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 |
|---|---|---|---|---|---|---|
__init__ | Initializes Diagnostic with neccessary attributes.
Args:
intro: A message to introduce the objectives and tasks of the diagnostic.
title: The name of the diagnostic.
checklist: An iterable of checkbase.Check objects to be run by the
diagnostic. | # Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | def __init__(self, intro, title, checklist):
"""Initializes Diagnostic with neccessary attributes.
Args:
intro: A message to introduce the objectives and tasks of the diagnostic.
title: The name of the diagnostic.
checklist: An iterable of checkbase.Check objects to be run by the
d... | 34 | 45 | # Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... |
RunChecks | Runs one or more checks, tries fixes, and outputs results.
Returns:
True if the diagnostic ultimately passed. | # Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | def RunChecks(self):
"""Runs one or more checks, tries fixes, and outputs results.
Returns:
True if the diagnostic ultimately passed.
"""
self._Print(self.intro)
num_checks_passed = 0
for check in self.checklist:
result, fixer = self._RunCheck(check)
if properties.VALUES.c... | 47 | 85 | # Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... |
_assert_proper_exception_context | assert that any exception we're catching does not have a __context__
without a __cause__, and that __suppress_context__ is never set.
Python 3 will report nested as exceptions as "during the handling of
error X, error Y occurred". That's not what we want to do. we want
these exceptions in a cause chain. | # testing/assertions.py
# Copyright (C) 2005-2021 the SQLAlchemy authors and contributors
# <see AUTHORS file>
#
# This module is part of SQLAlchemy and is released under
# the MIT License: http://www.opensource.org/licenses/mit-license.php
from __future__ import absolute_import
import contextlib
import re
import sys... | def _assert_proper_exception_context(exception):
"""assert that any exception we're catching does not have a __context__
without a __cause__, and that __suppress_context__ is never set.
Python 3 will report nested as exceptions as "during the handling of
error X, error Y occurred". That's not what we w... | 288 | 309 | # testing/assertions.py
# Copyright (C) 2005-2021 the SQLAlchemy authors and contributors
# <see AUTHORS file>
#
# This module is part of SQLAlchemy and is released under
# the MIT License: http://www.opensource.org/licenses/mit-license.php
from __future__ import absolute_import
import contextlib
import re
import sys... |
dict_scrape | Scrapes a dictionary for a given part of speech. POS tags in POS_tags.
POS(str), dictionaryfile(str-of-filename) -> list-of-strings | import sys
##print ("This is the name of the script: ", sys.argv[0])
##print ("Number of arguments: ", len(sys.argv))
##print ("The arguments are: " , str(sys.argv))
lemmas = []
lemmas_cleaned = []
nums = ['1','2','3','4','5','6','7','8','9','0']
alphabet = ['a','b','c','d','e','f','g','h','i','j','k','k','l','m','n',... | def dict_scrape(POS, dictionaryfile='as_freq.txt'):
"""Scrapes a dictionary for a given part of speech. POS tags in POS_tags.
POS(str), dictionaryfile(str-of-filename) -> list-of-strings
"""
if POS in POS_tags:
with open(dictionaryfile) as to_scrape:
for line in to_scrape:
... | 22 | 42 | import sys
##print ("This is the name of the script: ", sys.argv[0])
##print ("Number of arguments: ", len(sys.argv))
##print ("The arguments are: " , str(sys.argv))
lemmas = []
lemmas_cleaned = []
nums = ['1','2','3','4','5','6','7','8','9','0']
alphabet = ['a','b','c','d','e','f','g','h','i','j','k','k','l','m','n',... |
gcor | Compute the :abbr:`GCOR (global correlation)`.
:param numpy.ndarray func: input fMRI dataset, after motion correction
:param numpy.ndarray mask: 3D brain mask
:return: the computed GCOR value | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
# pylint: disable=no-member
#
# @Author: oesteban
# @Date: 2016-02-23 19:25:39
# @Email: code@oscaresteban.es
# @Last Modified by: oesteban
# @Last Modifie... | def gcor(func, mask):
"""
Compute the :abbr:`GCOR (global correlation)`.
:param numpy.ndarray func: input fMRI dataset, after motion correction
:param numpy.ndarray mask: 3D brain mask
:return: the computed GCOR value
"""
# Remove zero-variance voxels across time axis
tv_mask = zero_va... | 246 | 260 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
# pylint: disable=no-member
#
# @Author: oesteban
# @Date: 2016-02-23 19:25:39
# @Email: code@oscaresteban.es
# @Last Modified by: oesteban
# @Last Modifie... |
get | Wait until task is ready, and return its result.
.. warning::
Waiting for tasks within a task may lead to deadlocks.
Please read :ref:`task-synchronous-subtasks`.
:keyword timeout: How long to wait, in seconds, before the
operation times out.
:keyword propagate: Re-raise exception if the task... | # -*- coding: utf-8 -*-
"""
celery.result
~~~~~~~~~~~~~
Task results/state and groups of results.
"""
from __future__ import absolute_import
import time
import warnings
from collections import deque
from contextlib import contextmanager
from copy import copy
from kombu.utils import cached_property
from... | def get(self, timeout=None, propagate=True, interval=0.5,
no_ack=True, follow_parents=True,
EXCEPTION_STATES=states.EXCEPTION_STATES,
PROPAGATE_STATES=states.PROPAGATE_STATES):
"""Wait until task is ready, and return its result.
.. warning::
Waiting f... | 123 | 176 | # -*- coding: utf-8 -*-
"""
celery.result
~~~~~~~~~~~~~
Task results/state and groups of results.
"""
from __future__ import absolute_import
import time
import warnings
from collections import deque
from contextlib import contextmanager
from copy import copy
from kombu.utils import cached_property
from... |
iter_native | Backend optimized version of :meth:`iterate`.
.. versionadded:: 2.2
Note that this does not support collecting the results
for different task types using different backends.
This is currently only supported by the amqp, Redis and cache
result backends. | # -*- coding: utf-8 -*-
"""
celery.result
~~~~~~~~~~~~~
Task results/state and groups of results.
"""
from __future__ import absolute_import
import time
import warnings
from collections import deque
from contextlib import contextmanager
from copy import copy
from kombu.utils import cached_property
from... | def iter_native(self, timeout=None, interval=0.5, no_ack=True):
"""Backend optimized version of :meth:`iterate`.
.. versionadded:: 2.2
Note that this does not support collecting the results
for different task types using different backends.
This is currently only supported... | 650 | 668 | # -*- coding: utf-8 -*-
"""
celery.result
~~~~~~~~~~~~~
Task results/state and groups of results.
"""
from __future__ import absolute_import
import time
import warnings
from collections import deque
from contextlib import contextmanager
from copy import copy
from kombu.utils import cached_property
from... |
get_prepared_model | Function creates ANN model and compile.
Args:
stage ([str]): stage of experiment
no_classes ([INT]): No of classes for classification
input_shape ([int, int]): Input shape for model's input layer
loss ([str]): Loss function for model
optimizer ([str]): Optimizer for model
metrics ([str]): Metric... | import tensorflow as tf
from tensorflow.keras.models import Model
import pandas as pd
import matplotlib.pyplot as plt
import os
import logging
from .common import create_directories
# MASKED: get_prepared_model function (lines 12-80)
def save_model(model_dir: str, model: Model, model_suffix: str) -> None:
"""... | def get_prepared_model(stage: str, no_classes: int, input_shape: list, loss: str, optimizer: str, metrics: list) -> \
Model:
"""Function creates ANN model and compile.
Args:
stage ([str]): stage of experiment
no_classes ([INT]): No of classes for classification
input_shape ([int,... | 12 | 80 | import tensorflow as tf
from tensorflow.keras.models import Model
import pandas as pd
import matplotlib.pyplot as plt
import os
import logging
from .common import create_directories
def get_prepared_model(stage: str, no_classes: int, input_shape: list, loss: str, optimizer: str, metrics: list) -> \
Model:
... |
save_model | args:
model_dir: directory to save the model
model: model object to save
model_suffix: Suffix to save the model | import tensorflow as tf
from tensorflow.keras.models import Model
import pandas as pd
import matplotlib.pyplot as plt
import os
import logging
from .common import create_directories
def get_prepared_model(stage: str, no_classes: int, input_shape: list, loss: str, optimizer: str, metrics: list) -> \
Model:
... | def save_model(model_dir: str, model: Model, model_suffix: str) -> None:
"""
args:
model_dir: directory to save the model
model: model object to save
model_suffix: Suffix to save the model
"""
create_directories([model_dir])
model_file = os.path.join(model_dir, f"{model_suffi... | 83 | 93 | import tensorflow as tf
from tensorflow.keras.models import Model
import pandas as pd
import matplotlib.pyplot as plt
import os
import logging
from .common import create_directories
def get_prepared_model(stage: str, no_classes: int, input_shape: list, loss: str, optimizer: str, metrics: list) -> \
Model:
... |
save_history_plot | Args:
history: History object for plotting loss/accuracy curves
plot_dir: Directory to save plot files
stage: Stage name for training | import tensorflow as tf
from tensorflow.keras.models import Model
import pandas as pd
import matplotlib.pyplot as plt
import os
import logging
from .common import create_directories
def get_prepared_model(stage: str, no_classes: int, input_shape: list, loss: str, optimizer: str, metrics: list) -> \
Model:
... | def save_history_plot(history, plot_dir: str, stage: str) -> None:
"""
Args:
history: History object for plotting loss/accuracy curves
plot_dir: Directory to save plot files
stage: Stage name for training
"""
pd.DataFrame(history.history).plot(figsize=(10, 8))
plt.grid(True)
... | 96 | 108 | import tensorflow as tf
from tensorflow.keras.models import Model
import pandas as pd
import matplotlib.pyplot as plt
import os
import logging
from .common import create_directories
def get_prepared_model(stage: str, no_classes: int, input_shape: list, loss: str, optimizer: str, metrics: list) -> \
Model:
... |
get_callbacks | Args:
checkpoint_dir: Directory to save the model at checkpoint
tensorboard_logs: Directory to save tensorboard logs
stage: Stage name for training
Returns:
callback_list: List of created callbacks | import tensorflow as tf
from tensorflow.keras.models import Model
import pandas as pd
import matplotlib.pyplot as plt
import os
import logging
from .common import create_directories
def get_prepared_model(stage: str, no_classes: int, input_shape: list, loss: str, optimizer: str, metrics: list) -> \
Model:
... | def get_callbacks(checkpoint_dir: str, tensorboard_logs: str, stage: str) -> list:
"""
Args:
checkpoint_dir: Directory to save the model at checkpoint
tensorboard_logs: Directory to save tensorboard logs
stage: Stage name for training
Returns:
callback_list: List of created c... | 111 | 128 | import tensorflow as tf
from tensorflow.keras.models import Model
import pandas as pd
import matplotlib.pyplot as plt
import os
import logging
from .common import create_directories
def get_prepared_model(stage: str, no_classes: int, input_shape: list, loss: str, optimizer: str, metrics: list) -> \
Model:
... |
create_order | Create an order.
Args:
symbol: Trade target
action: Trade direction, `BUY` or `SELL`.
price: Price of each contract.
quantity: The buying or selling quantity.
order_type: Order type, `MARKET` or `LIMIT`.
Returns:
order_no: Order ID if created successfully, otherwise it's None.
error: Error... | # -*- coding:utf-8 -*-
"""
FTX Trade module.
https://docs.ftx.com/
Project: alphahunter
Author: HJQuant
Description: Asynchronous driven quantitative trading framework
"""
import time
import zlib
import json
import copy
import hmac
import base64
from urllib.parse import urljoin
from collections import defaultdict, d... | async def create_order(self, symbol, action, price, quantity, order_type=ORDER_TYPE_LIMIT, *args, **kwargs):
""" Create an order.
Args:
symbol: Trade target
action: Trade direction, `BUY` or `SELL`.
price: Price of each contract.
quantity: The buying ... | 231 | 273 | # -*- coding:utf-8 -*-
"""
FTX Trade module.
https://docs.ftx.com/
Project: alphahunter
Author: HJQuant
Description: Asynchronous driven quantitative trading framework
"""
import time
import zlib
import json
import copy
import hmac
import base64
from urllib.parse import urljoin
from collections import defaultdict, d... |
revoke_order | Revoke (an) order(s).
