xszheng2020/the_stack_dedup_python_hits_7 · Datasets at Hugging Face

77de46cc53dd3c999d04471ca5db870a003c1380

1,856

py

Python

test/pyaz/monitor/metrics/__init__.py

bigdatamoore/py-az-cli

54383a4ee7cc77556f6183e74e992eec95b28e01

[ "MIT" ]

null

test/pyaz/monitor/metrics/__init__.py

bigdatamoore/py-az-cli

54383a4ee7cc77556f6183e74e992eec95b28e01

[ "MIT" ]

9

2021-09-24T16:37:24.000Z

2021-12-24T00:39:19.000Z

test/pyaz/monitor/metrics/__init__.py

bigdatamoore/py-az-cli

54383a4ee7cc77556f6183e74e992eec95b28e01

[ "MIT" ]

null

import json, subprocess from ... pyaz_utils import get_cli_name, get_params def list(resource_namespace=None, resource_parent=None, resource_type=None, resource_group=None, resource, start_time=None, end_time=None, offset=None, interval=None, metadata=None, dimension=None, aggregation=None, metrics=None, filter=None, namespace=None, orderby=None, top=None): params = get_params(locals()) command = "az monitor metrics list " + params print(command) output = subprocess.run(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) stdout = output.stdout.decode("utf-8") stderr = output.stderr.decode("utf-8") if stdout: return json.loads(stdout) print(stdout) else: raise Exception(stderr) print(stderr) def list_definitions(resource_namespace=None, resource_parent=None, resource_type=None, resource_group=None, resource, namespace=None): params = get_params(locals()) command = "az monitor metrics list-definitions " + params print(command) output = subprocess.run(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) stdout = output.stdout.decode("utf-8") stderr = output.stderr.decode("utf-8") if stdout: return json.loads(stdout) print(stdout) else: raise Exception(stderr) print(stderr) def list_namespaces(resource_uri, start_time=None): params = get_params(locals()) command = "az monitor metrics list-namespaces " + params print(command) output = subprocess.run(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) stdout = output.stdout.decode("utf-8") stderr = output.stderr.decode("utf-8") if stdout: return json.loads(stdout) print(stdout) else: raise Exception(stderr) print(stderr)

40.347826

283

0.696659

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1,856

5.506494

0.233766

0.075472

0.04717

0.044811

0.787736

0

0.003992

0.190194

1,856

45

284

41.244444

0.842315

0

0.804878

0

0.067349

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null

0

0.04878

null

0.219512

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null

0

1

0

1

0

null

0

1

0

7

bb45c2e96b4ac9d457f9169b47bd7ad41205b790

212

py

Python

cortex/saver/image_saver.py

chib0/asd-winter2019

c7d95305b1e8b99013fd40da1e7ebe01c2d0102a

[ "Apache-2.0" ]

null

cortex/saver/image_saver.py

chib0/asd-winter2019

c7d95305b1e8b99013fd40da1e7ebe01c2d0102a

[ "Apache-2.0" ]

4

2021-02-02T22:38:53.000Z

2022-01-13T02:32:33.000Z

cortex/saver/image_saver.py

chib0/asd-winter2019

c7d95305b1e8b99013fd40da1e7ebe01c2d0102a

[ "Apache-2.0" ]

null

def save_color_image(db, data): db.update_snapshot(data['user'], data['timestamp'], data['result']) def save_depth_image(db, data): db.update_snapshot(data['user'], data['timestamp'], data['result'])

23.555556

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0.688679

30

212

4.666667

0.4

0.1

0.157143

0.185714

0.828571

0

0.113208

212

8

72

26.5

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0

0.5

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0.180952

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1

0.5

false

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null

0

1

0

1

0

null

0

1

0

9

248868b27836e431985923d632b95b655ae66824

2,382

py

Python

2019/python/aoccpu/opcodes.py

orhtej2/adventofcode

d273d28e394f58face0ae9873bfe43a667e427c4

[ "Unlicense" ]

null

2019/python/aoccpu/opcodes.py

orhtej2/adventofcode

d273d28e394f58face0ae9873bfe43a667e427c4

[ "Unlicense" ]

null

2019/python/aoccpu/opcodes.py

orhtej2/adventofcode

d273d28e394f58face0ae9873bfe43a667e427c4

[ "Unlicense" ]

null

class BreakoutException(Exception): pass class FunctionThrownException(Exception): def __init__(self, outer, step): self.outer = outer self.step = step def o_add(input_list, position, param1, param2, param3): param3(input_list, input_list[position+3], True, param1(input_list, input_list[position+1]) + param2(input_list, input_list[position+2])) return position + 4 def o_multiply(input_list, position, param1, param2, param3): param3(input_list, input_list[position+3], True, param1(input_list, input_list[position+1]) * param2(input_list, input_list[position+2])) return position + 4 def o_input(source, input_list, position, param1, *_): param1(input_list, input_list[position+1], True, source()) return position + 2 def o_output(sink, input_list, position, param1, *_): value = param1(input_list, input_list[position+1]) try: sink(value) return position + 2 except Exception as e: raise FunctionThrownException(e, position + 2) def o_jumpIfTrue(input_list, position, param1, param2, _): operand = param1(input_list, input_list[position+1]) if operand != 0: address = param2(input_list, input_list[position+2]) #print(f'(t{position})Jumping to {address} because of {operand}') return address #print(f'(t{position})Not jumping to because of {operand}') return position + 3 def o_jumpIfFalse(input_list, position, param1, param2, _): operand = param1(input_list, input_list[position+1]) if operand == 0: address = param2(input_list, input_list[position+2]) #print(f'(f{position})Jumping to {address} because of {operand}') return address #print(f'(f{position})Not jumping to because of {operand}') return position + 3 def o_lessThan(input_list, position, param1, param2, param3): x = param1(input_list, input_list[position+1]) y = param2(input_list, input_list[position+2]) param3(input_list, input_list[position+3], True, 1 if x < y else 0) return position + 4 def o_equal(input_list, position, param1, param2, param3): x = param1(input_list, input_list[position+1]) y = param2(input_list, input_list[position+2]) param3(input_list, input_list[position+3], True, 1 if x == y else 0) return position + 4 def o_done(input_list, position, *_): raise BreakoutException

39.04918

141

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333

2,382

4.798799

0.162162

0.253442

0.287234

0.202753

0.748436

0.707134

0

0.035494

0.183879

2,382

60

142

39.7

0.786523

0.102435

0

0.391304

0

1

0.217391

false

0.021739

0

0.478261

0

null

1

0

1

0

null

0

1

0

9

24afe3c62c29b12c5226936862c361986b33f4a4

77

py

Python

__init__.py

abhijit-buet/Learning-to-Detect-Pedestrian-Flow-inTraffic-Intersections-from-Synthetic-Data

c30334f29749dde01f998c7b648c9a8b3aa38650

[ "MIT" ]

1

2020-11-26T17:01:24.000Z

__init__.py

abhijit-buet/Learning-to-Detect-Pedestrian-Flow-inTraffic-Intersections-from-Synthetic-Data

c30334f29749dde01f998c7b648c9a8b3aa38650

[ "MIT" ]

null

__init__.py

abhijit-buet/Learning-to-Detect-Pedestrian-Flow-inTraffic-Intersections-from-Synthetic-Data

c30334f29749dde01f998c7b648c9a8b3aa38650

[ "MIT" ]

null

from .dataloader import customData #from .dataloader import customData_fiber

25.666667

41

0.857143

9

77

7.222222

0.555556

0.430769

0.615385

0.923077

0

0.103896

77

2

42

38.5

0.942029

0.519481

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

7

24b96115a998b559bd7747a253165dcc44204bf4

3,338

py

Python

Python Basics/Conditional Statements/More Exercises/Task09.py

DonikaChervenkova/SoftUni

bff579c037ec48f39ed193b34bc3502a32e90732

[ "MIT" ]

1

2022-03-16T10:23:04.000Z

Python Basics/Conditional Statements/More Exercises/Task09.py

IvanTodorovBG/SoftUni

7b667f6905d9f695ab1484efbb02b6715f6d569e

[ "MIT" ]

null

Python Basics/Conditional Statements/More Exercises/Task09.py

IvanTodorovBG/SoftUni

7b667f6905d9f695ab1484efbb02b6715f6d569e

[ "MIT" ]

1

2021-12-04T12:30:57.000Z

fuel_type = input() fuel_amount = float(input()) club_card = input() gasoline_price = 2.22 diesel_price = 2.33 gas_price = 0.93 if fuel_type == "Gas": if club_card == "Yes": if 20 <= fuel_amount <= 25: fuel_price = fuel_amount * (gas_price - 0.08) fuel_price = fuel_price - (fuel_price * 0.08) print(f"{fuel_price:.2f} lv.") elif fuel_amount < 20: fuel_price = fuel_amount * (gas_price - 0.08) print(f"{fuel_price:.2f} lv.") else: fuel_price = fuel_amount * (gas_price - 0.08) fuel_price = fuel_price - (fuel_price * 0.1) print(f"{fuel_price:.2f} lv.") elif club_card == "No": if 20 <= fuel_amount <= 25: fuel_price = fuel_amount * gas_price fuel_price = fuel_price - (fuel_price * 0.08) print(f"{fuel_price:.2f} lv.") elif fuel_amount < 20: fuel_price = fuel_amount * gas_price print(f"{fuel_price:.2f} lv.") else: fuel_price = fuel_amount * gas_price fuel_price = fuel_price - (fuel_price * 0.1) print(f"{fuel_price:.2f} lv.") elif fuel_type == "Gasoline": if club_card == "Yes": if 20 <= fuel_amount <= 25: fuel_price = fuel_amount * (gasoline_price - 0.18) fuel_price = fuel_price - (fuel_price * 0.08) print(f"{fuel_price:.2f} lv.") elif fuel_amount < 20: fuel_price = fuel_amount * (gasoline_price - 0.18) print(f"{fuel_price:.2f} lv.") else: fuel_price = fuel_amount * (gasoline_price - 0.18) fuel_price = fuel_price - (fuel_price * 0.1) print(f"{fuel_price:.2f} lv.") elif club_card == "No": if 20 <= fuel_amount <= 25: fuel_price = fuel_amount * gasoline_price fuel_price = fuel_price - (fuel_price * 0.08) print(f"{fuel_price:.2f} lv.") elif fuel_amount < 20: fuel_price = fuel_amount * gasoline_price print(f"{fuel_price:.2f} lv.") else: fuel_price = fuel_amount * gasoline_price fuel_price = fuel_price - (fuel_price * 0.1) print(f"{fuel_price:.2f} lv.") elif fuel_type == "Diesel": if club_card == "Yes": if 20 <= fuel_amount <= 25: fuel_price = fuel_amount * (diesel_price - 0.12) fuel_price = fuel_price - (fuel_price * 0.08) print(f"{fuel_price:.2f} lv.") elif fuel_amount < 20: fuel_price = fuel_amount * (diesel_price - 0.12) print(f"{fuel_price:.2f} lv.") else: fuel_price = fuel_amount * (diesel_price - 0.12) fuel_price = fuel_price - (fuel_price * 0.1) print(f"{fuel_price:.2f} lv.") elif club_card == "No": if 20 <= fuel_amount <= 25: fuel_price = fuel_amount * diesel_price fuel_price = fuel_price - (fuel_price * 0.08) print(f"{fuel_price:.2f} lv.") elif fuel_amount < 20: fuel_price = fuel_amount * diesel_price print(f"{fuel_price:.2f} lv.") else: fuel_price = fuel_amount * diesel_price fuel_price = fuel_price - (fuel_price * 0.1) print(f"{fuel_price:.2f} lv.")

39.270588

62

0.547334

448

3,338

3.776786

0.069196

0.382979

0.322695

0.255319

0.932624

0.929078

0.927305

0

0.0535

0.328041

3,338

84

63

39.738095

0.700847

0

0.888889

0

0.117471

0

1

0

false

0

0.222222

0

null

1

0

1

0

null

0

10

24f667fae77d29f5afd9e33142db60e9378ac6b2

119,503

py

Python

nova/tests/unit/virt/hyperv/test_vmops.py

bopopescu/nova-token

ec98f69dea7b3e2b9013b27fd55a2c1a1ac6bfb2

[ "Apache-2.0" ]

null

nova/tests/unit/virt/hyperv/test_vmops.py

bopopescu/nova-token

ec98f69dea7b3e2b9013b27fd55a2c1a1ac6bfb2

[ "Apache-2.0" ]

null

nova/tests/unit/virt/hyperv/test_vmops.py

bopopescu/nova-token

ec98f69dea7b3e2b9013b27fd55a2c1a1ac6bfb2

[ "Apache-2.0" ]

