function_name stringlengths 1 63 | docstring stringlengths 50 5.89k | masked_code stringlengths 50 882k | implementation stringlengths 169 12.9k | start_line int32 1 14.6k | end_line int32 16 14.6k | file_content stringlengths 274 882k |
|---|---|---|---|---|---|---|
read_namespaced_job_status | read status of the specified Job
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.read_namespaced_job_status(name, namespace, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str name: name of the Job (requi... | # coding: utf-8
"""
Kubernetes
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen)
OpenAPI spec version: v1.13.1
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import sys
import os
i... | def read_namespaced_job_status(self, name, namespace, **kwargs):
"""
read status of the specified Job
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.read_namespaced_job_status(name, na... | 1,108 | 1,129 | # coding: utf-8
"""
Kubernetes
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen)
OpenAPI spec version: v1.13.1
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import sys
import os
i... |
read_namespaced_job_status_with_http_info | read status of the specified Job
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.read_namespaced_job_status_with_http_info(name, namespace, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str name: name of... | # coding: utf-8
"""
Kubernetes
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen)
OpenAPI spec version: v1.13.1
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import sys
import os
i... | def read_namespaced_job_status_with_http_info(self, name, namespace, **kwargs):
"""
read status of the specified Job
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.read_namespaced_job_... | 1,131 | 1,213 | # coding: utf-8
"""
Kubernetes
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen)
OpenAPI spec version: v1.13.1
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import sys
import os
i... |
replace_namespaced_job | replace the specified Job
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.replace_namespaced_job(name, namespace, body, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str name: name of the Job (required)
... | # coding: utf-8
"""
Kubernetes
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen)
OpenAPI spec version: v1.13.1
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import sys
import os
i... | def replace_namespaced_job(self, name, namespace, body, **kwargs):
"""
replace the specified Job
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.replace_namespaced_job(name, namespace, ... | 1,215 | 1,238 | # coding: utf-8
"""
Kubernetes
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen)
OpenAPI spec version: v1.13.1
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import sys
import os
i... |
replace_namespaced_job_with_http_info | replace the specified Job
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.replace_namespaced_job_with_http_info(name, namespace, body, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str name: name of the ... | # coding: utf-8
"""
Kubernetes
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen)
OpenAPI spec version: v1.13.1
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import sys
import os
i... | def replace_namespaced_job_with_http_info(self, name, namespace, body, **kwargs):
"""
replace the specified Job
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.replace_namespaced_job_wi... | 1,240 | 1,331 | # coding: utf-8
"""
Kubernetes
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen)
OpenAPI spec version: v1.13.1
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import sys
import os
i... |
replace_namespaced_job_status | replace status of the specified Job
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.replace_namespaced_job_status(name, namespace, body, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str name: name of th... | # coding: utf-8
"""
Kubernetes
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen)
OpenAPI spec version: v1.13.1
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import sys
import os
i... | def replace_namespaced_job_status(self, name, namespace, body, **kwargs):
"""
replace status of the specified Job
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.replace_namespaced_job_... | 1,333 | 1,356 | # coding: utf-8
"""
Kubernetes
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen)
OpenAPI spec version: v1.13.1
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import sys
import os
i... |
replace_namespaced_job_status_with_http_info | replace status of the specified Job
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.replace_namespaced_job_status_with_http_info(name, namespace, body, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str n... | # coding: utf-8
"""
Kubernetes
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen)
OpenAPI spec version: v1.13.1
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import sys
import os
i... | def replace_namespaced_job_status_with_http_info(self, name, namespace, body, **kwargs):
"""
replace status of the specified Job
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.replace_... | 1,358 | 1,449 | # coding: utf-8
"""
Kubernetes
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen)
OpenAPI spec version: v1.13.1
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import sys
import os
i... |
_evaluate_steps | One step evaluation across replica.
Args:
per_replica_features: the batched features.
per_replica_labels: the batched labels.
Returns:
The loss corresponding to the given batch. | """Tensorflow trainer class."""
import logging
import math
import os
from typing import Callable, Dict, Optional
import numpy as np
import tensorflow as tf
from .modeling_tf_utils import TFPreTrainedModel, shape_list
from .optimization_tf import GradientAccumulator, create_optimizer
from .trainer_utils import PREFIX... | @tf.function
def _evaluate_steps(self, per_replica_features, per_replica_labels):
"""
One step evaluation across replica.
Args:
per_replica_features: the batched features.
per_replica_labels: the batched labels.
Returns:
The loss corresponding to the... | 161 | 180 | """Tensorflow trainer class."""
import logging
import math
import os
from typing import Callable, Dict, Optional
import numpy as np
import tensorflow as tf
from .modeling_tf_utils import TFPreTrainedModel, shape_list
from .optimization_tf import GradientAccumulator, create_optimizer
from .trainer_utils import PREFIX... |
_run_model | Computes the loss of the given features and labels pair.
Args:
features: the batched features.
labels: the batched labels.
training: run the model in training mode or not | """Tensorflow trainer class."""
import logging
import math
import os
from typing import Callable, Dict, Optional
import numpy as np
import tensorflow as tf
from .modeling_tf_utils import TFPreTrainedModel, shape_list
from .optimization_tf import GradientAccumulator, create_optimizer
from .trainer_utils import PREFIX... | def _run_model(self, features, labels, training):
"""
Computes the loss of the given features and labels pair.
Args:
features: the batched features.
labels: the batched labels.
training: run the model in training mode or not
"""
if self.args.mode... | 383 | 410 | """Tensorflow trainer class."""
import logging
import math
import os
from typing import Callable, Dict, Optional
import numpy as np
import tensorflow as tf
from .modeling_tf_utils import TFPreTrainedModel, shape_list
from .optimization_tf import GradientAccumulator, create_optimizer
from .trainer_utils import PREFIX... |
predict | Run prediction and return predictions and potential metrics.
Depending on the dataset and your use case, your test dataset may contain labels.
In that case, this method will also return metrics, like in evaluate().
Args:
test_dataset: something similar to a PT Dataset. This is just
temporary before to have a fram... | """Tensorflow trainer class."""
import logging
import math
import os
from typing import Callable, Dict, Optional
import numpy as np
import tensorflow as tf
from .modeling_tf_utils import TFPreTrainedModel, shape_list
from .optimization_tf import GradientAccumulator, create_optimizer
from .trainer_utils import PREFIX... | def predict(self, test_dataset: tf.data.Dataset) -> PredictionOutput:
"""
Run prediction and return predictions and potential metrics.
Depending on the dataset and your use case, your test dataset may contain labels.
In that case, this method will also return metrics, like in evaluat... | 412 | 424 | """Tensorflow trainer class."""
import logging
import math
import os
from typing import Callable, Dict, Optional
import numpy as np
import tensorflow as tf
from .modeling_tf_utils import TFPreTrainedModel, shape_list
from .optimization_tf import GradientAccumulator, create_optimizer
from .trainer_utils import PREFIX... |
create_tenant | Creates a new tenant.
Note this route only works when run against Prefect Server.
Args:
- name (str): the name of the tenant to create
- slug (str, optional): the slug of the tenant to create; defaults to name
Returns:
- str: the ID of the newly created tenant, or the ID of the currently active tenant
R... | import datetime
import json
import os
import re
import time
import uuid
import warnings
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Union
from urllib.parse import urljoin
# if simplejson is installed, `requests` defaults to using it instead of json
# this allows th... | def create_tenant(self, name: str, slug: str = None) -> str:
"""
Creates a new tenant.
Note this route only works when run against Prefect Server.
Args:
- name (str): the name of the tenant to create
- slug (str, optional): the slug of the tenant to create; ... | 128 | 159 | import datetime
import json
import os
import re
import time
import uuid
import warnings
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Union
from urllib.parse import urljoin
# if simplejson is installed, `requests` defaults to using it instead of json
# this allows th... |
get | Convenience function for calling the Prefect API with token auth and GET request
Args:
- path (str): the path of the API url. For example, to GET
http://prefect-server/v1/auth/login, path would be 'auth/login'.
- server (str, optional): the server to send the GET request to;
defaults to `self.a... | import datetime
import json
import os
import re
import time
import uuid
import warnings
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Union
from urllib.parse import urljoin
# if simplejson is installed, `requests` defaults to using it instead of json
# this allows th... | def get(
self,
path: str,
server: str = None,
headers: dict = None,
params: Dict[str, JSONLike] = None,
token: str = None,
retry_on_api_error: bool = True,
) -> dict:
"""
Convenience function for calling the Prefect API with token auth and ... | 164 | 202 | import datetime
import json
import os
import re
import time
import uuid
import warnings
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Union
from urllib.parse import urljoin
# if simplejson is installed, `requests` defaults to using it instead of json
# this allows th... |
post | Convenience function for calling the Prefect API with token auth and POST request
Args:
- path (str): the path of the API url. For example, to POST
http://prefect-server/v1/auth/login, path would be 'auth/login'.
- server (str, optional): the server to send the POST request to;
defaults to `sel... | import datetime
import json
import os
import re
import time
import uuid
import warnings
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Union
from urllib.parse import urljoin
# if simplejson is installed, `requests` defaults to using it instead of json
# this allows th... | def post(
self,
path: str,
server: str = None,
headers: dict = None,
params: Dict[str, JSONLike] = None,
token: str = None,
retry_on_api_error: bool = True,
) -> dict:
"""
Convenience function for calling the Prefect API with token auth and... | 204 | 242 | import datetime
import json
import os
import re
import time
import uuid
import warnings
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Union
from urllib.parse import urljoin
# if simplejson is installed, `requests` defaults to using it instead of json
# this allows th... |
get_auth_token | Returns an auth token:
- if no explicit access token is stored, returns the api token
- if there is an access token:
- if there's a refresh token and the access token expires in the next 30 seconds,
then we refresh the access token and store the result
- return the access token
Return... | import datetime
import json
import os
import re
import time
import uuid
import warnings
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Union
from urllib.parse import urljoin
# if simplejson is installed, `requests` defaults to using it instead of json
# this allows th... | def get_auth_token(self) -> str:
"""
Returns an auth token:
- if no explicit access token is stored, returns the api token
- if there is an access token:
- if there's a refresh token and the access token expires in the next 30 seconds,
then w... | 486 | 505 | import datetime
import json
import os
import re
import time
import uuid
import warnings
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Union
from urllib.parse import urljoin
# if simplejson is installed, `requests` defaults to using it instead of json
# this allows th... |
_refresh_access_token | Refresh the client's JWT access token.
NOTE: this should only be called by users who have provided a USER-scoped API token.
Returns:
- bool: True if the refresh succeeds | import datetime
import json
import os
import re
import time
import uuid
import warnings
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Union
from urllib.parse import urljoin
# if simplejson is installed, `requests` defaults to using it instead of json
# this allows th... | def _refresh_access_token(self) -> bool:
"""
Refresh the client's JWT access token.
NOTE: this should only be called by users who have provided a USER-scoped API token.
Returns:
- bool: True if the refresh succeeds
"""
payload = self.graphql(
... | 609 | 638 | import datetime
import json
import os
import re
import time
import uuid
import warnings
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Union
from urllib.parse import urljoin
# if simplejson is installed, `requests` defaults to using it instead of json
# this allows th... |
create_project | Create a new Project
Args:
- project_name (str): the project that should contain this flow
- project_description (str, optional): the project description
Returns:
- str: the ID of the newly-created project
Raises:
- ClientError: if the project creation failed | import datetime
import json
import os
import re
import time
import uuid
import warnings
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Union
from urllib.parse import urljoin
# if simplejson is installed, `requests` defaults to using it instead of json
# this allows th... | def create_project(self, project_name: str, project_description: str = None) -> str:
"""
Create a new Project
Args:
- project_name (str): the project that should contain this flow
- project_description (str, optional): the project description
Returns:
... | 869 | 900 | import datetime
import json
import os
import re
import time
import uuid
import warnings
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Union
from urllib.parse import urljoin
# if simplejson is installed, `requests` defaults to using it instead of json
# this allows th... |
get_flow_run_state | Retrieves the current state for a flow run.
Args:
- flow_run_id (str): the id for this flow run
Returns:
- State: a Prefect State object | import datetime
import json
import os
import re
import time
import uuid
import warnings
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Union
from urllib.parse import urljoin
# if simplejson is installed, `requests` defaults to using it instead of json
# this allows th... | def get_flow_run_state(self, flow_run_id: str) -> "prefect.engine.state.State":
"""
Retrieves the current state for a flow run.