Args:
symbol: Trade target
order_nos: Order id list, you can set this param to 0 or multiple items. If you set 0 param, you can cancel all orders for
this symbol. If you set 1 or multiple param, you can cancel an or multiple order.
Returns:
删除全部订单情况: 成功=(True, None), 失败=(False, ... | # -*- coding:utf-8 -*-
"""
FTX Trade module.
https://docs.ftx.com/
Project: alphahunter
Author: HJQuant
Description: Asynchronous driven quantitative trading framework
"""
import time
import zlib
import json
import copy
import hmac
import base64
from urllib.parse import urljoin
from collections import defaultdict, d... | async def revoke_order(self, symbol, *order_nos):
""" Revoke (an) order(s).
Args:
symbol: Trade target
order_nos: Order id list, you can set this param to 0 or multiple items. If you set 0 param, you can cancel all orders for
this symbol. If you set 1 or multipl... | 275 | 304 | # -*- coding:utf-8 -*-
"""
FTX Trade module.
https://docs.ftx.com/
Project: alphahunter
Author: HJQuant
Description: Asynchronous driven quantitative trading framework
"""
import time
import zlib
import json
import copy
import hmac
import base64
from urllib.parse import urljoin
from collections import defaultdict, d... |
get_assets | 获取交易账户资产信息
Args:
None
Returns:
assets: Asset if successfully, otherwise it's None.
error: Error information, otherwise it's None. | # -*- coding:utf-8 -*-
"""
FTX Trade module.
https://docs.ftx.com/
Project: alphahunter
Author: HJQuant
Description: Asynchronous driven quantitative trading framework
"""
import time
import zlib
import json
import copy
import hmac
import base64
from urllib.parse import urljoin
from collections import defaultdict, d... | async def get_assets(self):
""" 获取交易账户资产信息
Args:
None
Returns:
assets: Asset if successfully, otherwise it's None.
error: Error information, otherwise it's None.
"""
#{"result": {"backstopProvider": false, "collateral": 110.09426... | 306 | 346 | # -*- coding:utf-8 -*-
"""
FTX Trade module.
https://docs.ftx.com/
Project: alphahunter
Author: HJQuant
Description: Asynchronous driven quantitative trading framework
"""
import time
import zlib
import json
import copy
import hmac
import base64
from urllib.parse import urljoin
from collections import defaultdict, d... |
get_orders | 获取当前挂单列表
Args:
symbol: Trade target
Returns:
orders: Order list if successfully, otherwise it's None.
error: Error information, otherwise it's None. | # -*- coding:utf-8 -*-
"""
FTX Trade module.
https://docs.ftx.com/
Project: alphahunter
Author: HJQuant
Description: Asynchronous driven quantitative trading framework
"""
import time
import zlib
import json
import copy
import hmac
import base64
from urllib.parse import urljoin
from collections import defaultdict, d... | async def get_orders(self, symbol):
""" 获取当前挂单列表
Args:
symbol: Trade target
Returns:
orders: Order list if successfully, otherwise it's None.
error: Error information, otherwise it's None.
"""
#{"result": [{"avgFillPrice": null, ... | 389 | 418 | # -*- coding:utf-8 -*-
"""
FTX Trade module.
https://docs.ftx.com/
Project: alphahunter
Author: HJQuant
Description: Asynchronous driven quantitative trading framework
"""
import time
import zlib
import json
import copy
import hmac
import base64
from urllib.parse import urljoin
from collections import defaultdict, d... |
get_position | 获取当前持仓
Args:
symbol: Trade target
Returns:
position: Position if successfully, otherwise it's None.
error: Error information, otherwise it's None. | # -*- coding:utf-8 -*-
"""
FTX Trade module.
https://docs.ftx.com/
Project: alphahunter
Author: HJQuant
Description: Asynchronous driven quantitative trading framework
"""
import time
import zlib
import json
import copy
import hmac
import base64
from urllib.parse import urljoin
from collections import defaultdict, d... | async def get_position(self, symbol):
""" 获取当前持仓
Args:
symbol: Trade target
Returns:
position: Position if successfully, otherwise it's None.
error: Error information, otherwise it's None.
"""
#{"result": [{"collateralUsed": 0.35... | 420 | 485 | # -*- coding:utf-8 -*-
"""
FTX Trade module.
https://docs.ftx.com/
Project: alphahunter
Author: HJQuant
Description: Asynchronous driven quantitative trading framework
"""
import time
import zlib
import json
import copy
import hmac
import base64
from urllib.parse import urljoin
from collections import defaultdict, d... |
get_symbol_info | 获取指定符号相关信息
Args:
symbol: Trade target
Returns:
symbol_info: SymbolInfo if successfully, otherwise it's None.
error: Error information, otherwise it's None. | # -*- coding:utf-8 -*-
"""
FTX Trade module.
https://docs.ftx.com/
Project: alphahunter
Author: HJQuant
Description: Asynchronous driven quantitative trading framework
"""
import time
import zlib
import json
import copy
import hmac
import base64
from urllib.parse import urljoin
from collections import defaultdict, d... | async def get_symbol_info(self, symbol):
""" 获取指定符号相关信息
Args:
symbol: Trade target
Returns:
symbol_info: SymbolInfo if successfully, otherwise it's None.
error: Error information, otherwise it's None.
"""
"""
{
"success": ... | 487 | 537 | # -*- coding:utf-8 -*-
"""
FTX Trade module.
https://docs.ftx.com/
Project: alphahunter
Author: HJQuant
Description: Asynchronous driven quantitative trading framework
"""
import time
import zlib
import json
import copy
import hmac
import base64
from urllib.parse import urljoin
from collections import defaultdict, d... |
_RleCompress | Compresses mask using Run-length encoding provided by pycocotools.
Args:
masks: uint8 numpy array of shape [mask_height, mask_width] with values in
{0, 1}.
Returns:
A pycocotools Run-length encoding of the mask. | # Copyright 2017 The TensorFlow 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 _RleCompress(masks):
"""Compresses mask using Run-length encoding provided by pycocotools.
Args:
masks: uint8 numpy array of shape [mask_height, mask_width] with values in
{0, 1}.
Returns:
A pycocotools Run-length encoding of the mask.
"""
rle = mask.encode(np.asfortranarray(masks))
rle['c... | 347 | 359 | # Copyright 2017 The TensorFlow 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... |
ExportGroundtruthToCOCO | Export groundtruth detection annotations in numpy arrays to COCO API.
This function converts a set of groundtruth detection annotations represented
as numpy arrays to dictionaries that can be ingested by the COCO API.
Inputs to this function are three lists: image ids for each groundtruth image,
groundtruth boxes for ... | # Copyright 2017 The TensorFlow 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 ExportGroundtruthToCOCO(image_ids,
groundtruth_boxes,
groundtruth_classes,
categories,
output_path=None):
"""Export groundtruth detection annotations in numpy arrays to COCO API.
This function conver... | 484 | 556 | # Copyright 2017 The TensorFlow 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... |
ExportSingleImageDetectionBoxesToCoco | Export detections of a single image to COCO format.
This function converts detections represented as numpy arrays to dictionaries
that can be ingested by the COCO evaluation API. Note that the image_ids
provided here must match the ones given to the
ExporSingleImageDetectionBoxesToCoco. We assume that boxes, and class... | # Copyright 2017 The TensorFlow 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 ExportSingleImageDetectionBoxesToCoco(image_id,
category_id_set,
detection_boxes,
detection_scores,
detection_classes,
... | 559 | 650 | # Copyright 2017 The TensorFlow 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... |
ExportSingleImageDetectionMasksToCoco | Export detection masks of a single image to COCO format.
This function converts detections represented as numpy arrays to dictionaries
that can be ingested by the COCO evaluation API. We assume that
detection_masks, detection_scores, and detection_classes are in correspondence
- that is: detection_masks[i, :], detecti... | # Copyright 2017 The TensorFlow 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 ExportSingleImageDetectionMasksToCoco(image_id,
category_id_set,
detection_masks,
detection_scores,
detection_classes):
"""Export detection masks ... | 653 | 708 | # Copyright 2017 The TensorFlow 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... |
__init__ | COCOEvalWrapper constructor.
Note that for the area-based metrics to be meaningful, detection and
groundtruth boxes must be in image coordinates measured in pixels.
Args:
groundtruth: a coco.COCO (or coco_tools.COCOWrapper) object holding
groundtruth annotations
detections: a coco.COCO (or coco_tools.COCOWrap... | # Copyright 2017 The TensorFlow 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 __init__(self, groundtruth=None, detections=None, agnostic_mode=False,
iou_type='bbox', oks_sigmas=None):
"""COCOEvalWrapper constructor.
Note that for the area-based metrics to be meaningful, detection and
groundtruth boxes must be in image coordinates measured in pixels.
Args:
... | 160 | 183 | # Copyright 2017 The TensorFlow 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... |
validate | Validate the configuration dialog fields. For any field that is not valid
set the style sheet to the INVALID_STYLE_SHEET. Return the outcome of the
overall validity of the configuration. | import os
from PySide2 import QtWidgets
from mapclientplugins.filechooserstep.ui_configuredialog import Ui_ConfigureDialog
INVALID_STYLE_SHEET = 'background-color: rgba(239, 0, 0, 50)'
DEFAULT_STYLE_SHEET = ''
class ConfigureDialog(QtWidgets.QDialog):
"""
Configure dialog to present the user with the optio... | def validate(self):
"""
Validate the configuration dialog fields. For any field that is not valid
set the style sheet to the INVALID_STYLE_SHEET. Return the outcome of the
overall validity of the configuration.
"""
# Determine if the current identifier is unique thr... | 81 | 101 | import os
from PySide2 import QtWidgets
from mapclientplugins.filechooserstep.ui_configuredialog import Ui_ConfigureDialog
INVALID_STYLE_SHEET = 'background-color: rgba(239, 0, 0, 50)'
DEFAULT_STYLE_SHEET = ''
class ConfigureDialog(QtWidgets.QDialog):
"""
Configure dialog to present the user with the optio... |
split_section | Split a pipfile or a lockfile section out by section name and test function
:param dict input_file: A dictionary containing either a pipfile or lockfile
:param str section_suffix: A string of the name of the section
:param func test_function: A test function to test against the value in the key/value pair
... | # -*- coding: utf-8 -*-
import errno
import os
import re
import hashlib
import tempfile
import sys
import shutil
import logging
import click
import crayons
import delegator
import parse
import requests
import six
import stat
import warnings
try:
from weakref import finalize
except ImportError:
try:
fro... | def split_section(input_file, section_suffix, test_function):
"""
Split a pipfile or a lockfile section out by section name and test function
:param dict input_file: A dictionary containing either a pipfile or lockfile
:param str section_suffix: A string of the name of the section
:para... | 898 | 942 | # -*- coding: utf-8 -*-
import errno
import os
import re
import hashlib
import tempfile
import sys
import shutil
import logging
import click
import crayons
import delegator
import parse
import requests
import six
import stat
import warnings
try:
from weakref import finalize
except ImportError:
try:
fro... |
normalize_drive | Normalize drive in path so they stay consistent.
This currently only affects local drives on Windows, which can be
identified with either upper or lower cased drive names. The case is
always converted to uppercase because it seems to be preferred.
See: <https://github.com/pypa/pipenv/issues/1218> | # -*- coding: utf-8 -*-
import errno
import os
import re
import hashlib
import tempfile
import sys
import shutil
import logging
import click
import crayons
import delegator
import parse
import requests
import six
import stat
import warnings
try:
from weakref import finalize
except ImportError:
try:
fro... | def normalize_drive(path):
"""Normalize drive in path so they stay consistent.
This currently only affects local drives on Windows, which can be
identified with either upper or lower cased drive names. The case is
always converted to uppercase because it seems to be preferred.
See: <https://github... | 1,164 | 1,181 | # -*- coding: utf-8 -*-
import errno
import os
import re
import hashlib
import tempfile
import sys
import shutil
import logging
import click
import crayons
import delegator
import parse
import requests
import six
import stat
import warnings
try:
from weakref import finalize
except ImportError:
try:
fro... |
is_readonly_path | Check if a provided path exists and is readonly.
