kinetics.Tmin.value_si, self.Tmin, 6)
def test_Tmax(self):
"""
Test that the MultiArrhenius Tmax property was properly set.
"""
self.assertAlmostEqual(self.kinetics.Tmax.value_si, self.Tmax, 6)
def test_comment(self):
"""
Test that the MultiArrhenius comment property was properly set.
"""
self.assertEqual(self.kinetics.comment, self.comment)
def test_isTemperatureValid(self):
"""
Test the MultiArrhenius.isTemperatureValid() method.
"""
Tdata = numpy.array([200,400,600,800,1000,1200,1400,1600,1800,2000])
validdata = numpy.array([False,True,True,True,True,True,True,False,False,False], numpy.bool)
for T, valid in zip(Tdata, validdata):
valid0 = self.kinetics.isTemperatureValid(T)
self.assertEqual(valid0, valid)
def test_getRateCoefficient(self):
"""
Test the MultiArrhenius.getRateCoefficient() method.
"""
Tlist = numpy.array([200,400,600,800,1000,1200,1400,1600,1800,2000])
kexplist = numpy.array([2.85400e-06, 4.00384e-01, 2.73563e+01, 8.50699e+02, 1.20181e+04, 7.56312e+04, 2.84724e+05, 7.71702e+05, 1.67743e+06, 3.12290e+06])
for T, kexp in zip(Tlist, kexplist):
kact = self.kinetics.getRateCoefficient(T)
self.assertAlmostEqual(kexp, kact, delta=1e-4*kexp)
def test_pickle(self):
"""
Test that a MultiArrhenius object can be pickled and unpickled with no loss
of information.
"""
import cPickle
kinetics = cPickle.loads(cPickle.dumps(self.kinetics,-1))
self.assertEqual(len(self.kinetics.arrhenius), len(kinetics.arrhenius))
for arrh0, arrh in zip(self.kinetics.arrhenius, kinetics.arrhenius):
self.assertAlmostEqual(arrh0.A.value, arrh.A.value, delta=1e-18)
self.assertEqual(arrh0.A.units, arrh.A.units)
self.assertAlmostEqual(arrh0.n.value, arrh.n.value, 4)
self.assertAlmostEqual(arrh0.Ea.value, arrh.Ea.value, 4)
self.assertEqual(arrh0.Ea.units, arrh.Ea.units)
self.assertAlmostEqual(arrh0.T0.value, arrh.T0.value, 4)
self.assertEqual(arrh0.T0.units, arrh.T0.units)
self.assertAlmostEqual(self.kinetics.Tmin.value, kinetics.Tmin.value, 4)
self.assertEqual(self.kinetics.Tmin.units, kinetics.Tmin.units)
self.assertAlmostEqual(self.kinetics.Tmax.value, kinetics.Tmax.value, 4)
self.assertEqual(self.kinetics.Tmax.units, kinetics.Tmax.units)
self.assertEqual(self.kinetics.comment, kinetics.comment)
def test_repr(self):
"""
Test that a MultiArrhenius object can be reconstructed from its repr()
output with no loss of information.
"""
kinetics = None
exec('kinetics = {0!r}'.format(self.kinetics))
self.assertEqual(len(self.kinetics.arrhenius), len(kinetics.arrhenius))
for arrh0, arrh in zip(self.kinetics.arrhenius, kinetics.arrhenius):
self.assertAlmostEqual(arrh0.A.value, arrh.A.value, delta=1e-18)
self.assertEqual(arrh0.A.units, arrh.A.units)
self.assertAlmostEqual(arrh0.n.value, arrh.n.value, 4)
self.assertAlmostEqual(arrh0.Ea.value, arrh.Ea.value, 4)
self.assertEqual(arrh0.Ea.units, arrh.Ea.units)
self.assertAlmostEqual(arrh0.T0.value, arrh.T0.value, 4)
self.assertEqual(arrh0.T0.units, arrh.T0.units)
self.assertAlmostEqual(self.kinetics.Tmin.value, kinetics.Tmin.value, 4)
self.assertEqual(self.kinetics.Tmin.units, kinetics.Tmin.units)
self.assertAlmostEqual(self.kinetics.Tmax.value, kinetics.Tmax.value, 4)
self.assertEqual(self.kinetics.Tmax.units, kinetics.Tmax.units)
self.assertEqual(self.kinetics.comment, kinetics.comment)
def test_toArrhenius(self):
"""
Test that we can convert to an Arrhenius
"""
answer = self.single_kinetics.arrhenius[0]
fitted = self.single_kinetics.toArrhenius()
self.assertAlmostEqual(fitted.A.value_si, answer.A.value_si, delta=1e0)
self.assertAlmostEqual(fitted.n.value_si, answer.n.value_si, 1, 4)
self.assertAlmostEqual(fitted.Ea.value_si, answer.Ea.value_si, 2)
self.assertAlmostEqual(fitted.T0.value_si, answer.T0.value_si, 4)
def test_toArrheniusTrange(self):
"""
Test the toArrhenius temperature range is set correctly.
"""
answer = self.single_kinetics.arrhenius[0]
fitted = self.single_kinetics.toArrhenius(Tmin=800, Tmax=1200)
self.assertAlmostEqual(fitted.Tmin.value_si, 800.0)
self.assertAlmostEqual(fitted.Tmax.value_si, 1200.0)
for T in [800,1000,1200]:
self.assertAlmostEqual(fitted.getRateCoefficient(T) / answer.getRateCoefficient(T), 1.0)
def test_toArrheniusMultiple(self):
"""
Test the toArrhenius fitting multiple kinetics over a small range, see if we're within 5% at a few points
"""
answer = self.kinetics
fitted = self.kinetics.toArrhenius(Tmin=800, Tmax=1200)
self.assertAlmostEqual(fitted.Tmin.value_si, 800.0)
self.assertAlmostEqual(fitted.Tmax.value_si, 1200.0)
for T in [800,1000,1200]:
self.assertAlmostEqual(fitted.getRateCoefficient(T) / answer.getRateCoefficient(T), 1.0, delta=0.05)
def test_changeRate(self):
"""
Test the MultiArrhenius.changeRate() method.
"""
Tlist = numpy.array([300,400,500,600,700,800,900,1000,1100,1200,1300,1400,1500])
k0list = numpy.array([self.kinetics.getRateCoefficient(T) for T in Tlist])
self.kinetics.changeRate(2)
for T, kexp in zip(Tlist, k0list):
kact = self.kinetics.getRateCoefficient(T)
self.assertAlmostEqual(2*kexp, kact, delta=1e-6*kexp)
################################################################################
class TestMultiPDepArrhenius(unittest.TestCase):
"""
Contains unit tests of the :class:`MultiPDepArrhenius` class.
"""
def setUp(self):
"""
A function run before each unit test in this class.
"""
self.Tmin = 350.
self.Tmax = 1500.
self.Pmin = 1e-1
self.Pmax = 1e1
self.pressures = numpy.array([1e-1,1e1])
self.comment = 'CH3 + C2H6 <=> CH4 + C2H5 (Baulch 2005)'
self.arrhenius = [
PDepArrhenius(
pressures = (self.pressures,"bar"),
arrhenius = [
Arrhenius(
A = (9.3e-16,"cm^3/(molecule*s)"),
n = 0.0,
Ea = (4740*constants.R*0.001,"kJ/mol"),
T0 = (1,"K"),
Tmin = (self.Tmin,"K"),
Tmax = (self.Tmax,"K"),
comment = self.comment,
),
Arrhenius(
A = (9.3e-14,"cm^3/(molecule*s)"),
n = 0.0,
Ea = (4740*constants.R*0.001,"kJ/mol"),
T0 = (1,"K"),
Tmin = (self.Tmin,"K"),
Tmax = (self.Tmax,"K"),
comment = self.comment,
),
],
Tmin = (self.Tmin,"K"),
Tmax = (self.Tmax,"K"),
Pmin = (self.Pmin,"bar"),
Pmax = (self.Pmax,"bar"),
comment = self.comment,
),
PDepArrhenius(
pressures = (self.pressures,"bar"),
arrhenius = [
Arrhenius(
A = (1.4e-11,"cm^3/(molecule*s)"),
n = 0.0,
Ea = (11200*constants.R*0.001,"kJ/mol"),
T0 = (1,"K"),
Tmin = (self.Tmin,"K"),
Tmax = (self.Tmax,"K"),
comment = self.comment,
),
Arrhenius(
A = (1.4e-9,"cm^3/(molecule*s)"),
n = 0.0,
Ea = (11200*constants.R*0.001,"kJ/mol"),
T0 = (1,"K"),
Tmin = (self.Tmin,"K"),
Tmax = (self.Tmax,"K"),
comment = self.comment,
),
],
Tmin = (self.Tmin,"K"),
Tmax = (self.Tmax,"K"),
Pmin = (self.Pmin,"bar"),
Pmax = (self.Pmax,"bar"),
comment = self.comment,
),
]
self.kinetics = MultiPDepArrhenius(
arrhenius = self.arrhenius,
Tmin = (self.Tmin,"K"),
Tmax = (self.Tmax,"K"),
Pmin = (self.Pmin,"bar"),
Pmax = (self.Pmax,"bar"),
comment = self.comment,
)
def test_arrhenius(self):
"""
Test that the MultiPDepArrhenius arrhenius property was properly set.
"""
self.assertEqual(self.kinetics.arrhenius, self.arrhenius)
def test_Tmin(self):
"""
Test that the MultiPDepArrhenius Tmin property was properly set.
"""
self.assertAlmostEqual(self.kinetics.Tmin.value_si, self.Tmin, 6)
def test_Tmax(self):
"""
Test that the MultiPDepArrhenius Tmax property was properly set.
"""
self.assertAlmostEqual(self.kinetics.Tmax.value_si, self.Tmax, 6)
def test_Pmin(self):
"""
Test that the MultiPDepArrhenius Pmin property was properly set.
"""
self.assertAlmostEqual(self.kinetics.Pmin.value_si*1e-5, self.Pmin, 6)
def test_Pmax(self):
"""
Test that the MultiPDepArrhenius Pmax property was properly set.
"""
self.assertAlmostEqual(self.kinetics.Pmax.value_si*1e-5, self.Pmax, 6)
def test_comment(self):
"""
Test that the MultiPDepArrhenius comment property was properly set.
"""
self.assertEqual(self.kinetics.comment, self.comment)
def test_isTemperatureValid(self):
"""
Test the MultiPDepArrhenius.isTemperatureValid() method.
"""
Tdata = numpy.array([200,400,600,800,1000,1200,1400,1600,1800,2000])
validdata = numpy.array([False,True,True,True,True,True,True,False,False,False], numpy.bool)
for T, valid in zip(Tdata, validdata):
valid0 = self.kinetics.isTemperatureValid(T)
self.assertEqual(valid0, valid)
def test_isPressureValid(self):
"""
Test the MultiPDepArrhenius.isPressureValid() method.
"""
Pdata = numpy.array([1e3,1e4,1e5,1e6,1e7])
validdata = numpy.array([False,True,True,True,False], numpy.bool)
for P, valid in zip(Pdata, validdata):
valid0 = self.kinetics.isPressureValid(P)
self.assertEqual(valid0, valid)
def test_getRateCoefficient(self):
"""
Test the MultiPDepArrhenius.getRateCoefficient() method.
"""
Tlist = numpy.array([200,400,600,800,1000,1200,1400,1600,1800,2000])
Plist = numpy.array([1e4,1e5,1e6])
kexplist = numpy.array([
[2.85400e-08, 4.00384e-03, 2.73563e-01, 8.50699e+00, 1.20181e+02, 7.56312e+02, 2.84724e+03, 7.71702e+03, 1.67743e+04, 3.12290e+04],
[2.85400e-07, 4.00384e-02, 2.73563e+00, 8.50699e+01, 1.20181e+03, 7.56312e+03, 2.84724e+04, 7.71702e+04, 1.67743e+05, 3.12290e+05],
[2.85400e-06, 4.00384e-01, 2.73563e+01, 8.50699e+02, 1.20181e+04, 7.56312e+04, 2.84724e+05, 7.71702e+05, 1.67743e+06, 3.12290e+06],
]).T
for i in range(Tlist.shape[0]):
for j in range(Plist.shape[0]):
kexp = kexplist[i,j]
kact = self.kinetics.getRateCoefficient(Tlist[i], Plist[j])
self.assertAlmostEqual(kexp, kact, delta=1e-4*kexp)
def test_getRateCoefficient_diff_plist(self):
"""
Test the MultiPDepArrhenius.getRateCoefficient() when plists are different.
"""
# modify the MultiPDepArrhenius object with an additional entry
pressures = numpy.array([1e-1, 1e-1, 1e1])
self.kinetics.arrhenius[0].pressures = (pressures,"bar")
self.kinetics.arrhenius[0].arrhenius.insert(0, self.kinetics.arrhenius[0].arrhenius[0])
Tlist = numpy.array([200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000])
Plist = numpy.array([1e4, 1e5, 1e6])
kexplist = numpy.array([
[2.85400e-08, 4.00384e-03, 2.73563e-01, 8.50699e+00, 1.20181e+02, 7.56312e+02, 2.84724e+03, 7.71702e+03,
1.67743e+04, 3.12290e+04],
[2.85400e-07, 4.00384e-02, 2.73563e+00, 8.50699e+01, 1.20181e+03, 7.56312e+03, 2.84724e+04, 7.71702e+04,
1.67743e+05, 3.12290e+05],
[2.85400e-06, 4.00384e-01, 2.73563e+01, 8.50699e+02, 1.20181e+04, 7.56312e+04, 2.84724e+05, 7.71702e+05,
1.67743e+06, 3.12290e+06],
]).T
for i in range(Tlist.shape[0]):
for j in range(Plist.shape[0]):
kexp = kexplist[i, j]
kact = self.kinetics.getRateCoefficient(Tlist[i], Plist[j])
self.assertAlmostEqual(kexp, kact, delta=1e-4 * kexp)
def test_pickle(self):
"""
Test that a MultiPDepArrhenius object can be pickled and unpickled with
no loss of information.
"""
import cPickle
kinetics = cPickle.loads(cPickle.dumps(self.kinetics,-1))
self.assertEqual(len(self.kinetics.arrhenius), len(kinetics.arrhenius))
self.assertAlmostEqual(self.kinetics.Tmin.value, kinetics.Tmin.value, 4)
self.assertEqual(self.kinetics.Tmin.units, kinetics.Tmin.units)
self.assertAlmostEqual(self.kinetics.Tmax.value, kinetics.Tmax.value, 4)
self.assertEqual(self.kinetics.Tmax.units, kinetics.Tmax.units)
self.assertEqual(self.kinetics.comment, kinetics.comment)
def test_repr(self):
"""
Test that a MultiPDepArrhenius object can be reconstructed from its
repr() output with no loss of information.
"""
kinetics = None
exec('kinetics = {0!r}'.format(self.kinetics))
self.assertEqual(len(self.kinetics.arrhenius), len(kinetics.arrhenius))
self.assertAlmostEqual(self.kinetics.Tmin.value, kinetics.Tmin.value, 4)
self.assertEqual(self.kinetics.Tmin.units, kinetics.Tmin.units)
self.assertAlmostEqual(self.kinetics.Tmax.value, kinetics.Tmax.value, 4)
self.assertEqual(self.kinetics.Tmax.units, kinetics.Tmax.units)
self.assertEqual(self.kinetics.comment, kinetics.comment)
def test_changeRate(self):
"""
Test the PDepMultiArrhenius.changeRate() method.
"""
Tlist = numpy.array([300,400,500,600,700,800,900,1000,1100,1200,1300,1400,1500])
k0list = numpy.array([self.kinetics.getRateCoefficient(T,1e5) for T in Tlist])
self.kinetics.changeRate(2)
for T, kexp in zip(Tlist, k0list):
kact = self.kinetics.getRateCoefficient(T,1e5)
self.assertAlmostEqual(2*kexp, kact, delta=1e-6*kexp)
| 45.165 | 162 | 0.595638 | 5,306 | 45,165 | 5.044478 | 0.074444 | 0.057386 | 0.073788 | 0.053015 | 0.851267 | 0.810618 | 0.793619 | 0.776209 | 0.738773 | 0.723268 | 0 | 0.067551 | 0.270718 | 45,165 | 999 | 163 | 45.21021 | 0.745066 | 0.153482 | 0 | 0.751189 | 0 | 0 | 0.014387 | 0 | 0 | 0 | 0 | 0 | 0.340729 | 1 | 0.115689 | false | 0 | 0.015848 | 0 | 0.139461 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 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 | 7 |
ab0eb2080576eaa7205d6829d69e19df71b82f56 | 140 | py | Python | rastering/__init__.py | ndrplz/tensorflow_mesh_renderer | 208e7ce8e53b0a8a91dd88afe76e01413cd9ebb1 | [
"MIT"
] | 75 | 2019-01-05T09:14:06.000Z | 2022-03-22T12:31:24.000Z | rastering/__init__.py | ndrplz/tensorflow_mesh_renderer | 208e7ce8e53b0a8a91dd88afe76e01413cd9ebb1 | [
"MIT"
] | 1 | 2018-11-02T13:54:29.000Z | 2018-11-14T10:35:56.000Z | rastering/__init__.py | ndrplz/tensorflow_mesh_renderer | 208e7ce8e53b0a8a91dd88afe76e01413cd9ebb1 | [
"MIT"
] | 12 | 2019-01-01T05:15:04.000Z | 2021-08-12T23:53:38.000Z | from rastering.rasterer import Rasterer
from rastering.rototranslation import RotoTranslation
from rastering.rototranslation import Vector
| 28 | 53 | 0.885714 | 15 | 140 | 8.266667 | 0.4 | 0.314516 | 0.451613 | 0.548387 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.092857 | 140 | 4 | 54 | 35 | 0.976378 | 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 | 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 | 8 |
ab1d0d56139b4701c6e5b9459df87b084b7e5254 | 94 | py | Python | memethon/js/__init__.py | memethon/memethon | 39a7adf53f0fc142d1ba4b822053771d6e25d5b1 | [
"MIT"
] | 4 | 2021-07-08T15:49:39.000Z | 2021-12-05T16:12:07.000Z | memethon/js/__init__.py | Hunter2807/memethon | 39a7adf53f0fc142d1ba4b822053771d6e25d5b1 | [
"MIT"
] | 1 | 2021-07-10T03:53:16.000Z | 2021-09-23T16:54:32.000Z | memethon/js/__init__.py | memethon/memethon | 39a7adf53f0fc142d1ba4b822053771d6e25d5b1 | [
"MIT"
] | null | null | null | from memethon.js.console import console
from memethon.js.dtypes import (Array, String, Tuple)
| 31.333333 | 53 | 0.808511 | 14 | 94 | 5.428571 | 0.642857 | 0.315789 | 0.368421 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.106383 | 94 | 2 | 54 | 47 | 0.904762 | 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 | 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 | 7 |
db8cbbca5ad0b501ffb7f0490db667101227f58d | 215 | py | Python | metadata-ingestion/src/datahub/ingestion/source_report/time_window.py | cuong-pham/datahub | cb4eb001758f55622add0f4dc3650cf483609cba | [
"Apache-2.0"
] | 1,603 | 2016-03-03T17:21:03.000Z | 2020-01-22T22:12:02.000Z | metadata-ingestion/src/datahub/ingestion/source_report/time_window.py | cuong-pham/datahub | cb4eb001758f55622add0f4dc3650cf483609cba | [
"Apache-2.0"
] | 1,157 | 2016-03-03T19:29:22.000Z | 2020-01-20T14:41:59.000Z | metadata-ingestion/src/datahub/ingestion/source_report/time_window.py | cuong-pham/datahub | cb4eb001758f55622add0f4dc3650cf483609cba | [
"Apache-2.0"
] | 570 | 2016-03-03T17:21:05.000Z | 2020-01-21T06:54:10.000Z | from dataclasses import dataclass
from datetime import datetime
from typing import Optional
@dataclass
class BaseTimeWindowReport:
end_time: Optional[datetime] = None
start_time: Optional[datetime] = None
| 21.5 | 41 | 0.8 | 25 | 215 | 6.8 | 0.52 | 0.141176 | 0.235294 | 0.282353 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.153488 | 215 | 9 | 42 | 23.888889 | 0.934066 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.428571 | 0 | 0.857143 | 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 | 1 | 0 | 0 | 7 |
91bec4c62d07b5205bff497a37dc521e56e7409a | 15,742 | py | Python | api/portal_api/imports.py | mkeller3/mapping_portal_api | 2a7112e0ddea7c4b662f0ec1a8d7b1ee4627cdd6 | [
"Apache-2.0"
] | 2 | 2021-08-09T12:03:31.000Z | 2021-09-11T08:23:22.000Z | api/portal_api/imports.py | mkeller3/open_source_mapping_portal | 2a7112e0ddea7c4b662f0ec1a8d7b1ee4627cdd6 | [
"Apache-2.0"
] | null | null | null | api/portal_api/imports.py | mkeller3/open_source_mapping_portal | 2a7112e0ddea7c4b662f0ec1a8d7b1ee4627cdd6 | [
"Apache-2.0"
] | null | null | null | from rest_framework.views import APIView
from rest_framework.response import Response
from rest_framework import status
from rest_framework.permissions import IsAuthenticated
from .serializers import *
from .constants import *
from .helpers import *
from drf_yasg.utils import swagger_auto_schema
from rest_framework_tracking.mixins import LoggingMixin
from django.core.files.storage import FileSystemStorage
import sys
# Import geographic file such as geojson, shp, etc
class importGeographicFileView(LoggingMixin, APIView):
permission_classes = (IsAuthenticated),
@swagger_auto_schema(request_body=geographicFileSerializer, operation_description="Upload a geogrpahic file into Mapping Portal")
def post(self, request):
serializer = geographicFileSerializer(data=request.data)
serializer.is_valid(raise_exception=True)
if str(request.user.username) not in request.data['read_access_list']:
return Response({"error":f"Username is not in read_access_list. Add {str(request.user.username)} to read_access_list and try again."}, status=status.HTTP_400_BAD_REQUEST)
if str(request.user.username) not in request.data['write_access_list']:
return Response({"error":f"Username is not in write_access_list. Add {str(request.user.username)} to write_access_list and try again."}, status=status.HTTP_400_BAD_REQUEST)
file_name = serializer.validated_data['file_name']
temp_name = table_id_generator()
try:
for f in request.FILES.getlist('upload_files'):
fs = FileSystemStorage()
fs.save(temp_name + f.name, f)
valid_upload_files = False
for file in os.listdir(media_location):
if temp_name in os.path.splitext(file)[0]:
table_id = table_id_generator()
file_extension = os.path.splitext(file)[1]
file_name = os.path.splitext(file)[0]
formatted_file_name = file_name.replace(' ','_').replace('/','_')
os.rename(media_location+file_name+file_extension,media_location+formatted_file_name+file_extension)
if file_extension.lower() in ['.shp', '.tab', '.geojson', '.json', '.kml', '.gml']:
valid_upload_files = True
if valid_upload_files:
for file in os.listdir(media_location):
file_name = os.path.splitext(file)[0]
table_information = {
'username': request.user.username,
'table_id': table_id,
'title': serializer.validated_data['title'],
'updated_username': request.user.username,
'tags': serializer.validated_data['tags'],
'description': serializer.validated_data['description'],
'read_access_list': serializer.validated_data['read_access_list'],
'write_access_list': serializer.validated_data['write_access_list'],
'notification_access_list': serializer.validated_data['notification_access_list'],
'searchable': serializer.validated_data['searchable'],
'sensitive': serializer.validated_data['sensitive'],
'retention_date': serializer.validated_data['retention_date'],
"file": media_location+file_name+file_extension,
}
load_geographic_data_to_server(table_information)
clean_table(table_information['table_id'])
add_table_into_mapping_portal(table_information)
for file in os.listdir(media_location):
if temp_name in os.path.splitext(file)[0]:
os.remove(media_location+file)
return Response({'table_id':table_id})
else:
delete_data_backend(table_id, file_name)
for file in os.listdir(media_location):
file_name = os.path.splitext(file)[0]
if file_name == table_id:
os.remove(media_location+file)
return Response({"error":f"You have not upload a valid geogrpahic file type. Please try again."}, status=status.HTTP_400_BAD_REQUEST)
except Exception as e:
exc_type, exc_obj, exc_tb = sys.exc_info()
if 'table_id' in locals():
delete_data_backend(table_id, file_name)
if temp_name:
for file in os.listdir(media_location):
if temp_name in os.path.splitext(file)[0]:
os.remove(media_location+file)
return Response({
"error":str(e),
"line_number": exc_tb.tb_lineno,
}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)
# Import point file from file
class importPointFileView(LoggingMixin, APIView):
permission_classes = (IsAuthenticated),
@swagger_auto_schema(request_body=pointFileSerializer, operation_description="Upload a point file into Mapping Portal")
def post(self, request):
serializer = pointFileSerializer(data=request.data)
serializer.is_valid(raise_exception=True)
if str(request.user.username) not in request.data['read_access_list']:
return Response({"error":f"Username is not in read_access_list. Add {str(request.user.username)} to read_access_list and try again."}, status=status.HTTP_400_BAD_REQUEST)
if str(request.user.username) not in request.data['write_access_list']:
return Response({"error":f"Username is not in write_access_list. Add {str(request.user.username)} to write_access_list and try again."}, status=status.HTTP_400_BAD_REQUEST)
file_name = serializer.validated_data['file_name']
table_id = table_id_generator()
try:
for f in request.FILES.getlist('upload_files'):
fs = FileSystemStorage()
fs.save(f.name, f)
valid_upload_files = False
for file in os.listdir(media_location):
file_extension = os.path.splitext(file)[1]
file_name = os.path.splitext(file)[0]
formatted_file_name = file_name.replace(' ','_').replace('/','_')
os.rename(media_location+file_name+file_extension,media_location+formatted_file_name+file_extension)
if file_extension.lower() in ['.csv', '.txt', '.xlsx', '.xls']:
valid_upload_files = True
if valid_upload_files:
for file in os.listdir(media_location):
file_name = os.path.splitext(file)[0]
if file_extension.lower() in ['.csv', '.txt', '.xlsx', '.xls']:
table_information = {
'username': request.user.username,
'table_id': table_id,
'title': serializer.validated_data['title'],
'updated_username': request.user.username,
'tags': serializer.validated_data['tags'],
'description': serializer.validated_data['description'],
'read_access_list': serializer.validated_data['read_access_list'],
'write_access_list': serializer.validated_data['write_access_list'],
'searchable': serializer.validated_data['searchable'],
'sensitive': serializer.validated_data['sensitive'],
'retention_date': serializer.validated_data['retention_date'],
"file": media_location+file_name+file_extension,
"file_name": file_name,
"extenstion": file_extension.lower(),
"latitude_field": serializer.validated_data['latitude_field'],
"longitude_field": serializer.validated_data['longitude_field'],
}
load_point_data_to_server(table_information)
clean_table(table_information['table_id'])
add_table_into_mapping_portal(table_information)
for file in os.listdir(media_location):
file_name = os.path.splitext(file)[0]
if file_name == serializer.validated_data['file_name']:
os.remove(media_location+file)
return Response({'table_id':table_id})
else:
delete_data_backend(table_id, file_name)
return Response({"error":f"You have not upload a valid point file type. Please try again."}, status=status.HTTP_400_BAD_REQUEST)
except Exception as e:
exc_type, exc_obj, exc_tb = sys.exc_info()
delete_data_backend(table_id, file_name)
return Response({
"error":str(e),
"line_number": exc_tb.tb_lineno,
"file": os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)
# Import geographic data from csv and join on column to pre_built map_service
# Import geographic data from url and join on column to pre_built map_service
# Import point data from url
class importPointUrlView(LoggingMixin, APIView):
permission_classes = (IsAuthenticated),
@swagger_auto_schema(request_body=pointUrlSerializer, operation_description="Upload a point file into Mapping Portal")
def post(self, request):
serializer = pointUrlSerializer(data=request.data)
serializer.is_valid(raise_exception=True)
if str(request.user.username) not in request.data['read_access_list']:
return Response({"error":f"Username is not in read_access_list. Add {str(request.user.username)} to read_access_list and try again."}, status=status.HTTP_400_BAD_REQUEST)
if str(request.user.username) not in request.data['write_access_list']:
return Response({"error":f"Username is not in write_access_list. Add {str(request.user.username)} to write_access_list and try again."}, status=status.HTTP_400_BAD_REQUEST)
table_id = table_id_generator()
try:
table_information = {
'username': request.user.username,
'table_id': table_id,
'title': serializer.validated_data['title'],
'updated_username': request.user.username,
'tags': serializer.validated_data['tags'],
'description': serializer.validated_data['description'],
'read_access_list': serializer.validated_data['read_access_list'],
'write_access_list': serializer.validated_data['write_access_list'],
'searchable': serializer.validated_data['searchable'],
'sensitive': serializer.validated_data['sensitive'],
'retention_date': serializer.validated_data['retention_date'],
"file": media_location+file_name+file_extension,
"file_name": file_name,
"extenstion": file_extension.lower(),
"latitude_field": serializer.validated_data['latitude_field'],
"longitude_field": serializer.validated_data['longitude_field'],
}
load_point_data_to_server(table_information)
clean_table(table_information['table_id'])
add_table_into_mapping_portal(table_information)
for file in os.listdir(media_location):
file_name = os.path.splitext(file)[0]
if file_name == serializer.validated_data['file_name']:
os.remove(media_location+file)
return Response({'table_id':table_id})
except Exception as e:
exc_type, exc_obj, exc_tb = sys.exc_info()
delete_data_backend(table_id, file_name)
return Response({
"error":str(e),
"line_number": exc_tb.tb_lineno,
"file": os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)
# Import geographic file in json format and join on column to pre_built map_service
# Import json point data
# Import data from esri url
class importEsriUrlView(LoggingMixin, APIView):
permission_classes = (IsAuthenticated),
@swagger_auto_schema(request_body=esriServiceSerializer, operation_description="Upload a esri map service into Mapping Portal")
def post(self, request):
serializer = esriServiceSerializer(data=request.data)
serializer.is_valid(raise_exception=True)
if str(request.user.username) not in request.data['read_access_list']:
return Response({"error":f"Username is not in read_access_list. Add {str(request.user.username)} to read_access_list and try again."}, status=status.HTTP_400_BAD_REQUEST)
if str(request.user.username) not in request.data['write_access_list']:
return Response({"error":f"Username is not in write_access_list. Add {str(request.user.username)} to write_access_list and try again."}, status=status.HTTP_400_BAD_REQUEST)
try:
table_id = table_id_generator()
table_information = {
'username': request.user.username,
'table_id': table_id,
'title': serializer.validated_data['title'],
'updated_username': request.user.username,
'tags': serializer.validated_data['tags'],
'description': serializer.validated_data['description'],
'read_access_list': serializer.validated_data['read_access_list'],
'write_access_list': serializer.validated_data['write_access_list'],
'searchable': serializer.validated_data['searchable'],
'sensitive': serializer.validated_data['sensitive'],
'retention_date': serializer.validated_data['retention_date'],
'file': media_location+table_id+'.geojson',
}
download_esri_service_data(serializer.validated_data['url'], table_id)
load_geographic_data_to_server(table_information)
clean_table(table_information['table_id'])
add_table_into_mapping_portal(table_information)
for file in os.listdir(media_location):
file_name = os.path.splitext(file)[0]
if file_name == table_id:
os.remove(media_location+file)
return Response({'table_id':table_id})
except Exception as e:
exc_type, exc_obj, exc_tb = sys.exc_info()
delete_data_backend(table_id, table_id)
return Response({
"error":str(e),
"line_number": exc_tb.tb_lineno,
"file": os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)
# Import geo data from geojson string
# Update geo data from file
# Update point data from file | 51.276873 | 184 | 0.607038 | 1,727 | 15,742 | 5.248408 | 0.101332 | 0.046337 | 0.106575 | 0.038835 | 0.878861 | 0.870697 | 0.866505 | 0.862974 | 0.852714 | 0.842674 | 0 | 0.005258 | 0.299263 | 15,742 | 307 | 185 | 51.276873 | 0.816426 | 0.030238 | 0 | 0.834043 | 0 | 0.034043 | 0.173189 | 0.01783 | 0 | 0 | 0 | 0 | 0 | 1 | 0.017021 | false | 0 | 0.06383 | 0 | 0.191489 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 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 | 7 |
5308d04d06da3bdb154394d703d694b7bf3213ea | 164 | py | Python | sentence_transformers_extensions/evaluation/__init__.py | t-mesq/sentence-transformers | 5dbf13ce986e4d0938479a97e76077d396f47277 | [
"Apache-2.0"
] | null | null | null | sentence_transformers_extensions/evaluation/__init__.py | t-mesq/sentence-transformers | 5dbf13ce986e4d0938479a97e76077d396f47277 | [
"Apache-2.0"
] | null | null | null | sentence_transformers_extensions/evaluation/__init__.py | t-mesq/sentence-transformers | 5dbf13ce986e4d0938479a97e76077d396f47277 | [
"Apache-2.0"
] | null | null | null | from .DocumentRetrievalEvaluator import *
from .TemplateRetrievalEvaluator import *
from .QueryRetrievalEvaluator import *
from .StackedRetrievalEvaluators import * | 41 | 41 | 0.859756 | 12 | 164 | 11.75 | 0.5 | 0.212766 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.091463 | 164 | 4 | 42 | 41 | 0.946309 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 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 | 7 |
5359747b4a91dda26405dd91c56c7f6c59ea3f2e | 15,503 | py | Python | fhirclient/models/immunization_tests.py | carolinarsm/client-py | db1b6e3e28036dee11da75412003c7d90e591c6d | [
"Apache-2.0"
] | 418 | 2015-07-01T08:23:16.000Z | 2022-03-31T14:02:30.000Z | fhirclient/models/immunization_tests.py | carolinarsm/client-py | db1b6e3e28036dee11da75412003c7d90e591c6d | [
"Apache-2.0"
] | 312 | 2017-09-08T15:42:13.000Z | 2022-03-23T18:21:40.000Z | fhirclient/models/immunization_tests.py | carolinarsm/client-py | db1b6e3e28036dee11da75412003c7d90e591c6d | [
"Apache-2.0"
] | 185 | 2015-03-30T20:23:16.000Z | 2022-03-30T14:39:26.000Z | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Generated from FHIR 4.0.0-a53ec6ee1b on 2019-05-07.
# 2019, SMART Health IT.
import os
import io
import unittest
import json
from . import immunization
from .fhirdate import FHIRDate
class ImmunizationTests(unittest.TestCase):
def instantiate_from(self, filename):
datadir = os.environ.get('FHIR_UNITTEST_DATADIR') or ''
with io.open(os.path.join(datadir, filename), 'r', encoding='utf-8') as handle:
js = json.load(handle)
self.assertEqual("Immunization", js["resourceType"])
return immunization.Immunization(js)
def testImmunization1(self):
inst = self.instantiate_from("immunization-example.json")
self.assertIsNotNone(inst, "Must have instantiated a Immunization instance")
self.implImmunization1(inst)
js = inst.as_json()
self.assertEqual("Immunization", js["resourceType"])
inst2 = immunization.Immunization(js)
self.implImmunization1(inst2)
def implImmunization1(self, inst):
self.assertEqual(inst.doseQuantity.code, "mg")
self.assertEqual(inst.doseQuantity.system, "http://unitsofmeasure.org")
self.assertEqual(inst.doseQuantity.value, 5)
self.assertEqual(inst.education[0].documentType, "253088698300010311120702")
self.assertEqual(inst.education[0].presentationDate.date, FHIRDate("2013-01-10").date)
self.assertEqual(inst.education[0].presentationDate.as_json(), "2013-01-10")
self.assertEqual(inst.education[0].publicationDate.date, FHIRDate("2012-07-02").date)
self.assertEqual(inst.education[0].publicationDate.as_json(), "2012-07-02")
self.assertEqual(inst.expirationDate.date, FHIRDate("2015-02-15").date)
self.assertEqual(inst.expirationDate.as_json(), "2015-02-15")
self.assertEqual(inst.fundingSource.coding[0].code, "private")
self.assertEqual(inst.fundingSource.coding[0].system, "http://terminology.hl7.org/CodeSystem/immunization-funding-source")
self.assertEqual(inst.id, "example")
self.assertEqual(inst.identifier[0].system, "urn:ietf:rfc:3986")
self.assertEqual(inst.identifier[0].value, "urn:oid:1.3.6.1.4.1.21367.2005.3.7.1234")
self.assertTrue(inst.isSubpotent)
self.assertEqual(inst.lotNumber, "AAJN11K")
self.assertEqual(inst.meta.tag[0].code, "HTEST")
self.assertEqual(inst.meta.tag[0].display, "test health data")
self.assertEqual(inst.meta.tag[0].system, "http://terminology.hl7.org/CodeSystem/v3-ActReason")
self.assertEqual(inst.note[0].text, "Notes on adminstration of vaccine")
self.assertEqual(inst.occurrenceDateTime.date, FHIRDate("2013-01-10").date)
self.assertEqual(inst.occurrenceDateTime.as_json(), "2013-01-10")
self.assertEqual(inst.performer[0].function.coding[0].code, "OP")
self.assertEqual(inst.performer[0].function.coding[0].system, "http://terminology.hl7.org/CodeSystem/v2-0443")
self.assertEqual(inst.performer[1].function.coding[0].code, "AP")
self.assertEqual(inst.performer[1].function.coding[0].system, "http://terminology.hl7.org/CodeSystem/v2-0443")
self.assertTrue(inst.primarySource)
self.assertEqual(inst.programEligibility[0].coding[0].code, "ineligible")
self.assertEqual(inst.programEligibility[0].coding[0].system, "http://terminology.hl7.org/CodeSystem/immunization-program-eligibility")
self.assertEqual(inst.reasonCode[0].coding[0].code, "429060002")
self.assertEqual(inst.reasonCode[0].coding[0].system, "http://snomed.info/sct")
self.assertEqual(inst.route.coding[0].code, "IM")
self.assertEqual(inst.route.coding[0].display, "Injection, intramuscular")
self.assertEqual(inst.route.coding[0].system, "http://terminology.hl7.org/CodeSystem/v3-RouteOfAdministration")
self.assertEqual(inst.site.coding[0].code, "LA")
self.assertEqual(inst.site.coding[0].display, "left arm")
self.assertEqual(inst.site.coding[0].system, "http://terminology.hl7.org/CodeSystem/v3-ActSite")
self.assertEqual(inst.status, "completed")
self.assertEqual(inst.text.status, "generated")
self.assertEqual(inst.vaccineCode.coding[0].code, "FLUVAX")
self.assertEqual(inst.vaccineCode.coding[0].system, "urn:oid:1.2.36.1.2001.1005.17")
self.assertEqual(inst.vaccineCode.text, "Fluvax (Influenza)")
def testImmunization2(self):
inst = self.instantiate_from("immunization-example-historical.json")
self.assertIsNotNone(inst, "Must have instantiated a Immunization instance")
self.implImmunization2(inst)
js = inst.as_json()
self.assertEqual("Immunization", js["resourceType"])
inst2 = immunization.Immunization(js)
self.implImmunization2(inst2)
def implImmunization2(self, inst):
self.assertEqual(inst.id, "historical")
self.assertEqual(inst.identifier[0].system, "urn:ietf:rfc:3986")
self.assertEqual(inst.identifier[0].value, "urn:oid:1.3.6.1.4.1.21367.2005.3.7.1234")
self.assertEqual(inst.meta.tag[0].code, "HTEST")
self.assertEqual(inst.meta.tag[0].display, "test health data")
self.assertEqual(inst.meta.tag[0].system, "http://terminology.hl7.org/CodeSystem/v3-ActReason")
self.assertEqual(inst.note[0].text, "Notes on adminstration of a historical vaccine")
self.assertEqual(inst.occurrenceString, "January 2012")
self.assertFalse(inst.primarySource)
self.assertEqual(inst.reportOrigin.coding[0].code, "record")
self.assertEqual(inst.reportOrigin.coding[0].system, "http://terminology.hl7.org/CodeSystem/immunization-origin")
self.assertEqual(inst.reportOrigin.text, "Written Record")
self.assertEqual(inst.status, "completed")
self.assertEqual(inst.text.status, "generated")
self.assertEqual(inst.vaccineCode.coding[0].code, "GNFLU")
self.assertEqual(inst.vaccineCode.coding[0].system, "urn:oid:1.2.36.1.2001.1005.17")
self.assertEqual(inst.vaccineCode.text, "Influenza")
def testImmunization3(self):
inst = self.instantiate_from("immunization-example-protocol.json")
self.assertIsNotNone(inst, "Must have instantiated a Immunization instance")
self.implImmunization3(inst)
js = inst.as_json()
self.assertEqual("Immunization", js["resourceType"])
inst2 = immunization.Immunization(js)
self.implImmunization3(inst2)
def implImmunization3(self, inst):
self.assertEqual(inst.doseQuantity.code, "mg")
self.assertEqual(inst.doseQuantity.system, "http://unitsofmeasure.org")
self.assertEqual(inst.doseQuantity.value, 5)
self.assertEqual(inst.expirationDate.date, FHIRDate("2018-12-15").date)
self.assertEqual(inst.expirationDate.as_json(), "2018-12-15")
self.assertEqual(inst.fundingSource.coding[0].code, "private")
self.assertEqual(inst.fundingSource.coding[0].system, "http://terminology.hl7.org/CodeSystem/immunization-funding-source")
self.assertEqual(inst.id, "protocol")
self.assertEqual(inst.identifier[0].system, "urn:ietf:rfc:3986")
self.assertEqual(inst.identifier[0].value, "urn:oid:1.3.6.1.4.1.21367.2005.3.7.1234")
self.assertFalse(inst.isSubpotent)
self.assertEqual(inst.lotNumber, "PT123F")
self.assertEqual(inst.meta.tag[0].code, "HTEST")
self.assertEqual(inst.meta.tag[0].display, "test health data")
self.assertEqual(inst.meta.tag[0].system, "http://terminology.hl7.org/CodeSystem/v3-ActReason")
self.assertEqual(inst.occurrenceDateTime.date, FHIRDate("2018-06-18").date)
self.assertEqual(inst.occurrenceDateTime.as_json(), "2018-06-18")
self.assertEqual(inst.performer[0].function.coding[0].code, "OP")
self.assertEqual(inst.performer[0].function.coding[0].system, "http://terminology.hl7.org/CodeSystem/v2-0443")
self.assertEqual(inst.performer[1].function.coding[0].code, "AP")
self.assertEqual(inst.performer[1].function.coding[0].system, "http://terminology.hl7.org/CodeSystem/v2-0443")
self.assertTrue(inst.primarySource)
self.assertEqual(inst.programEligibility[0].coding[0].code, "ineligible")
self.assertEqual(inst.programEligibility[0].coding[0].system, "http://terminology.hl7.org/CodeSystem/immunization-program-eligibility")
self.assertEqual(inst.protocolApplied[0].doseNumberPositiveInt, 1)
self.assertEqual(inst.protocolApplied[0].series, "2-dose")
self.assertEqual(inst.protocolApplied[0].targetDisease[0].coding[0].code, "40468003")
self.assertEqual(inst.protocolApplied[0].targetDisease[0].coding[0].system, "http://snomed.info/sct")
self.assertEqual(inst.protocolApplied[1].doseNumberPositiveInt, 2)
self.assertEqual(inst.protocolApplied[1].series, "3-dose")
self.assertEqual(inst.protocolApplied[1].targetDisease[0].coding[0].code, "66071002")
self.assertEqual(inst.protocolApplied[1].targetDisease[0].coding[0].system, "http://snomed.info/sct")
self.assertEqual(inst.route.coding[0].code, "IM")
self.assertEqual(inst.route.coding[0].display, "Injection, intramuscular")
self.assertEqual(inst.route.coding[0].system, "http://terminology.hl7.org/CodeSystem/v3-RouteOfAdministration")
self.assertEqual(inst.site.coding[0].code, "LA")
self.assertEqual(inst.site.coding[0].display, "left arm")
self.assertEqual(inst.site.coding[0].system, "http://terminology.hl7.org/CodeSystem/v3-ActSite")
self.assertEqual(inst.status, "completed")
self.assertEqual(inst.text.status, "generated")
self.assertEqual(inst.vaccineCode.coding[0].code, "104")
self.assertEqual(inst.vaccineCode.coding[0].system, "http://hl7.org/fhir/sid/cvx")
self.assertEqual(inst.vaccineCode.text, "Twinrix (HepA/HepB)")
def testImmunization4(self):
inst = self.instantiate_from("immunization-example-refused.json")
self.assertIsNotNone(inst, "Must have instantiated a Immunization instance")
self.implImmunization4(inst)
js = inst.as_json()
self.assertEqual("Immunization", js["resourceType"])
inst2 = immunization.Immunization(js)
self.implImmunization4(inst2)
def implImmunization4(self, inst):
self.assertEqual(inst.id, "notGiven")
self.assertEqual(inst.meta.tag[0].code, "HTEST")
self.assertEqual(inst.meta.tag[0].display, "test health data")
self.assertEqual(inst.meta.tag[0].system, "http://terminology.hl7.org/CodeSystem/v3-ActReason")
self.assertEqual(inst.occurrenceDateTime.date, FHIRDate("2013-01-10").date)
self.assertEqual(inst.occurrenceDateTime.as_json(), "2013-01-10")
self.assertTrue(inst.primarySource)
self.assertEqual(inst.status, "not-done")
self.assertEqual(inst.statusReason.coding[0].code, "MEDPREC")
self.assertEqual(inst.statusReason.coding[0].display, "medical precaution")
self.assertEqual(inst.statusReason.coding[0].system, "http://terminology.hl7.org/CodeSystem/v3-ActReason")
self.assertEqual(inst.text.status, "generated")
self.assertEqual(inst.vaccineCode.coding[0].code, "01")
self.assertEqual(inst.vaccineCode.coding[0].display, "DTP")
self.assertEqual(inst.vaccineCode.coding[0].system, "http://hl7.org/fhir/sid/cvx")
def testImmunization5(self):
inst = self.instantiate_from("immunization-example-subpotent.json")
self.assertIsNotNone(inst, "Must have instantiated a Immunization instance")
self.implImmunization5(inst)
js = inst.as_json()
self.assertEqual("Immunization", js["resourceType"])
inst2 = immunization.Immunization(js)
self.implImmunization5(inst2)
def implImmunization5(self, inst):
self.assertEqual(inst.doseQuantity.code, "ml")
self.assertEqual(inst.doseQuantity.system, "http://unitsofmeasure.org")
self.assertEqual(inst.doseQuantity.value, 0.5)
self.assertEqual(inst.education[0].documentType, "253088698300010311120702")
self.assertEqual(inst.education[0].presentationDate.date, FHIRDate("2013-01-10").date)
self.assertEqual(inst.education[0].presentationDate.as_json(), "2013-01-10")
self.assertEqual(inst.education[0].publicationDate.date, FHIRDate("2012-07-02").date)
self.assertEqual(inst.education[0].publicationDate.as_json(), "2012-07-02")
self.assertEqual(inst.expirationDate.date, FHIRDate("2015-02-28").date)
self.assertEqual(inst.expirationDate.as_json(), "2015-02-28")
self.assertEqual(inst.fundingSource.coding[0].code, "private")
self.assertEqual(inst.fundingSource.coding[0].system, "http://terminology.hl7.org/CodeSystem/immunization-funding-source")
self.assertEqual(inst.id, "subpotent")
self.assertEqual(inst.identifier[0].system, "urn:ietf:rfc:3986")
self.assertEqual(inst.identifier[0].value, "urn:oid:1.3.6.1.4.1.21367.2005.3.7.1234")
self.assertFalse(inst.isSubpotent)
self.assertEqual(inst.lotNumber, "AAJN11K")
self.assertEqual(inst.meta.tag[0].code, "HTEST")
self.assertEqual(inst.meta.tag[0].display, "test health data")
self.assertEqual(inst.meta.tag[0].system, "http://terminology.hl7.org/CodeSystem/v3-ActReason")
self.assertEqual(inst.note[0].text, "Notes on adminstration of vaccine")
self.assertEqual(inst.occurrenceDateTime.date, FHIRDate("2015-01-15").date)
self.assertEqual(inst.occurrenceDateTime.as_json(), "2015-01-15")
self.assertEqual(inst.performer[0].function.coding[0].code, "OP")
self.assertEqual(inst.performer[0].function.coding[0].system, "http://terminology.hl7.org/CodeSystem/v2-0443")
self.assertEqual(inst.performer[1].function.coding[0].code, "AP")
self.assertEqual(inst.performer[1].function.coding[0].system, "http://terminology.hl7.org/CodeSystem/v2-0443")
self.assertTrue(inst.primarySource)
self.assertEqual(inst.programEligibility[0].coding[0].code, "ineligible")
self.assertEqual(inst.programEligibility[0].coding[0].system, "http://terminology.hl7.org/CodeSystem/immunization-program-eligibility")
self.assertEqual(inst.route.coding[0].code, "IM")
self.assertEqual(inst.route.coding[0].display, "Injection, intramuscular")
self.assertEqual(inst.route.coding[0].system, "http://terminology.hl7.org/CodeSystem/v3-RouteOfAdministration")
self.assertEqual(inst.site.coding[0].code, "LT")
self.assertEqual(inst.site.coding[0].display, "left thigh")
self.assertEqual(inst.site.coding[0].system, "http://terminology.hl7.org/CodeSystem/v3-ActSite")
self.assertEqual(inst.status, "completed")
self.assertEqual(inst.subpotentReason[0].coding[0].code, "partial")
self.assertEqual(inst.subpotentReason[0].coding[0].system, "http://terminology.hl7.org/CodeSystem/immunization-subpotent-reason")
self.assertEqual(inst.text.status, "generated")
self.assertEqual(inst.vaccineCode.coding[0].code, "GNHEP")
self.assertEqual(inst.vaccineCode.coding[0].system, "urn:oid:1.2.36.1.2001.1005.17")
self.assertEqual(inst.vaccineCode.text, "Hepatitis B")
| 63.277551 | 143 | 0.699155 | 1,862 | 15,503 | 5.808271 | 0.116541 | 0.220527 | 0.268793 | 0.040869 | 0.878964 | 0.859362 | 0.8319 | 0.784096 | 0.775959 | 0.744337 | 0 | 0.051113 | 0.148165 | 15,503 | 244 | 144 | 63.536885 | 0.767833 | 0.007676 | 0 | 0.589041 | 1 | 0.018265 | 0.227338 | 0.030888 | 0 | 0 | 0 | 0 | 0.785388 | 1 | 0.050228 | false | 0 | 0.027397 | 0 | 0.086758 | 0 | 0 | 0 | 0 | null | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
72d12b1a74ef542c0f777c53ba38a55eda1eb8a3 | 71,327 | py | Python | sysinv/sysinv/sysinv/sysinv/tests/api/test_storage_backends.py | etaivan/stx-config | 281e1f110973f96e077645fb01f67b646fc253cc | [
"Apache-2.0"
] | null | null | null | sysinv/sysinv/sysinv/sysinv/tests/api/test_storage_backends.py | etaivan/stx-config | 281e1f110973f96e077645fb01f67b646fc253cc | [
"Apache-2.0"
] | null | null | null | sysinv/sysinv/sysinv/sysinv/tests/api/test_storage_backends.py | etaivan/stx-config | 281e1f110973f96e077645fb01f67b646fc253cc | [
"Apache-2.0"
] | null | null | null | # vim: tabstop=4 shiftwidth=4 softtabstop=4
# -*- encoding: utf-8 -*-
#
#
# Copyright (c) 2017-2018 Wind River Systems, Inc.
#
# SPDX-License-Identifier: Apache-2.0
#
"""
Tests for the API /storage_backend/ methods.
"""
import mock
from six.moves import http_client
from sysinv.tests.api import base
from sysinv.tests.db import utils as dbutils
from sysinv.common import constants
from sysinv.common.storage_backend_conf import StorageBackendConfig
from oslo_serialization import jsonutils
from sysinv.api.controllers.v1 import storage_file as test_storage_file
from sysinv.api.controllers.v1 import storage_lvm as test_storage_lvm
from sysinv.api.controllers.v1 import storage_ceph as test_storage_ceph
from sysinv.api.controllers.v1.utils import SBApiHelper
# Monkey patches
#
# the hiera_data required for the file backend
test_storage_file.HIERA_DATA = {
'backend': ['test_bparam1'],
constants.SB_SVC_GLANCE: ['test_gparam1', 'test_gparam2']
}
test_storage_lvm.HIERA_DATA = {
'backend': [],
constants.SB_SVC_CINDER: ['test_cparam1', 'test_cparam2']
}
test_storage_ceph.HIERA_DATA = {
'backend': ['test_bparam3'],
constants.SB_SVC_CINDER: ['test_cparam3'],
constants.SB_SVC_RBD_PROVISIONER: ['test_rparam3'],
constants.SB_SVC_GLANCE: ['test_gparam3'],
constants.SB_SVC_SWIFT: ['test_sparam1'],
constants.SB_SVC_NOVA: ['test_nparam1'],
}
test_storage_ceph.CAPABILITIES = {
'backend': ['test_bparam3'],
constants.SB_SVC_CINDER: ['test_cparam3'],
constants.SB_SVC_RBD_PROVISIONER: ['test_rparam3'],
constants.SB_SVC_GLANCE: ['test_gparam3'],
constants.SB_SVC_SWIFT: ['test_sparam1'],
constants.SB_SVC_NOVA: ['test_nparam1'],
}
test_storage_ceph.MANDATORY_CAP = {
'backend': ['test_bparam3'],
constants.SB_SVC_CINDER: ['test_cparam3'],
constants.SB_SVC_RBD_PROVISIONER: ['test_rparam3'],
constants.SB_SVC_GLANCE: ['test_gparam3'],
constants.SB_SVC_SWIFT: ['test_sparam1'],
constants.SB_SVC_NOVA: ['test_nparam1'],
}
orig_set_backend_data = SBApiHelper.set_backend_data
def set_backend_state_configured(requested, defaults, checks, supported_svcs, current=None):
ret = orig_set_backend_data(requested, defaults, checks,
supported_svcs, current)
ret['state'] = constants.SB_STATE_CONFIGURED
return ret
class StorageBackendTestCases(base.FunctionalTest):
def setUp(self):
super(StorageBackendTestCases, self).setUp()
self.system = dbutils.create_test_isystem()
self.cluster = dbutils.create_test_cluster(system_id=self.system.id)
self.tier = dbutils.create_test_storage_tier(forclusterid=self.cluster.id)
self.load = dbutils.create_test_load()
self.host = dbutils.create_test_ihost(forisystemid=self.system.id)
def assertDeleted(self, fullPath):
self.get_json(fullPath, expect_errors=True) # Make sure this line raises an error
#
# StorageBackend API:
#
def test_post_no_backend(self):
response = self.post_json('/storage_backend', {}, expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, response.status_int)
self.assertEqual('application/json', response.content_type)
self.assertTrue(response.json['error_message'])
self.assertIn('This operation requires a storage backend to be specified',
response.json['error_message'])
#
# StorageBackend API: File
#
def test_post_file_missing_backend_param(self):
vals = {
'backend': constants.SB_TYPE_FILE
}
response = self.post_json('/storage_backend', vals, expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, response.status_int)
self.assertEqual('application/json', response.content_type)
self.assertTrue(response.json['error_message'])
self.assertIn('Missing required backend parameter: test_bparam1',
response.json['error_message'])
def test_post_file_missing_confirm(self):
vals = {
'backend': constants.SB_TYPE_FILE,
'capabilities': {'test_bparam1': 'foo'}
}
response = self.post_json('/storage_backend', vals, expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, response.status_int)
self.assertEqual('application/json', response.content_type)
self.assertTrue(response.json['error_message'])
self.assertIn('WARNING : THIS OPERATION IS NOT REVERSIBLE AND CANNOT BE CANCELLED',
response.json['error_message'])
def test_post_file_and_confirm(self):
vals = {
'backend': constants.SB_TYPE_FILE,
'capabilities': {'test_bparam1': 'foo'},
'confirmed': True
}
response = self.post_json('/storage_backend', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual(constants.SB_TYPE_FILE, # Expected
self.get_json('/storage_backend/%s/' % response.json['uuid'])['backend']) # Result
def test_post_file_with_invalid_svc_and_confirm(self):
vals = {
'backend': constants.SB_TYPE_FILE,
'services': constants.SB_SVC_CINDER,
'capabilities': {'test_bparam1': 'foo'},
'confirmed': True
}
response = self.post_json('/storage_backend', vals, expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, response.status_int)
self.assertEqual('application/json', response.content_type)
self.assertTrue(response.json['error_message'])
self.assertIn('Service cinder is not supported',
response.json['error_message'])
def test_post_file_with_valid_svc_no_svc_param_and_confirm(self):
vals = {
'backend': constants.SB_TYPE_FILE,
'services': constants.SB_SVC_GLANCE,
'capabilities': {'test_bparam1': 'foo'},
'confirmed': True
}
response = self.post_json('/storage_backend', vals, expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, response.status_int)
self.assertEqual('application/json', response.content_type)
self.assertTrue(response.json['error_message'])
self.assertIn('Missing required glance service parameter',
response.json['error_message'])
def test_post_file_and_confirm_modify_param(self):
vals = {
'backend': constants.SB_TYPE_FILE,
'capabilities': {'test_bparam1': 'foo'},
'confirmed': True
}
response = self.post_json('/storage_backend', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual(constants.SB_TYPE_FILE, # Expected
self.get_json('/storage_backend/%s/' % response.json['uuid'])['backend']) # Result
patch_response = self.patch_dict_json('/storage_backend/%s' % response.json['uuid'],
headers={'User-Agent': 'sysinv'},
capabilities=jsonutils.dumps({'test_bparam1': 'bar'}),
expect_errors=True)
self.assertEqual(http_client.OK, patch_response.status_int)
self.assertEqual({'test_bparam1': 'bar'}, # Expected
self.get_json('/storage_backend/%s/' % patch_response.json['uuid'])['capabilities']) # Result
def test_post_file_with_valid_svc_some_svc_param_and_confirm(self):
vals = {
'backend': constants.SB_TYPE_FILE,
'services': constants.SB_SVC_GLANCE,
'capabilities': {'test_bparam1': 'foo',
'test_gparam1': 'bar'},
'confirmed': True
}
response = self.post_json('/storage_backend', vals, expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, response.status_int)
self.assertEqual('application/json', response.content_type)
self.assertTrue(response.json['error_message'])
def test_post_file_with_valid_svc_all_svc_param_and_confirm(self):
vals = {
'backend': constants.SB_TYPE_FILE,
'services': constants.SB_SVC_GLANCE,
'capabilities': {'test_bparam1': 'foo',
'test_gparam1': 'bar',
'test_gparam2': 'far'},
'confirmed': True
}
response = self.post_json('/storage_backend', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual(constants.SB_TYPE_FILE, # Expected
self.get_json('/storage_backend/%s/' % response.json['uuid'])['backend']) # Result
def test_post_file_and_confirm_modify_with_invalid_svc(self):
vals = {
'backend': constants.SB_TYPE_FILE,
'capabilities': {'test_bparam1': 'foo'},
'confirmed': True
}
response = self.post_json('/storage_backend', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual(constants.SB_TYPE_FILE, # Expected
self.get_json('/storage_backend/%s/' % response.json['uuid'])['backend']) # Result
patch_response = self.patch_dict_json('/storage_backend/%s' % response.json['uuid'],
headers={'User-Agent': 'sysinv'},
services=constants.SB_SVC_CINDER,
expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, patch_response.status_int)
self.assertEqual('application/json', patch_response.content_type)
self.assertTrue(patch_response.json['error_message'])
self.assertIn('Service cinder is not supported', patch_response.json['error_message'])
def test_post_file_and_confirm_modify_with_svc_missing_params(self):
vals = {
'backend': constants.SB_TYPE_FILE,
'capabilities': {'test_bparam1': 'foo'},
'confirmed': True
}
response = self.post_json('/storage_backend', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual(constants.SB_TYPE_FILE, # Expected
self.get_json('/storage_backend/%s/' % response.json['uuid'])['backend']) # Result
patch_response = self.patch_dict_json('/storage_backend/%s' % response.json['uuid'],
headers={'User-Agent': 'sysinv'},
services=constants.SB_SVC_GLANCE,
expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, patch_response.status_int)
self.assertEqual('application/json', patch_response.content_type)
self.assertTrue(patch_response.json['error_message'])
self.assertIn('Missing required glance service parameter', patch_response.json['error_message'])
def test_post_file_and_confirm_modify_with_svc_missing_some_params(self):
vals = {
'backend': constants.SB_TYPE_FILE,
'capabilities': {'test_bparam1': 'foo'},
'confirmed': True
}
response = self.post_json('/storage_backend', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual(constants.SB_TYPE_FILE, # Expected
self.get_json('/storage_backend/%s/' % response.json['uuid'])['backend']) # Result
patch_response = self.patch_dict_json('/storage_backend/%s' % response.json['uuid'],
headers={'User-Agent': 'sysinv'},
services=constants.SB_SVC_GLANCE,
capabilities=jsonutils.dumps({'test_param2': 'bar'}),
expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, patch_response.status_int)
self.assertEqual('application/json', patch_response.content_type)
self.assertTrue(patch_response.json['error_message'])
self.assertIn('Missing required glance service parameter', patch_response.json['error_message'])
def test_post_file_and_confirm_modify_with_svc_with_params(self):
vals = {
'backend': constants.SB_TYPE_FILE,
'capabilities': {'test_bparam1': 'foo'},
'confirmed': True
}
response = self.post_json('/storage_backend', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual(constants.SB_TYPE_FILE, # Expected
self.get_json('/storage_backend/%s/' % response.json['uuid'])['backend']) # Result
patch_response = self.patch_dict_json('/storage_backend/%s' % response.json['uuid'],
headers={'User-Agent': 'sysinv'},
services=constants.SB_SVC_GLANCE,
capabilities=jsonutils.dumps({'test_gparam1': 'bar',
'test_gparam2': 'far'}),
expect_errors=False)
self.assertEqual(http_client.OK, patch_response.status_int)
self.assertEqual(constants.SB_SVC_GLANCE, # Expected
self.get_json('/storage_backend/%s/' % response.json['uuid'])['services']) # Result
self.assertEqual({'test_bparam1': 'foo',
'test_gparam1': 'bar',
'test_gparam2': 'far'}, # Expected
self.get_json('/storage_backend/%s/' % response.json['uuid'])['capabilities']) # Result
def test_post_file_and_list(self):
vals = {
'backend': constants.SB_TYPE_FILE,
'capabilities': {'test_bparam1': 'foo'},
'confirmed': True
}
response = self.post_json('/storage_backend/', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual(constants.SB_TYPE_FILE, # Expected
self.get_json('/storage_backend/%s/' % response.json['uuid'])['backend']) # Result
self.assertEqual(constants.SB_TYPE_FILE, self.get_json('/storage_backend')['storage_backends'][0]['backend'])
#
# StorageBackend API: LVM
#
@mock.patch('sysinv.api.controllers.v1.storage_lvm._discover_and_validate_cinder_hiera_data')
@mock.patch('sysinv.api.controllers.v1.storage_lvm._apply_backend_changes')
def test_post_lvm_missing_confirm(self, mock_apply, mock_validate,):
vals = {
'backend': constants.SB_TYPE_LVM,
'services': constants.SB_SVC_CINDER,
'capabilities': {'test_cparam1': 'bar',
'test_cparam2': 'far'},
}
response = self.post_json('/storage_backend', vals, expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, response.status_int)
self.assertEqual('application/json', response.content_type)
self.assertTrue(response.json['error_message'])
self.assertIn('WARNING : THIS OPERATION IS NOT REVERSIBLE AND CANNOT BE CANCELLED',
response.json['error_message'])
@mock.patch.object(StorageBackendConfig, 'set_img_conversions_defaults')
def test_post_lvm_without_svc_and_confirm(self, mock_img_conv):
vals = {
'backend': constants.SB_TYPE_LVM,
'confirmed': True
}
response = self.post_json('/storage_backend', vals, expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, response.status_int)
self.assertEqual('application/json', response.content_type)
self.assertTrue(response.json['error_message'])
self.assertIn('Service cinder is mandatory for the lvm backend.',
response.json['error_message'])
@mock.patch.object(StorageBackendConfig, 'set_img_conversions_defaults')
@mock.patch('sysinv.api.controllers.v1.storage_lvm._discover_and_validate_cinder_hiera_data')
@mock.patch('sysinv.api.controllers.v1.storage_lvm._apply_backend_changes')
def test_post_lvm_with_valid_svc_all_svc_param_and_confirm(self, mock_apply, mock_validate, mock_img_conv):
vals = {
'backend': constants.SB_TYPE_LVM,
'services': constants.SB_SVC_CINDER,
'capabilities': {'test_cparam1': 'bar',
'test_cparam2': 'far'},
'confirmed': True
}
response = self.post_json('/storage_backend', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual('lvm', # Expected
self.get_json('/storage_backend/%s/' % response.json['uuid'])['backend']) # Result
@mock.patch('sysinv.api.controllers.v1.storage_lvm._discover_and_validate_cinder_hiera_data')
@mock.patch('sysinv.api.controllers.v1.storage_lvm._apply_backend_changes')
def test_post_lvm_with_invalid_svc_and_confirm(self, mock_apply, mock_validate):
vals = {
'backend': constants.SB_TYPE_LVM,
'services': (',').join([constants.SB_SVC_CINDER, constants.SB_SVC_GLANCE]),
'capabilities': {'test_cparam1': 'bar',
'test_cparam2': 'far'},
'confirmed': True
}
response = self.post_json('/storage_backend', vals, expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, response.status_int)
self.assertEqual('application/json', response.content_type)
self.assertTrue(response.json['error_message'])
self.assertIn('Service glance is not supported',
response.json['error_message'])
@mock.patch('sysinv.api.controllers.v1.storage_lvm._discover_and_validate_cinder_hiera_data')
@mock.patch('sysinv.api.controllers.v1.storage_lvm._apply_backend_changes')
def test_post_lvm_with_valid_svc_no_svc_param_and_confirm(self, mock_apply, mock_validate):
vals = {
'backend': constants.SB_TYPE_LVM,
'services': constants.SB_SVC_CINDER,
'confirmed': True
}
response = self.post_json('/storage_backend', vals, expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, response.status_int)
self.assertEqual('application/json', response.content_type)
self.assertTrue(response.json['error_message'])
self.assertIn('Missing required cinder service parameter',
response.json['error_message'])
@mock.patch('sysinv.api.controllers.v1.storage_lvm._discover_and_validate_cinder_hiera_data')
@mock.patch('sysinv.api.controllers.v1.storage_lvm._apply_backend_changes')
def test_post_lvm_with_valid_svc_some_svc_param_and_confirm(self, mock_apply, mock_validate):
vals = {
'backend': constants.SB_TYPE_LVM,
'services': constants.SB_SVC_CINDER,
'capabilities': {'test_cparam1': 'bar'},
'confirmed': True
}
response = self.post_json('/storage_backend', vals, expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, response.status_int)
self.assertEqual('application/json', response.content_type)
self.assertTrue(response.json['error_message'])
self.assertIn('Missing required cinder service parameter',
response.json['error_message'])
@mock.patch.object(StorageBackendConfig, 'set_img_conversions_defaults')
@mock.patch('sysinv.api.controllers.v1.storage_lvm._discover_and_validate_cinder_hiera_data')
@mock.patch('sysinv.api.controllers.v1.storage_lvm._apply_backend_changes')
def test_post_lvm_and_remove_svc(self, mock_apply, mock_validate, mock_img_conv):
vals = {
'backend': constants.SB_TYPE_LVM,
'services': constants.SB_SVC_CINDER,
'capabilities': {'test_cparam1': 'bar',
'test_cparam2': 'far'},
'confirmed': True
}
response = self.post_json('/storage_backend', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual('lvm', # Expected
self.get_json('/storage_backend/%s/' % response.json['uuid'])['backend']) # Result
patch_response = self.patch_dict_json('/storage_backend/%s' % response.json['uuid'],
headers={'User-Agent': 'sysinv'},
services=constants.SB_SVC_GLANCE,
expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, patch_response.status_int)
self.assertEqual('application/json', patch_response.content_type)
self.assertTrue(patch_response.json['error_message'])
self.assertIn('Removing cinder is not supported', patch_response.json['error_message'])
@mock.patch.object(StorageBackendConfig, 'set_img_conversions_defaults')
@mock.patch('sysinv.api.controllers.v1.storage_lvm._discover_and_validate_cinder_hiera_data')
@mock.patch('sysinv.api.controllers.v1.storage_lvm._apply_backend_changes')
@mock.patch.object(SBApiHelper, 'set_backend_data',
side_effect=set_backend_state_configured)
def test_post_lvm_and_confirm_modify_with_invalid_svc(self, mock_set_backend_data, mock_apply,
mock_validate, mock_img_conv):
vals = {
'backend': constants.SB_TYPE_LVM,
'services': constants.SB_SVC_CINDER,
'capabilities': {'test_cparam1': 'bar',
'test_cparam2': 'far'},
'confirmed': True
}
response = self.post_json('/storage_backend', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual('lvm', # Expected
self.get_json('/storage_backend/%s/' % response.json['uuid'])['backend']) # Result
patch_response = self.patch_dict_json('/storage_backend/%s' % response.json['uuid'],
headers={'User-Agent': 'sysinv'},
services=(',').join([constants.SB_SVC_CINDER,
constants.SB_SVC_GLANCE]),
expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, patch_response.status_int)
self.assertEqual('application/json', patch_response.content_type)
self.assertTrue(patch_response.json['error_message'])
self.assertIn('Service glance is not supported', patch_response.json['error_message'])
@mock.patch.object(StorageBackendConfig, 'set_img_conversions_defaults')
@mock.patch('sysinv.api.controllers.v1.storage_lvm._discover_and_validate_cinder_hiera_data')
@mock.patch('sysinv.api.controllers.v1.storage_lvm._apply_backend_changes')
def test_post_lvm_and_confirm_modify_with_no_changes(self, mock_apply, mock_validate, mock_img_conv):
vals = {
'backend': constants.SB_TYPE_LVM,
'services': constants.SB_SVC_CINDER,
'capabilities': {'test_cparam1': 'bar',
'test_cparam2': 'far'},
'confirmed': True
}
response = self.post_json('/storage_backend', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual('lvm', # Expected
self.get_json('/storage_backend/%s/' % response.json['uuid'])['backend']) # Result
patch_response = self.patch_dict_json('/storage_backend/%s' % response.json['uuid'],
headers={'User-Agent': 'sysinv'},
services=constants.SB_SVC_CINDER,
expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, patch_response.status_int)
self.assertEqual('application/json', patch_response.content_type)
self.assertTrue(patch_response.json['error_message'])
self.assertIn('No changes to the existing backend settings were detected',
patch_response.json['error_message'])
@mock.patch.object(StorageBackendConfig, 'set_img_conversions_defaults')
@mock.patch('sysinv.api.controllers.v1.storage_lvm._discover_and_validate_cinder_hiera_data')
@mock.patch('sysinv.api.controllers.v1.storage_lvm._apply_backend_changes')
@mock.patch.object(SBApiHelper, 'set_backend_data',
side_effect=set_backend_state_configured)
def test_post_lvm_and_confirm_modify_with_svc_with_params(self, mock_set_backend_data,
mock_apply, mock_validate, mock_img_conv):
vals = {
'backend': constants.SB_TYPE_LVM,
'services': constants.SB_SVC_CINDER,
'capabilities': {'test_cparam1': 'bar',
'test_cparam2': 'far'},
'confirmed': True
}
response = self.post_json('/storage_backend', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual('lvm', # Expected
self.get_json('/storage_backend/%s/' % response.json['uuid'])['backend']) # Result
patch_response = self.patch_dict_json('/storage_backend/%s' % response.json['uuid'],
headers={'User-Agent': 'sysinv'},
services=constants.SB_SVC_CINDER,
capabilities=jsonutils.dumps({'test_cparam1': 'bar2',
'test_cparam2': 'far2'}),
expect_errors=False)
self.assertEqual(http_client.OK, patch_response.status_int)
self.assertEqual(constants.SB_SVC_CINDER, # Expected
self.get_json('/storage_backend/%s/' % response.json['uuid'])['services']) # Result
self.assertEqual({'test_cparam1': 'bar2',
'test_cparam2': 'far2'}, # Expected
self.get_json('/storage_backend/%s/' % response.json['uuid'])['capabilities']) # Result
@mock.patch.object(StorageBackendConfig, 'set_img_conversions_defaults')
@mock.patch('sysinv.api.controllers.v1.storage_lvm._discover_and_validate_cinder_hiera_data')
@mock.patch('sysinv.api.controllers.v1.storage_lvm._apply_backend_changes')
def test_post_lvm_and_list(self, mock_apply, mock_validate, mock_img_conv):
vals = {
'backend': constants.SB_TYPE_LVM,
'services': constants.SB_SVC_CINDER,
'capabilities': {'test_cparam1': 'bar',
'test_cparam2': 'far'},
'confirmed': True
}
response = self.post_json('/storage_backend/', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual(constants.SB_TYPE_LVM, # Expected
self.get_json('/storage_backend/%s/' % response.json['uuid'])['backend']) # Result
self.assertEqual(constants.SB_TYPE_LVM, self.get_json('/storage_backend')['storage_backends'][0]['backend'])
#
# StorageBackend API: Ceph
#
@mock.patch.object(StorageBackendConfig, 'get_ceph_mon_ip_addresses')
def test_post_ceph_missing_backend_param(self, mock_mon_ip):