2

2017-07-20T17:31:34.000Z

2020-07-24T02:42:19.000Z

begin_unit comment|'# Copyright 2014 IBM Corp.' nl|'\n' comment|'#' nl|'\n' comment|'# Licensed under the Apache License, Version 2.0 (the "License"); you may' nl|'\n' comment|'# not use this file except in compliance with the License. You may obtain' nl|'\n' comment|'# a copy of the License at' nl|'\n' comment|'#' nl|'\n' comment|'# http://www.apache.org/licenses/LICENSE-2.0' nl|'\n' comment|'#' nl|'\n' comment|'# Unless required by applicable law or agreed to in writing, software' nl|'\n' comment|'# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT' nl|'\n' comment|'# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the' nl|'\n' comment|'# License for the specific language governing permissions and limitations' nl|'\n' comment|'# under the License.' nl|'\n' nl|'\n' name|'import' name|'os' newline|'\n' nl|'\n' name|'from' name|'eventlet' name|'import' name|'timeout' name|'as' name|'etimeout' newline|'\n' name|'import' name|'mock' newline|'\n' name|'from' name|'os_win' name|'import' name|'constants' name|'as' name|'os_win_const' newline|'\n' name|'from' name|'os_win' name|'import' name|'exceptions' name|'as' name|'os_win_exc' newline|'\n' name|'from' name|'oslo_concurrency' name|'import' name|'processutils' newline|'\n' name|'from' name|'oslo_config' name|'import' name|'cfg' newline|'\n' name|'from' name|'oslo_utils' name|'import' name|'units' newline|'\n' name|'import' name|'six' newline|'\n' name|'import' name|'testtools' newline|'\n' nl|'\n' name|'from' name|'nova' name|'import' name|'exception' newline|'\n' name|'from' name|'nova' name|'import' name|'objects' newline|'\n' name|'from' name|'nova' op|'.' name|'tests' op|'.' name|'unit' name|'import' name|'fake_instance' newline|'\n' name|'from' name|'nova' op|'.' name|'tests' op|'.' name|'unit' op|'.' name|'objects' name|'import' name|'test_virtual_interface' newline|'\n' name|'from' name|'nova' op|'.' name|'tests' op|'.' name|'unit' op|'.' name|'virt' op|'.' name|'hyperv' name|'import' name|'test_base' newline|'\n' name|'from' name|'nova' op|'.' name|'virt' name|'import' name|'hardware' newline|'\n' name|'from' name|'nova' op|'.' name|'virt' op|'.' name|'hyperv' name|'import' name|'constants' newline|'\n' name|'from' name|'nova' op|'.' name|'virt' op|'.' name|'hyperv' name|'import' name|'vmops' newline|'\n' nl|'\n' DECL|variable|CONF name|'CONF' op|'=' name|'cfg' op|'.' name|'CONF' newline|'\n' nl|'\n' nl|'\n' DECL|class|VMOpsTestCase name|'class' name|'VMOpsTestCase' op|'(' name|'test_base' op|'.' name|'HyperVBaseTestCase' op|')' op|':' newline|'\n' indent|' ' string|'"""Unit tests for the Hyper-V VMOps class."""' newline|'\n' nl|'\n' DECL|variable|_FAKE_TIMEOUT name|'_FAKE_TIMEOUT' op|'=' number|'2' newline|'\n' DECL|variable|FAKE_SIZE name|'FAKE_SIZE' op|'=' number|'10' newline|'\n' DECL|variable|FAKE_DIR name|'FAKE_DIR' op|'=' string|"'fake_dir'" newline|'\n' DECL|variable|FAKE_ROOT_PATH name|'FAKE_ROOT_PATH' op|'=' string|"'C:\\\\path\\\\to\\\\fake.%s'" newline|'\n' DECL|variable|FAKE_CONFIG_DRIVE_ISO name|'FAKE_CONFIG_DRIVE_ISO' op|'=' string|"'configdrive.iso'" newline|'\n' DECL|variable|FAKE_CONFIG_DRIVE_VHD name|'FAKE_CONFIG_DRIVE_VHD' op|'=' string|"'configdrive.vhd'" newline|'\n' DECL|variable|FAKE_UUID name|'FAKE_UUID' op|'=' string|"'4f54fb69-d3a2-45b7-bb9b-b6e6b3d893b3'" newline|'\n' DECL|variable|FAKE_LOG name|'FAKE_LOG' op|'=' string|"'fake_log'" newline|'\n' nl|'\n' DECL|variable|_WIN_VERSION_6_3 name|'_WIN_VERSION_6_3' op|'=' string|"'6.3.0'" newline|'\n' DECL|variable|_WIN_VERSION_10 name|'_WIN_VERSION_10' op|'=' string|"'10.0'" newline|'\n' nl|'\n' DECL|variable|ISO9660 name|'ISO9660' op|'=' string|"'iso9660'" newline|'\n' DECL|variable|_FAKE_CONFIGDRIVE_PATH name|'_FAKE_CONFIGDRIVE_PATH' op|'=' string|"'C:/fake_instance_dir/configdrive.vhd'" newline|'\n' nl|'\n' DECL|member|setUp name|'def' name|'setUp' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'super' op|'(' name|'VMOpsTestCase' op|',' name|'self' op|')' op|'.' name|'setUp' op|'(' op|')' newline|'\n' name|'self' op|'.' name|'context' op|'=' string|"'fake-context'" newline|'\n' nl|'\n' name|'self' op|'.' name|'_vmops' op|'=' name|'vmops' op|'.' name|'VMOps' op|'(' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|'=' name|'mock' op|'.' name|'MagicMock' op|'(' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_metricsutils' op|'=' name|'mock' op|'.' name|'MagicMock' op|'(' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vhdutils' op|'=' name|'mock' op|'.' name|'MagicMock' op|'(' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_pathutils' op|'=' name|'mock' op|'.' name|'MagicMock' op|'(' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_hostutils' op|'=' name|'mock' op|'.' name|'MagicMock' op|'(' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.network.is_neutron'" op|')' newline|'\n' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.virt.hyperv.vmops.importutils.import_object'" op|')' newline|'\n' DECL|member|test_load_vif_driver_neutron name|'def' name|'test_load_vif_driver_neutron' op|'(' name|'self' op|',' name|'mock_import_object' op|',' name|'is_neutron' op|')' op|':' newline|'\n' indent|' ' name|'is_neutron' op|'.' name|'return_value' op|'=' name|'True' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_load_vif_driver_class' op|'(' op|')' newline|'\n' name|'mock_import_object' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'vmops' op|'.' name|'NEUTRON_VIF_DRIVER' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.network.is_neutron'" op|')' newline|'\n' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.virt.hyperv.vmops.importutils.import_object'" op|')' newline|'\n' DECL|member|test_load_vif_driver_nova name|'def' name|'test_load_vif_driver_nova' op|'(' name|'self' op|',' name|'mock_import_object' op|',' name|'is_neutron' op|')' op|':' newline|'\n' indent|' ' name|'is_neutron' op|'.' name|'return_value' op|'=' name|'False' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_load_vif_driver_class' op|'(' op|')' newline|'\n' name|'mock_import_object' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'vmops' op|'.' name|'NOVA_VIF_DRIVER' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.network.is_neutron'" op|')' newline|'\n' DECL|member|test_load_vif_driver_unknown name|'def' name|'test_load_vif_driver_unknown' op|'(' name|'self' op|',' name|'is_neutron' op|')' op|':' newline|'\n' comment|'# TODO(sdague): delete once network_api_class is removed from' nl|'\n' comment|'# config.' nl|'\n' indent|' ' name|'is_neutron' op|'.' name|'return_value' op|'=' name|'None' newline|'\n' name|'self' op|'.' name|'assertRaises' op|'(' name|'TypeError' op|',' name|'self' op|'.' name|'_vmops' op|'.' name|'_load_vif_driver_class' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.virt.hyperv.vmops.importutils.import_object'" op|')' newline|'\n' DECL|member|test_load_vif_driver_class_error name|'def' name|'test_load_vif_driver_class_error' op|'(' name|'self' op|',' name|'mock_import_object' op|')' op|':' newline|'\n' indent|' ' name|'mock_import_object' op|'.' name|'side_effect' op|'=' name|'KeyError' newline|'\n' name|'self' op|'.' name|'assertRaises' op|'(' name|'TypeError' op|',' name|'self' op|'.' name|'_vmops' op|'.' name|'_load_vif_driver_class' op|')' newline|'\n' nl|'\n' DECL|member|test_list_instances dedent|'' name|'def' name|'test_list_instances' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'mock_instance' op|'=' name|'mock' op|'.' name|'MagicMock' op|'(' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|'.' name|'list_instances' op|'.' name|'return_value' op|'=' op|'[' name|'mock_instance' op|']' newline|'\n' name|'response' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'list_instances' op|'(' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|'.' name|'list_instances' op|'.' name|'assert_called_once_with' op|'(' op|')' newline|'\n' name|'self' op|'.' name|'assertEqual' op|'(' name|'response' op|',' op|'[' name|'mock_instance' op|']' op|')' newline|'\n' nl|'\n' DECL|member|_test_get_info dedent|'' name|'def' name|'_test_get_info' op|'(' name|'self' op|',' name|'vm_exists' op|')' op|':' newline|'\n' indent|' ' name|'mock_instance' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|')' newline|'\n' name|'mock_info' op|'=' name|'mock' op|'.' name|'MagicMock' op|'(' name|'spec_set' op|'=' name|'dict' op|')' newline|'\n' name|'fake_info' op|'=' op|'{' string|"'EnabledState'" op|':' number|'2' op|',' nl|'\n' string|"'MemoryUsage'" op|':' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_MEM_KB' op|',' nl|'\n' string|"'NumberOfProcessors'" op|':' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_NUM_CPU' op|',' nl|'\n' string|"'UpTime'" op|':' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_CPU_NS' op|'}' newline|'\n' nl|'\n' DECL|function|getitem name|'def' name|'getitem' op|'(' name|'key' op|')' op|':' newline|'\n' indent|' ' name|'return' name|'fake_info' op|'[' name|'key' op|']' newline|'\n' dedent|'' name|'mock_info' op|'.' name|'__getitem__' op|'.' name|'side_effect' op|'=' name|'getitem' newline|'\n' nl|'\n' name|'expected' op|'=' name|'hardware' op|'.' name|'InstanceInfo' op|'(' name|'state' op|'=' name|'constants' op|'.' name|'HYPERV_POWER_STATE' op|'[' number|'2' op|']' op|',' nl|'\n' name|'max_mem_kb' op|'=' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_MEM_KB' op|',' nl|'\n' name|'mem_kb' op|'=' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_MEM_KB' op|',' nl|'\n' name|'num_cpu' op|'=' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_NUM_CPU' op|',' nl|'\n' name|'cpu_time_ns' op|'=' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_CPU_NS' op|')' newline|'\n' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|'.' name|'vm_exists' op|'.' name|'return_value' op|'=' name|'vm_exists' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|'.' name|'get_vm_summary_info' op|'.' name|'return_value' op|'=' name|'mock_info' newline|'\n' nl|'\n' name|'if' name|'not' name|'vm_exists' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'assertRaises' op|'(' name|'exception' op|'.' name|'InstanceNotFound' op|',' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'get_info' op|',' name|'mock_instance' op|')' newline|'\n' dedent|'' name|'else' op|':' newline|'\n' indent|' ' name|'response' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'get_info' op|'(' name|'mock_instance' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|'.' name|'vm_exists' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'mock_instance' op|'.' name|'name' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|'.' name|'get_vm_summary_info' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'mock_instance' op|'.' name|'name' op|')' newline|'\n' name|'self' op|'.' name|'assertEqual' op|'(' name|'response' op|',' name|'expected' op|')' newline|'\n' nl|'\n' DECL|member|test_get_info dedent|'' dedent|'' name|'def' name|'test_get_info' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_test_get_info' op|'(' name|'vm_exists' op|'=' name|'True' op|')' newline|'\n' nl|'\n' DECL|member|test_get_info_exception dedent|'' name|'def' name|'test_get_info_exception' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_test_get_info' op|'(' name|'vm_exists' op|'=' name|'False' op|')' newline|'\n' nl|'\n' DECL|member|_prepare_create_root_vhd_mocks dedent|'' name|'def' name|'_prepare_create_root_vhd_mocks' op|'(' name|'self' op|',' name|'use_cow_images' op|',' name|'vhd_format' op|',' nl|'\n' name|'vhd_size' op|')' op|':' newline|'\n' indent|' ' name|'mock_instance' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|')' newline|'\n' name|'mock_instance' op|'.' name|'root_gb' op|'=' name|'self' op|'.' name|'FAKE_SIZE' newline|'\n' name|'self' op|'.' name|'flags' op|'(' name|'use_cow_images' op|'=' name|'use_cow_images' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vhdutils' op|'.' name|'get_vhd_info' op|'.' name|'return_value' op|'=' op|'{' string|"'VirtualSize'" op|':' nl|'\n' name|'vhd_size' op|'*' name|'units' op|'.' name|'Gi' op|'}' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vhdutils' op|'.' name|'get_vhd_format' op|'.' name|'return_value' op|'=' name|'vhd_format' newline|'\n' name|'root_vhd_internal_size' op|'=' name|'mock_instance' op|'.' name|'root_gb' op|'*' name|'units' op|'.' name|'Gi' newline|'\n' name|'get_size' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'_vhdutils' op|'.' name|'get_internal_vhd_size_by_file_size' newline|'\n' name|'get_size' op|'.' name|'return_value' op|'=' name|'root_vhd_internal_size' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_pathutils' op|'.' name|'exists' op|'.' name|'return_value' op|'=' name|'True' newline|'\n' nl|'\n' name|'return' name|'mock_instance' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.virt.hyperv.imagecache.ImageCache.get_cached_image'" op|')' newline|'\n' DECL|member|_test_create_root_vhd_exception name|'def' name|'_test_create_root_vhd_exception' op|'(' name|'self' op|',' name|'mock_get_cached_image' op|',' nl|'\n' name|'vhd_format' op|')' op|':' newline|'\n' indent|' ' name|'mock_instance' op|'=' name|'self' op|'.' name|'_prepare_create_root_vhd_mocks' op|'(' nl|'\n' name|'use_cow_images' op|'=' name|'False' op|',' name|'vhd_format' op|'=' name|'vhd_format' op|',' nl|'\n' name|'vhd_size' op|'=' op|'(' name|'self' op|'.' name|'FAKE_SIZE' op|'+' number|'1' op|')' op|')' newline|'\n' name|'fake_vhd_path' op|'=' name|'self' op|'.' name|'FAKE_ROOT_PATH' op|'%' name|'vhd_format' newline|'\n' name|'mock_get_cached_image' op|'.' name|'return_value' op|'=' name|'fake_vhd_path' newline|'\n' name|'fake_root_path' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'_pathutils' op|'.' name|'get_root_vhd_path' op|'.' name|'return_value' newline|'\n' nl|'\n' name|'self' op|'.' name|'assertRaises' op|'(' name|'exception' op|'.' name|'FlavorDiskSmallerThanImage' op|',' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_create_root_vhd' op|',' name|'self' op|'.' name|'context' op|',' nl|'\n' name|'mock_instance' op|')' newline|'\n' nl|'\n' name|'self' op|'.' name|'assertFalse' op|'(' name|'self' op|'.' name|'_vmops' op|'.' name|'_vhdutils' op|'.' name|'resize_vhd' op|'.' name|'called' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_pathutils' op|'.' name|'exists' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'fake_root_path' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_pathutils' op|'.' name|'remove' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'fake_root_path' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.virt.hyperv.imagecache.ImageCache.get_cached_image'" op|')' newline|'\n' DECL|member|_test_create_root_vhd_qcow name|'def' name|'_test_create_root_vhd_qcow' op|'(' name|'self' op|',' name|'mock_get_cached_image' op|',' name|'vhd_format' op|')' op|':' newline|'\n' indent|' ' name|'mock_instance' op|'=' name|'self' op|'.' name|'_prepare_create_root_vhd_mocks' op|'(' nl|'\n' name|'use_cow_images' op|'=' name|'True' op|',' name|'vhd_format' op|'=' name|'vhd_format' op|',' nl|'\n' name|'vhd_size' op|'=' op|'(' name|'self' op|'.' name|'FAKE_SIZE' op|'-' number|'1' op|')' op|')' newline|'\n' name|'fake_vhd_path' op|'=' name|'self' op|'.' name|'FAKE_ROOT_PATH' op|'%' name|'vhd_format' newline|'\n' name|'mock_get_cached_image' op|'.' name|'return_value' op|'=' name|'fake_vhd_path' newline|'\n' nl|'\n' name|'fake_root_path' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'_pathutils' op|'.' name|'get_root_vhd_path' op|'.' name|'return_value' newline|'\n' name|'root_vhd_internal_size' op|'=' name|'mock_instance' op|'.' name|'root_gb' op|'*' name|'units' op|'.' name|'Gi' newline|'\n' name|'get_size' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'_vhdutils' op|'.' name|'get_internal_vhd_size_by_file_size' newline|'\n' nl|'\n' name|'response' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'_create_root_vhd' op|'(' name|'context' op|'=' name|'self' op|'.' name|'context' op|',' nl|'\n' name|'instance' op|'=' name|'mock_instance' op|')' newline|'\n' nl|'\n' name|'self' op|'.' name|'assertEqual' op|'(' name|'fake_root_path' op|',' name|'response' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_pathutils' op|'.' name|'get_root_vhd_path' op|'.' name|'assert_called_with' op|'(' nl|'\n' name|'mock_instance' op|'.' name|'name' op|',' name|'vhd_format' op|')' newline|'\n' name|'differencing_vhd' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'_vhdutils' op|'.' name|'create_differencing_vhd' newline|'\n' name|'differencing_vhd' op|'.' name|'assert_called_with' op|'(' name|'fake_root_path' op|',' name|'fake_vhd_path' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vhdutils' op|'.' name|'get_vhd_info' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'fake_vhd_path' op|')' newline|'\n' nl|'\n' name|'if' name|'vhd_format' name|'is' name|'constants' op|'.' name|'DISK_FORMAT_VHD' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'assertFalse' op|'(' name|'get_size' op|'.' name|'called' op|')' newline|'\n' name|'self' op|'.' name|'assertFalse' op|'(' name|'self' op|'.' name|'_vmops' op|'.' name|'_vhdutils' op|'.' name|'resize_vhd' op|'.' name|'called' op|')' newline|'\n' dedent|'' name|'else' op|':' newline|'\n' indent|' ' name|'get_size' op|'.' name|'assert_called_once_with' op|'(' name|'fake_vhd_path' op|',' nl|'\n' name|'root_vhd_internal_size' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vhdutils' op|'.' name|'resize_vhd' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'fake_root_path' op|',' name|'root_vhd_internal_size' op|',' name|'is_file_max_size' op|'=' name|'False' op|')' newline|'\n' nl|'\n' dedent|'' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.virt.hyperv.imagecache.ImageCache.get_cached_image'" op|')' newline|'\n' DECL|member|_test_create_root_vhd