Permissions check is `bool(path.stat & stat.S_IREAD)` or `not os.access(path, os.W_OK)` | # -*- coding: utf-8 -*-
import errno
import os
import re
import hashlib
import tempfile
import sys
import shutil
import logging
import click
import crayons
import delegator
import parse
import requests
import six
import stat
import warnings
try:
from weakref import finalize
except ImportError:
try:
fro... | def is_readonly_path(fn):
"""Check if a provided path exists and is readonly.
Permissions check is `bool(path.stat & stat.S_IREAD)` or `not os.access(path, os.W_OK)`
"""
if os.path.exists(fn):
return (os.stat(fn).st_mode & stat.S_IREAD) or not os.access(
fn, os.W_OK
)
r... | 1,184 | 1,194 | # -*- coding: utf-8 -*-
import errno
import os
import re
import hashlib
import tempfile
import sys
import shutil
import logging
import click
import crayons
import delegator
import parse
import requests
import six
import stat
import warnings
try:
from weakref import finalize
except ImportError:
try:
fro... |
download_progress_hook | A hook to report the progress of a download. This is mostly intended for users with
slow internet connections. Reports every 5% change in download progress. | import os
import sys
import tarfile
from six.moves.urllib.request import urlretrieve
url = 'https://commondatastorage.googleapis.com/books1000/'
last_percent_reported = None
data_root = '.' # Change me to store data elsewhere
# MASKED: download_progress_hook function (lines 11-26)
def maybe_download(filename, exp... | def download_progress_hook(count, blockSize, totalSize):
"""A hook to report the progress of a download. This is mostly intended for users with
slow internet connections. Reports every 5% change in download progress.
"""
global last_percent_reported
percent = int(count * blockSize * 100 / totalSize)... | 11 | 26 | import os
import sys
import tarfile
from six.moves.urllib.request import urlretrieve
url = 'https://commondatastorage.googleapis.com/books1000/'
last_percent_reported = None
data_root = '.' # Change me to store data elsewhere
def download_progress_hook(count, blockSize, totalSize):
"""A hook to report the progr... |
main | The entry point for the console script xbmcswift2.
The 'xbcmswift2' script is command bassed, so the second argument is always
the command to execute. Each command has its own parser options and usages.
If no command is provided or the -h flag is used without any other
commands, the general help message is shown. | '''
xbmcswift2.cli.cli
------------------
The main entry point for the xbmcswift2 console script. CLI commands can be
registered in this module.
:copyright: (c) 2012 by Jonathan Beluch
:license: GPLv3, see LICENSE for more details.
'''
import sys
from optparse import OptionParser
from xbmcswi... | def main():
'''The entry point for the console script xbmcswift2.
The 'xbcmswift2' script is command bassed, so the second argument is always
the command to execute. Each command has its own parser options and usages.
If no command is provided or the -h flag is used without any other
commands, the ... | 40 | 76 | '''
xbmcswift2.cli.cli
------------------
The main entry point for the xbmcswift2 console script. CLI commands can be
registered in this module.
:copyright: (c) 2012 by Jonathan Beluch
:license: GPLv3, see LICENSE for more details.
'''
import sys
from optparse import OptionParser
... |
compute_benchmark | Compute the scores of a synthesizer over a list of datasets.
The results are returned in a raw format as a ``pandas.DataFrame`` containing:
- One row for each dataset+scoring method (for example, a classifier)
- One column for each computed metric
- The columns:
- dataset
- distance
... | import logging
import os
import types
from datetime import datetime
import pandas as pd
from sdgym.data import load_dataset
from sdgym.evaluate import compute_scores
from sdgym.synthesizers import BaseSynthesizer
LOGGER = logging.getLogger(__name__)
BASE_DIR = os.path.dirname(__file__)
LEADERBOARD_PATH = os.path.jo... | def compute_benchmark(synthesizer, datasets=DEFAULT_DATASETS, iterations=3):
"""Compute the scores of a synthesizer over a list of datasets.
The results are returned in a raw format as a ``pandas.DataFrame`` containing:
- One row for each dataset+scoring method (for example, a classifier)
- One... | 36 | 79 | import logging
import os
import types
from datetime import datetime
import pandas as pd
from sdgym.data import load_dataset
from sdgym.evaluate import compute_scores
from sdgym.synthesizers import BaseSynthesizer
LOGGER = logging.getLogger(__name__)
BASE_DIR = os.path.dirname(__file__)
LEADERBOARD_PATH = os.path.jo... |
_summarize_scores | Computes a summary of the scores obtained by a synthesizer.
The raw scores returned by the ``compute_benchmark`` function are summarized
by grouping them by dataset and computing the average.
The results are then put in a ``pandas.Series`` object with one value per
dataset and metric.
As an example, the summary of a... | import logging
import os
import types
from datetime import datetime
import pandas as pd
from sdgym.data import load_dataset
from sdgym.evaluate import compute_scores
from sdgym.synthesizers import BaseSynthesizer
LOGGER = logging.getLogger(__name__)
BASE_DIR = os.path.dirname(__file__)
LEADERBOARD_PATH = os.path.jo... | def _summarize_scores(scores):
"""Computes a summary of the scores obtained by a synthesizer.
The raw scores returned by the ``compute_benchmark`` function are summarized
by grouping them by dataset and computing the average.
The results are then put in a ``pandas.Series`` object with one value per
... | 90 | 126 | import logging
import os
import types
from datetime import datetime
import pandas as pd
from sdgym.data import load_dataset
from sdgym.evaluate import compute_scores
from sdgym.synthesizers import BaseSynthesizer
LOGGER = logging.getLogger(__name__)
BASE_DIR = os.path.dirname(__file__)
LEADERBOARD_PATH = os.path.jo... |
_get_synthesizer_name | Get the name of the synthesizer function or class.
If the given synthesizer is a function, return its name.
If it is a method, return the name of the class to which
the method belongs.
Args:
synthesizer (function or method):
The synthesizer function or method.
Returns:
str:
Name of the functi... | import logging
import os
import types
from datetime import datetime
import pandas as pd
from sdgym.data import load_dataset
from sdgym.evaluate import compute_scores
from sdgym.synthesizers import BaseSynthesizer
LOGGER = logging.getLogger(__name__)
BASE_DIR = os.path.dirname(__file__)
LEADERBOARD_PATH = os.path.jo... | def _get_synthesizer_name(synthesizer):
"""Get the name of the synthesizer function or class.
If the given synthesizer is a function, return its name.
If it is a method, return the name of the class to which
the method belongs.
Args:
synthesizer (function or method):
The synthe... | 129 | 149 | import logging
import os
import types
from datetime import datetime
import pandas as pd
from sdgym.data import load_dataset
from sdgym.evaluate import compute_scores
from sdgym.synthesizers import BaseSynthesizer
LOGGER = logging.getLogger(__name__)
BASE_DIR = os.path.dirname(__file__)
LEADERBOARD_PATH = os.path.jo... |
scope_guard | Change the global/default scope instance by Python `with` statement. All
variable in runtime will assigned to the new scope.
Args:
scope: The new global/default scope.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy
new_scope = fluid.Scope()
with fl... | # Copyright (c) 2018 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 app... | @signature_safe_contextmanager
def scope_guard(scope):
"""
Change the global/default scope instance by Python `with` statement. All
variable in runtime will assigned to the new scope.
Args:
scope: The new global/default scope.
Examples:
.. code-block:: python
import pa... | 64 | 87 | # Copyright (c) 2018 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 app... |
as_numpy | Convert a Tensor to a numpy.ndarray, its only support Tensor without LoD information.
For higher dimensional sequence data, please use LoDTensor directly.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy
new_scope = fluid.Scope()
with fluid.scope_guard(new_scope)... | # Copyright (c) 2018 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 app... | def as_numpy(tensor):
"""
Convert a Tensor to a numpy.ndarray, its only support Tensor without LoD information.
For higher dimensional sequence data, please use LoDTensor directly.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy
new_sco... | 90 | 127 | # Copyright (c) 2018 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 app... |
has_feed_operators | Check whether the block already has feed operators.
Return false if the block does not have any feed operators.
If some feed operators have been prepended to the block, check that
the info contained in these feed operators matches the feed_targets
and feed_holder_name. Raise exception when any mismatch is found.
Retur... | # Copyright (c) 2018 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 app... | def has_feed_operators(block, feed_targets, feed_holder_name):
""" Check whether the block already has feed operators.
Return false if the block does not have any feed operators.
If some feed operators have been prepended to the block, check that
the info contained in these feed operators matches the f... | 130 | 165 | # Copyright (c) 2018 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 app... |
has_fetch_operators | Check whether the block already has fetch operators.
Return false if the block does not have any fetch operators.
If some fetch operators have been appended to the block, check that
the info contained in these fetch operators matches the fetch_targets
and fetch_holder_name. Raise exception when any mismatch is found.
... | # Copyright (c) 2018 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 app... | def has_fetch_operators(block, fetch_targets, fetch_holder_name):
""" Check whether the block already has fetch operators.
Return false if the block does not have any fetch operators.
If some fetch operators have been appended to the block, check that
the info contained in these fetch operators matches... | 168 | 205 | # Copyright (c) 2018 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 app... |
__init__ | Construct Exponential distribution with parameter `rate`.
Args:
rate: Floating point tensor, equivalent to `1 / mean`. Must contain only
positive values.
validate_args: Python `bool`, default `False`. When `True` distribution
parameters are checked for validity despite possibly degrading runtime
perfor... | # Copyright 2016 The TensorFlow 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 __init__(self,
rate,
validate_args=False,
allow_nan_stats=True,
name="Exponential"):
"""Construct Exponential distribution with parameter `rate`.
Args:
rate: Floating point tensor, equivalent to `1 / mean`. Must contain only
positi... | 73 | 110 | # Copyright 2016 The TensorFlow 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... |
create | Create each navigation componet, storing the layout. Then parent class to create application.
Args:
kwargs: keyword arguments passed to `self.create` | """Classes for more complex applications that have tabbed or paged navigation."""
from collections import OrderedDict
from copy import deepcopy
import dash_bootstrap_components as dbc
import dash_core_components as dcc
import dash_html_components as html
from implements import implements
from .utils_app import AppBa... | def create(self, **kwargs):
"""Create each navigation componet, storing the layout. Then parent class to create application.
Args:
kwargs: keyword arguments passed to `self.create`
"""
# Initialize the lookup for each tab then configure each tab
self.nav_lookup ... | 59 | 77 | """Classes for more complex applications that have tabbed or paged navigation."""
from collections import OrderedDict
from copy import deepcopy
import dash_bootstrap_components as dbc
import dash_core_components as dcc
import dash_html_components as html
from implements import implements
from .utils_app import AppBa... |
verify_app_initialization | Check that the app was properly initialized.
Raises:
RuntimeError: if child class has not called `self.register_uniq_ids` | """Classes for more complex applications that have tabbed or paged navigation."""
from collections import OrderedDict
from copy import deepcopy
import dash_bootstrap_components as dbc
import dash_core_components as dcc
import dash_html_components as html
from implements import implements
from .utils_app import AppBa... | def verify_app_initialization(self):
"""Check that the app was properly initialized.
Raises:
RuntimeError: if child class has not called `self.register_uniq_ids`
"""
super().verify_app_initialization()
allowed_locations = ('left', 'top', 'bottom', 'right')
... | 169 | 179 | """Classes for more complex applications that have tabbed or paged navigation."""
from collections import OrderedDict
from copy import deepcopy
import dash_bootstrap_components as dbc
import dash_core_components as dcc
import dash_html_components as html
from implements import implements
from .utils_app import AppBa... |
tab_menu | Return the HTML elements for the tab menu.
Returns:
dict: Dash HTML object | """Classes for more complex applications that have tabbed or paged navigation."""
from collections import OrderedDict
from copy import deepcopy
import dash_bootstrap_components as dbc
import dash_core_components as dcc
import dash_html_components as html
from implements import implements
from .utils_app import AppBa... | def tab_menu(self):
"""Return the HTML elements for the tab menu.
Returns:
dict: Dash HTML object
"""
tab_kwargs, tabs_kwargs, tabs_style = self.generate_tab_kwargs()
tabs = [dcc.Tab(label=name, value=name, **tab_kwargs) for name, tab in self.nav_lookup.items()]... | 249 | 265 | """Classes for more complex applications that have tabbed or paged navigation."""
from collections import OrderedDict
from copy import deepcopy
import dash_bootstrap_components as dbc
import dash_core_components as dcc
import dash_html_components as html
from implements import implements
from .utils_app import AppBa... |
nav_bar | Return the HTML elements for the navigation menu.