Args:
- flow_run_id (str): the id for this flow run
Returns:
- State: a Prefect State object
"""
query = {
... | 1,072 | 1,092 | import datetime
import json
import os
import re
import time
import uuid
import warnings
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Union
from urllib.parse import urljoin
# if simplejson is installed, `requests` defaults to using it instead of json
# this allows th... |
set_flow_run_state | Sets new state for a flow run in the database.
Args:
- flow_run_id (str): the id of the flow run to set state for
- state (State): the new state for this flow run
- version (int, optional): the current version of the flow run state. This is optional
but it can be supplied to enforce version-locking... | import datetime
import json
import os
import re
import time
import uuid
import warnings
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Union
from urllib.parse import urljoin
# if simplejson is installed, `requests` defaults to using it instead of json
# this allows th... | def set_flow_run_state(
self,
flow_run_id: str,
state: "prefect.engine.state.State",
version: int = None,
) -> "prefect.engine.state.State":
"""
Sets new state for a flow run in the database.
Args:
- flow_run_id (str): the id of the flow run t... | 1,094 | 1,150 | import datetime
import json
import os
import re
import time
import uuid
import warnings
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Union
from urllib.parse import urljoin
# if simplejson is installed, `requests` defaults to using it instead of json
# this allows th... |
get_task_run_info | Retrieves version and current state information for the given task run.
Args:
- flow_run_id (str): the id of the flow run that this task run lives in
- task_id (str): the task id for this task run
- map_index (int, optional): the mapping index for this task run; if
`None`, it is assumed this task i... | import datetime
import json
import os
import re
import time
import uuid
import warnings
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Union
from urllib.parse import urljoin
# if simplejson is installed, `requests` defaults to using it instead of json
# this allows th... | def get_task_run_info(
self, flow_run_id: str, task_id: str, map_index: Optional[int] = None
) -> TaskRunInfoResult:
"""
Retrieves version and current state information for the given task run.
Args:
- flow_run_id (str): the id of the flow run that this task run lives... | 1,192 | 1,255 | import datetime
import json
import os
import re
import time
import uuid
import warnings
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Union
from urllib.parse import urljoin
# if simplejson is installed, `requests` defaults to using it instead of json
# this allows th... |
get_task_run_state | Retrieves the current state for a task run.
Args:
- task_run_id (str): the id for this task run
Returns:
- State: a Prefect State object | import datetime
import json
import os
import re
import time
import uuid
import warnings
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Union
from urllib.parse import urljoin
# if simplejson is installed, `requests` defaults to using it instead of json
# this allows th... | def get_task_run_state(self, task_run_id: str) -> "prefect.engine.state.State":
"""
Retrieves the current state for a task run.
Args:
- task_run_id (str): the id for this task run
Returns:
- State: a Prefect State object
"""
query = {
... | 1,282 | 1,302 | import datetime
import json
import os
import re
import time
import uuid
import warnings
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Union
from urllib.parse import urljoin
# if simplejson is installed, `requests` defaults to using it instead of json
# this allows th... |
set_task_run_state | Sets new state for a task run.
Args:
- task_run_id (str): the id of the task run to set state for
- state (State): the new state for this task run
- version (int, optional): the current version of the task run state. This is optional
but it can be supplied to enforce version-locking.
- cache_fo... | import datetime
import json
import os
import re
import time
import uuid
import warnings
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Union
from urllib.parse import urljoin
# if simplejson is installed, `requests` defaults to using it instead of json
# this allows th... | def set_task_run_state(
self,
task_run_id: str,
state: "prefect.engine.state.State",
version: int = None,
cache_for: datetime.timedelta = None,
) -> "prefect.engine.state.State":
"""
Sets new state for a task run.
Args:
- task_run_id (... | 1,304 | 1,362 | import datetime
import json
import os
import re
import time
import uuid
import warnings
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Union
from urllib.parse import urljoin
# if simplejson is installed, `requests` defaults to using it instead of json
# this allows th... |
set_secret | Set a secret with the given name and value.
Args:
- name (str): the name of the secret; used for retrieving the secret
during task runs
- value (Any): the value of the secret
Raises:
- ClientError: if the GraphQL mutation is bad for any reason
- ValueError: if the secret-setting was unsuccessf... | import datetime
import json
import os
import re
import time
import uuid
import warnings
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Union
from urllib.parse import urljoin
# if simplejson is installed, `requests` defaults to using it instead of json
# this allows th... | def set_secret(self, name: str, value: Any) -> None:
"""
Set a secret with the given name and value.
Args:
- name (str): the name of the secret; used for retrieving the secret
during task runs
- value (Any): the value of the secret
Raises:
... | 1,364 | 1,388 | import datetime
import json
import os
import re
import time
import uuid
import warnings
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Union
from urllib.parse import urljoin
# if simplejson is installed, `requests` defaults to using it instead of json
# this allows th... |
write_run_logs | Uploads a collection of logs to Cloud.
Args:
- logs (List[Dict]): a list of log entries to add
Raises:
- ValueError: if uploading the logs fail | import datetime
import json
import os
import re
import time
import uuid
import warnings
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Union
from urllib.parse import urljoin
# if simplejson is installed, `requests` defaults to using it instead of json
# this allows th... | def write_run_logs(self, logs: List[Dict]) -> None:
"""
Uploads a collection of logs to Cloud.
Args:
- logs (List[Dict]): a list of log entries to add
Raises:
- ValueError: if uploading the logs fail
"""
mutation = {
"mutation($in... | 1,466 | 1,487 | import datetime
import json
import os
import re
import time
import uuid
import warnings
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Union
from urllib.parse import urljoin
# if simplejson is installed, `requests` defaults to using it instead of json
# this allows th... |
register_agent | Register an agent with a backend API
Args:
- agent_type (str): The type of agent being registered
- name: (str, optional): The name of the agent being registered
- labels (List[str], optional): A list of any present labels on the agent
being registered
- agent_config_id (str, optional): The ID ... | import datetime
import json
import os
import re
import time
import uuid
import warnings
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Union
from urllib.parse import urljoin
# if simplejson is installed, `requests` defaults to using it instead of json
# this allows th... | def register_agent(
self,
agent_type: str,
name: str = None,
labels: List[str] = None,
agent_config_id: str = None,
) -> str:
"""
Register an agent with a backend API
Args:
- agent_type (str): The type of agent being registered
... | 1,489 | 1,531 | import datetime
import json
import os
import re
import time
import uuid
import warnings
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Union
from urllib.parse import urljoin
# if simplejson is installed, `requests` defaults to using it instead of json
# this allows th... |
get_json_state | Gets a json-encoded description of the simulation's state.
As of now, it takes output and input capacities as arguments because the JSON state
is described through relative values. (For instance, first output at 0.3 capacity).
@param input_capacities An array containing the maximum capacities of the input.
@param out... | #!/usr/bin/python
# -*- coding: utf-8 -*-
#
# Copyright (C) 2005 onwards University of Deusto
# All rights reserved.
#
# This software is licensed as described in the file COPYING, which
# you should have received as part of this distribution.
#
# This software consists of contributions made by many individuals,
# list... | def get_json_state(self, input_capacities, output_capacities):
"""
Gets a json-encoded description of the simulation's state.
As of now, it takes output and input capacities as arguments because the JSON state
is described through relative values. (For instance, first output at 0.3 ... | 211 | 239 | #!/usr/bin/python
# -*- coding: utf-8 -*-
#
# Copyright (C) 2005 onwards University of Deusto
# All rights reserved.
#
# This software is licensed as described in the file COPYING, which
# you should have received as part of this distribution.
#
# This software consists of contributions made by many individuals,
# list... |
do_deactivate_realm | Deactivate this realm. Do NOT deactivate the users -- we need to be able to
tell the difference between users that were intentionally deactivated,
e.g. by a realm admin, and users who can't currently use Zulip because their
realm has been deactivated. | import datetime
import itertools
import logging
import os
import platform
import time
from collections import defaultdict
from operator import itemgetter
from typing import (
AbstractSet,
Any,
Callable,
Dict,
Iterable,
List,
Mapping,
MutableMapping,
Optional,
Sequence,
Set,
... | def do_deactivate_realm(realm: Realm, acting_user: Optional[UserProfile]=None) -> None:
"""
Deactivate this realm. Do NOT deactivate the users -- we need to be able to
tell the difference between users that were intentionally deactivated,
e.g. by a realm admin, and users who can't currently use Zulip be... | 795 | 827 | import datetime
import itertools
import logging
import os
import platform
import time
from collections import defaultdict
from operator import itemgetter
from typing import (
AbstractSet,
Any,
Callable,
Dict,
Iterable,
List,
Mapping,
MutableMapping,
Optional,
Sequence,
Set,
... |
_internal_prep_message | Create a message object and checks it, but doesn't send it or save it to the database.
The internal function that calls this can therefore batch send a bunch of created
messages together as one database query.
Call do_send_messages with a list of the return values of this method. | import datetime
import itertools
import logging
import os
import platform
import time
from collections import defaultdict
from operator import itemgetter
from typing import (
AbstractSet,
Any,
Callable,
Dict,
Iterable,
List,
Mapping,
MutableMapping,
Optional,
Sequence,
Set,
... | def _internal_prep_message(realm: Realm,
sender: UserProfile,
addressee: Addressee,
content: str) -> Optional[Dict[str, Any]]:
"""
Create a message object and checks it, but doesn't send it or save it to the database.
The inter... | 2,385 | 2,415 | import datetime
import itertools
import logging
import os
import platform
import time
from collections import defaultdict
from operator import itemgetter
from typing import (
AbstractSet,
Any,
Callable,
Dict,
Iterable,
List,
Mapping,
MutableMapping,
Optional,
Sequence,
Set,
... |
add_and_switch_to_new_task | Adds a new task to an existing TARS model. Sets necessary attributes and finally 'switches'
to the new task. Parameters are similar to the constructor except for model choice, batch
size and negative sampling. This method does not store the resultant model onto disk.
:param task_name: a string depicting the name of the... | import logging
from collections import OrderedDict
from pathlib import Path
from typing import List, Optional, Set, Tuple, Union
import numpy as np
import torch
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.preprocessing import minmax_scale
from tqdm import tqdm
import flair
from flair.data impo... | def add_and_switch_to_new_task(
self,
task_name,
label_dictionary: Union[List, Set, Dictionary, str],
label_type: str,
multi_label: bool = True,
force_switch: bool = False,
):
"""
Adds a new task to an existing TARS model. Sets necessary attributes... | 180 | 224 | import logging
from collections import OrderedDict
from pathlib import Path
from typing import List, Optional, Set, Tuple, Union
import numpy as np
import torch
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.preprocessing import minmax_scale
from tqdm import tqdm
import flair
from flair.data impo... |
__init__ | Initializes a TextClassifier
:param task_name: a string depicting the name of the task
:param label_dictionary: dictionary of labels you want to predict
:param embeddings: name of the pre-trained transformer model e.g.,
'bert-base-uncased' etc
:param num_negative_labels_to_sample: number of negative labels to sample fo... | import logging
from collections import OrderedDict
from pathlib import Path
from typing import List, Optional, Set, Tuple, Union
import numpy as np
import torch
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.preprocessing import minmax_scale
from tqdm import tqdm
import flair
from flair.data impo... | def __init__(
self,
task_name: Optional[str] = None,
label_dictionary: Optional[Dictionary] = None,
label_type: Optional[str] = None,
embeddings: Union[TransformerWordEmbeddings, str] = "bert-base-uncased",
num_negative_labels_to_sample: int = 2,
prefix: bool ... | 326 | 389 | import logging
from collections import OrderedDict
from pathlib import Path
from typing import List, Optional, Set, Tuple, Union
import numpy as np
import torch
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.preprocessing import minmax_scale
from tqdm import tqdm
import flair
from flair.data impo... |
__init__ | Initializes a TextClassifier
:param task_name: a string depicting the name of the task
:param label_dictionary: dictionary of labels you want to predict
:param embeddings: name of the pre-trained transformer model e.g.,
'bert-base-uncased' etc
:param num_negative_labels_to_sample: number of negative labels to sample fo... | import logging
from collections import OrderedDict
from pathlib import Path
from typing import List, Optional, Set, Tuple, Union
import numpy as np
import torch
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.preprocessing import minmax_scale
from tqdm import tqdm
import flair
from flair.data impo... | def __init__(
self,
task_name: Optional[str] = None,
label_dictionary: Optional[Dictionary] = None,
label_type: Optional[str] = None,
embeddings: Union[TransformerDocumentEmbeddings, str] = "bert-base-uncased",
num_negative_labels_to_sample: int = 2,
prefix: b... | 595 | 660 | import logging
from collections import OrderedDict
from pathlib import Path
from typing import List, Optional, Set, Tuple, Union
import numpy as np
import torch
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.preprocessing import minmax_scale
from tqdm import tqdm
import flair
from flair.data impo... |
__repr__ | Returns a string representation of this Point.
For each coordinate (x and y), the representation:
- Uses no decimal points if the number is close to an integer,
- Else it uses 2 decimal places after the decimal point.