name|'def' name|'_test_create_root_vhd' op|'(' name|'self' op|',' name|'mock_get_cached_image' op|',' name|'vhd_format' op|')' op|':' newline|'\n' indent|' ' name|'mock_instance' op|'=' name|'self' op|'.' name|'_prepare_create_root_vhd_mocks' op|'(' nl|'\n' name|'use_cow_images' op|'=' name|'False' op|',' name|'vhd_format' op|'=' name|'vhd_format' op|',' nl|'\n' name|'vhd_size' op|'=' op|'(' name|'self' op|'.' name|'FAKE_SIZE' op|'-' number|'1' op|')' op|')' newline|'\n' name|'fake_vhd_path' op|'=' name|'self' op|'.' name|'FAKE_ROOT_PATH' op|'%' name|'vhd_format' newline|'\n' name|'mock_get_cached_image' op|'.' name|'return_value' op|'=' name|'fake_vhd_path' newline|'\n' nl|'\n' name|'fake_root_path' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'_pathutils' op|'.' name|'get_root_vhd_path' op|'.' name|'return_value' newline|'\n' name|'root_vhd_internal_size' op|'=' name|'mock_instance' op|'.' name|'root_gb' op|'*' name|'units' op|'.' name|'Gi' newline|'\n' name|'get_size' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'_vhdutils' op|'.' name|'get_internal_vhd_size_by_file_size' newline|'\n' nl|'\n' name|'response' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'_create_root_vhd' op|'(' name|'context' op|'=' name|'self' op|'.' name|'context' op|',' nl|'\n' name|'instance' op|'=' name|'mock_instance' op|')' newline|'\n' nl|'\n' name|'self' op|'.' name|'assertEqual' op|'(' name|'fake_root_path' op|',' name|'response' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_pathutils' op|'.' name|'get_root_vhd_path' op|'.' name|'assert_called_with' op|'(' nl|'\n' name|'mock_instance' op|'.' name|'name' op|',' name|'vhd_format' op|')' newline|'\n' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_pathutils' op|'.' name|'copyfile' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'fake_vhd_path' op|',' name|'fake_root_path' op|')' newline|'\n' name|'get_size' op|'.' name|'assert_called_once_with' op|'(' name|'fake_vhd_path' op|',' name|'root_vhd_internal_size' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vhdutils' op|'.' name|'resize_vhd' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'fake_root_path' op|',' name|'root_vhd_internal_size' op|',' name|'is_file_max_size' op|'=' name|'False' op|')' newline|'\n' nl|'\n' DECL|member|test_create_root_vhd dedent|'' name|'def' name|'test_create_root_vhd' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_test_create_root_vhd' op|'(' name|'vhd_format' op|'=' name|'constants' op|'.' name|'DISK_FORMAT_VHD' op|')' newline|'\n' nl|'\n' DECL|member|test_create_root_vhdx dedent|'' name|'def' name|'test_create_root_vhdx' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_test_create_root_vhd' op|'(' name|'vhd_format' op|'=' name|'constants' op|'.' name|'DISK_FORMAT_VHDX' op|')' newline|'\n' nl|'\n' DECL|member|test_create_root_vhd_use_cow_images_true dedent|'' name|'def' name|'test_create_root_vhd_use_cow_images_true' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_test_create_root_vhd_qcow' op|'(' name|'vhd_format' op|'=' name|'constants' op|'.' name|'DISK_FORMAT_VHD' op|')' newline|'\n' nl|'\n' DECL|member|test_create_root_vhdx_use_cow_images_true dedent|'' name|'def' name|'test_create_root_vhdx_use_cow_images_true' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_test_create_root_vhd_qcow' op|'(' name|'vhd_format' op|'=' name|'constants' op|'.' name|'DISK_FORMAT_VHDX' op|')' newline|'\n' nl|'\n' DECL|member|test_create_root_vhdx_size_less_than_internal dedent|'' name|'def' name|'test_create_root_vhdx_size_less_than_internal' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_test_create_root_vhd_exception' op|'(' nl|'\n' name|'vhd_format' op|'=' name|'constants' op|'.' name|'DISK_FORMAT_VHD' op|')' newline|'\n' nl|'\n' DECL|member|test_is_resize_needed_exception dedent|'' name|'def' name|'test_is_resize_needed_exception' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'inst' op|'=' name|'mock' op|'.' name|'MagicMock' op|'(' op|')' newline|'\n' name|'self' op|'.' name|'assertRaises' op|'(' nl|'\n' name|'exception' op|'.' name|'FlavorDiskSmallerThanImage' op|',' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_is_resize_needed' op|',' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_PATH' op|',' name|'self' op|'.' name|'FAKE_SIZE' op|',' name|'self' op|'.' name|'FAKE_SIZE' op|'-' number|'1' op|',' name|'inst' op|')' newline|'\n' nl|'\n' DECL|member|test_is_resize_needed_true dedent|'' name|'def' name|'test_is_resize_needed_true' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'inst' op|'=' name|'mock' op|'.' name|'MagicMock' op|'(' op|')' newline|'\n' name|'self' op|'.' name|'assertTrue' op|'(' name|'self' op|'.' name|'_vmops' op|'.' name|'_is_resize_needed' op|'(' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_PATH' op|',' name|'self' op|'.' name|'FAKE_SIZE' op|',' name|'self' op|'.' name|'FAKE_SIZE' op|'+' number|'1' op|',' name|'inst' op|')' op|')' newline|'\n' nl|'\n' DECL|member|test_is_resize_needed_false dedent|'' name|'def' name|'test_is_resize_needed_false' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'inst' op|'=' name|'mock' op|'.' name|'MagicMock' op|'(' op|')' newline|'\n' name|'self' op|'.' name|'assertFalse' op|'(' name|'self' op|'.' name|'_vmops' op|'.' name|'_is_resize_needed' op|'(' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_PATH' op|',' name|'self' op|'.' name|'FAKE_SIZE' op|',' name|'self' op|'.' name|'FAKE_SIZE' op|',' name|'inst' op|')' op|')' newline|'\n' nl|'\n' DECL|member|test_create_ephemeral_vhd dedent|'' name|'def' name|'test_create_ephemeral_vhd' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'mock_instance' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|')' newline|'\n' name|'mock_instance' op|'.' name|'ephemeral_gb' op|'=' name|'self' op|'.' name|'FAKE_SIZE' newline|'\n' name|'best_supported' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'_vhdutils' op|'.' name|'get_best_supported_vhd_format' newline|'\n' name|'best_supported' op|'.' name|'return_value' op|'=' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_FORMAT' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_pathutils' op|'.' name|'get_ephemeral_vhd_path' op|'.' name|'return_value' op|'=' op|'(' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_PATH' op|')' newline|'\n' nl|'\n' name|'response' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'create_ephemeral_vhd' op|'(' name|'instance' op|'=' name|'mock_instance' op|')' newline|'\n' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_pathutils' op|'.' name|'get_ephemeral_vhd_path' op|'.' name|'assert_called_with' op|'(' nl|'\n' name|'mock_instance' op|'.' name|'name' op|',' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_FORMAT' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vhdutils' op|'.' name|'create_dynamic_vhd' op|'.' name|'assert_called_with' op|'(' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_PATH' op|',' name|'mock_instance' op|'.' name|'ephemeral_gb' op|'*' name|'units' op|'.' name|'Gi' op|')' newline|'\n' name|'self' op|'.' name|'assertEqual' op|'(' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_PATH' op|',' name|'response' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.virt.hyperv.vmops.VMOps.destroy'" op|')' newline|'\n' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.virt.hyperv.vmops.VMOps.power_on'" op|')' newline|'\n' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.virt.hyperv.vmops.VMOps.attach_config_drive'" op|')' newline|'\n' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.virt.hyperv.vmops.VMOps._create_config_drive'" op|')' newline|'\n' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.virt.configdrive.required_by'" op|')' newline|'\n' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.virt.hyperv.vmops.VMOps.create_instance'" op|')' newline|'\n' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.virt.hyperv.vmops.VMOps.get_image_vm_generation'" op|')' newline|'\n' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.virt.hyperv.vmops.VMOps.create_ephemeral_vhd'" op|')' newline|'\n' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.virt.hyperv.vmops.VMOps._create_root_vhd'" op|')' newline|'\n' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.virt.hyperv.volumeops.VolumeOps.'" nl|'\n' string|"'ebs_root_in_block_devices'" op|')' newline|'\n' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.virt.hyperv.vmops.VMOps._delete_disk_files'" op|')' newline|'\n' DECL|member|_test_spawn name|'def' name|'_test_spawn' op|'(' name|'self' op|',' name|'mock_delete_disk_files' op|',' nl|'\n' name|'mock_ebs_root_in_block_devices' op|',' name|'mock_create_root_vhd' op|',' nl|'\n' name|'mock_create_ephemeral_vhd' op|',' name|'mock_get_image_vm_gen' op|',' nl|'\n' name|'mock_create_instance' op|',' name|'mock_configdrive_required' op|',' nl|'\n' name|'mock_create_config_drive' op|',' name|'mock_attach_config_drive' op|',' nl|'\n' name|'mock_power_on' op|',' name|'mock_destroy' op|',' name|'exists' op|',' name|'boot_from_volume' op|',' nl|'\n' name|'configdrive_required' op|',' name|'fail' op|')' op|':' newline|'\n' indent|' ' name|'mock_instance' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|')' newline|'\n' name|'mock_image_meta' op|'=' name|'mock' op|'.' name|'MagicMock' op|'(' op|')' newline|'\n' nl|'\n' name|'fake_root_path' op|'=' name|'mock_create_root_vhd' op|'.' name|'return_value' newline|'\n' name|'fake_root_path' op|'=' name|'None' name|'if' name|'boot_from_volume' name|'else' name|'fake_root_path' newline|'\n' name|'fake_ephemeral_path' op|'=' name|'mock_create_ephemeral_vhd' op|'.' name|'return_value' newline|'\n' name|'fake_vm_gen' op|'=' name|'mock_get_image_vm_gen' op|'.' name|'return_value' newline|'\n' name|'fake_config_drive_path' op|'=' name|'mock_create_config_drive' op|'.' name|'return_value' newline|'\n' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|'.' name|'vm_exists' op|'.' name|'return_value' op|'=' name|'exists' newline|'\n' name|'mock_ebs_root_in_block_devices' op|'.' name|'return_value' op|'=' name|'boot_from_volume' newline|'\n' name|'mock_create_root_vhd' op|'.' name|'return_value' op|'=' name|'fake_root_path' newline|'\n' name|'mock_configdrive_required' op|'.' name|'return_value' op|'=' name|'configdrive_required' newline|'\n' name|'mock_create_instance' op|'.' name|'side_effect' op|'=' name|'fail' newline|'\n' name|'if' name|'exists' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'assertRaises' op|'(' name|'exception' op|'.' name|'InstanceExists' op|',' name|'self' op|'.' name|'_vmops' op|'.' name|'spawn' op|',' nl|'\n' name|'self' op|'.' name|'context' op|',' name|'mock_instance' op|',' name|'mock_image_meta' op|',' nl|'\n' op|'[' name|'mock' op|'.' name|'sentinel' op|'.' name|'FILE' op|']' op|',' name|'mock' op|'.' name|'sentinel' op|'.' name|'PASSWORD' op|',' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'INFO' op|',' name|'mock' op|'.' name|'sentinel' op|'.' name|'DEV_INFO' op|')' newline|'\n' dedent|'' name|'elif' name|'fail' name|'is' name|'os_win_exc' op|'.' name|'HyperVException' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'assertRaises' op|'(' name|'os_win_exc' op|'.' name|'HyperVException' op|',' name|'self' op|'.' name|'_vmops' op|'.' name|'spawn' op|',' nl|'\n' name|'self' op|'.' name|'context' op|',' name|'mock_instance' op|',' name|'mock_image_meta' op|',' nl|'\n' op|'[' name|'mock' op|'.' name|'sentinel' op|'.' name|'FILE' op|']' op|',' name|'mock' op|'.' name|'sentinel' op|'.' name|'PASSWORD' op|',' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'INFO' op|',' name|'mock' op|'.' name|'sentinel' op|'.' name|'DEV_INFO' op|')' newline|'\n' name|'mock_destroy' op|'.' name|'assert_called_once_with' op|'(' name|'mock_instance' op|')' newline|'\n' dedent|'' name|'else' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_vmops' op|'.' name|'spawn' op|'(' name|'self' op|'.' name|'context' op|',' name|'mock_instance' op|',' name|'mock_image_meta' op|',' nl|'\n' op|'[' name|'mock' op|'.' name|'sentinel' op|'.' name|'FILE' op|']' op|',' name|'mock' op|'.' name|'sentinel' op|'.' name|'PASSWORD' op|',' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'INFO' op|',' name|'mock' op|'.' name|'sentinel' op|'.' name|'DEV_INFO' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|'.' name|'vm_exists' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'mock_instance' op|'.' name|'name' op|')' newline|'\n' name|'mock_delete_disk_files' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'mock_instance' op|'.' name|'name' op|')' newline|'\n' name|'mock_ebs_root_in_block_devices' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'DEV_INFO' op|')' newline|'\n' name|'if' name|'not' name|'boot_from_volume' op|':' newline|'\n' indent|' ' name|'mock_create_root_vhd' op|'.' name|'assert_called_once_with' op|'(' name|'self' op|'.' name|'context' op|',' nl|'\n' name|'mock_instance' op|')' newline|'\n' dedent|'' name|'mock_create_ephemeral_vhd' op|'.' name|'assert_called_once_with' op|'(' name|'mock_instance' op|')' newline|'\n' name|'mock_get_image_vm_gen' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'mock_instance' op|'.' name|'uuid' op|',' name|'fake_root_path' op|',' name|'mock_image_meta' op|')' newline|'\n' name|'mock_create_instance' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'mock_instance' op|',' name|'mock' op|'.' name|'sentinel' op|'.' name|'INFO' op|',' name|'mock' op|'.' name|'sentinel' op|'.' name|'DEV_INFO' op|',' nl|'\n' name|'fake_root_path' op|',' name|'fake_ephemeral_path' op|',' name|'fake_vm_gen' op|')' newline|'\n' name|'mock_configdrive_required' op|'.' name|'assert_called_once_with' op|'(' name|'mock_instance' op|')' newline|'\n' name|'if' name|'configdrive_required' op|':' newline|'\n' indent|' ' name|'mock_create_config_drive' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'mock_instance' op|',' op|'[' name|'mock' op|'.' name|'sentinel' op|'.' name|'FILE' op|']' op|',' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'PASSWORD' op|',' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'INFO' op|')' newline|'\n' name|'mock_attach_config_drive' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'mock_instance' op|',' name|'fake_config_drive_path' op|',' name|'fake_vm_gen' op|')' newline|'\n' dedent|'' name|'mock_power_on' op|'.' name|'assert_called_once_with' op|'(' name|'mock_instance' op|')' newline|'\n' nl|'\n' DECL|member|test_spawn dedent|'' dedent|'' name|'def' name|'test_spawn' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_test_spawn' op|'(' name|'exists' op|'=' name|'False' op|',' name|'boot_from_volume' op|'=' name|'False' op|',' nl|'\n' name|'configdrive_required' op|'=' name|'True' op|',' name|'fail' op|'=' name|'None' op|')' newline|'\n' nl|'\n' DECL|member|test_spawn_instance_exists dedent|'' name|'def' name|'test_spawn_instance_exists' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_test_spawn' op|'(' name|'exists' op|'=' name|'True' op|',' name|'boot_from_volume' op|'=' name|'False' op|',' nl|'\n' name|'configdrive_required' op|'=' name|'True' op|',' name|'fail' op|'=' name|'None' op|')' newline|'\n' nl|'\n' DECL|member|test_spawn_create_instance_exception dedent|'' name|'def' name|'test_spawn_create_instance_exception' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_test_spawn' op|'(' name|'exists' op|'=' name|'False' op|',' name|'boot_from_volume' op|'=' name|'False' op|',' nl|'\n' name|'configdrive_required' op|'=' name|'True' op|',' nl|'\n' name|'fail' op|'=' name|'os_win_exc' op|'.' name|'HyperVException' op|')' newline|'\n' nl|'\n' DECL|member|test_spawn_not_required dedent|'' name|'def' name|'test_spawn_not_required' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_test_spawn' op|'(' name|'exists' op|'=' name|'False' op|',' name|'boot_from_volume' op|'=' name|'False' op|',' nl|'\n' name|'configdrive_required' op|'=' name|'False' op|',' name|'fail' op|'=' name|'None' op|')' newline|'\n' nl|'\n' DECL|member|test_spawn_root_in_block dedent|'' name|'def' name|'test_spawn_root_in_block' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_test_spawn' op|'(' name|'exists' op|'=' name|'False' op|',' name|'boot_from_volume' op|'=' name|'True' op|',' nl|'\n' name|'configdrive_required' op|'=' name|'False' op|',' name|'fail' op|'=' name|'None' op|')' newline|'\n' nl|'\n' DECL|member|test_spawn_no_admin_permissions dedent|'' name|'def' name|'test_spawn_no_admin_permissions' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|'.' name|'check_admin_permissions' op|'.' name|'side_effect' op|'=' op|'(' nl|'\n' name|'os_win_exc' op|'.' name|'HyperVException' op|')' newline|'\n' name|'self' op|'.' name|'assertRaises' op|'(' name|'os_win_exc' op|'.' name|'HyperVException' op|',' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'spawn' op|',' nl|'\n' name|'self' op|'.' name|'context' op|',' name|'mock' op|'.' name|'DEFAULT' op|',' name|'mock' op|'.' name|'DEFAULT' op|',' nl|'\n' op|'[' name|'mock' op|'.' name|'sentinel' op|'.' name|'FILE' op|']' op|',' name|'mock' op|'.' name|'sentinel' op|'.' name|'PASSWORD' op|',' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'INFO' op|',' name|'mock' op|'.' name|'sentinel' op|'.' name|'DEV_INFO' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.virt.hyperv.volumeops.VolumeOps'" nl|'\n' string|"'.attach_volumes'" op|')' newline|'\n' op|'@' name|'mock' op|'.' name|'patch' op|'.' name|'object' op|'(' name|'vmops' op|'.' name|'VMOps' op|',' string|"'_attach_drive'" op|')' newline|'\n' DECL|member|_test_create_instance