Returns:
dict: Dash HTML object | """Classes for more complex applications that have tabbed or paged navigation."""
from collections import OrderedDict
from copy import deepcopy
import dash_bootstrap_components as dbc
import dash_core_components as dcc
import dash_html_components as html
from implements import implements
from .utils_app import AppBa... | def nav_bar(self):
"""Return the HTML elements for the navigation menu.
Returns:
dict: Dash HTML object
"""
# Create brand icon and name where icon in optional
brand = []
if self.logo:
brand.append(dbc.Col(html.Img(src=self.logo, height='25px... | 304 | 365 | """Classes for more complex applications that have tabbed or paged navigation."""
from collections import OrderedDict
from copy import deepcopy
import dash_bootstrap_components as dbc
import dash_core_components as dcc
import dash_html_components as html
from implements import implements
from .utils_app import AppBa... |
analyze_correctness | For each training sample, determine whether the activation clustering method was correct.
:param assigned_clean_by_class: Result of clustering.
:param is_clean_by_class: is clean separated by class.
:return: Two variables are returned:
1) all_errors_by_class[i]: an array indicating the correctness of each ass... | # MIT License
#
# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
# r... | def analyze_correctness(
self, assigned_clean_by_class: Union[np.ndarray, List[np.ndarray]], is_clean_by_class: list
) -> Tuple[np.ndarray, str]:
"""
For each training sample, determine whether the activation clustering method was correct.
:param assigned_clean_by_class: Result ... | 42 | 101 | # MIT License
#
# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
# r... |
get_confusion_matrix | Computes and returns a json object that contains the confusion matrix for each class.
:param values: Array indicating the correctness of each assignment in the ith class.
:return: Json object with confusion matrix per-class. | # MIT License
#
# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
# r... | def get_confusion_matrix(self, values: np.ndarray) -> dict:
"""
Computes and returns a json object that contains the confusion matrix for each class.
:param values: Array indicating the correctness of each assignment in the ith class.
:return: Json object with confusion matrix per-c... | 103 | 174 | # MIT License
#
# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
# r... |
template_2a_1 | Returns:
QuantumCircuit: template as a quantum circuit. | # -*- coding: utf-8 -*-
# This code is part of Qiskit.
#
# (C) Copyright IBM 2020.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modif... | def template_2a_1():
"""
Returns:
QuantumCircuit: template as a quantum circuit.
"""
qc = QuantumCircuit(1)
qc.x(0)
qc.x(0)
return qc | 26 | 34 | # -*- coding: utf-8 -*-
# This code is part of Qiskit.
#
# (C) Copyright IBM 2020.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modif... |
adjust_bbox | Temporarily adjust the figure so that only the specified area
(bbox_inches) is saved.
It modifies fig.bbox, fig.bbox_inches,
fig.transFigure._boxout, and fig.patch. While the figure size
changes, the scale of the original figure is conserved. A
function which restores the original values are returned. | """
This module is to support *bbox_inches* option in savefig command.
"""
import warnings
from matplotlib.transforms import Bbox, TransformedBbox, Affine2D
# MASKED: adjust_bbox function (lines 10-56)
def adjust_bbox_png(fig, bbox_inches):
"""
adjust_bbox for png (Agg) format
"""
tr = fig.dpi_sc... | def adjust_bbox(fig, format, bbox_inches):
"""
Temporarily adjust the figure so that only the specified area
(bbox_inches) is saved.
It modifies fig.bbox, fig.bbox_inches,
fig.transFigure._boxout, and fig.patch. While the figure size
changes, the scale of the original figure is conserved. A
... | 10 | 56 | """
This module is to support *bbox_inches* option in savefig command.
"""
import warnings
from matplotlib.transforms import Bbox, TransformedBbox, Affine2D
def adjust_bbox(fig, format, bbox_inches):
"""
Temporarily adjust the figure so that only the specified area
(bbox_inches) is saved.
It modifi... |
process_figure_for_rasterizing | This need to be called when figure dpi changes during the drawing
(e.g., rasterizing). It recovers the bbox and re-adjust it with
the new dpi. | """
This module is to support *bbox_inches* option in savefig command.
"""
import warnings
from matplotlib.transforms import Bbox, TransformedBbox, Affine2D
def adjust_bbox(fig, format, bbox_inches):
"""
Temporarily adjust the figure so that only the specified area
(bbox_inches) is saved.
It modifi... | def process_figure_for_rasterizing(figure,
bbox_inches_restore, mode):
"""
This need to be called when figure dpi changes during the drawing
(e.g., rasterizing). It recovers the bbox and re-adjust it with
the new dpi.
"""
bbox_inches, restore_bbox = bbox_inch... | 114 | 128 | """
This module is to support *bbox_inches* option in savefig command.
"""
import warnings
from matplotlib.transforms import Bbox, TransformedBbox, Affine2D
def adjust_bbox(fig, format, bbox_inches):
"""
Temporarily adjust the figure so that only the specified area
(bbox_inches) is saved.
It modifi... |
validate_authorization_request | The client constructs the request URI by adding the following
parameters to the query component of the authorization endpoint URI
using the "application/x-www-form-urlencoded" format.
Per `Section 4.2.1`_.
response_type
REQUIRED. Value MUST be set to "token".
client_id
REQUIRED. The client identifier as d... | import logging
from authlib.common.urls import add_params_to_uri
from .base import BaseGrant, AuthorizationEndpointMixin
from ..errors import (
OAuth2Error,
UnauthorizedClientError,
AccessDeniedError,
)
log = logging.getLogger(__name__)
class ImplicitGrant(BaseGrant, AuthorizationEndpointMixin):
"""T... | def validate_authorization_request(self):
"""The client constructs the request URI by adding the following
parameters to the query component of the authorization endpoint URI
using the "application/x-www-form-urlencoded" format.
Per `Section 4.2.1`_.
response_type
... | 78 | 147 | import logging
from authlib.common.urls import add_params_to_uri
from .base import BaseGrant, AuthorizationEndpointMixin
from ..errors import (
OAuth2Error,
UnauthorizedClientError,
AccessDeniedError,
)
log = logging.getLogger(__name__)
class ImplicitGrant(BaseGrant, AuthorizationEndpointMixin):
"""T... |
cdsem_straight | Returns straight waveguide lines width sweep.
Args:
widths: for the sweep
length: for the line
cross_section: for the lines
text: optional text for labels
spacing: edge to edge spacing | """CD SEM structures."""
from functools import partial
from typing import Optional, Tuple
from gdsfactory.cell import cell
from gdsfactory.component import Component
from gdsfactory.components.straight import straight as straight_function
from gdsfactory.components.text_rectangular import text_rectangular
from gdsfact... | @cell
def cdsem_straight(
widths: Tuple[float, ...] = (0.4, 0.45, 0.5, 0.6, 0.8, 1.0),
length: float = LINE_LENGTH,
cross_section: CrossSectionFactory = strip,
text: Optional[ComponentFactory] = text_rectangular_mini,
spacing: float = 3,
) -> Component:
"""Returns straight waveguide lines width ... | 18 | 47 | """CD SEM structures."""
from functools import partial
from typing import Optional, Tuple
from gdsfactory.cell import cell
from gdsfactory.component import Component
from gdsfactory.components.straight import straight as straight_function
from gdsfactory.components.text_rectangular import text_rectangular
from gdsfact... |
_teacher_action | Extract teacher actions into variable.
:param obs: The observation.
:param ended: Whether the action seq is ended
:return: |
import json
import os
import sys
import numpy as np
import random
import math
import time
from tqdm import tqdm
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch import optim
import torch.nn.functional as F
from env import R2RBatch
from utils import padding_idx, add_idx, Tokenizer
imp... | def _teacher_action(self, obs, ended):
"""
Extract teacher actions into variable.
:param obs: The observation.
:param ended: Whether the action seq is ended
:return:
"""
a = np.zeros(len(obs), dtype=np.int64)
for i, ob in enumerate(obs):
if... | 169 | 188 |
import json
import os
import sys
import numpy as np
import random
import math
import time
from tqdm import tqdm
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch import optim
import torch.nn.functional as F
from env import R2RBatch
from utils import padding_idx, add_idx, Tokenizer
imp... |
type_from_ast | Given an AST node representing an annotation, return a
:class:`Value <pyanalyze.value.Value>`.
:param ast_node: AST node to evaluate.
:param visitor: Visitor class to use. This is used in the default
:class:`Context` to resolve names and show errors.
This is ignored if `ctx` is given.
... | """
Code for understanding type annotations.
This file contains functions that turn various representations of
Python type annotations into :class:`pyanalyze.value.Value` objects.
There are three major functions:
- :func:`type_from_runtime` takes a runtime Python object, for example
``type_from_value(int)`` -> ``... | @used # part of an API
def type_from_ast(
ast_node: ast.AST,
visitor: Optional["NameCheckVisitor"] = None,
ctx: Optional[Context] = None,
) -> Value:
"""Given an AST node representing an annotation, return a
:class:`Value <pyanalyze.value.Value>`.
:param ast_node: AST node to evaluate.
:p... | 227 | 247 | """
Code for understanding type annotations.
This file contains functions that turn various representations of
Python type annotations into :class:`pyanalyze.value.Value` objects.
There are three major functions:
- :func:`type_from_runtime` takes a runtime Python object, for example
``type_from_value(int)`` -> ``... |
test_compiled_js_dir_validation | Test that build.COMPILED_JS_DIR is validated correctly with
outDir in build.TSCONFIG_FILEPATH. | # coding: utf-8
#
# Copyright 2014 The Oppia 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 requi... | def test_compiled_js_dir_validation(self):
"""Test that build.COMPILED_JS_DIR is validated correctly with
outDir in build.TSCONFIG_FILEPATH.
"""
build.require_compiled_js_dir_to_be_valid()
out_dir = ''
with open(build.TSCONFIG_FILEPATH) as f:
config_data ... | 658 | 675 | # coding: utf-8
#
# Copyright 2014 The Oppia 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 requi... |
_encode_image | encodes an (numpy) image array to string.
Args:
image_array: (numpy) image array
fmt: image format to use
Returns:
encoded image string | # Copyright 2018 The TensorFlow 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 _encode_image(image_array, fmt='PNG'):
"""encodes an (numpy) image array to string.
Args:
image_array: (numpy) image array
fmt: image format to use
Returns:
encoded image string
"""
pil_image = Image.fromarray(image_array)
image_io = io.BytesIO()
pil_image.save(image_io, format=fmt)
re... | 261 | 274 | # Copyright 2018 The TensorFlow 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... |
write_warmup_requests | Writes warmup requests for inference into a tfrecord file.
Args:
savedmodel_dir: string, the file to the exported model folder.
model_name: string, a model name used inside the model server.
image_size: int, size of image, assuming image height and width.
batch_sizes: list, a list of batch sizes to create diff... | # Copyright 2018 The TensorFlow 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 write_warmup_requests(savedmodel_dir,
model_name,
image_size,
batch_sizes=None,
num_requests=8):
"""Writes warmup requests for inference into a tfrecord file.
Args:
savedmodel_dir: string, the file to th... | 277 | 311 | # Copyright 2018 The TensorFlow 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... |
add_text | Return component inside a new component with text geometry.
Args:
component:
text: text string.
text_offset: relative to component anchor. Defaults to center (cc).
text_anchor: relative to component (ce cw nc ne nw sc se sw center cc).
text_factory: function to add text labels. | """All functions return a Component so you can easily pipe or compose them.
There are two types of functions:
- decorators: return the original component
- containers: return a new component
"""
from functools import lru_cache
import numpy as np
from omegaconf import OmegaConf
from pydantic import validate_argument... | @cell
def add_text(
component: ComponentOrFactory,
text: str = "",
text_offset: Float2 = (0, 0),
text_anchor: Anchor = "cc",
text_factory: ComponentFactory = text_rectangular_multi_layer,
) -> Component:
"""Return component inside a new component with text geometry.
Args:
component:... | 41 | 68 | """All functions return a Component so you can easily pipe or compose them.
There are two types of functions:
- decorators: return the original component
- containers: return a new component
"""
from functools import lru_cache
import numpy as np
from omegaconf import OmegaConf
from pydantic import validate_argument... |
rotate | Return rotated component inside a new component.