Examples:
Point(10, 3.14)
Point(3.01, 2.99) | """
A simple Line class.
NOTE: This is NOT rosegraphics -- it is your OWN Line class.
Authors: David Mutchler, Vibha Alangar, Matt Boutell, Dave Fisher,
Mark Hays, Amanda Stouder, Aaron Wilkin, their colleagues,
and Jacob Jarski.
""" # DONE: 1. PUT YOUR NAME IN THE ABOVE LINE.
import math
impor... | def __repr__(self):
"""
Returns a string representation of this Point.
For each coordinate (x and y), the representation:
- Uses no decimal points if the number is close to an integer,
- Else it uses 2 decimal places after the decimal point.
Examples:
P... | 94 | 119 | """
A simple Line class.
NOTE: This is NOT rosegraphics -- it is your OWN Line class.
Authors: David Mutchler, Vibha Alangar, Matt Boutell, Dave Fisher,
Mark Hays, Amanda Stouder, Aaron Wilkin, their colleagues,
and Jacob Jarski.
""" # DONE: 1. PUT YOUR NAME IN THE ABOVE LINE.
import math
impor... |
__init__ | :param port: port number to start the redis server on. Specify none to automatically generate
:type port: int|None
:param extra_args: any extra arguments kwargs will be passed to redis server as --key val | import subprocess
import socket
import tempfile
import redis
import time
import os
import itertools
import sys
# Environment variable pointing to the redis executable
REDIS_PATH_ENVVAR = 'REDIS_PATH'
def get_random_port():
sock = socket.socket()
sock.listen(0)
_, port = sock.getsockname()
sock.close(... | def __init__(self, port=None, path='redis-server', **extra_args):
"""
:param port: port number to start the redis server on. Specify none to automatically generate
:type port: int|None
:param extra_args: any extra arguments kwargs will be passed to redis server as --key val
"... | 24 | 39 | import subprocess
import socket
import tempfile
import redis
import time
import os
import itertools
import sys
# Environment variable pointing to the redis executable
REDIS_PATH_ENVVAR = 'REDIS_PATH'
def get_random_port():
sock = socket.socket()
sock.listen(0)
_, port = sock.getsockname()
sock.close(... |
_sample_reduce | Reduce function used on the sample and choice functions.
Parameters
----------
reduce_iter : iterable
Each element is a tuple coming generated by the _sample_map_partitions function.
Returns a sequence of uniformly distributed samples; | import heapq
import math
import random as rnd
from functools import partial
from .core import Bag
def sample(population, k):
"""Chooses k unique random elements from a bag.
Returns a new bag containing elements from the population while
leaving the original population unchanged.
Parameters
---... | def _sample_reduce(reduce_iter, k, replace):
"""
Reduce function used on the sample and choice functions.
Parameters
----------
reduce_iter : iterable
Each element is a tuple coming generated by the _sample_map_partitions function.
Returns a sequence of uniformly distributed samples;
... | 93 | 126 | import heapq
import math
import random as rnd
from functools import partial
from .core import Bag
def sample(population, k):
"""Chooses k unique random elements from a bag.
Returns a new bag containing elements from the population while
leaving the original population unchanged.
Parameters
---... |
get_observations | Generate a `~gammapy.data.Observations`.
Parameters
----------
obs_id : list
Observation IDs.
skip_missing : bool, optional
Skip missing observations, default: False
Returns
-------
observations : `~gammapy.data.Observations`
Container holding a list of `~gammapy.data.DataStoreObservation` | # Licensed under a 3-clause BSD style license - see LICENSE.rst
from __future__ import absolute_import, division, print_function, unicode_literals
import logging
import subprocess
from ..utils.scripts import make_path
from ..utils.testing import Checker
from .obs_table import ObservationTable
from .hdu_index_table impo... | def get_observations(self, obs_id, skip_missing=False):
"""Generate a `~gammapy.data.Observations`.
Parameters
----------
obs_id : list
Observation IDs.
skip_missing : bool, optional
Skip missing observations, default: False
Returns
-... | 186 | 213 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
from __future__ import absolute_import, division, print_function, unicode_literals
import logging
import subprocess
from ..utils.scripts import make_path
from ..utils.testing import Checker
from .obs_table import ObservationTable
from .hdu_index_table impo... |
copy_obs | Create a new `~gammapy.data.DataStore` containing a subset of observations.
Parameters
----------
obs_id : array-like, `~gammapy.data.ObservationTable`
List of observations to copy
outdir : str, Path
Directory for the new store
hdu_class : list of str
see :attr:`gammapy.data.HDUIndexTable.VALID_HDU_CLASS`
... | # Licensed under a 3-clause BSD style license - see LICENSE.rst
from __future__ import absolute_import, division, print_function, unicode_literals
import logging
import subprocess
from ..utils.scripts import make_path
from ..utils.testing import Checker
from .obs_table import ObservationTable
from .hdu_index_table impo... | def copy_obs(self, obs_id, outdir, hdu_class=None, verbose=False, overwrite=False):
"""Create a new `~gammapy.data.DataStore` containing a subset of observations.
Parameters
----------
obs_id : array-like, `~gammapy.data.ObservationTable`
List of observations to copy
... | 215 | 262 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
from __future__ import absolute_import, division, print_function, unicode_literals
import logging
import subprocess
from ..utils.scripts import make_path
from ..utils.testing import Checker
from .obs_table import ObservationTable
from .hdu_index_table impo... |
get | Get an existing CSIStorageCapacity resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: ... | # coding=utf-8
# *** WARNING: this file was generated by pulumigen. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from ... import _utilities
from ... import me... | @staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None) -> 'CSIStorageCapacity':
"""
Get an existing CSIStorageCapacity resource's state with the given name, id, and optional extra
properties used to qualify ... | 259 | 282 | # coding=utf-8
# *** WARNING: this file was generated by pulumigen. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from ... import _utilities
from ... import me... |
commits | 获取分支的commits
@param branch:
@param kwargs:
@return: | # -*- coding: utf-8 -*-
"""
walle-web
:copyright: © 2015-2019 walle-web.io
:created time: 2019-02-24 10:47:53
:author: wushuiyong@walle-web.io
"""
import os
import re
import os.path as osp
import git as PyGit
from git import Repo as PyRepo
class Repo:
path = None
def __init__(self, path=Non... | def commits(self, branch):
'''
获取分支的commits
@param branch:
@param kwargs:
@return:
'''
self.checkout_2_branch(branch)
commit_log = PyGit.Git(self.path).log('--pretty=%h #@_@# %an #@_@# %s', max_count=50)
commit_list = commit_log.split('\n')
... | 118 | 144 | # -*- coding: utf-8 -*-
"""
walle-web
:copyright: © 2015-2019 walle-web.io
:created time: 2019-02-24 10:47:53
:author: wushuiyong@walle-web.io
"""
import os
import re
import os.path as osp
import git as PyGit
from git import Repo as PyRepo
class Repo:
path = None
def __init__(self, path=Non... |
quadprog | Input: Numpy arrays, the format follows MATLAB quadprog function: https://www.mathworks.com/help/optim/ug/quadprog.html
Output: Numpy array of the solution | from json import load
import os
import argparse
import random
from copy import deepcopy
import torchvision
import torchvision.transforms as transforms
from torch import nn
import sys
import torch
import numpy as np
import cvxopt
torch.manual_seed(0)
from fedlab.core.client.serial_trainer import SubsetSerialTrainer
fro... | def quadprog(Q, q, G, h, A, b):
"""
Input: Numpy arrays, the format follows MATLAB quadprog function: https://www.mathworks.com/help/optim/ug/quadprog.html
Output: Numpy array of the solution
"""
Q = cvxopt.matrix(Q.tolist())
q = cvxopt.matrix(q.tolist(), tc='d')
G = cvxopt.matrix(G.tolist()... | 24 | 36 | from json import load
import os
import argparse
import random
from copy import deepcopy
import torchvision
import torchvision.transforms as transforms
from torch import nn
import sys
import torch
import numpy as np
import cvxopt
torch.manual_seed(0)
from fedlab.core.client.serial_trainer import SubsetSerialTrainer
fro... |
refine_dict | Clean dictionary based on frequency and gap of frequency.
For example,
{'s1': ['t1': 999, 't2': 199, 't3':1],
's2': ['m1': 2000, 'm2': 100]}
=>
{'s1': ['t1': 999, 't2': 199],
's2': ['m1': 2000]}
Args:
full_mapping:
clean_dict_filename:
threshold:
ignore_gap:
Returns: | import argparse
import json
import os
from collections import Counter, defaultdict
from helper import _is_token_alnum
THRESHOLD = 0.01
GAP = 10
def get_full_mapping(src_filename, trg_filename, align_filename,
mapping_filename, reverse_src2trg=False, lowercase=True):
""" Get full mapping giv... | def refine_dict(full_mapping, clean_dict_filename, threshold, ignore_gap):
""" Clean dictionary based on frequency and gap of frequency.
For example,
{'s1': ['t1': 999, 't2': 199, 't3':1],
's2': ['m1': 2000, 'm2': 100]}
=>
{'s1': ['t1': 999, 't2': 199],
's2': ['m1': 2000]}
Args:
... | 92 | 134 | import argparse
import json
import os
from collections import Counter, defaultdict
from helper import _is_token_alnum
THRESHOLD = 0.01
GAP = 10
def get_full_mapping(src_filename, trg_filename, align_filename,
mapping_filename, reverse_src2trg=False, lowercase=True):
""" Get full mapping giv... |
generate_yaml_template | Args:
base_yaml: A string representation of one type job's base yaml.
slots_proto: A proto map object representation of modification
template's operable smallest units.
Returns:
string: A yaml_template | # Copyright 2020 The FedLearner Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | def generate_yaml_template(base_yaml, slots_proto):
"""
Args:
base_yaml: A string representation of one type job's base yaml.
slots_proto: A proto map object representation of modification
template's operable smallest units.
Returns:
string: A yaml_template
"""
slots ... | 48 | 63 | # Copyright 2020 The FedLearner Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... |
remote_shards | Returns a Dict[int, RRef] with keys being the RPC rank and values
being RRefs to shards on that rank. Need to initialize the
RPC framework for this functionality.
Raises an exception if ShardedTensor was created with ``init_rrefs=False`` | from dataclasses import dataclass, field
from enum import Enum
from typing import (
Callable,
Dict,
List,
Optional,
Union
)
import weakref
import threading
import torch
import torch.distributed as dist
from torch.distributed import rpc
from torch.distributed import distributed_c10d
from torch.distr... | def remote_shards(self) -> Dict[int, List[rpc.RRef[Shard]]]:
"""
Returns a Dict[int, RRef] with keys being the RPC rank and values
being RRefs to shards on that rank. Need to initialize the
RPC framework for this functionality.
Raises an exception if ShardedTensor was create... | 632 | 644 | from dataclasses import dataclass, field
from enum import Enum
from typing import (
Callable,
Dict,
List,
Optional,
Union
)
import weakref
import threading
import torch
import torch.distributed as dist
from torch.distributed import rpc
from torch.distributed import distributed_c10d
from torch.distr... |
prerelease_local_scheme | Return local scheme version unless building on master in CircleCI.
This function returns the local scheme version number
(e.g. 0.0.0.dev<N>+g<HASH>) unless building on CircleCI for a
pre-release in which case it ignores the hash and produces a
PEP440 compliant pre-release version number (e.g. 0.0.0.dev<N>). | import os
from setuptools import setup, find_packages
with open('README.rst') as readme_file:
readme = readme_file.read()
# MASKED: prerelease_local_scheme function (lines 8-22)
setup(
name='histomicsui',
use_scm_version={'local_scheme': prerelease_local_scheme},
setup_requires=['setuptools-scm'],
... | def prerelease_local_scheme(version):
"""
Return local scheme version unless building on master in CircleCI.