name|'def' name|'_test_create_instance' op|'(' name|'self' op|',' name|'mock_attach_drive' op|',' name|'mock_attach_volumes' op|',' nl|'\n' name|'fake_root_path' op|',' name|'fake_ephemeral_path' op|',' nl|'\n' name|'enable_instance_metrics' op|',' nl|'\n' name|'vm_gen' op|'=' name|'constants' op|'.' name|'VM_GEN_1' op|')' op|':' newline|'\n' indent|' ' name|'mock_vif_driver' op|'=' name|'mock' op|'.' name|'MagicMock' op|'(' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vif_driver' op|'=' name|'mock_vif_driver' newline|'\n' name|'self' op|'.' name|'flags' op|'(' name|'enable_instance_metrics_collection' op|'=' name|'enable_instance_metrics' op|',' nl|'\n' name|'group' op|'=' string|"'hyperv'" op|')' newline|'\n' name|'fake_network_info' op|'=' op|'{' string|"'id'" op|':' name|'mock' op|'.' name|'sentinel' op|'.' name|'ID' op|',' nl|'\n' string|"'address'" op|':' name|'mock' op|'.' name|'sentinel' op|'.' name|'ADDRESS' op|'}' newline|'\n' name|'mock_instance' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|')' newline|'\n' name|'instance_path' op|'=' name|'os' op|'.' name|'path' op|'.' name|'join' op|'(' name|'CONF' op|'.' name|'instances_path' op|',' name|'mock_instance' op|'.' name|'name' op|')' newline|'\n' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'create_instance' op|'(' name|'instance' op|'=' name|'mock_instance' op|',' nl|'\n' name|'network_info' op|'=' op|'[' name|'fake_network_info' op|']' op|',' nl|'\n' name|'block_device_info' op|'=' name|'mock' op|'.' name|'sentinel' op|'.' name|'DEV_INFO' op|',' nl|'\n' name|'root_vhd_path' op|'=' name|'fake_root_path' op|',' nl|'\n' name|'eph_vhd_path' op|'=' name|'fake_ephemeral_path' op|',' nl|'\n' name|'vm_gen' op|'=' name|'vm_gen' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|'.' name|'create_vm' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'mock_instance' op|'.' name|'name' op|',' name|'mock_instance' op|'.' name|'memory_mb' op|',' nl|'\n' name|'mock_instance' op|'.' name|'vcpus' op|',' name|'CONF' op|'.' name|'hyperv' op|'.' name|'limit_cpu_features' op|',' nl|'\n' name|'CONF' op|'.' name|'hyperv' op|'.' name|'dynamic_memory_ratio' op|',' name|'vm_gen' op|',' name|'instance_path' op|',' nl|'\n' op|'[' name|'mock_instance' op|'.' name|'uuid' op|']' op|')' newline|'\n' name|'expected' op|'=' op|'[' op|']' newline|'\n' name|'ctrl_type' op|'=' name|'vmops' op|'.' name|'VM_GENERATIONS_CONTROLLER_TYPES' op|'[' name|'vm_gen' op|']' newline|'\n' name|'ctrl_disk_addr' op|'=' number|'0' newline|'\n' name|'if' name|'fake_root_path' op|':' newline|'\n' indent|' ' name|'expected' op|'.' name|'append' op|'(' name|'mock' op|'.' name|'call' op|'(' name|'mock_instance' op|'.' name|'name' op|',' name|'fake_root_path' op|',' nl|'\n' number|'0' op|',' name|'ctrl_disk_addr' op|',' name|'ctrl_type' op|',' nl|'\n' name|'constants' op|'.' name|'DISK' op|')' op|')' newline|'\n' dedent|'' name|'ctrl_disk_addr' op|'=' number|'1' newline|'\n' name|'if' name|'fake_ephemeral_path' op|':' newline|'\n' indent|' ' name|'expected' op|'.' name|'append' op|'(' name|'mock' op|'.' name|'call' op|'(' name|'mock_instance' op|'.' name|'name' op|',' nl|'\n' name|'fake_ephemeral_path' op|',' number|'0' op|',' name|'ctrl_disk_addr' op|',' nl|'\n' name|'ctrl_type' op|',' name|'constants' op|'.' name|'DISK' op|')' op|')' newline|'\n' dedent|'' name|'mock_attach_drive' op|'.' name|'has_calls' op|'(' name|'expected' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|'.' name|'create_scsi_controller' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'mock_instance' op|'.' name|'name' op|')' newline|'\n' nl|'\n' name|'ebs_root' op|'=' name|'vm_gen' name|'is' name|'not' name|'constants' op|'.' name|'VM_GEN_2' name|'and' name|'fake_root_path' name|'is' name|'None' newline|'\n' name|'mock_attach_volumes' op|'.' name|'assert_called_once_with' op|'(' name|'mock' op|'.' name|'sentinel' op|'.' name|'DEV_INFO' op|',' nl|'\n' name|'mock_instance' op|'.' name|'name' op|',' nl|'\n' name|'ebs_root' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|'.' name|'create_nic' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'mock_instance' op|'.' name|'name' op|',' name|'mock' op|'.' name|'sentinel' op|'.' name|'ID' op|',' name|'mock' op|'.' name|'sentinel' op|'.' name|'ADDRESS' op|')' newline|'\n' name|'mock_vif_driver' op|'.' name|'plug' op|'.' name|'assert_called_once_with' op|'(' name|'mock_instance' op|',' nl|'\n' name|'fake_network_info' op|')' newline|'\n' name|'mock_enable' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'_metricsutils' op|'.' name|'enable_vm_metrics_collection' newline|'\n' name|'if' name|'enable_instance_metrics' op|':' newline|'\n' indent|' ' name|'mock_enable' op|'.' name|'assert_called_once_with' op|'(' name|'mock_instance' op|'.' name|'name' op|')' newline|'\n' nl|'\n' DECL|member|test_create_instance dedent|'' dedent|'' name|'def' name|'test_create_instance' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'fake_ephemeral_path' op|'=' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_EPHEMERAL_PATH' newline|'\n' name|'self' op|'.' name|'_test_create_instance' op|'(' name|'fake_root_path' op|'=' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_ROOT_PATH' op|',' nl|'\n' name|'fake_ephemeral_path' op|'=' name|'fake_ephemeral_path' op|',' nl|'\n' name|'enable_instance_metrics' op|'=' name|'True' op|')' newline|'\n' nl|'\n' DECL|member|test_create_instance_no_root_path dedent|'' name|'def' name|'test_create_instance_no_root_path' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'fake_ephemeral_path' op|'=' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_EPHEMERAL_PATH' newline|'\n' name|'self' op|'.' name|'_test_create_instance' op|'(' name|'fake_root_path' op|'=' name|'None' op|',' nl|'\n' name|'fake_ephemeral_path' op|'=' name|'fake_ephemeral_path' op|',' nl|'\n' name|'enable_instance_metrics' op|'=' name|'True' op|')' newline|'\n' nl|'\n' DECL|member|test_create_instance_no_ephemeral_path dedent|'' name|'def' name|'test_create_instance_no_ephemeral_path' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_test_create_instance' op|'(' name|'fake_root_path' op|'=' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_ROOT_PATH' op|',' nl|'\n' name|'fake_ephemeral_path' op|'=' name|'None' op|',' nl|'\n' name|'enable_instance_metrics' op|'=' name|'True' op|')' newline|'\n' nl|'\n' DECL|member|test_create_instance_no_path dedent|'' name|'def' name|'test_create_instance_no_path' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_test_create_instance' op|'(' name|'fake_root_path' op|'=' name|'None' op|',' nl|'\n' name|'fake_ephemeral_path' op|'=' name|'None' op|',' nl|'\n' name|'enable_instance_metrics' op|'=' name|'False' op|')' newline|'\n' nl|'\n' DECL|member|test_create_instance_enable_instance_metrics_false dedent|'' name|'def' name|'test_create_instance_enable_instance_metrics_false' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'fake_ephemeral_path' op|'=' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_EPHEMERAL_PATH' newline|'\n' name|'self' op|'.' name|'_test_create_instance' op|'(' name|'fake_root_path' op|'=' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_ROOT_PATH' op|',' nl|'\n' name|'fake_ephemeral_path' op|'=' name|'fake_ephemeral_path' op|',' nl|'\n' name|'enable_instance_metrics' op|'=' name|'False' op|')' newline|'\n' nl|'\n' DECL|member|test_create_instance_gen2 dedent|'' name|'def' name|'test_create_instance_gen2' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_test_create_instance' op|'(' name|'fake_root_path' op|'=' name|'None' op|',' nl|'\n' name|'fake_ephemeral_path' op|'=' name|'None' op|',' nl|'\n' name|'enable_instance_metrics' op|'=' name|'False' op|',' nl|'\n' name|'vm_gen' op|'=' name|'constants' op|'.' name|'VM_GEN_2' op|')' newline|'\n' nl|'\n' DECL|member|test_attach_drive_vm_to_scsi dedent|'' name|'def' name|'test_attach_drive_vm_to_scsi' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_vmops' op|'.' name|'_attach_drive' op|'(' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_VM_NAME' op|',' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_PATH' op|',' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_DRIVE_ADDR' op|',' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_CTRL_DISK_ADDR' op|',' nl|'\n' name|'constants' op|'.' name|'CTRL_TYPE_SCSI' op|')' newline|'\n' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|'.' name|'attach_scsi_drive' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_VM_NAME' op|',' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_PATH' op|',' nl|'\n' name|'constants' op|'.' name|'DISK' op|')' newline|'\n' nl|'\n' DECL|member|test_attach_drive_vm_to_ide dedent|'' name|'def' name|'test_attach_drive_vm_to_ide' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_vmops' op|'.' name|'_attach_drive' op|'(' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_VM_NAME' op|',' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_PATH' op|',' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_DRIVE_ADDR' op|',' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_CTRL_DISK_ADDR' op|',' nl|'\n' name|'constants' op|'.' name|'CTRL_TYPE_IDE' op|')' newline|'\n' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|'.' name|'attach_ide_drive' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_VM_NAME' op|',' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_PATH' op|',' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_DRIVE_ADDR' op|',' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_CTRL_DISK_ADDR' op|',' nl|'\n' name|'constants' op|'.' name|'DISK' op|')' newline|'\n' nl|'\n' DECL|member|test_get_image_vm_generation_default dedent|'' name|'def' name|'test_get_image_vm_generation_default' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'image_meta' op|'=' name|'objects' op|'.' name|'ImageMeta' op|'.' name|'from_dict' op|'(' op|'{' string|'"properties"' op|':' op|'{' op|'}' op|'}' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_hostutils' op|'.' name|'get_default_vm_generation' op|'.' name|'return_value' op|'=' op|'(' nl|'\n' name|'constants' op|'.' name|'IMAGE_PROP_VM_GEN_1' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_hostutils' op|'.' name|'get_supported_vm_types' op|'.' name|'return_value' op|'=' op|'[' nl|'\n' name|'constants' op|'.' name|'IMAGE_PROP_VM_GEN_1' op|',' name|'constants' op|'.' name|'IMAGE_PROP_VM_GEN_2' op|']' newline|'\n' nl|'\n' name|'response' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'get_image_vm_generation' op|'(' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'instance_id' op|',' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_PATH' op|',' name|'image_meta' op|')' newline|'\n' nl|'\n' name|'self' op|'.' name|'assertEqual' op|'(' name|'constants' op|'.' name|'VM_GEN_1' op|',' name|'response' op|')' newline|'\n' nl|'\n' DECL|member|test_get_image_vm_generation_gen2 dedent|'' name|'def' name|'test_get_image_vm_generation_gen2' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'image_meta' op|'=' name|'objects' op|'.' name|'ImageMeta' op|'.' name|'from_dict' op|'(' nl|'\n' op|'{' string|'"properties"' op|':' nl|'\n' op|'{' string|'"hw_machine_type"' op|':' name|'constants' op|'.' name|'IMAGE_PROP_VM_GEN_2' op|'}' op|'}' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_hostutils' op|'.' name|'get_supported_vm_types' op|'.' name|'return_value' op|'=' op|'[' nl|'\n' name|'constants' op|'.' name|'IMAGE_PROP_VM_GEN_1' op|',' name|'constants' op|'.' name|'IMAGE_PROP_VM_GEN_2' op|']' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vhdutils' op|'.' name|'get_vhd_format' op|'.' name|'return_value' op|'=' op|'(' nl|'\n' name|'constants' op|'.' name|'DISK_FORMAT_VHDX' op|')' newline|'\n' nl|'\n' name|'response' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'get_image_vm_generation' op|'(' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'instance_id' op|',' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_PATH' op|',' name|'image_meta' op|')' newline|'\n' nl|'\n' name|'self' op|'.' name|'assertEqual' op|'(' name|'constants' op|'.' name|'VM_GEN_2' op|',' name|'response' op|')' newline|'\n' nl|'\n' DECL|member|test_get_image_vm_generation_not_vhdx dedent|'' name|'def' name|'test_get_image_vm_generation_not_vhdx' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'image_meta' op|'=' name|'objects' op|'.' name|'ImageMeta' op|'.' name|'from_dict' op|'(' nl|'\n' op|'{' string|'"properties"' op|':' nl|'\n' op|'{' string|"'hw_machine_type'" op|':' name|'constants' op|'.' name|'IMAGE_PROP_VM_GEN_2' op|'}' op|'}' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_hostutils' op|'.' name|'get_supported_vm_types' op|'.' name|'return_value' op|'=' op|'[' nl|'\n' name|'constants' op|'.' name|'IMAGE_PROP_VM_GEN_1' op|',' name|'constants' op|'.' name|'IMAGE_PROP_VM_GEN_2' op|']' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vhdutils' op|'.' name|'get_vhd_format' op|'.' name|'return_value' op|'=' op|'(' nl|'\n' name|'constants' op|'.' name|'DISK_FORMAT_VHD' op|')' newline|'\n' nl|'\n' name|'self' op|'.' name|'assertRaises' op|'(' name|'exception' op|'.' name|'InstanceUnacceptable' op|',' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'get_image_vm_generation' op|',' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'instance_id' op|',' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_PATH' op|',' nl|'\n' name|'image_meta' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.api.metadata.base.InstanceMetadata'" op|')' newline|'\n' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.virt.configdrive.ConfigDriveBuilder'" op|')' newline|'\n' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.utils.execute'" op|')' newline|'\n' DECL|member|_test_create_config_drive name|'def' name|'_test_create_config_drive' op|'(' name|'self' op|',' name|'mock_execute' op|',' name|'mock_ConfigDriveBuilder' op|',' nl|'\n' name|'mock_InstanceMetadata' op|',' name|'config_drive_format' op|',' nl|'\n' name|'config_drive_cdrom' op|',' name|'side_effect' op|')' op|':' newline|'\n' indent|' ' name|'mock_instance' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|')' newline|'\n' name|'self' op|'.' name|'flags' op|'(' name|'config_drive_format' op|'=' name|'config_drive_format' op|')' newline|'\n' name|'self' op|'.' name|'flags' op|'(' name|'config_drive_cdrom' op|'=' name|'config_drive_cdrom' op|',' name|'group' op|'=' string|"'hyperv'" op|')' newline|'\n' name|'self' op|'.' name|'flags' op|'(' name|'config_drive_inject_password' op|'=' name|'True' op|',' name|'group' op|'=' string|"'hyperv'" op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_pathutils' op|'.' name|'get_instance_dir' op|'.' name|'return_value' op|'=' op|'(' nl|'\n' name|'self' op|'.' name|'FAKE_DIR' op|')' newline|'\n' name|'mock_ConfigDriveBuilder' op|'(' op|')' op|'.' name|'__enter__' op|'(' op|')' op|'.' name|'make_drive' op|'.' name|'side_effect' op|'=' op|'[' nl|'\n' name|'side_effect' op|']' newline|'\n' nl|'\n' name|'if' name|'config_drive_format' op|'!=' name|'self' op|'.' name|'ISO9660' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'assertRaises' op|'(' name|'exception' op|'.' name|'ConfigDriveUnsupportedFormat' op|',' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_create_config_drive' op|',' nl|'\n' name|'mock_instance' op|',' op|'[' name|'mock' op|'.' name|'sentinel' op|'.' name|'FILE' op|']' op|',' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'PASSWORD' op|',' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'NET_INFO' op|')' newline|'\n' dedent|'' name|'elif' name|'side_effect' name|'is' name|'processutils' op|'.' name|'ProcessExecutionError' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'assertRaises' op|'(' name|'processutils' op|'.' name|'ProcessExecutionError' op|',' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_create_config_drive' op|',' nl|'\n' name|'mock_instance' op|',' op|'[' name|'mock' op|'.' name|'sentinel' op|'.' name|'FILE' op|']' op|',' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'PASSWORD' op|',' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'NET_INFO' op|')' newline|'\n' dedent|'' name|'else' op|':' newline|'\n' indent|' ' name|'path' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'_create_config_drive' op|'(' name|'mock_instance' op|',' nl|'\n' op|'[' name|'mock' op|'.' name|'sentinel' op|'.' name|'FILE' op|']' op|',' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'PASSWORD' op|',' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'NET_INFO' op|')' newline|'\n' name|'mock_InstanceMetadata' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'mock_instance' op|',' name|'content' op|'=' op|'[' name|'mock' op|'.' name|'sentinel' op|'.' name|'FILE' op|']' op|',' nl|'\n' name|'extra_md' op|'=' op|'{' string|"'admin_pass'" op|':' name|'mock' op|'.' name|'sentinel' op|'.' name|'PASSWORD' op|'}' op|',' nl|'\n' name|'network_info' op|'=' name|'mock' op|'.' name|'sentinel' op|'.' name|'NET_INFO' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_pathutils' op|'.' name|'get_instance_dir' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'mock_instance' op|'.' name|'name' op|')' newline|'\n' name|'mock_ConfigDriveBuilder' op|'.' name|'assert_called_with' op|'(' nl|'\n' name|'instance_md' op|'=' name|'mock_InstanceMetadata' op|'(' op|')' op|')' newline|'\n' name|'mock_make_drive' op|'=' name|'mock_ConfigDriveBuilder' op|'(' op|')' op|'.' name|'__enter__' op|'(' op|')' op|'.' name|'make_drive' newline|'\n' name|'path_iso' op|'=' name|'os' op|'.' name|'path' op|'.' name|'join' op|'(' name|'self' op|'.' name|'FAKE_DIR' op|',' name|'self' op|'.' name|'FAKE_CONFIG_DRIVE_ISO' op|')' newline|'\n' name|'path_vhd' op|'=' name|'os' op|'.' name|'path' op|'.' name|'join' op|'(' name|'self' op|'.' name|'FAKE_DIR' op|',' name|'self' op|'.' name|'FAKE_CONFIG_DRIVE_VHD' op|')' newline|'\n' name|'mock_make_drive' op|'.' name|'assert_called_once_with' op|'(' name|'path_iso' op|')' newline|'\n' name|'if' name|'not' name|'CONF' op|'.' name|'hyperv' op|'.' name|'config_drive_cdrom' op|':' newline|'\n' indent|' ' name|'expected' op|'=' name|'path_vhd' newline|'\n' name|'mock_execute' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'CONF' op|'.' name|'hyperv' op|'.' name|'qemu_img_cmd' op|',' nl|'\n' string|"'convert'" op|',' string|"'-f'" op|',' string|"'raw'" op|',' string|"'-O'" op|',' string|"'vpc'" op|',' nl|'\n' name|'path_iso' op|',' name|'path_vhd' op|',' name|'attempts' op|'=' number|'1' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_pathutils' op|'.' name|'remove' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'os' op|'.' name|'path' op|'.' name|'join' op|'(' name|'self' op|'.' name|'FAKE_DIR' op|',' name|'self' op|'.' name|'FAKE_CONFIG_DRIVE_ISO' op|')' op|')' newline|'\n' dedent|'' name|'else' op|':' newline|'\n' indent|' ' name|'expected' op|'=' name|'path_iso' newline|'\n' nl|'\n' dedent|'' name|'self' op|'.' name|'assertEqual' op|'(' name|'expected' op|',' name|'path' op|')' newline|'\n' nl|'\n' DECL|member|test_create_config_drive_cdrom dedent|'' dedent|'' name|'def' name|'test_create_config_drive_cdrom' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_test_create_config_drive' op|'(' name|'config_drive_format' op|'=' name|'self' op|'.' name|'ISO9660' op|',' nl|'\n' name|'config_drive_cdrom' op|'=' name|'True' op|',' nl|'\n' name|'side_effect' op|'=' name|'None' op|')' newline|'\n' nl|'\n' DECL|member|test_create_config_drive_vhd dedent|'' name|'def' name|'test_create_config_drive_vhd' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_test_create_config_drive' op|'(' name|'config_drive_format' op|'=' name|'self' op|'.' name|'ISO9660' op|',' nl|'\n' name|'config_drive_cdrom' op|'=' name|'False' op|',' nl|'\n' name|'side_effect' op|'=' name|'None' op|')' newline|'\n' nl|'\n' DECL|member|test_create_config_drive_other_drive_format