# Test skipped. Fix later.
self.skipTest("Skipping to prevent failure notification on Jenkins")
vals = {
'backend': constants.SB_TYPE_CEPH
}
response = self.post_json('/storage_backend', vals, expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, response.status_int)
self.assertEqual('application/json', response.content_type)
self.assertTrue(response.json['error_message'])
self.assertIn('Missing required backend parameter: test_bparam3',
response.json['error_message'])
@mock.patch.object(StorageBackendConfig, 'get_ceph_mon_ip_addresses')
def test_post_ceph_missing_confirm(self, mock_mon_ip):
# Test skipped. Fix later.
self.skipTest("Skipping to prevent failure notification on Jenkins")
vals = {
'backend': constants.SB_TYPE_CEPH,
'capabilities': {'test_bparam3': 'foo'}
}
response = self.post_json('/storage_backend', vals, expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, response.status_int)
self.assertEqual('application/json', response.content_type)
self.assertTrue(response.json['error_message'])
self.assertIn('WARNING : THIS OPERATION IS NOT REVERSIBLE AND CANNOT BE CANCELLED',
response.json['error_message'])
@mock.patch.object(StorageBackendConfig, 'get_ceph_mon_ip_addresses')
@mock.patch.object(StorageBackendConfig, 'set_img_conversions_defaults')
def test_post_ceph_and_confirm(self, mock_img_conv, mock_mon_ip):
vals = {
'backend': constants.SB_TYPE_CEPH,
'capabilities': {'test_bparam3': 'foo'},
'confirmed': True
}
response = self.post_json('/storage_backend', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual('ceph', # Expected
self.get_json('/storage_backend/%s/' % response.json['uuid'])['backend']) # Result
@mock.patch.object(StorageBackendConfig, 'get_ceph_mon_ip_addresses')
def test_post_ceph_with_invalid_svc_and_confirm(self, mock_mon_ip):
vals = {
'backend': constants.SB_TYPE_CEPH,
'services': 'invalid_svc',
'capabilities': {'test_bparam3': 'foo'},
'confirmed': True
}
response = self.post_json('/storage_backend', vals, expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, response.status_int)
self.assertEqual('application/json', response.content_type)
self.assertTrue(response.json['error_message'])
self.assertIn('Service invalid_svc is not supported for the ceph backend',
response.json['error_message'])
@mock.patch.object(StorageBackendConfig, 'get_ceph_mon_ip_addresses')
@mock.patch('sysinv.api.controllers.v1.storage_ceph._discover_and_validate_cinder_capabilities')
@mock.patch('sysinv.api.controllers.v1.storage_ceph._apply_backend_changes')
def test_post_ceph_with_valid_svc_no_svc_param_and_confirm(self, mock_apply, mock_validate, mock_mon_ip):
# Test skipped. Fix later.
self.skipTest("Skipping to prevent failure notification on Jenkins")
vals = {
'backend': constants.SB_TYPE_CEPH,
'services': constants.SB_SVC_CINDER,
'capabilities': {'test_bparam3': 'foo'},
'confirmed': True
}
response = self.post_json('/storage_backend', vals, expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, response.status_int)
self.assertEqual('application/json', response.content_type)
self.assertTrue(response.json['error_message'])
self.assertIn('Missing required cinder service parameter',
response.json['error_message'])
@mock.patch.object(StorageBackendConfig, 'get_ceph_mon_ip_addresses')
@mock.patch('sysinv.api.controllers.v1.storage_ceph._discover_and_validate_cinder_capabilities')
@mock.patch('sysinv.api.controllers.v1.storage_ceph._apply_backend_changes')
def test_post_ceph_with_valid_svc_some_svc_param_and_confirm(self, mock_apply, mock_validate, mock_mon_ip):
# Test skipped. Fix later.
self.skipTest("Skipping to prevent failure notification on Jenkins")
vals = {
'backend': constants.SB_TYPE_CEPH,
'services': (',').join([constants.SB_SVC_CINDER, constants.SB_SVC_GLANCE]),
'capabilities': {'test_bparam3': 'foo',
'test_cparam3': 'bar'},
'confirmed': True
}
response = self.post_json('/storage_backend', vals, expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, response.status_int)
self.assertEqual('application/json', response.content_type)
self.assertTrue(response.json['error_message'])
self.assertIn('Missing required glance service parameter',
response.json['error_message'])
@mock.patch.object(StorageBackendConfig, 'get_ceph_mon_ip_addresses')
@mock.patch.object(StorageBackendConfig, 'set_img_conversions_defaults')
@mock.patch('sysinv.api.controllers.v1.storage_ceph._discover_and_validate_cinder_capabilities')
@mock.patch('sysinv.api.controllers.v1.storage_ceph._apply_backend_changes')
def test_post_ceph_with_valid_svc_all_svc_param_and_confirm(self, mock_apply, mock_validate, mock_img_conv, mock_mon_ip):
vals = {
'backend': constants.SB_TYPE_CEPH,
'services': (',').join([constants.SB_SVC_CINDER, constants.SB_SVC_GLANCE]),
'capabilities': {'test_bparam3': 'foo',
'test_cparam3': 'bar',
'test_gparam3': 'too'},
'confirmed': True
}
response = self.post_json('/storage_backend', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual('ceph', # Expected
self.get_json('/storage_backend/%s/' % response.json['uuid'])['backend']) # Result
@mock.patch.object(StorageBackendConfig, 'get_ceph_mon_ip_addresses')
@mock.patch.object(StorageBackendConfig, 'set_img_conversions_defaults')
@mock.patch.object(SBApiHelper, 'set_backend_data',
side_effect=set_backend_state_configured)
def test_post_ceph_and_confirm_modify_with_invalid_svc(self, mock_set_backend_data,
mock_img_conv, mock_mon_ip):
vals = {
'backend': constants.SB_TYPE_CEPH,
'capabilities': {'test_bparam3': 'foo'},
'confirmed': True
}
response = self.post_json('/storage_backend', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual('ceph', # Expected
self.get_json('/storage_backend/%s/' % response.json['uuid'])['backend']) # Result
patch_response = self.patch_dict_json('/storage_backend/%s' % response.json['uuid'],
headers={'User-Agent': 'sysinv'},
services='invalid_svc',
expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, patch_response.status_int)
self.assertEqual('application/json', patch_response.content_type)
self.assertTrue(patch_response.json['error_message'])
self.assertIn('Service invalid_svc is not supported for the ceph backend',
patch_response.json['error_message'])
@mock.patch.object(StorageBackendConfig, 'get_ceph_mon_ip_addresses')
@mock.patch.object(StorageBackendConfig, 'set_img_conversions_defaults')
@mock.patch('sysinv.api.controllers.v1.storage_ceph._discover_and_validate_cinder_capabilities')
@mock.patch('sysinv.api.controllers.v1.storage_ceph._apply_backend_changes')
@mock.patch.object(SBApiHelper, 'set_backend_data',
side_effect=set_backend_state_configured)
def test_post_ceph_and_confirm_modify_with_svc_missing_params(self, mock_set_backend_data,
mock_apply, mock_validate,
mock_img_conv, mock_mon_ip):
vals = {
'backend': constants.SB_TYPE_CEPH,
'capabilities': {'test_bparam3': 'foo'},
'confirmed': True
}
response = self.post_json('/storage_backend', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual('ceph', # Expected
self.get_json('/storage_backend/%s/' % response.json['uuid'])['backend']) # Result
patch_response = self.patch_dict_json('/storage_backend/%s' % response.json['uuid'],
headers={'User-Agent': 'sysinv'},
services=constants.SB_SVC_CINDER,
expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, patch_response.status_int)
self.assertEqual('application/json', patch_response.content_type)
self.assertTrue(patch_response.json['error_message'])
self.assertIn('Missing required cinder service parameter',
patch_response.json['error_message'])
@mock.patch.object(StorageBackendConfig, 'get_ceph_mon_ip_addresses')
@mock.patch.object(StorageBackendConfig, 'set_img_conversions_defaults')
@mock.patch('sysinv.api.controllers.v1.storage_ceph._discover_and_validate_cinder_capabilities')
@mock.patch('sysinv.api.controllers.v1.storage_ceph._apply_backend_changes')
@mock.patch.object(SBApiHelper, 'set_backend_data',
side_effect=set_backend_state_configured)
def test_post_ceph_and_confirm_modify_with_svc_missing_some_params(self, mock_set_backend_data, mock_apply,
mock_validate, mock_img_conv, mock_mon_ip):
vals = {
'backend': constants.SB_TYPE_CEPH,
'capabilities': {'test_bparam3': 'foo'},
'confirmed': True
}
response = self.post_json('/storage_backend', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual('ceph', # Expected
self.get_json('/storage_backend/%s/' % response.json['uuid'])['backend']) # Result
patch_response = self.patch_dict_json('/storage_backend/%s' % response.json['uuid'],
headers={'User-Agent': 'sysinv'},
services=(',').join([constants.SB_SVC_CINDER,
constants.SB_SVC_GLANCE]),
capabilities=jsonutils.dumps({'test_cparam3': 'bar'}),
expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, patch_response.status_int)
self.assertEqual('application/json', patch_response.content_type)
self.assertTrue(patch_response.json['error_message'])
self.assertIn('Missing required glance service parameter',
patch_response.json['error_message'])
@mock.patch.object(StorageBackendConfig, 'get_ceph_mon_ip_addresses')
@mock.patch.object(StorageBackendConfig, 'set_img_conversions_defaults')
@mock.patch('sysinv.api.controllers.v1.storage_ceph._discover_and_validate_cinder_capabilities')
@mock.patch('sysinv.api.controllers.v1.storage_ceph._apply_backend_changes')
@mock.patch.object(SBApiHelper, 'set_backend_data',
side_effect=set_backend_state_configured)
def test_post_ceph_and_confirm_modify_with_svc_with_params(self, mock_set_backend_data,
mock_apply, mock_validate,
mock_img_conv, mock_mon_ip):
vals = {
'backend': constants.SB_TYPE_CEPH,
'capabilities': {'test_bparam3': 'foo'},
'confirmed': True
}
services_string = '%s,%s' % (constants.SB_SVC_CINDER, constants.SB_SVC_GLANCE)
services_string2 = '%s,%s' % (constants.SB_SVC_GLANCE, constants.SB_SVC_CINDER)
response = self.post_json('/storage_backend', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual('ceph', # Expected
self.get_json('/storage_backend/%s/' % response.json['uuid'])['backend']) # Result
patch_response = self.patch_dict_json('/storage_backend/%s' % response.json['uuid'],
headers={'User-Agent': 'sysinv'},
services=services_string,
capabilities=jsonutils.dumps({'test_cparam3': 'bar',
'test_gparam3': 'too'}),
expect_errors=False)
self.assertEqual(http_client.OK, patch_response.status_int)
json_result = self.get_json('/storage_backend/%s/' % response.json['uuid'])['services']
self.assertTrue(services_string == json_result or services_string2 == json_result)
self.assertEqual({'test_bparam3': 'foo',
'test_cparam3': 'bar',
'test_gparam3': 'too'}, # Expected
self.get_json('/storage_backend/%s/' % response.json['uuid'])['capabilities']) # Result
@mock.patch.object(StorageBackendConfig, 'get_ceph_mon_ip_addresses')
@mock.patch.object(StorageBackendConfig, 'set_img_conversions_defaults')
def test_post_ceph_and_list(self, mock_img_conv, mock_mon_ip):
vals = {
'backend': constants.SB_TYPE_CEPH,
'capabilities': {'test_bparam3': 'foo'},
'confirmed': True
}
response = self.post_json('/storage_backend/', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual(constants.SB_TYPE_CEPH, # Expected
self.get_json('/storage_backend/%s/' % response.json['uuid'])['backend']) # Result
self.assertEqual(constants.SB_TYPE_CEPH, self.get_json('/storage_backend')['storage_backends'][0]['backend'])
class StorageFileTestCases(base.FunctionalTest):
def setUp(self):
super(StorageFileTestCases, self).setUp()
self.system = dbutils.create_test_isystem()
self.load = dbutils.create_test_load()
self.host = dbutils.create_test_ihost(forisystemid=self.system.id)
def assertDeleted(self, fullPath):
self.get_json(fullPath, expect_errors=True) # Make sure this line raises an error
#
# StorageFile API
#
def test_post_missing_backend_param(self):
vals = {
'backend': constants.SB_TYPE_FILE
}
response = self.post_json('/storage_file', vals, expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, response.status_int)
self.assertEqual('application/json', response.content_type)
self.assertTrue(response.json['error_message'])
self.assertIn('Missing required backend parameter: test_bparam1',
response.json['error_message'])
def test_post_missing_confirm(self):
# Test skipped. Fix later.
self.skipTest("Skipping to prevent failure notification on Jenkins")
vals = {
'backend': constants.SB_TYPE_FILE,
'capabilities': {'test_bparam1': 'foo'}
}
response = self.post_json('/storage_file', vals, expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, response.status_int)
self.assertEqual('application/json', response.content_type)
self.assertTrue(response.json['error_message'])
self.assertIn('WARNING : THIS OPERATION IS NOT REVERSIBLE AND CANNOT BE CANCELLED',
response.json['error_message'])
def test_post_and_confirm(self):
vals = {
'backend': constants.SB_TYPE_FILE,
'capabilities': {'test_bparam1': 'foo'},
'confirmed': True
}
response = self.post_json('/storage_file', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual(constants.SB_TYPE_FILE, # Expected
self.get_json('/storage_file/%s/' % response.json['uuid'])['backend']) # Result
def test_post_with_invalid_svc_and_confirm(self):
vals = {
'backend': constants.SB_TYPE_FILE,
'services': constants.SB_SVC_CINDER,
'capabilities': {'test_bparam1': 'foo'},
'confirmed': True
}
response = self.post_json('/storage_file', vals, expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, response.status_int)
self.assertEqual('application/json', response.content_type)
self.assertTrue(response.json['error_message'])
self.assertIn('Service cinder is not supported',
response.json['error_message'])
def test_post_with_valid_svc_no_svc_param_and_confirm(self):
vals = {
'backend': constants.SB_TYPE_FILE,
'services': constants.SB_SVC_GLANCE,
'capabilities': {'test_bparam1': 'foo'},
'confirmed': True
}
response = self.post_json('/storage_file', vals, expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, response.status_int)
self.assertEqual('application/json', response.content_type)
self.assertTrue(response.json['error_message'])
self.assertIn('Missing required glance service parameter: test_gparam1',
response.json['error_message'])
def test_post_and_confirm_modify_param(self):
vals = {
'backend': constants.SB_TYPE_FILE,
'capabilities': {'test_bparam1': 'foo'},
'confirmed': True
}
response = self.post_json('/storage_file', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual(constants.SB_TYPE_FILE, # Expected
self.get_json('/storage_file/%s/' % response.json['uuid'])['backend']) # Result
patch_response = self.patch_dict_json('/storage_file/%s' % response.json['uuid'],
headers={'User-Agent': 'sysinv'},
capabilities=jsonutils.dumps({'test_bparam1': 'bar'}),
expect_errors=True)
self.assertEqual(http_client.OK, patch_response.status_int)
self.assertEqual({'test_bparam1': 'bar'}, # Expected
self.get_json('/storage_file/%s/' % patch_response.json['uuid'])['capabilities']) # Result
def test_post_with_valid_svc_some_svc_param_and_confirm(self):
vals = {
'backend': constants.SB_TYPE_FILE,
'services': constants.SB_SVC_GLANCE,
'capabilities': {'test_bparam1': 'foo',
'test_gparam1': 'bar'},
'confirmed': True
}
response = self.post_json('/storage_file', vals, expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, response.status_int)
self.assertEqual('application/json', response.content_type)
self.assertTrue(response.json['error_message'])
self.assertIn('Missing required glance service parameter: test_gparam2',
response.json['error_message'])
def test_post_with_valid_svc_all_svc_param_and_confirm(self):
vals = {
'backend': constants.SB_TYPE_FILE,
'services': constants.SB_SVC_GLANCE,
'capabilities': {'test_bparam1': 'foo',
'test_gparam1': 'bar',
'test_gparam2': 'far'},
'confirmed': True
}
response = self.post_json('/storage_file', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual(constants.SB_TYPE_FILE, # Expected
self.get_json('/storage_file/%s/' % response.json['uuid'])['backend']) # Result
@mock.patch.object(SBApiHelper, 'set_backend_data',
side_effect=set_backend_state_configured)
def test_post_and_confirm_modify_with_invalid_svc(self, mock_set_backend_data):
vals = {
'backend': constants.SB_TYPE_FILE,
'capabilities': {'test_bparam1': 'foo'},
'confirmed': True
}
response = self.post_json('/storage_file', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual(constants.SB_TYPE_FILE, # Expected
self.get_json('/storage_file/%s/' % response.json['uuid'])['backend']) # Result
patch_response = self.patch_dict_json('/storage_file/%s' % response.json['uuid'],
headers={'User-Agent': 'sysinv'},
services=constants.SB_SVC_CINDER,
expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, patch_response.status_int)
self.assertEqual('application/json', patch_response.content_type)
self.assertTrue(patch_response.json['error_message'])
self.assertIn('Service cinder is not supported', patch_response.json['error_message'])
def test_post_and_confirm_modify_with_svc_missing_params(self):
vals = {
'backend': constants.SB_TYPE_FILE,
'capabilities': {'test_bparam1': 'foo'},
'confirmed': True
}
response = self.post_json('/storage_file', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual(constants.SB_TYPE_FILE, # Expected
self.get_json('/storage_file/%s/' % response.json['uuid'])['backend']) # Result
patch_response = self.patch_dict_json('/storage_file/%s' % response.json['uuid'],
headers={'User-Agent': 'sysinv'},
services=constants.SB_SVC_GLANCE,
expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, patch_response.status_int)
self.assertEqual('application/json', patch_response.content_type)
self.assertTrue(patch_response.json['error_message'])
self.assertIn('Missing required glance service parameter', patch_response.json['error_message'])
def test_post_and_confirm_modify_with_svc_missing_some_params(self):
vals = {
'backend': constants.SB_TYPE_FILE,
'capabilities': {'test_bparam1': 'foo'},
'confirmed': True
}
response = self.post_json('/storage_file', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual(constants.SB_TYPE_FILE, # Expected
self.get_json('/storage_file/%s/' % response.json['uuid'])['backend']) # Result
patch_response = self.patch_dict_json('/storage_file/%s' % response.json['uuid'],
headers={'User-Agent': 'sysinv'},
services=constants.SB_SVC_GLANCE,
capabilities=jsonutils.dumps({'test_gparam1': 'bar'}),
expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, patch_response.status_int)
self.assertEqual('application/json', patch_response.content_type)
self.assertTrue(patch_response.json['error_message'])
self.assertIn('Missing required glance service parameter', patch_response.json['error_message'])
def test_post_and_confirm_modify_with_svc_with_params(self):
# Test skipped. Fix later.
self.skipTest("Skipping to prevent failure notification on Jenkins")
vals = {
'backend': constants.SB_TYPE_FILE,
'capabilities': {'test_bparam1': 'foo'},
'confirmed': True
}
response = self.post_json('/storage_file', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual(constants.SB_TYPE_FILE, # Expected
self.get_json('/storage_file/%s/' % response.json['uuid'])['backend']) # Result
patch_response = self.patch_dict_json('/storage_file/%s' % response.json['uuid'],
headers={'User-Agent': 'sysinv'},
services=constants.SB_SVC_GLANCE,
capabilities=jsonutils.dumps({'test_gparam1': 'bar',
'test_gparam2': 'far'}),
expect_errors=False)
self.assertEqual(http_client.OK, patch_response.status_int)
self.assertEqual(constants.SB_SVC_GLANCE, # Expected
self.get_json('/storage_file/%s/' % response.json['uuid'])['services']) # Result
self.assertEqual({'test_bparam1': 'foo',
'test_gparam1': 'bar',
'test_gparam2': 'far'}, # Expected
self.get_json('/storage_file/%s/' % response.json['uuid'])['capabilities']) # Result
def test_post_and_list(self):
vals = {
'backend': constants.SB_TYPE_FILE,
'capabilities': {'test_bparam1': 'foo'},
'confirmed': True
}
response = self.post_json('/storage_file/', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual(constants.SB_TYPE_FILE, # Expected
self.get_json('/storage_file/%s/' % response.json['uuid'])['backend']) # Result
self.assertEqual(constants.SB_TYPE_FILE, self.get_json('/storage_backend')['storage_backends'][0]['backend'])
class StorageLvmTestCases(base.FunctionalTest):
def setUp(self):
super(StorageLvmTestCases, self).setUp()
self.system = dbutils.create_test_isystem()
self.load = dbutils.create_test_load()
self.host = dbutils.create_test_ihost(forisystemid=self.system.id)
def assertDeleted(self, fullPath):
self.get_json(fullPath, expect_errors=True) # Make sure this line raises an error
#
# StorageLvm API
#
@mock.patch('sysinv.api.controllers.v1.storage_lvm._discover_and_validate_cinder_hiera_data')
@mock.patch('sysinv.api.controllers.v1.storage_lvm._apply_backend_changes')
def test_post_missing_confirm(self, mock_apply, mock_validate,):
# Test skipped. Fix later.
self.skipTest("Skipping to prevent failure notification on Jenkins")
vals = {
'backend': constants.SB_TYPE_LVM,
'services': constants.SB_SVC_CINDER,
'capabilities': {'test_cparam1': 'bar',
'test_cparam2': 'far'},
}
response = self.post_json('/storage_lvm', vals, expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, response.status_int)
self.assertEqual('application/json', response.content_type)
self.assertTrue(response.json['error_message'])
self.assertIn('WARNING : THIS OPERATION IS NOT REVERSIBLE AND CANNOT BE CANCELLED',
response.json['error_message'])
@mock.patch.object(StorageBackendConfig, 'set_img_conversions_defaults')
@mock.patch('sysinv.api.controllers.v1.storage_lvm._discover_and_validate_cinder_hiera_data')
@mock.patch('sysinv.api.controllers.v1.storage_lvm._apply_backend_changes')
def test_post_and_confirm(self, mock_apply, mock_validate, mock_img_conv):
vals = {
'backend': constants.SB_TYPE_LVM,
'services': constants.SB_SVC_CINDER,
'capabilities': {'test_cparam1': 'bar',
'test_cparam2': 'far'},
'confirmed': True
}
response = self.post_json('/storage_lvm', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual(constants.SB_TYPE_LVM, # Expected
self.get_json('/storage_lvm/%s/' % response.json['uuid'])['backend']) # Result
@mock.patch('sysinv.api.controllers.v1.storage_lvm._discover_and_validate_cinder_hiera_data')
@mock.patch('sysinv.api.controllers.v1.storage_lvm._apply_backend_changes')
def test_post_with_invalid_svc_and_confirm(self, mock_apply, mock_validate):
vals = {
'backend': constants.SB_TYPE_LVM,
'services': (',').join([constants.SB_SVC_CINDER, constants.SB_SVC_GLANCE]),
'capabilities': {'test_cparam1': 'bar',
'test_cparam2': 'far'},
'confirmed': True
}
response = self.post_json('/storage_lvm', vals, expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, response.status_int)
self.assertEqual('application/json', response.content_type)
self.assertTrue(response.json['error_message'])
self.assertIn('Service glance is not supported',
response.json['error_message'])
@mock.patch('sysinv.api.controllers.v1.storage_lvm._discover_and_validate_cinder_hiera_data')
@mock.patch('sysinv.api.controllers.v1.storage_lvm._apply_backend_changes')
def test_post_with_valid_svc_no_svc_param_and_confirm(self, mock_apply, mock_validate):
vals = {
'backend': constants.SB_TYPE_LVM,
'services': constants.SB_SVC_CINDER,
'confirmed': True
}
response = self.post_json('/storage_lvm', vals, expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, response.status_int)
self.assertEqual('application/json', response.content_type)
self.assertTrue(response.json['error_message'])
self.assertIn('Missing required cinder service parameter',
response.json['error_message'])
@mock.patch('sysinv.api.controllers.v1.storage_lvm._discover_and_validate_cinder_hiera_data')
@mock.patch('sysinv.api.controllers.v1.storage_lvm._apply_backend_changes')
def test_post_with_valid_svc_some_svc_param_and_confirm(self, mock_apply, mock_validate):
vals = {
'backend': constants.SB_TYPE_LVM,
'services': constants.SB_SVC_CINDER,
'capabilities': {'test_cparam1': 'bar'},
'confirmed': True
}
response = self.post_json('/storage_lvm', vals, expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, response.status_int)
self.assertEqual('application/json', response.content_type)
self.assertTrue(response.json['error_message'])
self.assertIn('Missing required cinder service parameter',
response.json['error_message'])
@mock.patch.object(StorageBackendConfig, 'set_img_conversions_defaults')
@mock.patch('sysinv.api.controllers.v1.storage_lvm._discover_and_validate_cinder_hiera_data')
@mock.patch('sysinv.api.controllers.v1.storage_lvm._apply_backend_changes')
def test_post_with_valid_svc_all_svc_param_and_confirm(self, mock_apply, mock_validate, mock_img_conv):
vals = {
'backend': constants.SB_TYPE_LVM,
'services': constants.SB_SVC_CINDER,
'capabilities': {'test_cparam1': 'bar',
'test_cparam2': 'far'},
'confirmed': True
}
response = self.post_json('/storage_lvm', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual(constants.SB_TYPE_LVM, # Expected
self.get_json('/storage_lvm/%s/' % response.json['uuid'])['backend']) # Result
@mock.patch.object(StorageBackendConfig, 'set_img_conversions_defaults')
@mock.patch('sysinv.api.controllers.v1.storage_lvm._discover_and_validate_cinder_hiera_data')
@mock.patch('sysinv.api.controllers.v1.storage_lvm._apply_backend_changes')
@mock.patch.object(SBApiHelper, 'set_backend_data',
side_effect=set_backend_state_configured)
def test_post_and_confirm_modify_with_invalid_svc(self, mock_set_backend_data,
mock_apply, mock_validate, mock_img_conv):
vals = {
'backend': constants.SB_TYPE_LVM,
'services': constants.SB_SVC_CINDER,
'capabilities': {'test_cparam1': 'bar',
'test_cparam2': 'far'},
'confirmed': True
}
response = self.post_json('/storage_lvm', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual(constants.SB_TYPE_LVM, # Expected
self.get_json('/storage_lvm/%s/' % response.json['uuid'])['backend']) # Result
patch_response = self.patch_dict_json('/storage_lvm/%s' % response.json['uuid'],
headers={'User-Agent': 'sysinv'},
services=(',').join([constants.SB_SVC_CINDER,
constants.SB_SVC_GLANCE]),
expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, patch_response.status_int)
self.assertEqual('application/json', patch_response.content_type)
self.assertTrue(patch_response.json['error_message'])
self.assertIn('Service glance is not supported', patch_response.json['error_message'])
@mock.patch.object(StorageBackendConfig, 'set_img_conversions_defaults')
@mock.patch('sysinv.api.controllers.v1.storage_lvm._discover_and_validate_cinder_hiera_data')
@mock.patch('sysinv.api.controllers.v1.storage_lvm._apply_backend_changes')
def test_post_and_list(self, mock_apply, mock_validate, mock_img_conv):
vals = {
'backend': constants.SB_TYPE_LVM,
'services': constants.SB_SVC_CINDER,
'capabilities': {'test_cparam1': 'bar',
'test_cparam2': 'far'},
'confirmed': True
}
response = self.post_json('/storage_lvm/', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual(constants.SB_TYPE_LVM, # Expected
self.get_json('/storage_lvm/%s/' % response.json['uuid'])['backend']) # Result
self.assertEqual(constants.SB_TYPE_LVM, self.get_json('/storage_backend')['storage_backends'][0]['backend'])
class StorageCephTestCases(base.FunctionalTest):
def setUp(self):
super(StorageCephTestCases, self).setUp()
self.system = dbutils.create_test_isystem()
self.cluster = dbutils.create_test_cluster(system_id=self.system.id)
self.tier = dbutils.create_test_storage_tier(forclusterid=self.cluster.id)
self.load = dbutils.create_test_load()
self.host = dbutils.create_test_ihost(forisystemid=self.system.id)
def assertDeleted(self, fullPath):
self.get_json(fullPath, expect_errors=True) # Make sure this line raises an error
#
# StorageCeph API
#
@mock.patch.object(StorageBackendConfig, 'get_ceph_mon_ip_addresses')
def test_post_missing_confirm(self, mock_mon_ip):
# Test skipped. Fix later.
self.skipTest("Skipping to prevent failure notification on Jenkins")
vals = {
'backend': constants.SB_TYPE_CEPH,
'capabilities': {'test_bparam3': 'foo'}
}
response = self.post_json('/storage_ceph', vals, expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, response.status_int)
self.assertEqual('application/json', response.content_type)
self.assertTrue(response.json['error_message'])
self.assertIn('nWARNING : THIS OPERATION IS NOT REVERSIBLE AND CANNOT BE CANCELLED',
response.json['error_message'])
@mock.patch.object(StorageBackendConfig, 'get_ceph_mon_ip_addresses')
@mock.patch.object(StorageBackendConfig, 'set_img_conversions_defaults')
def test_post_and_confirm(self, mock_img_conv, mock_mon_ip):
vals = {
'backend': constants.SB_TYPE_CEPH,
'capabilities': {'test_bparam3': 'foo'},
'confirmed': True
}
response = self.post_json('/storage_ceph', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual(constants.SB_TYPE_CEPH, # Expected
self.get_json('/storage_ceph/%s/' % response.json['uuid'])['backend']) # Result
@mock.patch.object(StorageBackendConfig, 'get_ceph_mon_ip_addresses')
def test_post_with_invalid_svc_and_confirm(self, mock_mon_ip):
vals = {
'backend': constants.SB_TYPE_CEPH,
'services': 'invalid_svc',
'capabilities': {'test_bparam3': 'foo'},
'confirmed': True
}
response = self.post_json('/storage_ceph', vals, expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, response.status_int)
self.assertEqual('application/json', response.content_type)
self.assertTrue(response.json['error_message'])
self.assertIn('Service invalid_svc is not supported for the ceph backend',
response.json['error_message'])
@mock.patch.object(StorageBackendConfig, 'get_ceph_mon_ip_addresses')
@mock.patch.object(StorageBackendConfig, 'set_img_conversions_defaults')
def test_post_with_valid_svc_all_svc_param_and_confirm(self, mock_img_conv, mock_mon_ip):
vals = {
'backend': constants.SB_TYPE_CEPH,
'services': constants.SB_SVC_SWIFT,
'capabilities': {'test_bparam3': 'foo',
'test_sparam1': 'bar'},
'confirmed': True
}
response = self.post_json('/storage_ceph', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual(constants.SB_TYPE_CEPH, # Expected
self.get_json('/storage_ceph/%s/' % response.json['uuid'])['backend']) # Result
@mock.patch.object(StorageBackendConfig, 'get_ceph_mon_ip_addresses')
@mock.patch.object(StorageBackendConfig, 'set_img_conversions_defaults')
@mock.patch.object(SBApiHelper, 'set_backend_data',
side_effect=set_backend_state_configured)
def test_post_and_confirm_modify_with_invalid_svc(self, mock_set_backend_data, mock_img_conv, mock_mon_ip):
vals = {
'backend': constants.SB_TYPE_CEPH,
'capabilities': {'test_bparam3': 'foo'},
'confirmed': True
}
response = self.post_json('/storage_ceph', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual(constants.SB_TYPE_CEPH, # Expected
self.get_json('/storage_ceph/%s/' % response.json['uuid'])['backend']) # Result
patch_response = self.patch_dict_json('/storage_ceph/%s' % response.json['uuid'],
headers={'User-Agent': 'sysinv'},
services='invalid_svc',
expect_errors=True)
self.assertEqual(http_client.BAD_REQUEST, patch_response.status_int)
self.assertEqual('application/json', patch_response.content_type)
self.assertTrue(patch_response.json['error_message'])
self.assertIn('Service invalid_svc is not supported', patch_response.json['error_message'])
@mock.patch.object(StorageBackendConfig, 'get_ceph_mon_ip_addresses')
@mock.patch.object(StorageBackendConfig, 'set_img_conversions_defaults')
def test_post_and_confirm_modify_with_svc_with_params(self, mock_img_conv, mock_mon_ip):
# Test skipped. Fix later.
self.skipTest("Skipping to prevent failure notification on Jenkins")
vals = {
'backend': constants.SB_TYPE_CEPH,
'capabilities': {'test_bparam3': 'foo'},
'confirmed': True
}
response = self.post_json('/storage_ceph', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual(constants.SB_TYPE_CEPH, # Expected
self.get_json('/storage_ceph/%s/' % response.json['uuid'])['backend']) # Result
patch_response = self.patch_dict_json('/storage_ceph/%s' % response.json['uuid'],
headers={'User-Agent': 'sysinv'},
services=constants.SB_SVC_SWIFT,
capabilities=jsonutils.dumps({'test_sparam1': 'bar'}),
expect_errors=False)
self.assertEqual(http_client.OK, patch_response.status_int)
self.assertEqual(constants.SB_SVC_SWIFT, # Expected
self.get_json('/storage_ceph/%s/' % response.json['uuid'])['services']) # Result
self.assertEqual({'test_bparam3': 'foo',
'test_sparam1': 'bar'}, # Expected
self.get_json('/storage_ceph/%s/' % response.json['uuid'])['capabilities']) # Result
@mock.patch.object(StorageBackendConfig, 'get_ceph_mon_ip_addresses')
@mock.patch.object(StorageBackendConfig, 'set_img_conversions_defaults')
def test_post_and_list(self, mock_img_conv, mock_mon_ip):
vals = {
'backend': constants.SB_TYPE_CEPH,
'capabilities': {'test_bparam3': 'foo'},
'confirmed': True
}
response = self.post_json('/storage_ceph/', vals, expect_errors=False)
self.assertEqual(http_client.OK, response.status_int)
self.assertEqual(constants.SB_TYPE_CEPH, # Expected
self.get_json('/storage_ceph/%s/' % response.json['uuid'])['backend']) # Result
self.assertEqual(constants.SB_TYPE_CEPH, self.get_json('/storage_backend')['storage_backends'][0]['backend'])
| 53.994701 | 125 | 0.629411 | 7,684 | 71,327 | 5.520953 | 0.030323 | 0.064352 | 0.034297 | 0.05068 | 0.96443 | 0.961672 | 0.950216 | 0.944676 | 0.942178 | 0.937251 | 0 | 0.004094 | 0.256873 | 71,327 | 1,320 | 126 | 54.035606 | 0.796265 | 0.021731 | 0 | 0.817316 | 0 | 0 | 0.230959 | 0.063984 | 0 | 0 | 0 | 0 | 0.232035 | 1 | 0.064069 | false | 0 | 0.009524 | 0 | 0.077922 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 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 | 7 |
72edba50e5ba341d7bffed2cbdd9e36f06db734b | 38 | py | Python | tests/test_pycounts_syw.py | sy25wang/pycounts_syw | 23b6070a0342e21772d2b3b3cb90b4a6fd6d30be | [
"MIT"
] | null | null | null | tests/test_pycounts_syw.py | sy25wang/pycounts_syw | 23b6070a0342e21772d2b3b3cb90b4a6fd6d30be | [
"MIT"
] | null | null | null | tests/test_pycounts_syw.py | sy25wang/pycounts_syw | 23b6070a0342e21772d2b3b3cb90b4a6fd6d30be | [
"MIT"
] | null | null | null | from pycounts_syw import pycounts_syw
| 19 | 37 | 0.894737 | 6 | 38 | 5.333333 | 0.666667 | 0.6875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.105263 | 38 | 1 | 38 | 38 | 0.941176 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 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 | 1 | 0 | 0 | 7 |
f429683d99b83ced83141e9bc1c4539c365c370d | 8,881 | py | Python | integration/data/test_ha.py | vishnuitta/longhorn-engine | 7ffd3022737952bf53532127f75eb25efe961404 | [
"Apache-2.0"
] | 21 | 2017-01-06T13:57:48.000Z | 2019-07-08T18:23:45.000Z | integration/data/test_ha.py | vishnuitta/longhorn-engine | 7ffd3022737952bf53532127f75eb25efe961404 | [
"Apache-2.0"
] | 30 | 2016-11-15T15:51:42.000Z | 2020-06-09T06:04:22.000Z | integration/data/test_ha.py | vishnuitta/longhorn-engine | 7ffd3022737952bf53532127f75eb25efe961404 | [
"Apache-2.0"
] | 22 | 2016-11-14T09:29:04.000Z | 2018-10-29T17:55:54.000Z | import cmd
import common
from common import controller, replica1, replica2 # NOQA
from common import backing_replica1, backing_replica2 # NOQA
from common import prepare_backup_dir, BACKUP_DIR # NOQA
from common import open_replica, get_blockdev, cleanup_replica
from common import verify_read, verify_data, verify_async, VOLUME_HEAD
from snapshot_tree import snapshot_tree_build, snapshot_tree_verify
def test_ha_single_replica_failure(controller, replica1, replica2): # NOQA
open_replica(replica1)
open_replica(replica2)
replicas = controller.list_replica()
assert len(replicas) == 0
v = controller.list_volume()[0]
v = v.start(replicas=[
common.REPLICA1,
common.REPLICA2
])
assert v.replicaCount == 2
replicas = controller.list_replica()
assert len(replicas) == 2
assert replicas[0].mode == "RW"
assert replicas[1].mode == "RW"
dev = get_blockdev()
data = common.random_string(128)
data_offset = 1024
verify_data(dev, data_offset, data)
cleanup_replica(replica2)
verify_async(dev, 10, 128, 1)
common.verify_replica_state(controller, 1, "ERR")
verify_read(dev, data_offset, data)
def test_ha_single_replica_rebuild(controller, replica1, replica2): # NOQA
open_replica(replica1)
open_replica(replica2)
replicas = controller.list_replica()
assert len(replicas) == 0
v = controller.list_volume()[0]
v = v.start(replicas=[
common.REPLICA1,
common.REPLICA2
])
assert v.replicaCount == 2
replicas = controller.list_replica()
assert len(replicas) == 2
assert replicas[0].mode == "RW"
assert replicas[1].mode == "RW"
dev = get_blockdev()
data = common.random_string(128)
data_offset = 1024
verify_data(dev, data_offset, data)
# Cleanup replica2
cleanup_replica(replica2)
verify_async(dev, 10, 128, 1)
common.verify_replica_state(controller, 1, "ERR")
verify_read(dev, data_offset, data)
controller.delete(replicas[1])
# Rebuild replica2
common.open_replica(replica2)
cmd.add_replica(common.REPLICA2)
verify_async(dev, 10, 128, 1)
common.verify_replica_state(controller, 1, "RW")
verify_read(dev, data_offset, data)
# WORKAROUND for unable to remove the parent of volume head
newsnap = cmd.snapshot_create()
info = cmd.snapshot_info()
assert len(info) == 3
sysnap = info[newsnap]["parent"]
assert info[sysnap]["parent"] == ""
assert newsnap in info[sysnap]["children"]
assert info[sysnap]["usercreated"] is False
assert info[sysnap]["removed"] is False
cmd.snapshot_purge()
info = cmd.snapshot_info()
assert len(info) == 2
assert info[newsnap] is not None
assert info[VOLUME_HEAD] is not None
def test_ha_double_replica_rebuild(controller, replica1, replica2): # NOQA
open_replica(replica1)
open_replica(replica2)
replicas = controller.list_replica()
assert len(replicas) == 0
v = controller.list_volume()[0]
v = v.start(replicas=[
common.REPLICA1,
common.REPLICA2
])
assert v.replicaCount == 2
replicas = controller.list_replica()
assert len(replicas) == 2
assert replicas[0].mode == "RW"
assert replicas[1].mode == "RW"
dev = get_blockdev()
data1 = common.random_string(128)
data1_offset = 1024
verify_data(dev, data1_offset, data1)
# Close replica2
r2 = replica2.list_replica()[0]
assert r2.revisioncounter == 1
r2.close()
verify_async(dev, 10, 128, 1)
common.verify_replica_state(controller, 1, "ERR")
verify_read(dev, data1_offset, data1)
data2 = common.random_string(128)
data2_offset = 512
verify_data(dev, data2_offset, data2)
# Close replica1
r1 = replica1.list_replica()[0]
assert r1.revisioncounter == 12 # 1 + 10 + 1
r1.close()
# Restart volume
common.cleanup_controller(controller)
replicas = controller.list_replica()
assert len(replicas) == 0
v = controller.list_volume()[0]
# NOTE the order is reversed here
v = v.start(replicas=[
common.REPLICA2,
common.REPLICA1
])
assert v.replicaCount == 2
# replica2 is out because of lower revision counter
replicas = controller.list_replica()
assert len(replicas) == 2
assert replicas[0].mode == "ERR"
assert replicas[1].mode == "RW"
verify_read(dev, data1_offset, data1)
verify_read(dev, data2_offset, data2)
# Rebuild replica2
r2 = replica2.list_replica()[0]
assert r2.revisioncounter == 1
r2.close()
controller.delete(replicas[0])
cmd.add_replica(common.REPLICA2)
verify_async(dev, 10, 128, 1)
common.verify_replica_state(controller, 1, "RW")
verify_read(dev, data1_offset, data1)
verify_read(dev, data2_offset, data2)
r1 = replica1.list_replica()[0]
r2 = replica2.list_replica()[0]
assert r1.revisioncounter == 22 # 1 + 10 + 1 + 10
assert r2.revisioncounter == 22 # must be in sync with r1
def test_ha_revision_counter_consistency(controller, replica1, replica2): # NOQA
open_replica(replica1)
open_replica(replica2)
replicas = controller.list_replica()
assert len(replicas) == 0
v = controller.list_volume()[0]
v = v.start(replicas=[
common.REPLICA1,
common.REPLICA2
])
assert v.replicaCount == 2
replicas = controller.list_replica()
assert len(replicas) == 2
assert replicas[0].mode == "RW"
assert replicas[1].mode == "RW"
dev = get_blockdev()
common.verify_async(dev, 10, 128, 100)
r1 = replica1.list_replica()[0]
r2 = replica2.list_replica()[0]
# kernel can merge requests so backend may not receive 1000 writes
assert r1.revisioncounter > 0
assert r1.revisioncounter == r2.revisioncounter
def test_snapshot_tree_rebuild(controller, replica1, replica2): # NOQA
offset = 0
length = 128
open_replica(replica1)
open_replica(replica2)
replicas = controller.list_replica()
assert len(replicas) == 0
v = controller.list_volume()[0]
v = v.start(replicas=[
common.REPLICA1,
common.REPLICA2
])
assert v.replicaCount == 2
replicas = controller.list_replica()
assert len(replicas) == 2
assert replicas[0].mode == "RW"
assert replicas[1].mode == "RW"
dev = get_blockdev()
snap, snap_data = snapshot_tree_build(dev, offset, length)
data = common.random_string(128)
data_offset = 1024
verify_data(dev, data_offset, data)
# Cleanup replica2
cleanup_replica(replica2)
verify_async(dev, 10, 128, 1)
common.verify_replica_state(controller, 1, "ERR")
verify_read(dev, data_offset, data)
controller.delete(replicas[1])
# Rebuild replica2
common.open_replica(replica2)
cmd.add_replica(common.REPLICA2)
verify_async(dev, 10, 128, 1)
common.verify_replica_state(controller, 1, "RW")
snapshot_tree_verify(dev, offset, length, snap, snap_data)
def test_ha_single_backing_replica_rebuild(controller, # NOQA
backing_replica1, # NOQA
backing_replica2): # NOQA
prepare_backup_dir(BACKUP_DIR)
open_replica(backing_replica1)
open_replica(backing_replica2)
replicas = controller.list_replica()
assert len(replicas) == 0
v = controller.list_volume()[0]
v = v.start(replicas=[
common.BACKED_REPLICA1,
common.BACKED_REPLICA2
])
assert v.replicaCount == 2
replicas = controller.list_replica()
assert len(replicas) == 2
assert replicas[0].mode == "RW"
assert replicas[1].mode == "RW"
dev = get_blockdev()
data = common.random_string(128)
data_offset = 1024
verify_data(dev, data_offset, data)
# Cleanup replica2
cleanup_replica(backing_replica2)
verify_async(dev, 10, 128, 1)
common.verify_replica_state(controller, 1, "ERR")
verify_read(dev, data_offset, data)
controller.delete(replicas[1])
# Rebuild replica2
common.open_replica(backing_replica2)
cmd.add_replica(common.BACKED_REPLICA2)
verify_async(dev, 10, 128, 1)
common.verify_replica_state(controller, 1, "RW")
verify_read(dev, data_offset, data)
# WORKAROUND for unable to remove the parent of volume head
newsnap = cmd.snapshot_create()
info = cmd.snapshot_info()
assert len(info) == 3
sysnap = info[newsnap]["parent"]
assert info[sysnap]["parent"] == ""
assert newsnap in info[sysnap]["children"]
assert info[sysnap]["usercreated"] is False
assert info[sysnap]["removed"] is False
cmd.snapshot_purge()
info = cmd.snapshot_info()
assert len(info) == 2
assert info[newsnap] is not None
assert info[VOLUME_HEAD] is not None
| 25.742029 | 81 | 0.672109 | 1,121 | 8,881 | 5.145406 | 0.103479 | 0.050971 | 0.053398 | 0.070388 | 0.808773 | 0.753641 | 0.740811 | 0.740811 | 0.740811 | 0.740811 | 0 | 0.043862 | 0.22216 | 8,881 | 344 | 82 | 25.81686 | 0.791112 | 0.059903 | 0 | 0.8 | 0 | 0 | 0.015386 | 0 | 0 | 0 | 0 | 0 | 0.252174 | 1 | 0.026087 | false | 0 | 0.034783 | 0 | 0.06087 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 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 | 7 |
f436e55852306d2bbb81db8a529825e8b7d35f3f | 83 | py | Python | tests/parser/aggregates.count.12.test.py | veltri/DLV2 | 944aaef803aa75e7ec51d7e0c2b0d964687fdd0e | [
"Apache-2.0"
] | null | null | null | tests/parser/aggregates.count.12.test.py | veltri/DLV2 | 944aaef803aa75e7ec51d7e0c2b0d964687fdd0e | [
"Apache-2.0"
] | null | null | null | tests/parser/aggregates.count.12.test.py | veltri/DLV2 | 944aaef803aa75e7ec51d7e0c2b0d964687fdd0e | [
"Apache-2.0"
] | null | null | null | input = """
:- #count{V : a(V)} < X.
"""
output = """
:- #count{V : a(V)} < X.
"""
| 11.857143 | 24 | 0.349398 | 12 | 83 | 2.416667 | 0.5 | 0.413793 | 0.482759 | 0.551724 | 0.62069 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.240964 | 83 | 6 | 25 | 13.833333 | 0.460317 | 0 | 0 | 0.666667 | 0 | 0 | 0.626506 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
f4514b67ae7c57c67d0c90223acd17f48ed3898a | 4,481 | py | Python | tests/unittests/test_nebula_osquery.py | brannondorsey/hubble | 12d07149201b9772f95abb8b735cdacd01c5aef6 | [
"Apache-2.0"
] | null | null | null | tests/unittests/test_nebula_osquery.py | brannondorsey/hubble | 12d07149201b9772f95abb8b735cdacd01c5aef6 | [
"Apache-2.0"
] | null | null | null | tests/unittests/test_nebula_osquery.py | brannondorsey/hubble | 12d07149201b9772f95abb8b735cdacd01c5aef6 | [
"Apache-2.0"
] | null | null | null | import sys
import os
myPath = os.path.abspath(os.getcwd())
sys.path.insert(0, myPath)
import hubblestack.extmods.modules.nebula_osquery
class TestNebula():
def test__virtual__(self):
var = hubblestack.extmods.modules.nebula_osquery.__virtual__()
assert var == 'nebula'
def test_hubble_versions(self):
var = hubblestack.extmods.modules.nebula_osquery.hubble_versions()
assert ((var.get('hubble_versions')).get('result')) is True
def test_queries(self):
query_group = 'day'
query_file = 'tests/unittests/resources/hubblestack_nebula_queries.yaml'
def cp_cache_file(queryFile):
return 'tests/unittests/resources/hubblestack_nebula_queries.yaml'
def uptime():
return {}
def cmd_run(default):
return default
__salt__ = {}
__salt__['cp.cache_file'] = cp_cache_file
__salt__['status.uptime'] = uptime
__salt__['cmd.run'] = cmd_run
hubblestack.extmods.modules.nebula_osquery.__salt__ = __salt__
hubblestack.extmods.modules.nebula_osquery.__grains__ = {'osfinger': 'Ubuntu-16.04'}
def cmd_run_all(cmd):
return {'retcode': 0, 'pid': 3478,
'stdout': '[{"build":"","codename":"xenial","major":"16","minor":"4","name":"Ubuntu","patch":"",'
'"platform":"ubuntu","platform_like":"debian","query_time":"1500395829","version":"16.04.2 LTS (Xenial Xerus)"}]',
'stderr': ''}
__salt__['cmd.run_all'] = cmd_run_all
var = hubblestack.extmods.modules.nebula_osquery.queries(query_group, query_file, verbose=False, report_version_with_day=False)
assert len(var) != 0
assert var[0]['fallback_osfinger']['data'][0]['osfinger'] == 'Ubuntu-16.04'
def test_queries_for_report_version_with_day(self):
query_group = 'day'
query_file = 'tests/unittests/resources/hubblestack_nebula_queries.yaml'
def cp_cache_file(queryFile):
return 'tests/unittests/resources/hubblestack_nebula_queries.yaml'
def uptime():
return {}
def cmd_run(default):
return default
__salt__ = {}
__salt__['cp.cache_file'] = cp_cache_file
__salt__['status.uptime'] = uptime
__salt__['cmd.run'] = cmd_run
hubblestack.extmods.modules.nebula_osquery.__salt__ = __salt__
hubblestack.extmods.modules.nebula_osquery.__grains__ = {'osfinger': 'Ubuntu-16.04'}
def cmd_run_all(cmd):
return {'retcode': 0, 'pid': 3478,
'stdout': '[{"build":"","codename":"xenial","major":"16","minor":"4","name":"Ubuntu","patch":"",'
'"platform":"ubuntu","platform_like":"debian","query_time":"1500395829","version":"16.04.2 LTS (Xenial Xerus)"}]',
'stderr': ''}
__salt__['cmd.run_all'] = cmd_run_all
hubblestack.extmods.modules.nebula_osquery.__salt__ = __salt__
var = hubblestack.extmods.modules.nebula_osquery.queries(query_group, query_file, verbose=False, report_version_with_day=True)
hubblestack.extmods.modules.nebula_osquery.__salt__ = {}
assert len(var) != 0
assert (var[2]['hubble_versions']) is not None
def test_hubble_version(self):
var = hubblestack.extmods.modules.nebula_osquery.hubble_versions()
assert (var['hubble_versions']) is not None
def test_top(self):
__salt__ = {}
query_group = 'day'
topfile = 'tests/unittests/resources/top.nebula'
verbose = False,
report_version_with_day = True
def cp_cache_file(queryFile):
return 'tests/unittests/resources/top.nebula'
def match_compound(value):
return value
def status_uptime():
return {}
def cmd_run(default):
return default
__salt__['status.uptime'] = status_uptime
__salt__['cmd.run'] = cmd_run
__salt__['cp.cache_file'] = cp_cache_file
__salt__['match.compound'] = match_compound
hubblestack.extmods.modules.nebula_osquery.__salt__ = __salt__
var = hubblestack.extmods.modules.nebula_osquery.top(query_group, topfile, verbose, report_version_with_day)
hubblestack.extmods.modules.nebula_osquery.__salt__ = {}
assert len(var) != 0
assert var[0]['fallback_osfinger']['data'][0]['osfinger'] == 'Ubuntu-16.04'
| 41.110092 | 144 | 0.633341 | 505 | 4,481 | 5.186139 | 0.178218 | 0.103093 | 0.143184 | 0.177549 | 0.845743 | 0.814433 | 0.806033 | 0.750668 | 0.739213 | 0.702176 | 0 | 0.020676 | 0.233653 | 4,481 | 108 | 145 | 41.490741 | 0.741992 | 0 | 0 | 0.670455 | 0 | 0.045455 | 0.236331 | 0.144611 | 0 | 0 | 0 | 0 | 0.102273 | 1 | 0.204545 | false | 0 | 0.034091 | 0.136364 | 0.386364 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 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 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 9 |
f45dc8e4eac0aed3fd65bd342ab181923228157d | 166 | py | Python | src/embit/util/hashlib.py | arcbtc/embit | 70a125dcb42f84c2d549ffb41b1c878cff447191 | [
"MIT"
] | 1 | 2022-02-19T23:16:53.000Z | 2022-02-19T23:16:53.000Z | src/embit/util/hashlib.py | arcbtc/embit | 70a125dcb42f84c2d549ffb41b1c878cff447191 | [
"MIT"
] | null | null | null | src/embit/util/hashlib.py | arcbtc/embit | 70a125dcb42f84c2d549ffb41b1c878cff447191 | [
"MIT"
] | null | null | null | try:
from hashlib import hmac_sha512, pbkdf2_hmac_sha512, ripemd160
except:
from .pyhashlib import hmac_sha512, pbkdf2_hmac_sha512, ripemd160
from hashlib import * | 27.666667 | 66 | 0.831325 | 23 | 166 | 5.73913 | 0.434783 | 0.30303 | 0.257576 | 0.333333 | 0.621212 | 0.621212 | 0.621212 | 0 | 0 | 0 | 0 | 0.136986 | 0.120482 | 166 | 6 | 67 | 27.666667 | 0.767123 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 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 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 7 |
be4712c3431c184d39f7eca21ca1f8091661c621 | 247,900 | py | Python | platform/radio/efr32_multiphy_configurator/pycalcmodel/model_instance/base/Bindings.py | PascalGuenther/gecko_sdk | 2e82050dc8823c9fe0e8908c1b2666fb83056230 | [
"Zlib"
] | 82 | 2016-06-29T17:24:43.000Z | 2021-04-16T06:49:17.000Z | platform/radio/efr32_multiphy_configurator/pycalcmodel/model_instance/base/Bindings.py | PascalGuenther/gecko_sdk | 2e82050dc8823c9fe0e8908c1b2666fb83056230 | [
"Zlib"
] | 6 | 2022-01-12T18:22:08.000Z | 2022-03-25T10:19:27.000Z | platform/radio/efr32_multiphy_configurator/pycalcmodel/model_instance/base/Bindings.py | PascalGuenther/gecko_sdk | 2e82050dc8823c9fe0e8908c1b2666fb83056230 | [
"Zlib"
] | 56 | 2016-08-02T10:50:50.000Z | 2021-07-19T08:57:34.000Z | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Generated Tue Oct 13 17:05:21 2020 by generateDS.py version 2.12d.
#
# Command line options:
# ('-o', '..\\..\\model_instance\\base\\Bindings.py')
# ('--super', 'Bindings')
# ('-s', '..\\..\\model_instance\\base\\Template.py')
# ('--subclass-suffix', '')
# ('--external-encoding', 'ascii')
# ('-m', '')
# ('-f', '')
# ('--silence', '')
#
# Command line arguments:
# .\model_instance.xsd
#
# Command line:
# ..\..\generateDS\generateDS.py -o "..\..\model_instance\base\Bindings.py" --super="Bindings" -s "..\..\model_instance\base\Template.py" --subclass-suffix --external-encoding="ascii" -m -f --silence .\model_instance.xsd
#
# Current working directory (os.getcwd()):
# model_instance
#
import sys
import getopt
import re as re_
import base64
import datetime as datetime_
from pycalcmodel.py2_and_3_compatibility import *
etree_ = None
Verbose_import_ = False
(
XMLParser_import_none, XMLParser_import_lxml,
XMLParser_import_elementtree
) = range(3)
XMLParser_import_library = None
try:
# lxml
from lxml import etree as etree_
XMLParser_import_library = XMLParser_import_lxml
if Verbose_import_:
print("running with lxml.etree")
except ImportError:
try:
# cElementTree from Python 2.5+
import xml.etree.cElementTree as etree_
XMLParser_import_library = XMLParser_import_elementtree
if Verbose_import_:
print("running with cElementTree on Python 2.5+")
except ImportError:
try:
# ElementTree from Python 2.5+
import xml.etree.ElementTree as etree_
XMLParser_import_library = XMLParser_import_elementtree
if Verbose_import_:
print("running with ElementTree on Python 2.5+")
except ImportError:
try:
# normal cElementTree install
import cElementTree as etree_
XMLParser_import_library = XMLParser_import_elementtree
if Verbose_import_:
print("running with cElementTree")
except ImportError:
try:
# normal ElementTree install
import elementtree.ElementTree as etree_
XMLParser_import_library = XMLParser_import_elementtree
if Verbose_import_:
print("running with ElementTree")
except ImportError:
raise ImportError(
"Failed to import ElementTree from any known place")
def parsexml_(*args, **kwargs):
if (XMLParser_import_library == XMLParser_import_lxml and
'parser' not in kwargs):
# Use the lxml ElementTree compatible parser so that, e.g.,
# we ignore comments.
kwargs['parser'] = etree_.ETCompatXMLParser()
doc = etree_.parse(*args, **kwargs)
return doc
#
# User methods
#
# Calls to the methods in these classes are generated by generateDS.py.
# You can replace these methods by re-implementing the following class
# in a module named generatedssuper.py.
try:
from generatedssuper import GeneratedsSuper
except ImportError as exp:
class GeneratedsSuper(object):
tzoff_pattern = re_.compile(r'(\+|-)((0\d|1[0-3]):[0-5]\d|14:00)$')
class _FixedOffsetTZ(datetime_.tzinfo):
def __init__(self, offset, name):
self.__offset = datetime_.timedelta(minutes=offset)
self.__name = name
def utcoffset(self, dt):
return self.__offset
def tzname(self, dt):
return self.__name
def dst(self, dt):
return None
def gds_format_string(self, input_data, input_name=''):
return input_data
def gds_validate_string(self, input_data, node, input_name=''):
if not input_data:
return ''
else:
return input_data
def gds_format_base64(self, input_data, input_name=''):
return base64.b64encode(input_data)
def gds_validate_base64(self, input_data, node, input_name=''):
return input_data
def gds_format_integer(self, input_data, input_name=''):
return '%d' % input_data
def gds_validate_integer(self, input_data, node, input_name=''):
return input_data
def gds_format_integer_list(self, input_data, input_name=''):
return '%s' % input_data
def gds_validate_integer_list(self, input_data, node, input_name=''):
values = input_data.split()
for value in values:
try:
float(value)
except (TypeError, ValueError):
raise_parse_error(node, 'Requires sequence of integers')
return input_data
def gds_format_float(self, input_data, input_name=''):
return ('%.15f' % input_data).rstrip('0')
def gds_validate_float(self, input_data, node, input_name=''):
return input_data
def gds_format_float_list(self, input_data, input_name=''):
return '%s' % input_data
def gds_validate_float_list(self, input_data, node, input_name=''):
values = input_data.split()
for value in values:
try:
float(value)
except (TypeError, ValueError):
raise_parse_error(node, 'Requires sequence of floats')
return input_data
def gds_format_double(self, input_data, input_name=''):
return '%e' % input_data
def gds_validate_double(self, input_data, node, input_name=''):
return input_data
def gds_format_double_list(self, input_data, input_name=''):
return '%s' % input_data
def gds_validate_double_list(self, input_data, node, input_name=''):
values = input_data.split()
for value in values:
try:
float(value)
except (TypeError, ValueError):
raise_parse_error(node, 'Requires sequence of doubles')
return input_data
def gds_format_boolean(self, input_data, input_name=''):
return ('%s' % input_data).lower()
def gds_validate_boolean(self, input_data, node, input_name=''):
return input_data
def gds_format_boolean_list(self, input_data, input_name=''):
return '%s' % input_data
def gds_validate_boolean_list(self, input_data, node, input_name=''):
values = input_data.split()
for value in values:
if value not in ('true', '1', 'false', '0', ):
raise_parse_error(
node,
'Requires sequence of booleans '
'("true", "1", "false", "0")')
return input_data
def gds_validate_datetime(self, input_data, node, input_name=''):
return input_data
def gds_format_datetime(self, input_data, input_name=''):
if input_data.microsecond == 0:
_svalue = '%04d-%02d-%02dT%02d:%02d:%02d' % (
input_data.year,
input_data.month,
input_data.day,
input_data.hour,
input_data.minute,
input_data.second,
)
else:
_svalue = '%04d-%02d-%02dT%02d:%02d:%02d.%s' % (
input_data.year,
input_data.month,
input_data.day,
input_data.hour,
input_data.minute,
input_data.second,
('%f' % (float(input_data.microsecond) / 1000000))[2:],
)
if input_data.tzinfo is not None:
tzoff = input_data.tzinfo.utcoffset(input_data)
if tzoff is not None:
total_seconds = tzoff.seconds + (86400 * tzoff.days)
if total_seconds == 0:
_svalue += 'Z'
else:
if total_seconds < 0:
_svalue += '-'
total_seconds *= -1
else:
_svalue += '+'
hours = total_seconds // 3600
minutes = (total_seconds - (hours * 3600)) // 60
_svalue += '{0:02d}:{1:02d}'.format(hours, minutes)
return _svalue
@classmethod
def gds_parse_datetime(cls, input_data):
tz = None
if input_data[-1] == 'Z':
tz = GeneratedsSuper._FixedOffsetTZ(0, 'UTC')
input_data = input_data[:-1]
else:
results = GeneratedsSuper.tzoff_pattern.search(input_data)
if results is not None:
tzoff_parts = results.group(2).split(':')
tzoff = int(tzoff_parts[0]) * 60 + int(tzoff_parts[1])
if results.group(1) == '-':
tzoff *= -1
tz = GeneratedsSuper._FixedOffsetTZ(
tzoff, results.group(0))
input_data = input_data[:-6]
if len(input_data.split('.')) > 1:
dt = datetime_.datetime.strptime(
input_data, '%Y-%m-%dT%H:%M:%S.%f')
else:
dt = datetime_.datetime.strptime(
input_data, '%Y-%m-%dT%H:%M:%S')
dt = dt.replace(tzinfo=tz)
return dt
def gds_validate_date(self, input_data, node, input_name=''):
return input_data
def gds_format_date(self, input_data, input_name=''):
_svalue = '%04d-%02d-%02d' % (
input_data.year,
input_data.month,
input_data.day,
)
try:
if input_data.tzinfo is not None:
tzoff = input_data.tzinfo.utcoffset(input_data)
if tzoff is not None:
total_seconds = tzoff.seconds + (86400 * tzoff.days)
if total_seconds == 0:
_svalue += 'Z'
else:
if total_seconds < 0:
_svalue += '-'
total_seconds *= -1
else:
_svalue += '+'
hours = total_seconds // 3600
minutes = (total_seconds - (hours * 3600)) // 60
_svalue += '{0:02d}:{1:02d}'.format(hours, minutes)
except AttributeError:
pass
return _svalue
@classmethod
def gds_parse_date(cls, input_data):
tz = None
if input_data[-1] == 'Z':
tz = GeneratedsSuper._FixedOffsetTZ(0, 'UTC')
input_data = input_data[:-1]
else:
results = GeneratedsSuper.tzoff_pattern.search(input_data)
if results is not None:
tzoff_parts = results.group(2).split(':')
tzoff = int(tzoff_parts[0]) * 60 + int(tzoff_parts[1])
if results.group(1) == '-':
tzoff *= -1
tz = GeneratedsSuper._FixedOffsetTZ(
tzoff, results.group(0))
input_data = input_data[:-6]
dt = datetime_.datetime.strptime(input_data, '%Y-%m-%d')
dt = dt.replace(tzinfo=tz)
return dt.date()
def gds_validate_time(self, input_data, node, input_name=''):
return input_data
def gds_format_time(self, input_data, input_name=''):
if input_data.microsecond == 0:
_svalue = '%02d:%02d:%02d' % (
input_data.hour,
input_data.minute,
input_data.second,
)
else:
_svalue = '%02d:%02d:%02d.%s' % (
input_data.hour,
input_data.minute,
input_data.second,
('%f' % (float(input_data.microsecond) / 1000000))[2:],
)
if input_data.tzinfo is not None:
tzoff = input_data.tzinfo.utcoffset(input_data)
if tzoff is not None:
total_seconds = tzoff.seconds + (86400 * tzoff.days)
if total_seconds == 0:
_svalue += 'Z'
else:
if total_seconds < 0:
_svalue += '-'
total_seconds *= -1
else:
_svalue += '+'
hours = total_seconds // 3600
minutes = (total_seconds - (hours * 3600)) // 60
_svalue += '{0:02d}:{1:02d}'.format(hours, minutes)
return _svalue
@classmethod
def gds_parse_time(cls, input_data):
tz = None
if input_data[-1] == 'Z':
tz = GeneratedsSuper._FixedOffsetTZ(0, 'UTC')
input_data = input_data[:-1]
else:
results = GeneratedsSuper.tzoff_pattern.search(input_data)
if results is not None:
tzoff_parts = results.group(2).split(':')
tzoff = int(tzoff_parts[0]) * 60 + int(tzoff_parts[1])
if results.group(1) == '-':
tzoff *= -1
tz = GeneratedsSuper._FixedOffsetTZ(
tzoff, results.group(0))
input_data = input_data[:-6]
if len(input_data.split('.')) > 1:
dt = datetime_.datetime.strptime(input_data, '%H:%M:%S.%f')
else:
dt = datetime_.datetime.strptime(input_data, '%H:%M:%S')
dt = dt.replace(tzinfo=tz)
return dt.time()
def gds_str_lower(self, instring):
return instring.lower()
def get_path_(self, node):
path_list = []
self.get_path_list_(node, path_list)
path_list.reverse()
path = '/'.join(path_list)
return path
Tag_strip_pattern_ = re_.compile(r'\{.*\}')
def get_path_list_(self, node, path_list):
if node is None:
return
tag = GeneratedsSuper.Tag_strip_pattern_.sub('', node.tag)
if tag:
path_list.append(tag)
self.get_path_list_(node.getparent(), path_list)
def get_class_obj_(self, node, default_class=None):
class_obj1 = default_class
if 'xsi' in node.nsmap:
classname = node.get('{%s}type' % node.nsmap['xsi'])
if classname is not None:
names = classname.split(':')
if len(names) == 2:
classname = names[1]
class_obj2 = globals().get(classname)
if class_obj2 is not None:
class_obj1 = class_obj2
return class_obj1
def gds_build_any(self, node, type_name=None):
return None
@classmethod
def gds_reverse_node_mapping(cls, mapping):
return dict(((v, k) for k, v in mapping.iteritems()))
#
# If you have installed IPython you can uncomment and use the following.
# IPython is available from http://ipython.scipy.org/.
#
## from IPython.Shell import IPShellEmbed
## args = ''
## ipshell = IPShellEmbed(args,
## banner = 'Dropping into IPython',
## exit_msg = 'Leaving Interpreter, back to program.')
# Then use the following line where and when you want to drop into the
# IPython shell:
# ipshell('<some message> -- Entering ipshell.\nHit Ctrl-D to exit')
#
# Globals
#
ExternalEncoding = 'ascii'
Tag_pattern_ = re_.compile(r'({.*})?(.*)')
String_cleanup_pat_ = re_.compile(r"[\n\r\s]+")
Namespace_extract_pat_ = re_.compile(r'{(.*)}(.*)')
#
# Support/utility functions.
#
def showIndent(outfile, level, pretty_print=True):
if pretty_print:
for idx in range(level):
outfile.write(' ')
def quote_xml(inStr):
if not inStr:
return ''
s1 = (isinstance(inStr, basestring) and inStr or
'%s' % inStr)
s1 = s1.replace('&', '&')
s1 = s1.replace('<', '<')
s1 = s1.replace('>', '>')
return s1
def quote_attrib(inStr):
s1 = (isinstance(inStr, basestring) and inStr or
'%s' % inStr)
s1 = s1.replace('&', '&')
s1 = s1.replace('<', '<')
s1 = s1.replace('>', '>')
if '"' in s1:
if "'" in s1:
s1 = '"%s"' % s1.replace('"', """)
else:
s1 = "'%s'" % s1
else:
s1 = '"%s"' % s1
return s1
def quote_python(inStr):
s1 = inStr
if s1.find("'") == -1:
if s1.find('\n') == -1:
return "'%s'" % s1
else:
return "'''%s'''" % s1
else:
if s1.find('"') != -1:
s1 = s1.replace('"', '\\"')
if s1.find('\n') == -1:
return '"%s"' % s1
else:
return '"""%s"""' % s1
def get_all_text_(node):
if node.text is not None:
text = node.text
else:
text = ''
for child in node:
if child.tail is not None:
text += child.tail
return text
def find_attr_value_(attr_name, node):
attrs = node.attrib
attr_parts = attr_name.split(':')
value = None
if len(attr_parts) == 1:
value = attrs.get(attr_name)
elif len(attr_parts) == 2:
prefix, name = attr_parts
namespace = node.nsmap.get(prefix)
if namespace is not None:
value = attrs.get('{%s}%s' % (namespace, name, ))
return value
class GDSParseError(Exception):
pass
def raise_parse_error(node, msg):
if XMLParser_import_library == XMLParser_import_lxml:
msg = '%s (element %s/line %d)' % (
msg, node.tag, node.sourceline, )
else:
msg = '%s (element %s)' % (msg, node.tag, )
raise GDSParseError(msg)
class MixedContainer:
# Constants for category:
CategoryNone = 0
CategoryText = 1
CategorySimple = 2
CategoryComplex = 3
# Constants for content_type:
TypeNone = 0
TypeText = 1
TypeString = 2
TypeInteger = 3
TypeFloat = 4
TypeDecimal = 5
TypeDouble = 6
TypeBoolean = 7
TypeBase64 = 8
def __init__(self, category, content_type, name, value):
self.category = category
self.content_type = content_type
self.name = name
self.value = value
def getCategory(self):
return self.category
def getContenttype(self, content_type):
return self.content_type
def getValue(self):
return self.value
def getName(self):
return self.name
def export(self, outfile, level, name, namespace, pretty_print=True):
if self.category == MixedContainer.CategoryText:
# Prevent exporting empty content as empty lines.
if self.value.strip():
outfile.write(self.value)
elif self.category == MixedContainer.CategorySimple:
self.exportSimple(outfile, level, name)
else: # category == MixedContainer.CategoryComplex
self.value.export(outfile, level, namespace, name, pretty_print)
def exportSimple(self, outfile, level, name):
if self.content_type == MixedContainer.TypeString:
outfile.write('<%s>%s</%s>' % (
self.name, self.value, self.name))
elif self.content_type == MixedContainer.TypeInteger or \
self.content_type == MixedContainer.TypeBoolean:
outfile.write('<%s>%d</%s>' % (
self.name, self.value, self.name))
elif self.content_type == MixedContainer.TypeFloat or \
self.content_type == MixedContainer.TypeDecimal:
outfile.write('<%s>%f</%s>' % (
self.name, self.value, self.name))
elif self.content_type == MixedContainer.TypeDouble:
outfile.write('<%s>%g</%s>' % (
self.name, self.value, self.name))
elif self.content_type == MixedContainer.TypeBase64:
outfile.write('<%s>%s</%s>' % (
self.name, base64.b64encode(self.value), self.name))
def to_etree(self, element):
if self.category == MixedContainer.CategoryText:
# Prevent exporting empty content as empty lines.
if self.value.strip():
if len(element) > 0:
if element[-1].tail is None:
element[-1].tail = self.value
else:
element[-1].tail += self.value
else:
if element.text is None:
element.text = self.value
else:
element.text += self.value
elif self.category == MixedContainer.CategorySimple:
subelement = etree_.SubElement(element, '%s' % self.name)
subelement.text = self.to_etree_simple()
else: # category == MixedContainer.CategoryComplex
self.value.to_etree(element)
def to_etree_simple(self):
if self.content_type == MixedContainer.TypeString:
text = self.value
elif (self.content_type == MixedContainer.TypeInteger or
self.content_type == MixedContainer.TypeBoolean):
text = '%d' % self.value
elif (self.content_type == MixedContainer.TypeFloat or
self.content_type == MixedContainer.TypeDecimal):
text = '%f' % self.value
elif self.content_type == MixedContainer.TypeDouble:
text = '%g' % self.value
elif self.content_type == MixedContainer.TypeBase64:
text = '%s' % base64.b64encode(self.value)
return text
def exportLiteral(self, outfile, level, name):
if self.category == MixedContainer.CategoryText:
showIndent(outfile, level)
outfile.write(
'model_.MixedContainer(%d, %d, "%s", "%s"),\n' % (
self.category, self.content_type, self.name, self.value))
elif self.category == MixedContainer.CategorySimple:
showIndent(outfile, level)
outfile.write(
'model_.MixedContainer(%d, %d, "%s", "%s"),\n' % (
self.category, self.content_type, self.name, self.value))
else: # category == MixedContainer.CategoryComplex
showIndent(outfile, level)
outfile.write(
'model_.MixedContainer(%d, %d, "%s",\n' % (
self.category, self.content_type, self.name,))
self.value.exportLiteral(outfile, level + 1)
showIndent(outfile, level)
outfile.write(')\n')
class MemberSpec_(object):
def __init__(self, name='', data_type='', container=0):
self.name = name
self.data_type = data_type
self.container = container
def set_name(self, name): self.name = name
def get_name(self): return self.name
def set_data_type(self, data_type): self.data_type = data_type
def get_data_type_chain(self): return self.data_type
def get_data_type(self):
if isinstance(self.data_type, list):
if len(self.data_type) > 0:
return self.data_type[-1]
else:
return 'xs:string'
else:
return self.data_type
def set_container(self, container): self.container = container
def get_container(self): return self.container
def _cast(typ, value):
if typ is None or value is None:
return value
return typ(value)
#
# Data representation classes.
#
class features(GeneratedsSuper):
"""Defines a list of all features used to define the feature variables
used in all act_logic attributes."""