Most times you just need to place a reference and rotate it.
This rotate function just encapsulates the rotated reference into a new component.
Args:
component:
angle: in degrees | """All functions return a Component so you can easily pipe or compose them.
There are two types of functions:
- decorators: return the original component
- containers: return a new component
"""
from functools import lru_cache
import numpy as np
from omegaconf import OmegaConf
from pydantic import validate_argument... | @cell
def rotate(
component: ComponentOrFactory,
angle: float = 90,
) -> Component:
"""Return rotated component inside a new component.
Most times you just need to place a reference and rotate it.
This rotate function just encapsulates the rotated reference into a new component.
Args:
... | 95 | 116 | """All functions return a Component so you can easily pipe or compose them.
There are two types of functions:
- decorators: return the original component
- containers: return a new component
"""
from functools import lru_cache
import numpy as np
from omegaconf import OmegaConf
from pydantic import validate_argument... |
mirror | Return new Component with a mirrored reference.
Args:
p1: first point to define mirror axis
p2: second point to define mirror axis | """All functions return a Component so you can easily pipe or compose them.
There are two types of functions:
- decorators: return the original component
- containers: return a new component
"""
from functools import lru_cache
import numpy as np
from omegaconf import OmegaConf
from pydantic import validate_argument... | @cell
def mirror(component: Component, p1: Float2 = (0, 1), p2: Float2 = (0, 0)) -> Component:
"""Return new Component with a mirrored reference.
Args:
p1: first point to define mirror axis
p2: second point to define mirror axis
"""
component_new = Component()
component_new.componen... | 124 | 138 | """All functions return a Component so you can easily pipe or compose them.
There are two types of functions:
- decorators: return the original component
- containers: return a new component
"""
from functools import lru_cache
import numpy as np
from omegaconf import OmegaConf
from pydantic import validate_argument... |
move | Return new Component with a moved reference to the original component.
Args:
origin: of component
destination:
axis: x or y axis | """All functions return a Component so you can easily pipe or compose them.
There are two types of functions:
- decorators: return the original component
- containers: return a new component
"""
from functools import lru_cache
import numpy as np
from omegaconf import OmegaConf
from pydantic import validate_argument... | @cell
def move(
component: Component,
origin=(0, 0),
destination=None,
axis: Optional[Axis] = None,
) -> Component:
"""Return new Component with a moved reference to the original component.
Args:
origin: of component
destination:
axis: x or y axis
"""
component_n... | 141 | 161 | """All functions return a Component so you can easily pipe or compose them.
There are two types of functions:
- decorators: return the original component
- containers: return a new component
"""
from functools import lru_cache
import numpy as np
from omegaconf import OmegaConf
from pydantic import validate_argument... |
add_settings_label | Add a settings label to a component.
Args:
component:
layer_label:
settings: tuple or list of settings. if None, adds all changed settings | """All functions return a Component so you can easily pipe or compose them.
There are two types of functions:
- decorators: return the original component
- containers: return a new component
"""
from functools import lru_cache
import numpy as np
from omegaconf import OmegaConf
from pydantic import validate_argument... | @validate_arguments
def add_settings_label(
component: Component, layer_label: Layer = (66, 0), settings: Optional[Strs] = None
) -> Component:
"""Add a settings label to a component.
Args:
component:
layer_label:
settings: tuple or list of settings. if None, adds all changed settin... | 181 | 200 | """All functions return a Component so you can easily pipe or compose them.
There are two types of functions:
- decorators: return the original component
- containers: return a new component
"""
from functools import lru_cache
import numpy as np
from omegaconf import OmegaConf
from pydantic import validate_argument... |
write_fiducials | Write fiducials to a fiff file
Parameters
----------
fname : str
Destination file name.
pts : iterator of dict
Iterator through digitizer points. Each point is a dictionary with
the keys 'kind', 'ident' and 'r'.
coord_frame : int
The coordinate frame of the points (one of
mne.fiff.FIFF.FIFFV_COORD_... | # Authors: Alexandre Gramfort <gramfort@nmr.mgh.harvard.edu>
# Matti Hamalainen <msh@nmr.mgh.harvard.edu>
#
# License: BSD (3-clause)
from warnings import warn
from copy import deepcopy
import os.path as op
import numpy as np
from scipy import linalg
from ..externals.six import BytesIO
from datetime import da... | def write_fiducials(fname, pts, coord_frame=0):
"""Write fiducials to a fiff file
Parameters
----------
fname : str
Destination file name.
pts : iterator of dict
Iterator through digitizer points. Each point is a dictionary with
the keys 'kind', 'ident' and 'r'.
coord_fr... | 119 | 147 | # Authors: Alexandre Gramfort <gramfort@nmr.mgh.harvard.edu>
# Matti Hamalainen <msh@nmr.mgh.harvard.edu>
#
# License: BSD (3-clause)
from warnings import warn
from copy import deepcopy
import os.path as op
import numpy as np
from scipy import linalg
from ..externals.six import BytesIO
from datetime import da... |
write_info | Write measurement info in fif file.
Parameters
----------
fname : str
The name of the file. Should end by -info.fif.
info : instance of mne.fiff.meas_info.Info
The measurement info structure
data_type : int
The data_type in case it is necessary. Should be 4 (FIFFT_FLOAT),
5 (FIFFT_DOUBLE), or 16 (mne.f... | # Authors: Alexandre Gramfort <gramfort@nmr.mgh.harvard.edu>
# Matti Hamalainen <msh@nmr.mgh.harvard.edu>
#
# License: BSD (3-clause)
from warnings import warn
from copy import deepcopy
import os.path as op
import numpy as np
from scipy import linalg
from ..externals.six import BytesIO
from datetime import da... | def write_info(fname, info, data_type=None, reset_range=True):
"""Write measurement info in fif file.
Parameters
----------
fname : str
The name of the file. Should end by -info.fif.
info : instance of mne.fiff.meas_info.Info
The measurement info structure
data_type : int
... | 595 | 615 | # Authors: Alexandre Gramfort <gramfort@nmr.mgh.harvard.edu>
# Matti Hamalainen <msh@nmr.mgh.harvard.edu>
#
# License: BSD (3-clause)
from warnings import warn
from copy import deepcopy
import os.path as op
import numpy as np
from scipy import linalg
from ..externals.six import BytesIO
from datetime import da... |
loss_fn_kd | Compute the knowledge-distillation (KD) loss given outputs, labels.
"Hyperparameters": temperature and alpha
NOTE: the KL Divergence for PyTorch comparing the softmaxs of teacher
and student expects the input tensor to be log probabilities! See Issue #2 | """
Baseline CNN, losss function and metrics
Also customizes knowledge distillation (KD) loss function here
"""
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class Flatten(nn.Module):
def forward(self, input):
return input.view(input.size(0), -1)
"""
This is t... | def loss_fn_kd(outputs, labels, teacher_outputs, params):
"""
Compute the knowledge-distillation (KD) loss given outputs, labels.
"Hyperparameters": temperature and alpha
NOTE: the KL Divergence for PyTorch comparing the softmaxs of teacher
and student expects the input tensor to be log probabiliti... | 119 | 133 | """
Baseline CNN, losss function and metrics
Also customizes knowledge distillation (KD) loss function here
"""
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class Flatten(nn.Module):
def forward(self, input):
return input.view(input.size(0), -1)
"""
This is t... |
forward | This function defines how we use the components of our network to operate on an input batch.
Args:
s: (Variable) contains a batch of images, of dimension batch_size x 3 x 32 x 32 .
Returns:
out: (Variable) dimension batch_size x 6 with the log probabilities for the labels of each image.
Note: the dimensions ... | """
Baseline CNN, losss function and metrics
Also customizes knowledge distillation (KD) loss function here
"""
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class Flatten(nn.Module):
def forward(self, input):
return input.view(input.size(0), -1)
"""
This is t... | def forward(self, s):
"""
This function defines how we use the components of our network to operate on an input batch.
Args:
s: (Variable) contains a batch of images, of dimension batch_size x 3 x 32 x 32 .
Returns:
out: (Variable) dimension batch_size x 6 w... | 62 | 99 | """
Baseline CNN, losss function and metrics
Also customizes knowledge distillation (KD) loss function here
"""
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class Flatten(nn.Module):
def forward(self, input):
return input.view(input.size(0), -1)
"""
This is t... |
build_model | Build model.
Arguments:
n_stimuli: Integer indicating the number of stimuli in the
embedding.
n_dim: Integer indicating the dimensionality of the embedding.
Returns:
model: A TensorFlow Keras model. | # -*- coding: utf-8 -*-
# Copyright 2020 The PsiZ 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 r... | def build_model(n_stimuli, n_dim, n_group):
"""Build model.
Arguments:
n_stimuli: Integer indicating the number of stimuli in the
embedding.
n_dim: Integer indicating the dimensionality of the embedding.
Returns:
model: A TensorFlow Keras model.
"""
stimuli = t... | 259 | 293 | # -*- coding: utf-8 -*-
# Copyright 2020 The PsiZ 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 r... |
send_single_ans | Send a single message to specific id with a specific name.
:params ID: User quiz id.
:type ID: int
:params name: Name you want on the message.
:type name: str | import requests
import json
class BuddyAPI():
'''
An API of buddymojo.com
:returns: An API
'''
def __init__(self):
self.payload = {'type': 'friend',
'action': 'finish'}
self.payloadf = {'userQuizId': 1,
'type': 'friend',
... | def send_single_ans(self, ID, name: str):
'''
Send a single message to specific id with a specific name.
:params ID: User quiz id.
:type ID: int
:params name: Name you want on the message.
:type name: str
'''
self.data = {'userFullName': name,
... | 22 | 50 | import requests
import json
class BuddyAPI():
'''
An API of buddymojo.com
:returns: An API
'''
def __init__(self):
self.payload = {'type': 'friend',
'action': 'finish'}
self.payloadf = {'userQuizId': 1,
'type': 'friend',
... |
lazy_property | Allows to avoid recomputing a property over and over.
The result gets stored in a local var. Computation of the property
will happen once, on the first call of the property. All
succeeding calls will use the value stored in the private property. | import asyncio
import functools
import importlib
import inspect
import logging
from typing import Text, Dict, Optional, Any, List, Callable, Collection, Type
from rasa.shared.exceptions import RasaException
logger = logging.getLogger(__name__)
def class_from_module_path(
module_path: Text, lookup_path: Optional... | def lazy_property(function: Callable) -> Any:
"""Allows to avoid recomputing a property over and over.
The result gets stored in a local var. Computation of the property
will happen once, on the first call of the property. All
succeeding calls will use the value stored in the private property."""
... | 73 | 88 | import asyncio
import functools
import importlib
import inspect
import logging
from typing import Text, Dict, Optional, Any, List, Callable, Collection, Type
from rasa.shared.exceptions import RasaException
logger = logging.getLogger(__name__)
def class_from_module_path(
module_path: Text, lookup_path: Optional... |
cached_method | Caches method calls based on the call's `args` and `kwargs`.
Works for `async` and `sync` methods. Don't apply this to functions.
Args:
f: The decorated method whose return value should be cached.
Returns:
The return value which the method gives for the first call with the given
arguments. | import asyncio
import functools
import importlib
import inspect
import logging
from typing import Text, Dict, Optional, Any, List, Callable, Collection, Type
from rasa.shared.exceptions import RasaException
logger = logging.getLogger(__name__)
def class_from_module_path(
module_path: Text, lookup_path: Optional... | def cached_method(f: Callable[..., Any]) -> Callable[..., Any]:
"""Caches method calls based on the call's `args` and `kwargs`.
Works for `async` and `sync` methods. Don't apply this to functions.
Args:
f: The decorated method whose return value should be cached.
Returns:
The return v... | 91 | 150 | import asyncio
import functools
import importlib
import inspect
import logging
from typing import Text, Dict, Optional, Any, List, Callable, Collection, Type
from rasa.shared.exceptions import RasaException
logger = logging.getLogger(__name__)
def class_from_module_path(
module_path: Text, lookup_path: Optional... |
prepare_roidb | Enrich the imdb's roidb by adding some derived quantities that
are useful for training. This function precomputes the maximum
overlap, taken over ground-truth boxes, between each ROI and
each ground-truth box. The class with maximum overlap is also
recorded. | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
"""Transform a roidb into a trainable roidb by adding a bunch of metada... | def prepare_roidb(imdb):
"""Enrich the imdb's roidb by adding some derived quantities that
are useful for training. This function precomputes the maximum
overlap, taken over ground-truth boxes, between each ROI and
each ground-truth box. The class with maximum overlap is also
recorded.