dedent|'' name|'def' name|'test_create_config_drive_other_drive_format' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_test_create_config_drive' op|'(' name|'config_drive_format' op|'=' name|'mock' op|'.' name|'sentinel' op|'.' name|'OTHER' op|',' nl|'\n' name|'config_drive_cdrom' op|'=' name|'False' op|',' nl|'\n' name|'side_effect' op|'=' name|'None' op|')' newline|'\n' nl|'\n' DECL|member|test_create_config_drive_execution_error dedent|'' name|'def' name|'test_create_config_drive_execution_error' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_test_create_config_drive' op|'(' nl|'\n' name|'config_drive_format' op|'=' name|'self' op|'.' name|'ISO9660' op|',' nl|'\n' name|'config_drive_cdrom' op|'=' name|'False' op|',' nl|'\n' name|'side_effect' op|'=' name|'processutils' op|'.' name|'ProcessExecutionError' op|')' newline|'\n' nl|'\n' DECL|member|test_attach_config_drive_exception dedent|'' name|'def' name|'test_attach_config_drive_exception' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'instance' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|')' newline|'\n' name|'self' op|'.' name|'assertRaises' op|'(' name|'exception' op|'.' name|'InvalidDiskFormat' op|',' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'attach_config_drive' op|',' nl|'\n' name|'instance' op|',' string|"'C:/fake_instance_dir/configdrive.xxx'" op|',' nl|'\n' name|'constants' op|'.' name|'VM_GEN_1' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'.' name|'object' op|'(' name|'vmops' op|'.' name|'VMOps' op|',' string|"'_attach_drive'" op|')' newline|'\n' DECL|member|test_attach_config_drive name|'def' name|'test_attach_config_drive' op|'(' name|'self' op|',' name|'mock_attach_drive' op|')' op|':' newline|'\n' indent|' ' name|'instance' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'attach_config_drive' op|'(' name|'instance' op|',' nl|'\n' name|'self' op|'.' name|'_FAKE_CONFIGDRIVE_PATH' op|',' nl|'\n' name|'constants' op|'.' name|'VM_GEN_1' op|')' newline|'\n' name|'mock_attach_drive' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'instance' op|'.' name|'name' op|',' name|'self' op|'.' name|'_FAKE_CONFIGDRIVE_PATH' op|',' nl|'\n' number|'1' op|',' number|'0' op|',' name|'constants' op|'.' name|'CTRL_TYPE_IDE' op|',' name|'constants' op|'.' name|'DISK' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'.' name|'object' op|'(' name|'vmops' op|'.' name|'VMOps' op|',' string|"'_attach_drive'" op|')' newline|'\n' DECL|member|test_attach_config_drive_gen2 name|'def' name|'test_attach_config_drive_gen2' op|'(' name|'self' op|',' name|'mock_attach_drive' op|')' op|':' newline|'\n' indent|' ' name|'instance' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'attach_config_drive' op|'(' name|'instance' op|',' nl|'\n' name|'self' op|'.' name|'_FAKE_CONFIGDRIVE_PATH' op|',' nl|'\n' name|'constants' op|'.' name|'VM_GEN_2' op|')' newline|'\n' name|'mock_attach_drive' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'instance' op|'.' name|'name' op|',' name|'self' op|'.' name|'_FAKE_CONFIGDRIVE_PATH' op|',' nl|'\n' number|'1' op|',' number|'0' op|',' name|'constants' op|'.' name|'CTRL_TYPE_SCSI' op|',' name|'constants' op|'.' name|'DISK' op|')' newline|'\n' nl|'\n' DECL|member|test_delete_disk_files dedent|'' name|'def' name|'test_delete_disk_files' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'mock_instance' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_delete_disk_files' op|'(' name|'mock_instance' op|'.' name|'name' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_pathutils' op|'.' name|'get_instance_dir' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'mock_instance' op|'.' name|'name' op|',' name|'create_dir' op|'=' name|'False' op|',' name|'remove_dir' op|'=' name|'True' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.virt.hyperv.volumeops.VolumeOps.disconnect_volumes'" op|')' newline|'\n' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.virt.hyperv.vmops.VMOps._delete_disk_files'" op|')' newline|'\n' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.virt.hyperv.vmops.VMOps.power_off'" op|')' newline|'\n' DECL|member|test_destroy name|'def' name|'test_destroy' op|'(' name|'self' op|',' name|'mock_power_off' op|',' name|'mock_delete_disk_files' op|',' nl|'\n' name|'mock_disconnect_volumes' op|')' op|':' newline|'\n' indent|' ' name|'mock_instance' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|'.' name|'vm_exists' op|'.' name|'return_value' op|'=' name|'True' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vif_driver' op|'=' name|'mock' op|'.' name|'MagicMock' op|'(' op|')' newline|'\n' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'destroy' op|'(' name|'instance' op|'=' name|'mock_instance' op|',' nl|'\n' name|'network_info' op|'=' op|'[' name|'mock' op|'.' name|'sentinel' op|'.' name|'fake_vif' op|']' op|',' nl|'\n' name|'block_device_info' op|'=' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_BD_INFO' op|')' newline|'\n' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|'.' name|'vm_exists' op|'.' name|'assert_called_with' op|'(' nl|'\n' name|'mock_instance' op|'.' name|'name' op|')' newline|'\n' name|'mock_power_off' op|'.' name|'assert_called_once_with' op|'(' name|'mock_instance' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vif_driver' op|'.' name|'unplug' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'mock_instance' op|',' name|'mock' op|'.' name|'sentinel' op|'.' name|'fake_vif' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|'.' name|'destroy_vm' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'mock_instance' op|'.' name|'name' op|')' newline|'\n' name|'mock_disconnect_volumes' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_BD_INFO' op|')' newline|'\n' name|'mock_delete_disk_files' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'mock_instance' op|'.' name|'name' op|')' newline|'\n' nl|'\n' DECL|member|test_destroy_inexistent_instance dedent|'' name|'def' name|'test_destroy_inexistent_instance' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'mock_instance' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|'.' name|'vm_exists' op|'.' name|'return_value' op|'=' name|'False' newline|'\n' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'destroy' op|'(' name|'instance' op|'=' name|'mock_instance' op|')' newline|'\n' name|'self' op|'.' name|'assertFalse' op|'(' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|'.' name|'destroy_vm' op|'.' name|'called' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.virt.hyperv.vmops.VMOps.power_off'" op|')' newline|'\n' DECL|member|test_destroy_exception name|'def' name|'test_destroy_exception' op|'(' name|'self' op|',' name|'mock_power_off' op|')' op|':' newline|'\n' indent|' ' name|'mock_instance' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|'.' name|'destroy_vm' op|'.' name|'side_effect' op|'=' op|'(' nl|'\n' name|'os_win_exc' op|'.' name|'HyperVException' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|'.' name|'vm_exists' op|'.' name|'return_value' op|'=' name|'True' newline|'\n' nl|'\n' name|'self' op|'.' name|'assertRaises' op|'(' name|'os_win_exc' op|'.' name|'HyperVException' op|',' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'destroy' op|',' name|'mock_instance' op|')' newline|'\n' nl|'\n' DECL|member|test_reboot_hard dedent|'' name|'def' name|'test_reboot_hard' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_test_reboot' op|'(' name|'vmops' op|'.' name|'REBOOT_TYPE_HARD' op|',' nl|'\n' name|'os_win_const' op|'.' name|'HYPERV_VM_STATE_REBOOT' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|'"nova.virt.hyperv.vmops.VMOps._soft_shutdown"' op|')' newline|'\n' DECL|member|test_reboot_soft name|'def' name|'test_reboot_soft' op|'(' name|'self' op|',' name|'mock_soft_shutdown' op|')' op|':' newline|'\n' indent|' ' name|'mock_soft_shutdown' op|'.' name|'return_value' op|'=' name|'True' newline|'\n' name|'self' op|'.' name|'_test_reboot' op|'(' name|'vmops' op|'.' name|'REBOOT_TYPE_SOFT' op|',' nl|'\n' name|'os_win_const' op|'.' name|'HYPERV_VM_STATE_ENABLED' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|'"nova.virt.hyperv.vmops.VMOps._soft_shutdown"' op|')' newline|'\n' DECL|member|test_reboot_soft_failed name|'def' name|'test_reboot_soft_failed' op|'(' name|'self' op|',' name|'mock_soft_shutdown' op|')' op|':' newline|'\n' indent|' ' name|'mock_soft_shutdown' op|'.' name|'return_value' op|'=' name|'False' newline|'\n' name|'self' op|'.' name|'_test_reboot' op|'(' name|'vmops' op|'.' name|'REBOOT_TYPE_SOFT' op|',' nl|'\n' name|'os_win_const' op|'.' name|'HYPERV_VM_STATE_REBOOT' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|'"nova.virt.hyperv.vmops.VMOps.power_on"' op|')' newline|'\n' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|'"nova.virt.hyperv.vmops.VMOps._soft_shutdown"' op|')' newline|'\n' DECL|member|test_reboot_soft_exception name|'def' name|'test_reboot_soft_exception' op|'(' name|'self' op|',' name|'mock_soft_shutdown' op|',' name|'mock_power_on' op|')' op|':' newline|'\n' indent|' ' name|'mock_soft_shutdown' op|'.' name|'return_value' op|'=' name|'True' newline|'\n' name|'mock_power_on' op|'.' name|'side_effect' op|'=' name|'os_win_exc' op|'.' name|'HyperVException' op|'(' nl|'\n' string|'"Expected failure"' op|')' newline|'\n' name|'instance' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|')' newline|'\n' nl|'\n' name|'self' op|'.' name|'assertRaises' op|'(' name|'os_win_exc' op|'.' name|'HyperVException' op|',' name|'self' op|'.' name|'_vmops' op|'.' name|'reboot' op|',' nl|'\n' name|'instance' op|',' op|'{' op|'}' op|',' name|'vmops' op|'.' name|'REBOOT_TYPE_SOFT' op|')' newline|'\n' nl|'\n' name|'mock_soft_shutdown' op|'.' name|'assert_called_once_with' op|'(' name|'instance' op|')' newline|'\n' name|'mock_power_on' op|'.' name|'assert_called_once_with' op|'(' name|'instance' op|')' newline|'\n' nl|'\n' DECL|member|_test_reboot dedent|'' name|'def' name|'_test_reboot' op|'(' name|'self' op|',' name|'reboot_type' op|',' name|'vm_state' op|')' op|':' newline|'\n' indent|' ' name|'instance' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|')' newline|'\n' name|'with' name|'mock' op|'.' name|'patch' op|'.' name|'object' op|'(' name|'self' op|'.' name|'_vmops' op|',' string|"'_set_vm_state'" op|')' name|'as' name|'mock_set_state' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_vmops' op|'.' name|'reboot' op|'(' name|'instance' op|',' op|'{' op|'}' op|',' name|'reboot_type' op|')' newline|'\n' name|'mock_set_state' op|'.' name|'assert_called_once_with' op|'(' name|'instance' op|',' name|'vm_state' op|')' newline|'\n' nl|'\n' dedent|'' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|'"nova.virt.hyperv.vmops.VMOps._wait_for_power_off"' op|')' newline|'\n' DECL|member|test_soft_shutdown name|'def' name|'test_soft_shutdown' op|'(' name|'self' op|',' name|'mock_wait_for_power_off' op|')' op|':' newline|'\n' indent|' ' name|'instance' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|')' newline|'\n' name|'mock_wait_for_power_off' op|'.' name|'return_value' op|'=' name|'True' newline|'\n' nl|'\n' name|'result' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'_soft_shutdown' op|'(' name|'instance' op|',' name|'self' op|'.' name|'_FAKE_TIMEOUT' op|')' newline|'\n' nl|'\n' name|'mock_shutdown_vm' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|'.' name|'soft_shutdown_vm' newline|'\n' name|'mock_shutdown_vm' op|'.' name|'assert_called_once_with' op|'(' name|'instance' op|'.' name|'name' op|')' newline|'\n' name|'mock_wait_for_power_off' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'instance' op|'.' name|'name' op|',' name|'self' op|'.' name|'_FAKE_TIMEOUT' op|')' newline|'\n' nl|'\n' name|'self' op|'.' name|'assertTrue' op|'(' name|'result' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|'"time.sleep"' op|')' newline|'\n' DECL|member|test_soft_shutdown_failed name|'def' name|'test_soft_shutdown_failed' op|'(' name|'self' op|',' name|'mock_sleep' op|')' op|':' newline|'\n' indent|' ' name|'instance' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|')' newline|'\n' nl|'\n' name|'mock_shutdown_vm' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|'.' name|'soft_shutdown_vm' newline|'\n' name|'mock_shutdown_vm' op|'.' name|'side_effect' op|'=' name|'os_win_exc' op|'.' name|'HyperVException' op|'(' nl|'\n' string|'"Expected failure."' op|')' newline|'\n' nl|'\n' name|'result' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'_soft_shutdown' op|'(' name|'instance' op|',' name|'self' op|'.' name|'_FAKE_TIMEOUT' op|')' newline|'\n' nl|'\n' name|'mock_shutdown_vm' op|'.' name|'assert_called_once_with' op|'(' name|'instance' op|'.' name|'name' op|')' newline|'\n' name|'self' op|'.' name|'assertFalse' op|'(' name|'result' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|'"nova.virt.hyperv.vmops.VMOps._wait_for_power_off"' op|')' newline|'\n' DECL|member|test_soft_shutdown_wait name|'def' name|'test_soft_shutdown_wait' op|'(' name|'self' op|',' name|'mock_wait_for_power_off' op|')' op|':' newline|'\n' indent|' ' name|'instance' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|')' newline|'\n' name|'mock_wait_for_power_off' op|'.' name|'side_effect' op|'=' op|'[' name|'False' op|',' name|'True' op|']' newline|'\n' nl|'\n' name|'result' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'_soft_shutdown' op|'(' name|'instance' op|',' name|'self' op|'.' name|'_FAKE_TIMEOUT' op|',' number|'1' op|')' newline|'\n' nl|'\n' name|'calls' op|'=' op|'[' name|'mock' op|'.' name|'call' op|'(' name|'instance' op|'.' name|'name' op|',' number|'1' op|')' op|',' nl|'\n' name|'mock' op|'.' name|'call' op|'(' name|'instance' op|'.' name|'name' op|',' name|'self' op|'.' name|'_FAKE_TIMEOUT' op|'-' number|'1' op|')' op|']' newline|'\n' name|'mock_shutdown_vm' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|'.' name|'soft_shutdown_vm' newline|'\n' name|'mock_shutdown_vm' op|'.' name|'assert_called_with' op|'(' name|'instance' op|'.' name|'name' op|')' newline|'\n' name|'mock_wait_for_power_off' op|'.' name|'assert_has_calls' op|'(' name|'calls' op|')' newline|'\n' nl|'\n' name|'self' op|'.' name|'assertTrue' op|'(' name|'result' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|'"nova.virt.hyperv.vmops.VMOps._wait_for_power_off"' op|')' newline|'\n' DECL|member|test_soft_shutdown_wait_timeout name|'def' name|'test_soft_shutdown_wait_timeout' op|'(' name|'self' op|',' name|'mock_wait_for_power_off' op|')' op|':' newline|'\n' indent|' ' name|'instance' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|')' newline|'\n' name|'mock_wait_for_power_off' op|'.' name|'return_value' op|'=' name|'False' newline|'\n' nl|'\n' name|'result' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'_soft_shutdown' op|'(' name|'instance' op|',' name|'self' op|'.' name|'_FAKE_TIMEOUT' op|',' number|'1.5' op|')' newline|'\n' nl|'\n' name|'calls' op|'=' op|'[' name|'mock' op|'.' name|'call' op|'(' name|'instance' op|'.' name|'name' op|',' number|'1.5' op|')' op|',' nl|'\n' name|'mock' op|'.' name|'call' op|'(' name|'instance' op|'.' name|'name' op|',' name|'self' op|'.' name|'_FAKE_TIMEOUT' op|'-' number|'1.5' op|')' op|']' newline|'\n' name|'mock_shutdown_vm' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|'.' name|'soft_shutdown_vm' newline|'\n' name|'mock_shutdown_vm' op|'.' name|'assert_called_with' op|'(' name|'instance' op|'.' name|'name' op|')' newline|'\n' name|'mock_wait_for_power_off' op|'.' name|'assert_has_calls' op|'(' name|'calls' op|')' newline|'\n' nl|'\n' name|'self' op|'.' name|'assertFalse' op|'(' name|'result' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.virt.hyperv.vmops.VMOps._set_vm_state'" op|')' newline|'\n' DECL|member|test_pause name|'def' name|'test_pause' op|'(' name|'self' op|',' name|'mock_set_vm_state' op|')' op|':' newline|'\n' indent|' ' name|'mock_instance' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'pause' op|'(' name|'instance' op|'=' name|'mock_instance' op|')' newline|'\n' name|'mock_set_vm_state' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'mock_instance' op|',' name|'os_win_const' op|'.' name|'HYPERV_VM_STATE_PAUSED' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.virt.hyperv.vmops.VMOps._set_vm_state'" op|')' newline|'\n' DECL|member|test_unpause name|'def' name|'test_unpause' op|'(' name|'self' op|',' name|'mock_set_vm_state' op|')' op|':' newline|'\n' indent|' ' name|'mock_instance' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'unpause' op|'(' name|'instance' op|'=' name|'mock_instance' op|')' newline|'\n' name|'mock_set_vm_state' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'mock_instance' op|',' name|'os_win_const' op|'.' name|'HYPERV_VM_STATE_ENABLED' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.virt.hyperv.vmops.VMOps._set_vm_state'" op|')' newline|'\n' DECL|member|test_suspend name|'def' name|'test_suspend' op|'(' name|'self' op|',' name|'mock_set_vm_state' op|')' op|':' newline|'\n' indent|' ' name|'mock_instance' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'suspend' op|'(' name|'instance' op|'=' name|'mock_instance' op|')' newline|'\n' name|'mock_set_vm_state' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'mock_instance' op|',' name|'os_win_const' op|'.' name|'HYPERV_VM_STATE_SUSPENDED' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.virt.hyperv.vmops.VMOps._set_vm_state'" op|')' newline|'\n' DECL|member|test_resume name|'def' name|'test_resume' op|'(' name|'self' op|',' name|'mock_set_vm_state' op|')' op|':' newline|'\n' indent|' ' name|'mock_instance' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'resume' op|'(' name|'instance' op|'=' name|'mock_instance' op|')' newline|'\n' name|'mock_set_vm_state' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'mock_instance' op|',' name|'os_win_const' op|'.' name|'HYPERV_VM_STATE_ENABLED' op|')' newline|'\n' nl|'\n' DECL|member|_test_power_off dedent|'' name|'def' name|'_test_power_off' op|'(' name|'self' op|',' name|'timeout' op|',' name|'set_state_expected' op|'=' name|'True' op|')' op|':' newline|'\n' indent|' ' name|'instance' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|')' newline|'\n' name|'with' name|'mock' op|'.' name|'patch' op|'.' name|'object' op|'(' name|'self' op|'.' name|'_vmops' op|',' string|"'_set_vm_state'" op|')' name|'as' name|'mock_set_state' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_vmops' op|'.' name|'power_off' op|'(' name|'instance' op|',' name|'timeout' op|')' newline|'\n' nl|'\n' name|'if' name|'set_state_expected' op|':' newline|'\n' indent|' ' name|'mock_set_state' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'instance' op|',' name|'os_win_const' op|'.' name|'HYPERV_VM_STATE_DISABLED' op|')' newline|'\n' nl|'\n' DECL|member|test_power_off_hard