subclass = None
superclass = None
def __init__(self, feature=None):
self.original_tagname_ = None
if feature is None:
self.feature = []
else:
self.feature = feature
def factory(*args_, **kwargs_):
if features.subclass:
return features.subclass(*args_, **kwargs_)
else:
return features(*args_, **kwargs_)
factory = staticmethod(factory)
def get_feature(self): return self.feature
def set_feature(self, feature): self.feature = feature
def add_feature(self, value): self.feature.append(value)
def insert_feature(self, index, value): self.feature[index] = value
featureProp = property(get_feature, set_feature)
def hasContent_(self):
if (
self.feature
):
return True
else:
return False
def export(self, outfile, level, namespace_='', name_='features', namespacedef_='', pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.original_tagname_ is not None:
name_ = self.original_tagname_
showIndent(outfile, level, pretty_print)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = set()
self.exportAttributes(outfile, level, already_processed, namespace_, name_='features')
if self.hasContent_():
outfile.write('>%s' % (eol_, ))
self.exportChildren(outfile, level + 1, namespace_='', name_='features', pretty_print=pretty_print)
showIndent(outfile, level, pretty_print)
outfile.write('</%s%s>%s' % (namespace_, name_, eol_))
else:
outfile.write('/>%s' % (eol_, ))
def exportAttributes(self, outfile, level, already_processed, namespace_='', name_='features'):
pass
def exportChildren(self, outfile, level, namespace_='', name_='features', fromsubclass_=False, pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
for feature_ in self.feature:
feature_.export(outfile, level, namespace_, name_='feature', pretty_print=pretty_print)
def exportLiteral(self, outfile, level, name_='features'):
level += 1
already_processed = set()
self.exportLiteralAttributes(outfile, level, already_processed, name_)
if self.hasContent_():
self.exportLiteralChildren(outfile, level, name_)
def exportLiteralAttributes(self, outfile, level, already_processed, name_):
pass
def exportLiteralChildren(self, outfile, level, name_):
showIndent(outfile, level)
outfile.write('feature=[\n')
level += 1
for feature_ in self.feature:
showIndent(outfile, level)
outfile.write('model_.featureType(\n')
feature_.exportLiteral(outfile, level, name_='featureType')
showIndent(outfile, level)
outfile.write('),\n')
level -= 1
showIndent(outfile, level)
outfile.write('],\n')
def build(self, node):
already_processed = set()
self.buildAttributes(node, node.attrib, already_processed)
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_)
return self
def buildAttributes(self, node, attrs, already_processed):
pass
def buildChildren(self, child_, node, nodeName_, fromsubclass_=False):
if nodeName_ == 'feature':
obj_ = featureType.factory()
obj_.build(child_)
self.feature.append(obj_)
obj_.original_tagname_ = 'feature'
# end class features
class var_overrides(GeneratedsSuper):
"""An optional override value for this output. This value is used
during the calculations to override what would be normally
calculated. So it can be viewed as an "input" to the calculator.
When present, it will force the var_values above."""
subclass = None
superclass = None
def __init__(self, value=None):
self.original_tagname_ = None
if value is None:
self.value = []
else:
self.value = value
def factory(*args_, **kwargs_):
if var_overrides.subclass:
return var_overrides.subclass(*args_, **kwargs_)
else:
return var_overrides(*args_, **kwargs_)
factory = staticmethod(factory)
def get_value(self): return self.value
def set_value(self, value): self.value = value
def add_value(self, value): self.value.append(value)
def insert_value(self, index, value): self.value[index] = value
valueProp = property(get_value, set_value)
def hasContent_(self):
if (
self.value
):
return True
else:
return False
def export(self, outfile, level, namespace_='', name_='var_overrides', namespacedef_='', pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.original_tagname_ is not None:
name_ = self.original_tagname_
showIndent(outfile, level, pretty_print)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = set()
self.exportAttributes(outfile, level, already_processed, namespace_, name_='var_overrides')
if self.hasContent_():
outfile.write('>%s' % (eol_, ))
self.exportChildren(outfile, level + 1, namespace_='', name_='var_overrides', pretty_print=pretty_print)
showIndent(outfile, level, pretty_print)
outfile.write('</%s%s>%s' % (namespace_, name_, eol_))
else:
outfile.write('/>%s' % (eol_, ))
def exportAttributes(self, outfile, level, already_processed, namespace_='', name_='var_overrides'):
pass
def exportChildren(self, outfile, level, namespace_='', name_='var_overrides', fromsubclass_=False, pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
for value_ in self.value:
showIndent(outfile, level, pretty_print)
outfile.write('<%svalue>%s</%svalue>%s' % (namespace_, self.gds_format_string(quote_xml(value_), input_name='value'), namespace_, eol_))
def exportLiteral(self, outfile, level, name_='var_overrides'):
level += 1
already_processed = set()
self.exportLiteralAttributes(outfile, level, already_processed, name_)
if self.hasContent_():
self.exportLiteralChildren(outfile, level, name_)
def exportLiteralAttributes(self, outfile, level, already_processed, name_):
pass
def exportLiteralChildren(self, outfile, level, name_):
showIndent(outfile, level)
outfile.write('value=[\n')
level += 1
for value_ in self.value:
showIndent(outfile, level)
outfile.write('%s,\n' % quote_python(value_))
level -= 1
showIndent(outfile, level)
outfile.write('],\n')
def build(self, node):
already_processed = set()
self.buildAttributes(node, node.attrib, already_processed)
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_)
return self
def buildAttributes(self, node, attrs, already_processed):
pass
def buildChildren(self, child_, node, nodeName_, fromsubclass_=False):
if nodeName_ == 'value':
value_ = child_.text
value_ = self.gds_validate_string(value_, node, 'value')
self.value.append(value_)
# end class var_overrides
class overrides(GeneratedsSuper):
"""An optional override value for this output. This value is used
during the calculations to override what would be normally
calculated. This override can be viewed as an "input" to the
calculator. When present, it will force the var_values output in
the instance file."""
subclass = None
superclass = None
def __init__(self, value=None):
self.original_tagname_ = None
if value is None:
self.value = []
else:
self.value = value
def factory(*args_, **kwargs_):
if overrides.subclass:
return overrides.subclass(*args_, **kwargs_)
else:
return overrides(*args_, **kwargs_)
factory = staticmethod(factory)
def get_value(self): return self.value
def set_value(self, value): self.value = value
def add_value(self, value): self.value.append(value)
def insert_value(self, index, value): self.value[index] = value
valueProp = property(get_value, set_value)
def hasContent_(self):
if (
self.value
):
return True
else:
return False
def export(self, outfile, level, namespace_='', name_='overrides', namespacedef_='', pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.original_tagname_ is not None:
name_ = self.original_tagname_
showIndent(outfile, level, pretty_print)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = set()
self.exportAttributes(outfile, level, already_processed, namespace_, name_='overrides')
if self.hasContent_():
outfile.write('>%s' % (eol_, ))
self.exportChildren(outfile, level + 1, namespace_='', name_='overrides', pretty_print=pretty_print)
showIndent(outfile, level, pretty_print)
outfile.write('</%s%s>%s' % (namespace_, name_, eol_))
else:
outfile.write('/>%s' % (eol_, ))
def exportAttributes(self, outfile, level, already_processed, namespace_='', name_='overrides'):
pass
def exportChildren(self, outfile, level, namespace_='', name_='overrides', fromsubclass_=False, pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
for value_ in self.value:
showIndent(outfile, level, pretty_print)
outfile.write('<%svalue>%s</%svalue>%s' % (namespace_, self.gds_format_string(quote_xml(value_), input_name='value'), namespace_, eol_))
def exportLiteral(self, outfile, level, name_='overrides'):
level += 1
already_processed = set()
self.exportLiteralAttributes(outfile, level, already_processed, name_)
if self.hasContent_():
self.exportLiteralChildren(outfile, level, name_)
def exportLiteralAttributes(self, outfile, level, already_processed, name_):
pass
def exportLiteralChildren(self, outfile, level, name_):
showIndent(outfile, level)
outfile.write('value=[\n')
level += 1
for value_ in self.value:
showIndent(outfile, level)
outfile.write('%s,\n' % quote_python(value_))
level -= 1
showIndent(outfile, level)
outfile.write('],\n')
def build(self, node):
already_processed = set()
self.buildAttributes(node, node.attrib, already_processed)
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_)
return self
def buildAttributes(self, node, attrs, already_processed):
pass
def buildChildren(self, child_, node, nodeName_, fromsubclass_=False):
if nodeName_ == 'value':
value_ = child_.text
value_ = self.gds_validate_string(value_, node, 'value')
self.value.append(value_)
# end class overrides
class defaults(GeneratedsSuper):
"""The default value(s) to assign to the profile input variable."""
subclass = None
superclass = None
def __init__(self, value=None):
self.original_tagname_ = None
if value is None:
self.value = []
else:
self.value = value
def factory(*args_, **kwargs_):
if defaults.subclass:
return defaults.subclass(*args_, **kwargs_)
else:
return defaults(*args_, **kwargs_)
factory = staticmethod(factory)
def get_value(self): return self.value
def set_value(self, value): self.value = value
def add_value(self, value): self.value.append(value)
def insert_value(self, index, value): self.value[index] = value
valueProp = property(get_value, set_value)
def hasContent_(self):
if (
self.value
):
return True
else:
return False
def export(self, outfile, level, namespace_='', name_='defaults', namespacedef_='', pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.original_tagname_ is not None:
name_ = self.original_tagname_
showIndent(outfile, level, pretty_print)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = set()
self.exportAttributes(outfile, level, already_processed, namespace_, name_='defaults')
if self.hasContent_():
outfile.write('>%s' % (eol_, ))
self.exportChildren(outfile, level + 1, namespace_='', name_='defaults', pretty_print=pretty_print)
showIndent(outfile, level, pretty_print)
outfile.write('</%s%s>%s' % (namespace_, name_, eol_))
else:
outfile.write('/>%s' % (eol_, ))
def exportAttributes(self, outfile, level, already_processed, namespace_='', name_='defaults'):
pass
def exportChildren(self, outfile, level, namespace_='', name_='defaults', fromsubclass_=False, pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
for value_ in self.value:
showIndent(outfile, level, pretty_print)
outfile.write('<%svalue>%s</%svalue>%s' % (namespace_, self.gds_format_string(quote_xml(value_), input_name='value'), namespace_, eol_))
def exportLiteral(self, outfile, level, name_='defaults'):
level += 1
already_processed = set()
self.exportLiteralAttributes(outfile, level, already_processed, name_)
if self.hasContent_():
self.exportLiteralChildren(outfile, level, name_)
def exportLiteralAttributes(self, outfile, level, already_processed, name_):
pass
def exportLiteralChildren(self, outfile, level, name_):
showIndent(outfile, level)
outfile.write('value=[\n')
level += 1
for value_ in self.value:
showIndent(outfile, level)
outfile.write('%s,\n' % quote_python(value_))
level -= 1
showIndent(outfile, level)
outfile.write('],\n')
def build(self, node):
already_processed = set()
self.buildAttributes(node, node.attrib, already_processed)
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_)
return self
def buildAttributes(self, node, attrs, already_processed):
pass
def buildChildren(self, child_, node, nodeName_, fromsubclass_=False):
if nodeName_ == 'value':
value_ = child_.text
value_ = self.gds_validate_string(value_, node, 'value')
self.value.append(value_)
# end class defaults
class var_values(GeneratedsSuper):
"""Contains the variable's value."""
subclass = None
superclass = None
def __init__(self, value=None):
self.original_tagname_ = None
if value is None:
self.value = []
else:
self.value = value
def factory(*args_, **kwargs_):
if var_values.subclass:
return var_values.subclass(*args_, **kwargs_)
else:
return var_values(*args_, **kwargs_)
factory = staticmethod(factory)
def get_value(self): return self.value
def set_value(self, value): self.value = value
def add_value(self, value): self.value.append(value)
def insert_value(self, index, value): self.value[index] = value
valueProp = property(get_value, set_value)
def hasContent_(self):
if (
self.value
):
return True
else:
return False
def export(self, outfile, level, namespace_='', name_='var_values', namespacedef_='', pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.original_tagname_ is not None:
name_ = self.original_tagname_
showIndent(outfile, level, pretty_print)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = set()
self.exportAttributes(outfile, level, already_processed, namespace_, name_='var_values')
if self.hasContent_():
outfile.write('>%s' % (eol_, ))
self.exportChildren(outfile, level + 1, namespace_='', name_='var_values', pretty_print=pretty_print)
showIndent(outfile, level, pretty_print)
outfile.write('</%s%s>%s' % (namespace_, name_, eol_))
else:
outfile.write('/>%s' % (eol_, ))
def exportAttributes(self, outfile, level, already_processed, namespace_='', name_='var_values'):
pass
def exportChildren(self, outfile, level, namespace_='', name_='var_values', fromsubclass_=False, pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
for value_ in self.value:
showIndent(outfile, level, pretty_print)
outfile.write('<%svalue>%s</%svalue>%s' % (namespace_, self.gds_format_string(quote_xml(value_), input_name='value'), namespace_, eol_))
def exportLiteral(self, outfile, level, name_='var_values'):
level += 1
already_processed = set()
self.exportLiteralAttributes(outfile, level, already_processed, name_)
if self.hasContent_():
self.exportLiteralChildren(outfile, level, name_)
def exportLiteralAttributes(self, outfile, level, already_processed, name_):
pass
def exportLiteralChildren(self, outfile, level, name_):
showIndent(outfile, level)
outfile.write('value=[\n')
level += 1
for value_ in self.value:
showIndent(outfile, level)
outfile.write('%s,\n' % quote_python(value_))
level -= 1
showIndent(outfile, level)
outfile.write('],\n')
def build(self, node):
already_processed = set()
self.buildAttributes(node, node.attrib, already_processed)
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_)
return self
def buildAttributes(self, node, attrs, already_processed):
pass
def buildChildren(self, child_, node, nodeName_, fromsubclass_=False):
if nodeName_ == 'value':
value_ = child_.text
value_ = self.gds_validate_string(value_, node, 'value')
self.value.append(value_)
# end class var_values
class values(GeneratedsSuper):
"""The value(s) to assign to the profile variable."""
subclass = None
superclass = None
def __init__(self, value=None):
self.original_tagname_ = None
if value is None:
self.value = []
else:
self.value = value
def factory(*args_, **kwargs_):
if values.subclass:
return values.subclass(*args_, **kwargs_)
else:
return values(*args_, **kwargs_)
factory = staticmethod(factory)
def get_value(self): return self.value
def set_value(self, value): self.value = value
def add_value(self, value): self.value.append(value)
def insert_value(self, index, value): self.value[index] = value
valueProp = property(get_value, set_value)
def hasContent_(self):
if (
self.value
):
return True
else:
return False
def export(self, outfile, level, namespace_='', name_='values', namespacedef_='', pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.original_tagname_ is not None:
name_ = self.original_tagname_
showIndent(outfile, level, pretty_print)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = set()
self.exportAttributes(outfile, level, already_processed, namespace_, name_='values')
if self.hasContent_():
outfile.write('>%s' % (eol_, ))
self.exportChildren(outfile, level + 1, namespace_='', name_='values', pretty_print=pretty_print)
showIndent(outfile, level, pretty_print)
outfile.write('</%s%s>%s' % (namespace_, name_, eol_))
else:
outfile.write('/>%s' % (eol_, ))
def exportAttributes(self, outfile, level, already_processed, namespace_='', name_='values'):
pass
def exportChildren(self, outfile, level, namespace_='', name_='values', fromsubclass_=False, pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
for value_ in self.value:
showIndent(outfile, level, pretty_print)
outfile.write('<%svalue>%s</%svalue>%s' % (namespace_, self.gds_format_string(quote_xml(value_), input_name='value'), namespace_, eol_))
def exportLiteral(self, outfile, level, name_='values'):
level += 1
already_processed = set()
self.exportLiteralAttributes(outfile, level, already_processed, name_)
if self.hasContent_():
self.exportLiteralChildren(outfile, level, name_)
def exportLiteralAttributes(self, outfile, level, already_processed, name_):
pass
def exportLiteralChildren(self, outfile, level, name_):
showIndent(outfile, level)
outfile.write('value=[\n')
level += 1
for value_ in self.value:
showIndent(outfile, level)
outfile.write('%s,\n' % quote_python(value_))
level -= 1
showIndent(outfile, level)
outfile.write('],\n')
def build(self, node):
already_processed = set()
self.buildAttributes(node, node.attrib, already_processed)
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_)
return self
def buildAttributes(self, node, attrs, already_processed):
pass
def buildChildren(self, child_, node, nodeName_, fromsubclass_=False):
if nodeName_ == 'value':
value_ = child_.text
value_ = self.gds_validate_string(value_, node, 'value')
self.value.append(value_)
# end class values
class model(GeneratedsSuper):
"""The collection of optional phy initialization, single profile
instance, and related model variables. This file can be used in
two steps. First to generate a file to assign the profile inputs
directly or from a tracked phy. Second to capture the results of
the profile calculations.The party family. Combined with the
part_revision to define the calculator variables.The part
revision. The overall calculator version string for the model
instance.The version of the model_instance.xsd.A plain text
description of the model instance.Indicates the calculations
have run with the profile inputs. If true, see
result_code.Optional result code to mark the success of the
calculation run.Optional message to capture a calculator
exception from invalid profile inputs.Optional timestamp to mark
the time processed.(e.g. IC, Simulation, FPGA, etc...) Defaults
to 'IC", if not provided."""
subclass = None
superclass = None
def __init__(self, part_family=None, part_revision=None, calc_version=None, xsd_version=None, desc=None, processed=None, result_code=None, error_message=None, timestamp=None, target='IC', phys=None, profiles=None, variables=None, features=None, logs=None):
self.original_tagname_ = None
self.part_family = _cast(None, part_family)
self.part_revision = _cast(None, part_revision)
self.calc_version = _cast(None, calc_version)
self.xsd_version = _cast(None, xsd_version)
self.desc = _cast(None, desc)
self.processed = _cast(bool, processed)
self.result_code = _cast(int, result_code)
self.error_message = _cast(None, error_message)
if isinstance(timestamp, basestring):
initvalue_ = datetime_.datetime.strptime(timestamp, '%Y-%m-%dT%H:%M:%S')
else:
initvalue_ = timestamp
self.timestamp = initvalue_
self.target = _cast(None, target)
self.phys = phys
self.profiles = profiles
self.variables = variables
self.features = features
self.logs = logs
def factory(*args_, **kwargs_):
if model.subclass:
return model.subclass(*args_, **kwargs_)
else:
return model(*args_, **kwargs_)
factory = staticmethod(factory)
def get_phys(self): return self.phys
def set_phys(self, phys): self.phys = phys
physProp = property(get_phys, set_phys)
def get_profiles(self): return self.profiles
def set_profiles(self, profiles): self.profiles = profiles
profilesProp = property(get_profiles, set_profiles)
def get_variables(self): return self.variables
def set_variables(self, variables): self.variables = variables
variablesProp = property(get_variables, set_variables)
def get_features(self): return self.features
def set_features(self, features): self.features = features
featuresProp = property(get_features, set_features)
def get_logs(self): return self.logs
def set_logs(self, logs): self.logs = logs
logsProp = property(get_logs, set_logs)
def get_part_family(self): return self.part_family
def set_part_family(self, part_family): self.part_family = part_family
part_familyProp = property(get_part_family, set_part_family)
def get_part_revision(self): return self.part_revision
def set_part_revision(self, part_revision): self.part_revision = part_revision
part_revisionProp = property(get_part_revision, set_part_revision)
def get_calc_version(self): return self.calc_version
def set_calc_version(self, calc_version): self.calc_version = calc_version
calc_versionProp = property(get_calc_version, set_calc_version)
def get_xsd_version(self): return self.xsd_version
def set_xsd_version(self, xsd_version): self.xsd_version = xsd_version
xsd_versionProp = property(get_xsd_version, set_xsd_version)
def get_desc(self): return self.desc
def set_desc(self, desc): self.desc = desc
descProp = property(get_desc, set_desc)
def get_processed(self): return self.processed
def set_processed(self, processed): self.processed = processed
processedProp = property(get_processed, set_processed)
def get_result_code(self): return self.result_code
def set_result_code(self, result_code): self.result_code = result_code
result_codeProp = property(get_result_code, set_result_code)
def get_error_message(self): return self.error_message
def set_error_message(self, error_message): self.error_message = error_message
error_messageProp = property(get_error_message, set_error_message)
def get_timestamp(self): return self.timestamp
def set_timestamp(self, timestamp): self.timestamp = timestamp
timestampProp = property(get_timestamp, set_timestamp)
def get_target(self): return self.target
def set_target(self, target): self.target = target
targetProp = property(get_target, set_target)
def hasContent_(self):
if (
self.phys is not None or
self.profiles is not None or
self.variables is not None or
self.features is not None or
self.logs is not None
):
return True
else:
return False
def export(self, outfile, level, namespace_='', name_='model', namespacedef_='', pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.original_tagname_ is not None:
name_ = self.original_tagname_
showIndent(outfile, level, pretty_print)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = set()
self.exportAttributes(outfile, level, already_processed, namespace_, name_='model')
if self.hasContent_():
outfile.write('>%s' % (eol_, ))
self.exportChildren(outfile, level + 1, namespace_='', name_='model', pretty_print=pretty_print)
showIndent(outfile, level, pretty_print)
outfile.write('</%s%s>%s' % (namespace_, name_, eol_))
else:
outfile.write('/>%s' % (eol_, ))
def exportAttributes(self, outfile, level, already_processed, namespace_='', name_='model'):
if self.part_family is not None and 'part_family' not in already_processed:
already_processed.add('part_family')
outfile.write(' part_family=%s' % (self.gds_format_string(quote_attrib(self.part_family), input_name='part_family'), ))
if self.part_revision is not None and 'part_revision' not in already_processed:
already_processed.add('part_revision')
outfile.write(' part_revision=%s' % (self.gds_format_string(quote_attrib(self.part_revision), input_name='part_revision'), ))
if self.calc_version is not None and 'calc_version' not in already_processed:
already_processed.add('calc_version')
outfile.write(' calc_version=%s' % (self.gds_format_string(quote_attrib(self.calc_version), input_name='calc_version'), ))
if self.xsd_version is not None and 'xsd_version' not in already_processed:
already_processed.add('xsd_version')
outfile.write(' xsd_version=%s' % (self.gds_format_string(quote_attrib(self.xsd_version), input_name='xsd_version'), ))
if self.desc is not None and 'desc' not in already_processed:
already_processed.add('desc')
outfile.write(' desc=%s' % (self.gds_format_string(quote_attrib(self.desc), input_name='desc'), ))
if self.processed is not None and 'processed' not in already_processed:
already_processed.add('processed')
outfile.write(' processed="%s"' % self.gds_format_boolean(self.processed, input_name='processed'))
if self.result_code is not None and 'result_code' not in already_processed:
already_processed.add('result_code')
outfile.write(' result_code="%s"' % self.gds_format_integer(self.result_code, input_name='result_code'))
if self.error_message is not None and 'error_message' not in already_processed:
already_processed.add('error_message')
outfile.write(' error_message=%s' % (self.gds_format_string(quote_attrib(self.error_message), input_name='error_message'), ))
if self.timestamp is not None and 'timestamp' not in already_processed:
already_processed.add('timestamp')
outfile.write(' timestamp="%s"' % self.gds_format_datetime(self.timestamp, input_name='timestamp'))
if self.target is not None and 'target' not in already_processed:
already_processed.add('target')
outfile.write(' target=%s' % (self.gds_format_string(quote_attrib(self.target), input_name='target'), ))
def exportChildren(self, outfile, level, namespace_='', name_='model', fromsubclass_=False, pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.phys is not None:
self.phys.export(outfile, level, namespace_, name_='phys', pretty_print=pretty_print)
if self.profiles is not None:
self.profiles.export(outfile, level, namespace_, name_='profiles', pretty_print=pretty_print)
if self.variables is not None:
self.variables.export(outfile, level, namespace_, name_='variables', pretty_print=pretty_print)
if self.features is not None:
self.features.export(outfile, level, namespace_, name_='features', pretty_print=pretty_print)
if self.logs is not None:
self.logs.export(outfile, level, namespace_, name_='logs', pretty_print=pretty_print)
def exportLiteral(self, outfile, level, name_='model'):
level += 1
already_processed = set()
self.exportLiteralAttributes(outfile, level, already_processed, name_)
if self.hasContent_():
self.exportLiteralChildren(outfile, level, name_)
def exportLiteralAttributes(self, outfile, level, already_processed, name_):
if self.part_family is not None and 'part_family' not in already_processed:
already_processed.add('part_family')
showIndent(outfile, level)
outfile.write('part_family="%s",\n' % (self.part_family,))
if self.part_revision is not None and 'part_revision' not in already_processed:
already_processed.add('part_revision')
showIndent(outfile, level)
outfile.write('part_revision="%s",\n' % (self.part_revision,))
if self.calc_version is not None and 'calc_version' not in already_processed:
already_processed.add('calc_version')
showIndent(outfile, level)
outfile.write('calc_version="%s",\n' % (self.calc_version,))
if self.xsd_version is not None and 'xsd_version' not in already_processed:
already_processed.add('xsd_version')
showIndent(outfile, level)
outfile.write('xsd_version="%s",\n' % (self.xsd_version,))
if self.desc is not None and 'desc' not in already_processed:
already_processed.add('desc')
showIndent(outfile, level)
outfile.write('desc="%s",\n' % (self.desc,))
if self.processed is not None and 'processed' not in already_processed:
already_processed.add('processed')
showIndent(outfile, level)
outfile.write('processed=%s,\n' % (self.processed,))
if self.result_code is not None and 'result_code' not in already_processed:
already_processed.add('result_code')
showIndent(outfile, level)
outfile.write('result_code=%d,\n' % (self.result_code,))
if self.error_message is not None and 'error_message' not in already_processed:
already_processed.add('error_message')
showIndent(outfile, level)
outfile.write('error_message="%s",\n' % (self.error_message,))
if self.timestamp is not None and 'timestamp' not in already_processed:
already_processed.add('timestamp')
showIndent(outfile, level)
outfile.write('timestamp=model_.GeneratedsSuper.gds_parse_datetime("%s"),\n' % self.gds_format_datetime(self.timestamp, input_name='timestamp'))
if self.target is not None and 'target' not in already_processed:
already_processed.add('target')
showIndent(outfile, level)
outfile.write('target="%s",\n' % (self.target,))
def exportLiteralChildren(self, outfile, level, name_):
if self.phys is not None:
showIndent(outfile, level)
outfile.write('phys=model_.physType(\n')
self.phys.exportLiteral(outfile, level, name_='phys')
showIndent(outfile, level)
outfile.write('),\n')
if self.profiles is not None:
showIndent(outfile, level)
outfile.write('profiles=model_.profilesType(\n')
self.profiles.exportLiteral(outfile, level, name_='profiles')
showIndent(outfile, level)
outfile.write('),\n')
if self.variables is not None:
showIndent(outfile, level)
outfile.write('variables=model_.variablesType(\n')
self.variables.exportLiteral(outfile, level, name_='variables')
showIndent(outfile, level)
outfile.write('),\n')
if self.features is not None:
showIndent(outfile, level)
outfile.write('features=model_.features(\n')
self.features.exportLiteral(outfile, level)
showIndent(outfile, level)
outfile.write('),\n')
if self.logs is not None:
showIndent(outfile, level)
outfile.write('logs=model_.logsType(\n')
self.logs.exportLiteral(outfile, level, name_='logs')
showIndent(outfile, level)
outfile.write('),\n')
def build(self, node):
already_processed = set()
self.buildAttributes(node, node.attrib, already_processed)
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_)
return self
def buildAttributes(self, node, attrs, already_processed):
value = find_attr_value_('part_family', node)
if value is not None and 'part_family' not in already_processed:
already_processed.add('part_family')
self.part_family = value
value = find_attr_value_('part_revision', node)
if value is not None and 'part_revision' not in already_processed:
already_processed.add('part_revision')
self.part_revision = value
value = find_attr_value_('calc_version', node)
if value is not None and 'calc_version' not in already_processed:
already_processed.add('calc_version')
self.calc_version = value
value = find_attr_value_('xsd_version', node)
if value is not None and 'xsd_version' not in already_processed:
already_processed.add('xsd_version')
self.xsd_version = value
value = find_attr_value_('desc', node)
if value is not None and 'desc' not in already_processed:
already_processed.add('desc')
self.desc = value
value = find_attr_value_('processed', node)
if value is not None and 'processed' not in already_processed:
already_processed.add('processed')
if value in ('true', '1'):
self.processed = True
elif value in ('false', '0'):
self.processed = False
else:
raise_parse_error(node, 'Bad boolean attribute')
value = find_attr_value_('result_code', node)
if value is not None and 'result_code' not in already_processed:
already_processed.add('result_code')
try:
self.result_code = int(value)
except ValueError as exp:
raise_parse_error(node, 'Bad integer attribute: %s' % exp)
value = find_attr_value_('error_message', node)
if value is not None and 'error_message' not in already_processed:
already_processed.add('error_message')
self.error_message = value
value = find_attr_value_('timestamp', node)
if value is not None and 'timestamp' not in already_processed:
already_processed.add('timestamp')
try:
self.timestamp = self.gds_parse_datetime(value)
except ValueError as exp:
raise ValueError('Bad date-time attribute (timestamp): %s' % exp)
value = find_attr_value_('target', node)
if value is not None and 'target' not in already_processed:
already_processed.add('target')
self.target = value
def buildChildren(self, child_, node, nodeName_, fromsubclass_=False):
if nodeName_ == 'phys':
obj_ = physType.factory()
obj_.build(child_)
self.phys = obj_
obj_.original_tagname_ = 'phys'
elif nodeName_ == 'profiles':
obj_ = profilesType.factory()
obj_.build(child_)
self.profiles = obj_
obj_.original_tagname_ = 'profiles'
elif nodeName_ == 'variables':
obj_ = variablesType.factory()
obj_.build(child_)
self.variables = obj_
obj_.original_tagname_ = 'variables'
elif nodeName_ == 'features':
obj_ = features.factory()
obj_.build(child_)
self.features = obj_
obj_.original_tagname_ = 'features'
elif nodeName_ == 'logs':
obj_ = logsType.factory()
obj_.build(child_)
self.logs = obj_
obj_.original_tagname_ = 'logs'
# end class model
class featureType(GeneratedsSuper):
"""The feature name. This name will be combined with a 'feature_'
prefix to form the feature variable name used in the act_logic
attributes.A plain text description of the feature.The Boolean
value of the feature."""
subclass = None
superclass = None
def __init__(self, name=None, desc=None, value=None):
self.original_tagname_ = None
self.name = _cast(None, name)
self.desc = _cast(None, desc)
self.value = _cast(bool, value)
def factory(*args_, **kwargs_):
if featureType.subclass:
return featureType.subclass(*args_, **kwargs_)
else:
return featureType(*args_, **kwargs_)
factory = staticmethod(factory)
def get_name(self): return self.name
def set_name(self, name): self.name = name
nameProp = property(get_name, set_name)
def get_desc(self): return self.desc
def set_desc(self, desc): self.desc = desc
descProp = property(get_desc, set_desc)
def get_value(self): return self.value
def set_value(self, value): self.value = value
valueProp = property(get_value, set_value)
def validate_nameType(self, value):
# Validate type nameType, a restriction on xs:string.
pass
def hasContent_(self):
if (
):
return True
else:
return False
def export(self, outfile, level, namespace_='', name_='featureType', namespacedef_='', pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.original_tagname_ is not None:
name_ = self.original_tagname_
showIndent(outfile, level, pretty_print)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = set()
self.exportAttributes(outfile, level, already_processed, namespace_, name_='featureType')
if self.hasContent_():
outfile.write('>%s' % (eol_, ))
self.exportChildren(outfile, level + 1, namespace_='', name_='featureType', pretty_print=pretty_print)
outfile.write('</%s%s>%s' % (namespace_, name_, eol_))
else:
outfile.write('/>%s' % (eol_, ))
def exportAttributes(self, outfile, level, already_processed, namespace_='', name_='featureType'):
if self.name is not None and 'name' not in already_processed:
already_processed.add('name')
outfile.write(' name=%s' % (quote_attrib(self.name), ))
if self.desc is not None and 'desc' not in already_processed:
already_processed.add('desc')
outfile.write(' desc=%s' % (self.gds_format_string(quote_attrib(self.desc), input_name='desc'), ))
if self.value is not None and 'value' not in already_processed:
already_processed.add('value')
outfile.write(' value="%s"' % self.gds_format_boolean(self.value, input_name='value'))
def exportChildren(self, outfile, level, namespace_='', name_='featureType', fromsubclass_=False, pretty_print=True):
pass
def exportLiteral(self, outfile, level, name_='featureType'):
level += 1
already_processed = set()
self.exportLiteralAttributes(outfile, level, already_processed, name_)
if self.hasContent_():
self.exportLiteralChildren(outfile, level, name_)
def exportLiteralAttributes(self, outfile, level, already_processed, name_):
if self.name is not None and 'name' not in already_processed:
already_processed.add('name')
showIndent(outfile, level)
outfile.write('name="%s",\n' % (self.name,))
if self.desc is not None and 'desc' not in already_processed:
already_processed.add('desc')
showIndent(outfile, level)
outfile.write('desc="%s",\n' % (self.desc,))
if self.value is not None and 'value' not in already_processed:
already_processed.add('value')
showIndent(outfile, level)
outfile.write('value=%s,\n' % (self.value,))
def exportLiteralChildren(self, outfile, level, name_):
pass
def build(self, node):
already_processed = set()
self.buildAttributes(node, node.attrib, already_processed)
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_)
return self
def buildAttributes(self, node, attrs, already_processed):
value = find_attr_value_('name', node)
if value is not None and 'name' not in already_processed:
already_processed.add('name')
self.name = value
self.validate_nameType(self.name) # validate type nameType
value = find_attr_value_('desc', node)
if value is not None and 'desc' not in already_processed:
already_processed.add('desc')
self.desc = value
value = find_attr_value_('value', node)
if value is not None and 'value' not in already_processed:
already_processed.add('value')
if value in ('true', '1'):
self.value = True
elif value in ('false', '0'):
self.value = False
else:
raise_parse_error(node, 'Bad boolean attribute')
def buildChildren(self, child_, node, nodeName_, fromsubclass_=False):
pass
# end class featureType
class physType(GeneratedsSuper):
subclass = None
superclass = None
def __init__(self, phy=None):
self.original_tagname_ = None
self.phy = phy
def factory(*args_, **kwargs_):
if physType.subclass:
return physType.subclass(*args_, **kwargs_)
else:
return physType(*args_, **kwargs_)
factory = staticmethod(factory)
def get_phy(self): return self.phy
def set_phy(self, phy): self.phy = phy
phyProp = property(get_phy, set_phy)
def hasContent_(self):
if (
self.phy is not None
):
return True
else:
return False
def export(self, outfile, level, namespace_='', name_='physType', namespacedef_='', pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.original_tagname_ is not None:
name_ = self.original_tagname_
showIndent(outfile, level, pretty_print)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = set()
self.exportAttributes(outfile, level, already_processed, namespace_, name_='physType')
if self.hasContent_():
outfile.write('>%s' % (eol_, ))
self.exportChildren(outfile, level + 1, namespace_='', name_='physType', pretty_print=pretty_print)
showIndent(outfile, level, pretty_print)
outfile.write('</%s%s>%s' % (namespace_, name_, eol_))
else:
outfile.write('/>%s' % (eol_, ))
def exportAttributes(self, outfile, level, already_processed, namespace_='', name_='physType'):
pass
def exportChildren(self, outfile, level, namespace_='', name_='physType', fromsubclass_=False, pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.phy is not None:
self.phy.export(outfile, level, namespace_, name_='phy', pretty_print=pretty_print)
def exportLiteral(self, outfile, level, name_='physType'):
level += 1
already_processed = set()
self.exportLiteralAttributes(outfile, level, already_processed, name_)
if self.hasContent_():
self.exportLiteralChildren(outfile, level, name_)
def exportLiteralAttributes(self, outfile, level, already_processed, name_):
pass
def exportLiteralChildren(self, outfile, level, name_):
if self.phy is not None:
showIndent(outfile, level)
outfile.write('phy=model_.phyType(\n')
self.phy.exportLiteral(outfile, level, name_='phy')
showIndent(outfile, level)
outfile.write('),\n')
def build(self, node):
already_processed = set()
self.buildAttributes(node, node.attrib, already_processed)
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_)
return self
def buildAttributes(self, node, attrs, already_processed):
pass
def buildChildren(self, child_, node, nodeName_, fromsubclass_=False):
if nodeName_ == 'phy':
obj_ = phyType.factory()
obj_.build(child_)
self.phy = obj_
obj_.original_tagname_ = 'phy'
# end class physType
class phyType(GeneratedsSuper):
"""The phy name. The name to display in the GUI.A plain text
description of the phy.The name used to collect a group of
similar phys.The profile name to pass the phy input values.The
Boolean expression of feature_NAME variables with logical AND
(double ampersands), OR (double pipes), and/or NOT (exclamation)
operators. Use parenthesis to group. An empty string defaults to
true.User definable hastagsWhether this is a precaclaulted PHY,
and thus not neededin to run calcaultions againUnique ID used as
an alternative to phy name."""
subclass = None
superclass = None
def __init__(self, name=None, readable_name=None, desc=None, group_name=None, profile_name=None, act_logic=None, tags=None, locked=False, guid=None, profile_inputs=None, profile_outputs=None):
self.original_tagname_ = None
self.name = _cast(None, name)
self.readable_name = _cast(None, readable_name)
self.desc = _cast(None, desc)
self.group_name = _cast(None, group_name)
self.profile_name = _cast(None, profile_name)
self.act_logic = _cast(None, act_logic)
self.tags = _cast(None, tags)
self.locked = _cast(bool, locked)
self.guid = _cast(None, guid)
self.profile_inputs = profile_inputs
self.profile_outputs = profile_outputs
def factory(*args_, **kwargs_):
if phyType.subclass:
return phyType.subclass(*args_, **kwargs_)
else:
return phyType(*args_, **kwargs_)
factory = staticmethod(factory)
def get_profile_inputs(self): return self.profile_inputs
def set_profile_inputs(self, profile_inputs): self.profile_inputs = profile_inputs
profile_inputsProp = property(get_profile_inputs, set_profile_inputs)
def get_profile_outputs(self): return self.profile_outputs
def set_profile_outputs(self, profile_outputs): self.profile_outputs = profile_outputs
profile_outputsProp = property(get_profile_outputs, set_profile_outputs)
def get_name(self): return self.name
def set_name(self, name): self.name = name
nameProp = property(get_name, set_name)
def get_readable_name(self): return self.readable_name
def set_readable_name(self, readable_name): self.readable_name = readable_name
readable_nameProp = property(get_readable_name, set_readable_name)
def get_desc(self): return self.desc
def set_desc(self, desc): self.desc = desc
descProp = property(get_desc, set_desc)
def get_group_name(self): return self.group_name
def set_group_name(self, group_name): self.group_name = group_name
group_nameProp = property(get_group_name, set_group_name)
def get_profile_name(self): return self.profile_name
def set_profile_name(self, profile_name): self.profile_name = profile_name
profile_nameProp = property(get_profile_name, set_profile_name)
def get_act_logic(self): return self.act_logic
def set_act_logic(self, act_logic): self.act_logic = act_logic
act_logicProp = property(get_act_logic, set_act_logic)
def get_tags(self): return self.tags
def set_tags(self, tags): self.tags = tags
tagsProp = property(get_tags, set_tags)
def get_locked(self): return self.locked
def set_locked(self, locked): self.locked = locked
lockedProp = property(get_locked, set_locked)
def get_guid(self): return self.guid
def set_guid(self, guid): self.guid = guid
guidProp = property(get_guid, set_guid)
def validate_nameType(self, value):
# Validate type nameType, a restriction on xs:string.
pass
def hasContent_(self):
if (
self.profile_inputs is not None or
self.profile_outputs is not None
):
return True
else:
return False
def export(self, outfile, level, namespace_='', name_='phyType', namespacedef_='', pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.original_tagname_ is not None:
name_ = self.original_tagname_
showIndent(outfile, level, pretty_print)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = set()
self.exportAttributes(outfile, level, already_processed, namespace_, name_='phyType')
if self.hasContent_():
outfile.write('>%s' % (eol_, ))
self.exportChildren(outfile, level + 1, namespace_='', name_='phyType', pretty_print=pretty_print)
showIndent(outfile, level, pretty_print)
outfile.write('</%s%s>%s' % (namespace_, name_, eol_))
else:
outfile.write('/>%s' % (eol_, ))
def exportAttributes(self, outfile, level, already_processed, namespace_='', name_='phyType'):
if self.name is not None and 'name' not in already_processed:
already_processed.add('name')
outfile.write(' name=%s' % (quote_attrib(self.name), ))
if self.readable_name is not None and 'readable_name' not in already_processed:
already_processed.add('readable_name')
outfile.write(' readable_name=%s' % (self.gds_format_string(quote_attrib(self.readable_name), input_name='readable_name'), ))
if self.desc is not None and 'desc' not in already_processed:
already_processed.add('desc')
outfile.write(' desc=%s' % (self.gds_format_string(quote_attrib(self.desc), input_name='desc'), ))
if self.group_name is not None and 'group_name' not in already_processed:
already_processed.add('group_name')
outfile.write(' group_name=%s' % (quote_attrib(self.group_name), ))
if self.profile_name is not None and 'profile_name' not in already_processed:
already_processed.add('profile_name')
outfile.write(' profile_name=%s' % (quote_attrib(self.profile_name), ))
if self.act_logic is not None and 'act_logic' not in already_processed:
already_processed.add('act_logic')
outfile.write(' act_logic=%s' % (self.gds_format_string(quote_attrib(self.act_logic), input_name='act_logic'), ))
if self.tags is not None and 'tags' not in already_processed:
already_processed.add('tags')
outfile.write(' tags=%s' % (self.gds_format_string(quote_attrib(self.tags), input_name='tags'), ))
if self.locked is not None and 'locked' not in already_processed:
already_processed.add('locked')
outfile.write(' locked="%s"' % self.gds_format_boolean(self.locked, input_name='locked'))
if self.guid is not None and 'guid' not in already_processed:
already_processed.add('guid')
outfile.write(' guid=%s' % (self.gds_format_string(quote_attrib(self.guid), input_name='guid'), ))
def exportChildren(self, outfile, level, namespace_='', name_='phyType', fromsubclass_=False, pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.profile_inputs is not None:
self.profile_inputs.export(outfile, level, namespace_, name_='profile_inputs', pretty_print=pretty_print)
if self.profile_outputs is not None:
self.profile_outputs.export(outfile, level, namespace_, name_='profile_outputs', pretty_print=pretty_print)
def exportLiteral(self, outfile, level, name_='phyType'):
level += 1
already_processed = set()
self.exportLiteralAttributes(outfile, level, already_processed, name_)
if self.hasContent_():
self.exportLiteralChildren(outfile, level, name_)
def exportLiteralAttributes(self, outfile, level, already_processed, name_):
if self.name is not None and 'name' not in already_processed:
already_processed.add('name')
showIndent(outfile, level)
outfile.write('name="%s",\n' % (self.name,))
if self.readable_name is not None and 'readable_name' not in already_processed:
already_processed.add('readable_name')
showIndent(outfile, level)
outfile.write('readable_name="%s",\n' % (self.readable_name,))
if self.desc is not None and 'desc' not in already_processed:
already_processed.add('desc')
showIndent(outfile, level)
outfile.write('desc="%s",\n' % (self.desc,))
if self.group_name is not None and 'group_name' not in already_processed:
already_processed.add('group_name')
showIndent(outfile, level)
outfile.write('group_name="%s",\n' % (self.group_name,))
if self.profile_name is not None and 'profile_name' not in already_processed:
already_processed.add('profile_name')
showIndent(outfile, level)
outfile.write('profile_name="%s",\n' % (self.profile_name,))
if self.act_logic is not None and 'act_logic' not in already_processed:
already_processed.add('act_logic')
showIndent(outfile, level)
outfile.write('act_logic="%s",\n' % (self.act_logic,))
if self.tags is not None and 'tags' not in already_processed:
already_processed.add('tags')
showIndent(outfile, level)
outfile.write('tags="%s",\n' % (self.tags,))
if self.locked is not None and 'locked' not in already_processed:
already_processed.add('locked')
showIndent(outfile, level)
outfile.write('locked=%s,\n' % (self.locked,))
if self.guid is not None and 'guid' not in already_processed:
already_processed.add('guid')
showIndent(outfile, level)
outfile.write('guid="%s",\n' % (self.guid,))
def exportLiteralChildren(self, outfile, level, name_):
if self.profile_inputs is not None:
showIndent(outfile, level)
outfile.write('profile_inputs=model_.profile_inputsType(\n')
self.profile_inputs.exportLiteral(outfile, level, name_='profile_inputs')
showIndent(outfile, level)
outfile.write('),\n')
if self.profile_outputs is not None:
showIndent(outfile, level)
outfile.write('profile_outputs=model_.profile_outputsType(\n')
self.profile_outputs.exportLiteral(outfile, level, name_='profile_outputs')
showIndent(outfile, level)
outfile.write('),\n')
def build(self, node):
already_processed = set()
self.buildAttributes(node, node.attrib, already_processed)
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_)
return self
def buildAttributes(self, node, attrs, already_processed):
value = find_attr_value_('name', node)
if value is not None and 'name' not in already_processed:
already_processed.add('name')
self.name = value
self.validate_nameType(self.name) # validate type nameType
value = find_attr_value_('readable_name', node)
if value is not None and 'readable_name' not in already_processed:
already_processed.add('readable_name')
self.readable_name = value
value = find_attr_value_('desc', node)
if value is not None and 'desc' not in already_processed:
already_processed.add('desc')
self.desc = value
value = find_attr_value_('group_name', node)
if value is not None and 'group_name' not in already_processed:
already_processed.add('group_name')
self.group_name = value
self.validate_nameType(self.group_name) # validate type nameType
value = find_attr_value_('profile_name', node)
if value is not None and 'profile_name' not in already_processed:
already_processed.add('profile_name')
self.profile_name = value
self.validate_nameType(self.profile_name) # validate type nameType
value = find_attr_value_('act_logic', node)
if value is not None and 'act_logic' not in already_processed:
already_processed.add('act_logic')
self.act_logic = value
value = find_attr_value_('tags', node)
if value is not None and 'tags' not in already_processed:
already_processed.add('tags')
self.tags = value
value = find_attr_value_('locked', node)
if value is not None and 'locked' not in already_processed:
already_processed.add('locked')
if value in ('true', '1'):
self.locked = True
elif value in ('false', '0'):
self.locked = False
else:
raise_parse_error(node, 'Bad boolean attribute')
value = find_attr_value_('guid', node)
if value is not None and 'guid' not in already_processed:
already_processed.add('guid')
self.guid = value
def buildChildren(self, child_, node, nodeName_, fromsubclass_=False):
if nodeName_ == 'profile_inputs':
obj_ = profile_inputsType.factory()
obj_.build(child_)
self.profile_inputs = obj_
obj_.original_tagname_ = 'profile_inputs'
elif nodeName_ == 'profile_outputs':
obj_ = profile_outputsType.factory()
obj_.build(child_)
self.profile_outputs = obj_
obj_.original_tagname_ = 'profile_outputs'
# end class phyType
class profile_inputsType(GeneratedsSuper):
subclass = None
superclass = None
def __init__(self, profile_input=None):
self.original_tagname_ = None
if profile_input is None:
self.profile_input = []
else:
self.profile_input = profile_input
def factory(*args_, **kwargs_):
if profile_inputsType.subclass:
return profile_inputsType.subclass(*args_, **kwargs_)
else:
return profile_inputsType(*args_, **kwargs_)
factory = staticmethod(factory)
def get_profile_input(self): return self.profile_input
def set_profile_input(self, profile_input): self.profile_input = profile_input
def add_profile_input(self, value): self.profile_input.append(value)
def insert_profile_input(self, index, value): self.profile_input[index] = value
profile_inputProp = property(get_profile_input, set_profile_input)
def hasContent_(self):
if (
self.profile_input
):
return True
else:
return False
def export(self, outfile, level, namespace_='', name_='profile_inputsType', namespacedef_='', pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.original_tagname_ is not None:
name_ = self.original_tagname_
showIndent(outfile, level, pretty_print)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = set()
self.exportAttributes(outfile, level, already_processed, namespace_, name_='profile_inputsType')
if self.hasContent_():
outfile.write('>%s' % (eol_, ))
self.exportChildren(outfile, level + 1, namespace_='', name_='profile_inputsType', pretty_print=pretty_print)
showIndent(outfile, level, pretty_print)
outfile.write('</%s%s>%s' % (namespace_, name_, eol_))
else:
outfile.write('/>%s' % (eol_, ))
def exportAttributes(self, outfile, level, already_processed, namespace_='', name_='profile_inputsType'):
pass
def exportChildren(self, outfile, level, namespace_='', name_='profile_inputsType', fromsubclass_=False, pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
for profile_input_ in self.profile_input:
profile_input_.export(outfile, level, namespace_, name_='profile_input', pretty_print=pretty_print)
def exportLiteral(self, outfile, level, name_='profile_inputsType'):
level += 1
already_processed = set()
self.exportLiteralAttributes(outfile, level, already_processed, name_)
if self.hasContent_():
self.exportLiteralChildren(outfile, level, name_)
def exportLiteralAttributes(self, outfile, level, already_processed, name_):
pass
def exportLiteralChildren(self, outfile, level, name_):
showIndent(outfile, level)
outfile.write('profile_input=[\n')
level += 1
for profile_input_ in self.profile_input:
showIndent(outfile, level)
outfile.write('model_.profile_inputType(\n')
profile_input_.exportLiteral(outfile, level, name_='profile_inputType')
showIndent(outfile, level)
outfile.write('),\n')
level -= 1
showIndent(outfile, level)
outfile.write('],\n')
def build(self, node):
already_processed = set()
self.buildAttributes(node, node.attrib, already_processed)
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_)
return self
def buildAttributes(self, node, attrs, already_processed):
pass
def buildChildren(self, child_, node, nodeName_, fromsubclass_=False):
if nodeName_ == 'profile_input':
obj_ = profile_inputType.factory()
obj_.build(child_)
self.profile_input.append(obj_)
obj_.original_tagname_ = 'profile_input'
# end class profile_inputsType
class profile_inputType(GeneratedsSuper):
"""Specifies if this input has array of data."""
subclass = None
superclass = None
def __init__(self, is_array=None, readable_name=None, var_name=None, category=None, values=None):
self.original_tagname_ = None
self.is_array = _cast(bool, is_array)
self.readable_name = readable_name
self.var_name = var_name
self.category = category
self.values = values
def factory(*args_, **kwargs_):
if profile_inputType.subclass:
return profile_inputType.subclass(*args_, **kwargs_)
else:
return profile_inputType(*args_, **kwargs_)
factory = staticmethod(factory)
def get_readable_name(self): return self.readable_name
def set_readable_name(self, readable_name): self.readable_name = readable_name
readable_nameProp = property(get_readable_name, set_readable_name)
def get_var_name(self): return self.var_name
def set_var_name(self, var_name): self.var_name = var_name
var_nameProp = property(get_var_name, set_var_name)
def get_category(self): return self.category
def set_category(self, category): self.category = category
categoryProp = property(get_category, set_category)
def get_values(self): return self.values
def set_values(self, values): self.values = values
valuesProp = property(get_values, set_values)
def get_is_array(self): return self.is_array
def set_is_array(self, is_array): self.is_array = is_array
is_arrayProp = property(get_is_array, set_is_array)
def hasContent_(self):
if (
self.readable_name is not None or
self.var_name is not None or
self.category is not None or
self.values is not None
):
return True
else:
return False
def export(self, outfile, level, namespace_='', name_='profile_inputType', namespacedef_='', pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.original_tagname_ is not None:
name_ = self.original_tagname_
showIndent(outfile, level, pretty_print)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = set()
self.exportAttributes(outfile, level, already_processed, namespace_, name_='profile_inputType')
if self.hasContent_():
outfile.write('>%s' % (eol_, ))
self.exportChildren(outfile, level + 1, namespace_='', name_='profile_inputType', pretty_print=pretty_print)
showIndent(outfile, level, pretty_print)
outfile.write('</%s%s>%s' % (namespace_, name_, eol_))
else:
outfile.write('/>%s' % (eol_, ))
def exportAttributes(self, outfile, level, already_processed, namespace_='', name_='profile_inputType'):
if self.is_array is not None and 'is_array' not in already_processed:
already_processed.add('is_array')
outfile.write(' is_array="%s"' % self.gds_format_boolean(self.is_array, input_name='is_array'))
def exportChildren(self, outfile, level, namespace_='', name_='profile_inputType', fromsubclass_=False, pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.readable_name is not None:
showIndent(outfile, level, pretty_print)
outfile.write('<%sreadable_name>%s</%sreadable_name>%s' % (namespace_, self.gds_format_string(quote_xml(self.readable_name), input_name='readable_name'), namespace_, eol_))
if self.var_name is not None:
showIndent(outfile, level, pretty_print)
outfile.write('<%svar_name>%s</%svar_name>%s' % (namespace_, self.gds_format_string(quote_xml(self.var_name), input_name='var_name'), namespace_, eol_))
if self.category is not None:
showIndent(outfile, level, pretty_print)
outfile.write('<%scategory>%s</%scategory>%s' % (namespace_, self.gds_format_string(quote_xml(self.category), input_name='category'), namespace_, eol_))
if self.values is not None:
self.values.export(outfile, level, namespace_, name_='values', pretty_print=pretty_print)
def exportLiteral(self, outfile, level, name_='profile_inputType'):
level += 1
already_processed = set()
self.exportLiteralAttributes(outfile, level, already_processed, name_)
if self.hasContent_():
self.exportLiteralChildren(outfile, level, name_)
def exportLiteralAttributes(self, outfile, level, already_processed, name_):
if self.is_array is not None and 'is_array' not in already_processed:
already_processed.add('is_array')
showIndent(outfile, level)
outfile.write('is_array=%s,\n' % (self.is_array,))
def exportLiteralChildren(self, outfile, level, name_):
if self.readable_name is not None:
showIndent(outfile, level)
outfile.write('readable_name=%s,\n' % quote_python(self.readable_name))
if self.var_name is not None:
showIndent(outfile, level)
outfile.write('var_name=%s,\n' % quote_python(self.var_name))
if self.category is not None:
showIndent(outfile, level)
outfile.write('category=%s,\n' % quote_python(self.category))
if self.values is not None:
showIndent(outfile, level)
outfile.write('values=model_.values(\n')
self.values.exportLiteral(outfile, level)
showIndent(outfile, level)
outfile.write('),\n')
def build(self, node):
already_processed = set()
self.buildAttributes(node, node.attrib, already_processed)
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_)
return self
def buildAttributes(self, node, attrs, already_processed):
value = find_attr_value_('is_array', node)
if value is not None and 'is_array' not in already_processed:
already_processed.add('is_array')
if value in ('true', '1'):
self.is_array = True
elif value in ('false', '0'):
self.is_array = False
else:
raise_parse_error(node, 'Bad boolean attribute')
def buildChildren(self, child_, node, nodeName_, fromsubclass_=False):
if nodeName_ == 'readable_name':
readable_name_ = child_.text
readable_name_ = self.gds_validate_string(readable_name_, node, 'readable_name')
self.readable_name = readable_name_
elif nodeName_ == 'var_name':
var_name_ = child_.text
var_name_ = self.gds_validate_string(var_name_, node, 'var_name')
self.var_name = var_name_
elif nodeName_ == 'category':
category_ = child_.text
category_ = self.gds_validate_string(category_, node, 'category')
self.category = category_
elif nodeName_ == 'values':
obj_ = values.factory()
obj_.build(child_)
self.values = obj_
obj_.original_tagname_ = 'values'
# end class profile_inputType
class profile_outputsType(GeneratedsSuper):
subclass = None
superclass = None
def __init__(self, profile_output=None):
self.original_tagname_ = None
if profile_output is None:
self.profile_output = []
else:
self.profile_output = profile_output
def factory(*args_, **kwargs_):
if profile_outputsType.subclass:
return profile_outputsType.subclass(*args_, **kwargs_)
else:
return profile_outputsType(*args_, **kwargs_)
factory = staticmethod(factory)
def get_profile_output(self): return self.profile_output
def set_profile_output(self, profile_output): self.profile_output = profile_output
def add_profile_output(self, value): self.profile_output.append(value)
def insert_profile_output(self, index, value): self.profile_output[index] = value
profile_outputProp = property(get_profile_output, set_profile_output)
def hasContent_(self):
if (
self.profile_output
):
return True
else:
return False
def export(self, outfile, level, namespace_='', name_='profile_outputsType', namespacedef_='', pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.original_tagname_ is not None:
name_ = self.original_tagname_
showIndent(outfile, level, pretty_print)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = set()
self.exportAttributes(outfile, level, already_processed, namespace_, name_='profile_outputsType')
if self.hasContent_():
outfile.write('>%s' % (eol_, ))
self.exportChildren(outfile, level + 1, namespace_='', name_='profile_outputsType', pretty_print=pretty_print)
showIndent(outfile, level, pretty_print)
outfile.write('</%s%s>%s' % (namespace_, name_, eol_))
else:
outfile.write('/>%s' % (eol_, ))
def exportAttributes(self, outfile, level, already_processed, namespace_='', name_='profile_outputsType'):
pass
def exportChildren(self, outfile, level, namespace_='', name_='profile_outputsType', fromsubclass_=False, pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
for profile_output_ in self.profile_output:
profile_output_.export(outfile, level, namespace_, name_='profile_output', pretty_print=pretty_print)
def exportLiteral(self, outfile, level, name_='profile_outputsType'):
level += 1
already_processed = set()
self.exportLiteralAttributes(outfile, level, already_processed, name_)
if self.hasContent_():
self.exportLiteralChildren(outfile, level, name_)
def exportLiteralAttributes(self, outfile, level, already_processed, name_):
pass
def exportLiteralChildren(self, outfile, level, name_):
showIndent(outfile, level)
outfile.write('profile_output=[\n')
level += 1
for profile_output_ in self.profile_output:
showIndent(outfile, level)
outfile.write('model_.profile_outputType(\n')
profile_output_.exportLiteral(outfile, level, name_='profile_outputType')
showIndent(outfile, level)
outfile.write('),\n')
level -= 1
showIndent(outfile, level)
outfile.write('],\n')
def build(self, node):
already_processed = set()
self.buildAttributes(node, node.attrib, already_processed)
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_)
return self
def buildAttributes(self, node, attrs, already_processed):
pass
def buildChildren(self, child_, node, nodeName_, fromsubclass_=False):
if nodeName_ == 'profile_output':
obj_ = profile_outputType.factory()
obj_.build(child_)
self.profile_output.append(obj_)
obj_.original_tagname_ = 'profile_output'
# end class profile_outputsType
class profile_outputType(GeneratedsSuper):
"""Specifies if this output has array of data."""