"""
r... | 14 | 38 | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
"""Transform a roidb into a trainable roidb by adding a bunch of metada... |
create_game | Configure and create a game.
Creates a game with base settings equivalent to one of the default presets.
Allows user to customize the settings before starting the game.
Parameters
----------
gm : int
Game type to replicate:
0: Normal mode.
1: Advanced mode.
Returns
-------
BattleshipGame
Game... | #!/usr/bin/env python3
from string import ascii_uppercase
from re import fullmatch
from time import sleep
from random import Random
# Default game presets.
testing_preset = {'height': 10, 'width': 10, '5_ships': 0, '4_ships': 0, '3_ships': 0, '2_ships': 2, '1_ships': 0, 'allow_mines': True, 'allow_moves': True, 'mine... | def create_game(gm):
"""
Configure and create a game.
Creates a game with base settings equivalent to one of the default presets.
Allows user to customize the settings before starting the game.
Parameters
----------
gm : int
Game type to replicate:
0: Normal mode.
... | 1,152 | 1,237 | #!/usr/bin/env python3
from string import ascii_uppercase
from re import fullmatch
from time import sleep
from random import Random
# Default game presets.
testing_preset = {'height': 10, 'width': 10, '5_ships': 0, '4_ships': 0, '3_ships': 0, '2_ships': 2, '1_ships': 0, 'allow_mines': True, 'allow_moves': True, 'mine... |
box_string | Place a string into an ASCII box.
The result is placed inside of a ASCII box consisting of '+' characters for the corners and '-' characters for the edges.
Parameters
----------
string : str
String to be boxed.
min_width : int, optional
Specifies that the box be of a certain minimum width. Defaults to input s... | #!/usr/bin/env python3
from string import ascii_uppercase
from re import fullmatch
from time import sleep
from random import Random
# Default game presets.
testing_preset = {'height': 10, 'width': 10, '5_ships': 0, '4_ships': 0, '3_ships': 0, '2_ships': 2, '1_ships': 0, 'allow_mines': True, 'allow_moves': True, 'mine... | @staticmethod
def box_string(string, min_width=-1, print_string=False):
"""
Place a string into an ASCII box.
The result is placed inside of a ASCII box consisting of '+' characters for the corners and '-' characters for the edges.
Parameters
----------
string :... | 27 | 62 | #!/usr/bin/env python3
from string import ascii_uppercase
from re import fullmatch
from time import sleep
from random import Random
# Default game presets.
testing_preset = {'height': 10, 'width': 10, '5_ships': 0, '4_ships': 0, '3_ships': 0, '2_ships': 2, '1_ships': 0, 'allow_mines': True, 'allow_moves': True, 'mine... |
string_input | Take string-based user input.
The input question will be repeated until valid input is given, determined by the condition regex.
Parameters
----------
question : str
String to be displayed as the input question. Will be boxed with Utils#box_string before printing.
condition : r-string, optional
Regex to test ... | #!/usr/bin/env python3
from string import ascii_uppercase
from re import fullmatch
from time import sleep
from random import Random
# Default game presets.
testing_preset = {'height': 10, 'width': 10, '5_ships': 0, '4_ships': 0, '3_ships': 0, '2_ships': 2, '1_ships': 0, 'allow_mines': True, 'allow_moves': True, 'mine... | @staticmethod
def string_input(question, condition=r'.+'):
"""
Take string-based user input.
The input question will be repeated until valid input is given, determined by the condition regex.
Parameters
----------
question : str
String to be displaye... | 110 | 140 | #!/usr/bin/env python3
from string import ascii_uppercase
from re import fullmatch
from time import sleep
from random import Random
# Default game presets.
testing_preset = {'height': 10, 'width': 10, '5_ships': 0, '4_ships': 0, '3_ships': 0, '2_ships': 2, '1_ships': 0, 'allow_mines': True, 'allow_moves': True, 'mine... |
print_settings | Pretty-print a settings dictionary.
Parameters
----------
settings : dict
The settings dictionary to pretty-print.
Returns
-------
None | #!/usr/bin/env python3
from string import ascii_uppercase
from re import fullmatch
from time import sleep
from random import Random
# Default game presets.
testing_preset = {'height': 10, 'width': 10, '5_ships': 0, '4_ships': 0, '3_ships': 0, '2_ships': 2, '1_ships': 0, 'allow_mines': True, 'allow_moves': True, 'mine... | @staticmethod
def print_settings(settings):
"""
Pretty-print a settings dictionary.
Parameters
----------
settings : dict
The settings dictionary to pretty-print.
Returns
-------
None
"""
Utils.box_string('Current ... | 142 | 175 | #!/usr/bin/env python3
from string import ascii_uppercase
from re import fullmatch
from time import sleep
from random import Random
# Default game presets.
testing_preset = {'height': 10, 'width': 10, '5_ships': 0, '4_ships': 0, '3_ships': 0, '2_ships': 2, '1_ships': 0, 'allow_mines': True, 'allow_moves': True, 'mine... |
grid_pos_input | Take user-input in coordinate form.
The input question will be repeated until valid input is given.
The input must be a valid coordinate in battleship form (r'[A-Z]\d+').
The input coordinate must be inside of the grid defined by height and width.
Parameters
----------
height : int
Specifies the height of the gri... | #!/usr/bin/env python3
from string import ascii_uppercase
from re import fullmatch
from time import sleep
from random import Random
# Default game presets.
testing_preset = {'height': 10, 'width': 10, '5_ships': 0, '4_ships': 0, '3_ships': 0, '2_ships': 2, '1_ships': 0, 'allow_mines': True, 'allow_moves': True, 'mine... | @staticmethod
def grid_pos_input(height, width, question='Enter a Position:'):
"""
Take user-input in coordinate form.
The input question will be repeated until valid input is given.
The input must be a valid coordinate in battleship form (r'[A-Z]\d+').
The input coordin... | 177 | 218 | #!/usr/bin/env python3
from string import ascii_uppercase
from re import fullmatch
from time import sleep
from random import Random
# Default game presets.
testing_preset = {'height': 10, 'width': 10, '5_ships': 0, '4_ships': 0, '3_ships': 0, '2_ships': 2, '1_ships': 0, 'allow_mines': True, 'allow_moves': True, 'mine... |
print_board | Pretty-print the current boards of a player.
Prints both boards for a player, along with coordinate references, titles, and boxes around the grids.
Parameters
----------
player : int
Determines which player's grids to print. Zero-indexed.
Returns
-------
str
Same as the string that is printed. | #!/usr/bin/env python3
from string import ascii_uppercase
from re import fullmatch
from time import sleep
from random import Random
# Default game presets.
testing_preset = {'height': 10, 'width': 10, '5_ships': 0, '4_ships': 0, '3_ships': 0, '2_ships': 2, '1_ships': 0, 'allow_mines': True, 'allow_moves': True, 'mine... | def print_board(self, player):
"""
Pretty-print the current boards of a player.
Prints both boards for a player, along with coordinate references, titles, and boxes around the grids.
Parameters
----------
player : int
Determines which player's grids to p... | 335 | 383 | #!/usr/bin/env python3
from string import ascii_uppercase
from re import fullmatch
from time import sleep
from random import Random
# Default game presets.
testing_preset = {'height': 10, 'width': 10, '5_ships': 0, '4_ships': 0, '3_ships': 0, '2_ships': 2, '1_ships': 0, 'allow_mines': True, 'allow_moves': True, 'mine... |
setup_ship | Create a ship.
Creates a ship dictionary based on positional, directional, player, and size data and tests if placement is legal.
Parameters
----------
pos : tuple
(y,x) coordinate pair of top-left corner of the ship.
direction : int
Determines the direction of the ship:
0: Horizontal.
1: Vert... | #!/usr/bin/env python3
from string import ascii_uppercase
from re import fullmatch
from time import sleep
from random import Random
# Default game presets.
testing_preset = {'height': 10, 'width': 10, '5_ships': 0, '4_ships': 0, '3_ships': 0, '2_ships': 2, '1_ships': 0, 'allow_mines': True, 'allow_moves': True, 'mine... | def setup_ship(self, pos, direction, player, count, size):
"""
Create a ship.
Creates a ship dictionary based on positional, directional, player, and size data and tests if placement is legal.
Parameters
----------
pos : tuple
(y,x) coordinate pair of to... | 385 | 443 | #!/usr/bin/env python3
from string import ascii_uppercase
from re import fullmatch
from time import sleep
from random import Random
# Default game presets.
testing_preset = {'height': 10, 'width': 10, '5_ships': 0, '4_ships': 0, '3_ships': 0, '2_ships': 2, '1_ships': 0, 'allow_mines': True, 'allow_moves': True, 'mine... |
start_game | Start a new game.
Starts a game with the settings provided in the constructor.
All game code is contained here, with relevant helper methods also called here.
Every game has two stages: Setup and Play.
Returns
-------
int
Winning player's number. Zero-indexed. | #!/usr/bin/env python3
from string import ascii_uppercase
from re import fullmatch
from time import sleep
from random import Random
# Default game presets.
testing_preset = {'height': 10, 'width': 10, '5_ships': 0, '4_ships': 0, '3_ships': 0, '2_ships': 2, '1_ships': 0, 'allow_mines': True, 'allow_moves': True, 'mine... | def start_game(self):
"""
Start a new game.
Starts a game with the settings provided in the constructor.
All game code is contained here, with relevant helper methods also called here.
Every game has two stages: Setup and Play.
Returns
-------
int
... | 1,046 | 1,149 | #!/usr/bin/env python3
from string import ascii_uppercase
from re import fullmatch
from time import sleep
from random import Random
# Default game presets.
testing_preset = {'height': 10, 'width': 10, '5_ships': 0, '4_ships': 0, '3_ships': 0, '2_ships': 2, '1_ships': 0, 'allow_mines': True, 'allow_moves': True, 'mine... |
get_application_gateway | Application gateway resource.
:param str application_gateway_name: The name of the application gateway.
:param str resource_group_name: The name of the resource group. | # coding=utf-8
# *** WARNING: this file was generated by the Pulumi SDK Generator. ***
# *** 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
from ... import _utilities, _tables
from... | def get_application_gateway(application_gateway_name: Optional[str] = None,
resource_group_name: Optional[str] = None,
opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetApplicationGatewayResult:
"""
Application gateway resource.
:param str ... | 490 | 547 | # coding=utf-8
# *** WARNING: this file was generated by the Pulumi SDK Generator. ***
# *** 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
from ... import _utilities, _tables
from... |
f_regression_block | runs f_regression for each block separately (saves memory).
-------------------------
fun : method that returns statistics,pval
X : {array-like, sparse matrix} shape = (n_samples, n_features)
The set of regressors that will tested sequentially.
y : array of shape(n_samples).
The data matrix
block... | import numpy as np
import logging
import unittest
import os
import scipy.linalg as LA
import time
from sklearn.utils import safe_sqr, check_array
from scipy import stats
from pysnptools.snpreader import Bed,Pheno
from pysnptools.snpreader import SnpData,SnpReader
from pysnptools.kernelreader import KernelNpz
from pysn... | def f_regression_block(fun,X,y,blocksize=None,**args):
"""
runs f_regression for each block separately (saves memory).