This function returns the local scheme version number
(e.g. 0.0.0.dev<N>+g<HASH>) unless building on CircleCI for a
pre-release in which case it ignores the hash and produces a
PEP440 co... | 8 | 22 | import os
from setuptools import setup, find_packages
with open('README.rst') as readme_file:
readme = readme_file.read()
def prerelease_local_scheme(version):
"""
Return local scheme version unless building on master in CircleCI.
This function returns the local scheme version number
(e.g. 0.0.0... |
to_proto | Converts an on demand feature view object to its protobuf representation.
Returns:
A OnDemandFeatureViewProto protobuf. | import copy
import functools
import warnings
from types import MethodType
from typing import Dict, List, Optional, Type, Union
import dill
import pandas as pd
from feast.base_feature_view import BaseFeatureView
from feast.data_source import RequestSource
from feast.errors import RegistryInferenceFailure, SpecifiedFea... | def to_proto(self) -> OnDemandFeatureViewProto:
"""
Converts an on demand feature view object to its protobuf representation.
Returns:
A OnDemandFeatureViewProto protobuf.
"""
meta = OnDemandFeatureViewMeta()
if self.created_timestamp:
meta.cr... | 253 | 287 | import copy
import functools
import warnings
from types import MethodType
from typing import Dict, List, Optional, Type, Union
import dill
import pandas as pd
from feast.base_feature_view import BaseFeatureView
from feast.data_source import RequestSource
from feast.errors import RegistryInferenceFailure, SpecifiedFea... |
custom_name_func | A custom test name function that will ensure that the tests are run such that they're batched with all tests for a
given data set are run together, avoiding re-reading the data more than necessary. Tests are run in alphabetical
order, so put the test case first. An alternate option is to right justify the test number (... |
import ast
import csv
import logging
import math
import os
from nose_parameterized import parameterized
import numpy
import SimpleITK as sitk
import six
from radiomics import getTestCase, imageoperations
# Get the logger. This is done outside the class, as it is needed by both the class and the custom_name_func
log... | def custom_name_func(testcase_func, param_num, param):
"""
A custom test name function that will ensure that the tests are run such that they're batched with all tests for a
given data set are run together, avoiding re-reading the data more than necessary. Tests are run in alphabetical
order, so put the test ca... | 22 | 51 |
import ast
import csv
import logging
import math
import os
from nose_parameterized import parameterized
import numpy
import SimpleITK as sitk
import six
from radiomics import getTestCase, imageoperations
# Get the logger. This is done outside the class, as it is needed by both the class and the custom_name_func
log... |
readBaselineFiles | Reads the 'baseline' folder contained in dataDir. All files starting with 'baseline_' are read as baseline files.
These files should therefore be named as follows: 'baseline_<className>.csv'. |
import ast
import csv
import logging
import math
import os
from nose_parameterized import parameterized
import numpy
import SimpleITK as sitk
import six
from radiomics import getTestCase, imageoperations
# Get the logger. This is done outside the class, as it is needed by both the class and the custom_name_func
log... | def readBaselineFiles(self):
"""
Reads the 'baseline' folder contained in dataDir. All files starting with 'baseline_' are read as baseline files.
These files should therefore be named as follows: 'baseline_<className>.csv'.
"""
baselineFiles = [fileName for fileName in os.listdir(self._baselineDi... | 95 | 109 |
import ast
import csv
import logging
import math
import os
from nose_parameterized import parameterized
import numpy
import SimpleITK as sitk
import six
from radiomics import getTestCase, imageoperations
# Get the logger. This is done outside the class, as it is needed by both the class and the custom_name_func
log... |
getFeatureNames | Gets all features for which a baseline value is available for the current class and test case. Returns a list
containing the feature names (without image type and feature class specifiers, i.e. just the feature name). |
import ast
import csv
import logging
import math
import os
from nose_parameterized import parameterized
import numpy
import SimpleITK as sitk
import six
from radiomics import getTestCase, imageoperations
# Get the logger. This is done outside the class, as it is needed by both the class and the custom_name_func
log... | def getFeatureNames(self, className, test):
"""
Gets all features for which a baseline value is available for the current class and test case. Returns a list
containing the feature names (without image type and feature class specifiers, i.e. just the feature name).
"""
if className not in self._ba... | 117 | 124 |
import ast
import csv
import logging
import math
import os
from nose_parameterized import parameterized
import numpy
import SimpleITK as sitk
import six
from radiomics import getTestCase, imageoperations
# Get the logger. This is done outside the class, as it is needed by both the class and the custom_name_func
log... |
gradients | Take gradient of output node with respect to each node in node_list.
Parameters
----------
output_node: output node that we are taking derivative of.
node_list: list of nodes that we are taking derivative wrt.
Returns
-------
A list of gradient values, one for each node in node_list respectively. | """ library to take autodiff and execute a computation graph """
from __future__ import absolute_import
import numpy as np
from .Node import Op
from .. import ndarray
from ..stream import *
import ctypes
import os
from pynvml import *
FLAG_SHOW_GRAPH = False
G_NODE_ID = 0
NAME_RULE = 1
def communicate_init(worker_nu... | def gradients(output_node, node_list, scheduler_policy=None):
"""Take gradient of output node with respect to each node in node_list.
Parameters
----------
output_node: output node that we are taking derivative of.
node_list: list of nodes that we are taking derivative wrt.
Returns
-------... | 446 | 488 | """ library to take autodiff and execute a computation graph """
from __future__ import absolute_import
import numpy as np
from .Node import Op
from .. import ndarray
from ..stream import *
import ctypes
import os
from pynvml import *
FLAG_SHOW_GRAPH = False
G_NODE_ID = 0
NAME_RULE = 1
def communicate_init(worker_nu... |
distributed_gradients | Take gradient of output node with respect to each node in node_list.
Parameters
----------
output_node: output node that we are taking derivative of.
node_list: list of nodes that we are taking derivative wrt.
Returns
-------
A list of gradient values, one for each node in node_list respectively. | """ library to take autodiff and execute a computation graph """
from __future__ import absolute_import
import numpy as np
from .Node import Op
from .. import ndarray
from ..stream import *
import ctypes
import os
from pynvml import *
FLAG_SHOW_GRAPH = False
G_NODE_ID = 0
NAME_RULE = 1
def communicate_init(worker_nu... | def distributed_gradients(output_node, node_list, scheduler_policy=None):
"""Take gradient of output node with respect to each node in node_list.
Parameters
----------
output_node: output node that we are taking derivative of.
node_list: list of nodes that we are taking derivative wrt.
Returns... | 491 | 531 | """ library to take autodiff and execute a computation graph """
from __future__ import absolute_import
import numpy as np
from .Node import Op
from .. import ndarray
from ..stream import *
import ctypes
import os
from pynvml import *
FLAG_SHOW_GRAPH = False
G_NODE_ID = 0
NAME_RULE = 1
def communicate_init(worker_nu... |
find_topo_sort | Given a list of nodes, return a topo ordering of nodes ending in them.
A simple algorithm is to do a post-order DFS traversal on the given nodes,
going backwards based on input edges. Since a node is added to the ordering
after all its predecessors are traversed due to post-order DFS, we get a
topological sort. | """ library to take autodiff and execute a computation graph """
from __future__ import absolute_import
import numpy as np
from .Node import Op
from .. import ndarray
from ..stream import *
import ctypes
import os
from pynvml import *
FLAG_SHOW_GRAPH = False
G_NODE_ID = 0
NAME_RULE = 1
def communicate_init(worker_nu... | def find_topo_sort(node_list):
"""Given a list of nodes, return a topo ordering of nodes ending in them.
A simple algorithm is to do a post-order DFS traversal on the given nodes,
going backwards based on input edges. Since a node is added to the ordering
after all its predecessors are traversed due to... | 538 | 551 | """ library to take autodiff and execute a computation graph """
from __future__ import absolute_import
import numpy as np
from .Node import Op
from .. import ndarray
from ..stream import *
import ctypes
import os
from pynvml import *
FLAG_SHOW_GRAPH = False
G_NODE_ID = 0
NAME_RULE = 1
def communicate_init(worker_nu... |
broadcast_rule | Return output shape of broadcast shape_a, shape_b.
e.g. broadcast_rule((3,2), (4,3,2))
returns output_shape = (4,3,2)
Check out explanations and more examples at
https://docs.scipy.org/doc/numpy-1.10.0/user/basics.broadcasting.html
http://eli.thegreenplace.net/2015/broadcasting-arrays-in-numpy/ | """ library to take autodiff and execute a computation graph """
from __future__ import absolute_import
import numpy as np
from .Node import Op
from .. import ndarray
from ..stream import *
import ctypes
import os
from pynvml import *
FLAG_SHOW_GRAPH = False
G_NODE_ID = 0
NAME_RULE = 1
def communicate_init(worker_nu... | def broadcast_rule(shape_a, shape_b):
"""Return output shape of broadcast shape_a, shape_b.
e.g. broadcast_rule((3,2), (4,3,2))
returns output_shape = (4,3,2)
Check out explanations and more examples at
https://docs.scipy.org/doc/numpy-1.10.0/user/basics.broadcasting.html
http://eli.thegreenpla... | 571 | 597 | """ library to take autodiff and execute a computation graph """
from __future__ import absolute_import
import numpy as np
from .Node import Op
from .. import ndarray
from ..stream import *
import ctypes
import os
from pynvml import *
FLAG_SHOW_GRAPH = False
G_NODE_ID = 0
NAME_RULE = 1
def communicate_init(worker_nu... |
infer_shape | Given shapes of feed_dict nodes, infer shape for all nodes in graph.
Implementation note:
Iteratively calls node.op.infer_shape to infer shapes.
Node shapes stored in self.node_to_shape_map.
Parameters
----------
feed_shapes: node->shapes mapping for feed_dict nodes. | """ library to take autodiff and execute a computation graph """
from __future__ import absolute_import
import numpy as np
from .Node import Op
from .. import ndarray
from ..stream import *
import ctypes
import os
from pynvml import *
FLAG_SHOW_GRAPH = False
G_NODE_ID = 0
NAME_RULE = 1
def communicate_init(worker_nu... | def infer_shape(self, feed_shapes):
"""Given shapes of feed_dict nodes, infer shape for all nodes in graph.
Implementation note:
Iteratively calls node.op.infer_shape to infer shapes.
Node shapes stored in self.node_to_shape_map.
Parameters
----------
feed_s... | 97 | 118 | """ library to take autodiff and execute a computation graph """
from __future__ import absolute_import
import numpy as np
from .Node import Op
from .. import ndarray
from ..stream import *
import ctypes
import os
from pynvml import *
FLAG_SHOW_GRAPH = False
G_NODE_ID = 0
NAME_RULE = 1
def communicate_init(worker_nu... |
memory_plan | Allocates ndarray.NDArray for every node except feed_dict nodes.
Implementation note:
Option 1: Alloc a ndarray.NDArray per node that persists across run()
Option 2: Implement a memory pool to reuse memory for nodes of same
shapes. More details see Lecture 7.
For both options, self.node_to_arr_map stores node... | """ library to take autodiff and execute a computation graph """
from __future__ import absolute_import
import numpy as np
from .Node import Op
from .. import ndarray
from ..stream import *
import ctypes
import os
from pynvml import *
FLAG_SHOW_GRAPH = False
G_NODE_ID = 0
NAME_RULE = 1
def communicate_init(worker_nu... | def memory_plan(self, feed_shapes):
"""Allocates ndarray.NDArray for every node except feed_dict nodes.
Implementation note:
Option 1: Alloc a ndarray.NDArray per node that persists across run()
Option 2: Implement a memory pool to reuse memory for nodes of same
shap... | 120 | 149 | """ library to take autodiff and execute a computation graph """
from __future__ import absolute_import
import numpy as np
from .Node import Op
from .. import ndarray
from ..stream import *
import ctypes
import os
from pynvml import *
FLAG_SHOW_GRAPH = False
G_NODE_ID = 0
NAME_RULE = 1
def communicate_init(worker_nu... |
run | Parameters
----------
feed_dict: a dictionary of node->np.ndarray supplied by user.
convert_to_numpy_ret_vals: whether to convert ret vals to np.array
Returns
-------
A list of values for nodes in eval_node_list. NDArray or np.ndarray. | """ library to take autodiff and execute a computation graph """
from __future__ import absolute_import
import numpy as np
from .Node import Op
from .. import ndarray
from ..stream import *
import ctypes
import os
from pynvml import *
FLAG_SHOW_GRAPH = False
G_NODE_ID = 0
NAME_RULE = 1
def communicate_init(worker_nu... | def run(self, feed_dict, convert_to_numpy_ret_vals=False):
"""
Parameters
----------
feed_dict: a dictionary of node->np.ndarray supplied by user.
convert_to_numpy_ret_vals: whether to convert ret vals to np.array
Returns
-------
A list of values for ... | 151 | 214 | """ library to take autodiff and execute a computation graph """
from __future__ import absolute_import
import numpy as np
from .Node import Op
from .. import ndarray
from ..stream import *
import ctypes
import os
from pynvml import *
FLAG_SHOW_GRAPH = False
G_NODE_ID = 0
NAME_RULE = 1
def communicate_init(worker_nu... |
_callback | This will be called repeatedly every `self.interval` seconds.