subclass = None
superclass = None
def __init__(self, is_array=None, readable_name=None, category=None, var_name=None, overrides=None):
self.original_tagname_ = None
self.is_array = _cast(bool, is_array)
self.readable_name = readable_name
self.category = category
self.var_name = var_name
self.overrides = overrides
def factory(*args_, **kwargs_):
if profile_outputType.subclass:
return profile_outputType.subclass(*args_, **kwargs_)
else:
return profile_outputType(*args_, **kwargs_)
factory = staticmethod(factory)
def get_readable_name(self): return self.readable_name
def set_readable_name(self, readable_name): self.readable_name = readable_name
readable_nameProp = property(get_readable_name, set_readable_name)
def get_category(self): return self.category
def set_category(self, category): self.category = category
categoryProp = property(get_category, set_category)
def get_var_name(self): return self.var_name
def set_var_name(self, var_name): self.var_name = var_name
var_nameProp = property(get_var_name, set_var_name)
def get_overrides(self): return self.overrides
def set_overrides(self, overrides): self.overrides = overrides
overridesProp = property(get_overrides, set_overrides)
def get_is_array(self): return self.is_array
def set_is_array(self, is_array): self.is_array = is_array
is_arrayProp = property(get_is_array, set_is_array)
def hasContent_(self):
if (
self.readable_name is not None or
self.category is not None or
self.var_name is not None or
self.overrides is not None
):
return True
else:
return False
def export(self, outfile, level, namespace_='', name_='profile_outputType', namespacedef_='', pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.original_tagname_ is not None:
name_ = self.original_tagname_
showIndent(outfile, level, pretty_print)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = set()
self.exportAttributes(outfile, level, already_processed, namespace_, name_='profile_outputType')
if self.hasContent_():
outfile.write('>%s' % (eol_, ))
self.exportChildren(outfile, level + 1, namespace_='', name_='profile_outputType', pretty_print=pretty_print)
showIndent(outfile, level, pretty_print)
outfile.write('</%s%s>%s' % (namespace_, name_, eol_))
else:
outfile.write('/>%s' % (eol_, ))
def exportAttributes(self, outfile, level, already_processed, namespace_='', name_='profile_outputType'):
if self.is_array is not None and 'is_array' not in already_processed:
already_processed.add('is_array')
outfile.write(' is_array="%s"' % self.gds_format_boolean(self.is_array, input_name='is_array'))
def exportChildren(self, outfile, level, namespace_='', name_='profile_outputType', fromsubclass_=False, pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.readable_name is not None:
showIndent(outfile, level, pretty_print)
outfile.write('<%sreadable_name>%s</%sreadable_name>%s' % (namespace_, self.gds_format_string(quote_xml(self.readable_name), input_name='readable_name'), namespace_, eol_))
if self.category is not None:
showIndent(outfile, level, pretty_print)
outfile.write('<%scategory>%s</%scategory>%s' % (namespace_, self.gds_format_string(quote_xml(self.category), input_name='category'), namespace_, eol_))
if self.var_name is not None:
showIndent(outfile, level, pretty_print)
outfile.write('<%svar_name>%s</%svar_name>%s' % (namespace_, self.gds_format_string(quote_xml(self.var_name), input_name='var_name'), namespace_, eol_))
if self.overrides is not None:
self.overrides.export(outfile, level, namespace_, name_='overrides', pretty_print=pretty_print)
def exportLiteral(self, outfile, level, name_='profile_outputType'):
level += 1
already_processed = set()
self.exportLiteralAttributes(outfile, level, already_processed, name_)
if self.hasContent_():
self.exportLiteralChildren(outfile, level, name_)
def exportLiteralAttributes(self, outfile, level, already_processed, name_):
if self.is_array is not None and 'is_array' not in already_processed:
already_processed.add('is_array')
showIndent(outfile, level)
outfile.write('is_array=%s,\n' % (self.is_array,))
def exportLiteralChildren(self, outfile, level, name_):
if self.readable_name is not None:
showIndent(outfile, level)
outfile.write('readable_name=%s,\n' % quote_python(self.readable_name))
if self.category is not None:
showIndent(outfile, level)
outfile.write('category=%s,\n' % quote_python(self.category))
if self.var_name is not None:
showIndent(outfile, level)
outfile.write('var_name=%s,\n' % quote_python(self.var_name))
if self.overrides is not None:
showIndent(outfile, level)
outfile.write('overrides=model_.overrides(\n')
self.overrides.exportLiteral(outfile, level)
showIndent(outfile, level)
outfile.write('),\n')
def build(self, node):
already_processed = set()
self.buildAttributes(node, node.attrib, already_processed)
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_)
return self
def buildAttributes(self, node, attrs, already_processed):
value = find_attr_value_('is_array', node)
if value is not None and 'is_array' not in already_processed:
already_processed.add('is_array')
if value in ('true', '1'):
self.is_array = True
elif value in ('false', '0'):
self.is_array = False
else:
raise_parse_error(node, 'Bad boolean attribute')
def buildChildren(self, child_, node, nodeName_, fromsubclass_=False):
if nodeName_ == 'readable_name':
readable_name_ = child_.text
readable_name_ = self.gds_validate_string(readable_name_, node, 'readable_name')
self.readable_name = readable_name_
elif nodeName_ == 'category':
category_ = child_.text
category_ = self.gds_validate_string(category_, node, 'category')
self.category = category_
elif nodeName_ == 'var_name':
var_name_ = child_.text
var_name_ = self.gds_validate_string(var_name_, node, 'var_name')
self.var_name = var_name_
elif nodeName_ == 'overrides':
obj_ = overrides.factory()
obj_.build(child_)
self.overrides = obj_
obj_.original_tagname_ = 'overrides'
# end class profile_outputType
class profilesType(GeneratedsSuper):
subclass = None
superclass = None
def __init__(self, profile=None):
self.original_tagname_ = None
self.profile = profile
def factory(*args_, **kwargs_):
if profilesType.subclass:
return profilesType.subclass(*args_, **kwargs_)
else:
return profilesType(*args_, **kwargs_)
factory = staticmethod(factory)
def get_profile(self): return self.profile
def set_profile(self, profile): self.profile = profile
profileProp = property(get_profile, set_profile)
def hasContent_(self):
if (
self.profile is not None
):
return True
else:
return False
def export(self, outfile, level, namespace_='', name_='profilesType', namespacedef_='', pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.original_tagname_ is not None:
name_ = self.original_tagname_
showIndent(outfile, level, pretty_print)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = set()
self.exportAttributes(outfile, level, already_processed, namespace_, name_='profilesType')
if self.hasContent_():
outfile.write('>%s' % (eol_, ))
self.exportChildren(outfile, level + 1, namespace_='', name_='profilesType', pretty_print=pretty_print)
showIndent(outfile, level, pretty_print)
outfile.write('</%s%s>%s' % (namespace_, name_, eol_))
else:
outfile.write('/>%s' % (eol_, ))
def exportAttributes(self, outfile, level, already_processed, namespace_='', name_='profilesType'):
pass
def exportChildren(self, outfile, level, namespace_='', name_='profilesType', fromsubclass_=False, pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.profile is not None:
self.profile.export(outfile, level, namespace_, name_='profile', pretty_print=pretty_print)
def exportLiteral(self, outfile, level, name_='profilesType'):
level += 1
already_processed = set()
self.exportLiteralAttributes(outfile, level, already_processed, name_)
if self.hasContent_():
self.exportLiteralChildren(outfile, level, name_)
def exportLiteralAttributes(self, outfile, level, already_processed, name_):
pass
def exportLiteralChildren(self, outfile, level, name_):
if self.profile is not None:
showIndent(outfile, level)
outfile.write('profile=model_.profileType(\n')
self.profile.exportLiteral(outfile, level, name_='profile')
showIndent(outfile, level)
outfile.write('),\n')
def build(self, node):
already_processed = set()
self.buildAttributes(node, node.attrib, already_processed)
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_)
return self
def buildAttributes(self, node, attrs, already_processed):
pass
def buildChildren(self, child_, node, nodeName_, fromsubclass_=False):
if nodeName_ == 'profile':
obj_ = profileType.factory()
obj_.build(child_)
self.profile = obj_
obj_.original_tagname_ = 'profile'
# end class profilesType
class profileType(GeneratedsSuper):
"""The name of the calculation or configuration represented by this
profile.The name to display in the GUI.Used to organize profiles
for display.A plain text description of the profile. Explain
what happens here. What is computed.Indicates this is the
default profile to display to the user.The Boolean expression of
feature_NAME variables with logical AND (double ampersands), OR
(double pipes), and/or NOT (exclamation) operators. Use
parenthesis to group. An empty string defaults to true."""
subclass = None
superclass = None
def __init__(self, name=None, readable_name=None, category=None, desc=None, default=None, act_logic=None, inputs=None, forces=None, outputs=None, default_phys=None):
self.original_tagname_ = None
self.name = _cast(None, name)
self.readable_name = _cast(None, readable_name)
self.category = _cast(None, category)
self.desc = _cast(None, desc)
self.default = _cast(bool, default)
self.act_logic = _cast(None, act_logic)
self.inputs = inputs
self.forces = forces
self.outputs = outputs
self.default_phys = default_phys
def factory(*args_, **kwargs_):
if profileType.subclass:
return profileType.subclass(*args_, **kwargs_)
else:
return profileType(*args_, **kwargs_)
factory = staticmethod(factory)
def get_inputs(self): return self.inputs
def set_inputs(self, inputs): self.inputs = inputs
inputsProp = property(get_inputs, set_inputs)
def get_forces(self): return self.forces
def set_forces(self, forces): self.forces = forces
forcesProp = property(get_forces, set_forces)
def get_outputs(self): return self.outputs
def set_outputs(self, outputs): self.outputs = outputs
outputsProp = property(get_outputs, set_outputs)
def get_default_phys(self): return self.default_phys
def set_default_phys(self, default_phys): self.default_phys = default_phys
default_physProp = property(get_default_phys, set_default_phys)
def get_name(self): return self.name
def set_name(self, name): self.name = name
nameProp = property(get_name, set_name)
def get_readable_name(self): return self.readable_name
def set_readable_name(self, readable_name): self.readable_name = readable_name
readable_nameProp = property(get_readable_name, set_readable_name)
def get_category(self): return self.category
def set_category(self, category): self.category = category
categoryProp = property(get_category, set_category)
def get_desc(self): return self.desc
def set_desc(self, desc): self.desc = desc
descProp = property(get_desc, set_desc)
def get_default(self): return self.default
def set_default(self, default): self.default = default
defaultProp = property(get_default, set_default)
def get_act_logic(self): return self.act_logic
def set_act_logic(self, act_logic): self.act_logic = act_logic
act_logicProp = property(get_act_logic, set_act_logic)
def validate_nameType(self, value):
# Validate type nameType, a restriction on xs:string.
pass
def hasContent_(self):
if (
self.inputs is not None or
self.forces is not None or
self.outputs is not None or
self.default_phys is not None
):
return True
else:
return False
def export(self, outfile, level, namespace_='', name_='profileType', namespacedef_='', pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.original_tagname_ is not None:
name_ = self.original_tagname_
showIndent(outfile, level, pretty_print)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = set()
self.exportAttributes(outfile, level, already_processed, namespace_, name_='profileType')
if self.hasContent_():
outfile.write('>%s' % (eol_, ))
self.exportChildren(outfile, level + 1, namespace_='', name_='profileType', pretty_print=pretty_print)
showIndent(outfile, level, pretty_print)
outfile.write('</%s%s>%s' % (namespace_, name_, eol_))
else:
outfile.write('/>%s' % (eol_, ))
def exportAttributes(self, outfile, level, already_processed, namespace_='', name_='profileType'):
if self.name is not None and 'name' not in already_processed:
already_processed.add('name')
outfile.write(' name=%s' % (quote_attrib(self.name), ))
if self.readable_name is not None and 'readable_name' not in already_processed:
already_processed.add('readable_name')
outfile.write(' readable_name=%s' % (self.gds_format_string(quote_attrib(self.readable_name), input_name='readable_name'), ))
if self.category is not None and 'category' not in already_processed:
already_processed.add('category')
outfile.write(' category=%s' % (self.gds_format_string(quote_attrib(self.category), input_name='category'), ))
if self.desc is not None and 'desc' not in already_processed:
already_processed.add('desc')
outfile.write(' desc=%s' % (self.gds_format_string(quote_attrib(self.desc), input_name='desc'), ))
if self.default is not None and 'default' not in already_processed:
already_processed.add('default')
outfile.write(' default="%s"' % self.gds_format_boolean(self.default, input_name='default'))
if self.act_logic is not None and 'act_logic' not in already_processed:
already_processed.add('act_logic')
outfile.write(' act_logic=%s' % (self.gds_format_string(quote_attrib(self.act_logic), input_name='act_logic'), ))
def exportChildren(self, outfile, level, namespace_='', name_='profileType', fromsubclass_=False, pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.inputs is not None:
self.inputs.export(outfile, level, namespace_, name_='inputs', pretty_print=pretty_print)
if self.forces is not None:
self.forces.export(outfile, level, namespace_, name_='forces', pretty_print=pretty_print)
if self.outputs is not None:
self.outputs.export(outfile, level, namespace_, name_='outputs', pretty_print=pretty_print)
if self.default_phys is not None:
self.default_phys.export(outfile, level, namespace_, name_='default_phys', pretty_print=pretty_print)
def exportLiteral(self, outfile, level, name_='profileType'):
level += 1
already_processed = set()
self.exportLiteralAttributes(outfile, level, already_processed, name_)
if self.hasContent_():
self.exportLiteralChildren(outfile, level, name_)
def exportLiteralAttributes(self, outfile, level, already_processed, name_):
if self.name is not None and 'name' not in already_processed:
already_processed.add('name')
showIndent(outfile, level)
outfile.write('name="%s",\n' % (self.name,))
if self.readable_name is not None and 'readable_name' not in already_processed:
already_processed.add('readable_name')
showIndent(outfile, level)
outfile.write('readable_name="%s",\n' % (self.readable_name,))
if self.category is not None and 'category' not in already_processed:
already_processed.add('category')
showIndent(outfile, level)
outfile.write('category="%s",\n' % (self.category,))
if self.desc is not None and 'desc' not in already_processed:
already_processed.add('desc')
showIndent(outfile, level)
outfile.write('desc="%s",\n' % (self.desc,))
if self.default is not None and 'default' not in already_processed:
already_processed.add('default')
showIndent(outfile, level)
outfile.write('default=%s,\n' % (self.default,))
if self.act_logic is not None and 'act_logic' not in already_processed:
already_processed.add('act_logic')
showIndent(outfile, level)
outfile.write('act_logic="%s",\n' % (self.act_logic,))
def exportLiteralChildren(self, outfile, level, name_):
if self.inputs is not None:
showIndent(outfile, level)
outfile.write('inputs=model_.inputsType(\n')
self.inputs.exportLiteral(outfile, level, name_='inputs')
showIndent(outfile, level)
outfile.write('),\n')
if self.forces is not None:
showIndent(outfile, level)
outfile.write('forces=model_.forcesType(\n')
self.forces.exportLiteral(outfile, level, name_='forces')
showIndent(outfile, level)
outfile.write('),\n')
if self.outputs is not None:
showIndent(outfile, level)
outfile.write('outputs=model_.outputsType(\n')
self.outputs.exportLiteral(outfile, level, name_='outputs')
showIndent(outfile, level)
outfile.write('),\n')
if self.default_phys is not None:
showIndent(outfile, level)
outfile.write('default_phys=model_.default_physType(\n')
self.default_phys.exportLiteral(outfile, level, name_='default_phys')
showIndent(outfile, level)
outfile.write('),\n')
def build(self, node):
already_processed = set()
self.buildAttributes(node, node.attrib, already_processed)
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_)
return self
def buildAttributes(self, node, attrs, already_processed):
value = find_attr_value_('name', node)
if value is not None and 'name' not in already_processed:
already_processed.add('name')
self.name = value
self.validate_nameType(self.name) # validate type nameType
value = find_attr_value_('readable_name', node)
if value is not None and 'readable_name' not in already_processed:
already_processed.add('readable_name')
self.readable_name = value
value = find_attr_value_('category', node)
if value is not None and 'category' not in already_processed:
already_processed.add('category')
self.category = value
value = find_attr_value_('desc', node)
if value is not None and 'desc' not in already_processed:
already_processed.add('desc')
self.desc = value
value = find_attr_value_('default', node)
if value is not None and 'default' not in already_processed:
already_processed.add('default')
if value in ('true', '1'):
self.default = True
elif value in ('false', '0'):
self.default = False
else:
raise_parse_error(node, 'Bad boolean attribute')
value = find_attr_value_('act_logic', node)
if value is not None and 'act_logic' not in already_processed:
already_processed.add('act_logic')
self.act_logic = value
def buildChildren(self, child_, node, nodeName_, fromsubclass_=False):
if nodeName_ == 'inputs':
obj_ = inputsType.factory()
obj_.build(child_)
self.inputs = obj_
obj_.original_tagname_ = 'inputs'
elif nodeName_ == 'forces':
obj_ = forcesType.factory()
obj_.build(child_)
self.forces = obj_
obj_.original_tagname_ = 'forces'
elif nodeName_ == 'outputs':
obj_ = outputsType.factory()
obj_.build(child_)
self.outputs = obj_
obj_.original_tagname_ = 'outputs'
elif nodeName_ == 'default_phys':
obj_ = default_physType.factory()
obj_.build(child_)
self.default_phys = obj_
obj_.original_tagname_ = 'default_phys'
# end class profileType
class inputsType(GeneratedsSuper):
subclass = None
superclass = None
def __init__(self, input=None):
self.original_tagname_ = None
if input is None:
self.input = []
else:
self.input = input
def factory(*args_, **kwargs_):
if inputsType.subclass:
return inputsType.subclass(*args_, **kwargs_)
else:
return inputsType(*args_, **kwargs_)
factory = staticmethod(factory)
def get_input(self): return self.input
def set_input(self, input): self.input = input
def add_input(self, value): self.input.append(value)
def insert_input(self, index, value): self.input[index] = value
inputProp = property(get_input, set_input)
def hasContent_(self):
if (
self.input
):
return True
else:
return False
def export(self, outfile, level, namespace_='', name_='inputsType', namespacedef_='', pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.original_tagname_ is not None:
name_ = self.original_tagname_
showIndent(outfile, level, pretty_print)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = set()
self.exportAttributes(outfile, level, already_processed, namespace_, name_='inputsType')
if self.hasContent_():
outfile.write('>%s' % (eol_, ))
self.exportChildren(outfile, level + 1, namespace_='', name_='inputsType', pretty_print=pretty_print)
showIndent(outfile, level, pretty_print)
outfile.write('</%s%s>%s' % (namespace_, name_, eol_))
else:
outfile.write('/>%s' % (eol_, ))
def exportAttributes(self, outfile, level, already_processed, namespace_='', name_='inputsType'):
pass
def exportChildren(self, outfile, level, namespace_='', name_='inputsType', fromsubclass_=False, pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
for input_ in self.input:
input_.export(outfile, level, namespace_, name_='input', pretty_print=pretty_print)
def exportLiteral(self, outfile, level, name_='inputsType'):
level += 1
already_processed = set()
self.exportLiteralAttributes(outfile, level, already_processed, name_)
if self.hasContent_():
self.exportLiteralChildren(outfile, level, name_)
def exportLiteralAttributes(self, outfile, level, already_processed, name_):
pass
def exportLiteralChildren(self, outfile, level, name_):
showIndent(outfile, level)
outfile.write('input=[\n')
level += 1
for input_ in self.input:
showIndent(outfile, level)
outfile.write('model_.inputType1(\n')
input_.exportLiteral(outfile, level, name_='inputType1')
showIndent(outfile, level)
outfile.write('),\n')
level -= 1
showIndent(outfile, level)
outfile.write('],\n')
def build(self, node):
already_processed = set()
self.buildAttributes(node, node.attrib, already_processed)
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_)
return self
def buildAttributes(self, node, attrs, already_processed):
pass
def buildChildren(self, child_, node, nodeName_, fromsubclass_=False):
if nodeName_ == 'input':
obj_ = inputType1.factory()
obj_.build(child_)
self.input.append(obj_)
obj_.original_tagname_ = 'input'
# end class inputsType
class inputType1(GeneratedsSuper):
"""Specifies if this input has array of data.Describes the purpose of
this input variable.An optional minimum value, inclusive.An
optional maximum value, inclusive.Specifies the number of
fractional digits to display for a float or fixed point
value.Boolean that defines this input as deprecatedType of
visibility applicable to a GUIDefine the units multiplier when
shown on the GUI."""
subclass = None
superclass = None
def __init__(self, is_array=None, input_type=None, value_limit_min=None, value_limit_max=None, fractional_digits=None, deprecated=None, default_visiblity=None, units_multiplier=None, readable_name=None, category=None, var_name=None, var_values=None, defaults=None):
self.original_tagname_ = None
self.is_array = _cast(bool, is_array)
self.input_type = _cast(None, input_type)
self.value_limit_min = _cast(None, value_limit_min)
self.value_limit_max = _cast(None, value_limit_max)
self.fractional_digits = _cast(int, fractional_digits)
self.deprecated = _cast(bool, deprecated)
self.default_visiblity = _cast(None, default_visiblity)
self.units_multiplier = _cast(None, units_multiplier)
self.readable_name = readable_name
self.category = category
self.var_name = var_name
self.var_values = var_values
self.defaults = defaults
def factory(*args_, **kwargs_):
if inputType1.subclass:
return inputType1.subclass(*args_, **kwargs_)
else:
return inputType1(*args_, **kwargs_)
factory = staticmethod(factory)
def get_readable_name(self): return self.readable_name
def set_readable_name(self, readable_name): self.readable_name = readable_name
readable_nameProp = property(get_readable_name, set_readable_name)
def get_category(self): return self.category
def set_category(self, category): self.category = category
categoryProp = property(get_category, set_category)
def get_var_name(self): return self.var_name
def set_var_name(self, var_name): self.var_name = var_name
var_nameProp = property(get_var_name, set_var_name)
def get_var_values(self): return self.var_values
def set_var_values(self, var_values): self.var_values = var_values
var_valuesProp = property(get_var_values, set_var_values)
def get_defaults(self): return self.defaults
def set_defaults(self, defaults): self.defaults = defaults
defaultsProp = property(get_defaults, set_defaults)
def get_is_array(self): return self.is_array
def set_is_array(self, is_array): self.is_array = is_array
is_arrayProp = property(get_is_array, set_is_array)
def get_input_type(self): return self.input_type
def set_input_type(self, input_type): self.input_type = input_type
input_typeProp = property(get_input_type, set_input_type)
def get_value_limit_min(self): return self.value_limit_min
def set_value_limit_min(self, value_limit_min): self.value_limit_min = value_limit_min
value_limit_minProp = property(get_value_limit_min, set_value_limit_min)
def get_value_limit_max(self): return self.value_limit_max
def set_value_limit_max(self, value_limit_max): self.value_limit_max = value_limit_max
value_limit_maxProp = property(get_value_limit_max, set_value_limit_max)
def get_fractional_digits(self): return self.fractional_digits
def set_fractional_digits(self, fractional_digits): self.fractional_digits = fractional_digits
fractional_digitsProp = property(get_fractional_digits, set_fractional_digits)
def get_deprecated(self): return self.deprecated
def set_deprecated(self, deprecated): self.deprecated = deprecated
deprecatedProp = property(get_deprecated, set_deprecated)
def get_default_visiblity(self): return self.default_visiblity
def set_default_visiblity(self, default_visiblity): self.default_visiblity = default_visiblity
default_visiblityProp = property(get_default_visiblity, set_default_visiblity)
def get_units_multiplier(self): return self.units_multiplier
def set_units_multiplier(self, units_multiplier): self.units_multiplier = units_multiplier
units_multiplierProp = property(get_units_multiplier, set_units_multiplier)
def validate_inputType(self, value):
# Validate type inputType, a restriction on xs:string.
pass
def validate_visibilityType(self, value):
# Validate type visibilityType, a restriction on xs:string.
pass
def hasContent_(self):
if (
self.readable_name is not None or
self.category is not None or
self.var_name is not None or
self.var_values is not None or
self.defaults is not None
):
return True
else:
return False
def export(self, outfile, level, namespace_='', name_='inputType1', namespacedef_='', pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.original_tagname_ is not None:
name_ = self.original_tagname_
showIndent(outfile, level, pretty_print)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = set()
self.exportAttributes(outfile, level, already_processed, namespace_, name_='inputType1')
if self.hasContent_():
outfile.write('>%s' % (eol_, ))
self.exportChildren(outfile, level + 1, namespace_='', name_='inputType1', pretty_print=pretty_print)
showIndent(outfile, level, pretty_print)
outfile.write('</%s%s>%s' % (namespace_, name_, eol_))
else:
outfile.write('/>%s' % (eol_, ))
def exportAttributes(self, outfile, level, already_processed, namespace_='', name_='inputType1'):
if self.is_array is not None and 'is_array' not in already_processed:
already_processed.add('is_array')
outfile.write(' is_array="%s"' % self.gds_format_boolean(self.is_array, input_name='is_array'))
if self.input_type is not None and 'input_type' not in already_processed:
already_processed.add('input_type')
outfile.write(' input_type=%s' % (quote_attrib(self.input_type), ))
if self.value_limit_min is not None and 'value_limit_min' not in already_processed:
already_processed.add('value_limit_min')
outfile.write(' value_limit_min=%s' % (self.gds_format_string(quote_attrib(self.value_limit_min), input_name='value_limit_min'), ))
if self.value_limit_max is not None and 'value_limit_max' not in already_processed:
already_processed.add('value_limit_max')
outfile.write(' value_limit_max=%s' % (self.gds_format_string(quote_attrib(self.value_limit_max), input_name='value_limit_max'), ))
if self.fractional_digits is not None and 'fractional_digits' not in already_processed:
already_processed.add('fractional_digits')
outfile.write(' fractional_digits="%s"' % self.gds_format_integer(self.fractional_digits, input_name='fractional_digits'))
if self.deprecated is not None and 'deprecated' not in already_processed:
already_processed.add('deprecated')
outfile.write(' deprecated="%s"' % self.gds_format_boolean(self.deprecated, input_name='deprecated'))
if self.default_visiblity is not None and 'default_visiblity' not in already_processed:
already_processed.add('default_visiblity')
outfile.write(' default_visiblity=%s' % (quote_attrib(self.default_visiblity), ))
if self.units_multiplier is not None and 'units_multiplier' not in already_processed:
already_processed.add('units_multiplier')
outfile.write(' units_multiplier=%s' % (self.gds_format_string(quote_attrib(self.units_multiplier), input_name='units_multiplier'), ))
def exportChildren(self, outfile, level, namespace_='', name_='inputType1', fromsubclass_=False, pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.readable_name is not None:
showIndent(outfile, level, pretty_print)
outfile.write('<%sreadable_name>%s</%sreadable_name>%s' % (namespace_, self.gds_format_string(quote_xml(self.readable_name), input_name='readable_name'), namespace_, eol_))
if self.category is not None:
showIndent(outfile, level, pretty_print)
outfile.write('<%scategory>%s</%scategory>%s' % (namespace_, self.gds_format_string(quote_xml(self.category), input_name='category'), namespace_, eol_))
if self.var_name is not None:
showIndent(outfile, level, pretty_print)
outfile.write('<%svar_name>%s</%svar_name>%s' % (namespace_, self.gds_format_string(quote_xml(self.var_name), input_name='var_name'), namespace_, eol_))
if self.var_values is not None:
self.var_values.export(outfile, level, namespace_, name_='var_values', pretty_print=pretty_print)
if self.defaults is not None:
self.defaults.export(outfile, level, namespace_, name_='defaults', pretty_print=pretty_print)
def exportLiteral(self, outfile, level, name_='inputType1'):
level += 1
already_processed = set()
self.exportLiteralAttributes(outfile, level, already_processed, name_)
if self.hasContent_():
self.exportLiteralChildren(outfile, level, name_)
def exportLiteralAttributes(self, outfile, level, already_processed, name_):
if self.is_array is not None and 'is_array' not in already_processed:
already_processed.add('is_array')
showIndent(outfile, level)
outfile.write('is_array=%s,\n' % (self.is_array,))
if self.input_type is not None and 'input_type' not in already_processed:
already_processed.add('input_type')
showIndent(outfile, level)
outfile.write('input_type="%s",\n' % (self.input_type,))
if self.value_limit_min is not None and 'value_limit_min' not in already_processed:
already_processed.add('value_limit_min')
showIndent(outfile, level)
outfile.write('value_limit_min="%s",\n' % (self.value_limit_min,))
if self.value_limit_max is not None and 'value_limit_max' not in already_processed:
already_processed.add('value_limit_max')
showIndent(outfile, level)
outfile.write('value_limit_max="%s",\n' % (self.value_limit_max,))
if self.fractional_digits is not None and 'fractional_digits' not in already_processed:
already_processed.add('fractional_digits')
showIndent(outfile, level)
outfile.write('fractional_digits=%d,\n' % (self.fractional_digits,))
if self.deprecated is not None and 'deprecated' not in already_processed:
already_processed.add('deprecated')
showIndent(outfile, level)
outfile.write('deprecated=%s,\n' % (self.deprecated,))
if self.default_visiblity is not None and 'default_visiblity' not in already_processed:
already_processed.add('default_visiblity')
showIndent(outfile, level)
outfile.write('default_visiblity="%s",\n' % (self.default_visiblity,))
if self.units_multiplier is not None and 'units_multiplier' not in already_processed:
already_processed.add('units_multiplier')
showIndent(outfile, level)
outfile.write('units_multiplier="%s",\n' % (self.units_multiplier,))
def exportLiteralChildren(self, outfile, level, name_):
if self.readable_name is not None:
showIndent(outfile, level)
outfile.write('readable_name=%s,\n' % quote_python(self.readable_name))
if self.category is not None:
showIndent(outfile, level)
outfile.write('category=%s,\n' % quote_python(self.category))
if self.var_name is not None:
showIndent(outfile, level)
outfile.write('var_name=%s,\n' % quote_python(self.var_name))
if self.var_values is not None:
showIndent(outfile, level)
outfile.write('var_values=model_.var_values(\n')
self.var_values.exportLiteral(outfile, level)
showIndent(outfile, level)
outfile.write('),\n')
if self.defaults is not None:
showIndent(outfile, level)
outfile.write('defaults=model_.defaults(\n')
self.defaults.exportLiteral(outfile, level)
showIndent(outfile, level)
outfile.write('),\n')
def build(self, node):
already_processed = set()
self.buildAttributes(node, node.attrib, already_processed)
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_)
return self
def buildAttributes(self, node, attrs, already_processed):
value = find_attr_value_('is_array', node)
if value is not None and 'is_array' not in already_processed:
already_processed.add('is_array')
if value in ('true', '1'):
self.is_array = True
elif value in ('false', '0'):
self.is_array = False
else:
raise_parse_error(node, 'Bad boolean attribute')
value = find_attr_value_('input_type', node)
if value is not None and 'input_type' not in already_processed:
already_processed.add('input_type')
self.input_type = value
self.validate_inputType(self.input_type) # validate type inputType
value = find_attr_value_('value_limit_min', node)
if value is not None and 'value_limit_min' not in already_processed:
already_processed.add('value_limit_min')
self.value_limit_min = value
value = find_attr_value_('value_limit_max', node)
if value is not None and 'value_limit_max' not in already_processed:
already_processed.add('value_limit_max')
self.value_limit_max = value
value = find_attr_value_('fractional_digits', node)
if value is not None and 'fractional_digits' not in already_processed:
already_processed.add('fractional_digits')
try:
self.fractional_digits = int(value)
except ValueError as exp:
raise_parse_error(node, 'Bad integer attribute: %s' % exp)
if self.fractional_digits < 0:
raise_parse_error(node, 'Invalid NonNegativeInteger')
value = find_attr_value_('deprecated', node)
if value is not None and 'deprecated' not in already_processed:
already_processed.add('deprecated')
if value in ('true', '1'):
self.deprecated = True
elif value in ('false', '0'):
self.deprecated = False
else:
raise_parse_error(node, 'Bad boolean attribute')
value = find_attr_value_('default_visiblity', node)
if value is not None and 'default_visiblity' not in already_processed:
already_processed.add('default_visiblity')
self.default_visiblity = value
self.validate_visibilityType(self.default_visiblity) # validate type visibilityType
value = find_attr_value_('units_multiplier', node)
if value is not None and 'units_multiplier' not in already_processed:
already_processed.add('units_multiplier')
self.units_multiplier = value
def buildChildren(self, child_, node, nodeName_, fromsubclass_=False):
if nodeName_ == 'readable_name':
readable_name_ = child_.text
readable_name_ = self.gds_validate_string(readable_name_, node, 'readable_name')
self.readable_name = readable_name_
elif nodeName_ == 'category':
category_ = child_.text
category_ = self.gds_validate_string(category_, node, 'category')
self.category = category_
elif nodeName_ == 'var_name':
var_name_ = child_.text
var_name_ = self.gds_validate_string(var_name_, node, 'var_name')
self.var_name = var_name_
elif nodeName_ == 'var_values':
obj_ = var_values.factory()
obj_.build(child_)
self.var_values = obj_
obj_.original_tagname_ = 'var_values'
elif nodeName_ == 'defaults':
obj_ = defaults.factory()
obj_.build(child_)
self.defaults = obj_
obj_.original_tagname_ = 'defaults'
# end class inputType1
class forcesType(GeneratedsSuper):
subclass = None
superclass = None
def __init__(self, force=None):
self.original_tagname_ = None
if force is None:
self.force = []
else:
self.force = force
def factory(*args_, **kwargs_):
if forcesType.subclass:
return forcesType.subclass(*args_, **kwargs_)
else:
return forcesType(*args_, **kwargs_)
factory = staticmethod(factory)
def get_force(self): return self.force
def set_force(self, force): self.force = force
def add_force(self, value): self.force.append(value)
def insert_force(self, index, value): self.force[index] = value
forceProp = property(get_force, set_force)
def hasContent_(self):
if (
self.force
):
return True
else:
return False
def export(self, outfile, level, namespace_='', name_='forcesType', namespacedef_='', pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.original_tagname_ is not None:
name_ = self.original_tagname_
showIndent(outfile, level, pretty_print)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = set()
self.exportAttributes(outfile, level, already_processed, namespace_, name_='forcesType')
if self.hasContent_():
outfile.write('>%s' % (eol_, ))
self.exportChildren(outfile, level + 1, namespace_='', name_='forcesType', pretty_print=pretty_print)
showIndent(outfile, level, pretty_print)
outfile.write('</%s%s>%s' % (namespace_, name_, eol_))
else:
outfile.write('/>%s' % (eol_, ))
def exportAttributes(self, outfile, level, already_processed, namespace_='', name_='forcesType'):
pass
def exportChildren(self, outfile, level, namespace_='', name_='forcesType', fromsubclass_=False, pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
for force_ in self.force:
force_.export(outfile, level, namespace_, name_='force', pretty_print=pretty_print)
def exportLiteral(self, outfile, level, name_='forcesType'):
level += 1
already_processed = set()
self.exportLiteralAttributes(outfile, level, already_processed, name_)
if self.hasContent_():
self.exportLiteralChildren(outfile, level, name_)
def exportLiteralAttributes(self, outfile, level, already_processed, name_):
pass
def exportLiteralChildren(self, outfile, level, name_):
showIndent(outfile, level)
outfile.write('force=[\n')
level += 1
for force_ in self.force:
showIndent(outfile, level)
outfile.write('model_.forceType(\n')
force_.exportLiteral(outfile, level, name_='forceType')
showIndent(outfile, level)
outfile.write('),\n')
level -= 1
showIndent(outfile, level)
outfile.write('],\n')
def build(self, node):
already_processed = set()
self.buildAttributes(node, node.attrib, already_processed)
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_)
return self
def buildAttributes(self, node, attrs, already_processed):
pass
def buildChildren(self, child_, node, nodeName_, fromsubclass_=False):
if nodeName_ == 'force':
obj_ = forceType.factory()
obj_.build(child_)
self.force.append(obj_)
obj_.original_tagname_ = 'force'
# end class forcesType
class forceType(GeneratedsSuper):
"""Specifies if this force has array of data."""
subclass = None
superclass = None
def __init__(self, is_array=None, var_name=None, values=None):
self.original_tagname_ = None
self.is_array = _cast(bool, is_array)
self.var_name = var_name
self.values = values
def factory(*args_, **kwargs_):
if forceType.subclass:
return forceType.subclass(*args_, **kwargs_)
else:
return forceType(*args_, **kwargs_)
factory = staticmethod(factory)
def get_var_name(self): return self.var_name
def set_var_name(self, var_name): self.var_name = var_name
var_nameProp = property(get_var_name, set_var_name)
def get_values(self): return self.values
def set_values(self, values): self.values = values
valuesProp = property(get_values, set_values)
def get_is_array(self): return self.is_array
def set_is_array(self, is_array): self.is_array = is_array
is_arrayProp = property(get_is_array, set_is_array)
def hasContent_(self):
if (
self.var_name is not None or
self.values is not None
):
return True
else:
return False
def export(self, outfile, level, namespace_='', name_='forceType', namespacedef_='', pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.original_tagname_ is not None:
name_ = self.original_tagname_
showIndent(outfile, level, pretty_print)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = set()
self.exportAttributes(outfile, level, already_processed, namespace_, name_='forceType')
if self.hasContent_():
outfile.write('>%s' % (eol_, ))
self.exportChildren(outfile, level + 1, namespace_='', name_='forceType', pretty_print=pretty_print)
showIndent(outfile, level, pretty_print)
outfile.write('</%s%s>%s' % (namespace_, name_, eol_))
else:
outfile.write('/>%s' % (eol_, ))
def exportAttributes(self, outfile, level, already_processed, namespace_='', name_='forceType'):
if self.is_array is not None and 'is_array' not in already_processed:
already_processed.add('is_array')
outfile.write(' is_array="%s"' % self.gds_format_boolean(self.is_array, input_name='is_array'))
def exportChildren(self, outfile, level, namespace_='', name_='forceType', fromsubclass_=False, pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.var_name is not None:
showIndent(outfile, level, pretty_print)
outfile.write('<%svar_name>%s</%svar_name>%s' % (namespace_, self.gds_format_string(quote_xml(self.var_name), input_name='var_name'), namespace_, eol_))
if self.values is not None:
self.values.export(outfile, level, namespace_, name_='values', pretty_print=pretty_print)
def exportLiteral(self, outfile, level, name_='forceType'):
level += 1
already_processed = set()
self.exportLiteralAttributes(outfile, level, already_processed, name_)
if self.hasContent_():
self.exportLiteralChildren(outfile, level, name_)
def exportLiteralAttributes(self, outfile, level, already_processed, name_):
if self.is_array is not None and 'is_array' not in already_processed:
already_processed.add('is_array')
showIndent(outfile, level)
outfile.write('is_array=%s,\n' % (self.is_array,))
def exportLiteralChildren(self, outfile, level, name_):
if self.var_name is not None:
showIndent(outfile, level)
outfile.write('var_name=%s,\n' % quote_python(self.var_name))
if self.values is not None:
showIndent(outfile, level)
outfile.write('values=model_.values(\n')
self.values.exportLiteral(outfile, level)
showIndent(outfile, level)
outfile.write('),\n')
def build(self, node):
already_processed = set()
self.buildAttributes(node, node.attrib, already_processed)
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_)
return self
def buildAttributes(self, node, attrs, already_processed):
value = find_attr_value_('is_array', node)
if value is not None and 'is_array' not in already_processed:
already_processed.add('is_array')
if value in ('true', '1'):
self.is_array = True
elif value in ('false', '0'):
self.is_array = False
else:
raise_parse_error(node, 'Bad boolean attribute')
def buildChildren(self, child_, node, nodeName_, fromsubclass_=False):
if nodeName_ == 'var_name':
var_name_ = child_.text
var_name_ = self.gds_validate_string(var_name_, node, 'var_name')
self.var_name = var_name_
elif nodeName_ == 'values':
obj_ = values.factory()
obj_.build(child_)
self.values = obj_
obj_.original_tagname_ = 'values'
# end class forceType
class outputsType(GeneratedsSuper):
subclass = None
superclass = None
def __init__(self, output=None):
self.original_tagname_ = None
if output is None:
self.output = []
else:
self.output = output
def factory(*args_, **kwargs_):
if outputsType.subclass:
return outputsType.subclass(*args_, **kwargs_)
else:
return outputsType(*args_, **kwargs_)
factory = staticmethod(factory)
def get_output(self): return self.output
def set_output(self, output): self.output = output
def add_output(self, value): self.output.append(value)
def insert_output(self, index, value): self.output[index] = value
outputProp = property(get_output, set_output)
def hasContent_(self):
if (
self.output
):
return True
else:
return False
def export(self, outfile, level, namespace_='', name_='outputsType', namespacedef_='', pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.original_tagname_ is not None:
name_ = self.original_tagname_
showIndent(outfile, level, pretty_print)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = set()
self.exportAttributes(outfile, level, already_processed, namespace_, name_='outputsType')
if self.hasContent_():
outfile.write('>%s' % (eol_, ))
self.exportChildren(outfile, level + 1, namespace_='', name_='outputsType', pretty_print=pretty_print)
showIndent(outfile, level, pretty_print)
outfile.write('</%s%s>%s' % (namespace_, name_, eol_))
else:
outfile.write('/>%s' % (eol_, ))
def exportAttributes(self, outfile, level, already_processed, namespace_='', name_='outputsType'):
pass
def exportChildren(self, outfile, level, namespace_='', name_='outputsType', fromsubclass_=False, pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
for output_ in self.output:
output_.export(outfile, level, namespace_, name_='output', pretty_print=pretty_print)
def exportLiteral(self, outfile, level, name_='outputsType'):
level += 1
already_processed = set()
self.exportLiteralAttributes(outfile, level, already_processed, name_)
if self.hasContent_():
self.exportLiteralChildren(outfile, level, name_)
def exportLiteralAttributes(self, outfile, level, already_processed, name_):
pass
def exportLiteralChildren(self, outfile, level, name_):
showIndent(outfile, level)
outfile.write('output=[\n')
level += 1
for output_ in self.output:
showIndent(outfile, level)
outfile.write('model_.outputType2(\n')
output_.exportLiteral(outfile, level, name_='outputType2')
showIndent(outfile, level)
outfile.write('),\n')
level -= 1
showIndent(outfile, level)
outfile.write('],\n')
def build(self, node):
already_processed = set()
self.buildAttributes(node, node.attrib, already_processed)
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_)
return self
def buildAttributes(self, node, attrs, already_processed):
pass
def buildChildren(self, child_, node, nodeName_, fromsubclass_=False):
if nodeName_ == 'output':
obj_ = outputType2.factory()
obj_.build(child_)
self.output.append(obj_)
obj_.original_tagname_ = 'output'
# end class outputsType
class outputType2(GeneratedsSuper):
"""Specifies if this output has array of data.Describes the purpose of
this output variable.An optional minimum value, inclusive.An
optional maximum value, inclusive.Specifies the number of
fractional digits to display for a float or fixed point value."""
subclass = None
superclass = None
def __init__(self, is_array=None, output_type=None, value_limit_min=None, value_limit_max=None, fractional_digits=None, readable_name=None, category=None, var_name=None, var_values=None, var_overrides=None):
self.original_tagname_ = None
self.is_array = _cast(bool, is_array)
self.output_type = _cast(None, output_type)
self.value_limit_min = _cast(None, value_limit_min)
self.value_limit_max = _cast(None, value_limit_max)
self.fractional_digits = _cast(int, fractional_digits)
self.readable_name = readable_name
self.category = category
self.var_name = var_name
self.var_values = var_values
self.var_overrides = var_overrides
def factory(*args_, **kwargs_):
if outputType2.subclass:
return outputType2.subclass(*args_, **kwargs_)
else:
return outputType2(*args_, **kwargs_)
factory = staticmethod(factory)
def get_readable_name(self): return self.readable_name
def set_readable_name(self, readable_name): self.readable_name = readable_name
readable_nameProp = property(get_readable_name, set_readable_name)
def get_category(self): return self.category
def set_category(self, category): self.category = category
categoryProp = property(get_category, set_category)
def get_var_name(self): return self.var_name
def set_var_name(self, var_name): self.var_name = var_name
var_nameProp = property(get_var_name, set_var_name)
def get_var_values(self): return self.var_values
def set_var_values(self, var_values): self.var_values = var_values
var_valuesProp = property(get_var_values, set_var_values)
def get_var_overrides(self): return self.var_overrides
def set_var_overrides(self, var_overrides): self.var_overrides = var_overrides
var_overridesProp = property(get_var_overrides, set_var_overrides)
def get_is_array(self): return self.is_array
def set_is_array(self, is_array): self.is_array = is_array
is_arrayProp = property(get_is_array, set_is_array)
def get_output_type(self): return self.output_type
def set_output_type(self, output_type): self.output_type = output_type
output_typeProp = property(get_output_type, set_output_type)
def get_value_limit_min(self): return self.value_limit_min
def set_value_limit_min(self, value_limit_min): self.value_limit_min = value_limit_min
value_limit_minProp = property(get_value_limit_min, set_value_limit_min)
def get_value_limit_max(self): return self.value_limit_max
def set_value_limit_max(self, value_limit_max): self.value_limit_max = value_limit_max
value_limit_maxProp = property(get_value_limit_max, set_value_limit_max)
def get_fractional_digits(self): return self.fractional_digits
def set_fractional_digits(self, fractional_digits): self.fractional_digits = fractional_digits
fractional_digitsProp = property(get_fractional_digits, set_fractional_digits)
def validate_outputType(self, value):
# Validate type outputType, a restriction on xs:string.
pass
def hasContent_(self):
if (
self.readable_name is not None or
self.category is not None or
self.var_name is not None or
self.var_values is not None or
self.var_overrides is not None
):
return True
else:
return False
def export(self, outfile, level, namespace_='', name_='outputType2', namespacedef_='', pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.original_tagname_ is not None:
name_ = self.original_tagname_
showIndent(outfile, level, pretty_print)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = set()
self.exportAttributes(outfile, level, already_processed, namespace_, name_='outputType2')
if self.hasContent_():
outfile.write('>%s' % (eol_, ))
self.exportChildren(outfile, level + 1, namespace_='', name_='outputType2', pretty_print=pretty_print)
showIndent(outfile, level, pretty_print)
outfile.write('</%s%s>%s' % (namespace_, name_, eol_))
else:
outfile.write('/>%s' % (eol_, ))
def exportAttributes(self, outfile, level, already_processed, namespace_='', name_='outputType2'):
if self.is_array is not None and 'is_array' not in already_processed:
already_processed.add('is_array')
outfile.write(' is_array="%s"' % self.gds_format_boolean(self.is_array, input_name='is_array'))
if self.output_type is not None and 'output_type' not in already_processed:
already_processed.add('output_type')
outfile.write(' output_type=%s' % (quote_attrib(self.output_type), ))
if self.value_limit_min is not None and 'value_limit_min' not in already_processed:
already_processed.add('value_limit_min')
outfile.write(' value_limit_min=%s' % (self.gds_format_string(quote_attrib(self.value_limit_min), input_name='value_limit_min'), ))
if self.value_limit_max is not None and 'value_limit_max' not in already_processed:
already_processed.add('value_limit_max')
outfile.write(' value_limit_max=%s' % (self.gds_format_string(quote_attrib(self.value_limit_max), input_name='value_limit_max'), ))
if self.fractional_digits is not None and 'fractional_digits' not in already_processed:
already_processed.add('fractional_digits')
outfile.write(' fractional_digits="%s"' % self.gds_format_integer(self.fractional_digits, input_name='fractional_digits'))
def exportChildren(self, outfile, level, namespace_='', name_='outputType2', fromsubclass_=False, pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.readable_name is not None:
showIndent(outfile, level, pretty_print)
outfile.write('<%sreadable_name>%s</%sreadable_name>%s' % (namespace_, self.gds_format_string(quote_xml(self.readable_name), input_name='readable_name'), namespace_, eol_))
if self.category is not None:
showIndent(outfile, level, pretty_print)
outfile.write('<%scategory>%s</%scategory>%s' % (namespace_, self.gds_format_string(quote_xml(self.category), input_name='category'), namespace_, eol_))
if self.var_name is not None:
showIndent(outfile, level, pretty_print)
outfile.write('<%svar_name>%s</%svar_name>%s' % (namespace_, self.gds_format_string(quote_xml(self.var_name), input_name='var_name'), namespace_, eol_))
if self.var_values is not None:
self.var_values.export(outfile, level, namespace_, name_='var_values', pretty_print=pretty_print)
if self.var_overrides is not None:
self.var_overrides.export(outfile, level, namespace_, name_='var_overrides', pretty_print=pretty_print)
def exportLiteral(self, outfile, level, name_='outputType2'):
level += 1
already_processed = set()
self.exportLiteralAttributes(outfile, level, already_processed, name_)
if self.hasContent_():
self.exportLiteralChildren(outfile, level, name_)
def exportLiteralAttributes(self, outfile, level, already_processed, name_):
if self.is_array is not None and 'is_array' not in already_processed:
already_processed.add('is_array')
showIndent(outfile, level)
outfile.write('is_array=%s,\n' % (self.is_array,))
if self.output_type is not None and 'output_type' not in already_processed:
already_processed.add('output_type')
showIndent(outfile, level)
outfile.write('output_type="%s",\n' % (self.output_type,))
if self.value_limit_min is not None and 'value_limit_min' not in already_processed:
already_processed.add('value_limit_min')
showIndent(outfile, level)
outfile.write('value_limit_min="%s",\n' % (self.value_limit_min,))
if self.value_limit_max is not None and 'value_limit_max' not in already_processed:
already_processed.add('value_limit_max')
showIndent(outfile, level)
outfile.write('value_limit_max="%s",\n' % (self.value_limit_max,))
if self.fractional_digits is not None and 'fractional_digits' not in already_processed:
already_processed.add('fractional_digits')
showIndent(outfile, level)
outfile.write('fractional_digits=%d,\n' % (self.fractional_digits,))
def exportLiteralChildren(self, outfile, level, name_):
if self.readable_name is not None:
showIndent(outfile, level)
outfile.write('readable_name=%s,\n' % quote_python(self.readable_name))
if self.category is not None:
showIndent(outfile, level)
outfile.write('category=%s,\n' % quote_python(self.category))
if self.var_name is not None:
showIndent(outfile, level)
outfile.write('var_name=%s,\n' % quote_python(self.var_name))
if self.var_values is not None:
showIndent(outfile, level)
outfile.write('var_values=model_.var_values(\n')
self.var_values.exportLiteral(outfile, level)
showIndent(outfile, level)
outfile.write('),\n')
if self.var_overrides is not None:
showIndent(outfile, level)
outfile.write('var_overrides=model_.var_overrides(\n')
self.var_overrides.exportLiteral(outfile, level)
showIndent(outfile, level)
outfile.write('),\n')
def build(self, node):
already_processed = set()
self.buildAttributes(node, node.attrib, already_processed)
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_)
return self
def buildAttributes(self, node, attrs, already_processed):
value = find_attr_value_('is_array', node)
if value is not None and 'is_array' not in already_processed:
already_processed.add('is_array')
if value in ('true', '1'):
self.is_array = True
elif value in ('false', '0'):
self.is_array = False
else:
raise_parse_error(node, 'Bad boolean attribute')
value = find_attr_value_('output_type', node)
if value is not None and 'output_type' not in already_processed:
already_processed.add('output_type')
self.output_type = value
self.validate_outputType(self.output_type) # validate type outputType
value = find_attr_value_('value_limit_min', node)
if value is not None and 'value_limit_min' not in already_processed:
already_processed.add('value_limit_min')
self.value_limit_min = value
value = find_attr_value_('value_limit_max', node)
if value is not None and 'value_limit_max' not in already_processed:
already_processed.add('value_limit_max')
self.value_limit_max = value
value = find_attr_value_('fractional_digits', node)
if value is not None and 'fractional_digits' not in already_processed:
already_processed.add('fractional_digits')
try:
self.fractional_digits = int(value)
except ValueError as exp:
raise_parse_error(node, 'Bad integer attribute: %s' % exp)
if self.fractional_digits < 0:
raise_parse_error(node, 'Invalid NonNegativeInteger')
def buildChildren(self, child_, node, nodeName_, fromsubclass_=False):
if nodeName_ == 'readable_name':
readable_name_ = child_.text
readable_name_ = self.gds_validate_string(readable_name_, node, 'readable_name')
self.readable_name = readable_name_
elif nodeName_ == 'category':
category_ = child_.text
category_ = self.gds_validate_string(category_, node, 'category')
self.category = category_
elif nodeName_ == 'var_name':
var_name_ = child_.text
var_name_ = self.gds_validate_string(var_name_, node, 'var_name')
self.var_name = var_name_
elif nodeName_ == 'var_values':
obj_ = var_values.factory()
obj_.build(child_)
self.var_values = obj_
obj_.original_tagname_ = 'var_values'
elif nodeName_ == 'var_overrides':
obj_ = var_overrides.factory()
obj_.build(child_)
self.var_overrides = obj_
obj_.original_tagname_ = 'var_overrides'
# end class outputType2
class default_physType(GeneratedsSuper):
subclass = None
superclass = None
def __init__(self, default_phy=None):
self.original_tagname_ = None
if default_phy is None:
self.default_phy = []
else:
self.default_phy = default_phy
def factory(*args_, **kwargs_):
if default_physType.subclass:
return default_physType.subclass(*args_, **kwargs_)
else:
return default_physType(*args_, **kwargs_)
factory = staticmethod(factory)
def get_default_phy(self): return self.default_phy
def set_default_phy(self, default_phy): self.default_phy = default_phy
def add_default_phy(self, value): self.default_phy.append(value)
def insert_default_phy(self, index, value): self.default_phy[index] = value
default_phyProp = property(get_default_phy, set_default_phy)
def hasContent_(self):
if (
self.default_phy
):
return True
else:
return False
def export(self, outfile, level, namespace_='', name_='default_physType', namespacedef_='', pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.original_tagname_ is not None:
name_ = self.original_tagname_
showIndent(outfile, level, pretty_print)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = set()
self.exportAttributes(outfile, level, already_processed, namespace_, name_='default_physType')
if self.hasContent_():
outfile.write('>%s' % (eol_, ))
self.exportChildren(outfile, level + 1, namespace_='', name_='default_physType', pretty_print=pretty_print)
showIndent(outfile, level, pretty_print)
outfile.write('</%s%s>%s' % (namespace_, name_, eol_))
else:
outfile.write('/>%s' % (eol_, ))
def exportAttributes(self, outfile, level, already_processed, namespace_='', name_='default_physType'):
pass
def exportChildren(self, outfile, level, namespace_='', name_='default_physType', fromsubclass_=False, pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
for default_phy_ in self.default_phy:
default_phy_.export(outfile, level, namespace_, name_='default_phy', pretty_print=pretty_print)
def exportLiteral(self, outfile, level, name_='default_physType'):
level += 1
already_processed = set()
self.exportLiteralAttributes(outfile, level, already_processed, name_)
if self.hasContent_():
self.exportLiteralChildren(outfile, level, name_)
def exportLiteralAttributes(self, outfile, level, already_processed, name_):
pass
def exportLiteralChildren(self, outfile, level, name_):
showIndent(outfile, level)
outfile.write('default_phy=[\n')
level += 1
for default_phy_ in self.default_phy:
showIndent(outfile, level)
outfile.write('model_.default_phyType(\n')
default_phy_.exportLiteral(outfile, level, name_='default_phyType')
showIndent(outfile, level)
outfile.write('),\n')
level -= 1
showIndent(outfile, level)
outfile.write('],\n')
def build(self, node):
already_processed = set()
self.buildAttributes(node, node.attrib, already_processed)
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_)
return self
def buildAttributes(self, node, attrs, already_processed):
pass
def buildChildren(self, child_, node, nodeName_, fromsubclass_=False):
if nodeName_ == 'default_phy':
obj_ = default_phyType.factory()
obj_.build(child_)
self.default_phy.append(obj_)
obj_.original_tagname_ = 'default_phy'
# end class default_physType
class default_phyType(GeneratedsSuper):
"""The phy name."""
subclass = None
superclass = None
def __init__(self, phy_name=None):
self.original_tagname_ = None
self.phy_name = _cast(None, phy_name)
def factory(*args_, **kwargs_):
if default_phyType.subclass:
return default_phyType.subclass(*args_, **kwargs_)
else:
return default_phyType(*args_, **kwargs_)
factory = staticmethod(factory)
def get_phy_name(self): return self.phy_name
def set_phy_name(self, phy_name): self.phy_name = phy_name
phy_nameProp = property(get_phy_name, set_phy_name)
def validate_nameType(self, value):
# Validate type nameType, a restriction on xs:string.
pass
def hasContent_(self):
if (
):
return True
else:
return False
def export(self, outfile, level, namespace_='', name_='default_phyType', namespacedef_='', pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.original_tagname_ is not None:
name_ = self.original_tagname_
showIndent(outfile, level, pretty_print)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = set()
self.exportAttributes(outfile, level, already_processed, namespace_, name_='default_phyType')
if self.hasContent_():
outfile.write('>%s' % (eol_, ))
self.exportChildren(outfile, level + 1, namespace_='', name_='default_phyType', pretty_print=pretty_print)
outfile.write('</%s%s>%s' % (namespace_, name_, eol_))
else:
outfile.write('/>%s' % (eol_, ))
def exportAttributes(self, outfile, level, already_processed, namespace_='', name_='default_phyType'):
if self.phy_name is not None and 'phy_name' not in already_processed:
already_processed.add('phy_name')
outfile.write(' phy_name=%s' % (quote_attrib(self.phy_name), ))
def exportChildren(self, outfile, level, namespace_='', name_='default_phyType', fromsubclass_=False, pretty_print=True):
pass
def exportLiteral(self, outfile, level, name_='default_phyType'):
level += 1
already_processed = set()
self.exportLiteralAttributes(outfile, level, already_processed, name_)
if self.hasContent_():
self.exportLiteralChildren(outfile, level, name_)
def exportLiteralAttributes(self, outfile, level, already_processed, name_):
if self.phy_name is not None and 'phy_name' not in already_processed:
already_processed.add('phy_name')
showIndent(outfile, level)
outfile.write('phy_name="%s",\n' % (self.phy_name,))
def exportLiteralChildren(self, outfile, level, name_):
pass
def build(self, node):
already_processed = set()
self.buildAttributes(node, node.attrib, already_processed)
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_)
return self
def buildAttributes(self, node, attrs, already_processed):
value = find_attr_value_('phy_name', node)
if value is not None and 'phy_name' not in already_processed:
already_processed.add('phy_name')
self.phy_name = value
self.validate_nameType(self.phy_name) # validate type nameType
def buildChildren(self, child_, node, nodeName_, fromsubclass_=False):
pass
# end class default_phyType
class variablesType(GeneratedsSuper):
subclass = None
superclass = None
def __init__(self, variable=None):
self.original_tagname_ = None
if variable is None:
self.variable = []
else:
self.variable = variable
def factory(*args_, **kwargs_):
if variablesType.subclass:
return variablesType.subclass(*args_, **kwargs_)
else:
return variablesType(*args_, **kwargs_)
factory = staticmethod(factory)
def get_variable(self): return self.variable
def set_variable(self, variable): self.variable = variable
def add_variable(self, value): self.variable.append(value)
def insert_variable(self, index, value): self.variable[index] = value
variableProp = property(get_variable, set_variable)
def hasContent_(self):
if (
self.variable
):
return True
else:
return False
def export(self, outfile, level, namespace_='', name_='variablesType', namespacedef_='', pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.original_tagname_ is not None:
name_ = self.original_tagname_
showIndent(outfile, level, pretty_print)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = set()
self.exportAttributes(outfile, level, already_processed, namespace_, name_='variablesType')
if self.hasContent_():
outfile.write('>%s' % (eol_, ))
self.exportChildren(outfile, level + 1, namespace_='', name_='variablesType', pretty_print=pretty_print)
showIndent(outfile, level, pretty_print)
outfile.write('</%s%s>%s' % (namespace_, name_, eol_))
else:
outfile.write('/>%s' % (eol_, ))
def exportAttributes(self, outfile, level, already_processed, namespace_='', name_='variablesType'):
pass
def exportChildren(self, outfile, level, namespace_='', name_='variablesType', fromsubclass_=False, pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
for variable_ in self.variable:
variable_.export(outfile, level, namespace_, name_='variable', pretty_print=pretty_print)
def exportLiteral(self, outfile, level, name_='variablesType'):
level += 1
already_processed = set()
self.exportLiteralAttributes(outfile, level, already_processed, name_)
if self.hasContent_():
self.exportLiteralChildren(outfile, level, name_)
def exportLiteralAttributes(self, outfile, level, already_processed, name_):
pass
def exportLiteralChildren(self, outfile, level, name_):
showIndent(outfile, level)
outfile.write('variable=[\n')
level += 1
for variable_ in self.variable:
showIndent(outfile, level)
outfile.write('model_.variableType(\n')
variable_.exportLiteral(outfile, level, name_='variableType')
showIndent(outfile, level)
outfile.write('),\n')
level -= 1
showIndent(outfile, level)
outfile.write('],\n')
def build(self, node):
already_processed = set()
self.buildAttributes(node, node.attrib, already_processed)
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_)
return self
def buildAttributes(self, node, attrs, already_processed):
pass
def buildChildren(self, child_, node, nodeName_, fromsubclass_=False):
if nodeName_ == 'variable':
obj_ = variableType.factory()
obj_.build(child_)
self.variable.append(obj_)
obj_.original_tagname_ = 'variable'
# end class variablesType
class variableType(GeneratedsSuper):
"""The variable name. If a variable is referenced in a profile, then
this name will match the var_name in the profile section.The
variable type.Specifies if this variable has array of
data.Specify how to display the variable value.A plain text
description of the variable.Denotes that this variable can be
forced. Defaults to true. As the value_actual usage is
deprecated, this attribute will be set to false for actual
variables created for the reverse calculation flow. Specify this
variable maps directly to a CMSIS SVD register using
peripheral.register.field notation.Define the units.Used for
register or field types, do explicitly tag registers that are
"don't cares", or "not needed", when in a specific mode."""
subclass = None
superclass = None
def __init__(self, name=None, type_=None, is_array=None, format=None, desc=None, forceable=None, svd_mapping=None, units=None, value_do_not_care=False, enum=None, values=None, access_read=None, access_write=None):
self.original_tagname_ = None
self.name = _cast(None, name)
self.type_ = _cast(None, type_)
self.is_array = _cast(bool, is_array)
self.format = _cast(None, format)
self.desc = _cast(None, desc)
self.forceable = _cast(bool, forceable)
self.svd_mapping = _cast(None, svd_mapping)
self.units = _cast(None, units)
self.value_do_not_care = _cast(bool, value_do_not_care)
self.enum = enum
self.values = values
self.access_read = access_read
self.access_write = access_write
def factory(*args_, **kwargs_):
if variableType.subclass:
return variableType.subclass(*args_, **kwargs_)
else:
return variableType(*args_, **kwargs_)
factory = staticmethod(factory)
def get_enum(self): return self.enum
def set_enum(self, enum): self.enum = enum
enumProp = property(get_enum, set_enum)
def get_values(self): return self.values
def set_values(self, values): self.values = values
valuesProp = property(get_values, set_values)
def get_access_read(self): return self.access_read
def set_access_read(self, access_read): self.access_read = access_read
access_readProp = property(get_access_read, set_access_read)
def get_access_write(self): return self.access_write
def set_access_write(self, access_write): self.access_write = access_write
access_writeProp = property(get_access_write, set_access_write)
def get_name(self): return self.name
def set_name(self, name): self.name = name
nameProp = property(get_name, set_name)
def get_type(self): return self.type_
def set_type(self, type_): self.type_ = type_
typeProp = property(get_type, set_type)
def get_is_array(self): return self.is_array
def set_is_array(self, is_array): self.is_array = is_array
is_arrayProp = property(get_is_array, set_is_array)
def get_format(self): return self.format
def set_format(self, format): self.format = format
formatProp = property(get_format, set_format)
def get_desc(self): return self.desc
def set_desc(self, desc): self.desc = desc
descProp = property(get_desc, set_desc)
def get_forceable(self): return self.forceable
def set_forceable(self, forceable): self.forceable = forceable
forceableProp = property(get_forceable, set_forceable)
def get_svd_mapping(self): return self.svd_mapping
def set_svd_mapping(self, svd_mapping): self.svd_mapping = svd_mapping
svd_mappingProp = property(get_svd_mapping, set_svd_mapping)
def get_units(self): return self.units
def set_units(self, units): self.units = units
unitsProp = property(get_units, set_units)
def get_value_do_not_care(self): return self.value_do_not_care
def set_value_do_not_care(self, value_do_not_care): self.value_do_not_care = value_do_not_care
value_do_not_careProp = property(get_value_do_not_care, set_value_do_not_care)
def validate_nameType(self, value):
# Validate type nameType, a restriction on xs:string.
pass
def validate_varType(self, value):
# Validate type varType, a restriction on xs:string.
pass
def validate_formatType(self, value):
# Validate type formatType, a restriction on xs:string.
pass
def validate_svdType(self, value):
# Validate type svdType, a restriction on xs:string.
pass
def hasContent_(self):
if (
self.enum is not None or
self.values is not None or
self.access_read is not None or
self.access_write is not None
):
return True
else:
return False
def export(self, outfile, level, namespace_='', name_='variableType', namespacedef_='', pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.original_tagname_ is not None:
name_ = self.original_tagname_
showIndent(outfile, level, pretty_print)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = set()
self.exportAttributes(outfile, level, already_processed, namespace_, name_='variableType')
if self.hasContent_():
outfile.write('>%s' % (eol_, ))
self.exportChildren(outfile, level + 1, namespace_='', name_='variableType', pretty_print=pretty_print)
showIndent(outfile, level, pretty_print)
outfile.write('</%s%s>%s' % (namespace_, name_, eol_))
else:
outfile.write('/>%s' % (eol_, ))
def exportAttributes(self, outfile, level, already_processed, namespace_='', name_='variableType'):
if self.name is not None and 'name' not in already_processed:
already_processed.add('name')
outfile.write(' name=%s' % (quote_attrib(self.name), ))
if self.type_ is not None and 'type_' not in already_processed:
already_processed.add('type_')
outfile.write(' type=%s' % (quote_attrib(self.type_), ))
if self.is_array is not None and 'is_array' not in already_processed:
already_processed.add('is_array')
outfile.write(' is_array="%s"' % self.gds_format_boolean(self.is_array, input_name='is_array'))
if self.format is not None and 'format' not in already_processed:
already_processed.add('format')
outfile.write(' format=%s' % (quote_attrib(self.format), ))
if self.desc is not None and 'desc' not in already_processed:
already_processed.add('desc')
outfile.write(' desc=%s' % (self.gds_format_string(quote_attrib(self.desc), input_name='desc'), ))
if self.forceable is not None and 'forceable' not in already_processed:
already_processed.add('forceable')
outfile.write(' forceable="%s"' % self.gds_format_boolean(self.forceable, input_name='forceable'))
if self.svd_mapping is not None and 'svd_mapping' not in already_processed:
already_processed.add('svd_mapping')
outfile.write(' svd_mapping=%s' % (quote_attrib(self.svd_mapping), ))
if self.units is not None and 'units' not in already_processed:
already_processed.add('units')
outfile.write(' units=%s' % (self.gds_format_string(quote_attrib(self.units), input_name='units'), ))
if self.value_do_not_care is not None and 'value_do_not_care' not in already_processed:
already_processed.add('value_do_not_care')
outfile.write(' value_do_not_care="%s"' % self.gds_format_boolean(self.value_do_not_care, input_name='value_do_not_care'))
def exportChildren(self, outfile, level, namespace_='', name_='variableType', fromsubclass_=False, pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.enum is not None:
self.enum.export(outfile, level, namespace_, name_='enum', pretty_print=pretty_print)
if self.values is not None:
self.values.export(outfile, level, namespace_, name_='values', pretty_print=pretty_print)
if self.access_read is not None:
self.access_read.export(outfile, level, namespace_, name_='access_read', pretty_print=pretty_print)
if self.access_write is not None:
self.access_write.export(outfile, level, namespace_, name_='access_write', pretty_print=pretty_print)
def exportLiteral(self, outfile, level, name_='variableType'):
level += 1
already_processed = set()
self.exportLiteralAttributes(outfile, level, already_processed, name_)
if self.hasContent_():
self.exportLiteralChildren(outfile, level, name_)
def exportLiteralAttributes(self, outfile, level, already_processed, name_):
if self.name is not None and 'name' not in already_processed:
already_processed.add('name')
showIndent(outfile, level)
outfile.write('name="%s",\n' % (self.name,))
if self.type_ is not None and 'type_' not in already_processed:
already_processed.add('type_')
showIndent(outfile, level)
outfile.write('type_="%s",\n' % (self.type_,))
if self.is_array is not None and 'is_array' not in already_processed:
already_processed.add('is_array')
showIndent(outfile, level)
outfile.write('is_array=%s,\n' % (self.is_array,))
if self.format is not None and 'format' not in already_processed:
already_processed.add('format')
showIndent(outfile, level)
outfile.write('format="%s",\n' % (self.format,))
if self.desc is not None and 'desc' not in already_processed:
already_processed.add('desc')
showIndent(outfile, level)
outfile.write('desc="%s",\n' % (self.desc,))
if self.forceable is not None and 'forceable' not in already_processed:
already_processed.add('forceable')
showIndent(outfile, level)
outfile.write('forceable=%s,\n' % (self.forceable,))
if self.svd_mapping is not None and 'svd_mapping' not in already_processed:
already_processed.add('svd_mapping')
showIndent(outfile, level)
outfile.write('svd_mapping="%s",\n' % (self.svd_mapping,))
if self.units is not None and 'units' not in already_processed:
already_processed.add('units')
showIndent(outfile, level)
outfile.write('units="%s",\n' % (self.units,))
if self.value_do_not_care is not None and 'value_do_not_care' not in already_processed:
already_processed.add('value_do_not_care')
showIndent(outfile, level)
outfile.write('value_do_not_care=%s,\n' % (self.value_do_not_care,))
def exportLiteralChildren(self, outfile, level, name_):
if self.enum is not None:
showIndent(outfile, level)
outfile.write('enum=model_.enumType(\n')
self.enum.exportLiteral(outfile, level, name_='enum')
showIndent(outfile, level)
outfile.write('),\n')
if self.values is not None:
showIndent(outfile, level)
outfile.write('values=model_.valuesType(\n')
self.values.exportLiteral(outfile, level, name_='values')
showIndent(outfile, level)
outfile.write('),\n')
if self.access_read is not None:
showIndent(outfile, level)
outfile.write('access_read=model_.access_readType(\n')
self.access_read.exportLiteral(outfile, level, name_='access_read')
showIndent(outfile, level)
outfile.write('),\n')
if self.access_write is not None:
showIndent(outfile, level)
outfile.write('access_write=model_.access_writeType(\n')
self.access_write.exportLiteral(outfile, level, name_='access_write')
showIndent(outfile, level)
outfile.write('),\n')
def build(self, node):
already_processed = set()
self.buildAttributes(node, node.attrib, already_processed)
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_)
return self
def buildAttributes(self, node, attrs, already_processed):
value = find_attr_value_('name', node)
if value is not None and 'name' not in already_processed:
already_processed.add('name')
self.name = value
self.validate_nameType(self.name) # validate type nameType
value = find_attr_value_('type', node)
if value is not None and 'type' not in already_processed:
already_processed.add('type')
self.type_ = value
self.validate_varType(self.type_) # validate type varType
value = find_attr_value_('is_array', node)
if value is not None and 'is_array' not in already_processed:
already_processed.add('is_array')
if value in ('true', '1'):
self.is_array = True
elif value in ('false', '0'):
self.is_array = False
else:
raise_parse_error(node, 'Bad boolean attribute')
value = find_attr_value_('format', node)
if value is not None and 'format' not in already_processed:
already_processed.add('format')
self.format = value
self.validate_formatType(self.format) # validate type formatType
value = find_attr_value_('desc', node)
if value is not None and 'desc' not in already_processed:
already_processed.add('desc')
self.desc = value
value = find_attr_value_('forceable', node)
if value is not None and 'forceable' not in already_processed:
already_processed.add('forceable')
if value in ('true', '1'):
self.forceable = True
elif value in ('false', '0'):
self.forceable = False
else:
raise_parse_error(node, 'Bad boolean attribute')
value = find_attr_value_('svd_mapping', node)
if value is not None and 'svd_mapping' not in already_processed:
already_processed.add('svd_mapping')
self.svd_mapping = value
self.validate_svdType(self.svd_mapping) # validate type svdType
value = find_attr_value_('units', node)
if value is not None and 'units' not in already_processed:
already_processed.add('units')
self.units = value
value = find_attr_value_('value_do_not_care', node)
if value is not None and 'value_do_not_care' not in already_processed:
already_processed.add('value_do_not_care')
if value in ('true', '1'):
self.value_do_not_care = True
elif value in ('false', '0'):
self.value_do_not_care = False
else:
raise_parse_error(node, 'Bad boolean attribute')
def buildChildren(self, child_, node, nodeName_, fromsubclass_=False):
if nodeName_ == 'enum':
obj_ = enumType.factory()
obj_.build(child_)
self.enum = obj_
obj_.original_tagname_ = 'enum'
elif nodeName_ == 'values':
obj_ = valuesType.factory()
obj_.build(child_)
self.values = obj_
obj_.original_tagname_ = 'values'
elif nodeName_ == 'access_read':
obj_ = access_readType.factory()
obj_.build(child_)
self.access_read = obj_
obj_.original_tagname_ = 'access_read'
elif nodeName_ == 'access_write':
obj_ = access_writeType.factory()
obj_.build(child_)
self.access_write = obj_
obj_.original_tagname_ = 'access_write'
# end class variableType
class enumType(GeneratedsSuper):
"""The name of the enum.A plain text description of the enum."""