-------------------------
fun : method that returns statistics,pval
X : {array-like, sparse matrix} shape = (n_samples, n_features)
The set of regressors that will tested... | 286 | 315 | import numpy as np
import logging
import unittest
import os
import scipy.linalg as LA
import time
from sklearn.utils import safe_sqr, check_array
from scipy import stats
from pysnptools.snpreader import Bed,Pheno
from pysnptools.snpreader import SnpData,SnpReader
from pysnptools.kernelreader import KernelNp... |
f_regression_cov_alt | Implementation as derived in tex document
See pg 12 of following document for definition of F-statistic
http://www-stat.stanford.edu/~jtaylo/courses/stats191/notes/simple_diagnostics.pdf
Parameters
----------
X : {array-like, sparse matrix} shape = (n_samples, n_features)
The set of regressors that will tested s... | import numpy as np
import logging
import unittest
import os
import scipy.linalg as LA
import time
from sklearn.utils import safe_sqr, check_array
from scipy import stats
from pysnptools.snpreader import Bed,Pheno
from pysnptools.snpreader import SnpData,SnpReader
from pysnptools.kernelreader import KernelNpz
from pysn... | def f_regression_cov_alt(X, y, C):
"""
Implementation as derived in tex document
See pg 12 of following document for definition of F-statistic
http://www-stat.stanford.edu/~jtaylo/courses/stats191/notes/simple_diagnostics.pdf
Parameters
----------
X : {array-like, sparse matrix} shape = (... | 318 | 387 | import numpy as np
import logging
import unittest
import os
import scipy.linalg as LA
import time
from sklearn.utils import safe_sqr, check_array
from scipy import stats
from pysnptools.snpreader import Bed,Pheno
from pysnptools.snpreader import SnpData,SnpReader
from pysnptools.kernelreader import KernelNp... |
f_regression_cov | Univariate linear regression tests
Quick linear model for testing the effect of a single regressor,
sequentially for many regressors.
This is done in 3 steps:
1. the regressor of interest and the data are orthogonalized
wrt constant regressors
2. the cross correlation between data and regressors is computed
3. it is ... | import numpy as np
import logging
import unittest
import os
import scipy.linalg as LA
import time
from sklearn.utils import safe_sqr, check_array
from scipy import stats
from pysnptools.snpreader import Bed,Pheno
from pysnptools.snpreader import SnpData,SnpReader
from pysnptools.kernelreader import KernelNpz
from pysn... | def f_regression_cov(X, y, C):
"""Univariate linear regression tests
Quick linear model for testing the effect of a single regressor,
sequentially for many regressors.
This is done in 3 steps:
1. the regressor of interest and the data are orthogonalized
wrt constant regressors
2. the cross... | 390 | 442 | import numpy as np
import logging
import unittest
import os
import scipy.linalg as LA
import time
from sklearn.utils import safe_sqr, check_array
from scipy import stats
from pysnptools.snpreader import Bed,Pheno
from pysnptools.snpreader import SnpData,SnpReader
from pysnptools.kernelreader import KernelNp... |
description_of | Return a string describing the probable encoding of a file or
list of strings.
:param lines: The lines to get the encoding of.
:type lines: Iterable of bytes
:param name: Name of file or collection of lines
:type name: str | #!/usr/bin/env python
"""
Script which takes one or more file paths and reports on their detected
encodings
Example::
% chardetect somefile someotherfile
somefile: windows-1252 with confidence 0.5
someotherfile: ascii with confidence 1.0
If no paths are provided, it takes its input from stdin.
"""
# C... | def description_of(lines, name='stdin'):
"""
Return a string describing the probable encoding of a file or
list of strings.
:param lines: The lines to get the encoding of.
:type lines: Iterable of bytes
:param name: Name of file or collection of lines
:type name: str
"""
u = Univers... | 29 | 54 | #!/usr/bin/env python
"""
Script which takes one or more file paths and reports on their detected
encodings
Example::
% chardetect somefile someotherfile
somefile: windows-1252 with confidence 0.5
someotherfile: ascii with confidence 1.0
If no paths are provided, it takes its input from stdin.
"""
# C... |
main | Handles command line arguments and gets things started.
:param argv: List of arguments, as if specified on the command-line.
If None, ``sys.argv[1:]`` is used instead.
:type argv: list of str | #!/usr/bin/env python
"""
Script which takes one or more file paths and reports on their detected
encodings
Example::
% chardetect somefile someotherfile
somefile: windows-1252 with confidence 0.5
someotherfile: ascii with confidence 1.0
If no paths are provided, it takes its input from stdin.
"""
# C... | def main(argv=None):
"""
Handles command line arguments and gets things started.
:param argv: List of arguments, as if specified on the command-line.
If None, ``sys.argv[1:]`` is used instead.
:type argv: list of str
"""
# Get command line arguments
parser = argparse.Argume... | 57 | 84 | #!/usr/bin/env python
"""
Script which takes one or more file paths and reports on their detected
encodings
Example::
% chardetect somefile someotherfile
somefile: windows-1252 with confidence 0.5
someotherfile: ascii with confidence 1.0
If no paths are provided, it takes its input from stdin.
"""
# C... |
get | Get an existing ActionGroup 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: Options... | # coding=utf-8
# *** WARNING: this file was generated by the Pulumi SDK Generator. ***
# *** 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
from ... import _utilities, _tables
from... | @staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None) -> 'ActionGroup':
"""
Get an existing ActionGroup resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
... | 100 | 128 | # coding=utf-8
# *** WARNING: this file was generated by the Pulumi SDK Generator. ***
# *** 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
from ... import _utilities, _tables
from... |
_area_tables_binning_parallel | Construct area allocation and source-target correspondence tables using
a parallel spatial indexing approach
...
NOTE: currently, the largest df is chunked and the other one is shipped in
full to each core; within each process, the spatial index is built for the
largest set of geometries, and the other one used for `q... | """
Area Weighted Interpolation
"""
import numpy as np
import geopandas as gpd
from ._vectorized_raster_interpolation import _fast_append_profile_in_gdf
import warnings
from scipy.sparse import dok_matrix, diags, coo_matrix
import pandas as pd
import os
from tobler.util.util import _check_crs, _nan_check, _inf_check... | def _area_tables_binning_parallel(source_df, target_df, n_jobs=-1):
"""Construct area allocation and source-target correspondence tables using
a parallel spatial indexing approach
...
NOTE: currently, the largest df is chunked and the other one is shipped in
full to each core; within each process, ... | 60 | 137 | """
Area Weighted Interpolation
"""
import numpy as np
import geopandas as gpd
from ._vectorized_raster_interpolation import _fast_append_profile_in_gdf
import warnings
from scipy.sparse import dok_matrix, diags, coo_matrix
import pandas as pd
import os
from tobler.util.util import _check_crs, _nan_check, _inf_check... |
build_model | Build and return a Sequential model with Dense layers given by the layers argument.
Arguments
model (keras.Sequential) model to which layers will be added
input_dim (int) dimension of input
layers (tuple) sequence of 2-ples, one per layer, such as ((64, 'relu'), (64, 'relu'), (1, 'sigmoid'))
Ret... | """
Training and validation method for arbitrary models.
"""
import io
import os
import sys
import time
from keras import Sequential
from keras.layers import Dense, Dropout, BatchNormalization
from matplotlib.backends.backend_pdf import PdfPages
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
impo... | def build_model(layers, model=None, input_dim=None):
"""
Build and return a Sequential model with Dense layers given by the layers argument.
Arguments
model (keras.Sequential) model to which layers will be added
input_dim (int) dimension of input
layers (tuple) sequence of... | 123 | 155 | """
Training and validation method for arbitrary models.
"""
import io
import os
import sys
import time
from keras import Sequential
from keras.layers import Dense, Dropout, BatchNormalization
from matplotlib.backends.backend_pdf import PdfPages
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
impo... |
to_datetime | Based on what type when is, converts when to a Python DateTime object.
Type:
- int: returns now.plusMillis(when)
- openHAB number type: returns now.plusMillis(when.intValue())
- ISO8601 string: DateTime(when)
- Duration definition: see parse_duration_to_datetime
- java ZonedDateTime
For python m... | """
Copyright June 25, 2020 Richard Koshak
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing,... | def to_datetime(when, log=logging.getLogger("{}.time_utils".format(LOG_PREFIX)), output = 'Java'):
"""Based on what type when is, converts when to a Python DateTime object.
Type:
- int: returns now.plusMillis(when)
- openHAB number type: returns now.plusMillis(when.intValue())
- ISO8601 ... | 117 | 201 | """
Copyright June 25, 2020 Richard Koshak
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing,... |
draw_what_sheet | Draw a calendar page for a WHAT display.
Args:
image: The image to be drawn on to | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Display a calendar populated from google calendar data on an inky display."""
from PIL import Image, ImageDraw # type: ignore
# from typing import Tuple
# import time
# MASKED: draw_what_sheet function (lines 12-25)
if __name__ == "__main__":
palette = 3 * [... | def draw_what_sheet(image: Image.Image) -> None:
"""Draw a calendar page for a WHAT display.
Args:
image: The image to be drawn on to
"""
draw = ImageDraw.Draw(image)
# draw.rectangle([(7, 3), (392, 296)], outline=1)
draw.line([(7, 3), (392, 3)], fill=1)
for line in range(8):
... | 12 | 25 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Display a calendar populated from google calendar data on an inky display."""
from PIL import Image, ImageDraw # type: ignore
# from typing import Tuple
# import time
def draw_what_sheet(image: Image.Image) -> None:
"""Draw a calendar page for a WHAT display.
... |
get_shape | Return shape of the input tensor without batch size.
Parameters
----------
tensor : tf.Tensor
dynamic : bool
If True, returns tensor which represents shape. If False, returns list of ints and/or Nones.
Returns
-------
shape : tf.Tensor or list | """ Utility functions. """
import tensorflow as tf
# MASKED: get_shape function (lines 6-24)
def get_num_dims(tensor):
""" Return a number of semantic dimensions (i.e. excluding batch and channels axis)"""
shape = get_shape(tensor)
dim = len(shape)
return max(1, dim - 2)
def get_channels_axis(data... | def get_shape(tensor, dynamic=False):
""" Return shape of the input tensor without batch size.
Parameters
----------
tensor : tf.Tensor
dynamic : bool
If True, returns tensor which represents shape. If False, returns list of ints and/or Nones.
Returns
-------
shape : tf.Tensor... | 6 | 24 | """ Utility functions. """
import tensorflow as tf
def get_shape(tensor, dynamic=False):
""" Return shape of the input tensor without batch size.
Parameters
----------
tensor : tf.Tensor
dynamic : bool
If True, returns tensor which represents shape. If False, returns list of ints and/or... |
get_num_channels | Return number of channels in the input tensor.
Parameters
----------
tensor : tf.Tensor
Returns
-------
shape : tuple of ints | """ Utility functions. """
import tensorflow as tf
def get_shape(tensor, dynamic=False):
""" Return shape of the input tensor without batch size.
Parameters
----------
tensor : tf.Tensor
dynamic : bool
If True, returns tensor which represents shape. If False, returns list of ints and/or... | def get_num_channels(tensor, data_format='channels_last'):
""" Return number of channels in the input tensor.
Parameters
----------
tensor : tf.Tensor
Returns
-------
shape : tuple of ints
"""
shape = tensor.get_shape().as_list()
axis = get_channels_axis(data_format)
return... | 37 | 50 | """ Utility functions. """
import tensorflow as tf
def get_shape(tensor, dynamic=False):
""" Return shape of the input tensor without batch size.
Parameters
----------
tensor : tf.Tensor
dynamic : bool
If True, returns tensor which represents shape. If False, returns list of ints and/or... |
get_batch_size | Return batch size (the length of the first dimension) of the input tensor.
Parameters
----------
tensor : tf.Tensor
Returns
-------
batch size : int or None | """ Utility functions. """
import tensorflow as tf
def get_shape(tensor, dynamic=False):
""" Return shape of the input tensor without batch size.
Parameters
----------
tensor : tf.Tensor
dynamic : bool
If True, returns tensor which represents shape. If False, returns list of ints and/or... | def get_batch_size(tensor, dynamic=False):
""" Return batch size (the length of the first dimension) of the input tensor.
Parameters
----------
tensor : tf.Tensor
Returns
-------
batch size : int or None
"""
if dynamic:
return tf.shape(tensor)[0]
return tensor.get_shape... | 53 | 66 | """ Utility functions. """
import tensorflow as tf
def get_shape(tensor, dynamic=False):
""" Return shape of the input tensor without batch size.
Parameters
----------
tensor : tf.Tensor
dynamic : bool
If True, returns tensor which represents shape. If False, returns list of ints and/or... |
get_spatial_shape | Return the tensor spatial shape (without batch and channels dimensions).
Parameters
----------
tensor : tf.Tensor
dynamic : bool
If True, returns tensor which represents shape. If False, returns list of ints and/or Nones.
Returns
-------
shape : tf.Tensor or list | """ Utility functions. """
import tensorflow as tf
def get_shape(tensor, dynamic=False):
""" Return shape of the input tensor without batch size.