`self.subscriptions` contain tuples of (obj, args, kwargs) for
each subscribing object.
If overloading, this callback is expected to handle all
subscriptions when it is triggered. It should not return
anything and should not traceback on poorly designed hoo... | """
TickerHandler
This implements an efficient Ticker which uses a subscription
model to 'tick' subscribed objects at regular intervals.
The ticker mechanism is used by importing and accessing
the instantiated TICKER_HANDLER instance in this module. This
instance is run by the server; it will save its status across
s... | @inlineCallbacks
def _callback(self):
"""
This will be called repeatedly every `self.interval` seconds.
`self.subscriptions` contain tuples of (obj, args, kwargs) for
each subscribing object.
If overloading, this callback is expected to handle all
subscriptions w... | 88 | 139 | """
TickerHandler
This implements an efficient Ticker which uses a subscription
model to 'tick' subscribed objects at regular intervals.
The ticker mechanism is used by importing and accessing
the instantiated TICKER_HANDLER instance in this module. This
instance is run by the server; it will save its status across
s... |
__init__ | Set up the ticker
Args:
interval (int): The stepping interval. | """
TickerHandler
This implements an efficient Ticker which uses a subscription
model to 'tick' subscribed objects at regular intervals.
The ticker mechanism is used by importing and accessing
the instantiated TICKER_HANDLER instance in this module. This
instance is run by the server; it will save its status across
s... | def __init__(self, interval):
"""
Set up the ticker
Args:
interval (int): The stepping interval.
"""
self.interval = interval
self.subscriptions = {}
self._is_ticking = False
self._to_remove = []
self._to_add = []
# set up... | 142 | 156 | """
TickerHandler
This implements an efficient Ticker which uses a subscription
model to 'tick' subscribed objects at regular intervals.
The ticker mechanism is used by importing and accessing
the instantiated TICKER_HANDLER instance in this module. This
instance is run by the server; it will save its status across
s... |
validate | Start/stop the task depending on how many subscribers we have
using it.
Args:
start_delay (int): Time to way before starting. | """
TickerHandler
This implements an efficient Ticker which uses a subscription
model to 'tick' subscribed objects at regular intervals.
The ticker mechanism is used by importing and accessing
the instantiated TICKER_HANDLER instance in this module. This
instance is run by the server; it will save its status across
s... | def validate(self, start_delay=None):
"""
Start/stop the task depending on how many subscribers we have
using it.
Args:
start_delay (int): Time to way before starting.
"""
subs = self.subscriptions
if self.task.running:
if not subs:
... | 158 | 172 | """
TickerHandler
This implements an efficient Ticker which uses a subscription
model to 'tick' subscribed objects at regular intervals.
The ticker mechanism is used by importing and accessing
the instantiated TICKER_HANDLER instance in this module. This
instance is run by the server; it will save its status across
s... |
add | Add new ticker subscriber.
Args:
store_key (str): Unique storage hash.
args (any, optional): Arguments to send to the hook method. | """
TickerHandler
This implements an efficient Ticker which uses a subscription
model to 'tick' subscribed objects at regular intervals.
The ticker mechanism is used by importing and accessing
the instantiated TICKER_HANDLER instance in this module. This
instance is run by the server; it will save its status across
s... | def add(self, store_key, *args, **kwargs):
"""
Add new ticker subscriber.
Args:
store_key (str): Unique storage hash.
args (any, optional): Arguments to send to the hook method.
"""
_, _, _, interval, _, _ = store_key
if not interval:
... | 237 | 253 | """
TickerHandler
This implements an efficient Ticker which uses a subscription
model to 'tick' subscribed objects at regular intervals.
The ticker mechanism is used by importing and accessing
the instantiated TICKER_HANDLER instance in this module. This
instance is run by the server; it will save its status across
s... |
all_display | Get all tickers on an easily displayable form.
Returns:
tickers (dict): A list of all storekeys | """
TickerHandler
This implements an efficient Ticker which uses a subscription
model to 'tick' subscribed objects at regular intervals.
The ticker mechanism is used by importing and accessing
the instantiated TICKER_HANDLER instance in this module. This
instance is run by the server; it will save its status across
s... | def all_display(self):
"""
Get all tickers on an easily displayable form.
Returns:
tickers (dict): A list of all storekeys
"""
store_keys = []
for ticker in self.ticker_pool.tickers.itervalues():
for (objtup, callfunc, path, interval, idstrin... | 564 | 576 | """
TickerHandler
This implements an efficient Ticker which uses a subscription
model to 'tick' subscribed objects at regular intervals.
The ticker mechanism is used by importing and accessing
the instantiated TICKER_HANDLER instance in this module. This
instance is run by the server; it will save its status across
s... |
size_num_grads | Count total size of all gradient arrays of a given link
Args:
link (chainer.link.Link): Target link object. | import multiprocessing
import warnings
import six
from chainer.backends import cuda
from chainer.dataset import convert
from chainer import reporter
from chainer.training.updaters import standard_updater
try:
from cupy.cuda import nccl
_available = True
except Exception:
_available = False
import numpy... | def size_num_grads(link):
"""Count total size of all gradient arrays of a given link
Args:
link (chainer.link.Link): Target link object.
"""
size = 0
num = 0
for param in link.params():
if param.size == 0:
continue
size += param.size
num += 1
retu... | 276 | 289 | import multiprocessing
import warnings
import six
from chainer.backends import cuda
from chainer.dataset import convert
from chainer import reporter
from chainer.training.updaters import standard_updater
try:
from cupy.cuda import nccl
_available = True
except Exception:
_available = False
import numpy... |
gather_grads | Put together all gradient arrays and make a single array
Args:
link (chainer.link.Link): Target link object.
Return:
cupy.ndarray | import multiprocessing
import warnings
import six
from chainer.backends import cuda
from chainer.dataset import convert
from chainer import reporter
from chainer.training.updaters import standard_updater
try:
from cupy.cuda import nccl
_available = True
except Exception:
_available = False
import numpy... | def gather_grads(link):
"""Put together all gradient arrays and make a single array
Args:
link (chainer.link.Link): Target link object.
Return:
cupy.ndarray
"""
if link.xp is numpy:
raise RuntimeError('gather_grads works only on GPU.')
return _gather(link, 'grad') | 359 | 369 | import multiprocessing
import warnings
import six
from chainer.backends import cuda
from chainer.dataset import convert
from chainer import reporter
from chainer.training.updaters import standard_updater
try:
from cupy.cuda import nccl
_available = True
except Exception:
_available = False
import numpy... |
gather_params | Put together all gradient arrays and make a single array
Args:
link (chainer.link.Link): Target link object.
Return:
cupy.ndarray | import multiprocessing
import warnings
import six
from chainer.backends import cuda
from chainer.dataset import convert
from chainer import reporter
from chainer.training.updaters import standard_updater
try:
from cupy.cuda import nccl
_available = True
except Exception:
_available = False
import numpy... | def gather_params(link):
"""Put together all gradient arrays and make a single array
Args:
link (chainer.link.Link): Target link object.
Return:
cupy.ndarray
"""
if link.xp is numpy:
raise RuntimeError('Link.gather_params works only on GPU.')
return _gather(link, 'data') | 372 | 382 | import multiprocessing
import warnings
import six
from chainer.backends import cuda
from chainer.dataset import convert
from chainer import reporter
from chainer.training.updaters import standard_updater
try:
from cupy.cuda import nccl
_available = True
except Exception:
_available = False
import numpy... |
get_heaviest_peak | Returns: the header_hash, height, and weight of the heaviest block that one of our peers has notified
us of. | import asyncio
import logging
from typing import Dict, List, Optional, Set, Tuple
from seno.types.blockchain_format.sized_bytes import bytes32
from seno.util.ints import uint32, uint128
log = logging.getLogger(__name__)
class SyncStore:
# Whether or not we are syncing
sync_mode: bool
long_sync: bool
... | def get_heaviest_peak(self) -> Optional[Tuple[bytes32, uint32, uint128]]:
"""
Returns: the header_hash, height, and weight of the heaviest block that one of our peers has notified
us of.
"""
if len(self.peer_to_peak) == 0:
return None
heaviest_peak_hash: ... | 101 | 120 | import asyncio
import logging
from typing import Dict, List, Optional, Set, Tuple
from seno.types.blockchain_format.sized_bytes import bytes32
from seno.util.ints import uint32, uint128
log = logging.getLogger(__name__)
class SyncStore:
# Whether or not we are syncing
sync_mode: bool
long_sync: bool
... |
calculate_chunk_slices | Calculate slices for indexing an adapter.
Parameters
----------
items_per_chunk: int
Approximate number of items per chunk.
num_items: int
Total number of items.
Returns
-------
list of slices | #!/usr/bin/env python
import os
import re
import pickle
import json
import glob
import numpy as np
from abc import ABC, abstractmethod
from concurrent.futures import ProcessPoolExecutor
from contextlib import contextmanager
from collections import namedtuple, OrderedDict
from tqdm import tqdm
from .utils import img... | def calculate_chunk_slices(items_per_chunk, num_items):
"""Calculate slices for indexing an adapter.
Parameters
----------
items_per_chunk: int
Approximate number of items per chunk.
num_items: int
Total number of items.
Returns
-------
list of slices
"""
asser... | 462 | 480 | #!/usr/bin/env python
import os
import re
import pickle
import json
import glob
import numpy as np
from abc import ABC, abstractmethod
from concurrent.futures import ProcessPoolExecutor
from contextlib import contextmanager
from collections import namedtuple, OrderedDict
from tqdm import tqdm
from .utils import img... |
iter_all | Iterate over all frames in the gulp.
Parameters
----------
accepted_ids: list of str
A filter for accepted ids.
shuffle: bool
Shuffle the items or not.
Returns
-------
iterator
An iterator that yield a series of frames,meta tuples. See
`read_frames` for details. | #!/usr/bin/env python
import os
import re
import pickle
import json
import glob
import numpy as np
from abc import ABC, abstractmethod
from concurrent.futures import ProcessPoolExecutor
from contextlib import contextmanager
from collections import namedtuple, OrderedDict
from tqdm import tqdm
from .utils import img... | def iter_all(self, accepted_ids=None, shuffle=False):
""" Iterate over all frames in the gulp.
Parameters
----------
accepted_ids: list of str
A filter for accepted ids.
shuffle: bool
Shuffle the items or not.
Returns
-------
... | 393 | 423 | #!/usr/bin/env python
import os
import re
import pickle
import json
import glob
import numpy as np
from abc import ABC, abstractmethod
from concurrent.futures import ProcessPoolExecutor
from contextlib import contextmanager
from collections import namedtuple, OrderedDict
from tqdm import tqdm
from .utils import img... |
__init__ | Constructs a NsynthConfig.
Args:
gansynth_subset: bool, whether to use the subset of the dataset introduced
in the ICLR 2019 GANSynth paper (Engel, et al. 2018). This subset uses
acoustic-only instrument sources and limits the pitches to the interval
[24, 84]. The train and test splits are also modified ... | # coding=utf-8
# Copyright 2019 The TensorFlow Datasets 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 appl... | def __init__(self,
gansynth_subset=False,
estimate_f0_and_loudness=False,
**kwargs):
"""Constructs a NsynthConfig.
Args:
gansynth_subset: bool, whether to use the subset of the dataset introduced
in the ICLR 2019 GANSynth paper (Engel, et al. 2018). ... | 107 | 138 | # coding=utf-8
# Copyright 2019 The TensorFlow Datasets 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 appl... |
validate_linux_host_name | Validates a string as a legal host name component.
This validation will also occur server-side in the ARM API, but that may take
a minute or two before the user sees it. So it's more user-friendly to validate
in the CLI pre-flight. | # --------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# --------------------------------------------------------------------... | def validate_linux_host_name(namespace):
"""Validates a string as a legal host name component.