subclass = None
superclass = None
def __init__(self, name=None, desc=None, members=None):
self.original_tagname_ = None
self.name = _cast(None, name)
self.desc = _cast(None, desc)
self.members = members
def factory(*args_, **kwargs_):
if enumType.subclass:
return enumType.subclass(*args_, **kwargs_)
else:
return enumType(*args_, **kwargs_)
factory = staticmethod(factory)
def get_members(self): return self.members
def set_members(self, members): self.members = members
membersProp = property(get_members, set_members)
def get_name(self): return self.name
def set_name(self, name): self.name = name
nameProp = property(get_name, set_name)
def get_desc(self): return self.desc
def set_desc(self, desc): self.desc = desc
descProp = property(get_desc, set_desc)
def validate_nameType(self, value):
# Validate type nameType, a restriction on xs:string.
pass
def hasContent_(self):
if (
self.members is not None
):
return True
else:
return False
def export(self, outfile, level, namespace_='', name_='enumType', namespacedef_='', pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.original_tagname_ is not None:
name_ = self.original_tagname_
showIndent(outfile, level, pretty_print)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = set()
self.exportAttributes(outfile, level, already_processed, namespace_, name_='enumType')
if self.hasContent_():
outfile.write('>%s' % (eol_, ))
self.exportChildren(outfile, level + 1, namespace_='', name_='enumType', pretty_print=pretty_print)
showIndent(outfile, level, pretty_print)
outfile.write('</%s%s>%s' % (namespace_, name_, eol_))
else:
outfile.write('/>%s' % (eol_, ))
def exportAttributes(self, outfile, level, already_processed, namespace_='', name_='enumType'):
if self.name is not None and 'name' not in already_processed:
already_processed.add('name')
outfile.write(' name=%s' % (quote_attrib(self.name), ))
if self.desc is not None and 'desc' not in already_processed:
already_processed.add('desc')
outfile.write(' desc=%s' % (self.gds_format_string(quote_attrib(self.desc), input_name='desc'), ))
def exportChildren(self, outfile, level, namespace_='', name_='enumType', fromsubclass_=False, pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.members is not None:
self.members.export(outfile, level, namespace_, name_='members', pretty_print=pretty_print)
def exportLiteral(self, outfile, level, name_='enumType'):
level += 1
already_processed = set()
self.exportLiteralAttributes(outfile, level, already_processed, name_)
if self.hasContent_():
self.exportLiteralChildren(outfile, level, name_)
def exportLiteralAttributes(self, outfile, level, already_processed, name_):
if self.name is not None and 'name' not in already_processed:
already_processed.add('name')
showIndent(outfile, level)
outfile.write('name="%s",\n' % (self.name,))
if self.desc is not None and 'desc' not in already_processed:
already_processed.add('desc')
showIndent(outfile, level)
outfile.write('desc="%s",\n' % (self.desc,))
def exportLiteralChildren(self, outfile, level, name_):
if self.members is not None:
showIndent(outfile, level)
outfile.write('members=model_.membersType(\n')
self.members.exportLiteral(outfile, level, name_='members')
showIndent(outfile, level)
outfile.write('),\n')
def build(self, node):
already_processed = set()
self.buildAttributes(node, node.attrib, already_processed)
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_)
return self
def buildAttributes(self, node, attrs, already_processed):
value = find_attr_value_('name', node)
if value is not None and 'name' not in already_processed:
already_processed.add('name')
self.name = value
self.validate_nameType(self.name) # validate type nameType
value = find_attr_value_('desc', node)
if value is not None and 'desc' not in already_processed:
already_processed.add('desc')
self.desc = value
def buildChildren(self, child_, node, nodeName_, fromsubclass_=False):
if nodeName_ == 'members':
obj_ = membersType.factory()
obj_.build(child_)
self.members = obj_
obj_.original_tagname_ = 'members'
# end class enumType
class membersType(GeneratedsSuper):
subclass = None
superclass = None
def __init__(self, member=None):
self.original_tagname_ = None
if member is None:
self.member = []
else:
self.member = member
def factory(*args_, **kwargs_):
if membersType.subclass:
return membersType.subclass(*args_, **kwargs_)
else:
return membersType(*args_, **kwargs_)
factory = staticmethod(factory)
def get_member(self): return self.member
def set_member(self, member): self.member = member
def add_member(self, value): self.member.append(value)
def insert_member(self, index, value): self.member[index] = value
memberProp = property(get_member, set_member)
def hasContent_(self):
if (
self.member
):
return True
else:
return False
def export(self, outfile, level, namespace_='', name_='membersType', namespacedef_='', pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.original_tagname_ is not None:
name_ = self.original_tagname_
showIndent(outfile, level, pretty_print)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = set()
self.exportAttributes(outfile, level, already_processed, namespace_, name_='membersType')
if self.hasContent_():
outfile.write('>%s' % (eol_, ))
self.exportChildren(outfile, level + 1, namespace_='', name_='membersType', pretty_print=pretty_print)
showIndent(outfile, level, pretty_print)
outfile.write('</%s%s>%s' % (namespace_, name_, eol_))
else:
outfile.write('/>%s' % (eol_, ))
def exportAttributes(self, outfile, level, already_processed, namespace_='', name_='membersType'):
pass
def exportChildren(self, outfile, level, namespace_='', name_='membersType', fromsubclass_=False, pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
for member_ in self.member:
member_.export(outfile, level, namespace_, name_='member', pretty_print=pretty_print)
def exportLiteral(self, outfile, level, name_='membersType'):
level += 1
already_processed = set()
self.exportLiteralAttributes(outfile, level, already_processed, name_)
if self.hasContent_():
self.exportLiteralChildren(outfile, level, name_)
def exportLiteralAttributes(self, outfile, level, already_processed, name_):
pass
def exportLiteralChildren(self, outfile, level, name_):
showIndent(outfile, level)
outfile.write('member=[\n')
level += 1
for member_ in self.member:
showIndent(outfile, level)
outfile.write('model_.memberType(\n')
member_.exportLiteral(outfile, level, name_='memberType')
showIndent(outfile, level)
outfile.write('),\n')
level -= 1
showIndent(outfile, level)
outfile.write('],\n')
def build(self, node):
already_processed = set()
self.buildAttributes(node, node.attrib, already_processed)
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_)
return self
def buildAttributes(self, node, attrs, already_processed):
pass
def buildChildren(self, child_, node, nodeName_, fromsubclass_=False):
if nodeName_ == 'member':
obj_ = memberType.factory()
obj_.build(child_)
self.member.append(obj_)
obj_.original_tagname_ = 'member'
# end class membersType
class memberType(GeneratedsSuper):
"""The string name.The integer value.A plain text description of the
enum member."""
subclass = None
superclass = None
def __init__(self, name=None, value=None, desc=None):
self.original_tagname_ = None
self.name = _cast(None, name)
self.value = _cast(int, value)
self.desc = _cast(None, desc)
def factory(*args_, **kwargs_):
if memberType.subclass:
return memberType.subclass(*args_, **kwargs_)
else:
return memberType(*args_, **kwargs_)
factory = staticmethod(factory)
def get_name(self): return self.name
def set_name(self, name): self.name = name
nameProp = property(get_name, set_name)
def get_value(self): return self.value
def set_value(self, value): self.value = value
valueProp = property(get_value, set_value)
def get_desc(self): return self.desc
def set_desc(self, desc): self.desc = desc
descProp = property(get_desc, set_desc)
def validate_nameType(self, value):
# Validate type nameType, a restriction on xs:string.
pass
def hasContent_(self):
if (
):
return True
else:
return False
def export(self, outfile, level, namespace_='', name_='memberType', namespacedef_='', pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.original_tagname_ is not None:
name_ = self.original_tagname_
showIndent(outfile, level, pretty_print)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = set()
self.exportAttributes(outfile, level, already_processed, namespace_, name_='memberType')
if self.hasContent_():
outfile.write('>%s' % (eol_, ))
self.exportChildren(outfile, level + 1, namespace_='', name_='memberType', pretty_print=pretty_print)
outfile.write('</%s%s>%s' % (namespace_, name_, eol_))
else:
outfile.write('/>%s' % (eol_, ))
def exportAttributes(self, outfile, level, already_processed, namespace_='', name_='memberType'):
if self.name is not None and 'name' not in already_processed:
already_processed.add('name')
outfile.write(' name=%s' % (quote_attrib(self.name), ))
if self.value is not None and 'value' not in already_processed:
already_processed.add('value')
outfile.write(' value="%s"' % self.gds_format_integer(self.value, input_name='value'))
if self.desc is not None and 'desc' not in already_processed:
already_processed.add('desc')
outfile.write(' desc=%s' % (self.gds_format_string(quote_attrib(self.desc), input_name='desc'), ))
def exportChildren(self, outfile, level, namespace_='', name_='memberType', fromsubclass_=False, pretty_print=True):
pass
def exportLiteral(self, outfile, level, name_='memberType'):
level += 1
already_processed = set()
self.exportLiteralAttributes(outfile, level, already_processed, name_)
if self.hasContent_():
self.exportLiteralChildren(outfile, level, name_)
def exportLiteralAttributes(self, outfile, level, already_processed, name_):
if self.name is not None and 'name' not in already_processed:
already_processed.add('name')
showIndent(outfile, level)
outfile.write('name="%s",\n' % (self.name,))
if self.value is not None and 'value' not in already_processed:
already_processed.add('value')
showIndent(outfile, level)
outfile.write('value=%d,\n' % (self.value,))
if self.desc is not None and 'desc' not in already_processed:
already_processed.add('desc')
showIndent(outfile, level)
outfile.write('desc="%s",\n' % (self.desc,))
def exportLiteralChildren(self, outfile, level, name_):
pass
def build(self, node):
already_processed = set()
self.buildAttributes(node, node.attrib, already_processed)
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_)
return self
def buildAttributes(self, node, attrs, already_processed):
value = find_attr_value_('name', node)
if value is not None and 'name' not in already_processed:
already_processed.add('name')
self.name = value
self.validate_nameType(self.name) # validate type nameType
value = find_attr_value_('value', node)
if value is not None and 'value' not in already_processed:
already_processed.add('value')
try:
self.value = int(value)
except ValueError as exp:
raise_parse_error(node, 'Bad integer attribute: %s' % exp)
value = find_attr_value_('desc', node)
if value is not None and 'desc' not in already_processed:
already_processed.add('desc')
self.desc = value
def buildChildren(self, child_, node, nodeName_, fromsubclass_=False):
pass
# end class memberType
class valuesType(GeneratedsSuper):
subclass = None
superclass = None
def __init__(self, calculated=None, forced=None):
self.original_tagname_ = None
self.calculated = calculated
self.forced = forced
def factory(*args_, **kwargs_):
if valuesType.subclass:
return valuesType.subclass(*args_, **kwargs_)
else:
return valuesType(*args_, **kwargs_)
factory = staticmethod(factory)
def get_calculated(self): return self.calculated
def set_calculated(self, calculated): self.calculated = calculated
calculatedProp = property(get_calculated, set_calculated)
def get_forced(self): return self.forced
def set_forced(self, forced): self.forced = forced
forcedProp = property(get_forced, set_forced)
def hasContent_(self):
if (
self.calculated is not None or
self.forced is not None
):
return True
else:
return False
def export(self, outfile, level, namespace_='', name_='valuesType', namespacedef_='', pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.original_tagname_ is not None:
name_ = self.original_tagname_
showIndent(outfile, level, pretty_print)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = set()
self.exportAttributes(outfile, level, already_processed, namespace_, name_='valuesType')
if self.hasContent_():
outfile.write('>%s' % (eol_, ))
self.exportChildren(outfile, level + 1, namespace_='', name_='valuesType', pretty_print=pretty_print)
showIndent(outfile, level, pretty_print)
outfile.write('</%s%s>%s' % (namespace_, name_, eol_))
else:
outfile.write('/>%s' % (eol_, ))
def exportAttributes(self, outfile, level, already_processed, namespace_='', name_='valuesType'):
pass
def exportChildren(self, outfile, level, namespace_='', name_='valuesType', fromsubclass_=False, pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.calculated is not None:
self.calculated.export(outfile, level, namespace_, name_='calculated', pretty_print=pretty_print)
if self.forced is not None:
self.forced.export(outfile, level, namespace_, name_='forced', pretty_print=pretty_print)
def exportLiteral(self, outfile, level, name_='valuesType'):
level += 1
already_processed = set()
self.exportLiteralAttributes(outfile, level, already_processed, name_)
if self.hasContent_():
self.exportLiteralChildren(outfile, level, name_)
def exportLiteralAttributes(self, outfile, level, already_processed, name_):
pass
def exportLiteralChildren(self, outfile, level, name_):
if self.calculated is not None:
showIndent(outfile, level)
outfile.write('calculated=model_.calculatedType(\n')
self.calculated.exportLiteral(outfile, level, name_='calculated')
showIndent(outfile, level)
outfile.write('),\n')
if self.forced is not None:
showIndent(outfile, level)
outfile.write('forced=model_.forcedType(\n')
self.forced.exportLiteral(outfile, level, name_='forced')
showIndent(outfile, level)
outfile.write('),\n')
def build(self, node):
already_processed = set()
self.buildAttributes(node, node.attrib, already_processed)
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_)
return self
def buildAttributes(self, node, attrs, already_processed):
pass
def buildChildren(self, child_, node, nodeName_, fromsubclass_=False):
if nodeName_ == 'calculated':
obj_ = calculatedType.factory()
obj_.build(child_)
self.calculated = obj_
obj_.original_tagname_ = 'calculated'
elif nodeName_ == 'forced':
obj_ = forcedType.factory()
obj_.build(child_)
self.forced = obj_
obj_.original_tagname_ = 'forced'
# end class valuesType
class calculatedType(GeneratedsSuper):
subclass = None
superclass = None
def __init__(self, value=None):
self.original_tagname_ = None
if value is None:
self.value = []
else:
self.value = value
def factory(*args_, **kwargs_):
if calculatedType.subclass:
return calculatedType.subclass(*args_, **kwargs_)
else:
return calculatedType(*args_, **kwargs_)
factory = staticmethod(factory)
def get_value(self): return self.value
def set_value(self, value): self.value = value
def add_value(self, value): self.value.append(value)
def insert_value(self, index, value): self.value[index] = value
valueProp = property(get_value, set_value)
def hasContent_(self):
if (
self.value
):
return True
else:
return False
def export(self, outfile, level, namespace_='', name_='calculatedType', namespacedef_='', pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.original_tagname_ is not None:
name_ = self.original_tagname_
showIndent(outfile, level, pretty_print)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = set()
self.exportAttributes(outfile, level, already_processed, namespace_, name_='calculatedType')
if self.hasContent_():
outfile.write('>%s' % (eol_, ))
self.exportChildren(outfile, level + 1, namespace_='', name_='calculatedType', pretty_print=pretty_print)
showIndent(outfile, level, pretty_print)
outfile.write('</%s%s>%s' % (namespace_, name_, eol_))
else:
outfile.write('/>%s' % (eol_, ))
def exportAttributes(self, outfile, level, already_processed, namespace_='', name_='calculatedType'):
pass
def exportChildren(self, outfile, level, namespace_='', name_='calculatedType', fromsubclass_=False, pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
for value_ in self.value:
showIndent(outfile, level, pretty_print)
outfile.write('<%svalue>%s</%svalue>%s' % (namespace_, self.gds_format_string(quote_xml(value_), input_name='value'), namespace_, eol_))
def exportLiteral(self, outfile, level, name_='calculatedType'):
level += 1
already_processed = set()
self.exportLiteralAttributes(outfile, level, already_processed, name_)
if self.hasContent_():
self.exportLiteralChildren(outfile, level, name_)
def exportLiteralAttributes(self, outfile, level, already_processed, name_):
pass
def exportLiteralChildren(self, outfile, level, name_):
showIndent(outfile, level)
outfile.write('value=[\n')
level += 1
for value_ in self.value:
showIndent(outfile, level)
outfile.write('%s,\n' % quote_python(value_))
level -= 1
showIndent(outfile, level)
outfile.write('],\n')
def build(self, node):
already_processed = set()
self.buildAttributes(node, node.attrib, already_processed)
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_)
return self
def buildAttributes(self, node, attrs, already_processed):
pass
def buildChildren(self, child_, node, nodeName_, fromsubclass_=False):
if nodeName_ == 'value':
value_ = child_.text
value_ = self.gds_validate_string(value_, node, 'value')
self.value.append(value_)
# end class calculatedType
class forcedType(GeneratedsSuper):
subclass = None
superclass = None
def __init__(self, value=None):
self.original_tagname_ = None
if value is None:
self.value = []
else:
self.value = value
def factory(*args_, **kwargs_):
if forcedType.subclass:
return forcedType.subclass(*args_, **kwargs_)
else:
return forcedType(*args_, **kwargs_)
factory = staticmethod(factory)
def get_value(self): return self.value
def set_value(self, value): self.value = value
def add_value(self, value): self.value.append(value)
def insert_value(self, index, value): self.value[index] = value
valueProp = property(get_value, set_value)
def hasContent_(self):
if (
self.value
):
return True
else:
return False
def export(self, outfile, level, namespace_='', name_='forcedType', namespacedef_='', pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.original_tagname_ is not None:
name_ = self.original_tagname_
showIndent(outfile, level, pretty_print)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = set()
self.exportAttributes(outfile, level, already_processed, namespace_, name_='forcedType')
if self.hasContent_():
outfile.write('>%s' % (eol_, ))
self.exportChildren(outfile, level + 1, namespace_='', name_='forcedType', pretty_print=pretty_print)
showIndent(outfile, level, pretty_print)
outfile.write('</%s%s>%s' % (namespace_, name_, eol_))
else:
outfile.write('/>%s' % (eol_, ))
def exportAttributes(self, outfile, level, already_processed, namespace_='', name_='forcedType'):
pass
def exportChildren(self, outfile, level, namespace_='', name_='forcedType', fromsubclass_=False, pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
for value_ in self.value:
showIndent(outfile, level, pretty_print)
outfile.write('<%svalue>%s</%svalue>%s' % (namespace_, self.gds_format_string(quote_xml(value_), input_name='value'), namespace_, eol_))
def exportLiteral(self, outfile, level, name_='forcedType'):
level += 1
already_processed = set()
self.exportLiteralAttributes(outfile, level, already_processed, name_)
if self.hasContent_():
self.exportLiteralChildren(outfile, level, name_)
def exportLiteralAttributes(self, outfile, level, already_processed, name_):
pass
def exportLiteralChildren(self, outfile, level, name_):
showIndent(outfile, level)
outfile.write('value=[\n')
level += 1
for value_ in self.value:
showIndent(outfile, level)
outfile.write('%s,\n' % quote_python(value_))
level -= 1
showIndent(outfile, level)
outfile.write('],\n')
def build(self, node):
already_processed = set()
self.buildAttributes(node, node.attrib, already_processed)
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_)
return self
def buildAttributes(self, node, attrs, already_processed):
pass
def buildChildren(self, child_, node, nodeName_, fromsubclass_=False):
if nodeName_ == 'value':
value_ = child_.text
value_ = self.gds_validate_string(value_, node, 'value')
self.value.append(value_)
# end class forcedType
class access_readType(GeneratedsSuper):
subclass = None
superclass = None
def __init__(self, name=None):
self.original_tagname_ = None
if name is None:
self.name = []
else:
self.name = name
def factory(*args_, **kwargs_):
if access_readType.subclass:
return access_readType.subclass(*args_, **kwargs_)
else:
return access_readType(*args_, **kwargs_)
factory = staticmethod(factory)
def get_name(self): return self.name
def set_name(self, name): self.name = name
def add_name(self, value): self.name.append(value)
def insert_name(self, index, value): self.name[index] = value
nameProp = property(get_name, set_name)
def hasContent_(self):
if (
self.name
):
return True
else:
return False
def export(self, outfile, level, namespace_='', name_='access_readType', namespacedef_='', pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.original_tagname_ is not None:
name_ = self.original_tagname_
showIndent(outfile, level, pretty_print)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = set()
self.exportAttributes(outfile, level, already_processed, namespace_, name_='access_readType')
if self.hasContent_():
outfile.write('>%s' % (eol_, ))
self.exportChildren(outfile, level + 1, namespace_='', name_='access_readType', pretty_print=pretty_print)
showIndent(outfile, level, pretty_print)
outfile.write('</%s%s>%s' % (namespace_, name_, eol_))
else:
outfile.write('/>%s' % (eol_, ))
def exportAttributes(self, outfile, level, already_processed, namespace_='', name_='access_readType'):
pass
def exportChildren(self, outfile, level, namespace_='', name_='access_readType', fromsubclass_=False, pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
for name_ in self.name:
showIndent(outfile, level, pretty_print)
outfile.write('<%sname>%s</%sname>%s' % (namespace_, self.gds_format_string(quote_xml(name_), input_name='name'), namespace_, eol_))
def exportLiteral(self, outfile, level, name_='access_readType'):
level += 1
already_processed = set()
self.exportLiteralAttributes(outfile, level, already_processed, name_)
if self.hasContent_():
self.exportLiteralChildren(outfile, level, name_)
def exportLiteralAttributes(self, outfile, level, already_processed, name_):
pass
def exportLiteralChildren(self, outfile, level, name_):
showIndent(outfile, level)
outfile.write('name=[\n')
level += 1
for name_ in self.name:
showIndent(outfile, level)
outfile.write('%s,\n' % quote_python(name_))
level -= 1
showIndent(outfile, level)
outfile.write('],\n')
def build(self, node):
already_processed = set()
self.buildAttributes(node, node.attrib, already_processed)
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_)
return self
def buildAttributes(self, node, attrs, already_processed):
pass
def buildChildren(self, child_, node, nodeName_, fromsubclass_=False):
if nodeName_ == 'name':
name_ = child_.text
name_ = self.gds_validate_string(name_, node, 'name')
self.name.append(name_)
# end class access_readType
class access_writeType(GeneratedsSuper):
subclass = None
superclass = None
def __init__(self, name=None):
self.original_tagname_ = None
self.name = name
def factory(*args_, **kwargs_):
if access_writeType.subclass:
return access_writeType.subclass(*args_, **kwargs_)
else:
return access_writeType(*args_, **kwargs_)
factory = staticmethod(factory)
def get_name(self): return self.name
def set_name(self, name): self.name = name
nameProp = property(get_name, set_name)
def hasContent_(self):
if (
self.name is not None
):
return True
else:
return False
def export(self, outfile, level, namespace_='', name_='access_writeType', namespacedef_='', pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.original_tagname_ is not None:
name_ = self.original_tagname_
showIndent(outfile, level, pretty_print)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = set()
self.exportAttributes(outfile, level, already_processed, namespace_, name_='access_writeType')
if self.hasContent_():
outfile.write('>%s' % (eol_, ))
self.exportChildren(outfile, level + 1, namespace_='', name_='access_writeType', pretty_print=pretty_print)
showIndent(outfile, level, pretty_print)
outfile.write('</%s%s>%s' % (namespace_, name_, eol_))
else:
outfile.write('/>%s' % (eol_, ))
def exportAttributes(self, outfile, level, already_processed, namespace_='', name_='access_writeType'):
pass
def exportChildren(self, outfile, level, namespace_='', name_='access_writeType', fromsubclass_=False, pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.name is not None:
showIndent(outfile, level, pretty_print)
outfile.write('<%sname>%s</%sname>%s' % (namespace_, self.gds_format_string(quote_xml(self.name), input_name='name'), namespace_, eol_))
def exportLiteral(self, outfile, level, name_='access_writeType'):
level += 1
already_processed = set()
self.exportLiteralAttributes(outfile, level, already_processed, name_)
if self.hasContent_():
self.exportLiteralChildren(outfile, level, name_)
def exportLiteralAttributes(self, outfile, level, already_processed, name_):
pass
def exportLiteralChildren(self, outfile, level, name_):
if self.name is not None:
showIndent(outfile, level)
outfile.write('name=%s,\n' % quote_python(self.name))
def build(self, node):
already_processed = set()
self.buildAttributes(node, node.attrib, already_processed)
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_)
return self
def buildAttributes(self, node, attrs, already_processed):
pass
def buildChildren(self, child_, node, nodeName_, fromsubclass_=False):
if nodeName_ == 'name':
name_ = child_.text
name_ = self.gds_validate_string(name_, node, 'name')
self.name = name_
# end class access_writeType
class logsType(GeneratedsSuper):
subclass = None
superclass = None
def __init__(self, log=None):
self.original_tagname_ = None
if log is None:
self.log = []
else:
self.log = log
def factory(*args_, **kwargs_):
if logsType.subclass:
return logsType.subclass(*args_, **kwargs_)
else:
return logsType(*args_, **kwargs_)
factory = staticmethod(factory)
def get_log(self): return self.log
def set_log(self, log): self.log = log
def add_log(self, value): self.log.append(value)
def insert_log(self, index, value): self.log[index] = value
logProp = property(get_log, set_log)
def hasContent_(self):
if (
self.log
):
return True
else:
return False
def export(self, outfile, level, namespace_='', name_='logsType', namespacedef_='', pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.original_tagname_ is not None:
name_ = self.original_tagname_
showIndent(outfile, level, pretty_print)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = set()
self.exportAttributes(outfile, level, already_processed, namespace_, name_='logsType')
if self.hasContent_():
outfile.write('>%s' % (eol_, ))
self.exportChildren(outfile, level + 1, namespace_='', name_='logsType', pretty_print=pretty_print)
showIndent(outfile, level, pretty_print)
outfile.write('</%s%s>%s' % (namespace_, name_, eol_))
else:
outfile.write('/>%s' % (eol_, ))
def exportAttributes(self, outfile, level, already_processed, namespace_='', name_='logsType'):
pass
def exportChildren(self, outfile, level, namespace_='', name_='logsType', fromsubclass_=False, pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
for log_ in self.log:
log_.export(outfile, level, namespace_, name_='log', pretty_print=pretty_print)
def exportLiteral(self, outfile, level, name_='logsType'):
level += 1
already_processed = set()
self.exportLiteralAttributes(outfile, level, already_processed, name_)
if self.hasContent_():
self.exportLiteralChildren(outfile, level, name_)
def exportLiteralAttributes(self, outfile, level, already_processed, name_):
pass
def exportLiteralChildren(self, outfile, level, name_):
showIndent(outfile, level)
outfile.write('log=[\n')
level += 1
for log_ in self.log:
showIndent(outfile, level)
outfile.write('model_.logType3(\n')
log_.exportLiteral(outfile, level, name_='logType3')
showIndent(outfile, level)
outfile.write('),\n')
level -= 1
showIndent(outfile, level)
outfile.write('],\n')
def build(self, node):
already_processed = set()
self.buildAttributes(node, node.attrib, already_processed)
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_)
return self
def buildAttributes(self, node, attrs, already_processed):
pass
def buildChildren(self, child_, node, nodeName_, fromsubclass_=False):
if nodeName_ == 'log':
obj_ = logType3.factory()
obj_.build(child_)
self.log.append(obj_)
obj_.original_tagname_ = 'log'
# end class logsType
class logType3(GeneratedsSuper):
"""Log type (e.g. error, warning, information)"""
subclass = None
superclass = None
def __init__(self, type_=None, valueOf_=None):
self.original_tagname_ = None
self.type_ = _cast(None, type_)
self.valueOf_ = valueOf_
def factory(*args_, **kwargs_):
if logType3.subclass:
return logType3.subclass(*args_, **kwargs_)
else:
return logType3(*args_, **kwargs_)
factory = staticmethod(factory)
def get_type(self): return self.type_
def set_type(self, type_): self.type_ = type_
typeProp = property(get_type, set_type)
def get_valueOf_(self): return self.valueOf_
def set_valueOf_(self, valueOf_): self.valueOf_ = valueOf_
def validate_logType(self, value):
# Validate type logType, a restriction on xs:string.
pass
def hasContent_(self):
if (
self.valueOf_
):
return True
else:
return False
def export(self, outfile, level, namespace_='', name_='logType3', namespacedef_='', pretty_print=True):
if pretty_print:
eol_ = '\n'
else:
eol_ = ''
if self.original_tagname_ is not None:
name_ = self.original_tagname_
showIndent(outfile, level, pretty_print)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = set()
self.exportAttributes(outfile, level, already_processed, namespace_, name_='logType3')
if self.hasContent_():
outfile.write('>')
outfile.write(str(self.valueOf_))
self.exportChildren(outfile, level + 1, namespace_='', name_='logType3', pretty_print=pretty_print)
outfile.write('</%s%s>%s' % (namespace_, name_, eol_))
else:
outfile.write('/>%s' % (eol_, ))
def exportAttributes(self, outfile, level, already_processed, namespace_='', name_='logType3'):
if self.type_ is not None and 'type_' not in already_processed:
already_processed.add('type_')
outfile.write(' type=%s' % (quote_attrib(self.type_), ))
def exportChildren(self, outfile, level, namespace_='', name_='logType3', fromsubclass_=False, pretty_print=True):
pass
def exportLiteral(self, outfile, level, name_='logType3'):
level += 1
already_processed = set()
self.exportLiteralAttributes(outfile, level, already_processed, name_)
if self.hasContent_():
self.exportLiteralChildren(outfile, level, name_)
showIndent(outfile, level)
outfile.write('valueOf_ = """%s""",\n' % (self.valueOf_,))
def exportLiteralAttributes(self, outfile, level, already_processed, name_):
if self.type_ is not None and 'type_' not in already_processed:
already_processed.add('type_')
showIndent(outfile, level)
outfile.write('type_="%s",\n' % (self.type_,))
def exportLiteralChildren(self, outfile, level, name_):
pass
def build(self, node):
already_processed = set()
self.buildAttributes(node, node.attrib, already_processed)
self.valueOf_ = get_all_text_(node)
for child in node:
nodeName_ = Tag_pattern_.match(child.tag).groups()[-1]
self.buildChildren(child, node, nodeName_)
return self
def buildAttributes(self, node, attrs, already_processed):
value = find_attr_value_('type', node)
if value is not None and 'type' not in already_processed:
already_processed.add('type')
self.type_ = value
self.validate_logType(self.type_) # validate type logType
def buildChildren(self, child_, node, nodeName_, fromsubclass_=False):
pass
# end class logType3
GDSClassesMapping = {
'feature': featureType,
'phys': physType,
'profiles': profilesType,
'variables': variablesType,
'logs': logsType,
'phy': phyType,
'profile_inputs': profile_inputsType,
'profile_outputs': profile_outputsType,
'profile_input': profile_inputType,
'profile_output': profile_outputType,
'profile': profileType,
'inputs': inputsType,
'forces': forcesType,
'outputs': outputsType,
'default_phys': default_physType,
'input': inputType1,
'force': forceType,
'output': outputType2,
'default_phy': default_phyType,
'variable': variableType,
'enum': enumType,
'values': valuesType,
'access_read': access_readType,
'access_write': access_writeType,
'members': membersType,
'member': memberType,
'calculated': calculatedType,
'forced': forcedType,
'log': logType3,
}
USAGE_TEXT = """
Usage: python <Parser>.py [ -s ] <in_xml_file>
"""
def usage():
print(USAGE_TEXT)
sys.exit(1)
def get_root_tag(node):
tag = Tag_pattern_.match(node.tag).groups()[-1]
rootClass = GDSClassesMapping.get(tag)
if rootClass is None:
rootClass = globals().get(tag)
return tag, rootClass
def parse(inFileName, silence=False):
doc = parsexml_(inFileName)
rootNode = doc.getroot()
rootTag, rootClass = get_root_tag(rootNode)
if rootClass is None:
rootTag = 'features'
rootClass = features
rootObj = rootClass.factory()
rootObj.build(rootNode)
# Enable Python to collect the space used by the DOM.
doc = None
## if not silence:
## sys.stdout.write('<?xml version="1.0" ?>\n')
## rootObj.export(
## sys.stdout, 0, name_=rootTag,
## namespacedef_='',
## pretty_print=True)
return rootObj
def parseEtree(inFileName, silence=False):
doc = parsexml_(inFileName)
rootNode = doc.getroot()
rootTag, rootClass = get_root_tag(rootNode)
if rootClass is None:
rootTag = 'features'
rootClass = features
rootObj = rootClass.factory()
rootObj.build(rootNode)
# Enable Python to collect the space used by the DOM.
doc = None
mapping = {}
rootElement = rootObj.to_etree(None, name_=rootTag, mapping_=mapping)
reverse_mapping = rootObj.gds_reverse_node_mapping(mapping)
## if not silence:
## content = etree_.tostring(
## rootElement, pretty_print=True,
## xml_declaration=True, encoding="utf-8")
## sys.stdout.write(content)
## sys.stdout.write('\n')
return rootObj, rootElement, mapping, reverse_mapping
def parseString(inString, silence=False):
from StringIO import StringIO
doc = parsexml_(StringIO(inString))
rootNode = doc.getroot()
rootTag, rootClass = get_root_tag(rootNode)
if rootClass is None:
rootTag = 'features'
rootClass = features
rootObj = rootClass.factory()
rootObj.build(rootNode)
# Enable Python to collect the space used by the DOM.
doc = None
## if not silence:
## sys.stdout.write('<?xml version="1.0" ?>\n')
## rootObj.export(
## sys.stdout, 0, name_=rootTag,
## namespacedef_='')
return rootObj
def parseLiteral(inFileName, silence=False):
doc = parsexml_(inFileName)
rootNode = doc.getroot()
rootTag, rootClass = get_root_tag(rootNode)
if rootClass is None:
rootTag = 'features'
rootClass = features
rootObj = rootClass.factory()
rootObj.build(rootNode)
# Enable Python to collect the space used by the DOM.
doc = None
## if not silence:
## sys.stdout.write('#from Bindings import *\n\n')
## sys.stdout.write('import Bindings as model_\n\n')
## sys.stdout.write('rootObj = model_.rootClass(\n')
## rootObj.exportLiteral(sys.stdout, 0, name_=rootTag)
## sys.stdout.write(')\n')
return rootObj
def main():
args = sys.argv[1:]
if len(args) == 1:
parse(args[0])
else:
usage()
if __name__ == '__main__':
#import pdb; pdb.set_trace()
main()
__all__ = [
"access_readType",
"access_writeType",
"calculatedType",
"default_phyType",
"default_physType",
"defaults",
"enumType",
"featureType",
"features",
"forceType",
"forcedType",
"forcesType",
"inputType1",
"inputsType",
"logType3",
"logsType",
"memberType",
"membersType",
"model",
"outputType2",
"outputsType",
"overrides",
"phyType",
"physType",
"profileType",
"profile_inputType",
"profile_inputsType",
"profile_outputType",
"profile_outputsType",
"profilesType",
"values",
"valuesType",
"var_overrides",
"var_values",
"variableType",
"variablesType"
]
| 45.856456 | 269 | 0.629367 | 28,298 | 247,900 | 5.255954 | 0.023889 | 0.058736 | 0.02136 | 0.038411 | 0.826212 | 0.779518 | 0.736723 | 0.71149 | 0.689618 | 0.668318 | 0 | 0.002964 | 0.263643 | 247,900 | 5,405 | 270 | 45.86494 | 0.81182 | 0.03808 | 0 | 0.705626 | 1 | 0.000198 | 0.066205 | 0.00912 | 0 | 0 | 0 | 0 | 0 | 1 | 0.157488 | false | 0.018621 | 0.00733 | 0.029913 | 0.269216 | 0.062005 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
be681d89066a0b812f6634a1716afd478fcec7c6 | 36,213 | py | Python | notebooks/tbeucler_devlog/colorbrewer.py | wallingTACC/CBRAIN-CAM | 10589f92ac0c3bf09e5b019ce5b2d80f9967c6b2 | [
"MIT"
] | 12 | 2019-03-30T07:40:07.000Z | 2022-01-11T11:53:02.000Z | notebooks/tbeucler_devlog/colorbrewer.py | wallingTACC/CBRAIN-CAM | 10589f92ac0c3bf09e5b019ce5b2d80f9967c6b2 | [
"MIT"
] | 2 | 2015-02-10T03:48:24.000Z | 2015-02-12T10:21:37.000Z | notebooks/tbeucler_devlog/colorbrewer.py | wallingTACC/CBRAIN-CAM | 10589f92ac0c3bf09e5b019ce5b2d80f9967c6b2 | [
"MIT"
] | 11 | 2015-01-30T10:12:22.000Z | 2022-03-27T10:46:54.000Z | #!/usr/bin/env python
# -*- coding: utf-8 -*-
__version__ = '1.0.0'
VERSION = tuple(map(int, __version__.split('.')))
YlGn = {
3: ["rgb(247,252,185)", "rgb(173,221,142)", "rgb(49,163,84)"],
4: ["rgb(255,255,204)", "rgb(194,230,153)", "rgb(120,198,121)", "rgb(35,132,67)"],
5: ["rgb(255,255,204)", "rgb(194,230,153)", "rgb(120,198,121)", "rgb(49,163,84)", "rgb(0,104,55)"],
6: ["rgb(255,255,204)", "rgb(217,240,163)", "rgb(173,221,142)", "rgb(120,198,121)", "rgb(49,163,84)", "rgb(0,104,55)"],
7: ["rgb(255,255,204)", "rgb(217,240,163)", "rgb(173,221,142)", "rgb(120,198,121)", "rgb(65,171,93)", "rgb(35,132,67)", "rgb(0,90,50)"],
8: ["rgb(255,255,229)", "rgb(247,252,185)", "rgb(217,240,163)", "rgb(173,221,142)", "rgb(120,198,121)", "rgb(65,171,93)", "rgb(35,132,67)", "rgb(0,90,50)"],
9: ["rgb(255,255,229)", "rgb(247,252,185)", "rgb(217,240,163)", "rgb(173,221,142)", "rgb(120,198,121)", "rgb(65,171,93)", "rgb(35,132,67)", "rgb(0,104,55)", "rgb(0,69,41)"]
}
YlGnBu = {
3: ["rgb(237,248,177)", "rgb(127,205,187)", "rgb(44,127,184)"],
4: ["rgb(255,255,204)", "rgb(161,218,180)", "rgb(65,182,196)", "rgb(34,94,168)"],
5: ["rgb(255,255,204)", "rgb(161,218,180)", "rgb(65,182,196)", "rgb(44,127,184)", "rgb(37,52,148)"],
6: ["rgb(255,255,204)", "rgb(199,233,180)", "rgb(127,205,187)", "rgb(65,182,196)", "rgb(44,127,184)", "rgb(37,52,148)"],
7: ["rgb(255,255,204)", "rgb(199,233,180)", "rgb(127,205,187)", "rgb(65,182,196)", "rgb(29,145,192)", "rgb(34,94,168)", "rgb(12,44,132)"],
8: ["rgb(255,255,217)", "rgb(237,248,177)", "rgb(199,233,180)", "rgb(127,205,187)", "rgb(65,182,196)", "rgb(29,145,192)", "rgb(34,94,168)", "rgb(12,44,132)"],
9: ["rgb(255,255,217)", "rgb(237,248,177)", "rgb(199,233,180)", "rgb(127,205,187)", "rgb(65,182,196)", "rgb(29,145,192)", "rgb(34,94,168)", "rgb(37,52,148)", "rgb(8,29,88)"]
}
GnBu = {
3: ["rgb(224,243,219)", "rgb(168,221,181)", "rgb(67,162,202)"],
4: ["rgb(240,249,232)", "rgb(186,228,188)", "rgb(123,204,196)", "rgb(43,140,190)"],
5: ["rgb(240,249,232)", "rgb(186,228,188)", "rgb(123,204,196)", "rgb(67,162,202)", "rgb(8,104,172)"],
6: ["rgb(240,249,232)", "rgb(204,235,197)", "rgb(168,221,181)", "rgb(123,204,196)", "rgb(67,162,202)", "rgb(8,104,172)"],
7: ["rgb(240,249,232)", "rgb(204,235,197)", "rgb(168,221,181)", "rgb(123,204,196)", "rgb(78,179,211)", "rgb(43,140,190)", "rgb(8,88,158)"],
8: ["rgb(247,252,240)", "rgb(224,243,219)", "rgb(204,235,197)", "rgb(168,221,181)", "rgb(123,204,196)", "rgb(78,179,211)", "rgb(43,140,190)", "rgb(8,88,158)"],
9: ["rgb(247,252,240)", "rgb(224,243,219)", "rgb(204,235,197)", "rgb(168,221,181)", "rgb(123,204,196)", "rgb(78,179,211)", "rgb(43,140,190)", "rgb(8,104,172)", "rgb(8,64,129)"]
}
BuGn = {
3: ["rgb(229,245,249)", "rgb(153,216,201)", "rgb(44,162,95)"],
4: ["rgb(237,248,251)", "rgb(178,226,226)", "rgb(102,194,164)", "rgb(35,139,69)"],
5: ["rgb(237,248,251)", "rgb(178,226,226)", "rgb(102,194,164)", "rgb(44,162,95)", "rgb(0,109,44)"],
6: ["rgb(237,248,251)", "rgb(204,236,230)", "rgb(153,216,201)", "rgb(102,194,164)", "rgb(44,162,95)", "rgb(0,109,44)"],
7: ["rgb(237,248,251)", "rgb(204,236,230)", "rgb(153,216,201)", "rgb(102,194,164)", "rgb(65,174,118)", "rgb(35,139,69)", "rgb(0,88,36)"],
8: ["rgb(247,252,253)", "rgb(229,245,249)", "rgb(204,236,230)", "rgb(153,216,201)", "rgb(102,194,164)", "rgb(65,174,118)", "rgb(35,139,69)", "rgb(0,88,36)"],
9: ["rgb(247,252,253)", "rgb(229,245,249)", "rgb(204,236,230)", "rgb(153,216,201)", "rgb(102,194,164)", "rgb(65,174,118)", "rgb(35,139,69)", "rgb(0,109,44)", "rgb(0,68,27)"]
}
PuBuGn = {
3: ["rgb(236,226,240)", "rgb(166,189,219)", "rgb(28,144,153)"],
4: ["rgb(246,239,247)", "rgb(189,201,225)", "rgb(103,169,207)", "rgb(2,129,138)"],
5: ["rgb(246,239,247)", "rgb(189,201,225)", "rgb(103,169,207)", "rgb(28,144,153)", "rgb(1,108,89)"],
6: ["rgb(246,239,247)", "rgb(208,209,230)", "rgb(166,189,219)", "rgb(103,169,207)", "rgb(28,144,153)", "rgb(1,108,89)"],
7: ["rgb(246,239,247)", "rgb(208,209,230)", "rgb(166,189,219)", "rgb(103,169,207)", "rgb(54,144,192)", "rgb(2,129,138)", "rgb(1,100,80)"],
8: ["rgb(255,247,251)", "rgb(236,226,240)", "rgb(208,209,230)", "rgb(166,189,219)", "rgb(103,169,207)", "rgb(54,144,192)", "rgb(2,129,138)", "rgb(1,100,80)"],
9: ["rgb(255,247,251)", "rgb(236,226,240)", "rgb(208,209,230)", "rgb(166,189,219)", "rgb(103,169,207)", "rgb(54,144,192)", "rgb(2,129,138)", "rgb(1,108,89)", "rgb(1,70,54)"]
}
PuBu = {
3: ["rgb(236,231,242)", "rgb(166,189,219)", "rgb(43,140,190)"],
4: ["rgb(241,238,246)", "rgb(189,201,225)", "rgb(116,169,207)", "rgb(5,112,176)"],
5: ["rgb(241,238,246)", "rgb(189,201,225)", "rgb(116,169,207)", "rgb(43,140,190)", "rgb(4,90,141)"],
6: ["rgb(241,238,246)", "rgb(208,209,230)", "rgb(166,189,219)", "rgb(116,169,207)", "rgb(43,140,190)", "rgb(4,90,141)"],
7: ["rgb(241,238,246)", "rgb(208,209,230)", "rgb(166,189,219)", "rgb(116,169,207)", "rgb(54,144,192)", "rgb(5,112,176)", "rgb(3,78,123)"],
8: ["rgb(255,247,251)", "rgb(236,231,242)", "rgb(208,209,230)", "rgb(166,189,219)", "rgb(116,169,207)", "rgb(54,144,192)", "rgb(5,112,176)", "rgb(3,78,123)"],
9: ["rgb(255,247,251)", "rgb(236,231,242)", "rgb(208,209,230)", "rgb(166,189,219)", "rgb(116,169,207)", "rgb(54,144,192)", "rgb(5,112,176)", "rgb(4,90,141)", "rgb(2,56,88)"]
}
BuPu = {
3: ["rgb(224,236,244)", "rgb(158,188,218)", "rgb(136,86,167)"],
4: ["rgb(237,248,251)", "rgb(179,205,227)", "rgb(140,150,198)", "rgb(136,65,157)"],
5: ["rgb(237,248,251)", "rgb(179,205,227)", "rgb(140,150,198)", "rgb(136,86,167)", "rgb(129,15,124)"],
6: ["rgb(237,248,251)", "rgb(191,211,230)", "rgb(158,188,218)", "rgb(140,150,198)", "rgb(136,86,167)", "rgb(129,15,124)"],
7: ["rgb(237,248,251)", "rgb(191,211,230)", "rgb(158,188,218)", "rgb(140,150,198)", "rgb(140,107,177)", "rgb(136,65,157)", "rgb(110,1,107)"],
8: ["rgb(247,252,253)", "rgb(224,236,244)", "rgb(191,211,230)", "rgb(158,188,218)", "rgb(140,150,198)", "rgb(140,107,177)", "rgb(136,65,157)", "rgb(110,1,107)"],
9: ["rgb(247,252,253)", "rgb(224,236,244)", "rgb(191,211,230)", "rgb(158,188,218)", "rgb(140,150,198)", "rgb(140,107,177)", "rgb(136,65,157)", "rgb(129,15,124)", "rgb(77,0,75)"]
}
RdPu = {
3: ["rgb(253,224,221)", "rgb(250,159,181)", "rgb(197,27,138)"],
4: ["rgb(254,235,226)", "rgb(251,180,185)", "rgb(247,104,161)", "rgb(174,1,126)"],
5: ["rgb(254,235,226)", "rgb(251,180,185)", "rgb(247,104,161)", "rgb(197,27,138)", "rgb(122,1,119)"],
6: ["rgb(254,235,226)", "rgb(252,197,192)", "rgb(250,159,181)", "rgb(247,104,161)", "rgb(197,27,138)", "rgb(122,1,119)"],
7: ["rgb(254,235,226)", "rgb(252,197,192)", "rgb(250,159,181)", "rgb(247,104,161)", "rgb(221,52,151)", "rgb(174,1,126)", "rgb(122,1,119)"],
8: ["rgb(255,247,243)", "rgb(253,224,221)", "rgb(252,197,192)", "rgb(250,159,181)", "rgb(247,104,161)", "rgb(221,52,151)", "rgb(174,1,126)", "rgb(122,1,119)"],
9: ["rgb(255,247,243)", "rgb(253,224,221)", "rgb(252,197,192)", "rgb(250,159,181)", "rgb(247,104,161)", "rgb(221,52,151)", "rgb(174,1,126)", "rgb(122,1,119)", "rgb(73,0,106)"]
}
PuRd = {
3: ["rgb(231,225,239)", "rgb(201,148,199)", "rgb(221,28,119)"],
4: ["rgb(241,238,246)", "rgb(215,181,216)", "rgb(223,101,176)", "rgb(206,18,86)"],
5: ["rgb(241,238,246)", "rgb(215,181,216)", "rgb(223,101,176)", "rgb(221,28,119)", "rgb(152,0,67)"],
6: ["rgb(241,238,246)", "rgb(212,185,218)", "rgb(201,148,199)", "rgb(223,101,176)", "rgb(221,28,119)", "rgb(152,0,67)"],
7: ["rgb(241,238,246)", "rgb(212,185,218)", "rgb(201,148,199)", "rgb(223,101,176)", "rgb(231,41,138)", "rgb(206,18,86)", "rgb(145,0,63)"],
8: ["rgb(247,244,249)", "rgb(231,225,239)", "rgb(212,185,218)", "rgb(201,148,199)", "rgb(223,101,176)", "rgb(231,41,138)", "rgb(206,18,86)", "rgb(145,0,63)"],
9: ["rgb(247,244,249)", "rgb(231,225,239)", "rgb(212,185,218)", "rgb(201,148,199)", "rgb(223,101,176)", "rgb(231,41,138)", "rgb(206,18,86)", "rgb(152,0,67)", "rgb(103,0,31)"]
}
OrRd = {
3: ["rgb(254,232,200)", "rgb(253,187,132)", "rgb(227,74,51)"],
4: ["rgb(254,240,217)", "rgb(253,204,138)", "rgb(252,141,89)", "rgb(215,48,31)"],
5: ["rgb(254,240,217)", "rgb(253,204,138)", "rgb(252,141,89)", "rgb(227,74,51)", "rgb(179,0,0)"],
6: ["rgb(254,240,217)", "rgb(253,212,158)", "rgb(253,187,132)", "rgb(252,141,89)", "rgb(227,74,51)", "rgb(179,0,0)"],
7: ["rgb(254,240,217)", "rgb(253,212,158)", "rgb(253,187,132)", "rgb(252,141,89)", "rgb(239,101,72)", "rgb(215,48,31)", "rgb(153,0,0)"],
8: ["rgb(255,247,236)", "rgb(254,232,200)", "rgb(253,212,158)", "rgb(253,187,132)", "rgb(252,141,89)", "rgb(239,101,72)", "rgb(215,48,31)", "rgb(153,0,0)"],
9: ["rgb(255,247,236)", "rgb(254,232,200)", "rgb(253,212,158)", "rgb(253,187,132)", "rgb(252,141,89)", "rgb(239,101,72)", "rgb(215,48,31)", "rgb(179,0,0)", "rgb(127,0,0)"]
}
YlOrRd = {
3: ["rgb(255,237,160)", "rgb(254,178,76)", "rgb(240,59,32)"],
4: ["rgb(255,255,178)", "rgb(254,204,92)", "rgb(253,141,60)", "rgb(227,26,28)"],
5: ["rgb(255,255,178)", "rgb(254,204,92)", "rgb(253,141,60)", "rgb(240,59,32)", "rgb(189,0,38)"],
6: ["rgb(255,255,178)", "rgb(254,217,118)", "rgb(254,178,76)", "rgb(253,141,60)", "rgb(240,59,32)", "rgb(189,0,38)"],
7: ["rgb(255,255,178)", "rgb(254,217,118)", "rgb(254,178,76)", "rgb(253,141,60)", "rgb(252,78,42)", "rgb(227,26,28)", "rgb(177,0,38)"],
8: ["rgb(255,255,204)", "rgb(255,237,160)", "rgb(254,217,118)", "rgb(254,178,76)", "rgb(253,141,60)", "rgb(252,78,42)", "rgb(227,26,28)", "rgb(177,0,38)"],
9: ["rgb(255,255,204)", "rgb(255,237,160)", "rgb(254,217,118)", "rgb(254,178,76)", "rgb(253,141,60)", "rgb(252,78,42)", "rgb(227,26,28)", "rgb(189,0,38)", "rgb(128,0,38)"]
}
YlOrBr = {
3: ["rgb(255,247,188)", "rgb(254,196,79)", "rgb(217,95,14)"],
4: ["rgb(255,255,212)", "rgb(254,217,142)", "rgb(254,153,41)", "rgb(204,76,2)"],
5: ["rgb(255,255,212)", "rgb(254,217,142)", "rgb(254,153,41)", "rgb(217,95,14)", "rgb(153,52,4)"],
6: ["rgb(255,255,212)", "rgb(254,227,145)", "rgb(254,196,79)", "rgb(254,153,41)", "rgb(217,95,14)", "rgb(153,52,4)"],
7: ["rgb(255,255,212)", "rgb(254,227,145)", "rgb(254,196,79)", "rgb(254,153,41)", "rgb(236,112,20)", "rgb(204,76,2)", "rgb(140,45,4)"],
8: ["rgb(255,255,229)", "rgb(255,247,188)", "rgb(254,227,145)", "rgb(254,196,79)", "rgb(254,153,41)", "rgb(236,112,20)", "rgb(204,76,2)", "rgb(140,45,4)"],
9: ["rgb(255,255,229)", "rgb(255,247,188)", "rgb(254,227,145)", "rgb(254,196,79)", "rgb(254,153,41)", "rgb(236,112,20)", "rgb(204,76,2)", "rgb(153,52,4)", "rgb(102,37,6)"]
}
Purples = {
3: ["rgb(239,237,245)", "rgb(188,189,220)", "rgb(117,107,177)"],
4: ["rgb(242,240,247)", "rgb(203,201,226)", "rgb(158,154,200)", "rgb(106,81,163)"],
5: ["rgb(242,240,247)", "rgb(203,201,226)", "rgb(158,154,200)", "rgb(117,107,177)", "rgb(84,39,143)"],
6: ["rgb(242,240,247)", "rgb(218,218,235)", "rgb(188,189,220)", "rgb(158,154,200)", "rgb(117,107,177)", "rgb(84,39,143)"],
7: ["rgb(242,240,247)", "rgb(218,218,235)", "rgb(188,189,220)", "rgb(158,154,200)", "rgb(128,125,186)", "rgb(106,81,163)", "rgb(74,20,134)"],
8: ["rgb(252,251,253)", "rgb(239,237,245)", "rgb(218,218,235)", "rgb(188,189,220)", "rgb(158,154,200)", "rgb(128,125,186)", "rgb(106,81,163)", "rgb(74,20,134)"],
9: ["rgb(252,251,253)", "rgb(239,237,245)", "rgb(218,218,235)", "rgb(188,189,220)", "rgb(158,154,200)", "rgb(128,125,186)", "rgb(106,81,163)", "rgb(84,39,143)", "rgb(63,0,125)"]
}
Blues = {
3: ["rgb(222,235,247)", "rgb(158,202,225)", "rgb(49,130,189)"],
4: ["rgb(239,243,255)", "rgb(189,215,231)", "rgb(107,174,214)", "rgb(33,113,181)"],
5: ["rgb(239,243,255)", "rgb(189,215,231)", "rgb(107,174,214)", "rgb(49,130,189)", "rgb(8,81,156)"],
6: ["rgb(239,243,255)", "rgb(198,219,239)", "rgb(158,202,225)", "rgb(107,174,214)", "rgb(49,130,189)", "rgb(8,81,156)"],
7: ["rgb(239,243,255)", "rgb(198,219,239)", "rgb(158,202,225)", "rgb(107,174,214)", "rgb(66,146,198)", "rgb(33,113,181)", "rgb(8,69,148)"],
8: ["rgb(247,251,255)", "rgb(222,235,247)", "rgb(198,219,239)", "rgb(158,202,225)", "rgb(107,174,214)", "rgb(66,146,198)", "rgb(33,113,181)", "rgb(8,69,148)"],
9: ["rgb(247,251,255)", "rgb(222,235,247)", "rgb(198,219,239)", "rgb(158,202,225)", "rgb(107,174,214)", "rgb(66,146,198)", "rgb(33,113,181)", "rgb(8,81,156)", "rgb(8,48,107)"]
}
Greens = {
3: ["rgb(229,245,224)", "rgb(161,217,155)", "rgb(49,163,84)"],
4: ["rgb(237,248,233)", "rgb(186,228,179)", "rgb(116,196,118)", "rgb(35,139,69)"],
5: ["rgb(237,248,233)", "rgb(186,228,179)", "rgb(116,196,118)", "rgb(49,163,84)", "rgb(0,109,44)"],
6: ["rgb(237,248,233)", "rgb(199,233,192)", "rgb(161,217,155)", "rgb(116,196,118)", "rgb(49,163,84)", "rgb(0,109,44)"],
7: ["rgb(237,248,233)", "rgb(199,233,192)", "rgb(161,217,155)", "rgb(116,196,118)", "rgb(65,171,93)", "rgb(35,139,69)", "rgb(0,90,50)"],
8: ["rgb(247,252,245)", "rgb(229,245,224)", "rgb(199,233,192)", "rgb(161,217,155)", "rgb(116,196,118)", "rgb(65,171,93)", "rgb(35,139,69)", "rgb(0,90,50)"],
9: ["rgb(247,252,245)", "rgb(229,245,224)", "rgb(199,233,192)", "rgb(161,217,155)", "rgb(116,196,118)", "rgb(65,171,93)", "rgb(35,139,69)", "rgb(0,109,44)", "rgb(0,68,27)"]
}
Oranges = {
3: ["rgb(254,230,206)", "rgb(253,174,107)", "rgb(230,85,13)"],
4: ["rgb(254,237,222)", "rgb(253,190,133)", "rgb(253,141,60)", "rgb(217,71,1)"],
5: ["rgb(254,237,222)", "rgb(253,190,133)", "rgb(253,141,60)", "rgb(230,85,13)", "rgb(166,54,3)"],
6: ["rgb(254,237,222)", "rgb(253,208,162)", "rgb(253,174,107)", "rgb(253,141,60)", "rgb(230,85,13)", "rgb(166,54,3)"],
7: ["rgb(254,237,222)", "rgb(253,208,162)", "rgb(253,174,107)", "rgb(253,141,60)", "rgb(241,105,19)", "rgb(217,72,1)", "rgb(140,45,4)"],
8: ["rgb(255,245,235)", "rgb(254,230,206)", "rgb(253,208,162)", "rgb(253,174,107)", "rgb(253,141,60)", "rgb(241,105,19)", "rgb(217,72,1)", "rgb(140,45,4)"],
9: ["rgb(255,245,235)", "rgb(254,230,206)", "rgb(253,208,162)", "rgb(253,174,107)", "rgb(253,141,60)", "rgb(241,105,19)", "rgb(217,72,1)", "rgb(166,54,3)", "rgb(127,39,4)"]
}
Reds = {
3: ["rgb(254,224,210)", "rgb(252,146,114)", "rgb(222,45,38)"],
4: ["rgb(254,229,217)", "rgb(252,174,145)", "rgb(251,106,74)", "rgb(203,24,29)"],
5: ["rgb(254,229,217)", "rgb(252,174,145)", "rgb(251,106,74)", "rgb(222,45,38)", "rgb(165,15,21)"],
6: ["rgb(254,229,217)", "rgb(252,187,161)", "rgb(252,146,114)", "rgb(251,106,74)", "rgb(222,45,38)", "rgb(165,15,21)"],
7: ["rgb(254,229,217)", "rgb(252,187,161)", "rgb(252,146,114)", "rgb(251,106,74)", "rgb(239,59,44)", "rgb(203,24,29)", "rgb(153,0,13)"],
8: ["rgb(255,245,240)", "rgb(254,224,210)", "rgb(252,187,161)", "rgb(252,146,114)", "rgb(251,106,74)", "rgb(239,59,44)", "rgb(203,24,29)", "rgb(153,0,13)"],
9: ["rgb(255,245,240)", "rgb(254,224,210)", "rgb(252,187,161)", "rgb(252,146,114)", "rgb(251,106,74)", "rgb(239,59,44)", "rgb(203,24,29)", "rgb(165,15,21)", "rgb(103,0,13)"]
}
Greys = {
3: ["rgb(240,240,240)", "rgb(189,189,189)", "rgb(99,99,99)"],
4: ["rgb(247,247,247)", "rgb(204,204,204)", "rgb(150,150,150)", "rgb(82,82,82)"],
5: ["rgb(247,247,247)", "rgb(204,204,204)", "rgb(150,150,150)", "rgb(99,99,99)", "rgb(37,37,37)"],
6: ["rgb(247,247,247)", "rgb(217,217,217)", "rgb(189,189,189)", "rgb(150,150,150)", "rgb(99,99,99)", "rgb(37,37,37)"],
7: ["rgb(247,247,247)", "rgb(217,217,217)", "rgb(189,189,189)", "rgb(150,150,150)", "rgb(115,115,115)", "rgb(82,82,82)", "rgb(37,37,37)"],
8: ["rgb(255,255,255)", "rgb(240,240,240)", "rgb(217,217,217)", "rgb(189,189,189)", "rgb(150,150,150)", "rgb(115,115,115)", "rgb(82,82,82)", "rgb(37,37,37)"],
9: ["rgb(255,255,255)", "rgb(240,240,240)", "rgb(217,217,217)", "rgb(189,189,189)", "rgb(150,150,150)", "rgb(115,115,115)", "rgb(82,82,82)", "rgb(37,37,37)", "rgb(0,0,0)"]
}
sequential = {
'multihue' : { 'YlGn': YlGn, 'YlGnBu': YlGnBu, 'GnBu': GnBu, 'BuGn': BuGn, 'PuBuGn': PuBuGn, 'PuBu': PuBu, 'BuPu': BuPu, 'RdPu': RdPu, 'PuRd': PuRd, 'OrRd': OrRd, 'YlOrRd': YlOrRd, 'YlOrBr': YlOrBr },
'singlehue': { 'Purples': Purples, 'Blues': Blues, 'Greens': Greens, 'Oranges': Oranges, 'Reds': Reds, 'Greys': Greys },
}
sequential.update(sequential['multihue'])
sequential.update(sequential['singlehue'])
PuOr = {
3: ["rgb(241,163,64)", "rgb(247,247,247)", "rgb(153,142,195)"],
4: ["rgb(230,97,1)", "rgb(253,184,99)", "rgb(178,171,210)", "rgb(94,60,153)"],
5: ["rgb(230,97,1)", "rgb(253,184,99)", "rgb(247,247,247)", "rgb(178,171,210)", "rgb(94,60,153)"],
6: ["rgb(179,88,6)", "rgb(241,163,64)", "rgb(254,224,182)", "rgb(216,218,235)", "rgb(153,142,195)", "rgb(84,39,136)"],
7: ["rgb(179,88,6)", "rgb(241,163,64)", "rgb(254,224,182)", "rgb(247,247,247)", "rgb(216,218,235)", "rgb(153,142,195)", "rgb(84,39,136)"],
8: ["rgb(179,88,6)", "rgb(224,130,20)", "rgb(253,184,99)", "rgb(254,224,182)", "rgb(216,218,235)", "rgb(178,171,210)", "rgb(128,115,172)", "rgb(84,39,136)"],
9: ["rgb(179,88,6)", "rgb(224,130,20)", "rgb(253,184,99)", "rgb(254,224,182)", "rgb(247,247,247)", "rgb(216,218,235)", "rgb(178,171,210)", "rgb(128,115,172)", "rgb(84,39,136)"],
10: ["rgb(127,59,8)", "rgb(179,88,6)", "rgb(224,130,20)", "rgb(253,184,99)", "rgb(254,224,182)", "rgb(216,218,235)", "rgb(178,171,210)", "rgb(128,115,172)", "rgb(84,39,136)", "rgb(45,0,75)"],
11: ["rgb(127,59,8)", "rgb(179,88,6)", "rgb(224,130,20)", "rgb(253,184,99)", "rgb(254,224,182)", "rgb(247,247,247)", "rgb(216,218,235)", "rgb(178,171,210)", "rgb(128,115,172)", "rgb(84,39,136)", "rgb(45,0,75)"]
}
BrBG = {
3: ["rgb(216,179,101)", "rgb(245,245,245)", "rgb(90,180,172)"],
4: ["rgb(166,97,26)", "rgb(223,194,125)", "rgb(128,205,193)", "rgb(1,133,113)"],
5: ["rgb(166,97,26)", "rgb(223,194,125)", "rgb(245,245,245)", "rgb(128,205,193)", "rgb(1,133,113)"],
6: ["rgb(140,81,10)", "rgb(216,179,101)", "rgb(246,232,195)", "rgb(199,234,229)", "rgb(90,180,172)", "rgb(1,102,94)"],
7: ["rgb(140,81,10)", "rgb(216,179,101)", "rgb(246,232,195)", "rgb(245,245,245)", "rgb(199,234,229)", "rgb(90,180,172)", "rgb(1,102,94)"],
8: ["rgb(140,81,10)", "rgb(191,129,45)", "rgb(223,194,125)", "rgb(246,232,195)", "rgb(199,234,229)", "rgb(128,205,193)", "rgb(53,151,143)", "rgb(1,102,94)"],
9: ["rgb(140,81,10)", "rgb(191,129,45)", "rgb(223,194,125)", "rgb(246,232,195)", "rgb(245,245,245)", "rgb(199,234,229)", "rgb(128,205,193)", "rgb(53,151,143)", "rgb(1,102,94)"],
10: ["rgb(84,48,5)", "rgb(140,81,10)", "rgb(191,129,45)", "rgb(223,194,125)", "rgb(246,232,195)", "rgb(199,234,229)", "rgb(128,205,193)", "rgb(53,151,143)", "rgb(1,102,94)", "rgb(0,60,48)"],
11: ["rgb(84,48,5)", "rgb(140,81,10)", "rgb(191,129,45)", "rgb(223,194,125)", "rgb(246,232,195)", "rgb(245,245,245)", "rgb(199,234,229)", "rgb(128,205,193)", "rgb(53,151,143)", "rgb(1,102,94)", "rgb(0,60,48)"]
}
PRGn = {
3: ["rgb(175,141,195)", "rgb(247,247,247)", "rgb(127,191,123)"],
4: ["rgb(123,50,148)", "rgb(194,165,207)", "rgb(166,219,160)", "rgb(0,136,55)"],
5: ["rgb(123,50,148)", "rgb(194,165,207)", "rgb(247,247,247)", "rgb(166,219,160)", "rgb(0,136,55)"],
6: ["rgb(118,42,131)", "rgb(175,141,195)", "rgb(231,212,232)", "rgb(217,240,211)", "rgb(127,191,123)", "rgb(27,120,55)"],
7: ["rgb(118,42,131)", "rgb(175,141,195)", "rgb(231,212,232)", "rgb(247,247,247)", "rgb(217,240,211)", "rgb(127,191,123)", "rgb(27,120,55)"],
8: ["rgb(118,42,131)", "rgb(153,112,171)", "rgb(194,165,207)", "rgb(231,212,232)", "rgb(217,240,211)", "rgb(166,219,160)", "rgb(90,174,97)", "rgb(27,120,55)"],
9: ["rgb(118,42,131)", "rgb(153,112,171)", "rgb(194,165,207)", "rgb(231,212,232)", "rgb(247,247,247)", "rgb(217,240,211)", "rgb(166,219,160)", "rgb(90,174,97)", "rgb(27,120,55)"],
10: ["rgb(64,0,75)", "rgb(118,42,131)", "rgb(153,112,171)", "rgb(194,165,207)", "rgb(231,212,232)", "rgb(217,240,211)", "rgb(166,219,160)", "rgb(90,174,97)", "rgb(27,120,55)", "rgb(0,68,27)"],
11: ["rgb(64,0,75)", "rgb(118,42,131)", "rgb(153,112,171)", "rgb(194,165,207)", "rgb(231,212,232)", "rgb(247,247,247)", "rgb(217,240,211)", "rgb(166,219,160)", "rgb(90,174,97)", "rgb(27,120,55)", "rgb(0,68,27)"]
}
PiYG = {
3: ["rgb(233,163,201)", "rgb(247,247,247)", "rgb(161,215,106)"],
4: ["rgb(208,28,139)", "rgb(241,182,218)", "rgb(184,225,134)", "rgb(77,172,38)"],
5: ["rgb(208,28,139)", "rgb(241,182,218)", "rgb(247,247,247)", "rgb(184,225,134)", "rgb(77,172,38)"],
6: ["rgb(197,27,125)", "rgb(233,163,201)", "rgb(253,224,239)", "rgb(230,245,208)", "rgb(161,215,106)", "rgb(77,146,33)"],
7: ["rgb(197,27,125)", "rgb(233,163,201)", "rgb(253,224,239)", "rgb(247,247,247)", "rgb(230,245,208)", "rgb(161,215,106)", "rgb(77,146,33)"],
8: ["rgb(197,27,125)", "rgb(222,119,174)", "rgb(241,182,218)", "rgb(253,224,239)", "rgb(230,245,208)", "rgb(184,225,134)", "rgb(127,188,65)", "rgb(77,146,33)"],
9: ["rgb(197,27,125)", "rgb(222,119,174)", "rgb(241,182,218)", "rgb(253,224,239)", "rgb(247,247,247)", "rgb(230,245,208)", "rgb(184,225,134)", "rgb(127,188,65)", "rgb(77,146,33)"],
10: ["rgb(142,1,82)", "rgb(197,27,125)", "rgb(222,119,174)", "rgb(241,182,218)", "rgb(253,224,239)", "rgb(230,245,208)", "rgb(184,225,134)", "rgb(127,188,65)", "rgb(77,146,33)", "rgb(39,100,25)"],
11: ["rgb(142,1,82)", "rgb(197,27,125)", "rgb(222,119,174)", "rgb(241,182,218)", "rgb(253,224,239)", "rgb(247,247,247)", "rgb(230,245,208)", "rgb(184,225,134)", "rgb(127,188,65)", "rgb(77,146,33)", "rgb(39,100,25)"]
}
RdBu = {
3: ["rgb(239,138,98)", "rgb(247,247,247)", "rgb(103,169,207)"],
4: ["rgb(202,0,32)", "rgb(244,165,130)", "rgb(146,197,222)", "rgb(5,113,176)"],
5: ["rgb(202,0,32)", "rgb(244,165,130)", "rgb(247,247,247)", "rgb(146,197,222)", "rgb(5,113,176)"],
6: ["rgb(178,24,43)", "rgb(239,138,98)", "rgb(253,219,199)", "rgb(209,229,240)", "rgb(103,169,207)", "rgb(33,102,172)"],
7: ["rgb(178,24,43)", "rgb(239,138,98)", "rgb(253,219,199)", "rgb(247,247,247)", "rgb(209,229,240)", "rgb(103,169,207)", "rgb(33,102,172)"],
8: ["rgb(178,24,43)", "rgb(214,96,77)", "rgb(244,165,130)", "rgb(253,219,199)", "rgb(209,229,240)", "rgb(146,197,222)", "rgb(67,147,195)", "rgb(33,102,172)"],
9: ["rgb(178,24,43)", "rgb(214,96,77)", "rgb(244,165,130)", "rgb(253,219,199)", "rgb(247,247,247)", "rgb(209,229,240)", "rgb(146,197,222)", "rgb(67,147,195)", "rgb(33,102,172)"],
10: ["rgb(103,0,31)", "rgb(178,24,43)", "rgb(214,96,77)", "rgb(244,165,130)", "rgb(253,219,199)", "rgb(209,229,240)", "rgb(146,197,222)", "rgb(67,147,195)", "rgb(33,102,172)", "rgb(5,48,97)"],
11: ["rgb(103,0,31)", "rgb(178,24,43)", "rgb(214,96,77)", "rgb(244,165,130)", "rgb(253,219,199)", "rgb(247,247,247)", "rgb(209,229,240)", "rgb(146,197,222)", "rgb(67,147,195)", "rgb(33,102,172)", "rgb(5,48,97)"]
}
RdGy = {
3: ["rgb(239,138,98)", "rgb(255,255,255)", "rgb(153,153,153)"],
4: ["rgb(202,0,32)", "rgb(244,165,130)", "rgb(186,186,186)", "rgb(64,64,64)"],
5: ["rgb(202,0,32)", "rgb(244,165,130)", "rgb(255,255,255)", "rgb(186,186,186)", "rgb(64,64,64)"],
6: ["rgb(178,24,43)", "rgb(239,138,98)", "rgb(253,219,199)", "rgb(224,224,224)", "rgb(153,153,153)", "rgb(77,77,77)"],
7: ["rgb(178,24,43)", "rgb(239,138,98)", "rgb(253,219,199)", "rgb(255,255,255)", "rgb(224,224,224)", "rgb(153,153,153)", "rgb(77,77,77)"],
8: ["rgb(178,24,43)", "rgb(214,96,77)", "rgb(244,165,130)", "rgb(253,219,199)", "rgb(224,224,224)", "rgb(186,186,186)", "rgb(135,135,135)", "rgb(77,77,77)"],
9: ["rgb(178,24,43)", "rgb(214,96,77)", "rgb(244,165,130)", "rgb(253,219,199)", "rgb(255,255,255)", "rgb(224,224,224)", "rgb(186,186,186)", "rgb(135,135,135)", "rgb(77,77,77)"],
10: ["rgb(103,0,31)", "rgb(178,24,43)", "rgb(214,96,77)", "rgb(244,165,130)", "rgb(253,219,199)", "rgb(224,224,224)", "rgb(186,186,186)", "rgb(135,135,135)", "rgb(77,77,77)", "rgb(26,26,26)"],
11: ["rgb(103,0,31)", "rgb(178,24,43)", "rgb(214,96,77)", "rgb(244,165,130)", "rgb(253,219,199)", "rgb(255,255,255)", "rgb(224,224,224)", "rgb(186,186,186)", "rgb(135,135,135)", "rgb(77,77,77)", "rgb(26,26,26)"]
}
RdYlBu = {
3: ["rgb(252,141,89)", "rgb(255,255,191)", "rgb(145,191,219)"],
4: ["rgb(215,25,28)", "rgb(253,174,97)", "rgb(171,217,233)", "rgb(44,123,182)"],
5: ["rgb(215,25,28)", "rgb(253,174,97)", "rgb(255,255,191)", "rgb(171,217,233)", "rgb(44,123,182)"],
6: ["rgb(215,48,39)", "rgb(252,141,89)", "rgb(254,224,144)", "rgb(224,243,248)", "rgb(145,191,219)", "rgb(69,117,180)"],
7: ["rgb(215,48,39)", "rgb(252,141,89)", "rgb(254,224,144)", "rgb(255,255,191)", "rgb(224,243,248)", "rgb(145,191,219)", "rgb(69,117,180)"],
8: ["rgb(215,48,39)", "rgb(244,109,67)", "rgb(253,174,97)", "rgb(254,224,144)", "rgb(224,243,248)", "rgb(171,217,233)", "rgb(116,173,209)", "rgb(69,117,180)"],
9: ["rgb(215,48,39)", "rgb(244,109,67)", "rgb(253,174,97)", "rgb(254,224,144)", "rgb(255,255,191)", "rgb(224,243,248)", "rgb(171,217,233)", "rgb(116,173,209)", "rgb(69,117,180)"],