dedent|'' dedent|'' dedent|'' name|'def' name|'test_power_off_hard' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_test_power_off' op|'(' name|'timeout' op|'=' number|'0' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|'"nova.virt.hyperv.vmops.VMOps._soft_shutdown"' op|')' newline|'\n' DECL|member|test_power_off_exception name|'def' name|'test_power_off_exception' op|'(' name|'self' op|',' name|'mock_soft_shutdown' op|')' op|':' newline|'\n' indent|' ' name|'mock_soft_shutdown' op|'.' name|'return_value' op|'=' name|'False' newline|'\n' name|'self' op|'.' name|'_test_power_off' op|'(' name|'timeout' op|'=' number|'1' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|'"nova.virt.hyperv.vmops.VMOps._set_vm_state"' op|')' newline|'\n' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|'"nova.virt.hyperv.vmops.VMOps._soft_shutdown"' op|')' newline|'\n' DECL|member|test_power_off_soft name|'def' name|'test_power_off_soft' op|'(' name|'self' op|',' name|'mock_soft_shutdown' op|',' name|'mock_set_state' op|')' op|':' newline|'\n' indent|' ' name|'instance' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|')' newline|'\n' name|'mock_soft_shutdown' op|'.' name|'return_value' op|'=' name|'True' newline|'\n' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'power_off' op|'(' name|'instance' op|',' number|'1' op|',' number|'0' op|')' newline|'\n' nl|'\n' name|'mock_soft_shutdown' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'instance' op|',' number|'1' op|',' name|'vmops' op|'.' name|'SHUTDOWN_TIME_INCREMENT' op|')' newline|'\n' name|'self' op|'.' name|'assertFalse' op|'(' name|'mock_set_state' op|'.' name|'called' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|'"nova.virt.hyperv.vmops.VMOps._soft_shutdown"' op|')' newline|'\n' DECL|member|test_power_off_unexisting_instance name|'def' name|'test_power_off_unexisting_instance' op|'(' name|'self' op|',' name|'mock_soft_shutdown' op|')' op|':' newline|'\n' indent|' ' name|'mock_soft_shutdown' op|'.' name|'side_effect' op|'=' name|'os_win_exc' op|'.' name|'HyperVVMNotFoundException' op|'(' nl|'\n' name|'vm_name' op|'=' name|'mock' op|'.' name|'sentinel' op|'.' name|'vm_name' op|')' newline|'\n' name|'self' op|'.' name|'_test_power_off' op|'(' name|'timeout' op|'=' number|'1' op|',' name|'set_state_expected' op|'=' name|'False' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.virt.hyperv.vmops.VMOps._set_vm_state'" op|')' newline|'\n' DECL|member|test_power_on name|'def' name|'test_power_on' op|'(' name|'self' op|',' name|'mock_set_vm_state' op|')' op|':' newline|'\n' indent|' ' name|'mock_instance' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|')' newline|'\n' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'power_on' op|'(' name|'mock_instance' op|')' newline|'\n' nl|'\n' name|'mock_set_vm_state' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'mock_instance' op|',' name|'os_win_const' op|'.' name|'HYPERV_VM_STATE_ENABLED' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.virt.hyperv.volumeops.VolumeOps'" nl|'\n' string|"'.fix_instance_volume_disk_paths'" op|')' newline|'\n' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.virt.hyperv.vmops.VMOps._set_vm_state'" op|')' newline|'\n' DECL|member|test_power_on_having_block_devices name|'def' name|'test_power_on_having_block_devices' op|'(' name|'self' op|',' name|'mock_set_vm_state' op|',' nl|'\n' name|'mock_fix_instance_vol_paths' op|')' op|':' newline|'\n' indent|' ' name|'mock_instance' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|')' newline|'\n' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'power_on' op|'(' name|'mock_instance' op|',' name|'mock' op|'.' name|'sentinel' op|'.' name|'block_device_info' op|')' newline|'\n' nl|'\n' name|'mock_fix_instance_vol_paths' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'mock_instance' op|'.' name|'name' op|',' name|'mock' op|'.' name|'sentinel' op|'.' name|'block_device_info' op|')' newline|'\n' name|'mock_set_vm_state' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'mock_instance' op|',' name|'os_win_const' op|'.' name|'HYPERV_VM_STATE_ENABLED' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'.' name|'object' op|'(' name|'vmops' op|'.' name|'VMOps' op|',' string|"'log_vm_serial_output'" op|')' newline|'\n' op|'@' name|'mock' op|'.' name|'patch' op|'.' name|'object' op|'(' name|'vmops' op|'.' name|'VMOps' op|',' string|"'_delete_vm_console_log'" op|')' newline|'\n' DECL|member|_test_set_vm_state name|'def' name|'_test_set_vm_state' op|'(' name|'self' op|',' name|'mock_delete_vm_console_log' op|',' nl|'\n' name|'mock_log_vm_output' op|',' name|'state' op|')' op|':' newline|'\n' indent|' ' name|'mock_instance' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|')' newline|'\n' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_set_vm_state' op|'(' name|'mock_instance' op|',' name|'state' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|'.' name|'set_vm_state' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'mock_instance' op|'.' name|'name' op|',' name|'state' op|')' newline|'\n' name|'if' name|'state' name|'in' op|'(' name|'os_win_const' op|'.' name|'HYPERV_VM_STATE_DISABLED' op|',' nl|'\n' name|'os_win_const' op|'.' name|'HYPERV_VM_STATE_REBOOT' op|')' op|':' newline|'\n' indent|' ' name|'mock_delete_vm_console_log' op|'.' name|'assert_called_once_with' op|'(' name|'mock_instance' op|')' newline|'\n' dedent|'' name|'if' name|'state' name|'in' op|'(' name|'os_win_const' op|'.' name|'HYPERV_VM_STATE_ENABLED' op|',' nl|'\n' name|'os_win_const' op|'.' name|'HYPERV_VM_STATE_REBOOT' op|')' op|':' newline|'\n' indent|' ' name|'mock_log_vm_output' op|'.' name|'assert_called_once_with' op|'(' name|'mock_instance' op|'.' name|'name' op|',' nl|'\n' name|'mock_instance' op|'.' name|'uuid' op|')' newline|'\n' nl|'\n' DECL|member|test_set_vm_state_disabled dedent|'' dedent|'' name|'def' name|'test_set_vm_state_disabled' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_test_set_vm_state' op|'(' name|'state' op|'=' name|'os_win_const' op|'.' name|'HYPERV_VM_STATE_DISABLED' op|')' newline|'\n' nl|'\n' DECL|member|test_set_vm_state_enabled dedent|'' name|'def' name|'test_set_vm_state_enabled' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_test_set_vm_state' op|'(' name|'state' op|'=' name|'os_win_const' op|'.' name|'HYPERV_VM_STATE_ENABLED' op|')' newline|'\n' nl|'\n' DECL|member|test_set_vm_state_reboot dedent|'' name|'def' name|'test_set_vm_state_reboot' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_test_set_vm_state' op|'(' name|'state' op|'=' name|'os_win_const' op|'.' name|'HYPERV_VM_STATE_REBOOT' op|')' newline|'\n' nl|'\n' DECL|member|test_set_vm_state_exception dedent|'' name|'def' name|'test_set_vm_state_exception' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'mock_instance' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|'.' name|'set_vm_state' op|'.' name|'side_effect' op|'=' op|'(' nl|'\n' name|'os_win_exc' op|'.' name|'HyperVException' op|')' newline|'\n' name|'self' op|'.' name|'assertRaises' op|'(' name|'os_win_exc' op|'.' name|'HyperVException' op|',' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_set_vm_state' op|',' nl|'\n' name|'mock_instance' op|',' name|'mock' op|'.' name|'sentinel' op|'.' name|'STATE' op|')' newline|'\n' nl|'\n' DECL|member|test_get_vm_state dedent|'' name|'def' name|'test_get_vm_state' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'summary_info' op|'=' op|'{' string|"'EnabledState'" op|':' name|'os_win_const' op|'.' name|'HYPERV_VM_STATE_DISABLED' op|'}' newline|'\n' nl|'\n' name|'with' name|'mock' op|'.' name|'patch' op|'.' name|'object' op|'(' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|',' nl|'\n' string|"'get_vm_summary_info'" op|')' name|'as' name|'mock_get_summary_info' op|':' newline|'\n' indent|' ' name|'mock_get_summary_info' op|'.' name|'return_value' op|'=' name|'summary_info' newline|'\n' nl|'\n' name|'response' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'_get_vm_state' op|'(' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_VM_NAME' op|')' newline|'\n' name|'self' op|'.' name|'assertEqual' op|'(' name|'response' op|',' name|'os_win_const' op|'.' name|'HYPERV_VM_STATE_DISABLED' op|')' newline|'\n' nl|'\n' dedent|'' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'.' name|'object' op|'(' name|'vmops' op|'.' name|'VMOps' op|',' string|"'_get_vm_state'" op|')' newline|'\n' DECL|member|test_wait_for_power_off_true name|'def' name|'test_wait_for_power_off_true' op|'(' name|'self' op|',' name|'mock_get_state' op|')' op|':' newline|'\n' indent|' ' name|'mock_get_state' op|'.' name|'return_value' op|'=' name|'os_win_const' op|'.' name|'HYPERV_VM_STATE_DISABLED' newline|'\n' name|'result' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'_wait_for_power_off' op|'(' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_VM_NAME' op|',' name|'vmops' op|'.' name|'SHUTDOWN_TIME_INCREMENT' op|')' newline|'\n' name|'mock_get_state' op|'.' name|'assert_called_with' op|'(' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_VM_NAME' op|')' newline|'\n' name|'self' op|'.' name|'assertTrue' op|'(' name|'result' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'.' name|'object' op|'(' name|'vmops' op|'.' name|'etimeout' op|',' string|'"with_timeout"' op|')' newline|'\n' DECL|member|test_wait_for_power_off_false name|'def' name|'test_wait_for_power_off_false' op|'(' name|'self' op|',' name|'mock_with_timeout' op|')' op|':' newline|'\n' indent|' ' name|'mock_with_timeout' op|'.' name|'side_effect' op|'=' name|'etimeout' op|'.' name|'Timeout' op|'(' op|')' newline|'\n' name|'result' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'_wait_for_power_off' op|'(' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_VM_NAME' op|',' name|'vmops' op|'.' name|'SHUTDOWN_TIME_INCREMENT' op|')' newline|'\n' name|'self' op|'.' name|'assertFalse' op|'(' name|'result' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'.' name|'object' op|'(' name|'vmops' op|'.' name|'ioutils' op|',' string|"'IOThread'" op|')' newline|'\n' DECL|member|_test_log_vm_serial_output name|'def' name|'_test_log_vm_serial_output' op|'(' name|'self' op|',' name|'mock_io_thread' op|',' nl|'\n' name|'worker_running' op|'=' name|'False' op|',' nl|'\n' name|'worker_exists' op|'=' name|'False' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_vmops' op|'.' name|'_pathutils' op|'.' name|'get_vm_console_log_paths' op|'.' name|'return_value' op|'=' op|'(' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'log_path' op|',' op|')' newline|'\n' name|'fake_instance_uuid' op|'=' string|"'fake-uuid'" newline|'\n' name|'fake_existing_worker' op|'=' name|'mock' op|'.' name|'Mock' op|'(' op|')' newline|'\n' name|'fake_existing_worker' op|'.' name|'is_active' op|'.' name|'return_value' op|'=' name|'worker_running' newline|'\n' name|'fake_log_writers' op|'=' op|'{' name|'fake_instance_uuid' op|':' name|'fake_existing_worker' op|'}' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vm_log_writers' op|'=' op|'(' nl|'\n' name|'fake_log_writers' name|'if' name|'worker_exists' name|'else' op|'{' op|'}' op|')' newline|'\n' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'log_vm_serial_output' op|'(' name|'mock' op|'.' name|'sentinel' op|'.' name|'instance_name' op|',' nl|'\n' name|'fake_instance_uuid' op|')' newline|'\n' nl|'\n' name|'if' name|'not' op|'(' name|'worker_exists' name|'and' name|'worker_running' op|')' op|':' newline|'\n' indent|' ' name|'expected_pipe_path' op|'=' string|"r'\\\\.\\pipe\\%s'" op|'%' name|'fake_instance_uuid' newline|'\n' name|'expected_current_worker' op|'=' name|'mock_io_thread' op|'.' name|'return_value' newline|'\n' name|'expected_current_worker' op|'.' name|'start' op|'.' name|'assert_called_once_with' op|'(' op|')' newline|'\n' name|'mock_io_thread' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'expected_pipe_path' op|',' name|'mock' op|'.' name|'sentinel' op|'.' name|'log_path' op|',' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_MAX_CONSOLE_LOG_FILE_SIZE' op|')' newline|'\n' dedent|'' name|'else' op|':' newline|'\n' indent|' ' name|'expected_current_worker' op|'=' name|'fake_existing_worker' newline|'\n' dedent|'' name|'self' op|'.' name|'assertEqual' op|'(' name|'expected_current_worker' op|',' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vm_log_writers' op|'[' name|'fake_instance_uuid' op|']' op|')' newline|'\n' nl|'\n' DECL|member|test_log_vm_serial_output_unexisting_worker dedent|'' name|'def' name|'test_log_vm_serial_output_unexisting_worker' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_test_log_vm_serial_output' op|'(' op|')' newline|'\n' nl|'\n' DECL|member|test_log_vm_serial_output_worker_stopped dedent|'' name|'def' name|'test_log_vm_serial_output_worker_stopped' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_test_log_vm_serial_output' op|'(' name|'worker_exists' op|'=' name|'True' op|')' newline|'\n' nl|'\n' DECL|member|test_log_vm_serial_output_worker_running dedent|'' name|'def' name|'test_log_vm_serial_output_worker_running' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_test_log_vm_serial_output' op|'(' name|'worker_exists' op|'=' name|'True' op|',' nl|'\n' name|'worker_running' op|'=' name|'True' op|')' newline|'\n' nl|'\n' DECL|member|test_copy_vm_console_logs dedent|'' name|'def' name|'test_copy_vm_console_logs' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'fake_local_paths' op|'=' op|'(' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_PATH' op|',' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_PATH_ARCHIVED' op|')' newline|'\n' name|'fake_remote_paths' op|'=' op|'(' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_REMOTE_PATH' op|',' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_REMOTE_PATH_ARCHIVED' op|')' newline|'\n' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_pathutils' op|'.' name|'get_vm_console_log_paths' op|'.' name|'side_effect' op|'=' op|'[' nl|'\n' name|'fake_local_paths' op|',' name|'fake_remote_paths' op|']' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_pathutils' op|'.' name|'exists' op|'.' name|'side_effect' op|'=' op|'[' name|'True' op|',' name|'False' op|']' newline|'\n' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'copy_vm_console_logs' op|'(' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_VM_NAME' op|',' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_DEST' op|')' newline|'\n' nl|'\n' name|'calls' op|'=' op|'[' name|'mock' op|'.' name|'call' op|'(' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_VM_NAME' op|')' op|',' nl|'\n' name|'mock' op|'.' name|'call' op|'(' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_VM_NAME' op|',' nl|'\n' name|'remote_server' op|'=' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_DEST' op|')' op|']' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_pathutils' op|'.' name|'get_vm_console_log_paths' op|'.' name|'assert_has_calls' op|'(' name|'calls' op|')' newline|'\n' nl|'\n' name|'calls' op|'=' op|'[' name|'mock' op|'.' name|'call' op|'(' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_PATH' op|')' op|',' nl|'\n' name|'mock' op|'.' name|'call' op|'(' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_PATH_ARCHIVED' op|')' op|']' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_pathutils' op|'.' name|'exists' op|'.' name|'assert_has_calls' op|'(' name|'calls' op|')' newline|'\n' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_pathutils' op|'.' name|'copy' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_PATH' op|',' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_REMOTE_PATH' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'.' name|'object' op|'(' name|'vmops' op|'.' name|'ioutils' op|',' string|"'IOThread'" op|')' newline|'\n' DECL|member|test_log_vm_serial_output name|'def' name|'test_log_vm_serial_output' op|'(' name|'self' op|',' name|'fake_iothread' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_vmops' op|'.' name|'_pathutils' op|'.' name|'get_vm_console_log_paths' op|'.' name|'return_value' op|'=' op|'[' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_PATH' op|']' newline|'\n' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'log_vm_serial_output' op|'(' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_VM_NAME' op|',' nl|'\n' name|'self' op|'.' name|'FAKE_UUID' op|')' newline|'\n' nl|'\n' name|'pipe_path' op|'=' string|"r'\\\\.