This validation will also occur server-side in the ARM API, but that may take
a minute or two before the user sees it. So it's more user-friendly to validate
in the CLI pre-flight.
"""
# https://stack... | 71 | 83 | # --------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# --------------------------------------------------------------------... |
__init__ | Provides a proxy protocol policy, which allows an ELB to carry a client connection information to a backend.
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[list] instance_ports: List of instance ports to which the policy
shou... | # coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import json
import warnings
import pulumi
import pulumi.runtime
from .. import utilities, tables
class ProxyProtocolPolicy(pulumi.Cust... | def __init__(__self__, resource_name, opts=None, instance_ports=None, load_balancer=None, __name__=None, __opts__=None):
"""
Provides a proxy protocol policy, which allows an ELB to carry a client connection information to a backend.
:param str resource_name: The name of the resourc... | 22 | 60 | # coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import json
import warnings
import pulumi
import pulumi.runtime
from .. import utilities, tables
class ProxyProtocolPolicy(pulumi.Cust... |
transform_audio | Add background noise audio.
Note that this is an in-place transformation.
:param audio_segment: Audio segment to add effects to.
:type audio_segment: AudioSegmenet|SpeechSegment | # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | def transform_audio(self, audio_segment):
"""Add background noise audio.
Note that this is an in-place transformation.
:param audio_segment: Audio segment to add effects to.
:type audio_segment: AudioSegmenet|SpeechSegment
"""
noise_json = self._rng.choice(self._noi... | 45 | 64 | # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... |
symrcm | Returns the permutation array that orders a sparse CSR or CSC matrix or Qobj
in Reverse-Cuthill McKee ordering. Since the input matrix must be symmetric,
this routine works on the matrix A+Trans(A) if the sym flag is set to False (Default).
It is assumed by default (*sym=False*) that the input matrix is not symmetric... | # This file is part of QuTiP: Quantum Toolbox in Python.
#
# Copyright (c) 2011 and later, Paul D. Nation and Robert J. Johansson.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met... | def symrcm(A, sym=False):
"""
Returns the permutation array that orders a sparse CSR or CSC matrix or Qobj
in Reverse-Cuthill McKee ordering. Since the input matrix must be symmetric,
this routine works on the matrix A+Trans(A) if the sym flag is set to False (Default).
It is assumed by defaul... | 104 | 149 | # This file is part of QuTiP: Quantum Toolbox in Python.
#
# Copyright (c) 2011 and later, Paul D. Nation and Robert J. Johansson.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met... |
inner | Inner is called when a generated method (publicGetX) is called.
_self is a reference to self created by function.__get__(exchange, type(exchange))
https://en.wikipedia.org/wiki/Closure_(computer_programming) equivalent to functools.partial | # -*- coding: utf-8 -*-
"""Base exchange class"""
# -----------------------------------------------------------------------------
__version__ = '1.18.575'
# -----------------------------------------------------------------------------
from ccxt.base.errors import ExchangeError
from ccxt.base.errors import NetworkE... | @functools.wraps(entry)
def inner(_self, params=None):
"""
Inner is called when a generated method (publicGetX) is called.
_self is a reference to self created by function.__get__(exchange... | 396 | 406 | # -*- coding: utf-8 -*-
"""Base exchange class"""
# -----------------------------------------------------------------------------
__version__ = '1.18.575'
# -----------------------------------------------------------------------------
from ccxt.base.errors import ExchangeError
from ccxt.base.errors import NetworkE... |
is_story_file | Checks if a file is a Rasa story file.
Args:
file_path: Path of the file which should be checked.
Returns:
`True` if it's a story file, otherwise `False`. | import logging
import os
import shutil
import tempfile
import uuid
import re
from typing import Tuple, List, Text, Set, Union, Optional, Iterable
from rasa.nlu.training_data import loading
from rasa.utils.io import DEFAULT_ENCODING
logger = logging.getLogger(__name__)
def get_core_directory(paths: Optional[Union[Tex... | def is_story_file(file_path: Text) -> bool:
"""Checks if a file is a Rasa story file.
Args:
file_path: Path of the file which should be checked.
Returns:
`True` if it's a story file, otherwise `False`.
"""
if not file_path.endswith(".md"):
return False
try:
wi... | 140 | 167 | import logging
import os
import shutil
import tempfile
import uuid
import re
from typing import Tuple, List, Text, Set, Union, Optional, Iterable
from rasa.nlu.training_data import loading
from rasa.utils.io import DEFAULT_ENCODING
logger = logging.getLogger(__name__)
def get_core_directory(paths: Optional[Union[Tex... |
predict | predict function
Args:
model: keras model fit by fit_model
X_test: Test features
cate_cols: categorical columns list
Returns: y_pred | from typing import Tuple, Union
import numpy as np
import pandas as pd
import tensorflow as tf
from src.models.dnn_regressor_funcs import (
_compile_model,
_create_keras_model,
_fit_model,
_to_input_list,
)
# MASKED: predict function (lines 15-28)
def train(
X_train: pd.DataFrame,
y_train:... | def predict(model: tf.keras.Model, X_test: pd.DataFrame, cate_cols: list) -> np.array:
"""
predict function
Args:
model: keras model fit by fit_model
X_test: Test features
cate_cols: categorical columns list
Returns: y_pred
"""
X_test_list = _to_input_list(df=X_test, ca... | 15 | 28 | from typing import Tuple, Union
import numpy as np
import pandas as pd
import tensorflow as tf
from src.models.dnn_regressor_funcs import (
_compile_model,
_create_keras_model,
_fit_model,
_to_input_list,
)
def predict(model: tf.keras.Model, X_test: pd.DataFrame, cate_cols: list) -> np.array:
""... |
train | Training main function that takes dataset and parameters as input and returns the trained model with history
Args:
X_train: Train features
y_train: train labels
X_val: Validation labels
y_val: validation labels
layers: List of nodes in hidden layers
num_classes: Number of classes in target varia... | from typing import Tuple, Union
import numpy as np
import pandas as pd
import tensorflow as tf
from src.models.dnn_regressor_funcs import (
_compile_model,
_create_keras_model,
_fit_model,
_to_input_list,
)
def predict(model: tf.keras.Model, X_test: pd.DataFrame, cate_cols: list) -> np.array:
""... | def train(
X_train: pd.DataFrame,
y_train: Union[pd.Series, np.array],
X_val: pd.DataFrame,
y_val: Union[pd.Series, np.array],
layers: list,
num_classes: int,
cate_cols: list,
learning_rate: float,
epochs: int,
batch_size: int,
dropout_rate: float = 0.3,
) -> Tuple[tf.keras.c... | 31 | 97 | from typing import Tuple, Union
import numpy as np
import pandas as pd
import tensorflow as tf
from src.models.dnn_regressor_funcs import (
_compile_model,
_create_keras_model,
_fit_model,
_to_input_list,
)
def predict(model: tf.keras.Model, X_test: pd.DataFrame, cate_cols: list) -> np.array:
""... |
contact_list | Displays a list of :model:`rr.Contact` linked to
:model:`rr.ServiceProvider`.
Includes a ModelForm for adding :model:`rr.Contact` to
:model:`rr.ServiceProvider`.
**Context**
``object_list``
List of :model:`rr.Contact`.
``form``
ModelForm for creating a :model:`rr.Contact`
``object``
An instance of :mod... | import logging
from django.contrib import messages
from django.contrib.auth.decorators import login_required
from django.shortcuts import render
from django.utils import timezone
from django.utils.translation import ugettext as _
from rr.forms.contact import ContactForm
from rr.models.contact import Contact
from rr.u... | @login_required
def contact_list(request, pk):
"""
Displays a list of :model:`rr.Contact` linked to
:model:`rr.ServiceProvider`.
Includes a ModelForm for adding :model:`rr.Contact` to
:model:`rr.ServiceProvider`.
**Context**
``object_list``
List of :model:`rr.Contact`.
``form... | 16 | 50 | import logging
from django.contrib import messages
from django.contrib.auth.decorators import login_required
from django.shortcuts import render
from django.utils import timezone
from django.utils.translation import ugettext as _
from rr.forms.contact import ContactForm
from rr.models.contact import Contact
from rr.u... |
parametrize | Parametrizes the CirqOperation.
Args:
*args (float): the parameters for the operations | # Copyright 2019-2020 Xanadu Quantum Technologies Inc.
# 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... | def parametrize(self, *args):
"""Parametrizes the CirqOperation.
Args:
*args (float): the parameters for the operations
"""
self.parametrized_cirq_gates = self.parametrization(*args)
if not isinstance(self.parametrized_cirq_gates, Sequence):
self.par... | 54 | 67 | # Copyright 2019-2020 Xanadu Quantum Technologies Inc.
# 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... |
apply | Applies the CirqOperation.
Args:
*qubits (Cirq:Qid): the qubits on which the Cirq gates should be performed. | # Copyright 2019-2020 Xanadu Quantum Technologies Inc.
# 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... | def apply(self, *qubits):
"""Applies the CirqOperation.
Args:
*qubits (Cirq:Qid): the qubits on which the Cirq gates should be performed.
"""
if not self.parametrized_cirq_gates:
raise qml.DeviceError("CirqOperation must be parametrized before it can be appli... | 69 | 78 | # Copyright 2019-2020 Xanadu Quantum Technologies Inc.
# 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... |
save | Saves the current module dictionary.
Args:
filename (str): name of output file |
import os
import urllib
import torch
from torch.utils import model_zoo
class CheckpointIO(object):
''' CheckpointIO class.
It handles saving and loading checkpoints.
Args:
checkpoint_dir (str): path where checkpoints are saved
'''
def __init__(self, checkpoint_dir='./chkpts', **kwargs):... | def save(self, filename, **kwargs):
''' Saves the current module dictionary.
Args:
filename (str): name of output file
'''
if not os.path.isabs(filename):
filename = os.path.join(self.checkpoint_dir, filename)
outdict = kwargs
for k, v in sel... | 27 | 39 |
import os
import urllib
import torch
from torch.utils import model_zoo
class CheckpointIO(object):
''' CheckpointIO class.
It handles saving and loading checkpoints.
Args:
checkpoint_dir (str): path where checkpoints are saved
'''
def __init__(self, checkpoint_dir='./chkpts', **kwargs):... |
parse_state_dict | Parse state_dict of model and return scalars.
Args:
state_dict (dict): State dict of model |
import os
import urllib
import torch
from torch.utils import model_zoo
class CheckpointIO(object):
''' CheckpointIO class.
It handles saving and loading checkpoints.
Args:
checkpoint_dir (str): path where checkpoints are saved
'''
def __init__(self, checkpoint_dir='./chkpts', **kwargs):... | def parse_state_dict(self, state_dict):
'''Parse state_dict of model and return scalars.
Args:
state_dict (dict): State dict of model
'''
for k, v in self.module_dict.items():
if k in state_dict:
v.load_state_dict(state_dict[k])
... | 83 | 97 |
import os
import urllib
import torch
from torch.utils import model_zoo
class CheckpointIO(object):
''' CheckpointIO class.
It handles saving and loading checkpoints.
Args:
checkpoint_dir (str): path where checkpoints are saved
'''
def __init__(self, checkpoint_dir='./chkpts', **kwargs):... |
get_extra_rules | Helper to provide custom (project level/user level) anonymization
rules as a mapping of tags -> action function.
Args:
use_extra (bool): If use extra rules.
extra_json_path (Path_Str): Path to extra rules json file.
It should be flat json with action as a key and list of tags as value.
Returns:
Option... | """ This module is intended to extend functionality of the code provided by original authors.
The process is as follows:
1. User has to provide source root path containing (possibly nested) folders with dicom files
2. The program will recreate the structure in the destination root path and anonymize all
... | def get_extra_rules(
use_extra: bool,
extra_json_path: Path_Str,
) -> Optional[ActionsDict]:
"""Helper to provide custom (project level/user level) anonymization
rules as a mapping of tags -> action function.
Args:
use_extra (bool): If use extra rules.
extra_json_path (Path_Str): Pa... | 47 | 73 | """ This module is intended to extend functionality of the code provided by original authors.
The process is as follows:
1. User has to provide source root path containing (possibly nested) folders with dicom files
2. The program will recreate the structure in the destination root path and anonymize all
... |
anonymize_dicom_folder | Anonymize dicom files in `in_path`, if `in_path` doesn't
contain dicom files, will do nothing. Debug == True will do
sort of dry run to check if all good for the large data storages
Args:
in_path (Path_Str): path to the folder containing dicom files
out_path (Path_Str): path to the folder there anonymized copi... | """ This module is intended to extend functionality of the code provided by original authors.