10: ["rgb(165,0,38)", "rgb(215,48,39)", "rgb(244,109,67)", "rgb(253,174,97)", "rgb(254,224,144)", "rgb(224,243,248)", "rgb(171,217,233)", "rgb(116,173,209)", "rgb(69,117,180)", "rgb(49,54,149)"],
11: ["rgb(165,0,38)", "rgb(215,48,39)", "rgb(244,109,67)", "rgb(253,174,97)", "rgb(254,224,144)", "rgb(255,255,191)", "rgb(224,243,248)", "rgb(171,217,233)", "rgb(116,173,209)", "rgb(69,117,180)", "rgb(49,54,149)"]
}
Spectral = {
3: ["rgb(252,141,89)", "rgb(255,255,191)", "rgb(153,213,148)"],
4: ["rgb(215,25,28)", "rgb(253,174,97)", "rgb(171,221,164)", "rgb(43,131,186)"],
5: ["rgb(215,25,28)", "rgb(253,174,97)", "rgb(255,255,191)", "rgb(171,221,164)", "rgb(43,131,186)"],
6: ["rgb(213,62,79)", "rgb(252,141,89)", "rgb(254,224,139)", "rgb(230,245,152)", "rgb(153,213,148)", "rgb(50,136,189)"],
7: ["rgb(213,62,79)", "rgb(252,141,89)", "rgb(254,224,139)", "rgb(255,255,191)", "rgb(230,245,152)", "rgb(153,213,148)", "rgb(50,136,189)"],
8: ["rgb(213,62,79)", "rgb(244,109,67)", "rgb(253,174,97)", "rgb(254,224,139)", "rgb(230,245,152)", "rgb(171,221,164)", "rgb(102,194,165)", "rgb(50,136,189)"],
9: ["rgb(213,62,79)", "rgb(244,109,67)", "rgb(253,174,97)", "rgb(254,224,139)", "rgb(255,255,191)", "rgb(230,245,152)", "rgb(171,221,164)", "rgb(102,194,165)", "rgb(50,136,189)"],
10: ["rgb(158,1,66)", "rgb(213,62,79)", "rgb(244,109,67)", "rgb(253,174,97)", "rgb(254,224,139)", "rgb(230,245,152)", "rgb(171,221,164)", "rgb(102,194,165)", "rgb(50,136,189)", "rgb(94,79,162)"],
11: ["rgb(158,1,66)", "rgb(213,62,79)", "rgb(244,109,67)", "rgb(253,174,97)", "rgb(254,224,139)", "rgb(255,255,191)", "rgb(230,245,152)", "rgb(171,221,164)", "rgb(102,194,165)", "rgb(50,136,189)", "rgb(94,79,162)"]
}
RdYlGn = {
3: ["rgb(252,141,89)", "rgb(255,255,191)", "rgb(145,207,96)"],
4: ["rgb(215,25,28)", "rgb(253,174,97)", "rgb(166,217,106)", "rgb(26,150,65)"],
5: ["rgb(215,25,28)", "rgb(253,174,97)", "rgb(255,255,191)", "rgb(166,217,106)", "rgb(26,150,65)"],
6: ["rgb(215,48,39)", "rgb(252,141,89)", "rgb(254,224,139)", "rgb(217,239,139)", "rgb(145,207,96)", "rgb(26,152,80)"],
7: ["rgb(215,48,39)", "rgb(252,141,89)", "rgb(254,224,139)", "rgb(255,255,191)", "rgb(217,239,139)", "rgb(145,207,96)", "rgb(26,152,80)"],
8: ["rgb(215,48,39)", "rgb(244,109,67)", "rgb(253,174,97)", "rgb(254,224,139)", "rgb(217,239,139)", "rgb(166,217,106)", "rgb(102,189,99)", "rgb(26,152,80)"],
9: ["rgb(215,48,39)", "rgb(244,109,67)", "rgb(253,174,97)", "rgb(254,224,139)", "rgb(255,255,191)", "rgb(217,239,139)", "rgb(166,217,106)", "rgb(102,189,99)", "rgb(26,152,80)"],
10: ["rgb(165,0,38)", "rgb(215,48,39)", "rgb(244,109,67)", "rgb(253,174,97)", "rgb(254,224,139)", "rgb(217,239,139)", "rgb(166,217,106)", "rgb(102,189,99)", "rgb(26,152,80)", "rgb(0,104,55)"],
11: ["rgb(165,0,38)", "rgb(215,48,39)", "rgb(244,109,67)", "rgb(253,174,97)", "rgb(254,224,139)", "rgb(255,255,191)", "rgb(217,239,139)", "rgb(166,217,106)", "rgb(102,189,99)", "rgb(26,152,80)", "rgb(0,104,55)"]
}
diverging = { 'PuOr': PuOr, 'BrBG': BrBG, 'PRGn': PRGn, 'PiYG': PiYG, 'RdBu': RdBu, 'RdGy': RdGy, 'RdYlBu': RdYlBu, 'Spectral': Spectral, 'RdYlGn': RdYlGn }
Accent = {
3: ['rgb(127,201,127)', 'rgb(190,174,212)', 'rgb(253,192,134)'],
4: ['rgb(127,201,127)', 'rgb(190,174,212)', 'rgb(253,192,134)', 'rgb(255,255,153)'],
5: ['rgb(127,201,127)', 'rgb(190,174,212)', 'rgb(253,192,134)', 'rgb(255,255,153)', 'rgb(56,108,176)'],
6: ['rgb(127,201,127)', 'rgb(190,174,212)', 'rgb(253,192,134)', 'rgb(255,255,153)', 'rgb(56,108,176)', 'rgb(240,2,127)'],
7: ['rgb(127,201,127)', 'rgb(190,174,212)', 'rgb(253,192,134)', 'rgb(255,255,153)', 'rgb(56,108,176)', 'rgb(240,2,127)', 'rgb(191,91,23)'],
8: ['rgb(127,201,127)', 'rgb(190,174,212)', 'rgb(253,192,134)', 'rgb(255,255,153)', 'rgb(56,108,176)', 'rgb(240,2,127)', 'rgb(191,91,23)', 'rgb(102,102,102)']
}
Dark2 = {
3: ['rgb(27,158,119)', 'rgb(217,95,2)', 'rgb(117,112,179)'],
4: ['rgb(27,158,119)', 'rgb(217,95,2)', 'rgb(117,112,179)', 'rgb(231,41,138)'],
5: ['rgb(27,158,119)', 'rgb(217,95,2)', 'rgb(117,112,179)', 'rgb(231,41,138)', 'rgb(102,166,30)'],
6: ['rgb(27,158,119)', 'rgb(217,95,2)', 'rgb(117,112,179)', 'rgb(231,41,138)', 'rgb(102,166,30)', 'rgb(230,171,2)'],
7: ['rgb(27,158,119)', 'rgb(217,95,2)', 'rgb(117,112,179)', 'rgb(231,41,138)', 'rgb(102,166,30)', 'rgb(230,171,2)', 'rgb(166,118,29)'],
8: ['rgb(27,158,119)', 'rgb(217,95,2)', 'rgb(117,112,179)', 'rgb(231,41,138)', 'rgb(102,166,30)', 'rgb(230,171,2)', 'rgb(166,118,29)', 'rgb(102,102,102)']
}
Paired = {
3: ['rgb(166,206,227)', 'rgb(31,120,180)', 'rgb(178,223,138)'],
4: ['rgb(166,206,227)', 'rgb(31,120,180)', 'rgb(178,223,138)', 'rgb(51,160,44)'],
5: ['rgb(166,206,227)', 'rgb(31,120,180)', 'rgb(178,223,138)', 'rgb(51,160,44)', 'rgb(251,154,153)'],
6: ['rgb(166,206,227)', 'rgb(31,120,180)', 'rgb(178,223,138)', 'rgb(51,160,44)', 'rgb(251,154,153)', 'rgb(227,26,28)'],
7: ['rgb(166,206,227)', 'rgb(31,120,180)', 'rgb(178,223,138)', 'rgb(51,160,44)', 'rgb(251,154,153)', 'rgb(227,26,28)', 'rgb(253,191,111)'],
8: ['rgb(166,206,227)', 'rgb(31,120,180)', 'rgb(178,223,138)', 'rgb(51,160,44)', 'rgb(251,154,153)', 'rgb(227,26,28)', 'rgb(253,191,111)', 'rgb(255,127,0)'],
9: ['rgb(166,206,227)', 'rgb(31,120,180)', 'rgb(178,223,138)', 'rgb(51,160,44)', 'rgb(251,154,153)', 'rgb(227,26,28)', 'rgb(253,191,111)', 'rgb(255,127,0)', 'rgb(202,178,214)'],
10: ['rgb(166,206,227)', 'rgb(31,120,180)', 'rgb(178,223,138)', 'rgb(51,160,44)', 'rgb(251,154,153)', 'rgb(227,26,28)', 'rgb(253,191,111)', 'rgb(255,127,0)', 'rgb(202,178,214)', 'rgb(106,61,154)'],
11: ['rgb(166,206,227)', 'rgb(31,120,180)', 'rgb(178,223,138)', 'rgb(51,160,44)', 'rgb(251,154,153)', 'rgb(227,26,28)', 'rgb(253,191,111)', 'rgb(255,127,0)', 'rgb(202,178,214)', 'rgb(106,61,154)', 'rgb(255,255,153)'],
12: ['rgb(166,206,227)', 'rgb(31,120,180)', 'rgb(178,223,138)', 'rgb(51,160,44)', 'rgb(251,154,153)', 'rgb(227,26,28)', 'rgb(253,191,111)', 'rgb(255,127,0)', 'rgb(202,178,214)', 'rgb(106,61,154)', 'rgb(255,255,153)', 'rgb(177,89,40)']
}
Pastel1 = {
3: ['rgb(251,180,174)', 'rgb(179,205,227)', 'rgb(204,235,197)'],
4: ['rgb(251,180,174)', 'rgb(179,205,227)', 'rgb(204,235,197)', 'rgb(222,203,228)'],
5: ['rgb(251,180,174)', 'rgb(179,205,227)', 'rgb(204,235,197)', 'rgb(222,203,228)', 'rgb(254,217,166)'],
6: ['rgb(251,180,174)', 'rgb(179,205,227)', 'rgb(204,235,197)', 'rgb(222,203,228)', 'rgb(254,217,166)', 'rgb(255,255,204)'],
7: ['rgb(251,180,174)', 'rgb(179,205,227)', 'rgb(204,235,197)', 'rgb(222,203,228)', 'rgb(254,217,166)', 'rgb(255,255,204)', 'rgb(229,216,189)'],
8: ['rgb(251,180,174)', 'rgb(179,205,227)', 'rgb(204,235,197)', 'rgb(222,203,228)', 'rgb(254,217,166)', 'rgb(255,255,204)', 'rgb(229,216,189)', 'rgb(253,218,236)'],
9: ['rgb(251,180,174)', 'rgb(179,205,227)', 'rgb(204,235,197)', 'rgb(222,203,228)', 'rgb(254,217,166)', 'rgb(255,255,204)', 'rgb(229,216,189)', 'rgb(253,218,236)', 'rgb(242,242,242)']
}
Pastel2 = {
3: ['rgb(179,226,205)', 'rgb(253,205,172)', 'rgb(203,213,232)'],
4: ['rgb(179,226,205)', 'rgb(253,205,172)', 'rgb(203,213,232)', 'rgb(244,202,228)'],
5: ['rgb(179,226,205)', 'rgb(253,205,172)', 'rgb(203,213,232)', 'rgb(244,202,228)', 'rgb(230,245,201)'],
6: ['rgb(179,226,205)', 'rgb(253,205,172)', 'rgb(203,213,232)', 'rgb(244,202,228)', 'rgb(230,245,201)', 'rgb(255,242,174)'],
7: ['rgb(179,226,205)', 'rgb(253,205,172)', 'rgb(203,213,232)', 'rgb(244,202,228)', 'rgb(230,245,201)', 'rgb(255,242,174)', 'rgb(241,226,204)'],
8: ['rgb(179,226,205)', 'rgb(253,205,172)', 'rgb(203,213,232)', 'rgb(244,202,228)', 'rgb(230,245,201)', 'rgb(255,242,174)', 'rgb(241,226,204)', 'rgb(204,204,204)']
}
Set1 = {
3: ['rgb(228,26,28)', 'rgb(55,126,184)', 'rgb(77,175,74)'],
4: ['rgb(228,26,28)', 'rgb(55,126,184)', 'rgb(77,175,74)', 'rgb(152,78,163)'],
5: ['rgb(228,26,28)', 'rgb(55,126,184)', 'rgb(77,175,74)', 'rgb(152,78,163)', 'rgb(255,127,0)'],
6: ['rgb(228,26,28)', 'rgb(55,126,184)', 'rgb(77,175,74)', 'rgb(152,78,163)', 'rgb(255,127,0)', 'rgb(255,255,51)'],
7: ['rgb(228,26,28)', 'rgb(55,126,184)', 'rgb(77,175,74)', 'rgb(152,78,163)', 'rgb(255,127,0)', 'rgb(255,255,51)', 'rgb(166,86,40)'],
8: ['rgb(228,26,28)', 'rgb(55,126,184)', 'rgb(77,175,74)', 'rgb(152,78,163)', 'rgb(255,127,0)', 'rgb(255,255,51)', 'rgb(166,86,40)', 'rgb(247,129,191)'],
9: ['rgb(228,26,28)', 'rgb(55,126,184)', 'rgb(77,175,74)', 'rgb(152,78,163)', 'rgb(255,127,0)', 'rgb(255,255,51)', 'rgb(166,86,40)', 'rgb(247,129,191)', 'rgb(153,153,153)']
}
Set2 = {
3: ['rgb(102,194,165)', 'rgb(252,141,98)', 'rgb(141,160,203)'],
4: ['rgb(102,194,165)', 'rgb(252,141,98)', 'rgb(141,160,203)', 'rgb(231,138,195)'],
5: ['rgb(102,194,165)', 'rgb(252,141,98)', 'rgb(141,160,203)', 'rgb(231,138,195)', 'rgb(166,216,84)'],
6: ['rgb(102,194,165)', 'rgb(252,141,98)', 'rgb(141,160,203)', 'rgb(231,138,195)', 'rgb(166,216,84)', 'rgb(255,217,47)'],
7: ['rgb(102,194,165)', 'rgb(252,141,98)', 'rgb(141,160,203)', 'rgb(231,138,195)', 'rgb(166,216,84)', 'rgb(255,217,47)', 'rgb(229,196,148)'],
8: ['rgb(102,194,165)', 'rgb(252,141,98)', 'rgb(141,160,203)', 'rgb(231,138,195)', 'rgb(166,216,84)', 'rgb(255,217,47)', 'rgb(229,196,148)', 'rgb(179,179,179)']
}
Set3 = {
3: ['rgb(141,211,199)', 'rgb(255,255,179)', 'rgb(190,186,218)'],
4: ['rgb(141,211,199)', 'rgb(255,255,179)', 'rgb(190,186,218)', 'rgb(251,128,114)'],
5: ['rgb(141,211,199)', 'rgb(255,255,179)', 'rgb(190,186,218)', 'rgb(251,128,114)', 'rgb(128,177,211)'],
6: ['rgb(141,211,199)', 'rgb(255,255,179)', 'rgb(190,186,218)', 'rgb(251,128,114)', 'rgb(128,177,211)', 'rgb(253,180,98)'],
7: ['rgb(141,211,199)', 'rgb(255,255,179)', 'rgb(190,186,218)', 'rgb(251,128,114)', 'rgb(128,177,211)', 'rgb(253,180,98)', 'rgb(179,222,105)'],
8: ['rgb(141,211,199)', 'rgb(255,255,179)', 'rgb(190,186,218)', 'rgb(251,128,114)', 'rgb(128,177,211)', 'rgb(253,180,98)', 'rgb(179,222,105)', 'rgb(252,205,229)'],
9: ['rgb(141,211,199)', 'rgb(255,255,179)', 'rgb(190,186,218)', 'rgb(251,128,114)', 'rgb(128,177,211)', 'rgb(253,180,98)', 'rgb(179,222,105)', 'rgb(252,205,229)', 'rgb(217,217,217)'],
10: ['rgb(141,211,199)', 'rgb(255,255,179)', 'rgb(190,186,218)', 'rgb(251,128,114)', 'rgb(128,177,211)', 'rgb(253,180,98)', 'rgb(179,222,105)', 'rgb(252,205,229)', 'rgb(217,217,217)', 'rgb(188,128,189)'],
11: ['rgb(141,211,199)', 'rgb(255,255,179)', 'rgb(190,186,218)', 'rgb(251,128,114)', 'rgb(128,177,211)', 'rgb(253,180,98)', 'rgb(179,222,105)', 'rgb(252,205,229)', 'rgb(217,217,217)', 'rgb(188,128,189)', 'rgb(204,235,197)'],
12: ['rgb(141,211,199)', 'rgb(255,255,179)', 'rgb(190,186,218)', 'rgb(251,128,114)', 'rgb(128,177,211)', 'rgb(253,180,98)', 'rgb(179,222,105)', 'rgb(252,205,229)', 'rgb(217,217,217)', 'rgb(188,128,189)', 'rgb(204,235,197)', 'rgb(255,237,111)']
}
qualitative = { 'Pastel2':Pastel2, 'Pastel1':Pastel1, 'Dark2':Dark2, 'Accent':Accent, 'Paired':Paired, 'Set1':Set1, 'Set2':Set2, 'Set3':Set3 }
from bunch import bunchify
for k, v in globals().items():
if isinstance(v, dict): globals()[k] = bunchify(v)
| 100.313019 | 247 | 0.552757 | 7,174 | 36,213 | 2.7891 | 0.043351 | 0.031786 | 0.031936 | 0.014394 | 0.920186 | 0.891249 | 0.882753 | 0.882353 | 0.863961 | 0.853966 | 0 | 0.430554 | 0.090796 | 36,213 | 360 | 248 | 100.591667 | 0.17716 | 0.00116 | 0 | 0 | 0 | 0 | 0.716241 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.002874 | 0 | 0.002874 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
be991d31eeca9c8691eac8c31a7c6f2ddf4b80db | 166 | py | Python | src/users/fixtures.py | denkasyanov/education-backend | c796b6f2f1cc1cd09f83cab2ca0cc45344906ef5 | [
"MIT"
] | 151 | 2020-04-21T09:58:57.000Z | 2021-09-12T09:01:21.000Z | src/users/fixtures.py | denkasyanov/education-backend | c796b6f2f1cc1cd09f83cab2ca0cc45344906ef5 | [
"MIT"
] | 163 | 2020-05-29T20:52:00.000Z | 2021-09-11T12:44:56.000Z | src/users/fixtures.py | boochamoocha/education-backend | c6ffb0c00bc066c8f1e0a8c0ffe4d0215c7c416a | [
"MIT"
] | 39 | 2020-04-21T12:28:16.000Z | 2021-09-12T15:33:47.000Z | import pytest
@pytest.fixture
def user(mixer):
return mixer.blend('users.User')
@pytest.fixture
def another_user(mixer):
return mixer.blend('users.User')
| 13.833333 | 36 | 0.722892 | 23 | 166 | 5.173913 | 0.434783 | 0.218487 | 0.268908 | 0.336134 | 0.571429 | 0.571429 | 0.571429 | 0 | 0 | 0 | 0 | 0 | 0.144578 | 166 | 11 | 37 | 15.090909 | 0.838028 | 0 | 0 | 0.571429 | 0 | 0 | 0.120482 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.285714 | false | 0 | 0.142857 | 0.285714 | 0.714286 | 0 | 1 | 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 | 7 |
fe2b0189c02de69e82b71cf3835116c8d36b3fc6 | 242 | py | Python | hypothesis/auto/training/__init__.py | boyali/hypothesis-sre | f44d25eb281d49663d49d134ee73ad542849714b | [
"BSD-3-Clause"
] | null | null | null | hypothesis/auto/training/__init__.py | boyali/hypothesis-sre | f44d25eb281d49663d49d134ee73ad542849714b | [
"BSD-3-Clause"
] | null | null | null | hypothesis/auto/training/__init__.py | boyali/hypothesis-sre | f44d25eb281d49663d49d134ee73ad542849714b | [
"BSD-3-Clause"
] | null | null | null | from .base import BaseTrainer
from .amortized_ratio_estimation import BaseAmortizedRatioEstimatorTrainer
from .amortized_ratio_estimation import LikelihoodToEvidenceRatioEstimatorTrainer
from .amortized_ratio_estimation import create_trainer
| 48.4 | 81 | 0.917355 | 23 | 242 | 9.347826 | 0.478261 | 0.181395 | 0.251163 | 0.390698 | 0.474419 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.066116 | 242 | 4 | 82 | 60.5 | 0.951327 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 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 | 1 | 0 | 0 | 7 |
fe93d8e7f37398bb80286d51493e54204659612c | 680 | py | Python | Day 3/Swap_first_WITH_last.py | tushartrip1010/100_days_code_py | ee74b429e98cdd8bdf8661cf987da67c9fee5a3e | [
"Apache-2.0"
] | null | null | null | Day 3/Swap_first_WITH_last.py | tushartrip1010/100_days_code_py | ee74b429e98cdd8bdf8661cf987da67c9fee5a3e | [
"Apache-2.0"
] | null | null | null | Day 3/Swap_first_WITH_last.py | tushartrip1010/100_days_code_py | ee74b429e98cdd8bdf8661cf987da67c9fee5a3e | [
"Apache-2.0"
] | null | null | null | # Approach 1:
def Swap_Last_with_First(Arr):
temp = Arr[0]
Arr[0] = Arr[len(Arr)-1]
Arr[len(Arr)-1] = temp
return Arr
a = []
n = int(input("How Many Elements: "))
print("Enter The Elements: ")
for i in range(0, n):
element = int(input())
a.append(element)
print(f"List After Swapping: {Swap_Last_with_First(a)}")
# Approach 2:
def Swap_Last_with_First(Arr):
Arr[0], Arr[-1] = Arr[-1], Arr[0]
return Arr
a = []
n = int(input("How Many Elements: "))
print("Enter The Elements: ")
for i in range(0, n):
element = int(input())
a.append(element)
print(f"List After Swapping: {Swap_Last_with_First(a)}")
| 20 | 57 | 0.594118 | 108 | 680 | 3.62963 | 0.305556 | 0.081633 | 0.122449 | 0.173469 | 0.821429 | 0.821429 | 0.704082 | 0.704082 | 0.704082 | 0.704082 | 0 | 0.023166 | 0.238235 | 680 | 33 | 58 | 20.606061 | 0.733591 | 0.033824 | 0 | 0.818182 | 0 | 0 | 0.273752 | 0.080515 | 0 | 0 | 0 | 0 | 0 | 1 | 0.090909 | false | 0 | 0 | 0 | 0.181818 | 0.181818 | 0 | 0 | 0 | null | 0 | 0 | 1 | 1 | 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 | 8 |
fe9aa9ad892aca551e21bc03fb1a6d183eb19a79 | 4,373 | py | Python | homog/tests/test_quat.py | bcov77/homog | 526c3f07c720f76333bc8be0cd64b436015ff509 | [
"Apache-2.0"
] | null | null | null | homog/tests/test_quat.py | bcov77/homog | 526c3f07c720f76333bc8be0cd64b436015ff509 | [
"Apache-2.0"
] | 104 | 2018-02-02T00:54:14.000Z | 2022-03-28T11:20:24.000Z | homog/tests/test_quat.py | bcov77/homog | 526c3f07c720f76333bc8be0cd64b436015ff509 | [
"Apache-2.0"
] | 5 | 2018-02-01T20:34:36.000Z | 2021-06-22T17:59:30.000Z | import homog
from homog.quat import *
import pytest
try:
import numba
only_if_numba = lambda f: f
except ImportError:
import pytest
only_if_numba = pytest.mark.skip
def test_rand_quat():
rq = rand_quat((1, 2, 3, 5))
assert rq.shape == (1, 2, 3, 5, 4)
assert np.allclose(np.linalg.norm(rq, axis=-1), 1)
def test_quat_mult():
# from pyquaternion
assert list(quat_multiply([1, 0, 0, 0], [1, 0, 0, 0])) == [1, 0, 0, 0]
assert list(quat_multiply([1, 0, 0, 0], [0, 1, 0, 0])) == [0, 1, 0, 0]
assert list(quat_multiply([1, 0, 0, 0], [0, 0, 1, 0])) == [0, 0, 1, 0]
assert list(quat_multiply([1, 0, 0, 0], [0, 0, 0, 1])) == [0, 0, 0, 1]
assert list(quat_multiply([0, 1, 0, 0], [1, 0, 0, 0])) == [0, 1, 0, 0]
assert list(quat_multiply([0, 1, 0, 0], [0, 1, 0, 0])) == [-1, 0, 0, 0]
assert list(quat_multiply([0, 1, 0, 0], [0, 0, 1, 0])) == [0, 0, 0, 1]
assert list(quat_multiply([0, 1, 0, 0], [0, 0, 0, 1])) == [0, 0, -1, 0]
assert list(quat_multiply([0, 0, 1, 0], [1, 0, 0, 0])) == [0, 0, 1, 0]
assert list(quat_multiply([0, 0, 1, 0], [0, 1, 0, 0])) == [0, 0, 0, -1]
assert list(quat_multiply([0, 0, 1, 0], [0, 0, 1, 0])) == [-1, 0, 0, 0]
assert list(quat_multiply([0, 0, 1, 0], [0, 0, 0, 1])) == [0, 1, 0, 0]
assert list(quat_multiply([0, 0, 0, 1], [1, 0, 0, 0])) == [0, 0, 0, 1]
assert list(quat_multiply([0, 0, 0, 1], [0, 1, 0, 0])) == [0, 0, 1, 0]
assert list(quat_multiply([0, 0, 0, 1], [0, 0, 1, 0])) == [0, -1, 0, 0]
assert list(quat_multiply([0, 0, 0, 1], [0, 0, 0, 1])) == [-1, 0, 0, 0]
@only_if_numba
def test_numba_quat_mult():
Q = [[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1],
[0.5, 0.5, 0.5, 0.5]]
for q in Q:
for r in Q:
q = np.array(q, dtype='f8')
r = np.array(r, dtype='f8')
assert np.all(quat_multiply(q, r) == gu_quat_multiply(q, r))
q = homog.quat.rand_quat(1000)
r = homog.quat.rand_quat(1000)
assert np.allclose(quat_multiply(q, r), gu_quat_multiply(q, r))
def test_rot_quat_conversion_rand():
x = homog.rand_xform((5, 6, 7), cart_sd=0)
assert np.all(homog.is_homog_xform(x))
q = rot_to_quat(x)
assert np.all(is_valid_quat_rot(q))
y = quat_to_xform(q)
assert np.all(homog.is_homog_xform(y))
assert x.shape == y.shape
assert np.allclose(x, y)
q = homog.quat.rand_quat()
assert np.all(is_valid_quat_rot(q))
x = quat_to_xform(q)
assert np.all(homog.is_homog_xform(x))
p = rot_to_quat(x)
assert np.all(is_valid_quat_rot(p))
assert p.shape == q.shape
assert np.allclose(p, q)
@only_if_numba
def test_rot_quat_conversion_rand_numba():
x = homog.rand_xform((5, 6, 7), cart_sd=0)
assert np.all(homog.is_homog_xform(x))
q = gu_rot_to_quat(x)
assert np.all(is_valid_quat_rot(q))
y = quat_to_xform(q)
assert np.all(homog.is_homog_xform(y))
assert x.shape == y.shape
assert np.allclose(x, y)
q = homog.quat.rand_quat()
assert np.all(is_valid_quat_rot(q))
x = quat_to_xform(q)
assert np.all(homog.is_homog_xform(x))
p = gu_rot_to_quat(x)
assert np.all(is_valid_quat_rot(p))
assert p.shape == q.shape
assert np.allclose(p, q)
def test_rot_quat_conversion_cases():
R22 = np.sqrt(2) / 2
cases = np.array([[1.00, 0.00, 0.00, 0.00], [0.00, 1.00, 0.00, 0.00], [
0.00, 0.00, 1.00, 0.00
], [0.00, 0.00, 0.00,
1.00], [+0.5, +0.5, +0.5, +0.5], [+0.5, -0.5, -0.5, -0.5], [
+0.5, -0.5, +0.5, +0.5
], [+0.5, +0.5, -0.5, -0.5], [+0.5, +0.5, -0.5, +0.5], [
+0.5, -0.5, +0.5, -0.5
], [+0.5, -0.5, -0.5, +0.5], [+0.5, +0.5, +0.5, -0.5], [
+R22, +R22, 0.00, 0.00
], [+R22, 0.00, +R22, 0.00], [+R22, 0.00, 0.00, +R22], [
0.00, +R22, +R22, 0.00
], [0.00, +R22, 0.00,
+R22], [0.00, 0.00, +R22, +R22], [+R22, -R22, 0.00,
0.00], [+R22, 0.00, -R22, 0.00],
[+R22, 0.00, 0.00, -R22], [0.00, +R22, -R22, 0.00],
[0.00, +R22, 0.00, -R22], [0.00, 0.00, +R22, -R22]])
assert np.all(is_valid_quat_rot(cases))
x = quat_to_xform(cases)
assert homog.is_homog_xform(x)
q = xform_to_quat(x)
assert np.all(is_valid_quat_rot(q))
assert np.allclose(cases, q)
| 37.376068 | 78 | 0.52847 | 850 | 4,373 | 2.578824 | 0.074118 | 0.090328 | 0.073905 | 0.062044 | 0.80885 | 0.765055 | 0.730839 | 0.719434 | 0.702099 | 0.668339 | 0 | 0.14714 | 0.252458 | 4,373 | 116 | 79 | 37.698276 | 0.523402 | 0.003887 | 0 | 0.326733 | 0 | 0 | 0.000919 | 0 | 0 | 0 | 0 | 0 | 0.435644 | 1 | 0.059406 | false | 0 | 0.059406 | 0 | 0.118812 | 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 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
22bed1b4106396053da1f98c490ad701743f0c84 | 8,720 | py | Python | powerbot/api/logs_api.py | rogerarmstrong/python-samples | df73b5dab70090f820fc47096b0ae5490c7779b6 | [
"Apache-2.0"
] | null | null | null | powerbot/api/logs_api.py | rogerarmstrong/python-samples | df73b5dab70090f820fc47096b0ae5490c7779b6 | [
"Apache-2.0"
] | null | null | null | powerbot/api/logs_api.py | rogerarmstrong/python-samples | df73b5dab70090f820fc47096b0ae5490c7779b6 | [
"Apache-2.0"
] | null | null | null | # coding: utf-8
"""
Powerbot Server
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) # noqa: E501
OpenAPI spec version: 1.0.5
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import re # noqa: F401
# python 2 and python 3 compatibility library
import six
from powerbot.api_client import ApiClient
class LogsApi(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
Ref: https://github.com/swagger-api/swagger-codegen
"""
def __init__(self, api_client=None):
if api_client is None:
api_client = ApiClient()
self.api_client = api_client
def add_log_entry(self, value, **kwargs): # noqa: E501
"""Add a new log entry # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.add_log_entry(value, async=True)
>>> result = thread.get()
:param async bool
:param LogEntry value: (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.add_log_entry_with_http_info(value, **kwargs) # noqa: E501
else:
(data) = self.add_log_entry_with_http_info(value, **kwargs) # noqa: E501
return data
def add_log_entry_with_http_info(self, value, **kwargs): # noqa: E501
"""Add a new log entry # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.add_log_entry_with_http_info(value, async=True)
>>> result = thread.get()
:param async bool
:param LogEntry value: (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['value'] # noqa: E501
all_params.append('async')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method add_log_entry" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'value' is set
if ('value' not in params or
params['value'] is None):
raise ValueError("Missing the required parameter `value` when calling `add_log_entry`") # noqa: E501
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'value' in params:
body_params = params['value']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['api_key_security'] # noqa: E501
return self.api_client.call_api(
'/logs', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type=None, # noqa: E501
auth_settings=auth_settings,
async=params.get('async'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_logs(self, **kwargs): # noqa: E501
"""Retrieves log entries # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.get_logs(async=True)
>>> result = thread.get()
:param async bool
:param int offset: Offset when loading a list of items
:param int limit: Limits the number of loaded items
:param str severity_at_least:
:param datetime received_from: from timestamp is 'inclusive' (i.e. >=)
:param datetime received_to: to timestamp is 'exclusive' (i.e. <)
:return: list[LogEntry]
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.get_logs_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.get_logs_with_http_info(**kwargs) # noqa: E501
return data
def get_logs_with_http_info(self, **kwargs): # noqa: E501
"""Retrieves log entries # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.get_logs_with_http_info(async=True)
>>> result = thread.get()
:param async bool
:param int offset: Offset when loading a list of items
:param int limit: Limits the number of loaded items
:param str severity_at_least:
:param datetime received_from: from timestamp is 'inclusive' (i.e. >=)
:param datetime received_to: to timestamp is 'exclusive' (i.e. <)
:return: list[LogEntry]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['offset', 'limit', 'severity_at_least', 'received_from', 'received_to'] # noqa: E501
all_params.append('async')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_logs" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
if 'offset' in params:
query_params.append(('offset', params['offset'])) # noqa: E501
if 'limit' in params:
query_params.append(('limit', params['limit'])) # noqa: E501
if 'severity_at_least' in params:
query_params.append(('severity_at_least', params['severity_at_least'])) # noqa: E501
if 'received_from' in params:
query_params.append(('received_from', params['received_from'])) # noqa: E501
if 'received_to' in params:
query_params.append(('received_to', params['received_to'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['api_key_security'] # noqa: E501
return self.api_client.call_api(
'/logs', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='list[LogEntry]', # noqa: E501
auth_settings=auth_settings,
async=params.get('async'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
| 36.182573 | 119 | 0.602179 | 1,018 | 8,720 | 4.93222 | 0.169941 | 0.049393 | 0.030472 | 0.02868 | 0.812786 | 0.791675 | 0.750448 | 0.713404 | 0.713404 | 0.699462 | 0 | 0.016727 | 0.300688 | 8,720 | 240 | 120 | 36.333333 | 0.806658 | 0.062041 | 0 | 0.655738 | 1 | 0 | 0.170593 | 0.025076 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.032787 | null | null | 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 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
22d0c10a6330f676a69b87645d9fa1b9e828221f | 38,313 | py | Python | hpomb_requirements/ip.py | secanonm/HPomb | dc6e3eef3a3ac3f754279b5f93b8fb944b7eea28 | [
"MIT"
] | 96 | 2020-12-16T00:06:12.000Z | 2022-03-22T15:10:35.000Z | hpomb_requirements/ip.py | secanonm/HPomb | dc6e3eef3a3ac3f754279b5f93b8fb944b7eea28 | [
"MIT"
] | 6 | 2020-12-25T03:48:47.000Z | 2021-02-27T07:52:27.000Z | hpomb_requirements/ip.py | secanonm/HPomb | dc6e3eef3a3ac3f754279b5f93b8fb944b7eea28 | [
"MIT"
] | 44 | 2020-12-25T03:25:34.000Z | 2022-02-02T12:57:03.000Z | from pytransform import pyarmor_runtime
pyarmor_runtime()
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2) | 12,771 | 38,255 | 0.750189 | 9,566 | 38,313 | 3.003136 | 0.027702 | 0.003968 | 0.004073 | 0.003342 | 0.001358 | 0.000835 | 0.000835 | 0 | 0 | 0 | 0 | 0.314502 | 0.000209 | 38,313 | 3 | 38,255 | 12,771 | 0.435478 | 0 | 0 | 0 | 0 | 0.333333 | 0.997442 | 0.997442 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.333333 | 0 | 0.333333 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | null | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 11 |
a3e45932a41a0a1da655b8bbd30a94995adc4059 | 78,596 | py | Python | sdk/webpubsub/azure-messaging-webpubsubservice/azure/messaging/webpubsubservice/_operations/_operations.py | xolve/azure-sdk-for-python | 9f5baa19c392f77f811d936ee43450e4ea524002 | [
"MIT"
] | 2,728 | 2015-01-09T10:19:32.000Z | 2022-03-31T14:50:33.000Z | sdk/webpubsub/azure-messaging-webpubsubservice/azure/messaging/webpubsubservice/_operations/_operations.py | v-xuto/azure-sdk-for-python | 9c6296d22094c5ede410bc83749e8df8694ccacc | [
"MIT"
] | 17,773 | 2015-01-05T15:57:17.000Z | 2022-03-31T23:50:25.000Z | sdk/webpubsub/azure-messaging-webpubsubservice/azure/messaging/webpubsubservice/_operations/_operations.py | v-xuto/azure-sdk-for-python | 9c6296d22094c5ede410bc83749e8df8694ccacc | [
"MIT"
] | 1,916 | 2015-01-19T05:05:41.000Z | 2022-03-31T19:36:44.000Z | # coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
import functools
from typing import TYPE_CHECKING
import warnings
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import HttpResponse
from azure.core.rest import HttpRequest
from azure.core.tracing.decorator import distributed_trace
from msrest import Serializer
from .._vendor import _format_url_section
if TYPE_CHECKING:
# pylint: disable=unused-import,ungrouped-imports
from typing import Any, Callable, Dict, Generic, IO, List, Optional, TypeVar, Union
T = TypeVar('T')
JSONType = Any
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
_SERIALIZER = Serializer()
_SERIALIZER.client_side_validation = False
# fmt: off
def build_get_client_access_token_request(
hub, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
user_id = kwargs.pop('user_id', None) # type: Optional[str]
roles = kwargs.pop('roles', None) # type: Optional[List[str]]
minutes_to_expire = kwargs.pop('minutes_to_expire', 60) # type: Optional[int]
accept = "application/json, text/json"
# Construct URL
url = kwargs.pop("template_url", '/api/hubs/{hub}/:generateToken')
path_format_arguments = {
"hub": _SERIALIZER.url("hub", hub, 'str', pattern=r'^[A-Za-z][A-Za-z0-9_`,.[\]]{0,127}$'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if user_id is not None:
query_parameters['userId'] = _SERIALIZER.query("user_id", user_id, 'str')
if roles is not None:
query_parameters['role'] = [_SERIALIZER.query("roles", q, 'str') if q is not None else '' for q in roles]
if minutes_to_expire is not None:
query_parameters['minutesToExpire'] = _SERIALIZER.query("minutes_to_expire", minutes_to_expire, 'int')
query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_close_all_connections_request(
hub, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
excluded = kwargs.pop('excluded', None) # type: Optional[List[str]]
reason = kwargs.pop('reason', None) # type: Optional[str]
accept = "application/json, text/json"
# Construct URL
url = kwargs.pop("template_url", '/api/hubs/{hub}/:closeConnections')
path_format_arguments = {
"hub": _SERIALIZER.url("hub", hub, 'str', pattern=r'^[A-Za-z][A-Za-z0-9_`,.[\]]{0,127}$'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if excluded is not None:
query_parameters['excluded'] = [_SERIALIZER.query("excluded", q, 'str') if q is not None else '' for q in excluded]
if reason is not None:
query_parameters['reason'] = _SERIALIZER.query("reason", reason, 'str')
query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_send_to_all_request(
hub, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
content_type = kwargs.pop('content_type', None) # type: Optional[str]
excluded = kwargs.pop('excluded', None) # type: Optional[List[str]]
accept = "application/json, text/json"
# Construct URL
url = kwargs.pop("template_url", '/api/hubs/{hub}/:send')
path_format_arguments = {
"hub": _SERIALIZER.url("hub", hub, 'str', pattern=r'^[A-Za-z][A-Za-z0-9_`,.[\]]{0,127}$'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if excluded is not None:
query_parameters['excluded'] = [_SERIALIZER.query("excluded", q, 'str') if q is not None else '' for q in excluded]
query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_connection_exists_request(
hub, # type: str
connection_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
accept = "application/json, text/json"
# Construct URL
url = kwargs.pop("template_url", '/api/hubs/{hub}/connections/{connectionId}')
path_format_arguments = {
"hub": _SERIALIZER.url("hub", hub, 'str', pattern=r'^[A-Za-z][A-Za-z0-9_`,.[\]]{0,127}$'),
"connectionId": _SERIALIZER.url("connection_id", connection_id, 'str', min_length=1),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="HEAD",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_close_connection_request(
hub, # type: str
connection_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
reason = kwargs.pop('reason', None) # type: Optional[str]
accept = "application/json, text/json"
# Construct URL
url = kwargs.pop("template_url", '/api/hubs/{hub}/connections/{connectionId}')
path_format_arguments = {
"hub": _SERIALIZER.url("hub", hub, 'str', pattern=r'^[A-Za-z][A-Za-z0-9_`,.[\]]{0,127}$'),
"connectionId": _SERIALIZER.url("connection_id", connection_id, 'str', min_length=1),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if reason is not None:
query_parameters['reason'] = _SERIALIZER.query("reason", reason, 'str')
query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="DELETE",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_send_to_connection_request(
hub, # type: str
connection_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
content_type = kwargs.pop('content_type', None) # type: Optional[str]
accept = "application/json, text/json"
# Construct URL
url = kwargs.pop("template_url", '/api/hubs/{hub}/connections/{connectionId}/:send')
path_format_arguments = {
"hub": _SERIALIZER.url("hub", hub, 'str', pattern=r'^[A-Za-z][A-Za-z0-9_`,.[\]]{0,127}$'),
"connectionId": _SERIALIZER.url("connection_id", connection_id, 'str', min_length=1),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_group_exists_request(
hub, # type: str
group, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
accept = "application/json, text/json"
# Construct URL
url = kwargs.pop("template_url", '/api/hubs/{hub}/groups/{group}')
path_format_arguments = {
"hub": _SERIALIZER.url("hub", hub, 'str', pattern=r'^[A-Za-z][A-Za-z0-9_`,.[\]]{0,127}$'),
"group": _SERIALIZER.url("group", group, 'str', max_length=1024, min_length=1),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="HEAD",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_close_group_connections_request(
hub, # type: str
group, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
excluded = kwargs.pop('excluded', None) # type: Optional[List[str]]
reason = kwargs.pop('reason', None) # type: Optional[str]
accept = "application/json, text/json"
# Construct URL
url = kwargs.pop("template_url", '/api/hubs/{hub}/groups/{group}/:closeConnections')
path_format_arguments = {
"hub": _SERIALIZER.url("hub", hub, 'str', pattern=r'^[A-Za-z][A-Za-z0-9_`,.[\]]{0,127}$'),
"group": _SERIALIZER.url("group", group, 'str', max_length=1024, min_length=1),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if excluded is not None:
query_parameters['excluded'] = [_SERIALIZER.query("excluded", q, 'str') if q is not None else '' for q in excluded]
if reason is not None:
query_parameters['reason'] = _SERIALIZER.query("reason", reason, 'str')
query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_send_to_group_request(
hub, # type: str
group, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
content_type = kwargs.pop('content_type', None) # type: Optional[str]
excluded = kwargs.pop('excluded', None) # type: Optional[List[str]]
accept = "application/json, text/json"
# Construct URL
url = kwargs.pop("template_url", '/api/hubs/{hub}/groups/{group}/:send')
path_format_arguments = {
"hub": _SERIALIZER.url("hub", hub, 'str', pattern=r'^[A-Za-z][A-Za-z0-9_`,.[\]]{0,127}$'),
"group": _SERIALIZER.url("group", group, 'str', max_length=1024, min_length=1),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if excluded is not None:
query_parameters['excluded'] = [_SERIALIZER.query("excluded", q, 'str') if q is not None else '' for q in excluded]
query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_add_connection_to_group_request(
hub, # type: str
group, # type: str
connection_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
accept = "application/json, text/json"
# Construct URL
url = kwargs.pop("template_url", '/api/hubs/{hub}/groups/{group}/connections/{connectionId}')
path_format_arguments = {
"hub": _SERIALIZER.url("hub", hub, 'str', pattern=r'^[A-Za-z][A-Za-z0-9_`,.[\]]{0,127}$'),
"group": _SERIALIZER.url("group", group, 'str', max_length=1024, min_length=1),
"connectionId": _SERIALIZER.url("connection_id", connection_id, 'str', min_length=1),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="PUT",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_remove_connection_from_group_request(
hub, # type: str
group, # type: str
connection_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
accept = "application/json, text/json"
# Construct URL
url = kwargs.pop("template_url", '/api/hubs/{hub}/groups/{group}/connections/{connectionId}')
path_format_arguments = {
"hub": _SERIALIZER.url("hub", hub, 'str', pattern=r'^[A-Za-z][A-Za-z0-9_`,.[\]]{0,127}$'),
"group": _SERIALIZER.url("group", group, 'str', max_length=1024, min_length=1),
"connectionId": _SERIALIZER.url("connection_id", connection_id, 'str', min_length=1),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="DELETE",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_user_exists_request(
hub, # type: str
user_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
accept = "application/json, text/json"
# Construct URL
url = kwargs.pop("template_url", '/api/hubs/{hub}/users/{userId}')
path_format_arguments = {
"hub": _SERIALIZER.url("hub", hub, 'str', pattern=r'^[A-Za-z][A-Za-z0-9_`,.[\]]{0,127}$'),
"userId": _SERIALIZER.url("user_id", user_id, 'str', min_length=1),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="HEAD",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_close_user_connections_request(
hub, # type: str
user_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
excluded = kwargs.pop('excluded', None) # type: Optional[List[str]]
reason = kwargs.pop('reason', None) # type: Optional[str]
accept = "application/json, text/json"
# Construct URL
url = kwargs.pop("template_url", '/api/hubs/{hub}/users/{userId}/:closeConnections')
path_format_arguments = {
"hub": _SERIALIZER.url("hub", hub, 'str', pattern=r'^[A-Za-z][A-Za-z0-9_`,.[\]]{0,127}$'),
"userId": _SERIALIZER.url("user_id", user_id, 'str', min_length=1),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if excluded is not None:
query_parameters['excluded'] = [_SERIALIZER.query("excluded", q, 'str') if q is not None else '' for q in excluded]
if reason is not None:
query_parameters['reason'] = _SERIALIZER.query("reason", reason, 'str')
query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_send_to_user_request(
hub, # type: str
user_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
content_type = kwargs.pop('content_type', None) # type: Optional[str]
accept = "application/json, text/json"
# Construct URL
url = kwargs.pop("template_url", '/api/hubs/{hub}/users/{userId}/:send')
path_format_arguments = {
"hub": _SERIALIZER.url("hub", hub, 'str', pattern=r'^[A-Za-z][A-Za-z0-9_`,.[\]]{0,127}$'),
"userId": _SERIALIZER.url("user_id", user_id, 'str', min_length=1),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_add_user_to_group_request(
hub, # type: str
group, # type: str
user_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
accept = "application/json, text/json"
# Construct URL
url = kwargs.pop("template_url", '/api/hubs/{hub}/users/{userId}/groups/{group}')
path_format_arguments = {
"hub": _SERIALIZER.url("hub", hub, 'str', pattern=r'^[A-Za-z][A-Za-z0-9_`,.[\]]{0,127}$'),
"group": _SERIALIZER.url("group", group, 'str', max_length=1024, min_length=1),
"userId": _SERIALIZER.url("user_id", user_id, 'str', min_length=1),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="PUT",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_remove_user_from_group_request(
hub, # type: str
group, # type: str
user_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
accept = "application/json, text/json"
# Construct URL
url = kwargs.pop("template_url", '/api/hubs/{hub}/users/{userId}/groups/{group}')
path_format_arguments = {
"hub": _SERIALIZER.url("hub", hub, 'str', pattern=r'^[A-Za-z][A-Za-z0-9_`,.[\]]{0,127}$'),
"group": _SERIALIZER.url("group", group, 'str', max_length=1024, min_length=1),
"userId": _SERIALIZER.url("user_id", user_id, 'str', min_length=1),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="DELETE",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_remove_user_from_all_groups_request(
hub, # type: str
user_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
accept = "application/json, text/json"
# Construct URL
url = kwargs.pop("template_url", '/api/hubs/{hub}/users/{userId}/groups')
path_format_arguments = {
"hub": _SERIALIZER.url("hub", hub, 'str', pattern=r'^[A-Za-z][A-Za-z0-9_`,.[\]]{0,127}$'),
"userId": _SERIALIZER.url("user_id", user_id, 'str', min_length=1),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="DELETE",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_grant_permission_request(
hub, # type: str
permission, # type: str
connection_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
target_name = kwargs.pop('target_name', None) # type: Optional[str]
accept = "application/json, text/json"
# Construct URL
url = kwargs.pop("template_url", '/api/hubs/{hub}/permissions/{permission}/connections/{connectionId}')
path_format_arguments = {
"hub": _SERIALIZER.url("hub", hub, 'str', pattern=r'^[A-Za-z][A-Za-z0-9_`,.[\]]{0,127}$'),
"permission": _SERIALIZER.url("permission", permission, 'str'),
"connectionId": _SERIALIZER.url("connection_id", connection_id, 'str', min_length=1),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if target_name is not None:
query_parameters['targetName'] = _SERIALIZER.query("target_name", target_name, 'str')
query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="PUT",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_revoke_permission_request(
hub, # type: str
permission, # type: str
connection_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
target_name = kwargs.pop('target_name', None) # type: Optional[str]
accept = "application/json, text/json"
# Construct URL
url = kwargs.pop("template_url", '/api/hubs/{hub}/permissions/{permission}/connections/{connectionId}')
path_format_arguments = {
"hub": _SERIALIZER.url("hub", hub, 'str', pattern=r'^[A-Za-z][A-Za-z0-9_`,.[\]]{0,127}$'),
"permission": _SERIALIZER.url("permission", permission, 'str'),
"connectionId": _SERIALIZER.url("connection_id", connection_id, 'str', min_length=1),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if target_name is not None:
query_parameters['targetName'] = _SERIALIZER.query("target_name", target_name, 'str')
query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="DELETE",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_has_permission_request(
hub, # type: str
permission, # type: str
connection_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
target_name = kwargs.pop('target_name', None) # type: Optional[str]
accept = "application/json, text/json"
# Construct URL
url = kwargs.pop("template_url", '/api/hubs/{hub}/permissions/{permission}/connections/{connectionId}')
path_format_arguments = {
"hub": _SERIALIZER.url("hub", hub, 'str', pattern=r'^[A-Za-z][A-Za-z0-9_`,.[\]]{0,127}$'),
"permission": _SERIALIZER.url("permission", permission, 'str'),
"connectionId": _SERIALIZER.url("connection_id", connection_id, 'str', min_length=1),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if target_name is not None:
query_parameters['targetName'] = _SERIALIZER.query("target_name", target_name, 'str')
query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="HEAD",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
# fmt: on
class WebPubSubServiceClientOperationsMixin(object):
@distributed_trace
def get_client_access_token(
self,
**kwargs # type: Any
):
# type: (...) -> JSONType
"""Generate token for the client to connect Azure Web PubSub service.
Generate token for the client to connect Azure Web PubSub service.
:keyword user_id: User Id.
:paramtype user_id: str
:keyword roles: Roles that the connection with the generated token will have.
:paramtype roles: list[str]
:keyword minutes_to_expire: The expire time of the generated token.
:paramtype minutes_to_expire: int
:keyword api_version: Api Version. The default value is "2021-10-01". Note that overriding this
default value may result in unsupported behavior.
:paramtype api_version: str
:return: JSON object
:rtype: JSONType
:raises: ~azure.core.exceptions.HttpResponseError
Example:
.. code-block:: python
# response body for status code(s): 200
response.json() == {
"token": "str" # Optional. The token value for the WebSocket client to connect to the service.
}
"""
cls = kwargs.pop('cls', None) # type: ClsType[JSONType]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
user_id = kwargs.pop('user_id', None) # type: Optional[str]
roles = kwargs.pop('roles', None) # type: Optional[List[str]]
minutes_to_expire = kwargs.pop('minutes_to_expire', 60) # type: Optional[int]
request = build_get_client_access_token_request(
hub=self._config.hub,
api_version=api_version,
user_id=user_id,
roles=roles,
minutes_to_expire=minutes_to_expire,
template_url=self.get_client_access_token.metadata['url'],
)
path_format_arguments = {
"Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
request.url = self._client.format_url(request.url, **path_format_arguments)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response)
if response.content:
deserialized = response.json()
else:
deserialized = None
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_client_access_token.metadata = {'url': '/api/hubs/{hub}/:generateToken'} # type: ignore
@distributed_trace
def close_all_connections(
self,
**kwargs # type: Any
):
# type: (...) -> None
"""Close the connections in the hub.
Close the connections in the hub.
:keyword excluded: Exclude these connectionIds when closing the connections in the hub.
:paramtype excluded: list[str]
:keyword reason: The reason closing the client connection.
:paramtype reason: str
:keyword api_version: Api Version. The default value is "2021-10-01". Note that overriding this
default value may result in unsupported behavior.
:paramtype api_version: str
:return: None
:rtype: None
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[None]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
excluded = kwargs.pop('excluded', None) # type: Optional[List[str]]
reason = kwargs.pop('reason', None) # type: Optional[str]
request = build_close_all_connections_request(
hub=self._config.hub,
api_version=api_version,
excluded=excluded,
reason=reason,
template_url=self.close_all_connections.metadata['url'],
)
path_format_arguments = {
"Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
request.url = self._client.format_url(request.url, **path_format_arguments)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [204]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response)
if cls:
return cls(pipeline_response, None, {})
close_all_connections.metadata = {'url': '/api/hubs/{hub}/:closeConnections'} # type: ignore
@distributed_trace
def send_to_all(
self,
message, # type: Union[IO, str, JSONType]
**kwargs # type: Any
):
# type: (...) -> None
"""Broadcast content inside request body to all the connected client connections.
Broadcast content inside request body to all the connected client connections.
:param message: The payload body.
:type message: IO or str or JSONType
:keyword excluded: Excluded connection Ids.
:paramtype excluded: list[str]
:keyword api_version: Api Version. The default value is "2021-10-01". Note that overriding this
default value may result in unsupported behavior.
:paramtype api_version: str
:keyword str content_type: Media type of the body sent to the API. Default value is
"application/json". Allowed values are: "application/json", "application/octet-stream",
"text/plain."
:return: None
:rtype: None
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[None]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
excluded = kwargs.pop('excluded', None) # type: Optional[List[str]]
json = None
content = None
if content_type.split(";")[0] in ['application/json']:
json = message
elif content_type.split(";")[0] in ['application/octet-stream', 'text/plain']:
content = message
else:
raise ValueError(
"The content_type '{}' is not one of the allowed values: "
"['application/json', 'application/octet-stream', 'text/plain']".format(content_type)
)
request = build_send_to_all_request(
hub=self._config.hub,
api_version=api_version,
content_type=content_type,
json=json,
content=content,
excluded=excluded,
template_url=self.send_to_all.metadata['url'],
)
path_format_arguments = {
"Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
request.url = self._client.format_url(request.url, **path_format_arguments)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [202]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response)
if cls:
return cls(pipeline_response, None, {})
send_to_all.metadata = {'url': '/api/hubs/{hub}/:send'} # type: ignore
@distributed_trace
def connection_exists(
self,
connection_id, # type: str
**kwargs # type: Any
):
# type: (...) -> bool
"""Check if the connection with the given connectionId exists.
Check if the connection with the given connectionId exists.
:param connection_id: The connection Id.
:type connection_id: str
:keyword api_version: Api Version. The default value is "2021-10-01". Note that overriding this
default value may result in unsupported behavior.
:paramtype api_version: str
:return: bool
:rtype: bool
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[None]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
request = build_connection_exists_request(
hub=self._config.hub,
connection_id=connection_id,
api_version=api_version,
template_url=self.connection_exists.metadata['url'],
)
path_format_arguments = {
"Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
request.url = self._client.format_url(request.url, **path_format_arguments)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200, 404]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response)
if cls:
return cls(pipeline_response, None, {})
return 200 <= response.status_code <= 299
connection_exists.metadata = {'url': '/api/hubs/{hub}/connections/{connectionId}'} # type: ignore
@distributed_trace
def close_connection(
self,
connection_id, # type: str
**kwargs # type: Any
):
# type: (...) -> None
"""Close the client connection.
Close the client connection.
:param connection_id: Target connection Id.
:type connection_id: str
:keyword reason: The reason closing the client connection.
:paramtype reason: str
:keyword api_version: Api Version. The default value is "2021-10-01". Note that overriding this
default value may result in unsupported behavior.
:paramtype api_version: str
:return: None
:rtype: None
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[None]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
reason = kwargs.pop('reason', None) # type: Optional[str]
request = build_close_connection_request(
hub=self._config.hub,
connection_id=connection_id,
api_version=api_version,
reason=reason,
template_url=self.close_connection.metadata['url'],
)
path_format_arguments = {
"Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
request.url = self._client.format_url(request.url, **path_format_arguments)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [204]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response)
if cls:
return cls(pipeline_response, None, {})
close_connection.metadata = {'url': '/api/hubs/{hub}/connections/{connectionId}'} # type: ignore
@distributed_trace
def send_to_connection(
self,
connection_id, # type: str
message, # type: Union[IO, str, JSONType]
**kwargs # type: Any
):
# type: (...) -> None
"""Send content inside request body to the specific connection.
Send content inside request body to the specific connection.
:param connection_id: The connection Id.
:type connection_id: str
:param message: The payload body.
:type message: IO or str or JSONType
:keyword api_version: Api Version. The default value is "2021-10-01". Note that overriding this
default value may result in unsupported behavior.
:paramtype api_version: str
:keyword str content_type: Media type of the body sent to the API. Default value is
"application/json". Allowed values are: "application/json", "application/octet-stream",
"text/plain."
:return: None
:rtype: None
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[None]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
json = None
content = None
if content_type.split(";")[0] in ['application/json']:
json = message
elif content_type.split(";")[0] in ['application/octet-stream', 'text/plain']:
content = message
else:
raise ValueError(
"The content_type '{}' is not one of the allowed values: "
"['application/json', 'application/octet-stream', 'text/plain']".format(content_type)
)
request = build_send_to_connection_request(
hub=self._config.hub,
connection_id=connection_id,
api_version=api_version,
content_type=content_type,
json=json,
content=content,
template_url=self.send_to_connection.metadata['url'],
)
path_format_arguments = {
"Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
request.url = self._client.format_url(request.url, **path_format_arguments)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [202]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response)
if cls:
return cls(pipeline_response, None, {})
send_to_connection.metadata = {'url': '/api/hubs/{hub}/connections/{connectionId}/:send'} # type: ignore
@distributed_trace
def group_exists(
self,
group, # type: str
**kwargs # type: Any
):
# type: (...) -> bool
"""Check if there are any client connections inside the given group.
Check if there are any client connections inside the given group.
:param group: Target group name, which length should be greater than 0 and less than 1025.
:type group: str
:keyword api_version: Api Version. The default value is "2021-10-01". Note that overriding this
default value may result in unsupported behavior.
:paramtype api_version: str
:return: bool
:rtype: bool
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[None]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
request = build_group_exists_request(
hub=self._config.hub,
group=group,
api_version=api_version,
template_url=self.group_exists.metadata['url'],
)
path_format_arguments = {
"Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
request.url = self._client.format_url(request.url, **path_format_arguments)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200, 404]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response)
if cls:
return cls(pipeline_response, None, {})
return 200 <= response.status_code <= 299
group_exists.metadata = {'url': '/api/hubs/{hub}/groups/{group}'} # type: ignore
@distributed_trace
def close_group_connections(
self,
group, # type: str
**kwargs # type: Any
):
# type: (...) -> None
"""Close connections in the specific group.
Close connections in the specific group.
:param group: Target group name, which length should be greater than 0 and less than 1025.
:type group: str
:keyword excluded: Exclude these connectionIds when closing the connections in the group.
:paramtype excluded: list[str]
:keyword reason: The reason closing the client connection.
:paramtype reason: str
:keyword api_version: Api Version. The default value is "2021-10-01". Note that overriding this
default value may result in unsupported behavior.
:paramtype api_version: str
:return: None
:rtype: None
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[None]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
excluded = kwargs.pop('excluded', None) # type: Optional[List[str]]
reason = kwargs.pop('reason', None) # type: Optional[str]
request = build_close_group_connections_request(
hub=self._config.hub,
group=group,
api_version=api_version,
excluded=excluded,
reason=reason,
template_url=self.close_group_connections.metadata['url'],
)
path_format_arguments = {
"Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
request.url = self._client.format_url(request.url, **path_format_arguments)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [204]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response)
if cls:
return cls(pipeline_response, None, {})
close_group_connections.metadata = {'url': '/api/hubs/{hub}/groups/{group}/:closeConnections'} # type: ignore
@distributed_trace
def send_to_group(
self,
group, # type: str
message, # type: Union[IO, str, JSONType]
**kwargs # type: Any
):
# type: (...) -> None
"""Send content inside request body to a group of connections.
Send content inside request body to a group of connections.
:param group: Target group name, which length should be greater than 0 and less than 1025.
:type group: str
:param message: The payload body.
:type message: IO or str or JSONType
:keyword excluded: Excluded connection Ids.
:paramtype excluded: list[str]
:keyword api_version: Api Version. The default value is "2021-10-01". Note that overriding this
default value may result in unsupported behavior.
:paramtype api_version: str
:keyword str content_type: Media type of the body sent to the API. Default value is
"application/json". Allowed values are: "application/json", "application/octet-stream",
"text/plain."
:return: None
:rtype: None
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[None]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
excluded = kwargs.pop('excluded', None) # type: Optional[List[str]]
json = None
content = None
if content_type.split(";")[0] in ['application/json']:
json = message
elif content_type.split(";")[0] in ['application/octet-stream', 'text/plain']:
content = message
else:
raise ValueError(
"The content_type '{}' is not one of the allowed values: "
"['application/json', 'application/octet-stream', 'text/plain']".format(content_type)
)
request = build_send_to_group_request(
hub=self._config.hub,
group=group,
api_version=api_version,
content_type=content_type,
json=json,
content=content,
excluded=excluded,
template_url=self.send_to_group.metadata['url'],
)
path_format_arguments = {
"Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
request.url = self._client.format_url(request.url, **path_format_arguments)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [202]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response)
if cls:
return cls(pipeline_response, None, {})
send_to_group.metadata = {'url': '/api/hubs/{hub}/groups/{group}/:send'} # type: ignore
@distributed_trace
def add_connection_to_group(
self,
group, # type: str
connection_id, # type: str
**kwargs # type: Any
):
# type: (...) -> None
"""Add a connection to the target group.
Add a connection to the target group.
:param group: Target group name, which length should be greater than 0 and less than 1025.
:type group: str
:param connection_id: Target connection Id.
:type connection_id: str
:keyword api_version: Api Version. The default value is "2021-10-01". Note that overriding this
default value may result in unsupported behavior.
:paramtype api_version: str
:return: None
:rtype: None
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[None]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
request = build_add_connection_to_group_request(
hub=self._config.hub,
group=group,
connection_id=connection_id,
api_version=api_version,
template_url=self.add_connection_to_group.metadata['url'],
)
path_format_arguments = {
"Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
request.url = self._client.format_url(request.url, **path_format_arguments)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response)
if cls:
return cls(pipeline_response, None, {})
add_connection_to_group.metadata = {'url': '/api/hubs/{hub}/groups/{group}/connections/{connectionId}'} # type: ignore
@distributed_trace
def remove_connection_from_group(
self,
group, # type: str
connection_id, # type: str
**kwargs # type: Any
):
# type: (...) -> None
"""Remove a connection from the target group.
Remove a connection from the target group.
:param group: Target group name, which length should be greater than 0 and less than 1025.
:type group: str
:param connection_id: Target connection Id.
:type connection_id: str
:keyword api_version: Api Version. The default value is "2021-10-01". Note that overriding this
default value may result in unsupported behavior.
:paramtype api_version: str
:return: None
:rtype: None
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[None]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
request = build_remove_connection_from_group_request(
hub=self._config.hub,
group=group,
connection_id=connection_id,
api_version=api_version,
template_url=self.remove_connection_from_group.metadata['url'],
)
path_format_arguments = {
"Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
request.url = self._client.format_url(request.url, **path_format_arguments)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [204]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response)
if cls:
return cls(pipeline_response, None, {})
remove_connection_from_group.metadata = {'url': '/api/hubs/{hub}/groups/{group}/connections/{connectionId}'} # type: ignore
@distributed_trace
def user_exists(
self,
user_id, # type: str
**kwargs # type: Any
):
# type: (...) -> bool
"""Check if there are any client connections connected for the given user.
Check if there are any client connections connected for the given user.
:param user_id: Target user Id.
:type user_id: str
:keyword api_version: Api Version. The default value is "2021-10-01". Note that overriding this
default value may result in unsupported behavior.
:paramtype api_version: str
:return: bool
:rtype: bool
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[None]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
request = build_user_exists_request(
hub=self._config.hub,
user_id=user_id,
api_version=api_version,
template_url=self.user_exists.metadata['url'],
)
path_format_arguments = {
"Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
request.url = self._client.format_url(request.url, **path_format_arguments)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200, 404]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response)
if cls:
return cls(pipeline_response, None, {})
return 200 <= response.status_code <= 299
user_exists.metadata = {'url': '/api/hubs/{hub}/users/{userId}'} # type: ignore
@distributed_trace
def close_user_connections(
self,
user_id, # type: str
**kwargs # type: Any
):
# type: (...) -> None
"""Close connections for the specific user.
Close connections for the specific user.
:param user_id: The user Id.