Parameters
----------
tensor : tf.Tensor
dynamic : bool
If True, returns tensor which represents shape. If False, returns list of ints and/or... | def get_spatial_shape(tensor, data_format='channels_last', dynamic=False):
""" Return the tensor spatial shape (without batch and channels dimensions).
Parameters
----------
tensor : tf.Tensor
dynamic : bool
If True, returns tensor which represents shape. If False, returns list of ints and... | 82 | 101 | """ Utility functions. """
import tensorflow as tf
def get_shape(tensor, dynamic=False):
""" Return shape of the input tensor without batch size.
Parameters
----------
tensor : tf.Tensor
dynamic : bool
If True, returns tensor which represents shape. If False, returns list of ints and/or... |
call_function | Call a function after properly setting up its arguments and return address.
Args:
addr : function address
args : a sequence of arguments to pass to the function; may be empty
ret : return address; may be None | #!/usr/bin/env python3
#
# Cross Platform and Multi Architecture Advanced Binary Emulation Framework
#
from typing import Sequence
from pefile import PE
from qiling.const import QL_ARCH
from qiling.exception import QlErrorArch, QlMemoryMappedError
from qiling.loader.loader import QlLoader
from qiling.os.memory import... | def call_function(self, addr: int, args: Sequence[int], ret: int):
"""Call a function after properly setting up its arguments and return address.
Args:
addr : function address
args : a sequence of arguments to pass to the function; may be empty
ret : return addr... | 141 | 162 | #!/usr/bin/env python3
#
# Cross Platform and Multi Architecture Advanced Binary Emulation Framework
#
from typing import Sequence
from pefile import PE
from qiling.const import QL_ARCH
from qiling.exception import QlErrorArch, QlMemoryMappedError
from qiling.loader.loader import QlLoader
from qiling.os.memory import... |
validate_required_primitive_elements_594 | https://www.hl7.org/fhir/extensibility.html#Special-Case
In some cases, implementers might find that they do not have appropriate data for
an element with minimum cardinality = 1. In this case, the element must be present,
but unless the resource or a profile on it has made the actual value of the primitive
data type m... | # -*- coding: utf-8 -*-
"""
Profile: http://hl7.org/fhir/StructureDefinition/Task
Release: R4
Version: 4.0.1
Build ID: 9346c8cc45
Last updated: 2019-11-01T09:29:23.356+11:00
"""
import typing
from pydantic import Field, root_validator
from pydantic.error_wrappers import ErrorWrapper, ValidationError
from pydantic.erro... | @root_validator(pre=True, allow_reuse=True)
def validate_required_primitive_elements_594(
cls, values: typing.Dict[str, typing.Any]
) -> typing.Dict[str, typing.Any]:
"""https://www.hl7.org/fhir/extensibility.html#Special-Case
In some cases, implementers might find that they do not h... | 452 | 509 | # -*- coding: utf-8 -*-
"""
Profile: http://hl7.org/fhir/StructureDefinition/Task
Release: R4
Version: 4.0.1
Build ID: 9346c8cc45
Last updated: 2019-11-01T09:29:23.356+11:00
"""
import typing
from pydantic import Field, root_validator
from pydantic.error_wrappers import ErrorWrapper, ValidationError
from pydantic.erro... |
run_decider_state | Runs the decider state of the barrier concurrency state. The decider state decides on which outcome the
barrier concurrency is left.
:param decider_state: the decider state of the barrier concurrency state
:param child_errors: error of the concurrent branches
:param final_outcomes_dict: dictionary of all outcomes of t... | # Copyright (C) 2014-2018 DLR
#
# All rights reserved. This program and the accompanying materials are made
# available under the terms of the Eclipse Public License v1.0 which
# accompanies this distribution, and is available at
# http://www.eclipse.org/legal/epl-v10.html
#
# Contributors:
# Annika Wollschlaeger <anni... | def run_decider_state(self, decider_state, child_errors, final_outcomes_dict):
""" Runs the decider state of the barrier concurrency state. The decider state decides on which outcome the
barrier concurrency is left.
:param decider_state: the decider state of the barrier concurrency state
... | 183 | 208 | # Copyright (C) 2014-2018 DLR
#
# All rights reserved. This program and the accompanying materials are made
# available under the terms of the Eclipse Public License v1.0 which
# accompanies this distribution, and is available at
# http://www.eclipse.org/legal/epl-v10.html
#
# Contributors:
# Annika Wollschlaeger <anni... |
states | Overwrite the setter of the container state base class as special handling for the decider state is needed.
:param states: the dictionary of new states
:raises exceptions.TypeError: if the states parameter is not of type dict | # Copyright (C) 2014-2018 DLR
#
# All rights reserved. This program and the accompanying materials are made
# available under the terms of the Eclipse Public License v1.0 which
# accompanies this distribution, and is available at
# http://www.eclipse.org/legal/epl-v10.html
#
# Contributors:
# Annika Wollschlaeger <anni... | @ContainerState.states.setter
@lock_state_machine
@Observable.observed
def states(self, states):
""" Overwrite the setter of the container state base class as special handling for the decider state is needed.
:param states: the dictionary of new states
:raises exceptions.TypeErr... | 260 | 285 | # Copyright (C) 2014-2018 DLR
#
# All rights reserved. This program and the accompanying materials are made
# available under the terms of the Eclipse Public License v1.0 which
# accompanies this distribution, and is available at
# http://www.eclipse.org/legal/epl-v10.html
#
# Contributors:
# Annika Wollschlaeger <anni... |
get_outcome_for_state_name | Returns the final outcome of the child state specified by name.
Note: This is utility function that is used by the programmer to make a decision based on the final outcome
of its child states. A state is not uniquely specified by the name, but as the programmer normally does not want
to use state-ids in his code this ... | # Copyright (C) 2014-2018 DLR
#
# All rights reserved. This program and the accompanying materials are made
# available under the terms of the Eclipse Public License v1.0 which
# accompanies this distribution, and is available at
# http://www.eclipse.org/legal/epl-v10.html
#
# Contributors:
# Annika Wollschlaeger <anni... | def get_outcome_for_state_name(self, name):
""" Returns the final outcome of the child state specified by name.
Note: This is utility function that is used by the programmer to make a decision based on the final outcome
of its child states. A state is not uniquely specified by the name, but... | 352 | 367 | # Copyright (C) 2014-2018 DLR
#
# All rights reserved. This program and the accompanying materials are made
# available under the terms of the Eclipse Public License v1.0 which
# accompanies this distribution, and is available at
# http://www.eclipse.org/legal/epl-v10.html
#
# Contributors:
# Annika Wollschlaeger <anni... |
get_errors_for_state_name | Returns the error message of the child state specified by name.
Note: This is utility function that is used by the programmer to make a decision based on the final outcome
of its child states. A state is not uniquely specified by the name, but as the programmer normally does not want
to use state-ids in his code this ... | # Copyright (C) 2014-2018 DLR
#
# All rights reserved. This program and the accompanying materials are made
# available under the terms of the Eclipse Public License v1.0 which
# accompanies this distribution, and is available at
# http://www.eclipse.org/legal/epl-v10.html
#
# Contributors:
# Annika Wollschlaeger <anni... | def get_errors_for_state_name(self, name):
""" Returns the error message of the child state specified by name.
Note: This is utility function that is used by the programmer to make a decision based on the final outcome
of its child states. A state is not uniquely specified by the name, but ... | 382 | 397 | # Copyright (C) 2014-2018 DLR
#
# All rights reserved. This program and the accompanying materials are made
# available under the terms of the Eclipse Public License v1.0 which
# accompanies this distribution, and is available at
# http://www.eclipse.org/legal/epl-v10.html
#
# Contributors:
# Annika Wollschlaeger <anni... |
tag_instances_on_cluster | Adds project tag to untagged instances in a given cluster.
Parameters
----------
cluster_name : str
The name of the AWS ECS cluster in which running instances
should be tagged.
project : str
The name of the project to tag instances with. | from __future__ import absolute_import, print_function, unicode_literals
import pickle
from builtins import dict, str
import os
import re
import boto3
import logging
import botocore.session
from time import sleep
import matplotlib as mpl
from numpy import median, arange, array
from indra.tools.reading.util.reporter ... | def tag_instances_on_cluster(cluster_name, project='cwc'):
"""Adds project tag to untagged instances in a given cluster.
Parameters
----------
cluster_name : str
The name of the AWS ECS cluster in which running instances
should be tagged.
project : str
The name of the projec... | 319 | 345 | from __future__ import absolute_import, print_function, unicode_literals
import pickle
from builtins import dict, str
import os
import re
import boto3
import logging
import botocore.session
from time import sleep
import matplotlib as mpl
from numpy import median, arange, array
from indra.tools.reading.util.reporter ... |
submit_reading | Submit an old-style pmid-centered no-database s3 only reading job.
This function is provided for the sake of backward compatibility. It is
preferred that you use the object-oriented PmidSubmitter and the
submit_reading job going forward. | from __future__ import absolute_import, print_function, unicode_literals
import pickle
from builtins import dict, str
import os
import re
import boto3
import logging
import botocore.session
from time import sleep
import matplotlib as mpl
from numpy import median, arange, array
from indra.tools.reading.util.reporter ... | def submit_reading(basename, pmid_list_filename, readers, start_ix=None,
end_ix=None, pmids_per_job=3000, num_tries=2,
force_read=False, force_fulltext=False, project_name=None):
"""Submit an old-style pmid-centered no-database s3 only reading job.
This function is provide... | 536 | 549 | from __future__ import absolute_import, print_function, unicode_literals
import pickle
from builtins import dict, str
import os
import re
import boto3
import logging
import botocore.session
from time import sleep
import matplotlib as mpl
from numpy import median, arange, array
from indra.tools.reading.util.reporter ... |
submit_combine | Submit a batch job to combine the outputs of a reading job.
This function is provided for backwards compatibility. You should use the
PmidSubmitter and submit_combine methods. | from __future__ import absolute_import, print_function, unicode_literals
import pickle
from builtins import dict, str
import os
import re
import boto3
import logging
import botocore.session
from time import sleep
import matplotlib as mpl
from numpy import median, arange, array
from indra.tools.reading.util.reporter ... | def submit_combine(basename, readers, job_ids=None, project_name=None):
"""Submit a batch job to combine the outputs of a reading job.
This function is provided for backwards compatibility. You should use the
PmidSubmitter and submit_combine methods.
"""
sub = PmidSubmitter(basename, readers, proje... | 552 | 561 | from __future__ import absolute_import, print_function, unicode_literals
import pickle
from builtins import dict, str
import os
import re
import boto3
import logging
import botocore.session
from time import sleep
import matplotlib as mpl
from numpy import median, arange, array
from indra.tools.reading.util.reporter ... |
get_ticket_multiline_description_sale | Compute a multiline description of this ticket, in the context of sales.
It will often be used as the default description of a sales order line referencing this ticket.
1. the first line is the ticket name
2. the second line is the event name (if it exists, which should be the case with a normal workflow) or the p... | # -*- coding: utf-8 -*-
# Part of Odoo. See LICENSE file for full copyright and licensing details.
from odoo import api, fields, models, _
from odoo.exceptions import ValidationError, UserError
from odoo.addons import decimal_precision as dp
from odoo.tools import float_is_zero
class EventType(models.Model):
_i... | def get_ticket_multiline_description_sale(self):
""" Compute a multiline description of this ticket, in the context of sales.
It will often be used as the default description of a sales order line referencing this ticket.
1. the first line is the ticket name
2. the second line i... | 163 | 181 | # -*- coding: utf-8 -*-
# Part of Odoo. See LICENSE file for full copyright and licensing details.
from odoo import api, fields, models, _
from odoo.exceptions import ValidationError, UserError
from odoo.addons import decimal_precision as dp
from odoo.tools import float_is_zero
class EventType(models.Model):
_i... |
add_pusher | Creates a new pusher and adds it to the pool
Returns:
The newly created pusher. | # Copyright 2015, 2016 OpenMarket Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | async def add_pusher(
self,
user_id: str,
access_token: Optional[int],
kind: str,
app_id: str,
app_display_name: str,
device_display_name: str,
pushkey: str,
lang: Optional[str],
data: JsonDict,
profile_tag: str = "",
) -> O... | 97 | 169 | # Copyright 2015, 2016 OpenMarket Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... |
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