The process is as follows:
1. User has to provide source root path containing (possibly nested) folders with dicom files
2. The program will recreate the structure in the destination root path and anonymize all
... | def anonymize_dicom_folder(
in_path: Path_Str, out_path: Path_Str, debug: bool = False, **kwargs
):
"""Anonymize dicom files in `in_path`, if `in_path` doesn't
contain dicom files, will do nothing. Debug == True will do
sort of dry run to check if all good for the large data storages
Args:
... | 76 | 122 | """ This module is intended to extend functionality of the code provided by original authors.
The process is as follows:
1. User has to provide source root path containing (possibly nested) folders with dicom files
2. The program will recreate the structure in the destination root path and anonymize all
... |
_cmdf_in | `{cmd}` - Adds you to the game.
This command also allows moderators to add other users and arbitrary strings as participants.
**Example:** `{cmd} an elephant` - Adds "an elephant" as a participant. | import asyncio
import random
import re
import textwrap
import discord
from .. import utils, errors, cmd
from ..servermodule import ServerModule, registered
from ..enums import PrivilegeLevel
@registered
class TruthGame(ServerModule):
MODULE_NAME = "Truth Game"
MODULE_SHORT_DESCRIPTION = "Tools to play *Truth*... | @cmd.add(_cmdd, "in", top=True)
async def _cmdf_in(self, substr, msg, privilege_level):
"""
`{cmd}` - Adds you to the game.
This command also allows moderators to add other users and arbitrary strings as participants.
**Example:** `{cmd} an elephant` - Adds "an elephant" as a participant.... | 84 | 107 | import asyncio
import random
import re
import textwrap
import discord
from .. import utils, errors, cmd
from ..servermodule import ServerModule, registered
from ..enums import PrivilegeLevel
@registered
class TruthGame(ServerModule):
MODULE_NAME = "Truth Game"
MODULE_SHORT_DESCRIPTION = "Tools to play *Truth*... |
resize_axis | Truncates or pads a tensor to new_size on on a given axis.
Truncate or extend tensor such that tensor.shape[axis] == new_size. If the
size increases, the padding will be performed at the end, using fill_value.
Args:
tensor: The tensor to be resized.
axis: An integer representing the dimension to be sliced.
new_si... | import io
import os
import random
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from PIL import Image
# MASKED: resize_axis function (lines 12-43)
class CircleLoss(torch.nn.Module):
def __init__(self, m=0.25, gamma=256):
super(CircleLoss, self).__init__()
... | def resize_axis(tensor, axis, new_size, fill_value=0, random_sampling=False):
"""Truncates or pads a tensor to new_size on on a given axis.
Truncate or extend tensor such that tensor.shape[axis] == new_size. If the
size increases, the padding will be performed at the end, using fill_value.
Args:
t... | 12 | 43 | import io
import os
import random
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from PIL import Image
def resize_axis(tensor, axis, new_size, fill_value=0, random_sampling=False):
"""Truncates or pads a tensor to new_size on on a given axis.
Truncate or extend tensor s... |
_query_for_init | 返回某些类型合约的 query
todo: 为了兼容旧版提供给用户的 api._data["quote"].items() 类似用法，应该限制交易所 ["SHFE", "DCE", "CZCE", "INE", "CFFEX", "KQ"] | #!usr/bin/env python3
# -*- coding:utf-8 -*-
__author__ = 'yanqiong'
import random
import secrets
from bisect import bisect_right
from sgqlc.operation import Operation
from pandas.core.internals import BlockManager
from tqsdk.ins_schema import ins_schema, _add_all_frags
RD = random.Random(secrets.randbits(128)) #... | def _query_for_init():
"""
返回某些类型合约的 query
todo: 为了兼容旧版提供给用户的 api._data["quote"].items() 类似用法，应该限制交易所 ["SHFE", "DCE", "CZCE", "INE", "CFFEX", "KQ"]
"""
op = Operation(ins_schema.rootQuery)
query = op.multi_symbol_info(class_=["FUTURE", "INDEX", "OPTION", "COMBINE", "CONT"],
... | 39 | 48 | #!usr/bin/env python3
# -*- coding:utf-8 -*-
__author__ = 'yanqiong'
import random
import secrets
from bisect import bisect_right
from sgqlc.operation import Operation
from pandas.core.internals import BlockManager
from tqsdk.ins_schema import ins_schema, _add_all_frags
RD = random.Random(secrets.randbits(128)) #... |
solve_for_target_return | Solve for the weights of the minimum variance portfolio which has
a specific targeted return.
Constraints:
sum of weights = 1,
weights bound by [0, 0.2],
portfolio return = target return,
Returns the weights and the jacobian used to generate the solution. | '''
A collection of functions to perform portfolio analysis.
Max Gosselin, 2019
'''
import numpy as np
import pandas as pd
from scipy import optimize
def portfolio_metrics(weights, avg_xs_returns, covariance_matrix):
''' Compute basic portfolio metrics: return, stdv, sharpe ratio '''
por... | def solve_for_target_return(xs_avg, covariance_matrix, target):
''' Solve for the weights of the minimum variance portfolio which has
a specific targeted return.
Constraints:
sum of weights = 1,
weights bound by [0, 0.2],
portfolio return = target return,
... | 103 | 135 | '''
A collection of functions to perform portfolio analysis.
Max Gosselin, 2019
'''
import numpy as np
import pandas as pd
from scipy import optimize
def portfolio_metrics(weights, avg_xs_returns, covariance_matrix):
''' Compute basic portfolio metrics: return, stdv, sharpe ratio '''
por... |
_consistency_check | Required defintions:
-- WHITESPACE (Default done automatically) => Assert.
-- NEWLINE (Default done automatically) => Assert.
Inadmissible 'eat-into'.
-- SUPPRESSOR shall not eat into [NEWLINE]
-- NEWLINE shall not eat into [WHITESPACE, BADSPACE, SUSPEND, SUPPRESSOR]
-- WHITESPACE shall not eat in... | # Project Quex (http://quex.sourceforge.net); License: MIT;
# (C) 2005-2020 Frank-Rene Schaefer;
#_______________________________________________________________________________
from quex.input.setup import NotificationDB
from quex.input.regular_expression.pattern import Patt... | def _consistency_check(self):
"""
Required defintions:
-- WHITESPACE (Default done automatically) => Assert.
-- NEWLINE (Default done automatically) => Assert.
Inadmissible 'eat-into'.
-- SUPPRESSOR shall not eat into [NEWLINE]
-- NEWLINE sh... | 258 | 342 | # Project Quex (http://quex.sourceforge.net); License: MIT;
# (C) 2005-2020 Frank-Rene Schaefer;
#_______________________________________________________________________________
from quex.input.setup import NotificationDB
from quex.input.regular_expression.pattern import Patt... |
get_enrollment_dates | Takes a course object and returns student dates of enrollment.
Useful for handling late registrations and modified deadlines.
Example:
course.get_enrollment_date() | import requests
import urllib.parse
import posixpath
import pandas as pd
# MASKED: get_enrollment_dates function (lines 6-34)
def get_assignments(course):
'''Takes a course object and returns
a Pandas data frame with all existing assignments and their attributes/data
Example:
course.get_assignments()... | def get_enrollment_dates(course):
'''Takes a course object and returns student dates of enrollment.
Useful for handling late registrations and modified deadlines.
Example:
course.get_enrollment_date()'''
url_path = posixpath.join("api", "v1", "courses", course['course_id'], "enrollments")
api_u... | 6 | 34 | import requests
import urllib.parse
import posixpath
import pandas as pd
def get_enrollment_dates(course):
'''Takes a course object and returns student dates of enrollment.
Useful for handling late registrations and modified deadlines.
Example:
course.get_enrollment_date()'''
url_path = posixpath.... |
get_assignments | Takes a course object and returns
a Pandas data frame with all existing assignments and their attributes/data
Example:
course.get_assignments() | import requests
import urllib.parse
import posixpath
import pandas as pd
def get_enrollment_dates(course):
'''Takes a course object and returns student dates of enrollment.
Useful for handling late registrations and modified deadlines.
Example:
course.get_enrollment_date()'''
url_path = posixpath.... | def get_assignments(course):
'''Takes a course object and returns
a Pandas data frame with all existing assignments and their attributes/data
Example:
course.get_assignments()'''
url_path = posixpath.join("api", "v1", "courses", course['course_id'], "assignments")
api_url = urllib.parse.urljoin... | 36 | 57 | import requests
import urllib.parse
import posixpath
import pandas as pd
def get_enrollment_dates(course):
'''Takes a course object and returns student dates of enrollment.
Useful for handling late registrations and modified deadlines.
Example:
course.get_enrollment_date()'''
url_path = posixpath.... |
get_assignment_lock_date | Takes a course object and the name of a Canvas assignment and returns the due date. Returns None if no due date assigned.
Example:
course.get_assignment_due_date('worksheet_01') | import requests
import urllib.parse
import posixpath
import pandas as pd
def get_enrollment_dates(course):
'''Takes a course object and returns student dates of enrollment.
Useful for handling late registrations and modified deadlines.
Example:
course.get_enrollment_date()'''
url_path = posixpath.... | def get_assignment_lock_date(course, assignment):
'''Takes a course object and the name of a Canvas assignment and returns the due date. Returns None if no due date assigned.
Example:
course.get_assignment_due_date('worksheet_01')'''
assignments = get_assignments(course)
assignments = assignmen... | 59 | 71 | import requests
import urllib.parse
import posixpath
import pandas as pd
def get_enrollment_dates(course):
'''Takes a course object and returns student dates of enrollment.
Useful for handling late registrations and modified deadlines.
Example:
course.get_enrollment_date()'''
url_path = posixpath.... |
get_assignment_due_date | Takes a course object and the name of a Canvas assignment and returns the due date. Returns None if no due date assigned.
Example:
course.get_assignment_due_date('worksheet_01') | import requests
import urllib.parse
import posixpath
import pandas as pd
def get_enrollment_dates(course):
'''Takes a course object and returns student dates of enrollment.
Useful for handling late registrations and modified deadlines.
Example:
course.get_enrollment_date()'''
url_path = posixpath.... | def get_assignment_due_date(course, assignment):
'''Takes a course object and the name of a Canvas assignment and returns the due date. Returns None if no due date assigned.
Example:
course.get_assignment_due_date('worksheet_01')'''
assignments = get_assignments(course)
assignments = assignment... | 75 | 87 | import requests
import urllib.parse
import posixpath
import pandas as pd
def get_enrollment_dates(course):
'''Takes a course object and returns student dates of enrollment.
Useful for handling late registrations and modified deadlines.
Example:
course.get_enrollment_date()'''
url_path = posixpath.... |
get_assignment_unlock_date | Takes a course object and the name of a Canvas assignment and returns the due date. Returns None if no due date assigned.
Example:
course.get_assignment_unlock_date('worksheet_01') | import requests
import urllib.parse
import posixpath
import pandas as pd
def get_enrollment_dates(course):
'''Takes a course object and returns student dates of enrollment.
Useful for handling late registrations and modified deadlines.
Example:
course.get_enrollment_date()'''
url_path = posixpath.... | def get_assignment_unlock_date(course, assignment):
'''Takes a course object and the name of a Canvas assignment and returns the due date. Returns None if no due date assigned.
Example:
course.get_assignment_unlock_date('worksheet_01')'''
assignments = get_assignments(course)
assignments = assi... | 89 | 100 | import requests
import urllib.parse
import posixpath
import pandas as pd
def get_enrollment_dates(course):
'''Takes a course object and returns student dates of enrollment.
Useful for handling late registrations and modified deadlines.
Example:
course.get_enrollment_date()'''
url_path = posixpath.... |
get_assignment_id | Takes a course object and the name of a Canvas assignment and returns the Canvas ID.
Example:
course.get_assignment_id('worksheet_01') | import requests
import urllib.parse
import posixpath
import pandas as pd
def get_enrollment_dates(course):
'''Takes a course object and returns student dates of enrollment.
Useful for handling late registrations and modified deadlines.
Example:
course.get_enrollment_date()'''
url_path = posixpath.... | def get_assignment_id(course, assignment):
'''Takes a course object and the name of a Canvas assignment and returns the Canvas ID.