:type user_id: str
:keyword excluded: Exclude these connectionIds when closing the connections for the user.
:paramtype excluded: list[str]
:keyword reason: The reason closing the client connection.
:paramtype reason: str
:keyword api_version: Api Version. The default value is "2021-10-01". Note that overriding this
default value may result in unsupported behavior.
:paramtype api_version: str
:return: None
:rtype: None
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[None]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
excluded = kwargs.pop('excluded', None) # type: Optional[List[str]]
reason = kwargs.pop('reason', None) # type: Optional[str]
request = build_close_user_connections_request(
hub=self._config.hub,
user_id=user_id,
api_version=api_version,
excluded=excluded,
reason=reason,
template_url=self.close_user_connections.metadata['url'],
)
path_format_arguments = {
"Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
request.url = self._client.format_url(request.url, **path_format_arguments)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [204]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response)
if cls:
return cls(pipeline_response, None, {})
close_user_connections.metadata = {'url': '/api/hubs/{hub}/users/{userId}/:closeConnections'} # type: ignore
@distributed_trace
def send_to_user(
self,
user_id, # type: str
message, # type: Union[IO, str, JSONType]
**kwargs # type: Any
):
# type: (...) -> None
"""Send content inside request body to the specific user.
Send content inside request body to the specific user.
:param user_id: The user Id.
:type user_id: str
:param message: The payload body.
:type message: IO or str or JSONType
:keyword api_version: Api Version. The default value is "2021-10-01". Note that overriding this
default value may result in unsupported behavior.
:paramtype api_version: str
:keyword str content_type: Media type of the body sent to the API. Default value is
"application/json". Allowed values are: "application/json", "application/octet-stream",
"text/plain."
:return: None
:rtype: None
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[None]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
json = None
content = None
if content_type.split(";")[0] in ['application/json']:
json = message
elif content_type.split(";")[0] in ['application/octet-stream', 'text/plain']:
content = message
else:
raise ValueError(
"The content_type '{}' is not one of the allowed values: "
"['application/json', 'application/octet-stream', 'text/plain']".format(content_type)
)
request = build_send_to_user_request(
hub=self._config.hub,
user_id=user_id,
api_version=api_version,
content_type=content_type,
json=json,
content=content,
template_url=self.send_to_user.metadata['url'],
)
path_format_arguments = {
"Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
request.url = self._client.format_url(request.url, **path_format_arguments)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [202]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response)
if cls:
return cls(pipeline_response, None, {})
send_to_user.metadata = {'url': '/api/hubs/{hub}/users/{userId}/:send'} # type: ignore
@distributed_trace
def add_user_to_group(
self,
group, # type: str
user_id, # type: str
**kwargs # type: Any
):
# type: (...) -> None
"""Add a user to the target group.
Add a user to the target group.
:param group: Target group name, which length should be greater than 0 and less than 1025.
:type group: str
:param user_id: Target user Id.
:type user_id: str
:keyword api_version: Api Version. The default value is "2021-10-01". Note that overriding this
default value may result in unsupported behavior.
:paramtype api_version: str
:return: None
:rtype: None
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[None]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
request = build_add_user_to_group_request(
hub=self._config.hub,
group=group,
user_id=user_id,
api_version=api_version,
template_url=self.add_user_to_group.metadata['url'],
)
path_format_arguments = {
"Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
request.url = self._client.format_url(request.url, **path_format_arguments)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response)
if cls:
return cls(pipeline_response, None, {})
add_user_to_group.metadata = {'url': '/api/hubs/{hub}/users/{userId}/groups/{group}'} # type: ignore
@distributed_trace
def remove_user_from_group(
self,
group, # type: str
user_id, # type: str
**kwargs # type: Any
):
# type: (...) -> None
"""Remove a user from the target group.
Remove a user from the target group.
:param group: Target group name, which length should be greater than 0 and less than 1025.
:type group: str
:param user_id: Target user Id.
:type user_id: str
:keyword api_version: Api Version. The default value is "2021-10-01". Note that overriding this
default value may result in unsupported behavior.
:paramtype api_version: str
:return: None
:rtype: None
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[None]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
request = build_remove_user_from_group_request(
hub=self._config.hub,
group=group,
user_id=user_id,
api_version=api_version,
template_url=self.remove_user_from_group.metadata['url'],
)
path_format_arguments = {
"Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
request.url = self._client.format_url(request.url, **path_format_arguments)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [204]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response)
if cls:
return cls(pipeline_response, None, {})
remove_user_from_group.metadata = {'url': '/api/hubs/{hub}/users/{userId}/groups/{group}'} # type: ignore
@distributed_trace
def remove_user_from_all_groups(
self,
user_id, # type: str
**kwargs # type: Any
):
# type: (...) -> None
"""Remove a user from all groups.
Remove a user from all groups.
:param user_id: Target user Id.
:type user_id: str
:keyword api_version: Api Version. The default value is "2021-10-01". Note that overriding this
default value may result in unsupported behavior.
:paramtype api_version: str
:return: None
:rtype: None
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[None]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
request = build_remove_user_from_all_groups_request(
hub=self._config.hub,
user_id=user_id,
api_version=api_version,
template_url=self.remove_user_from_all_groups.metadata['url'],
)
path_format_arguments = {
"Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
request.url = self._client.format_url(request.url, **path_format_arguments)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [204]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response)
if cls:
return cls(pipeline_response, None, {})
remove_user_from_all_groups.metadata = {'url': '/api/hubs/{hub}/users/{userId}/groups'} # type: ignore
@distributed_trace
def grant_permission(
self,
permission, # type: str
connection_id, # type: str
**kwargs # type: Any
):
# type: (...) -> None
"""Grant permission to the connection.
Grant permission to the connection.
:param permission: The permission: current supported actions are joinLeaveGroup and
sendToGroup. Possible values are: "sendToGroup" or "joinLeaveGroup".
:type permission: str
:param connection_id: Target connection Id.
:type connection_id: str
:keyword target_name: The meaning of the target depends on the specific permission. For
joinLeaveGroup and sendToGroup, targetName is a required parameter standing for the group name.
:paramtype target_name: str
:keyword api_version: Api Version. The default value is "2021-10-01". Note that overriding this
default value may result in unsupported behavior.
:paramtype api_version: str
:return: None
:rtype: None
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[None]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
target_name = kwargs.pop('target_name', None) # type: Optional[str]
request = build_grant_permission_request(
hub=self._config.hub,
permission=permission,
connection_id=connection_id,
api_version=api_version,
target_name=target_name,
template_url=self.grant_permission.metadata['url'],
)
path_format_arguments = {
"Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
request.url = self._client.format_url(request.url, **path_format_arguments)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response)
if cls:
return cls(pipeline_response, None, {})
grant_permission.metadata = {'url': '/api/hubs/{hub}/permissions/{permission}/connections/{connectionId}'} # type: ignore
@distributed_trace
def revoke_permission(
self,
permission, # type: str
connection_id, # type: str
**kwargs # type: Any
):
# type: (...) -> None
"""Revoke permission for the connection.
Revoke permission for the connection.
:param permission: The permission: current supported actions are joinLeaveGroup and
sendToGroup. Possible values are: "sendToGroup" or "joinLeaveGroup".
:type permission: str
:param connection_id: Target connection Id.
:type connection_id: str
:keyword target_name: The meaning of the target depends on the specific permission. For
joinLeaveGroup and sendToGroup, targetName is a required parameter standing for the group name.
:paramtype target_name: str
:keyword api_version: Api Version. The default value is "2021-10-01". Note that overriding this
default value may result in unsupported behavior.
:paramtype api_version: str
:return: None
:rtype: None
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[None]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
target_name = kwargs.pop('target_name', None) # type: Optional[str]
request = build_revoke_permission_request(
hub=self._config.hub,
permission=permission,
connection_id=connection_id,
api_version=api_version,
target_name=target_name,
template_url=self.revoke_permission.metadata['url'],
)
path_format_arguments = {
"Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
request.url = self._client.format_url(request.url, **path_format_arguments)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [204]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response)
if cls:
return cls(pipeline_response, None, {})
revoke_permission.metadata = {'url': '/api/hubs/{hub}/permissions/{permission}/connections/{connectionId}'} # type: ignore
@distributed_trace
def has_permission(
self,
permission, # type: str
connection_id, # type: str
**kwargs # type: Any
):
# type: (...) -> bool
"""Check if a connection has permission to the specified action.
Check if a connection has permission to the specified action.
:param permission: The permission: current supported actions are joinLeaveGroup and
sendToGroup. Possible values are: "sendToGroup" or "joinLeaveGroup".
:type permission: str
:param connection_id: Target connection Id.
:type connection_id: str
:keyword target_name: The meaning of the target depends on the specific permission. For
joinLeaveGroup and sendToGroup, targetName is a required parameter standing for the group name.
:paramtype target_name: str
:keyword api_version: Api Version. The default value is "2021-10-01". Note that overriding this
default value may result in unsupported behavior.
:paramtype api_version: str
:return: bool
:rtype: bool
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[None]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = kwargs.pop('api_version', "2021-10-01") # type: str
target_name = kwargs.pop('target_name', None) # type: Optional[str]
request = build_has_permission_request(
hub=self._config.hub,
permission=permission,
connection_id=connection_id,
api_version=api_version,
target_name=target_name,
template_url=self.has_permission.metadata['url'],
)
path_format_arguments = {
"Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
request.url = self._client.format_url(request.url, **path_format_arguments)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200, 404]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response)
if cls:
return cls(pipeline_response, None, {})
return 200 <= response.status_code <= 299
has_permission.metadata = {'url': '/api/hubs/{hub}/permissions/{permission}/connections/{connectionId}'} # type: ignore
| 38.470876 | 133 | 0.633951 | 9,093 | 78,596 | 5.288244 | 0.031453 | 0.049911 | 0.03161 | 0.027451 | 0.960487 | 0.950505 | 0.934284 | 0.92164 | 0.915589 | 0.905711 | 0 | 0.01637 | 0.241412 | 78,596 | 2,042 | 134 | 38.489716 | 0.790145 | 0.237328 | 0 | 0.806907 | 0 | 0 | 0.146468 | 0.054048 | 0 | 0 | 0 | 0 | 0 | 1 | 0.031397 | false | 0 | 0.008634 | 0 | 0.076138 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 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 | 7 |
4a875687b3406dbe0f916064127c99df55266a8d | 2,639 | py | Python | tests/cli/config/test_show.py | daobook/hatch | 1cf39ad1a11ce90bc77fb7fdc4b9202433509179 | [
"MIT"
] | null | null | null | tests/cli/config/test_show.py | daobook/hatch | 1cf39ad1a11ce90bc77fb7fdc4b9202433509179 | [
"MIT"
] | null | null | null | tests/cli/config/test_show.py | daobook/hatch | 1cf39ad1a11ce90bc77fb7fdc4b9202433509179 | [
"MIT"
] | null | null | null | def test_default_scrubbed(hatch, config_file, helpers, default_cache_dir, default_data_dir):
config_file.model.project = 'foo'
config_file.model.publish['pypi']['auth'] = 'bar'
config_file.save()
result = hatch('config', 'show')
default_cache_directory = str(default_cache_dir).replace('\\', '\\\\')
default_data_directory = str(default_data_dir).replace('\\', '\\\\')
assert result.exit_code == 0, result.output
assert result.output == helpers.dedent(
f"""
mode = "local"
project = "foo"
shell = ""
[dirs]
project = []
python = "isolated"
data = "{default_data_directory}"
cache = "{default_cache_directory}"
[dirs.env]
[projects]
[template]
name = "Foo Bar"
email = "foo@bar.baz"
[template.licenses]
headers = true
default = [
"MIT",
]
[template.plugins.default]
tests = true
ci = false
src-layout = false
[terminal.styles]
info = "bold"
success = "bold cyan"
error = "bold red"
warning = "bold yellow"
waiting = "bold magenta"
debug = "bold"
spinner = "simpleDotsScrolling"
"""
)
def test_reveal(hatch, config_file, helpers, default_cache_dir, default_data_dir):
config_file.model.project = 'foo'
config_file.model.publish['pypi']['auth'] = 'bar'
config_file.save()
result = hatch('config', 'show', '-a')
default_cache_directory = str(default_cache_dir).replace('\\', '\\\\')
default_data_directory = str(default_data_dir).replace('\\', '\\\\')
assert result.exit_code == 0, result.output
assert result.output == helpers.dedent(
f"""
mode = "local"
project = "foo"
shell = ""
[dirs]
project = []
python = "isolated"
data = "{default_data_directory}"
cache = "{default_cache_directory}"
[dirs.env]
[projects]
[publish.pypi]
user = ""
auth = "bar"
[template]
name = "Foo Bar"
email = "foo@bar.baz"
[template.licenses]
headers = true
default = [
"MIT",
]
[template.plugins.default]
tests = true
ci = false
src-layout = false
[terminal.styles]
info = "bold"
success = "bold cyan"
error = "bold red"
warning = "bold yellow"
waiting = "bold magenta"
debug = "bold"
spinner = "simpleDotsScrolling"
"""
)
| 23.774775 | 92 | 0.533914 | 257 | 2,639 | 5.307393 | 0.276265 | 0.058651 | 0.043988 | 0.032258 | 0.957478 | 0.957478 | 0.957478 | 0.957478 | 0.957478 | 0.957478 | 0 | 0.001134 | 0.331565 | 2,639 | 110 | 93 | 23.990909 | 0.772109 | 0 | 0 | 0.873563 | 0 | 0 | 0.642289 | 0.075786 | 0 | 0 | 0 | 0 | 0.045977 | 1 | 0.022989 | false | 0 | 0 | 0 | 0.022989 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
4a8bec2641a7e8d7c07b8ce408d2ad3f2a6b95fd | 95 | py | Python | genorate_key.py | PyOfile/Django_blog | 76f10dc7e6aa76ca949349cdb8b1a356b9e34b1d | [
"MIT"
] | 24 | 2017-03-19T16:17:37.000Z | 2021-11-07T15:35:33.000Z | scripts/generate_secret_key.py | GrantHeaslip/ghweb | d0ec54afdea4df45769d4876d82fd14037e16e56 | [
"BSD-3-Clause"
] | 117 | 2016-04-19T12:35:10.000Z | 2022-02-22T13:19:05.000Z | scripts/generate_secret_key.py | GrantHeaslip/ghweb | d0ec54afdea4df45769d4876d82fd14037e16e56 | [
"BSD-3-Clause"
] | 11 | 2017-08-08T12:11:39.000Z | 2021-12-08T05:34:06.000Z | from django.core.management.utils import get_random_secret_key
print(get_random_secret_key())
| 23.75 | 62 | 0.863158 | 15 | 95 | 5.066667 | 0.733333 | 0.236842 | 0.394737 | 0.473684 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.063158 | 95 | 3 | 63 | 31.666667 | 0.853933 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0.5 | 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 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 8 |
4ab369b71f111c043daecd9c13b854de3122eb38 | 30,803 | py | Python | hoomd/test-py/test_gsd.py | InnocentBug/hoomd-blue | 2be99602b7e76ab3ec8e018e194d49f3f209bf60 | [
"BSD-3-Clause"
] | 1 | 2019-05-21T00:48:37.000Z | 2019-05-21T00:48:37.000Z | hoomd/test-py/test_gsd.py | FeynmanDNA/hoomd-blue | 707e5163b0e15e5d2bae1f992f985c7d97ebef32 | [
"BSD-3-Clause"
] | null | null | null | hoomd/test-py/test_gsd.py | FeynmanDNA/hoomd-blue | 707e5163b0e15e5d2bae1f992f985c7d97ebef32 | [
"BSD-3-Clause"
] | null | null | null | # -*- coding: iso-8859-1 -*-
# Maintainer: joaander
from hoomd import *
import hoomd;
import unittest
import os
import numpy
import tempfile
# unit tests for dump.gsd
class gsd_write_tests (unittest.TestCase):
def setUp(self):
context.initialize()
if hoomd.comm.get_rank() == 0:
tmp = tempfile.mkstemp(suffix='.test.gsd');
self.tmp_file = tmp[1];
else:
self.tmp_file = "invalid";
self.snapshot = data.make_snapshot(N=4, box=data.boxdim(Lx=10, Ly=20, Lz=30), dtype='float');
if comm.get_rank() == 0:
# particles
self.snapshot.particles.position[0] = [0,1,2];
self.snapshot.particles.position[1] = [1,2,3];
self.snapshot.particles.position[2] = [0,-1,-2];
self.snapshot.particles.position[3] = [-1, -2, -3];
self.snapshot.particles.velocity[0] = [10, 11, 12];
self.snapshot.particles.velocity[1] = [11, 12, 13];
self.snapshot.particles.velocity[2] = [12, 13, 14];
self.snapshot.particles.velocity[3] = [13, 14, 15];
self.snapshot.particles.acceleration[0] = [20, 21, 22];
self.snapshot.particles.acceleration[1] = [21, 22, 23];
self.snapshot.particles.acceleration[2] = [22, 23, 24];
self.snapshot.particles.acceleration[3] = [23, 24, 25];
self.snapshot.particles.typeid[:] = [0,0,1,1];
self.snapshot.particles.mass[:] = [33, 34, 35, 36];
self.snapshot.particles.charge[:] = [44, 45, 46, 47];
self.snapshot.particles.diameter[:] = [55, 56, 57, 58];
self.snapshot.particles.image[0] = [60, 61, 62];
self.snapshot.particles.image[1] = [61, 62, 63];
self.snapshot.particles.image[2] = [62, 63, 64];
self.snapshot.particles.image[3] = [63, 64, 65];
self.snapshot.particles.types = ['p1', 'p2'];
# bonds
self.snapshot.bonds.types = ['b1', 'b2'];
self.snapshot.bonds.resize(2);
self.snapshot.bonds.typeid[:] = [0, 1];
self.snapshot.bonds.group[0] = [0, 1];
self.snapshot.bonds.group[1] = [2, 3];
# angles
self.snapshot.angles.types = ['a1', 'a2'];
self.snapshot.angles.resize(2);
self.snapshot.angles.typeid[:] = [1, 0];
self.snapshot.angles.group[0] = [0, 1, 2];
self.snapshot.angles.group[1] = [2, 3, 0];
# dihedrals
self.snapshot.dihedrals.types = ['d1'];
self.snapshot.dihedrals.resize(1);
self.snapshot.dihedrals.typeid[:] = [0];
self.snapshot.dihedrals.group[0] = [0, 1, 2, 3];
# impropers
self.snapshot.impropers.types = ['i1'];
self.snapshot.impropers.resize(1);
self.snapshot.impropers.typeid[:] = [0];
self.snapshot.impropers.group[0] = [3, 2, 1, 0];
# constraints
self.snapshot.constraints.resize(1)
self.snapshot.constraints.group[0] = [0, 1]
self.snapshot.constraints.value[0] = 2.5
# special pairs
self.snapshot.pairs.types = ['p1', 'p2'];
self.snapshot.pairs.resize(2);
self.snapshot.pairs.typeid[:] = [0, 1];
self.snapshot.pairs.group[0] = [0, 1];
self.snapshot.pairs.group[1] = [2, 3];
self.s = init.read_snapshot(self.snapshot);
context.current.sorter.set_params(grid=8)
# tests basic creation of the dump
def test(self):
dump.gsd(filename=self.tmp_file, group=group.all(), period=1, overwrite=True);
run(5);
# ensure 5 frames are written to the file
data.gsd_snapshot(self.tmp_file, frame=4);
if comm.get_rank() == 0:
self.assertRaises(RuntimeError, data.gsd_snapshot, self.tmp_file, frame=5);
# tests with phase
def test_phase(self):
dump.gsd(filename=self.tmp_file, group=group.all(), period=1, phase=0, overwrite=True);
run(1);
data.gsd_snapshot(self.tmp_file, frame=0);
if comm.get_rank() == 0:
self.assertRaises(RuntimeError, data.gsd_snapshot, self.tmp_file, frame=1);
# tests overwrite
def test_overwrite(self):
if comm.get_rank() == 0:
with open(self.tmp_file, 'wt') as f:
f.write('Hello');
dump.gsd(filename=self.tmp_file, group=group.all(), period=1, overwrite=True);
run(1);
data.gsd_snapshot(self.tmp_file, frame=0);
if comm.get_rank() == 0:
self.assertRaises(RuntimeError, data.gsd_snapshot, self.tmp_file, frame=1);
# tests truncate
def test_truncate(self):
dump.gsd(filename=self.tmp_file, group=group.all(), period=1, truncate=True, overwrite=True);
run(5);
data.gsd_snapshot(self.tmp_file, frame=0);
if comm.get_rank() == 0:
self.assertRaises(RuntimeError, data.gsd_snapshot, self.tmp_file, frame=1);
# tests write_restart
def write_restart(self):
g = dump.gsd(filename=self.tmp_file, group=group.all(), period=1000000, truncate=True, overwrite=True);
run(5);
g.write_restart();
data.gsd_snapshot(self.tmp_file, frame=0);
if comm.get_rank() == 0:
self.assertRaises(RuntimeError, data.gsd_snapshot, self.tmp_file, frame=1);
# test all static quantities
def test_all_static(self):
dump.gsd(filename=self.tmp_file, group=group.all(), period=1, static=['attribute', 'property', 'momentum', 'topology'], overwrite=True);
run(1);
data.gsd_snapshot(self.tmp_file, frame=0);
if comm.get_rank() == 0:
self.assertRaises(RuntimeError, data.gsd_snapshot, self.tmp_file, frame=1);
def test_dynamic(self):
dump.gsd(filename=self.tmp_file, group=group.all(), period=1, dynamic=['momentum'], overwrite=True);
run(1);
data.gsd_snapshot(self.tmp_file, frame=0);
if comm.get_rank() == 0:
self.assertRaises(RuntimeError, data.gsd_snapshot, self.tmp_file, frame=1);
# test write file
def test_write_immediate(self):
dump.gsd(filename=self.tmp_file, group=group.all(), period=None, time_step=1000, overwrite=True);
data.gsd_snapshot(self.tmp_file, frame=0);
if comm.get_rank() == 0:
self.assertRaises(RuntimeError, data.gsd_snapshot, self.tmp_file, frame=1);
# tests init.read_gsd
def test_read_gsd(self):
dump.gsd(filename=self.tmp_file, group=group.all(), period=1, overwrite=True);
run(5);
context.initialize();
init.read_gsd(filename=self.tmp_file, frame=4);
self.assertEqual(get_step(), 4)
if comm.get_rank() == 0:
self.assertRaises(RuntimeError, init.read_gsd, self.tmp_file, frame=5);
# tests init.read_gsd time_step
def test_read_gsd_time_step(self):
dump.gsd(filename=self.tmp_file, group=group.all(), period=1, overwrite=True);
run(5);
context.initialize();
# test that time_step is set appropriately
init.read_gsd(filename=self.tmp_file, frame=4, time_step=1000);
self.assertEqual(get_step(), 1000)
# when restart is present, the time_step field should be ignored
context.initialize();
init.read_gsd(filename=self.tmp_file, restart=self.tmp_file, frame=4, time_step=1000);
self.assertEqual(get_step(), 4)
# tests with zero particles
def test_zero_particles(self):
self.s.particles.remove(0)
self.s.particles.remove(1)
self.s.particles.remove(2)
self.s.particles.remove(3)
dump.gsd(filename=self.tmp_file, group=group.all(), period=1, overwrite=True);
def tearDown(self):
if (hoomd.comm.get_rank()==0):
os.remove(self.tmp_file);
comm.barrier_all();
# unit tests for dump.gsd
class gsd_read_tests (unittest.TestCase):
def setUp(self):
context.initialize()
if hoomd.comm.get_rank() == 0:
tmp = tempfile.mkstemp(suffix='.test.gsd');
self.tmp_file = tmp[1];
else:
self.tmp_file = "invalid";
self.snapshot = data.make_snapshot(N=4, box=data.boxdim(L=10), dtype='float');
if comm.get_rank() == 0:
# particles
self.snapshot.particles.position[0] = [0,1,2];
self.snapshot.particles.position[1] = [1,2,3];
self.snapshot.particles.position[2] = [0,-1,-2];
self.snapshot.particles.position[3] = [-1, -2, -3];
self.snapshot.particles.velocity[0] = [10, 11, 12];
self.snapshot.particles.velocity[1] = [11, 12, 13];
self.snapshot.particles.velocity[2] = [12, 13, 14];
self.snapshot.particles.velocity[3] = [13, 14, 15];
self.snapshot.particles.orientation[0] = [19, 20, 21, 22];
self.snapshot.particles.orientation[1] = [20, 21, 22, 23];
self.snapshot.particles.orientation[2] = [21, 22, 23, 24];
self.snapshot.particles.orientation[3] = [22, 23, 24, 25];
self.snapshot.particles.angmom[0] = [119, 220, 321, 422];
self.snapshot.particles.angmom[1] = [120, 221, 322, 423];
self.snapshot.particles.angmom[2] = [121, 222, 323, 424];
self.snapshot.particles.angmom[3] = [122, 223, 324, 425];
self.snapshot.particles.typeid[:] = [0,0,1,1];
self.snapshot.particles.mass[:] = [33, 34, 35, 36];
self.snapshot.particles.charge[:] = [44, 45, 46, 47];
self.snapshot.particles.diameter[:] = [55, 56, 57, 58];
self.snapshot.particles.body[:] = [-1, -1, -1, -1];
self.snapshot.particles.image[0] = [60, 61, 62];
self.snapshot.particles.image[1] = [61, 62, 63];
self.snapshot.particles.image[2] = [62, 63, 64];
self.snapshot.particles.image[3] = [63, 64, 65];
self.snapshot.particles.moment_inertia[0] = [50, 51, 52];
self.snapshot.particles.moment_inertia[1] = [51, 52, 53];
self.snapshot.particles.moment_inertia[2] = [52, 53, 54];
self.snapshot.particles.moment_inertia[3] = [53, 54, 55];
self.snapshot.particles.types = ['p1', 'p2'];
# bonds
self.snapshot.bonds.types = ['b1', 'b2'];
self.snapshot.bonds.resize(2);
self.snapshot.bonds.typeid[:] = [0, 1];
self.snapshot.bonds.group[0] = [0, 1];
self.snapshot.bonds.group[1] = [2, 3];
# angles
self.snapshot.angles.types = ['a1', 'a2'];
self.snapshot.angles.resize(2);
self.snapshot.angles.typeid[:] = [1, 0];
self.snapshot.angles.group[0] = [0, 1, 2];
self.snapshot.angles.group[1] = [2, 3, 0];
# dihedrals
self.snapshot.dihedrals.types = ['d1'];
self.snapshot.dihedrals.resize(1);
self.snapshot.dihedrals.typeid[:] = [0];
self.snapshot.dihedrals.group[0] = [0, 1, 2, 3];
# impropers
self.snapshot.impropers.types = ['i1'];
self.snapshot.impropers.resize(1);
self.snapshot.impropers.typeid[:] = [0];
self.snapshot.impropers.group[0] = [3, 2, 1, 0];
# constraints
self.snapshot.constraints.resize(1)
self.snapshot.constraints.group[0] = [0, 1]
self.snapshot.constraints.value[0] = 2.5
# pairs
self.snapshot.pairs.types = ['p1', 'p2'];
self.snapshot.pairs.resize(2);
self.snapshot.pairs.typeid[:] = [0, 1];
self.snapshot.pairs.group[0] = [0, 1];
self.snapshot.pairs.group[1] = [2, 3];
self.s = init.read_snapshot(self.snapshot);
context.current.sorter.set_params(grid=8)
# tests data.gsd_snapshot
def test_gsd_snapshot(self):
dump.gsd(filename=self.tmp_file, group=group.all(), period=None, overwrite=True);
snap = data.gsd_snapshot(self.tmp_file, frame=0);
if comm.get_rank() == 0:
self.assertEqual(snap.box.dimensions, self.snapshot.box.dimensions);
self.assertEqual(snap.box.Lx, self.snapshot.box.Lx);
self.assertEqual(snap.box.Ly, self.snapshot.box.Ly);
self.assertEqual(snap.box.Lz, self.snapshot.box.Lz);
self.assertEqual(snap.box.xy, self.snapshot.box.xy);
self.assertEqual(snap.box.xz, self.snapshot.box.xz);
self.assertEqual(snap.box.yz, self.snapshot.box.yz);
self.assertEqual(snap.particles.N, self.snapshot.particles.N);
self.assertEqual(snap.particles.types, self.snapshot.particles.types);
numpy.testing.assert_array_equal(snap.particles.typeid, self.snapshot.particles.typeid);
numpy.testing.assert_array_equal(snap.particles.mass, self.snapshot.particles.mass);
numpy.testing.assert_array_equal(snap.particles.charge, self.snapshot.particles.charge);
numpy.testing.assert_array_equal(snap.particles.diameter, self.snapshot.particles.diameter);
numpy.testing.assert_array_equal(snap.particles.body, self.snapshot.particles.body);
numpy.testing.assert_array_equal(snap.particles.moment_inertia, self.snapshot.particles.moment_inertia);
numpy.testing.assert_array_equal(snap.particles.position, self.snapshot.particles.position);
numpy.testing.assert_array_equal(snap.particles.orientation, self.snapshot.particles.orientation);
numpy.testing.assert_array_equal(snap.particles.velocity, self.snapshot.particles.velocity);
numpy.testing.assert_array_equal(snap.particles.angmom, self.snapshot.particles.angmom);
numpy.testing.assert_array_equal(snap.particles.image, self.snapshot.particles.image);
self.assertEqual(snap.bonds.N, self.snapshot.bonds.N);
self.assertEqual(snap.bonds.types, self.snapshot.bonds.types);
numpy.testing.assert_array_equal(snap.bonds.typeid, self.snapshot.bonds.typeid);
numpy.testing.assert_array_equal(snap.bonds.group, self.snapshot.bonds.group);
self.assertEqual(snap.angles.N, self.snapshot.angles.N);
self.assertEqual(snap.angles.types, self.snapshot.angles.types);
numpy.testing.assert_array_equal(snap.angles.typeid, self.snapshot.angles.typeid);
numpy.testing.assert_array_equal(snap.angles.group, self.snapshot.angles.group);
self.assertEqual(snap.dihedrals.N, self.snapshot.dihedrals.N);
self.assertEqual(snap.dihedrals.types, self.snapshot.dihedrals.types);
numpy.testing.assert_array_equal(snap.dihedrals.typeid, self.snapshot.dihedrals.typeid);
numpy.testing.assert_array_equal(snap.dihedrals.group, self.snapshot.dihedrals.group);
self.assertEqual(snap.impropers.N, self.snapshot.impropers.N);
self.assertEqual(snap.impropers.types, self.snapshot.impropers.types);
numpy.testing.assert_array_equal(snap.impropers.typeid, self.snapshot.impropers.typeid);
numpy.testing.assert_array_equal(snap.impropers.group, self.snapshot.impropers.group);
self.assertEqual(snap.constraints.N, self.snapshot.constraints.N);
numpy.testing.assert_array_equal(snap.constraints.group, self.snapshot.constraints.group);
numpy.testing.assert_array_equal(snap.constraints.value, self.snapshot.constraints.value);
self.assertEqual(snap.pairs.N, self.snapshot.pairs.N);
self.assertEqual(snap.pairs.types, self.snapshot.pairs.types);
numpy.testing.assert_array_equal(snap.pairs.typeid, self.snapshot.pairs.typeid);
numpy.testing.assert_array_equal(snap.pairs.group, self.snapshot.pairs.group);
# test changing the order particles
def test_remove(self):
# remove particle so that tag 2 points to no particle, and particle tags are no longer contiguous
self.s.particles.remove(2)
self.snapshot = self.s.take_snapshot(all=True)
dump.gsd(filename=self.tmp_file, group=group.all(), period=None, overwrite=True);
snap = data.gsd_snapshot(self.tmp_file, frame=0);
if comm.get_rank() == 0:
self.assertEqual(snap.box.dimensions, self.snapshot.box.dimensions);
self.assertEqual(snap.box.Lx, self.snapshot.box.Lx);
self.assertEqual(snap.box.Ly, self.snapshot.box.Ly);
self.assertEqual(snap.box.Lz, self.snapshot.box.Lz);
self.assertEqual(snap.box.xy, self.snapshot.box.xy);
self.assertEqual(snap.box.xz, self.snapshot.box.xz);
self.assertEqual(snap.box.yz, self.snapshot.box.yz);
self.assertEqual(snap.particles.N, self.snapshot.particles.N);
self.assertEqual(snap.particles.types, self.snapshot.particles.types);
numpy.testing.assert_array_equal(snap.particles.typeid, self.snapshot.particles.typeid);
numpy.testing.assert_array_equal(snap.particles.mass, self.snapshot.particles.mass);
numpy.testing.assert_array_equal(snap.particles.charge, self.snapshot.particles.charge);
numpy.testing.assert_array_equal(snap.particles.diameter, self.snapshot.particles.diameter);
numpy.testing.assert_array_equal(snap.particles.body, self.snapshot.particles.body);
numpy.testing.assert_array_equal(snap.particles.moment_inertia, self.snapshot.particles.moment_inertia);
numpy.testing.assert_array_equal(snap.particles.position, self.snapshot.particles.position);
numpy.testing.assert_array_equal(snap.particles.orientation, self.snapshot.particles.orientation);
numpy.testing.assert_array_equal(snap.particles.velocity, self.snapshot.particles.velocity);
numpy.testing.assert_array_equal(snap.particles.angmom, self.snapshot.particles.angmom);
numpy.testing.assert_array_equal(snap.particles.image, self.snapshot.particles.image);
self.assertEqual(snap.bonds.N, self.snapshot.bonds.N);
self.assertEqual(snap.bonds.types, self.snapshot.bonds.types);
numpy.testing.assert_array_equal(snap.bonds.typeid, self.snapshot.bonds.typeid);
numpy.testing.assert_array_equal(snap.bonds.group, self.snapshot.bonds.group);
self.assertEqual(snap.angles.N, self.snapshot.angles.N);
self.assertEqual(snap.angles.types, self.snapshot.angles.types);
numpy.testing.assert_array_equal(snap.angles.typeid, self.snapshot.angles.typeid);
numpy.testing.assert_array_equal(snap.angles.group, self.snapshot.angles.group);
self.assertEqual(snap.dihedrals.N, self.snapshot.dihedrals.N);
self.assertEqual(snap.dihedrals.types, self.snapshot.dihedrals.types);
numpy.testing.assert_array_equal(snap.dihedrals.typeid, self.snapshot.dihedrals.typeid);
numpy.testing.assert_array_equal(snap.dihedrals.group, self.snapshot.dihedrals.group);
self.assertEqual(snap.impropers.N, self.snapshot.impropers.N);
self.assertEqual(snap.impropers.types, self.snapshot.impropers.types);
numpy.testing.assert_array_equal(snap.impropers.typeid, self.snapshot.impropers.typeid);
numpy.testing.assert_array_equal(snap.impropers.group, self.snapshot.impropers.group);
self.assertEqual(snap.constraints.N, self.snapshot.constraints.N);
numpy.testing.assert_array_equal(snap.constraints.group, self.snapshot.constraints.group);
numpy.testing.assert_array_equal(snap.constraints.value, self.snapshot.constraints.value);
self.assertEqual(snap.pairs.N, self.snapshot.pairs.N);
self.assertEqual(snap.pairs.types, self.snapshot.pairs.types);
numpy.testing.assert_array_equal(snap.pairs.typeid, self.snapshot.pairs.typeid);
numpy.testing.assert_array_equal(snap.pairs.group, self.snapshot.pairs.group);
# tests init.read_gsd
def test_read_gsd(self):
dump.gsd(filename=self.tmp_file, group=group.all(), period=None, overwrite=True);
context.initialize();
init.read_gsd(filename=self.tmp_file, frame=-1);
def tearDown(self):
if comm.get_rank() == 0:
os.remove(self.tmp_file);
comm.barrier_all();
# unit tests for dump.gsd with default type
class gsd_default_type (unittest.TestCase):
def setUp(self):
context.initialize()
if hoomd.comm.get_rank() == 0:
tmp = tempfile.mkstemp(suffix='.test.gsd');
self.tmp_file = tmp[1];
else:
self.tmp_file = "invalid";
self.snapshot = data.make_snapshot(N=4, box=data.boxdim(L=10), dtype='float');
if comm.get_rank() == 0:
# particles
self.snapshot.particles.position[0] = [0,1,2];
self.snapshot.particles.position[1] = [1,2,3];
self.snapshot.particles.position[2] = [0,-1,-2];
self.snapshot.particles.position[3] = [-1, -2, -3];
self.snapshot.particles.velocity[0] = [10, 11, 12];
self.snapshot.particles.velocity[1] = [11, 12, 13];
self.snapshot.particles.velocity[2] = [12, 13, 14];
self.snapshot.particles.velocity[3] = [13, 14, 15];
self.snapshot.particles.types = ['A'];
self.s = init.read_snapshot(self.snapshot);
context.current.sorter.set_params(grid=8)
# tests data.gsd_snapshot
def test_gsd(self):
dump.gsd(filename=self.tmp_file, group=group.all(), period=None, overwrite=True);
snap = data.gsd_snapshot(self.tmp_file, frame=-1);
if comm.get_rank() == 0:
self.assertEqual(snap.particles.N, self.snapshot.particles.N);
self.assertEqual(snap.particles.types, self.snapshot.particles.types);
# tests init.read_gsd
def test_read_gsd(self):
dump.gsd(filename=self.tmp_file, group=group.all(), period=None, overwrite=True);
context.initialize();
init.read_gsd(filename=self.tmp_file);
def tearDown(self):
if comm.get_rank() == 0:
os.remove(self.tmp_file);
comm.barrier_all();
class gsd_default_type (unittest.TestCase):
def setUp(self):
context.initialize();
if hoomd.comm.get_rank() == 0:
tmp = tempfile.mkstemp(suffix='.test.gsd');
self.tmp_file = tmp[1];
else:
self.tmp_file = "invalid";
def validate_append(self, name, default_val, nondefault_val):
self.snapshot = data.make_snapshot(N=4, box=data.boxdim(L=10), dtype='float');
if comm.get_rank() == 0:
# particles
self.snapshot.particles.types = ['A', 'B', 'C'];
print(dir(self.snapshot.particles))
getattr(self.snapshot.particles, name)[:] = nondefault_val
self.s = init.read_snapshot(self.snapshot);
context.current.sorter.set_params(grid=8)
# write out frame 0
dump.gsd(filename=self.tmp_file, group=group.all(), period=None, overwrite=True);
# reset values to default and write out the second frame
if comm.get_rank() == 0:
getattr(self.snapshot.particles, name)[:] = default_val
self.s.restore_snapshot(self.snapshot);
run(1)
dump.gsd(filename=self.tmp_file, group=group.all(), dynamic=['attribute', 'momentum'], period=None);
# validate the resulting gsd file
snap = data.gsd_snapshot(self.tmp_file, frame=0);
if comm.get_rank() == 0:
self.assertEqual(snap.particles.N, self.snapshot.particles.N);
numpy.testing.assert_array_equal(getattr(snap.particles, name), nondefault_val);
snap = data.gsd_snapshot(self.tmp_file, frame=1);
if comm.get_rank() == 0:
self.assertEqual(snap.particles.N, self.snapshot.particles.N);
numpy.testing.assert_array_equal(getattr(snap.particles, name), default_val);
def validate_fullwrite(self, name, default_val, nondefault_val):
self.snapshot = data.make_snapshot(N=4, box=data.boxdim(L=10), dtype='float');
if comm.get_rank() == 0:
# particles
self.snapshot.particles.types = ['A', 'B', 'C'];
print(dir(self.snapshot.particles))
getattr(self.snapshot.particles, name)[:] = nondefault_val
self.s = init.read_snapshot(self.snapshot);
context.current.sorter.set_params(grid=8)
# write out frame 0
dump.gsd(filename=self.tmp_file, group=group.all(), period=1, overwrite=True, dynamic=['attribute', 'momentum']);
run(1)
# reset values to default and write out the second frame
if comm.get_rank() == 0:
getattr(self.snapshot.particles, name)[:] = default_val
self.s.restore_snapshot(self.snapshot);
run(1)
# validate the resulting gsd file
snap = data.gsd_snapshot(self.tmp_file, frame=0);
if comm.get_rank() == 0:
self.assertEqual(snap.particles.N, self.snapshot.particles.N);
numpy.testing.assert_array_equal(getattr(snap.particles, name), nondefault_val);
snap = data.gsd_snapshot(self.tmp_file, frame=1);
if comm.get_rank() == 0:
self.assertEqual(snap.particles.N, self.snapshot.particles.N);
numpy.testing.assert_array_equal(getattr(snap.particles, name), default_val);
def test_nondefault_typeid(self):
self.validate_append(name='typeid',
default_val = [0, 0, 0, 0],
nondefault_val = [2, 1, 0, 2])
def test_nondefault_typeid2(self):
self.validate_fullwrite(name='typeid',
default_val = [0, 0, 0, 0],
nondefault_val = [2, 1, 0, 2])
def test_nondefault_mass(self):
self.validate_append(name='mass',
default_val = [1, 1, 1, 1],
nondefault_val = [3, 2, 1, 3])
def test_nondefault_mass2(self):
self.validate_fullwrite(name='mass',
default_val = [1, 1, 1, 1],
nondefault_val = [3, 2, 1, 3])
def test_nondefault_charge(self):
self.validate_append(name='charge',
default_val = [0, 0, 0, 0],
nondefault_val = [1, -1, 3, -3])
def test_nondefault_charge2(self):
self.validate_fullwrite(name='charge',
default_val = [0, 0, 0, 0],
nondefault_val = [1, -1, 3, -3])
def test_nondefault_diameter(self):
self.validate_append(name='diameter',
default_val = [1, 1, 1, 1],
nondefault_val = [2, 3, 4, 1])
def test_nondefault_diameter2(self):
self.validate_fullwrite(name='diameter',
default_val = [1, 1, 1, 1],
nondefault_val = [2, 3, 4, 1])
def test_nondefault_body(self):
self.validate_append(name='body',
default_val = [4294967295, 4294967295, 4294967295, 4294967295],
nondefault_val = [0, 1, 2, 4294967295])
def test_nondefault_body2(self):
self.validate_fullwrite(name='body',
default_val = [4294967295, 4294967295, 4294967295, 4294967295],
nondefault_val = [0, 1, 2, 4294967295])
def test_nondefault_moment_inertia(self):
self.validate_append(name='moment_inertia',
default_val = [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]],
nondefault_val = [[1, 0, 0], [1, 2, 0], [1, 1, 1], [2, 3, 4]])
def test_nondefault_moment_inertia2(self):
self.validate_fullwrite(name='moment_inertia',
default_val = [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]],
nondefault_val = [[1, 0, 0], [1, 2, 0], [1, 1, 1], [2, 3, 4]])
def test_nondefault_velocity(self):
self.validate_append(name='velocity',
default_val = [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]],
nondefault_val = [[1, 0, 0], [1, 2, 0], [1, 1, 1], [2, 3, 4]])
def test_nondefault_velocity2(self):
self.validate_fullwrite(name='velocity',
default_val = [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]],
nondefault_val = [[1, 0, 0], [1, 2, 0], [1, 1, 1], [2, 3, 4]])
def test_nondefault_image(self):
self.validate_append(name='image',
default_val = [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]],
nondefault_val = [[1, 0, 0], [1, 2, 0], [1, 1, 1], [2, 3, 4]])
def test_nondefault_image2(self):
self.validate_fullwrite(name='image',
default_val = [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]],
nondefault_val = [[1, 0, 0], [1, 2, 0], [1, 1, 1], [2, 3, 4]])
def test_nondefault_orientation(self):
self.validate_append(name='orientation',
default_val = [[1, 0, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0]],
nondefault_val = [[1, 1, 0, 0], [1, 1, 2, 0], [1, 1, 1, 1], [1, 2, 3, 4]])
def test_nondefault_orientation2(self):
self.validate_fullwrite(name='orientation',
default_val = [[1, 0, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0]],
nondefault_val = [[1, 1, 0, 0], [1, 1, 2, 0], [1, 1, 1, 1], [1, 2, 3, 4]])
def test_nondefault_angmom(self):
self.validate_append(name='angmom',
default_val = [[1, 0, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0]],
nondefault_val = [[1, 1, 0, 0], [1, 1, 2, 0], [1, 1, 1, 1], [1, 2, 3, 4]])
def test_nondefault_angmom2(self):
self.validate_fullwrite(name='angmom',
default_val = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
nondefault_val = [[1, 1, 0, 0], [1, 1, 2, 0], [1, 1, 1, 1], [1, 2, 3, 4]])
def tearDown(self):
if comm.get_rank() == 0:
os.remove(self.tmp_file);
comm.barrier_all();
if __name__ == '__main__':
unittest.main(argv = ['test.py', '-v'])
| 47.535494 | 144 | 0.598481 | 3,921 | 30,803 | 4.592196 | 0.06631 | 0.14995 | 0.116628 | 0.015773 | 0.91414 | 0.879485 | 0.859491 | 0.855826 | 0.855826 | 0.847495 | 0 | 0.048617 | 0.256111 | 30,803 | 647 | 145 | 47.608964 | 0.737191 | 0.035354 | 0 | 0.772541 | 0 | 0 | 0.012776 | 0 | 0 | 0 | 0 | 0 | 0.221311 | 1 | 0.094262 | false | 0 | 0.012295 | 0 | 0.114754 | 0.004098 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 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 | 7 |
4ac3e7fe9cba6945dde6fcd4e9464c199ce3a5ff | 122,083 | py | Python | src/sentry/integrations/github/testutils.py | AlexWayfer/sentry | ef935cda2b2e960bd602fda590540882d1b0712d | [
"BSD-3-Clause"
] | 1 | 2022-02-09T22:56:49.000Z | 2022-02-09T22:56:49.000Z | src/sentry/integrations/github/testutils.py | AlexWayfer/sentry | ef935cda2b2e960bd602fda590540882d1b0712d | [
"BSD-3-Clause"
] | 6 | 2018-10-19T10:04:23.000Z | 2019-12-09T20:29:12.000Z | src/sentry/integrations/github/testutils.py | AlexWayfer/sentry | ef935cda2b2e960bd602fda590540882d1b0712d | [
"BSD-3-Clause"
] | 1 | 2020-07-03T00:52:19.000Z | 2020-07-03T00:52:19.000Z | # -*- coding: utf-8 -*-
from __future__ import absolute_import
# we keep this as a raw string as order matters for hmac signing
PUSH_EVENT_EXAMPLE = b"""{
"ref": "refs/heads/changes",
"before": "9049f1265b7d61be4a8904a9a27120d2064dab3b",
"after": "0d1a26e67d8f5eaf1f6ba5c57fc3c7d91ac0fd1c",
"created": false,
"deleted": false,
"forced": false,
"base_ref": null,
"compare": "https://github.com/baxterthehacker/public-repo/compare/9049f1265b7d...0d1a26e67d8f",
"commits": [
{
"id": "133d60480286590a610a0eb7352ff6e02b9674c4",
"tree_id": "f9d2a07e9488b91af2641b26b9407fe22a451433",
"distinct": true,
"message": "Update README.md (àgain)",
"timestamp": "2015-05-05T19:45:15-04:00",
"url": "https://github.com/baxterthehacker/public-repo/commit/133d60480286590a610a0eb7352ff6e02b9674c4",
"author": {
"name": "bàxterthehacker",
"email": "baxterthehacker@users.noreply.github.com",
"username": "baxterthehacker"
},
"committer": {
"name": "baxterthehacker",
"email": "baxterthehacker@users.noreply.github.com",
"username": "baxterthehacker"
},
"added": [
],
"removed": [
],
"modified": [
"README.md"
]
},
{
"id": "0d1a26e67d8f5eaf1f6ba5c57fc3c7d91ac0fd1c",
"tree_id": "f9d2a07e9488b91af2641b26b9407fe22a451433",
"distinct": true,
"message": "Update README.md",
"timestamp": "2015-05-05T19:40:15-04:00",
"url": "https://github.com/baxterthehacker/public-repo/commit/0d1a26e67d8f5eaf1f6ba5c57fc3c7d91ac0fd1c",
"author": {
"name": "bàxterthehacker",
"email": "baxterthehacker@users.noreply.github.com",
"username": "baxterthehacker"
},
"committer": {
"name": "baxterthehacker",
"email": "baxterthehacker@users.noreply.github.com",
"username": "baxterthehacker"
},
"added": [
],
"removed": [
],
"modified": [
"README.md"
]
},
{
"id": "0d1a26e67d8f5eaf1f6ba5c57fc3c7d91ac0fd1c",
"tree_id": "f9d2a07e9488b91af2641b26b9407fe22a451433",
"distinct": true,
"message": "fix widget #skipsentry",
"timestamp": "2015-05-05T19:40:15-04:00",
"url": "https://github.com/baxterthehacker/public-repo/commit/0d1a26e67d8f5eaf1f6ba5c57fc3c7d91ac0fd1c",
"author": {
"name": "bàxterthehacker",
"email": "baxterthehacker@users.noreply.github.com",
"username": "baxterthehacker"
},
"committer": {
"name": "baxterthehacker",
"email": "baxterthehacker@users.noreply.github.com",
"username": "baxterthehacker"
},
"added": [
],
"removed": [
],
"modified": [
"README.md"
]
}
],
"head_commit": {
"id": "0d1a26e67d8f5eaf1f6ba5c57fc3c7d91ac0fd1c",
"tree_id": "f9d2a07e9488b91af2641b26b9407fe22a451433",
"distinct": true,
"message": "Update README.md",
"timestamp": "2015-05-05T19:40:15-04:00",
"url": "https://github.com/baxterthehacker/public-repo/commit/0d1a26e67d8f5eaf1f6ba5c57fc3c7d91ac0fd1c",
"author": {
"name": "baxterthehacker",
"email": "baxterthehacker@users.noreply.github.com",
"username": "baxterthehacker"
},
"committer": {
"name": "baxterthehacker",
"email": "baxterthehacker@users.noreply.github.com",
"username": "baxterthehacker"
},
"added": [
],
"removed": [
],
"modified": [
"README.md"
]
},
"repository": {
"id": 35129377,
"name": "public-repo",
"full_name": "baxterthehacker/public-repo",
"owner": {
"name": "baxterthehacker",
"email": "baxterthehacker@users.noreply.github.com"
},
"private": false,
"html_url": "https://github.com/baxterthehacker/public-repo",
"description": "",
"fork": false,
"url": "https://github.com/baxterthehacker/public-repo",
"forks_url": "https://api.github.com/repos/baxterthehacker/public-repo/forks",
"keys_url": "https://api.github.com/repos/baxterthehacker/public-repo/keys{/key_id}",
"collaborators_url": "https://api.github.com/repos/baxterthehacker/public-repo/collaborators{/collaborator}",
"teams_url": "https://api.github.com/repos/baxterthehacker/public-repo/teams",
"hooks_url": "https://api.github.com/repos/baxterthehacker/public-repo/hooks",
"issue_events_url": "https://api.github.com/repos/baxterthehacker/public-repo/issues/events{/number}",
"events_url": "https://api.github.com/repos/baxterthehacker/public-repo/events",
"assignees_url": "https://api.github.com/repos/baxterthehacker/public-repo/assignees{/user}",
"branches_url": "https://api.github.com/repos/baxterthehacker/public-repo/branches{/branch}",
"tags_url": "https://api.github.com/repos/baxterthehacker/public-repo/tags",
"blobs_url": "https://api.github.com/repos/baxterthehacker/public-repo/git/blobs{/sha}",
"git_tags_url": "https://api.github.com/repos/baxterthehacker/public-repo/git/tags{/sha}",
"git_refs_url": "https://api.github.com/repos/baxterthehacker/public-repo/git/refs{/sha}",
"trees_url": "https://api.github.com/repos/baxterthehacker/public-repo/git/trees{/sha}",
"statuses_url": "https://api.github.com/repos/baxterthehacker/public-repo/statuses/{sha}",
"languages_url": "https://api.github.com/repos/baxterthehacker/public-repo/languages",
"stargazers_url": "https://api.github.com/repos/baxterthehacker/public-repo/stargazers",
"contributors_url": "https://api.github.com/repos/baxterthehacker/public-repo/contributors",
"subscribers_url": "https://api.github.com/repos/baxterthehacker/public-repo/subscribers",
"subscription_url": "https://api.github.com/repos/baxterthehacker/public-repo/subscription",
"commits_url": "https://api.github.com/repos/baxterthehacker/public-repo/commits{/sha}",
"git_commits_url": "https://api.github.com/repos/baxterthehacker/public-repo/git/commits{/sha}",
"comments_url": "https://api.github.com/repos/baxterthehacker/public-repo/comments{/number}",
"issue_comment_url": "https://api.github.com/repos/baxterthehacker/public-repo/issues/comments{/number}",
"contents_url": "https://api.github.com/repos/baxterthehacker/public-repo/contents/{+path}",
"compare_url": "https://api.github.com/repos/baxterthehacker/public-repo/compare/{base}...{head}",
"merges_url": "https://api.github.com/repos/baxterthehacker/public-repo/merges",
"archive_url": "https://api.github.com/repos/baxterthehacker/public-repo/{archive_format}{/ref}",
"downloads_url": "https://api.github.com/repos/baxterthehacker/public-repo/downloads",
"issues_url": "https://api.github.com/repos/baxterthehacker/public-repo/issues{/number}",
"pulls_url": "https://api.github.com/repos/baxterthehacker/public-repo/pulls{/number}",
"milestones_url": "https://api.github.com/repos/baxterthehacker/public-repo/milestones{/number}",
"notifications_url": "https://api.github.com/repos/baxterthehacker/public-repo/notifications{?since,all,participating}",
"labels_url": "https://api.github.com/repos/baxterthehacker/public-repo/labels{/name}",
"releases_url": "https://api.github.com/repos/baxterthehacker/public-repo/releases{/id}",
"created_at": 1430869212,
"updated_at": "2015-05-05T23:40:12Z",
"pushed_at": 1430869217,
"git_url": "git://github.com/baxterthehacker/public-repo.git",
"ssh_url": "git@github.com:baxterthehacker/public-repo.git",
"clone_url": "https://github.com/baxterthehacker/public-repo.git",
"svn_url": "https://github.com/baxterthehacker/public-repo",
"homepage": null,
"size": 0,
"stargazers_count": 0,
"watchers_count": 0,
"language": null,
"has_issues": true,
"has_downloads": true,
"has_wiki": true,
"has_pages": true,
"forks_count": 0,
"mirror_url": null,
"open_issues_count": 0,
"forks": 0,
"open_issues": 0,
"watchers": 0,
"default_branch": "master",
"stargazers": 0,
"master_branch": "master"
},
"pusher": {
"name": "baxterthehacker",
"email": "baxterthehacker@users.noreply.github.com"
},
"sender": {
"login": "baxterthehacker",
"id": 6752317,
"avatar_url": "https://avatars.githubusercontent.com/u/6752317?v=3",
"gravatar_id": "",
"url": "https://api.github.com/users/baxterthehacker",
"html_url": "https://github.com/baxterthehacker",
"followers_url": "https://api.github.com/users/baxterthehacker/followers",
"following_url": "https://api.github.com/users/baxterthehacker/following{/other_user}",
"gists_url": "https://api.github.com/users/baxterthehacker/gists{/gist_id}",
"starred_url": "https://api.github.com/users/baxterthehacker/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/baxterthehacker/subscriptions",
"organizations_url": "https://api.github.com/users/baxterthehacker/orgs",
"repos_url": "https://api.github.com/users/baxterthehacker/repos",
"events_url": "https://api.github.com/users/baxterthehacker/events{/privacy}",
"received_events_url": "https://api.github.com/users/baxterthehacker/received_events",
"type": "User",
"site_admin": false
}
}"""
PUSH_EVENT_EXAMPLE_INSTALLATION = b"""{
"ref": "refs/heads/changes",
"installation" : {
"id": 12345
},
"before": "9049f1265b7d61be4a8904a9a27120d2064dab3b",
"after": "0d1a26e67d8f5eaf1f6ba5c57fc3c7d91ac0fd1c",
"created": false,
"deleted": false,
"forced": false,
"base_ref": null,
"compare": "https://github.com/baxterthehacker/public-repo/compare/9049f1265b7d...0d1a26e67d8f",
"commits": [
{
"id": "133d60480286590a610a0eb7352ff6e02b9674c4",
"tree_id": "f9d2a07e9488b91af2641b26b9407fe22a451433",
"distinct": true,
"message": "Update README.md (àgain)",
"timestamp": "2015-05-05T19:45:15-04:00",
"url": "https://github.com/baxterthehacker/public-repo/commit/133d60480286590a610a0eb7352ff6e02b9674c4",
"author": {
"name": "bàxterthehacker",
"email": "baxterthehacker@users.noreply.github.com",
"username": "baxterthehacker"
},
"committer": {
"name": "baxterthehacker",
"email": "baxterthehacker@users.noreply.github.com",
"username": "baxterthehacker"
},
"added": [
],
"removed": [
],
"modified": [
"README.md"
]
},
{
"id": "0d1a26e67d8f5eaf1f6ba5c57fc3c7d91ac0fd1c",
"tree_id": "f9d2a07e9488b91af2641b26b9407fe22a451433",
"distinct": true,
"message": "Update README.md",
"timestamp": "2015-05-05T19:40:15-04:00",
"url": "https://github.com/baxterthehacker/public-repo/commit/0d1a26e67d8f5eaf1f6ba5c57fc3c7d91ac0fd1c",
"author": {
"name": "bàxterthehacker",
"email": "baxterthehacker@users.noreply.github.com",
"username": "baxterthehacker"
},
"committer": {
"name": "baxterthehacker",
"email": "baxterthehacker@users.noreply.github.com",
"username": "baxterthehacker"
},
"added": [
],
"removed": [
],
"modified": [
"README.md"
]
},
{
"id": "0d1a26e67d8f5eaf1f6ba5c57fc3c7d91ac0fd1c",
"tree_id": "f9d2a07e9488b91af2641b26b9407fe22a451433",
"distinct": true,
"message": "fix widget #skipsentry",
"timestamp": "2015-05-05T19:40:15-04:00",
"url": "https://github.com/baxterthehacker/public-repo/commit/0d1a26e67d8f5eaf1f6ba5c57fc3c7d91ac0fd1c",
"author": {
"name": "bàxterthehacker",
"email": "baxterthehacker@users.noreply.github.com",
"username": "baxterthehacker"
},
"committer": {
"name": "baxterthehacker",
"email": "baxterthehacker@users.noreply.github.com",
"username": "baxterthehacker"
},
"added": [
],
"removed": [
],
"modified": [
"README.md"
]
}
],
"head_commit": {
"id": "0d1a26e67d8f5eaf1f6ba5c57fc3c7d91ac0fd1c",
"tree_id": "f9d2a07e9488b91af2641b26b9407fe22a451433",
"distinct": true,
"message": "Update README.md",
"timestamp": "2015-05-05T19:40:15-04:00",
"url": "https://github.com/baxterthehacker/public-repo/commit/0d1a26e67d8f5eaf1f6ba5c57fc3c7d91ac0fd1c",
"author": {
"name": "baxterthehacker",
"email": "baxterthehacker@users.noreply.github.com",
"username": "baxterthehacker"
},
"committer": {
"name": "baxterthehacker",
"email": "baxterthehacker@users.noreply.github.com",
"username": "baxterthehacker"
},
"added": [
],
"removed": [
],
"modified": [
"README.md"
]
},
"repository": {
"id": 35129377,
"name": "public-repo",
"full_name": "baxterthehacker/public-repo",
"owner": {
"name": "baxterthehacker",
"email": "baxterthehacker@users.noreply.github.com"
},
"private": false,
"html_url": "https://github.com/baxterthehacker/public-repo",
"description": "",
"fork": false,
"url": "https://github.com/baxterthehacker/public-repo",
"forks_url": "https://api.github.com/repos/baxterthehacker/public-repo/forks",
"keys_url": "https://api.github.com/repos/baxterthehacker/public-repo/keys{/key_id}",
"collaborators_url": "https://api.github.com/repos/baxterthehacker/public-repo/collaborators{/collaborator}",
"teams_url": "https://api.github.com/repos/baxterthehacker/public-repo/teams",
"hooks_url": "https://api.github.com/repos/baxterthehacker/public-repo/hooks",
"issue_events_url": "https://api.github.com/repos/baxterthehacker/public-repo/issues/events{/number}",
"events_url": "https://api.github.com/repos/baxterthehacker/public-repo/events",
"assignees_url": "https://api.github.com/repos/baxterthehacker/public-repo/assignees{/user}",
"branches_url": "https://api.github.com/repos/baxterthehacker/public-repo/branches{/branch}",
"tags_url": "https://api.github.com/repos/baxterthehacker/public-repo/tags",
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"starred_url": "https://api.github.com/users/octocat/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/octocat/subscriptions",
"organizations_url": "https://api.github.com/users/octocat/orgs",
"repos_url": "https://api.github.com/users/octocat/repos",
"events_url": "https://api.github.com/users/octocat/events{/privacy}",
"received_events_url": "https://api.github.com/users/octocat/received_events",
"type": "User",
"site_admin": false
},
"access_tokens_url": "https://api.github.com/installations/2/access_tokens",
"repositories_url": "https://api.github.com/installation/repositories"
},
"sender": {
"login": "octocat",
"id": 1,
"avatar_url": "https://github.com/images/error/octocat_happy.gif",
"gravatar_id": "",
"url": "https://api.github.com/users/octocat",
"html_url": "https://github.com/octocat",
"followers_url": "https://api.github.com/users/octocat/followers",
"following_url": "https://api.github.com/users/octocat/following{/other_user}",
"gists_url": "https://api.github.com/users/octocat/gists{/gist_id}",
"starred_url": "https://api.github.com/users/octocat/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/octocat/subscriptions",
"organizations_url": "https://api.github.com/users/octocat/orgs",
"repos_url": "https://api.github.com/users/octocat/repos",
"events_url": "https://api.github.com/users/octocat/events{/privacy}",
"received_events_url": "https://api.github.com/users/octocat/received_events",
"type": "User",
"site_admin": false
}
}"""
INSTALLATION_REPO_EVENT = """{
"action": "added",
"installation": {
"id": 2,
"account": {
"login": "octocat",
"id": 1,
"avatar_url": "https://github.com/images/error/octocat_happy.gif",
"gravatar_id": "",
"url": "https://api.github.com/users/octocat",
"html_url": "https://github.com/octocat",
"followers_url": "https://api.github.com/users/octocat/followers",
"following_url": "https://api.github.com/users/octocat/following{/other_user}",
"gists_url": "https://api.github.com/users/octocat/gists{/gist_id}",
"starred_url": "https://api.github.com/users/octocat/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/octocat/subscriptions",
"organizations_url": "https://api.github.com/users/octocat/orgs",
"repos_url": "https://api.github.com/users/octocat/repos",
"events_url": "https://api.github.com/users/octocat/events{/privacy}",
"received_events_url": "https://api.github.com/users/octocat/received_events",
"type": "User",
"site_admin": false
},
"access_tokens_url": "https://api.github.com/installations/2/access_tokens",
"repositories_url": "https://api.github.com/installation/repositories",
"html_url": "https://github.com/settings/installations/2"
},
"repository_selection": "selected",
"repositories_added": [
{
"id": 1296269,
"name": "Hello-World",
"full_name": "octocat/Hello-World"
}
],
"repositories_removed": [
],
"sender": {
"login": "octocat",
"id": 1,
"avatar_url": "https://github.com/images/error/octocat_happy.gif",
"gravatar_id": "",
"url": "https://api.github.com/users/octocat",
"html_url": "https://github.com/octocat",
"followers_url": "https://api.github.com/users/octocat/followers",
"following_url": "https://api.github.com/users/octocat/following{/other_user}",
"gists_url": "https://api.github.com/users/octocat/gists{/gist_id}",
"starred_url": "https://api.github.com/users/octocat/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/octocat/subscriptions",
"organizations_url": "https://api.github.com/users/octocat/orgs",
"repos_url": "https://api.github.com/users/octocat/repos",
"events_url": "https://api.github.com/users/octocat/events{/privacy}",
"received_events_url": "https://api.github.com/users/octocat/received_events",
"type": "User",
"site_admin": false
}
}"""
INTSTALLATION_REPOSITORIES_API_RESPONSE = """{
"total_count": 1,
"repositories": [
{
"id": 1296269,
"owner": {
"login": "octocat",
"id": 1,
"avatar_url": "https://github.com/images/error/octocat_happy.gif",
"gravatar_id": "",
"url": "https://api.github.com/users/octocat",
"html_url": "https://github.com/octocat",
"followers_url": "https://api.github.com/users/octocat/followers",
"following_url": "https://api.github.com/users/octocat/following{/other_user}",
"gists_url": "https://api.github.com/users/octocat/gists{/gist_id}",
"starred_url": "https://api.github.com/users/octocat/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/octocat/subscriptions",
"organizations_url": "https://api.github.com/users/octocat/orgs",
"repos_url": "https://api.github.com/users/octocat/repos",
"events_url": "https://api.github.com/users/octocat/events{/privacy}",
"received_events_url": "https://api.github.com/users/octocat/received_events",
"type": "User",
"site_admin": false
},
"name": "Hello-World",
"full_name": "octocat/Hello-World",
"description": "This your first repo!",
"private": false,
"fork": false,
"url": "https://api.github.com/repos/octocat/Hello-World",
"html_url": "https://github.com/octocat/Hello-World",
"archive_url": "http://api.github.com/repos/octocat/Hello-World/{archive_format}{/ref}",
"assignees_url": "http://api.github.com/repos/octocat/Hello-World/assignees{/user}",
"blobs_url": "http://api.github.com/repos/octocat/Hello-World/git/blobs{/sha}",
"branches_url": "http://api.github.com/repos/octocat/Hello-World/branches{/branch}",
"clone_url": "https://github.com/octocat/Hello-World.git",
"collaborators_url": "http://api.github.com/repos/octocat/Hello-World/collaborators{/collaborator}",
"comments_url": "http://api.github.com/repos/octocat/Hello-World/comments{/number}",
"commits_url": "http://api.github.com/repos/octocat/Hello-World/commits{/sha}",
"compare_url": "http://api.github.com/repos/octocat/Hello-World/compare/{base}...{head}",
"contents_url": "http://api.github.com/repos/octocat/Hello-World/contents/{+path}",
"contributors_url": "http://api.github.com/repos/octocat/Hello-World/contributors",
"deployments_url": "http://api.github.com/repos/octocat/Hello-World/deployments",
"downloads_url": "http://api.github.com/repos/octocat/Hello-World/downloads",
"events_url": "http://api.github.com/repos/octocat/Hello-World/events",
"forks_url": "http://api.github.com/repos/octocat/Hello-World/forks",
"git_commits_url": "http://api.github.com/repos/octocat/Hello-World/git/commits{/sha}",
"git_refs_url": "http://api.github.com/repos/octocat/Hello-World/git/refs{/sha}",
"git_tags_url": "http://api.github.com/repos/octocat/Hello-World/git/tags{/sha}",
"git_url": "git:github.com/octocat/Hello-World.git",
"hooks_url": "http://api.github.com/repos/octocat/Hello-World/hooks",
"issue_comment_url": "http://api.github.com/repos/octocat/Hello-World/issues/comments{/number}",
"issue_events_url": "http://api.github.com/repos/octocat/Hello-World/issues/events{/number}",
"issues_url": "http://api.github.com/repos/octocat/Hello-World/issues{/number}",
"keys_url": "http://api.github.com/repos/octocat/Hello-World/keys{/key_id}",
"labels_url": "http://api.github.com/repos/octocat/Hello-World/labels{/name}",
"languages_url": "http://api.github.com/repos/octocat/Hello-World/languages",
"merges_url": "http://api.github.com/repos/octocat/Hello-World/merges",
"milestones_url": "http://api.github.com/repos/octocat/Hello-World/milestones{/number}",
"mirror_url": "git:git.example.com/octocat/Hello-World",
"notifications_url": "http://api.github.com/repos/octocat/Hello-World/notifications{?since, all, participating}",
"pulls_url": "http://api.github.com/repos/octocat/Hello-World/pulls{/number}",
"releases_url": "http://api.github.com/repos/octocat/Hello-World/releases{/id}",
"ssh_url": "git@github.com:octocat/Hello-World.git",
"stargazers_url": "http://api.github.com/repos/octocat/Hello-World/stargazers",
"statuses_url": "http://api.github.com/repos/octocat/Hello-World/statuses/{sha}",
"subscribers_url": "http://api.github.com/repos/octocat/Hello-World/subscribers",
"subscription_url": "http://api.github.com/repos/octocat/Hello-World/subscription",
"svn_url": "https://svn.github.com/octocat/Hello-World",
"tags_url": "http://api.github.com/repos/octocat/Hello-World/tags",
"teams_url": "http://api.github.com/repos/octocat/Hello-World/teams",
"trees_url": "http://api.github.com/repos/octocat/Hello-World/git/trees{/sha}",
"homepage": "https://github.com",
"language": null,
"forks_count": 9,
"stargazers_count": 80,
"watchers_count": 80,
"size": 108,
"default_branch": "master",
"open_issues_count": 0,
"topics": [
"octocat",
"atom",
"electron",
"API"
],
"has_issues": true,
"has_wiki": true,
"has_pages": false,
"has_downloads": true,
"pushed_at": "2011-01-26T19:06:43Z",
"created_at": "2011-01-26T19:01:12Z",
"updated_at": "2011-01-26T19:14:43Z",
"allow_rebase_merge": true,
"allow_squash_merge": true,
"allow_merge_commit": true,
"subscribers_count": 42,
"network_count": 0
}
]
}"""
LIST_INSTALLATION_API_RESPONSE = """{
"total_count": 2,
"installations": [
{
"id": 1,
"account": {