\\pipe\\%s'" op|'%' name|'self' op|'.' name|'FAKE_UUID' newline|'\n' name|'fake_iothread' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'pipe_path' op|',' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_PATH' op|',' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_MAX_CONSOLE_LOG_FILE_SIZE' op|')' newline|'\n' name|'fake_iothread' op|'.' name|'return_value' op|'.' name|'start' op|'.' name|'assert_called_once_with' op|'(' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'testtools' op|'.' name|'skip' op|'(' string|"'mock_open in 1.2 read only works once 1475661'" op|')' newline|'\n' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|'"os.path.exists"' op|')' newline|'\n' DECL|member|test_get_console_output name|'def' name|'test_get_console_output' op|'(' name|'self' op|',' name|'fake_path_exists' op|')' op|':' newline|'\n' indent|' ' name|'mock_instance' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|')' newline|'\n' nl|'\n' name|'fake_path_exists' op|'.' name|'return_value' op|'=' name|'True' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_pathutils' op|'.' name|'get_vm_console_log_paths' op|'.' name|'return_value' op|'=' op|'(' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_PATH' op|',' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_PATH_ARCHIVED' op|')' newline|'\n' nl|'\n' name|'with' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.virt.hyperv.vmops.open'" op|',' nl|'\n' name|'mock' op|'.' name|'mock_open' op|'(' name|'read_data' op|'=' name|'self' op|'.' name|'FAKE_LOG' op|')' op|',' nl|'\n' name|'create' op|'=' name|'True' op|')' op|':' newline|'\n' indent|' ' name|'instance_log' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'get_console_output' op|'(' name|'mock_instance' op|')' newline|'\n' comment|'# get_vm_console_log_paths returns 2 paths.' nl|'\n' name|'self' op|'.' name|'assertEqual' op|'(' name|'self' op|'.' name|'FAKE_LOG' op|'*' number|'2' op|',' name|'instance_log' op|')' newline|'\n' nl|'\n' name|'expected_calls' op|'=' op|'[' name|'mock' op|'.' name|'call' op|'(' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_PATH_ARCHIVED' op|')' op|',' nl|'\n' name|'mock' op|'.' name|'call' op|'(' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_PATH' op|')' op|']' newline|'\n' name|'fake_path_exists' op|'.' name|'assert_has_calls' op|'(' name|'expected_calls' op|',' name|'any_order' op|'=' name|'False' op|')' newline|'\n' nl|'\n' dedent|'' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'.' name|'object' op|'(' name|'six' op|'.' name|'moves' op|'.' name|'builtins' op|',' string|"'open'" op|')' newline|'\n' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|'"os.path.exists"' op|')' newline|'\n' DECL|member|test_get_console_output_exception name|'def' name|'test_get_console_output_exception' op|'(' name|'self' op|',' name|'fake_path_exists' op|',' name|'fake_open' op|')' op|':' newline|'\n' indent|' ' name|'fake_vm' op|'=' name|'mock' op|'.' name|'MagicMock' op|'(' op|')' newline|'\n' name|'fake_open' op|'.' name|'side_effect' op|'=' name|'IOError' newline|'\n' name|'fake_path_exists' op|'.' name|'return_value' op|'=' name|'True' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_pathutils' op|'.' name|'get_vm_console_log_paths' op|'.' name|'return_value' op|'=' op|'(' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'fake_console_log_path' op|',' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'fake_console_log_archived' op|')' newline|'\n' nl|'\n' name|'with' name|'mock' op|'.' name|'patch' op|'(' string|"'nova.virt.hyperv.vmops.open'" op|',' name|'fake_open' op|',' name|'create' op|'=' name|'True' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'assertRaises' op|'(' name|'exception' op|'.' name|'ConsoleLogOutputException' op|',' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'get_console_output' op|',' nl|'\n' name|'fake_vm' op|')' newline|'\n' nl|'\n' dedent|'' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'.' name|'object' op|'(' name|'vmops' op|'.' name|'fileutils' op|',' string|"'delete_if_exists'" op|')' newline|'\n' DECL|member|test_delete_vm_console_log name|'def' name|'test_delete_vm_console_log' op|'(' name|'self' op|',' name|'mock_delete_if_exists' op|')' op|':' newline|'\n' indent|' ' name|'mock_instance' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_pathutils' op|'.' name|'get_vm_console_log_paths' op|'.' name|'return_value' op|'=' op|'(' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_PATH' op|',' op|')' newline|'\n' name|'mock_log_writer' op|'=' name|'mock' op|'.' name|'MagicMock' op|'(' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vm_log_writers' op|'[' name|'mock_instance' op|'[' string|"'uuid'" op|']' op|']' op|'=' name|'mock_log_writer' newline|'\n' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_delete_vm_console_log' op|'(' name|'mock_instance' op|')' newline|'\n' nl|'\n' name|'mock_log_writer' op|'.' name|'join' op|'.' name|'assert_called_once_with' op|'(' op|')' newline|'\n' name|'mock_delete_if_exists' op|'.' name|'assert_called_once_with' op|'(' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_PATH' op|')' newline|'\n' nl|'\n' DECL|member|test_create_vm_com_port_pipe dedent|'' name|'def' name|'test_create_vm_com_port_pipe' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'mock_instance' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|')' newline|'\n' name|'pipe_path' op|'=' string|"r'\\\\.\\pipe\\%s'" op|'%' name|'mock_instance' op|'[' string|"'uuid'" op|']' newline|'\n' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_create_vm_com_port_pipe' op|'(' name|'mock_instance' op|')' newline|'\n' nl|'\n' name|'get_vm_serial_port' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|'.' name|'get_vm_serial_port_connection' newline|'\n' name|'get_vm_serial_port' op|'.' name|'assert_called_once_with' op|'(' name|'mock_instance' op|'[' string|"'name'" op|']' op|',' nl|'\n' name|'update_connection' op|'=' name|'pipe_path' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'.' name|'object' op|'(' name|'vmops' op|'.' name|'VMOps' op|',' string|'"log_vm_serial_output"' op|')' newline|'\n' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|'"os.path.basename"' op|')' newline|'\n' op|'@' name|'mock' op|'.' name|'patch' op|'(' string|'"os.path.exists"' op|')' newline|'\n' DECL|member|test_restart_vm_log_writers name|'def' name|'test_restart_vm_log_writers' op|'(' name|'self' op|',' name|'mock_exists' op|',' name|'mock_basename' op|',' nl|'\n' name|'mock_log_vm_output' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|'.' name|'get_active_instances' op|'.' name|'return_value' op|'=' op|'[' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_VM_NAME' op|',' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_VM_NAME_OTHER' op|']' newline|'\n' name|'mock_exists' op|'.' name|'side_effect' op|'=' op|'[' name|'True' op|',' name|'False' op|']' newline|'\n' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'restart_vm_log_writers' op|'(' op|')' newline|'\n' nl|'\n' name|'calls' op|'=' op|'[' name|'mock' op|'.' name|'call' op|'(' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_VM_NAME' op|')' op|',' nl|'\n' name|'mock' op|'.' name|'call' op|'(' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_VM_NAME_OTHER' op|')' op|']' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_pathutils' op|'.' name|'get_instance_dir' op|'.' name|'assert_has_calls' op|'(' name|'calls' op|')' newline|'\n' name|'get_vm_serial_port' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|'.' name|'get_vm_serial_port_connection' newline|'\n' name|'get_vm_serial_port' op|'.' name|'assert_called_once_with' op|'(' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_VM_NAME' op|')' newline|'\n' name|'mock_log_vm_output' op|'.' name|'assert_called_once_with' op|'(' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_VM_NAME' op|',' nl|'\n' name|'mock_basename' op|'.' name|'return_value' op|')' newline|'\n' nl|'\n' DECL|member|test_list_instance_uuids dedent|'' name|'def' name|'test_list_instance_uuids' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'fake_uuid' op|'=' string|"'4f54fb69-d3a2-45b7-bb9b-b6e6b3d893b3'" newline|'\n' name|'with' name|'mock' op|'.' name|'patch' op|'.' name|'object' op|'(' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|',' nl|'\n' string|"'list_instance_notes'" op|')' name|'as' name|'mock_list_notes' op|':' newline|'\n' indent|' ' name|'mock_list_notes' op|'.' name|'return_value' op|'=' op|'[' op|'(' string|"'fake_name'" op|',' op|'[' name|'fake_uuid' op|']' op|')' op|']' newline|'\n' nl|'\n' name|'response' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'list_instance_uuids' op|'(' op|')' newline|'\n' name|'mock_list_notes' op|'.' name|'assert_called_once_with' op|'(' op|')' newline|'\n' nl|'\n' dedent|'' name|'self' op|'.' name|'assertEqual' op|'(' name|'response' op|',' op|'[' name|'fake_uuid' op|']' op|')' newline|'\n' nl|'\n' DECL|member|test_copy_vm_dvd_disks dedent|'' name|'def' name|'test_copy_vm_dvd_disks' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'fake_paths' op|'=' op|'[' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_DVD_PATH1' op|',' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_DVD_PATH2' op|']' newline|'\n' name|'mock_copy' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'_pathutils' op|'.' name|'copyfile' newline|'\n' name|'mock_get_dvd_disk_paths' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|'.' name|'get_vm_dvd_disk_paths' newline|'\n' name|'mock_get_dvd_disk_paths' op|'.' name|'return_value' op|'=' name|'fake_paths' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_pathutils' op|'.' name|'get_instance_dir' op|'.' name|'return_value' op|'=' op|'(' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_DEST_PATH' op|')' newline|'\n' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'copy_vm_dvd_disks' op|'(' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_VM_NAME' op|',' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_DEST_HOST' op|')' newline|'\n' nl|'\n' name|'mock_get_dvd_disk_paths' op|'.' name|'assert_called_with' op|'(' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_VM_NAME' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_pathutils' op|'.' name|'get_instance_dir' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_VM_NAME' op|',' nl|'\n' name|'remote_server' op|'=' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_DEST_HOST' op|')' newline|'\n' name|'mock_copy' op|'.' name|'has_calls' op|'(' name|'mock' op|'.' name|'call' op|'(' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_DVD_PATH1' op|',' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_DEST_PATH' op|')' op|',' nl|'\n' name|'mock' op|'.' name|'call' op|'(' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_DVD_PATH2' op|',' nl|'\n' name|'mock' op|'.' name|'sentinel' op|'.' name|'FAKE_DEST_PATH' op|')' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'.' name|'object' op|'(' name|'vmops' op|'.' name|'VMOps' op|',' string|"'_get_vm_state'" op|')' newline|'\n' DECL|member|test_check_hotplug_available_vm_disabled name|'def' name|'test_check_hotplug_available_vm_disabled' op|'(' name|'self' op|',' name|'mock_get_vm_state' op|')' op|':' newline|'\n' indent|' ' name|'fake_vm' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|')' newline|'\n' name|'mock_get_vm_state' op|'.' name|'return_value' op|'=' name|'os_win_const' op|'.' name|'HYPERV_VM_STATE_DISABLED' newline|'\n' nl|'\n' name|'result' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'_check_hotplug_available' op|'(' name|'fake_vm' op|')' newline|'\n' nl|'\n' name|'self' op|'.' name|'assertTrue' op|'(' name|'result' op|')' newline|'\n' name|'mock_get_vm_state' op|'.' name|'assert_called_once_with' op|'(' name|'fake_vm' op|'.' name|'name' op|')' newline|'\n' name|'self' op|'.' name|'assertFalse' op|'(' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_hostutils' op|'.' name|'check_min_windows_version' op|'.' name|'called' op|')' newline|'\n' name|'self' op|'.' name|'assertFalse' op|'(' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|'.' name|'get_vm_generation' op|'.' name|'called' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'.' name|'object' op|'(' name|'vmops' op|'.' name|'VMOps' op|',' string|"'_get_vm_state'" op|')' newline|'\n' DECL|member|_test_check_hotplug_available name|'def' name|'_test_check_hotplug_available' op|'(' nl|'\n' name|'self' op|',' name|'mock_get_vm_state' op|',' name|'expected_result' op|'=' name|'False' op|',' nl|'\n' name|'vm_gen' op|'=' name|'constants' op|'.' name|'VM_GEN_2' op|',' name|'windows_version' op|'=' name|'_WIN_VERSION_10' op|')' op|':' newline|'\n' nl|'\n' indent|' ' name|'fake_vm' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|')' newline|'\n' name|'mock_get_vm_state' op|'.' name|'return_value' op|'=' name|'os_win_const' op|'.' name|'HYPERV_VM_STATE_ENABLED' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|'.' name|'get_vm_generation' op|'.' name|'return_value' op|'=' name|'vm_gen' newline|'\n' name|'fake_check_win_vers' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'_hostutils' op|'.' name|'check_min_windows_version' newline|'\n' name|'fake_check_win_vers' op|'.' name|'return_value' op|'=' op|'(' nl|'\n' name|'windows_version' op|'==' name|'self' op|'.' name|'_WIN_VERSION_10' op|')' newline|'\n' nl|'\n' name|'result' op|'=' name|'self' op|'.' name|'_vmops' op|'.' name|'_check_hotplug_available' op|'(' name|'fake_vm' op|')' newline|'\n' nl|'\n' name|'self' op|'.' name|'assertEqual' op|'(' name|'expected_result' op|',' name|'result' op|')' newline|'\n' name|'mock_get_vm_state' op|'.' name|'assert_called_once_with' op|'(' name|'fake_vm' op|'.' name|'name' op|')' newline|'\n' name|'fake_check_win_vers' op|'.' name|'assert_called_once_with' op|'(' number|'10' op|',' number|'0' op|')' newline|'\n' nl|'\n' DECL|member|test_check_if_hotplug_available dedent|'' name|'def' name|'test_check_if_hotplug_available' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_test_check_hotplug_available' op|'(' name|'expected_result' op|'=' name|'True' op|')' newline|'\n' nl|'\n' DECL|member|test_check_if_hotplug_available_gen1 dedent|'' name|'def' name|'test_check_if_hotplug_available_gen1' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_test_check_hotplug_available' op|'(' nl|'\n' name|'expected_result' op|'=' name|'False' op|',' name|'vm_gen' op|'=' name|'constants' op|'.' name|'VM_GEN_1' op|')' newline|'\n' nl|'\n' DECL|member|test_check_if_hotplug_available_win_6_3 dedent|'' name|'def' name|'test_check_if_hotplug_available_win_6_3' op|'(' name|'self' op|')' op|':' newline|'\n' indent|' ' name|'self' op|'.' name|'_test_check_hotplug_available' op|'(' nl|'\n' name|'expected_result' op|'=' name|'False' op|',' name|'windows_version' op|'=' name|'self' op|'.' name|'_WIN_VERSION_6_3' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'.' name|'object' op|'(' name|'vmops' op|'.' name|'VMOps' op|',' string|"'_check_hotplug_available'" op|')' newline|'\n' DECL|member|test_attach_interface name|'def' name|'test_attach_interface' op|'(' name|'self' op|',' name|'mock_check_hotplug_available' op|')' op|':' newline|'\n' indent|' ' name|'mock_check_hotplug_available' op|'.' name|'return_value' op|'=' name|'True' newline|'\n' name|'fake_vm' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|')' newline|'\n' name|'fake_vif' op|'=' name|'test_virtual_interface' op|'.' name|'fake_vif' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vif_driver' op|'=' name|'mock' op|'.' name|'MagicMock' op|'(' op|')' newline|'\n' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'attach_interface' op|'(' name|'fake_vm' op|',' name|'fake_vif' op|')' newline|'\n' nl|'\n' name|'mock_check_hotplug_available' op|'.' name|'assert_called_once_with' op|'(' name|'fake_vm' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vif_driver' op|'.' name|'plug' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'fake_vm' op|',' name|'fake_vif' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|'.' name|'create_nic' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'fake_vm' op|'.' name|'name' op|',' name|'fake_vif' op|'[' string|"'id'" op|']' op|',' name|'fake_vif' op|'[' string|"'address'" op|']' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'.' name|'object' op|'(' name|'vmops' op|'.' name|'VMOps' op|',' string|"'_check_hotplug_available'" op|')' newline|'\n' DECL|member|test_attach_interface_failed name|'def' name|'test_attach_interface_failed' op|'(' name|'self' op|',' name|'mock_check_hotplug_available' op|')' op|':' newline|'\n' indent|' ' name|'mock_check_hotplug_available' op|'.' name|'return_value' op|'=' name|'False' newline|'\n' name|'self' op|'.' name|'assertRaises' op|'(' name|'exception' op|'.' name|'InterfaceAttachFailed' op|',' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'attach_interface' op|',' nl|'\n' name|'mock' op|'.' name|'MagicMock' op|'(' op|')' op|',' name|'mock' op|'.' name|'sentinel' op|'.' name|'fake_vif' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'.' name|'object' op|'(' name|'vmops' op|'.' name|'VMOps' op|',' string|"'_check_hotplug_available'" op|')' newline|'\n' DECL|member|test_detach_interface name|'def' name|'test_detach_interface' op|'(' name|'self' op|',' name|'mock_check_hotplug_available' op|')' op|':' newline|'\n' indent|' ' name|'mock_check_hotplug_available' op|'.' name|'return_value' op|'=' name|'True' newline|'\n' name|'fake_vm' op|'=' name|'fake_instance' op|'.' name|'fake_instance_obj' op|'(' name|'self' op|'.' name|'context' op|')' newline|'\n' name|'fake_vif' op|'=' name|'test_virtual_interface' op|'.' name|'fake_vif' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vif_driver' op|'=' name|'mock' op|'.' name|'MagicMock' op|'(' op|')' newline|'\n' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'detach_interface' op|'(' name|'fake_vm' op|',' name|'fake_vif' op|')' newline|'\n' nl|'\n' name|'mock_check_hotplug_available' op|'.' name|'assert_called_once_with' op|'(' name|'fake_vm' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vif_driver' op|'.' name|'unplug' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'fake_vm' op|',' name|'fake_vif' op|')' newline|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'_vmutils' op|'.' name|'destroy_nic' op|'.' name|'assert_called_once_with' op|'(' nl|'\n' name|'fake_vm' op|'.' name|'name' op|',' name|'fake_vif' op|'[' string|"'id'" op|']' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'.' name|'object' op|'(' name|'vmops' op|'.' name|'VMOps' op|',' string|"'_check_hotplug_available'" op|')' newline|'\n' DECL|member|test_detach_interface_failed name|'def' name|'test_detach_interface_failed' op|'(' name|'self' op|',' name|'mock_check_hotplug_available' op|')' op|':' newline|'\n' indent|' ' name|'mock_check_hotplug_available' op|'.' name|'return_value' op|'=' name|'False' newline|'\n' name|'self' op|'.' name|'assertRaises' op|'(' name|'exception' op|'.' name|'InterfaceDetachFailed' op|',' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'detach_interface' op|',' nl|'\n' name|'mock' op|'.' name|'MagicMock' op|'(' op|')' op|',' name|'mock' op|'.' name|'sentinel' op|'.' name|'fake_vif' op|')' newline|'\n' nl|'\n' dedent|'' op|'@' name|'mock' op|'.' name|'patch' op|'.' name|'object' op|'(' name|'vmops' op|'.' name|'VMOps' op|',' string|"'_check_hotplug_available'" op|')' newline|'\n' DECL|member|test_detach_interface_missing_instance name|'def' name|'test_detach_interface_missing_instance' op|'(' name|'self' op|',' name|'mock_check_hotplug' op|')' op|':' newline|'\n' indent|' ' name|'mock_check_hotplug' op|'.' name|'side_effect' op|'=' name|'os_win_exc' op|'.' name|'HyperVVMNotFoundException' op|'(' nl|'\n' name|'vm_name' op|'=' string|"'fake_vm'" op|')' newline|'\n' name|'self' op|'.' name|'assertRaises' op|'(' name|'exception' op|'.' name|'InterfaceDetachFailed' op|',' nl|'\n' name|'self' op|'.' name|'_vmops' op|'.' name|'detach_interface' op|',' nl|'\n' name|'mock' op|'.' name|'MagicMock' op|'(' op|')' op|',' name|'mock' op|'.' name|'sentinel' op|'.' name|'fake_vif' op|')' newline|'\n' dedent|'' dedent|'' endmarker|'' end_unit