Example:
course.get_assignment_id('worksheet_01')'''
assignments = get_assignments(course)
assignments = assignments[['name', 'id']].query('name == @assignment')
... | 103 | 110 | import requests
import urllib.parse
import posixpath
import pandas as pd
def get_enrollment_dates(course):
'''Takes a course object and returns student dates of enrollment.
Useful for handling late registrations and modified deadlines.
Example:
course.get_enrollment_date()'''
url_path = posixpath.... |
post_grade | Takes a course object, an assignment name, student id, and score to upload. Posts to Canvas.
Example:
course.post_grades(dsci100, 'worksheet_01', '23423', 10) | import requests
import urllib.parse
import posixpath
import pandas as pd
def get_enrollment_dates(course):
'''Takes a course object and returns student dates of enrollment.
Useful for handling late registrations and modified deadlines.
Example:
course.get_enrollment_date()'''
url_path = posixpath.... | def post_grade(course, assignment, student, score):
'''Takes a course object, an assignment name, student id, and score to upload. Posts to Canvas.
Example:
course.post_grades(dsci100, 'worksheet_01', '23423', 10)'''
assignment_id = get_assignment_id(course, assignment)
url_post_path = posixpath.jo... | 168 | 186 | import requests
import urllib.parse
import posixpath
import pandas as pd
def get_enrollment_dates(course):
'''Takes a course object and returns student dates of enrollment.
Useful for handling late registrations and modified deadlines.
Example:
course.get_enrollment_date()'''
url_path = posixpath.... |
make_kinetic_precond | Preconditioner
P = 1 / (||k|| + ε)
Keyword Arguments:
kpointset -- | from ..coefficient_array import PwCoeffs
from scipy.sparse import dia_matrix
import numpy as np
# MASKED: make_kinetic_precond function (lines 6-43)
class Preconditioner:
def __init__(self):
pass
class DiagonalPreconditioner(Preconditioner):
"""
Apply diagonal preconditioner and project result... | def make_kinetic_precond(kpointset, c0, eps=0.1, asPwCoeffs=True):
"""
Preconditioner
P = 1 / (||k|| + ε)
Keyword Arguments:
kpointset --
"""
nk = len(kpointset)
nc = kpointset.ctx().num_spins()
if nc == 1 and nk == 1 and not asPwCoeffs:
# return as np.matrix
kp = k... | 6 | 43 | from ..coefficient_array import PwCoeffs
from scipy.sparse import dia_matrix
import numpy as np
def make_kinetic_precond(kpointset, c0, eps=0.1, asPwCoeffs=True):
"""
Preconditioner
P = 1 / (||k|| + ε)
Keyword Arguments:
kpointset --
"""
nk = len(kpointset)
nc = kpointset.ctx().num_s... |
rbbox2d_to_near_bbox | convert rotated bbox to nearest 'standing' or 'lying' bbox.
Args:
rbboxes: [N, 5(x, y, xdim, ydim, rad)] rotated bboxes
Returns:
bboxes: [N, 4(xmin, ymin, xmax, ymax)] bboxes | from pathlib import Path
import numba
import numpy as np
from det3d.core.bbox.geometry import (
points_count_convex_polygon_3d_jit,
points_in_convex_polygon_3d_jit,
)
try:
from spconv.utils import rbbox_intersection, rbbox_iou
except:
print("Import spconv fail, no support for sparse convolution!")
de... | def rbbox2d_to_near_bbox(rbboxes):
"""convert rotated bbox to nearest 'standing' or 'lying' bbox.
Args:
rbboxes: [N, 5(x, y, xdim, ydim, rad)] rotated bboxes
Returns:
bboxes: [N, 4(xmin, ymin, xmax, ymax)] bboxes
"""
rots = rbboxes[..., -1]
rots_0_pi_div_2 = np.abs(limit_period(r... | 131 | 143 | from pathlib import Path
import numba
import numpy as np
from det3d.core.bbox.geometry import (
points_count_convex_polygon_3d_jit,
points_in_convex_polygon_3d_jit,
)
try:
from spconv.utils import rbbox_intersection, rbbox_iou
except:
print("Import spconv fail, no support for sparse convolut... |
rotation_2d | rotation 2d points based on origin point clockwise when angle positive.
Args:
points (float array, shape=[N, point_size, 2]): points to be rotated.
angles (float array, shape=[N]): rotation angle.
Returns:
float array: same shape as points | from pathlib import Path
import numba
import numpy as np
from det3d.core.bbox.geometry import (
points_count_convex_polygon_3d_jit,
points_in_convex_polygon_3d_jit,
)
try:
from spconv.utils import rbbox_intersection, rbbox_iou
except:
print("Import spconv fail, no support for sparse convolution!")
de... | def rotation_2d(points, angles):
"""rotation 2d points based on origin point clockwise when angle positive.
Args:
points (float array, shape=[N, point_size, 2]): points to be rotated.
angles (float array, shape=[N]): rotation angle.
Returns:
float array: same shape as points
""... | 207 | 220 | from pathlib import Path
import numba
import numpy as np
from det3d.core.bbox.geometry import (
points_count_convex_polygon_3d_jit,
points_in_convex_polygon_3d_jit,
)
try:
from spconv.utils import rbbox_intersection, rbbox_iou
except:
print("Import spconv fail, no support for sparse convolut... |
rotation_box | rotation 2d points based on origin point clockwise when angle positive.
Args:
points (float array, shape=[N, point_size, 2]): points to be rotated.
angle (float): rotation angle.
Returns:
float array: same shape as points | from pathlib import Path
import numba
import numpy as np
from det3d.core.bbox.geometry import (
points_count_convex_polygon_3d_jit,
points_in_convex_polygon_3d_jit,
)
try:
from spconv.utils import rbbox_intersection, rbbox_iou
except:
print("Import spconv fail, no support for sparse convolution!")
de... | def rotation_box(box_corners, angle):
"""rotation 2d points based on origin point clockwise when angle positive.
Args:
points (float array, shape=[N, point_size, 2]): points to be rotated.
angle (float): rotation angle.
Returns:
float array: same shape as points
"""
rot_sin... | 223 | 238 | from pathlib import Path
import numba
import numpy as np
from det3d.core.bbox.geometry import (
points_count_convex_polygon_3d_jit,
points_in_convex_polygon_3d_jit,
)
try:
from spconv.utils import rbbox_intersection, rbbox_iou
except:
print("Import spconv fail, no support for sparse convolut... |
center_to_corner_box3d | convert kitti locations, dimensions and angles to corners
Args:
centers (float array, shape=[N, 3]): locations in kitti label file.
dims (float array, shape=[N, 3]): dimensions in kitti label file.
angles (float array, shape=[N]): rotation_y in kitti label file.
origin (list or array or float): origin ... | from pathlib import Path
import numba
import numpy as np
from det3d.core.bbox.geometry import (
points_count_convex_polygon_3d_jit,
points_in_convex_polygon_3d_jit,
)
try:
from spconv.utils import rbbox_intersection, rbbox_iou
except:
print("Import spconv fail, no support for sparse convolution!")
de... | def center_to_corner_box3d(centers, dims, angles=None, origin=(0.5, 0.5, 0.5), axis=2):
"""convert kitti locations, dimensions and angles to corners
Args:
centers (float array, shape=[N, 3]): locations in kitti label file.
dims (float array, shape=[N, 3]): dimensions in kitti label file.
... | 241 | 262 | from pathlib import Path
import numba
import numpy as np
from det3d.core.bbox.geometry import (
points_count_convex_polygon_3d_jit,
points_in_convex_polygon_3d_jit,
)
try:
from spconv.utils import rbbox_intersection, rbbox_iou
except:
print("Import spconv fail, no support for sparse convolut... |
center_to_corner_box2d | convert kitti locations, dimensions and angles to corners.
format: center(xy), dims(xy), angles(clockwise when positive)
Args:
centers (float array, shape=[N, 2]): locations in kitti label file.
dims (float array, shape=[N, 2]): dimensions in kitti label file.
angles (float array, shape=[N]): rotation_y in... | from pathlib import Path
import numba
import numpy as np
from det3d.core.bbox.geometry import (
points_count_convex_polygon_3d_jit,
points_in_convex_polygon_3d_jit,
)
try:
from spconv.utils import rbbox_intersection, rbbox_iou
except:
print("Import spconv fail, no support for sparse convolution!")
de... | def center_to_corner_box2d(centers, dims, angles=None, origin=0.5):
"""convert kitti locations, dimensions and angles to corners.
format: center(xy), dims(xy), angles(clockwise when positive)
Args:
centers (float array, shape=[N, 2]): locations in kitti label file.
dims (float array, shape=... | 265 | 285 | from pathlib import Path
import numba
import numpy as np
from det3d.core.bbox.geometry import (
points_count_convex_polygon_3d_jit,
points_in_convex_polygon_3d_jit,
)
try:
from spconv.utils import rbbox_intersection, rbbox_iou
except:
print("Import spconv fail, no support for sparse convolut... |
iou_jit | calculate box iou. note that jit version runs 2x faster than cython in
my machine!
Parameters
----------
boxes: (N, 4) ndarray of float
query_boxes: (K, 4) ndarray of float
Returns
-------
overlaps: (N, K) ndarray of overlap between boxes and query_boxes | from pathlib import Path
import numba
import numpy as np
from det3d.core.bbox.geometry import (
points_count_convex_polygon_3d_jit,
points_in_convex_polygon_3d_jit,
)
try:
from spconv.utils import rbbox_intersection, rbbox_iou
except:
print("Import spconv fail, no support for sparse convolution!")
de... | @numba.jit(nopython=True)
def iou_jit(boxes, query_boxes, eps=1.0):
"""calculate box iou. note that jit version runs 2x faster than cython in
my machine!
Parameters
----------
boxes: (N, 4) ndarray of float
query_boxes: (K, 4) ndarray of float
Returns
-------
overlaps: (N, K) ndarray... | 496 | 535 | from pathlib import Path
import numba
import numpy as np
from det3d.core.bbox.geometry import (
points_count_convex_polygon_3d_jit,
points_in_convex_polygon_3d_jit,
)
try:
from spconv.utils import rbbox_intersection, rbbox_iou
except:
print("Import spconv fail, no support for sparse convolut... |
image_box_region_area | check a 2d voxel is contained by a box. used to filter empty
anchors.
Summed-area table algorithm:
==> W
------------------
| | |
|------A---------B
| | |
| | |
|----- C---------D
Iabcd = ID-IB-IC+IA
Args:
img_cumsum: [M, H, W](yx) cumsumed image.
bbox: [N, 4](xyxy) boundi... | from pathlib import Path
import numba
import numpy as np
from det3d.core.bbox.geometry import (
points_count_convex_polygon_3d_jit,
points_in_convex_polygon_3d_jit,
)
try:
from spconv.utils import rbbox_intersection, rbbox_iou
except:
print("Import spconv fail, no support for sparse convolution!")
de... | def image_box_region_area(img_cumsum, bbox):
"""check a 2d voxel is contained by a box. used to filter empty
anchors.
Summed-area table algorithm:
==> W
------------------
| | |
|------A---------B
| | |
| | |
|----- C---------D
Iabcd = I... | 742 | 766 | from pathlib import Path
import numba
import numpy as np
from det3d.core.bbox.geometry import (
points_count_convex_polygon_3d_jit,
points_in_convex_polygon_3d_jit,
)
try:
from spconv.utils import rbbox_intersection, rbbox_iou
except:
print("Import spconv fail, no support for sparse convolut... |
parse_command_args | This parses the arguments and returns a tuple containing:
(args, command, command_args)
For example, "--config=bar start --with=baz" would return:
(['--config=bar'], 'start', ['--with=baz']) | """
logan.runner
~~~~~~~~~~~~
:copyright: (c) 2012 David Cramer.
:license: Apache License 2.0, see NOTICE for more details.
"""
import argparse
import os
import re
import sys
from django.core import management
from nautobot import __version__
from . import importer
from .settings import create_default_settings
__... | def parse_command_args(args):
"""
This parses the arguments and returns a tuple containing:
(args, command, command_args)
For example, "--config=bar start --with=baz" would return:
(['--config=bar'], 'start', ['--with=baz'])
"""
index = None
for arg_i, arg in enumerate(args):
... | 29 | 49 | """
logan.runner
~~~~~~~~~~~~
:copyright: (c) 2012 David Cramer.
:license: Apache License 2.0, see NOTICE for more details.
"""
import argparse
import os
import re
import sys
from django.core import management
from nautobot import __version__
from . import importer
from .settings import create_default_settings
__... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.