"login": "github",
"id": 1,
"url": "https://api.github.com/orgs/github",
"repos_url": "https://api.github.com/orgs/github/repos",
"events_url": "https://api.github.com/orgs/github/events",
"hooks_url": "https://api.github.com/orgs/github/hooks",
"issues_url": "https://api.github.com/orgs/github/issues",
"members_url": "https://api.github.com/orgs/github/members{/member}",
"public_members_url": "https://api.github.com/orgs/github/public_members{/member}",
"avatar_url": "https://github.com/images/error/octocat_happy.gif",
"description": "A great organization"
},
"access_tokens_url": "https://api.github.com/installations/1/access_tokens",
"repositories_url": "https://api.github.com/installation/repositories",
"html_url": "https://github.com/organizations/github/settings/installations/1",
"app_id": 1,
"target_id": 1,
"target_type": "Organization",
"permissions": {
"metadata": "read",
"contents": "read",
"issues": "write",
"single_file": "write"
},
"events": [
"push",
"pull_request"
],
"single_file_name": "config.yml"
},
{
"id": 3,
"account": {
"login": "octocat",
"id": 2,
"avatar_url": "https://github.com/images/error/octocat_happy.gif",
"gravatar_id": "",
"url": "https://api.github.com/users/octocat",
"html_url": "https://github.com/octocat",
"followers_url": "https://api.github.com/users/octocat/followers",
"following_url": "https://api.github.com/users/octocat/following{/other_user}",
"gists_url": "https://api.github.com/users/octocat/gists{/gist_id}",
"starred_url": "https://api.github.com/users/octocat/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/octocat/subscriptions",
"organizations_url": "https://api.github.com/users/octocat/orgs",
"repos_url": "https://api.github.com/users/octocat/repos",
"events_url": "https://api.github.com/users/octocat/events{/privacy}",
"received_events_url": "https://api.github.com/users/octocat/received_events",
"type": "User",
"site_admin": false
},
"access_tokens_url": "https://api.github.com/installations/1/access_tokens",
"repositories_url": "https://api.github.com/installation/repositories",
"html_url": "https://github.com/organizations/github/settings/installations/1",
"app_id": 1,
"target_id": 1,
"target_type": "Organization",
"permissions": {
"metadata": "read",
"contents": "read",
"issues": "write",
"single_file": "write"
},
"events": [
"push",
"pull_request"
],
"single_file_name": "config.yml"
}
]
}"""
PULL_REQUEST_OPENED_EVENT_EXAMPLE = b"""{
"action": "opened",
"number": 1,
"pull_request": {
"url": "https://api.github.com/repos/baxterthehacker/public-repo/pulls/1",
"id": 34778301,
"html_url": "https://github.com/baxterthehacker/public-repo/pull/1",
"diff_url": "https://github.com/baxterthehacker/public-repo/pull/1.diff",
"patch_url": "https://github.com/baxterthehacker/public-repo/pull/1.patch",
"issue_url": "https://api.github.com/repos/baxterthehacker/public-repo/issues/1",
"number": 1,
"state": "open",
"locked": false,
"title": "Update the README with new information",
"user": {
"login": "baxterthehacker",
"id": 6752317,
"avatar_url": "https://avatars.githubusercontent.com/u/6752317?v=3",
"gravatar_id": "",
"url": "https://api.github.com/users/baxterthehacker",
"html_url": "https://github.com/baxterthehacker",
"followers_url": "https://api.github.com/users/baxterthehacker/followers",
"following_url": "https://api.github.com/users/baxterthehacker/following{/other_user}",
"gists_url": "https://api.github.com/users/baxterthehacker/gists{/gist_id}",
"starred_url": "https://api.github.com/users/baxterthehacker/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/baxterthehacker/subscriptions",
"organizations_url": "https://api.github.com/users/baxterthehacker/orgs",
"repos_url": "https://api.github.com/users/baxterthehacker/repos",
"events_url": "https://api.github.com/users/baxterthehacker/events{/privacy}",
"received_events_url": "https://api.github.com/users/baxterthehacker/received_events",
"type": "User",
"site_admin": false
},
"body": "This is a pretty simple change that we need to pull into master.",
"created_at": "2015-05-05T23:40:27Z",
"updated_at": "2015-05-05T23:40:27Z",
"closed_at": null,
"merged_at": null,
"merge_commit_sha": null,
"assignee": null,
"milestone": null,
"commits_url": "https://api.github.com/repos/baxterthehacker/public-repo/pulls/1/commits",
"review_comments_url": "https://api.github.com/repos/baxterthehacker/public-repo/pulls/1/comments",
"review_comment_url": "https://api.github.com/repos/baxterthehacker/public-repo/pulls/comments{/number}",
"comments_url": "https://api.github.com/repos/baxterthehacker/public-repo/issues/1/comments",
"statuses_url": "https://api.github.com/repos/baxterthehacker/public-repo/statuses/0d1a26e67d8f5eaf1f6ba5c57fc3c7d91ac0fd1c",
"head": {
"label": "baxterthehacker:changes",
"ref": "changes",
"sha": "0d1a26e67d8f5eaf1f6ba5c57fc3c7d91ac0fd1c",
"user": {
"login": "baxterthehacker",
"id": 6752317,
"avatar_url": "https://avatars.githubusercontent.com/u/6752317?v=3",
"gravatar_id": "",
"url": "https://api.github.com/users/baxterthehacker",
"html_url": "https://github.com/baxterthehacker",
"followers_url": "https://api.github.com/users/baxterthehacker/followers",
"following_url": "https://api.github.com/users/baxterthehacker/following{/other_user}",
"gists_url": "https://api.github.com/users/baxterthehacker/gists{/gist_id}",
"starred_url": "https://api.github.com/users/baxterthehacker/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/baxterthehacker/subscriptions",
"organizations_url": "https://api.github.com/users/baxterthehacker/orgs",
"repos_url": "https://api.github.com/users/baxterthehacker/repos",
"events_url": "https://api.github.com/users/baxterthehacker/events{/privacy}",
"received_events_url": "https://api.github.com/users/baxterthehacker/received_events",
"type": "User",
"site_admin": false
},
"repo": {
"id": 35129377,
"name": "public-repo",
"full_name": "baxterthehacker/public-repo",
"owner": {
"login": "baxterthehacker",
"id": 6752317,
"avatar_url": "https://avatars.githubusercontent.com/u/6752317?v=3",
"gravatar_id": "",
"url": "https://api.github.com/users/baxterthehacker",
"html_url": "https://github.com/baxterthehacker",
"followers_url": "https://api.github.com/users/baxterthehacker/followers",
"following_url": "https://api.github.com/users/baxterthehacker/following{/other_user}",
"gists_url": "https://api.github.com/users/baxterthehacker/gists{/gist_id}",
"starred_url": "https://api.github.com/users/baxterthehacker/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/baxterthehacker/subscriptions",
"organizations_url": "https://api.github.com/users/baxterthehacker/orgs",
"repos_url": "https://api.github.com/users/baxterthehacker/repos",
"events_url": "https://api.github.com/users/baxterthehacker/events{/privacy}",
"received_events_url": "https://api.github.com/users/baxterthehacker/received_events",
"type": "User",
"site_admin": false
},
"private": false,
"html_url": "https://github.com/baxterthehacker/public-repo",
"description": "",
"fork": false,
"url": "https://api.github.com/repos/baxterthehacker/public-repo",
"forks_url": "https://api.github.com/repos/baxterthehacker/public-repo/forks",
"keys_url": "https://api.github.com/repos/baxterthehacker/public-repo/keys{/key_id}",
"collaborators_url": "https://api.github.com/repos/baxterthehacker/public-repo/collaborators{/collaborator}",
"teams_url": "https://api.github.com/repos/baxterthehacker/public-repo/teams",
"hooks_url": "https://api.github.com/repos/baxterthehacker/public-repo/hooks",
"issue_events_url": "https://api.github.com/repos/baxterthehacker/public-repo/issues/events{/number}",
"events_url": "https://api.github.com/repos/baxterthehacker/public-repo/events",
"assignees_url": "https://api.github.com/repos/baxterthehacker/public-repo/assignees{/user}",
"branches_url": "https://api.github.com/repos/baxterthehacker/public-repo/branches{/branch}",
"tags_url": "https://api.github.com/repos/baxterthehacker/public-repo/tags",
"blobs_url": "https://api.github.com/repos/baxterthehacker/public-repo/git/blobs{/sha}",
"git_tags_url": "https://api.github.com/repos/baxterthehacker/public-repo/git/tags{/sha}",
"git_refs_url": "https://api.github.com/repos/baxterthehacker/public-repo/git/refs{/sha}",
"trees_url": "https://api.github.com/repos/baxterthehacker/public-repo/git/trees{/sha}",
"statuses_url": "https://api.github.com/repos/baxterthehacker/public-repo/statuses/{sha}",
"languages_url": "https://api.github.com/repos/baxterthehacker/public-repo/languages",
"stargazers_url": "https://api.github.com/repos/baxterthehacker/public-repo/stargazers",
"contributors_url": "https://api.github.com/repos/baxterthehacker/public-repo/contributors",
"subscribers_url": "https://api.github.com/repos/baxterthehacker/public-repo/subscribers",
"subscription_url": "https://api.github.com/repos/baxterthehacker/public-repo/subscription",
"commits_url": "https://api.github.com/repos/baxterthehacker/public-repo/commits{/sha}",
"git_commits_url": "https://api.github.com/repos/baxterthehacker/public-repo/git/commits{/sha}",
"comments_url": "https://api.github.com/repos/baxterthehacker/public-repo/comments{/number}",
"issue_comment_url": "https://api.github.com/repos/baxterthehacker/public-repo/issues/comments{/number}",
"contents_url": "https://api.github.com/repos/baxterthehacker/public-repo/contents/{+path}",
"compare_url": "https://api.github.com/repos/baxterthehacker/public-repo/compare/{base}...{head}",
"merges_url": "https://api.github.com/repos/baxterthehacker/public-repo/merges",
"archive_url": "https://api.github.com/repos/baxterthehacker/public-repo/{archive_format}{/ref}",
"downloads_url": "https://api.github.com/repos/baxterthehacker/public-repo/downloads",
"issues_url": "https://api.github.com/repos/baxterthehacker/public-repo/issues{/number}",
"pulls_url": "https://api.github.com/repos/baxterthehacker/public-repo/pulls{/number}",
"milestones_url": "https://api.github.com/repos/baxterthehacker/public-repo/milestones{/number}",
"notifications_url": "https://api.github.com/repos/baxterthehacker/public-repo/notifications{?since,all,participating}",
"labels_url": "https://api.github.com/repos/baxterthehacker/public-repo/labels{/name}",
"releases_url": "https://api.github.com/repos/baxterthehacker/public-repo/releases{/id}",
"created_at": "2015-05-05T23:40:12Z",
"updated_at": "2015-05-05T23:40:12Z",
"pushed_at": "2015-05-05T23:40:26Z",
"git_url": "git://github.com/baxterthehacker/public-repo.git",
"ssh_url": "git@github.com:baxterthehacker/public-repo.git",
"clone_url": "https://github.com/baxterthehacker/public-repo.git",
"svn_url": "https://github.com/baxterthehacker/public-repo",
"homepage": null,
"size": 0,
"stargazers_count": 0,
"watchers_count": 0,
"language": null,
"has_issues": true,
"has_downloads": true,
"has_wiki": true,
"has_pages": true,
"forks_count": 0,
"mirror_url": null,
"open_issues_count": 1,
"forks": 0,
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"hooks_url": "https://api.github.com/repos/baxterthehacker/public-repo/hooks",
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},
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"collaborators_url": "https://api.github.com/repos/baxterthehacker/public-repo/collaborators{/collaborator}",
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"pulls_url": "https://api.github.com/repos/baxterthehacker/public-repo/pulls{/number}",
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"html_url": "https://github.com/baxterthehacker",
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}"""
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"received_events_url": "https://api.github.com/users/baxterthehacker/received_events",
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},
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"html_url": "https://github.com/baxterthehacker",
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"collaborators_url": "https://api.github.com/repos/baxterthehacker/public-repo/collaborators{/collaborator}",
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"hooks_url": "https://api.github.com/repos/baxterthehacker/public-repo/hooks",
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"events_url": "https://api.github.com/repos/baxterthehacker/public-repo/events",
"assignees_url": "https://api.github.com/repos/baxterthehacker/public-repo/assignees{/user}",
"branches_url": "https://api.github.com/repos/baxterthehacker/public-repo/branches{/branch}",
"tags_url": "https://api.github.com/repos/baxterthehacker/public-repo/tags",
"blobs_url": "https://api.github.com/repos/baxterthehacker/public-repo/git/blobs{/sha}",
"git_tags_url": "https://api.github.com/repos/baxterthehacker/public-repo/git/tags{/sha}",
"git_refs_url": "https://api.github.com/repos/baxterthehacker/public-repo/git/refs{/sha}",
"trees_url": "https://api.github.com/repos/baxterthehacker/public-repo/git/trees{/sha}",
"statuses_url": "https://api.github.com/repos/baxterthehacker/public-repo/statuses/{sha}",
"languages_url": "https://api.github.com/repos/baxterthehacker/public-repo/languages",
"stargazers_url": "https://api.github.com/repos/baxterthehacker/public-repo/stargazers",
"contributors_url": "https://api.github.com/repos/baxterthehacker/public-repo/contributors",
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"subscription_url": "https://api.github.com/repos/baxterthehacker/public-repo/subscription",
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"contents_url": "https://api.github.com/repos/baxterthehacker/public-repo/contents/{+path}",
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},
"html": {
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},
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"forks_url": "https://api.github.com/repos/baxterthehacker/public-repo/forks",
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"collaborators_url": "https://api.github.com/repos/baxterthehacker/public-repo/collaborators{/collaborator}",
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"hooks_url": "https://api.github.com/repos/baxterthehacker/public-repo/hooks",
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"events_url": "https://api.github.com/repos/baxterthehacker/public-repo/events",
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"blobs_url": "https://api.github.com/repos/baxterthehacker/public-repo/git/blobs{/sha}",
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"languages_url": "https://api.github.com/repos/baxterthehacker/public-repo/languages",
"stargazers_url": "https://api.github.com/repos/baxterthehacker/public-repo/stargazers",
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"pulls_url": "https://api.github.com/repos/baxterthehacker/public-repo/pulls{/number}",
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"html_url": "https://github.com/baxterthehacker",
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}"""
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"received_events_url": "https://api.github.com/users/baxterthehacker/received_events",
"type": "User",
"site_admin": false
},
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"updated_at": "2015-05-05T23:40:27Z",
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"commits_url": "https://api.github.com/repos/baxterthehacker/public-repo/pulls/1/commits",
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"comments_url": "https://api.github.com/repos/baxterthehacker/public-repo/issues/1/comments",
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"received_events_url": "https://api.github.com/users/baxterthehacker/received_events",
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},
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},
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| 53.288084 | 168 | 0.664261 | 13,864 | 122,083 | 5.73341 | 0.023442 | 0.119792 | 0.13285 | 0.180505 | 0.970071 | 0.969052 | 0.967907 | 0.965227 | 0.95978 | 0.94574 | 0 | 0.034813 | 0.148014 | 122,083 | 2,290 | 169 | 53.311354 | 0.729399 | 0.000688 | 0 | 0.859025 | 0 | 0.235837 | 0.996065 | 0.058346 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.000439 | null | null | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 |
4373a42e56b1688b00c60595fbc74852878c3c70 | 167 | py | Python | setup.py | Zeroto521/my-data-toolkit | bde37f625aa81e65b97648798535f6d931864888 | [
"MIT"
] | 1 | 2021-10-09T04:50:58.000Z | 2021-10-09T04:50:58.000Z | setup.py | Zeroto521/my-data-toolkit | bde37f625aa81e65b97648798535f6d931864888 | [
"MIT"
] | 427 | 2021-06-04T02:40:22.000Z | 2022-03-30T12:55:52.000Z | setup.py | Zeroto521/my-data-toolkit | bde37f625aa81e65b97648798535f6d931864888 | [
"MIT"
] | 1 | 2021-07-09T09:56:44.000Z | 2021-07-09T09:56:44.000Z | from setuptools import setup
from versioneer import get_cmdclass
from versioneer import get_version
setup(
version=get_version(),
cmdclass=get_cmdclass(),
)
| 16.7 | 35 | 0.784431 | 21 | 167 | 6.047619 | 0.380952 | 0.220472 | 0.314961 | 0.362205 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.155689 | 167 | 9 | 36 | 18.555556 | 0.900709 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 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 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 7 |
43bb222673b11ca2a5cf2f88767d5a40c03ab04d | 983 | py | Python | links.py | ytx21cn/material-icons-navitation-arrows | ba7b095b3154267c58535b1da4a0b4957659a49c | [
"Apache-2.0"
] | null | null | null | links.py | ytx21cn/material-icons-navitation-arrows | ba7b095b3154267c58535b1da4a0b4957659a49c | [
"Apache-2.0"
] | null | null | null | links.py | ytx21cn/material-icons-navitation-arrows | ba7b095b3154267c58535b1da4a0b4957659a49c | [
"Apache-2.0"
] | null | null | null | all_links = [
# Group 1
'https://fonts.gstatic.com/s/i/materialicons/arrow_back/v6/24px.svg',
'https://fonts.gstatic.com/s/i/materialicons/arrow_downward/v6/24px.svg',
'https://fonts.gstatic.com/s/i/materialicons/arrow_forward/v6/24px.svg',
'https://fonts.gstatic.com/s/i/materialicons/arrow_upward/v5/24px.svg',
# Group 2
'https://fonts.gstatic.com/s/i/materialicons/east/v2/24px.svg',
'https://fonts.gstatic.com/s/i/materialicons/north/v2/24px.svg',
'https://fonts.gstatic.com/s/i/materialicons/north_east/v2/24px.svg',
'https://fonts.gstatic.com/s/i/materialicons/north_west/v2/24px.svg',
'https://fonts.gstatic.com/s/i/materialicons/south/v2/24px.svg',
'https://fonts.gstatic.com/s/i/materialicons/south_east/v2/24px.svg',
'https://fonts.gstatic.com/s/i/materialicons/south_west/v2/24px.svg',
'https://fonts.gstatic.com/s/i/materialicons/west/v2/24px.svg',
]
common_prefix = 'https://fonts.gstatic.com/s/i/materialicons/'
| 51.736842 | 77 | 0.716175 | 155 | 983 | 4.477419 | 0.180645 | 0.18732 | 0.318444 | 0.37464 | 0.886167 | 0.886167 | 0.886167 | 0.785303 | 0.727666 | 0.727666 | 0 | 0.042175 | 0.083418 | 983 | 18 | 78 | 54.611111 | 0.72808 | 0.015259 | 0 | 0 | 0 | 0.8 | 0.85285 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
78f7c0eb438c385bf26b1cab9bdd465df152294c | 8,606 | py | Python | AlphaBeta.py | fatalkiller/Teeko | c89ae47e4d3f88d921497964c2e2c32cb682621c | [
"MIT"
] | null | null | null | AlphaBeta.py | fatalkiller/Teeko | c89ae47e4d3f88d921497964c2e2c32cb682621c | [
"MIT"
] | null | null | null | AlphaBeta.py | fatalkiller/Teeko | c89ae47e4d3f88d921497964c2e2c32cb682621c | [
"MIT"
] | null | null | null | def min_pose(model, p, alpha, beta, eval_enable):
# Teste si on doit poser ou déplacer un pion
if model.pose == 0:
return min_deplace(model, p, alpha, beta, eval_enable)
# Vérif noeud terminal
if model.gagnant:
model.gagnant = False
return 100 + p
if p == 0:
if eval_enable:
return model.evaluation()
else:
return 0
# Init v pour un min
v = 1000
# Parcoure du tableau de jeu
for i in range(5):
for j in range(5):
# Test si plateau[j][i] déjà pris
# On pose le pion à l'endroit souhaité
if model.pose_pion(j, i):
val = max_pose(model, p - 1, alpha, beta, eval_enable)
# p impair, alors on fait un min
v = min(v, val)
# On annule le coup effectué
model.plateau[j][i] = 0
model.change_tour()
model.pose += 1
# Arrêter la recherche dans cette branche
# si valeur min actuelle est de toute façon minimale
if v <= alpha:
return v
# Diminue beta pour accélerer la recherche du max
beta = min(beta, v)
return v
def max_pose(model, p, alpha, beta, eval_enable):
# Teste si on doit poser ou déplacer un pion
if model.pose == 0:
return max_deplace(model, p, alpha, beta, eval_enable)
# Vérif noeud terminal
if model.gagnant:
model.gagnant = False
return -100 - p
if p == 0:
if eval_enable:
return model.evaluation()
else:
return 0
# Init v pour un max
v = -1000
# Parcoure du tableau de jeu
for i in range(5):
for j in range(5):
# Test si plateau[j][i] déjà pris
# On pose le pion à l'endroit souhaité
if model.pose_pion(j, i):
val = min_pose(model, p - 1, alpha, beta, eval_enable)
# p pair, alors on fait un max
v = max(v, val)
# On annule le coup effectué
model.plateau[j][i] = 0
model.change_tour()
model.pose += 1
# Arrêter la recherche dans cette branche
# si valeur max actuelle est de toute façon maximale
if v >= beta:
return v
# Diminue alpha pour accélerer la recherche du min
alpha = max(alpha, v)
return v
def min_deplace(model, p, alpha, beta, eval_enable):
# Vérif noeud terminal
if model.gagnant:
model.gagnant = False
return 100 + p
if p == 0:
if eval_enable:
return model.evaluation()
else:
return 0
# Init v pour un min
v = 1000
# Parcoure du tableau de jeu
for i in range(5):
for j in range(5):
# Teste si la position courante est occupée
if model.plateau[j][i] == model.tour:
moves = model.mouvement_possible(j, i)
# On parcoure les mouvements possibles
for m in moves:
# On déplace le pion
model.plateau[m[0]][m[1]] = model.tour
# On enlève le pion à l'emplacement précédent
model.plateau[j][i] = 0
model.change_tour()
val = max_deplace(model, p - 1, alpha, beta, eval_enable)
# p impair, alors on fait un min
v = min(v, val)
# On annule le coup effectué
model.change_tour()
model.plateau[m[0]][m[1]] = 0
model.plateau[j][i] = model.tour
# Arrêter la recherche dans cette branche
# si valeur min actuelle est de toute façon minimale
if v <= alpha:
return v
# Diminue beta pour accélerer la recherche du max
beta = min(beta, v)
return v
def max_deplace(model, p, alpha, beta, eval_enable):
# Vérif noeud terminal
if model.gagnant:
model.gagnant = False
return -100 - p
if p == 0:
if eval_enable:
return model.evaluation()
else:
return 0
# Init v pour un max
v = -1000
# Parcoure du tableau de jeu
for i in range(5):
for j in range(5):
# Teste si la position courante est occupée
if model.plateau[j][i] == model.tour:
moves = model.mouvement_possible(j, i)
# On parcoure les mouvements possibles
for m in moves:
# On déplace le pion
model.plateau[m[0]][m[1]] = model.tour
# On enlève le pion à l'emplacement précédent
model.plateau[j][i] = 0
model.change_tour()
val = min_deplace(model, p - 1, alpha, beta, eval_enable)
# p pair, alors on fait un max
v = max(v, val)
# On annule le coup effectué
model.change_tour()
model.plateau[m[0]][m[1]] = 0
model.plateau[j][i] = model.tour
# Arrêter la recherche dans cette branche
# si valeur max actuelle est de toute façon maximale
if v >= beta:
return v
# Diminue alpha pour accélerer la recherche du min
alpha = max(alpha, v)
return v
def min_max_pose(model, p, alpha, beta, eval_enable):
# Init du coup retourné
coup = []
# Init v pour un max
v = -1000
# Parcoure du tableau de jeu
for i in range(5):
for j in range(5):
# Test si plateau[j][i] déjà pris
# On pose le pion à l'endroit souhaité
if model.pose_pion(j, i):
val = min_pose(model, p - 1, alpha, beta, eval_enable)
# p pair, alors on fait un max
if v < val:
v = val
coup = [j, i]
# On annule le coup effectué
model.plateau[j][i] = 0
model.change_tour()
model.pose += 1
# Diminue alpha pour accélerer la recherche du min
alpha = max(alpha, v)
# On pose le pion au meilleur emplacement
model.pose_pion(coup[0], coup[1])
def min_max_deplace(model, p, alpha, beta, eval_enable):
# Init du coup retourné
coup = []
# Init v pour un max
v = -1000
# Parcoure du tableau de jeu
for i in range(5):
for j in range(5):
# Teste si la position courante est occupée
if model.plateau[j][i] == model.tour:
moves = model.mouvement_possible(j, i)
# On parcoure les mouvements possibles
for m in moves:
# On déplace le pion
model.plateau[m[0]][m[1]] = model.tour
# On enlève le pion à l'emplacement précédent
model.plateau[j][i] = 0
model.change_tour()
val = min_deplace(model, p - 1, alpha, beta, eval_enable)
# p pair, alors on fait un max
if v < val:
v = val
model.pion = [j, i]
coup = m
# On annule le coup effectué
model.change_tour()
model.plateau[m[0]][m[1]] = 0
model.plateau[j][i] = model.tour
# Diminue alpha pour accélerer la recherche du min
alpha = max(alpha, v)
# On déplace le pion au meilleur emplacement
model.deplace_pion(coup[0], coup[1])
def min_max(model, p, eval_enable):
# Init alpha et beta a +/- "infini"
alpha = -1000
beta = 1000
model.ia_en_cours = model.tour
if model.pose > 0:
min_max_pose(model, p, alpha, beta, eval_enable)
else:
min_max_deplace(model, p, alpha, beta, eval_enable)
model.ia_en_cours = 0
| 31.756458 | 78 | 0.478039 | 1,059 | 8,606 | 3.825307 | 0.098206 | 0.011355 | 0.051345 | 0.075043 | 0.959269 | 0.959269 | 0.943471 | 0.943471 | 0.933597 | 0.907924 | 0 | 0.021451 | 0.447479 | 8,606 | 270 | 79 | 31.874074 | 0.830494 | 0.259703 | 0 | 0.843537 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.047619 | false | 0 | 0 | 0 | 0.197279 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 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 | 7 |
60a9fd4745a5405c7c5a0df0f2f28949a1596303 | 4,574 | py | Python | src/sqlTools/BookTools.py | XPH0904/Library-management-system | 9990654070caa9f757af9a6f4771ce4b1b484083 | [
"Apache-2.0"
] | null | null | null | src/sqlTools/BookTools.py | XPH0904/Library-management-system | 9990654070caa9f757af9a6f4771ce4b1b484083 | [
"Apache-2.0"
] | null | null | null | src/sqlTools/BookTools.py | XPH0904/Library-management-system | 9990654070caa9f757af9a6f4771ce4b1b484083 | [
"Apache-2.0"
] | null | null | null | import traceback
import mysql.connector
from ..model.Book import *
from ..database.database import DatabaseTools
class BookTools:
def BookData(self):
db = DatabaseTools()
conn = db.getConn()
result_set = None
ls = []
try :
sql = "select idBook,nameBook,price,kind,author,publisher from Book"
mycursor = conn.cursor()
mycursor.execute(sql)
result_set = mycursor.fetchall()
for row in result_set:
book = Book()
book.setIdBook(row[0])
book.setNameBook(row[1])
book.setPrice(row[2])
book.setType(row[3])
book.setAuthor(row[4])
book.setPublisher(row[5])
ls.append(book.list_return())
mycursor.close()
conn.close()
except Exception as e:
traceback.print_exc()
return ls
def BookDataName(self, nameBook):
db = DatabaseTools()
conn = db.getConn()
result_set = None
ls = []
try:
sql = "select idBook,nameBook,price,kind,author,publisher from Book where nameBook like %s"
answer= ("%"+str(nameBook)+"%",)
mycursor = conn.cursor()
mycursor.execute(sql,answer)
result_set = mycursor.fetchall()
for row in result_set:
book = Book()
book.setIdBook(row[0])
book.setNameBook(row[1])
book.setPrice(row[2])
book.setType(row[3])
book.setAuthor(row[4])
book.setPublisher(row[5])
ls.append(book.list_return())
mycursor.close()
conn.close()
except Exception as e:
traceback.print_exc()
return ls
def Search_Book(self, idBook):
db = DatabaseTools()
conn = db.getConn()
result_set = None
book = None
ls = []
try:
sql = "select idBook,nameBook,price,kind,author,publisher from Book where idBook= %s "
answer = (str(idBook),)
mycursor = conn.cursor()
mycursor.execute(sql,answer)
result_set = mycursor.fetchall()
for row in result_set:
book = Book()
book.setIdBook(row[0])
book.setNameBook(row[1])
book.setPrice(row[2])
book.setType(row[3])
book.setAuthor(row[4])
book.setPublisher(row[5])
ls.append(book.list_return())
mycursor.close()
conn.close()
except Exception as e:
traceback.print_exc()
return ls
def AddBook(self, Book):
i = 0
db = DatabaseTools()
conn = db.getConn()
try :
sql = "insert into book (idBook,nameBook,price,kind,author,publisher)values(%s,%s,%s,%s,%s,%s)"
answer = (str(Book.idBook), str(Book.nameBook), str(Book.price), str(Book.type_), str(Book.author), str(Book.publisher))
mycursor = conn.cursor()
mycursor.execute(sql,answer)
i = mycursor.rowcount
mycursor.close()
conn.commit()
conn.close()
except Exception as e:
traceback.print_exc()
return i
def UpdateBook(self, Book):
i = 0
db = DatabaseTools()
conn = db.getConn()
try :
sql = "update book set idBook=%s,nameBook=%s,price=%s,kind=%s,author=%s,publisher=%s where idBook=%s"
answer = (str(Book.idBook), str(Book.nameBook), str(Book.price), str(Book.type_), str(Book.author), str(Book.publisher), str(Book.idBook))
mycursor = conn.cursor()
mycursor.execute(sql,answer)
i = mycursor.rowcount
mycursor.close()
conn.commit()
conn.close()
except Exception as e:
traceback.print_exc()
return i
def DeteleBook(self, idbook):
i = 0
db = DatabaseTools()
conn = db.getConn()
try:
sql = "delete from Book where idBook=%s"
answer = (str(idbook),)
mycursor = conn.cursor()
mycursor.execute(sql,answer)
i = mycursor.rowcount
mycursor.close()
conn.commit()
conn.close()
except Exception as e:
traceback.print_exc()
return i
| 28.234568 | 150 | 0.510931 | 480 | 4,574 | 4.825 | 0.1625 | 0.039292 | 0.049223 | 0.054404 | 0.858808 | 0.8519 | 0.819948 | 0.819948 | 0.802245 | 0.786701 | 0 | 0.007379 | 0.377787 | 4,574 | 161 | 151 | 28.409938 | 0.806395 | 0 | 0 | 0.8125 | 0 | 0.015625 | 0.095103 | 0.056843 | 0 | 0 | 0 | 0 | 0 | 1 | 0.046875 | false | 0 | 0.03125 | 0 | 0.132813 | 0.046875 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 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 | 7 |
714879d4044a66588932d860ecfd3c666fff44ac | 42,685 | py | Python | ee/clickhouse/queries/funnels/test/test_funnel_trends.py | augustogoulart/posthog | 39416b7e94553b05f880c769714d986e057b64be | [
"MIT"
] | null | null | null | ee/clickhouse/queries/funnels/test/test_funnel_trends.py | augustogoulart/posthog | 39416b7e94553b05f880c769714d986e057b64be | [
"MIT"
] | null | null | null | ee/clickhouse/queries/funnels/test/test_funnel_trends.py | augustogoulart/posthog | 39416b7e94553b05f880c769714d986e057b64be | [
"MIT"
] | null | null | null | from datetime import datetime, timedelta
from uuid import uuid4
import pytz
from ee.clickhouse.models.event import create_event
from ee.clickhouse.queries.funnels import ClickhouseFunnel, ClickhouseFunnelStrict, ClickhouseFunnelUnordered
from ee.clickhouse.queries.funnels.funnel_trends import ClickhouseFunnelTrends
from ee.clickhouse.queries.funnels.funnel_trends_persons import ClickhouseFunnelTrendsPersons
from ee.clickhouse.util import ClickhouseTestMixin
from posthog.constants import INSIGHT_FUNNELS, TRENDS_LINEAR
from posthog.models.filters import Filter
from posthog.models.person import Person
from posthog.test.base import APIBaseTest
FORMAT_TIME = "%Y-%m-%d %H:%M:%S"
FORMAT_TIME_DAY_END = "%Y-%m-%d 23:59:59"
def _create_person(**kwargs):
person = Person.objects.create(**kwargs)
return Person(id=person.uuid, uuid=person.uuid)
def _create_event(**kwargs):
kwargs.update({"event_uuid": uuid4()})
create_event(**kwargs)
class TestFunnelTrends(ClickhouseTestMixin, APIBaseTest):
maxDiff = None
def _create_sample_data(self):
# five people, three steps
_create_person(distinct_ids=["user_one"], team=self.team)
_create_person(distinct_ids=["user_two"], team=self.team)
_create_person(distinct_ids=["user_three"], team=self.team)
_create_person(distinct_ids=["user_four"], team=self.team)
_create_person(distinct_ids=["user_five"], team=self.team)
_create_person(distinct_ids=["user_six"], team=self.team)
_create_person(distinct_ids=["user_seven"], team=self.team)
_create_person(distinct_ids=["user_eight"], team=self.team)
# user_one, funnel steps: one, two three
_create_event(event="step one", distinct_id="user_one", team=self.team, timestamp="2021-05-01 00:00:00")
_create_event(event="step two", distinct_id="user_one", team=self.team, timestamp="2021-05-03 00:00:00")
_create_event(event="step three", distinct_id="user_one", team=self.team, timestamp="2021-05-05 00:00:00")
# user_two, funnel steps: one, two
_create_event(event="step one", distinct_id="user_two", team=self.team, timestamp="2021-05-02 00:00:00")
_create_event(event="step two", distinct_id="user_two", team=self.team, timestamp="2021-05-04 00:00:00")
# user_three, funnel steps: one
_create_event(event="step one", distinct_id="user_three", team=self.team, timestamp="2021-05-06 00:00:00")
# user_four, funnel steps: none
_create_event(event="step none", distinct_id="user_four", team=self.team, timestamp="2021-05-06 00:00:00")
# user_five, funnel steps: one, two, three in the same day
_create_event(event="step one", distinct_id="user_five", team=self.team, timestamp="2021-05-01 01:00:00")
_create_event(event="step two", distinct_id="user_five", team=self.team, timestamp="2021-05-01 02:00:00")
_create_event(event="step three", distinct_id="user_five", team=self.team, timestamp="2021-05-01 03:00:00")
# user_six, funnel steps: one, two three
_create_event(event="step one", distinct_id="user_six", team=self.team, timestamp="2021-05-01 00:00:00")
_create_event(event="step two", distinct_id="user_six", team=self.team, timestamp="2021-05-03 00:00:00")
_create_event(event="step three", distinct_id="user_six", team=self.team, timestamp="2021-05-05 00:00:00")
# user_seven, funnel steps: one, two
_create_event(event="step one", distinct_id="user_seven", team=self.team, timestamp="2021-05-02 00:00:00")
_create_event(event="step two", distinct_id="user_seven", team=self.team, timestamp="2021-05-04 00:00:00")
def test_no_event_in_period(self):
_create_person(distinct_ids=["user a"], team=self.team)
_create_event(event="step one", distinct_id="user a", team=self.team, timestamp="2021-06-06 21:00:00")
filter = Filter(
data={
"insight": INSIGHT_FUNNELS,
"display": TRENDS_LINEAR,
"interval": "day",
"date_from": "2021-06-07 00:00:00",
"date_to": "2021-06-13 23:59:59",
"funnel_window_days": 7,
"events": [
{"id": "step one", "order": 0},
{"id": "step two", "order": 1},
{"id": "step three", "order": 2},
],
}
)
funnel_trends = ClickhouseFunnelTrends(filter, self.team, ClickhouseFunnel)
results = funnel_trends._exec_query()
formatted_results = funnel_trends._format_results(results)
self.assertEqual(len(results), 7)
self.assertEqual(formatted_results[0]["days"][0], "2021-06-07")
def test_only_one_user_reached_one_step(self):
_create_person(distinct_ids=["user a"], team=self.team)
_create_event(event="step one", distinct_id="user a", team=self.team, timestamp="2021-06-07 19:00:00")
filter = Filter(
data={
"insight": INSIGHT_FUNNELS,
"display": TRENDS_LINEAR,
"interval": "day",
"date_from": "2021-06-07 00:00:00",
"date_to": "2021-06-13 23:59:59",
"funnel_window_days": 7,
"events": [
{"id": "step one", "order": 0},
{"id": "step two", "order": 1},
{"id": "step three", "order": 2},
],
}
)
funnel_trends = ClickhouseFunnelTrends(filter, self.team, ClickhouseFunnel)
results = funnel_trends._exec_query()
self.assertEqual(
results,
[
{
"reached_to_step_count": 0,
"is_period_final": True,
"conversion_rate": 0,
"reached_from_step_count": 1,
"timestamp": datetime(2021, 6, 7, 0, 0).replace(tzinfo=pytz.UTC),
},
{
"reached_to_step_count": 0,
"is_period_final": True,
"conversion_rate": 0,
"reached_from_step_count": 0,
"timestamp": datetime(2021, 6, 8, 0, 0).replace(tzinfo=pytz.UTC),
},
{
"reached_to_step_count": 0,
"is_period_final": True,
"conversion_rate": 0,
"reached_from_step_count": 0,
"timestamp": datetime(2021, 6, 9, 0, 0).replace(tzinfo=pytz.UTC),
},
{
"reached_to_step_count": 0,
"is_period_final": True,
"conversion_rate": 0,
"reached_from_step_count": 0,
"timestamp": datetime(2021, 6, 10, 0, 0).replace(tzinfo=pytz.UTC),
},
{
"reached_to_step_count": 0,
"is_period_final": True,
"conversion_rate": 0,
"reached_from_step_count": 0,
"timestamp": datetime(2021, 6, 11, 0, 0).replace(tzinfo=pytz.UTC),
},
{
"reached_to_step_count": 0,
"is_period_final": True,
"conversion_rate": 0,
"reached_from_step_count": 0,
"timestamp": datetime(2021, 6, 12, 0, 0).replace(tzinfo=pytz.UTC),
},
{
"reached_to_step_count": 0,
"is_period_final": True,
"conversion_rate": 0,
"reached_from_step_count": 0,
"timestamp": datetime(2021, 6, 13, 0, 0).replace(tzinfo=pytz.UTC),
},
],
)
# 1 user who dropped off starting 2021-06-07
funnel_trends_persons_existent_dropped_off_results, _ = ClickhouseFunnelTrendsPersons(
Filter({**filter._data, "entrance_period_start": "2021-06-07 00:00:00", "drop_off": True}),
self.team,
ClickhouseFunnel,
).run()
self.assertEqual(
len(funnel_trends_persons_existent_dropped_off_results), 1,
)
self.assertEqual(
[person["distinct_ids"] for person in funnel_trends_persons_existent_dropped_off_results], [["user a"]],
)
# No users converted 2021-06-07
funnel_trends_persons_nonexistent_converted_results, _ = ClickhouseFunnelTrendsPersons(
Filter({**filter._data, "entrance_period_start": "2021-06-07 00:00:00", "drop_off": False}),
self.team,
ClickhouseFunnel,
).run()
self.assertEqual(
len(funnel_trends_persons_nonexistent_converted_results), 0,
)
# No users dropped off 2021-06-08
funnel_trends_persons_nonexistent_converted_results, _ = ClickhouseFunnelTrendsPersons(
Filter({**filter._data, "entrance_period_start": "2021-06-08 00:00:00", "drop_off": True}),
self.team,
ClickhouseFunnel,
).run()
self.assertEqual(
len(funnel_trends_persons_nonexistent_converted_results), 0,
)
# minute, hour, day, week, month
def test_hour_interval(self):
filter = Filter(
data={
"insight": INSIGHT_FUNNELS,
"display": TRENDS_LINEAR,
"interval": "hour",
"date_from": "2021-05-01 00:00:00",
"date_to": "2021-05-07 00:00:00",
"funnel_window_days": 7,
"events": [
{"id": "step one", "order": 0},
{"id": "step two", "order": 1},
{"id": "step three", "order": 2},
],
}
)
results = ClickhouseFunnelTrends(filter, self.team, ClickhouseFunnel)._exec_query()
self.assertEqual(len(results), 145)
def test_day_interval(self):
filter = Filter(
data={
"insight": INSIGHT_FUNNELS,
"display": TRENDS_LINEAR,
"interval": "day",
"date_from": "2021-05-01 00:00:00",
"date_to": "2021-05-07 00:00:00",
"funnel_window_days": 7,
"events": [
{"id": "step one", "order": 0},
{"id": "step two", "order": 1},
{"id": "step three", "order": 2},
],
}
)
results = ClickhouseFunnelTrends(filter, self.team, ClickhouseFunnel)._exec_query()
self.assertEqual(len(results), 7)
def test_week_interval(self):
filter = Filter(
data={
"insight": INSIGHT_FUNNELS,
"display": TRENDS_LINEAR,
"interval": "week",
"date_from": "2021-05-01 00:00:00",
"date_to": "2021-05-07 00:00:00",
"funnel_window_days": 7,
"events": [
{"id": "step one", "order": 0},
{"id": "step two", "order": 1},
{"id": "step three", "order": 2},
],
}
)
results = ClickhouseFunnelTrends(filter, self.team, ClickhouseFunnel)._exec_query()
self.assertEqual(2, len(results))
def test_month_interval(self):
filter = Filter(
data={
"insight": INSIGHT_FUNNELS,
"display": TRENDS_LINEAR,
"interval": "month",
"date_from": "2021-05-01 00:00:00",
"date_to": "2021-05-07 00:00:00",
"funnel_window_days": 7,
"events": [
{"id": "step one", "order": 0},
{"id": "step two", "order": 1},
{"id": "step three", "order": 2},
],
}
)
results = ClickhouseFunnelTrends(filter, self.team, ClickhouseFunnel)._exec_query()
self.assertEqual(len(results), 1)
def test_all_results_for_day_interval(self):
self._create_sample_data()
filter = Filter(
data={
"insight": INSIGHT_FUNNELS,
"display": TRENDS_LINEAR,
"interval": "day",
"date_from": "2021-05-01 00:00:00",
"date_to": "2021-05-07 00:00:00",
"funnel_window_days": 7,
"events": [
{"id": "step one", "order": 0},
{"id": "step two", "order": 1},
{"id": "step three", "order": 2},
],
}
)
results = ClickhouseFunnelTrends(filter, self.team, ClickhouseFunnel)._exec_query()
saturday = results[0] # 5/1
self.assertEqual(3, saturday["reached_to_step_count"])
self.assertEqual(3, saturday["reached_from_step_count"])
self.assertEqual(100, saturday["conversion_rate"])
self.assertEqual(True, saturday["is_period_final"])
sunday = results[1] # 5/2
self.assertEqual(0, sunday["reached_to_step_count"])
self.assertEqual(2, sunday["reached_from_step_count"])
self.assertEqual(0, sunday["conversion_rate"])
self.assertEqual(True, sunday["is_period_final"])
monday = results[2] # 5/3
self.assertEqual(0, monday["reached_to_step_count"])
self.assertEqual(0, monday["reached_from_step_count"])
self.assertEqual(0, monday["conversion_rate"])
self.assertEqual(True, monday["is_period_final"])
tuesday = results[3] # 5/4
self.assertEqual(0, tuesday["reached_to_step_count"])
self.assertEqual(0, tuesday["reached_from_step_count"])
self.assertEqual(0, tuesday["conversion_rate"])
self.assertEqual(True, tuesday["is_period_final"])
wednesday = results[4] # 5/5
self.assertEqual(0, wednesday["reached_to_step_count"])
self.assertEqual(0, wednesday["reached_from_step_count"])
self.assertEqual(0, wednesday["conversion_rate"])
self.assertEqual(True, wednesday["is_period_final"])
thursday = results[5] # 5/6
self.assertEqual(0, thursday["reached_to_step_count"])
self.assertEqual(1, thursday["reached_from_step_count"])
self.assertEqual(0, thursday["conversion_rate"])
self.assertEqual(True, thursday["is_period_final"])
friday = results[6] # 5/7
self.assertEqual(0, friday["reached_to_step_count"])
self.assertEqual(0, friday["reached_from_step_count"])
self.assertEqual(0, friday["conversion_rate"])
self.assertEqual(True, friday["is_period_final"])
def test_window_size_one_day(self):
self._create_sample_data()
filter = Filter(
data={
"insight": INSIGHT_FUNNELS,
"display": TRENDS_LINEAR,
"interval": "day",
"date_from": "2021-05-01 00:00:00",
"date_to": "2021-05-07 00:00:00",
"funnel_window_days": 1,
"events": [
{"id": "step one", "order": 0},
{"id": "step two", "order": 1},
{"id": "step three", "order": 2},
],
}
)
results = ClickhouseFunnelTrends(filter, self.team, ClickhouseFunnel)._exec_query()
saturday = results[0] # 5/1
self.assertEqual(1, saturday["reached_to_step_count"])
self.assertEqual(3, saturday["reached_from_step_count"])
self.assertEqual(33.33, saturday["conversion_rate"])
self.assertEqual(True, saturday["is_period_final"])
sunday = results[1] # 5/2
self.assertEqual(0, sunday["reached_to_step_count"])
self.assertEqual(2, sunday["reached_from_step_count"])
self.assertEqual(0, sunday["conversion_rate"])
self.assertEqual(True, sunday["is_period_final"])
monday = results[2] # 5/3
self.assertEqual(0, monday["reached_to_step_count"])
self.assertEqual(0, monday["reached_from_step_count"])
self.assertEqual(0, monday["conversion_rate"])
self.assertEqual(True, monday["is_period_final"])
tuesday = results[3] # 5/4
self.assertEqual(0, tuesday["reached_to_step_count"])
self.assertEqual(0, tuesday["reached_from_step_count"])
self.assertEqual(0, tuesday["conversion_rate"])
self.assertEqual(True, tuesday["is_period_final"])
wednesday = results[4] # 5/5
self.assertEqual(0, wednesday["reached_to_step_count"])
self.assertEqual(0, wednesday["reached_from_step_count"])
self.assertEqual(0, wednesday["conversion_rate"])
self.assertEqual(True, wednesday["is_period_final"])
thursday = results[5] # 5/6
self.assertEqual(0, thursday["reached_to_step_count"])
self.assertEqual(1, thursday["reached_from_step_count"])
self.assertEqual(0, thursday["conversion_rate"])
self.assertEqual(True, thursday["is_period_final"])
friday = results[6] # 5/7
self.assertEqual(0, friday["reached_to_step_count"])
self.assertEqual(0, friday["reached_from_step_count"])
self.assertEqual(0, friday["conversion_rate"])
self.assertEqual(True, friday["is_period_final"])
def test_period_not_final(self):
now = datetime.now()
_create_person(distinct_ids=["user_eight"], team=self.team)
_create_event(event="step one", distinct_id="user_eight", team=self.team, timestamp=now.strftime(FORMAT_TIME))
_create_event(
event="step two",
distinct_id="user_eight",
team=self.team,
timestamp=(now + timedelta(minutes=1)).strftime(FORMAT_TIME),
)
_create_event(
event="step three",
distinct_id="user_eight",
team=self.team,
timestamp=(now + timedelta(minutes=2)).strftime(FORMAT_TIME),
)
filter = Filter(
data={
"insight": INSIGHT_FUNNELS,
"display": TRENDS_LINEAR,
"interval": "day",
"date_from": (now - timedelta(1)).strftime(FORMAT_TIME),
"date_to": now.strftime(FORMAT_TIME_DAY_END),
"funnel_window_days": 1,
"events": [
{"id": "step one", "order": 0},
{"id": "step two", "order": 1},
{"id": "step three", "order": 2},
],
}
)
results = ClickhouseFunnelTrends(filter, self.team, ClickhouseFunnel)._exec_query()
self.assertEqual(len(results), 2)
day = results[0] # yesterday
self.assertEqual(day["reached_from_step_count"], 0)
self.assertEqual(day["reached_to_step_count"], 0)
self.assertEqual(day["conversion_rate"], 0)
self.assertEqual(
day["timestamp"].replace(tzinfo=pytz.UTC),
(datetime(now.year, now.month, now.day) - timedelta(1)).replace(tzinfo=pytz.UTC),
)
self.assertEqual(day["is_period_final"], True) # this window can't be affected anymore
day = results[1] # today
self.assertEqual(day["reached_from_step_count"], 1)
self.assertEqual(day["reached_to_step_count"], 1)
self.assertEqual(day["conversion_rate"], 100)
self.assertEqual(
day["timestamp"].replace(tzinfo=pytz.UTC), datetime(now.year, now.month, now.day).replace(tzinfo=pytz.UTC)
)
self.assertEqual(day["is_period_final"], False) # events coming in now may stil affect this
def test_two_runs_by_single_user_in_one_period(self):
_create_person(distinct_ids=["user_one"], team=self.team)
# 1st full run
_create_event(event="step one", distinct_id="user_one", team=self.team, timestamp="2021-05-01 00:00:00")
_create_event(event="step two", distinct_id="user_one", team=self.team, timestamp="2021-05-01 01:00:00")
_create_event(event="step three", distinct_id="user_one", team=self.team, timestamp="2021-05-01 02:00:00")
# 2nd full run
_create_event(event="step one", distinct_id="user_one", team=self.team, timestamp="2021-05-01 13:00:00")
_create_event(event="step two", distinct_id="user_one", team=self.team, timestamp="2021-05-01 14:00:00")
_create_event(event="step three", distinct_id="user_one", team=self.team, timestamp="2021-05-01 15:00:00")
filter = Filter(
data={
"insight": INSIGHT_FUNNELS,
"display": TRENDS_LINEAR,
"interval": "day",
"date_from": "2021-05-01 00:00:00",
"date_to": "2021-05-01 23:59:59",
"funnel_window_days": 1,
"events": [
{"id": "step one", "order": 0},
{"id": "step two", "order": 1},
{"id": "step three", "order": 2},
],
}
)
results = ClickhouseFunnelTrends(filter, self.team, ClickhouseFunnel)._exec_query()
self.assertEqual(len(results), 1)
day = results[0] # 2021-05-01
self.assertEqual(day["reached_from_step_count"], 1)
self.assertEqual(day["reached_to_step_count"], 1)
self.assertEqual(day["conversion_rate"], 100)
self.assertEqual(day["is_period_final"], True)
def test_steps_performed_in_period_but_in_reverse(self):
_create_person(distinct_ids=["user_one"], team=self.team)
_create_event(event="step three", distinct_id="user_one", team=self.team, timestamp="2021-05-01 01:00:00")
_create_event(event="step two", distinct_id="user_one", team=self.team, timestamp="2021-05-01 02:00:00")
_create_event(event="step one", distinct_id="user_one", team=self.team, timestamp="2021-05-01 03:00:00")
filter = Filter(
data={
"insight": INSIGHT_FUNNELS,
"display": TRENDS_LINEAR,
"interval": "day",
"date_from": "2021-05-01 00:00:00",
"date_to": "2021-05-01 23:59:59",
"funnel_window_days": 1,
"events": [
{"id": "step one", "order": 0},
{"id": "step two", "order": 1},
{"id": "step three", "order": 2},
],
}
)
results = ClickhouseFunnelTrends(filter, self.team, ClickhouseFunnel)._exec_query()
self.assertEqual(len(results), 1)
day_1 = results[0] # 2021-05-01
self.assertEqual(day_1["reached_from_step_count"], 1)
self.assertEqual(day_1["reached_to_step_count"], 0)
self.assertEqual(day_1["conversion_rate"], 0)
self.assertEqual(day_1["is_period_final"], True)
def test_one_person_in_multiple_periods_and_windows(self):
_create_person(distinct_ids=["user_one"], team=self.team)
_create_person(distinct_ids=["user_two"], team=self.team)
# 1st user's 1st complete run
_create_event(event="step one", distinct_id="user_one", team=self.team, timestamp="2021-05-01 01:00:00")
_create_event(event="step two", distinct_id="user_one", team=self.team, timestamp="2021-05-01 02:00:00")
_create_event(event="step three", distinct_id="user_one", team=self.team, timestamp="2021-05-01 03:00:00")
# 1st user's incomplete run
_create_event(event="step one", distinct_id="user_one", team=self.team, timestamp="2021-05-03 01:00:00")
_create_event(event="step two", distinct_id="user_one", team=self.team, timestamp="2021-05-03 02:00:00")
# 2nd user's incomplete run
_create_event(event="step one", distinct_id="user_two", team=self.team, timestamp="2021-05-04 18:00:00")
# 1st user's 2nd complete run
_create_event(event="step one", distinct_id="user_one", team=self.team, timestamp="2021-05-04 11:00:00")
_create_event(event="step two", distinct_id="user_one", team=self.team, timestamp="2021-05-04 12:00:00")
_create_event(event="step three", distinct_id="user_one", team=self.team, timestamp="2021-05-04 13:00:00")
filter = Filter(
data={
"insight": INSIGHT_FUNNELS,
"display": TRENDS_LINEAR,
"interval": "day",
"date_from": "2021-05-01 00:00:00",
"date_to": "2021-05-04 23:59:59",
"funnel_window_days": 1,
"events": [
{"id": "step one", "order": 0},
{"id": "step two", "order": 1},
{"id": "step three", "order": 2},
],
}
)
results = ClickhouseFunnelTrends(filter, self.team, ClickhouseFunnel)._exec_query()
self.assertEqual(len(results), 4)
day_1 = results[0] # 2021-05-01
self.assertEqual(day_1["reached_from_step_count"], 1)
self.assertEqual(day_1["reached_to_step_count"], 1)
self.assertEqual(day_1["conversion_rate"], 100)
self.assertEqual(day_1["is_period_final"], True)
day_2 = results[1] # 2021-05-02
self.assertEqual(day_2["reached_from_step_count"], 0)
self.assertEqual(day_2["reached_to_step_count"], 0)
self.assertEqual(day_2["conversion_rate"], 0)
self.assertEqual(day_2["is_period_final"], True)
day_3 = results[2] # 2021-05-03
self.assertEqual(day_3["reached_from_step_count"], 1)
self.assertEqual(day_3["reached_to_step_count"], 0)
self.assertEqual(day_3["conversion_rate"], 0)
self.assertEqual(day_3["is_period_final"], True)
day_4 = results[3] # 2021-05-04
self.assertEqual(day_4["reached_from_step_count"], 2)
self.assertEqual(day_4["reached_to_step_count"], 1)
self.assertEqual(day_4["conversion_rate"], 50)
self.assertEqual(day_4["is_period_final"], True)
# 1 user who dropped off starting # 2021-05-04
funnel_trends_persons_existent_dropped_off_results, _ = ClickhouseFunnelTrendsPersons(
Filter({**filter._data, "entrance_period_start": "2021-05-04 00:00:00", "drop_off": True}),
self.team,
ClickhouseFunnel,
).run()
self.assertEqual(
len(funnel_trends_persons_existent_dropped_off_results), 1,
)
self.assertEqual(
[person["distinct_ids"] for person in funnel_trends_persons_existent_dropped_off_results], [["user_two"]],
)
# 1 user who converted starting # 2021-05-04
funnel_trends_persons_existent_dropped_off_results, _ = ClickhouseFunnelTrendsPersons(
Filter({**filter._data, "entrance_period_start": "2021-05-04 00:00:00", "drop_off": False}),
self.team,
ClickhouseFunnel,
).run()
self.assertEqual(
len(funnel_trends_persons_existent_dropped_off_results), 1,
)
self.assertEqual(
[person["distinct_ids"] for person in funnel_trends_persons_existent_dropped_off_results], [["user_one"]],
)
def test_from_second_step(self):
_create_person(distinct_ids=["user_one"], team=self.team)
_create_person(distinct_ids=["user_two"], team=self.team)
_create_person(distinct_ids=["user_three"], team=self.team)
_create_person(distinct_ids=["user_four"], team=self.team)
# 1st user's complete run - should fall into the 2021-05-01 bucket even though counting only from 2nd step
_create_event(event="step one", distinct_id="user_one", team=self.team, timestamp="2021-05-01 01:00:00")
_create_event(event="step two", distinct_id="user_one", team=self.team, timestamp="2021-05-02 02:00:00")
_create_event(event="step three", distinct_id="user_one", team=self.team, timestamp="2021-05-02 03:00:00")
# 2nd user's incomplete run - should not count at all since not reaching 2nd step
_create_event(event="step one", distinct_id="user_two", team=self.team, timestamp="2021-05-01 01:00:00")
# 3rd user's incomplete run - should not count at all since reaching 2nd step BUT not the 1st one
_create_event(event="step two", distinct_id="user_three", team=self.team, timestamp="2021-05-02 02:00:00")
_create_event(event="step three", distinct_id="user_three", team=self.team, timestamp="2021-05-02 03:00:00")
# 4th user's incomplete run - should fall into the 2021-05-02 bucket as entered but not completed
_create_event(event="step one", distinct_id="user_four", team=self.team, timestamp="2021-05-02 01:00:00")
_create_event(event="step two", distinct_id="user_four", team=self.team, timestamp="2021-05-02 02:00:00")
filter = Filter(
data={
"insight": INSIGHT_FUNNELS,
"display": TRENDS_LINEAR,
"interval": "day",
"date_from": "2021-05-01 00:00:00",
"date_to": "2021-05-02 23:59:59",
"funnel_window_days": 3,
"funnel_from_step": 1,
"events": [
{"id": "step one", "order": 0},
{"id": "step two", "order": 1},
{"id": "step three", "order": 2},
],
}
)
results = ClickhouseFunnelTrends(filter, self.team, ClickhouseFunnel)._exec_query()
self.assertEqual(len(results), 2)
day_1 = results[0] # 2021-05-01
self.assertEqual(day_1["reached_from_step_count"], 1)
self.assertEqual(day_1["reached_to_step_count"], 1)
self.assertEqual(day_1["conversion_rate"], 100)
self.assertEqual(day_1["is_period_final"], True)
day_2 = results[1] # 2021-05-02
self.assertEqual(day_2["reached_from_step_count"], 1)
self.assertEqual(day_2["reached_to_step_count"], 0)
self.assertEqual(day_2["conversion_rate"], 0)
self.assertEqual(day_2["is_period_final"], True)
def test_to_second_step(self):
_create_person(distinct_ids=["user_one"], team=self.team)
_create_person(distinct_ids=["user_two"], team=self.team)
_create_person(distinct_ids=["user_three"], team=self.team)
_create_person(distinct_ids=["user_four"], team=self.team)
# 1st user's complete run - should fall into the 2021-05-01 bucket
_create_event(event="step one", distinct_id="user_one", team=self.team, timestamp="2021-05-01 01:00:00")
_create_event(event="step two", distinct_id="user_one", team=self.team, timestamp="2021-05-02 02:00:00")
_create_event(event="step three", distinct_id="user_one", team=self.team, timestamp="2021-05-02 03:00:00")
# 2nd user's incomplete run - should count as incomplete
_create_event(event="step one", distinct_id="user_two", team=self.team, timestamp="2021-05-01 01:00:00")
# 3rd user's incomplete run - should not count at all since reaching 2nd step BUT not the 1st one
_create_event(event="step two", distinct_id="user_three", team=self.team, timestamp="2021-05-02 02:00:00")
_create_event(event="step three", distinct_id="user_three", team=self.team, timestamp="2021-05-02 03:00:00")
# 4th user's incomplete run - should fall into the 2021-05-02 bucket as entered and completed
_create_event(event="step one", distinct_id="user_four", team=self.team, timestamp="2021-05-02 01:00:00")
_create_event(event="step two", distinct_id="user_four", team=self.team, timestamp="2021-05-02 02:00:00")
filter = Filter(
data={
"insight": INSIGHT_FUNNELS,
"display": TRENDS_LINEAR,
"interval": "day",
"date_from": "2021-05-01 00:00:00",
"date_to": "2021-05-02 23:59:59",
"funnel_window_days": 3,
"funnel_to_step": 1,
"events": [
{"id": "step one", "order": 0},
{"id": "step two", "order": 1},
{"id": "step three", "order": 2},
],
}
)
results = ClickhouseFunnelTrends(filter, self.team, ClickhouseFunnel)._exec_query()
self.assertEqual(len(results), 2)
day_1 = results[0] # 2021-05-01
self.assertEqual(day_1["reached_from_step_count"], 2)
self.assertEqual(day_1["reached_to_step_count"], 1)
self.assertEqual(day_1["conversion_rate"], 50)
self.assertEqual(day_1["is_period_final"], True)
day_2 = results[1] # 2021-05-02
self.assertEqual(day_2["reached_from_step_count"], 1)
self.assertEqual(day_2["reached_to_step_count"], 1)
self.assertEqual(day_2["conversion_rate"], 100)
self.assertEqual(day_2["is_period_final"], True)
def test_window_size_one_day_not_broken_by_breakdown(self):
self._create_sample_data()
filter = Filter(
data={
"insight": INSIGHT_FUNNELS,
"display": TRENDS_LINEAR,
"interval": "day",
"date_from": "2021-05-01 00:00:00",
"date_to": "2021-05-07 00:00:00",
"funnel_window_days": 1,
"events": [
{"id": "step one", "order": 0},
{"id": "step two", "order": 1},
{"id": "step three", "order": 2},
],
}
)
results = ClickhouseFunnelTrends(filter, self.team, ClickhouseFunnel)._exec_query()
filter_breakdown = Filter(
data={
"insight": INSIGHT_FUNNELS,
"display": TRENDS_LINEAR,
"interval": "day",
"date_from": "2021-05-01 00:00:00",
"date_to": "2021-05-07 00:00:00",
"funnel_window_days": 1,
"breakdown": "x",
"breakdown_type": "event",
"events": [
{"id": "step one", "order": 0},
{"id": "step two", "order": 1},
{"id": "step three", "order": 2},
],
}
)
results_breakdown = ClickhouseFunnelTrends(filter_breakdown, self.team, ClickhouseFunnel)._exec_query()
self.assertEqual(results_breakdown, results)
def test_one_person_in_multiple_periods_and_windows_in_unordered_funnel(self):
_create_person(distinct_ids=["user_one"], team=self.team)
_create_person(distinct_ids=["user_two"], team=self.team)
# 1st user's 1st complete run
_create_event(event="step one", distinct_id="user_one", team=self.team, timestamp="2021-05-01 01:00:00")
_create_event(event="step three", distinct_id="user_one", team=self.team, timestamp="2021-05-01 02:00:00")
_create_event(event="step two", distinct_id="user_one", team=self.team, timestamp="2021-05-01 03:00:00")
# 1st user's incomplete run
_create_event(event="step two", distinct_id="user_one", team=self.team, timestamp="2021-05-03 01:00:00")
_create_event(event="step one", distinct_id="user_one", team=self.team, timestamp="2021-05-03 02:00:00")
# 2nd user's incomplete run
_create_event(event="step one", distinct_id="user_two", team=self.team, timestamp="2021-05-04 18:00:00")
# 1st user's 2nd complete run
_create_event(event="step three", distinct_id="user_one", team=self.team, timestamp="2021-05-04 11:00:00")
_create_event(event="step one", distinct_id="user_one", team=self.team, timestamp="2021-05-04 12:00:00")
_create_event(event="step two", distinct_id="user_one", team=self.team, timestamp="2021-05-04 13:00:00")
filter = Filter(
data={
"insight": INSIGHT_FUNNELS,
"display": TRENDS_LINEAR,
"interval": "day",
"date_from": "2021-05-01 00:00:00",
"date_to": "2021-05-04 23:59:59",
"funnel_window_days": 1,
"events": [
{"id": "step one", "order": 0},
{"id": "step two", "order": 1},
{"id": "step three", "order": 2},
],
}
)
results = ClickhouseFunnelTrends(filter, self.team, ClickhouseFunnelUnordered)._exec_query()
self.assertEqual(len(results), 4)
day_1 = results[0] # 2021-05-01
self.assertEqual(day_1["reached_from_step_count"], 1)
self.assertEqual(day_1["reached_to_step_count"], 1)
self.assertEqual(day_1["conversion_rate"], 100)
self.assertEqual(day_1["is_period_final"], True)
day_2 = results[1] # 2021-05-02
self.assertEqual(day_2["reached_from_step_count"], 0)
self.assertEqual(day_2["reached_to_step_count"], 0)
self.assertEqual(day_2["conversion_rate"], 0)
self.assertEqual(day_2["is_period_final"], True)
day_3 = results[2] # 2021-05-03
self.assertEqual(day_3["reached_from_step_count"], 1)
self.assertEqual(day_3["reached_to_step_count"], 0)
self.assertEqual(day_3["conversion_rate"], 0)
self.assertEqual(day_3["is_period_final"], True)
day_4 = results[3] # 2021-05-04
self.assertEqual(day_4["reached_from_step_count"], 2)
self.assertEqual(day_4["reached_to_step_count"], 1)
self.assertEqual(day_4["conversion_rate"], 50)
self.assertEqual(day_4["is_period_final"], True)
# 1 user who dropped off starting # 2021-05-04
funnel_trends_persons_existent_dropped_off_results, _ = ClickhouseFunnelTrendsPersons(
Filter({**filter._data, "entrance_period_start": "2021-05-04 00:00:00", "drop_off": True}),
self.team,
ClickhouseFunnelUnordered,
).run()
self.assertEqual(
len(funnel_trends_persons_existent_dropped_off_results), 1,
)
self.assertEqual(
[person["distinct_ids"] for person in funnel_trends_persons_existent_dropped_off_results], [["user_two"]],
)
# 1 user who converted starting # 2021-05-04
funnel_trends_persons_existent_dropped_off_results, _ = ClickhouseFunnelTrendsPersons(
Filter({**filter._data, "entrance_period_start": "2021-05-04 00:00:00", "drop_off": False}),
self.team,
ClickhouseFunnelUnordered,
).run()
self.assertEqual(
len(funnel_trends_persons_existent_dropped_off_results), 1,
)
self.assertEqual(
[person["distinct_ids"] for person in funnel_trends_persons_existent_dropped_off_results], [["user_one"]],
)
def test_one_person_in_multiple_periods_and_windows_in_strict_funnel(self):
_create_person(distinct_ids=["user_one"], team=self.team)
_create_person(distinct_ids=["user_two"], team=self.team)
# 1st user's 1st complete run
_create_event(event="step one", distinct_id="user_one", team=self.team, timestamp="2021-05-01 01:00:00")
_create_event(event="step two", distinct_id="user_one", team=self.team, timestamp="2021-05-01 02:00:00")
_create_event(event="step three", distinct_id="user_one", team=self.team, timestamp="2021-05-01 03:00:00")
# 1st user's incomplete run
_create_event(event="step one", distinct_id="user_one", team=self.team, timestamp="2021-05-03 01:00:00")
_create_event(event="step two", distinct_id="user_one", team=self.team, timestamp="2021-05-03 02:00:00")
# broken because strict
_create_event(event="blah", distinct_id="user_one", team=self.team, timestamp="2021-05-03 02:30:00")
_create_event(event="step three", distinct_id="user_one", team=self.team, timestamp="2021-05-03 03:00:00")
# 2nd user's incomplete run
_create_event(event="step one", distinct_id="user_two", team=self.team, timestamp="2021-05-04 18:00:00")
# broken because strict
_create_event(event="blah", distinct_id="user_two", team=self.team, timestamp="2021-05-04 18:20:00")
_create_event(event="step two", distinct_id="user_two", team=self.team, timestamp="2021-05-04 19:00:00")
# 1st user's 2nd complete run
_create_event(event="step one", distinct_id="user_one", team=self.team, timestamp="2021-05-04 11:00:00")
_create_event(event="step two", distinct_id="user_one", team=self.team, timestamp="2021-05-04 12:00:00")
_create_event(event="step three", distinct_id="user_one", team=self.team, timestamp="2021-05-04 13:00:00")
filter = Filter(
data={
"insight": INSIGHT_FUNNELS,
"display": TRENDS_LINEAR,
"interval": "day",
"date_from": "2021-05-01 00:00:00",
"date_to": "2021-05-04 23:59:59",
"funnel_window_days": 1,
"events": [
{"id": "step one", "order": 0},
{"id": "step two", "order": 1},
{"id": "step three", "order": 2},
],
}
)
results = ClickhouseFunnelTrends(filter, self.team, ClickhouseFunnelStrict)._exec_query()
self.assertEqual(len(results), 4)
day_1 = results[0] # 2021-05-01
self.assertEqual(day_1["reached_from_step_count"], 1)
self.assertEqual(day_1["reached_to_step_count"], 1)
self.assertEqual(day_1["conversion_rate"], 100)
self.assertEqual(day_1["is_period_final"], True)
day_2 = results[1] # 2021-05-02
self.assertEqual(day_2["reached_from_step_count"], 0)
self.assertEqual(day_2["reached_to_step_count"], 0)
self.assertEqual(day_2["conversion_rate"], 0)
self.assertEqual(day_2["is_period_final"], True)
day_3 = results[2] # 2021-05-03
self.assertEqual(day_3["reached_from_step_count"], 1)
self.assertEqual(day_3["reached_to_step_count"], 0)
self.assertEqual(day_3["conversion_rate"], 0)
self.assertEqual(day_3["is_period_final"], True)
day_4 = results[3] # 2021-05-04
self.assertEqual(day_4["reached_from_step_count"], 2)
self.assertEqual(day_4["reached_to_step_count"], 1)
self.assertEqual(day_4["conversion_rate"], 50)
self.assertEqual(day_4["is_period_final"], True)
| 45.217161 | 118 | 0.590512 | 5,254 | 42,685 | 4.548915 | 0.042444 | 0.104184 | 0.051715 | 0.066778 | 0.925565 | 0.920335 | 0.915188 | 0.904561 | 0.888661 | 0.882469 | 0 | 0.075298 | 0.272578 | 42,685 | 943 | 119 | 45.265111 | 0.694428 | 0.047417 | 0 | 0.742523 | 0 | 0 | 0.227107 | 0.048078 | 0 | 0 | 0 | 0 | 0.215865 | 1 | 0.026008 | false | 0 | 0.015605 | 0 | 0.045514 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 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 | 7 |
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