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0.212965

0.078907

0.079632

0.949603

0.921051

0.878698

0.836805

0.803931

0.773707

0

0.001607

0.088684

119,503

9,252

89

12.916451

0.657041

0

0.95882

0

0.398626

0.099738

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0.016213

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null

0.001189

0.002918

null

0

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0

1

0

1

0

null

0

1

0

9

701e715db82b8bc5e8bb5750c5a19654ee0bf908

24,401

py

Python

kernel.py

JeremyLinux/PTGPBO

2ff498d1752be9f377236f5bbffd8ad433dce1b4

[ "MIT" ]

1

2020-09-29T15:54:11.000Z

kernel.py

JeremyLinux/PTGPBO

2ff498d1752be9f377236f5bbffd8ad433dce1b4

[ "MIT" ]

2

2019-10-23T13:44:18.000Z

2022-01-20T17:52:51.000Z

kernel.py

JeremyLinux/PTGPBO

2ff498d1752be9f377236f5bbffd8ad433dce1b4

[ "MIT" ]

2

2021-01-01T18:16:54.000Z

2022-01-20T20:34:50.000Z

import torch import torch.nn as nn from math import pi class Kernel(nn.Module): def __init__(self): super(Kernel, self).__init__() def __add__(self, other): return SumKernel(self, other) def __mul__(self, other): return ProductKernel(self, other) class SumKernel(Kernel): def __init__(self, k1, k2): super(SumKernel, self).__init__() self.k1 = k1 self.k2 = k2 self.num_params = k1.num_params + k2.num_params def __call__(self, x1, x2): out = self.k1(x1, x2) + self.k2(x1, x2) return out def grad(self, x1, x2): out = self.k1.grad(x1, x2) + self.k2.grad(x1, x2) return out def reset_params(self): self.k1.reset_params() self.k2.reset_params() def randomise_params(self): self.k1.randomise_params() self.k2.randomise_params() def get_params(self): k1_params = self.k1.get_params() k2_params = self.k2.get_params() params = k1_params + k2_params return params def set_params(self, p): self.k1.set_params = p[:self.k1.num_params] self.k2.set_params = p[self.k1.num_params:] class ProductKernel(Kernel): def __init__(self, k1, k2): super(ProductKernel, self).__init__() self.k1 = k1 self.k2 = k2 self.num_params = k1.num_params + k2.num_params def __call__(self, x1, x2): out = self.k1(x1, x2) * self.k2(x1, x2) return out def grad(self, x1, x2): out = self.k1(x1, x2).unsqueeze(-1) * self.k2.grad(x1, x2) + \ self.k1.grad(x1, x2) * self.k2(x1, x2).unsqueeze(-1) return out def reset_params(self): self.k1.reset_params() self.k2.reset_params() def randomise_params(self): self.k1.randomise_params() self.k2.randomise_params() def get_params(self): k1_params = self.k1.get_params() k2_params = self.k2.get_params() params = k1_params + k2_params return params def set_params(self, p): self.k1.set_params = p[:self.k1.num_params] self.k2.set_params = p[self.k1.num_params:] class LinearKernel(Kernel): def __init__(self, sigma, rho, c): super(LinearKernel, self).__init__() self.sigma = nn.Parameter(torch.tensor(float(sigma))) self.rho = nn.Parameter(torch.tensor(float(rho))) self.c = nn.Parameter(torch.tensor(float(c))) self.init_sigma = torch.tensor(float(sigma)) self.init_rho = torch.tensor(float(rho)) self.init_c = torch.tensor(float(c)) self.num_params = 3 def __call__(self, x1, x2): z = ((x1 - self.c) * (x2 - self.c)).sum(-1) out = self.sigma.pow(2) + self.rho.pow(2)*z return out def grad(self, x1, x2): out = self.rho.pow(2) * (x2 - self.c) return out def reset_params(self): self.sigma.data = self.init_sigma.clone() self.rho.data = self.init_rho.clone() self.c.data = self.init_c.clone() def randomise_params(self): self.sigma.data = torch.rand(1) - 0.5 self.rho.data = torch.rand(1) - 0.5 self.c.data = torch.rand(1) - 0.5 def get_params(self): params = [self.sigma.clone(), self.rho.clone(), self.c.clone()] return params def set_params(self, p): self.sigma.data = p[0] self.rho.data = p[1] self.c.data = p[2] class Matern32Kernel(Kernel): def __init__(self, sigma, rho): super(Matern32Kernel, self).__init__() self.sigma = nn.Parameter(torch.tensor(float(sigma))) self.rho = nn.Parameter(torch.tensor(float(rho))) self.init_sigma = torch.tensor(float(sigma)) self.init_rho = torch.tensor(float(rho)) self.num_params = 2 def __call__(self, x1, x2): d = (x1 - x2).pow(2).sum(-1).pow(0.5) out = self.sigma.pow(2) * (1 + 3**0.5 * d/self.rho) * \ torch.exp(-3**0.5 * d/self.rho) return out def grad(self, x1, x2): diff = x1 - x2 d = diff.pow(2).sum(-1).pow(0.5).unsqueeze(-1) out = -self.sigma.pow(2) * ((3*diff) / self.rho.pow(2)) * \ torch.exp(-3**0.5 * d/self.rho) out[out != out] = 0.0 return out def reset_params(self): self.sigma.data = self.init_sigma.clone() self.rho.data = self.init_rho.clone() def randomise_params(self): self.sigma.data = 1 + 4*torch.rand(1) self.rho.data = 1 + 4*torch.rand(1) def get_params(self): params = [self.sigma.clone(), self.rho.clone()] return params def set_params(self, p): self.sigma.data = p[0] self.rho.data = p[1] class Matern52Kernel(Kernel): def __init__(self, sigma, rho): super(Matern52Kernel, self).__init__() self.sigma = nn.Parameter(torch.tensor(float(sigma))) self.rho = nn.Parameter(torch.tensor(float(rho))) self.init_sigma = torch.tensor(float(sigma)) self.init_rho = torch.tensor(float(rho)) self.num_params = 2 def __call__(self, x1, x2): d = (x1 - x2).pow(2).sum(-1).pow(0.5) out = self.sigma.pow(2) * \ (1 + 5**0.5 * d/self.rho + (5*d.pow(2))/(3*self.rho.pow(2))) * \ torch.exp(-5**0.5 * d/self.rho) return out def grad(self, x1, x2): diff = x1 - x2 d = diff.pow(2).sum(-1).pow(0.5).unsqueeze(-1) out = -self.sigma.pow(2) * \ ((5*diff)/(3*self.rho.pow(2)) + (5*5**0.5*diff*d)/(3*self.rho.pow(3))) * \ torch.exp(-5**0.5 * d/self.rho) out[out != out] = 0.0 return out def reset_params(self): self.sigma.data = self.init_sigma.clone() self.rho.data = self.init_rho.clone() def randomise_params(self): self.sigma.data = 1 + 4*torch.rand(1) self.rho.data = 1 + 4*torch.rand(1) def get_params(self): params = [self.sigma.clone(), self.rho.clone()] return params def set_params(self, p): self.sigma.data = p[0] self.rho.data = p[1] class SquaredExponentialKernel(Kernel): def __init__(self, sigma, rho): super(SquaredExponentialKernel, self).__init__() self.sigma = nn.Parameter(torch.tensor(float(sigma))) self.rho = nn.Parameter(torch.tensor(float(rho))) self.init_sigma = torch.tensor(float(sigma)) self.init_rho = torch.tensor(float(rho)) self.num_params = 2 def __call__(self, x1, x2): d = (x1 - x2).pow(2).sum(-1).pow(0.5) out = self.sigma.pow(2) * torch.exp(-d.pow(2) / (2 * self.rho.pow(2))) return out def grad(self, x1, x2): diff = x1 - x2 d = diff.pow(2).sum(-1).pow(0.5).unsqueeze(-1) out = -self.sigma.pow(2) * (diff / (self.rho.pow(2))) * \ torch.exp(-d.pow(2) / (2 * self.rho.pow(2))) return out def reset_params(self): self.sigma.data = self.init_sigma.clone() self.rho.data = self.init_rho.clone() def randomise_params(self): self.sigma.data = 1 + 4*torch.rand(1) self.rho.data = 1 + 4*torch.rand(1) def get_params(self): params = [self.sigma.clone(), self.rho.clone()] return params def set_params(self, p): self.sigma.data = p[0] self.rho.data = p[1] class PeriodicKernel(Kernel): def __init__(self, sigma, rho, period): super(PeriodicKernel, self).__init__() self.sigma = nn.Parameter(torch.tensor(float(sigma))) self.rho = nn.Parameter(torch.tensor(float(rho))) self.period = nn.Parameter(torch.tensor(float(period))) self.init_sigma = torch.tensor(float(sigma)) self.init_rho = torch.tensor(float(rho)) self.init_period = torch.tensor(float(period)) self.num_params = 3 def __call__(self, x1, x2): d = (x1 - x2).pow(2).sum(-1).pow(0.5) out = self.sigma.pow(2) * \ torch.exp(-2*torch.sin(pi*d/self.period).pow(2)/self.rho.pow(2)) return out def grad(self, x1, x2): diff = x1 - x2 d = diff.pow(2).sum(-1).pow(0.5).unsqueeze(-1) inner = pi*d/self.period out = -((2*self.sigma.pow(2)*pi*diff)/(self.period*d*self.rho.pow(2))) * \ torch.sin(2*inner) * \ torch.exp(-2*torch.sin(inner).pow(2)/self.rho.pow(2)) out[out != out] = 0.0 return out def reset_params(self): self.sigma.data = self.init_sigma.clone() self.rho.data = self.init_rho.clone() self.period.data = self.init_period.clone() def randomise_params(self): self.sigma.data = 1 + 4*torch.rand(1) self.rho.data = 1 + 4*torch.rand(1) self.period.data = 0.5 + 4.5*torch.rand(1) def get_params(self): params = [self.sigma.data.clone(), self.rho.data.clone(), self.period.data.clone()] return params def set_params(self, p): self.sigma.data = p[0] self.rho.data = p[1] self.period.data = p[2] class AdditiveLinearKernel(Kernel): def __init__(self, sigma, rho, c): super(AdditiveLinearKernel, self).__init__() self.sigma = nn.Parameter(sigma.float()) self.rho = nn.Parameter(rho.float()) self.c = nn.Parameter(c.float()) self.init_sigma = sigma.float().clone() self.init_rho = rho.float().clone() self.init_c = c.float().clone() self.num_params = 3 def __call__(self, x1, x2): sigma = self.sigma.unsqueeze(0).unsqueeze(0) rho = self.rho.unsqueeze(0).unsqueeze(0) c = self.c.unsqueeze(0).unsqueeze(0) z = ((x1 - c) * (x2 - c)) out = (sigma.pow(2) + rho.pow(2)*z).sum(-1) return out def grad(self, x1, x2): rho = self.rho.unsqueeze(0).unsqueeze(0) c = self.c.unsqueeze(0).unsqueeze(0) out = rho.pow(2) * (x2 - c) return out def reset_params(self): self.sigma.data = self.init_sigma.clone() self.rho.data = self.init_rho.clone() self.c.data = self.init_c.clone() def randomise_params(self): self.sigma.data = torch.rand(self.sigma.size()) - 0.5 self.rho.data = torch.rand(self.rho.size()) - 0.5 self.c.data = torch.rand(self.c.size()) - 0.5 def get_params(self): params = [self.sigma.clone(), self.rho.clone(), self.c.clone()] return params def set_params(self, p): self.sigma.data = p[0] self.rho.data = p[1] self.c.data = p[2] class AdditiveMatern32Kernel(Kernel): def __init__(self, sigma, rho): super(AdditiveMatern32Kernel, self).__init__() self.sigma = nn.Parameter(sigma.float()) self.rho = nn.Parameter(rho.float()) self.init_sigma = sigma.float().clone() self.init_rho = rho.float().clone() self.num_params = 2 def __call__(self, x1, x2): sigma = self.sigma.unsqueeze(0).unsqueeze(0) rho = self.rho.unsqueeze(0).unsqueeze(0) diff = torch.abs(x1 - x2) out = (sigma.pow(2) * (1 + 3**0.5*diff/rho) * \ torch.exp(-3**0.5*diff*rho)).sum(-1) return out def grad(self, x1, x2): sigma = self.sigma.unsqueeze(0).unsqueeze(0) rho = self.rho.unsqueeze(0).unsqueeze(0) diff = x1 - x2 out = -sigma.pow(2) * \ (3*torch.sign(diff)*torch.abs(diff) / rho.pow(2)) * \ torch.exp(-3*diff/rho) return out def reset_params(self): self.sigma.data = self.init_sigma.clone() self.rho.data = self.init_rho.clone() def randomise_params(self): self.sigma.data = 1 + 4*torch.rand(self.sigma.size()) self.rho.data = 1 + 4*torch.rand(self.rho.size()) def get_params(self): params = [self.sigma.clone(), self.rho.clone()] return params def set_params(self, p): self.sigma.data = p[0] self.rho.data = p[1] class AdditiveMatern52Kernel(Kernel): def __init__(self, sigma, rho): super(AdditiveMatern52Kernel, self).__init__() self.sigma = nn.Parameter(sigma.float()) self.rho = nn.Parameter(rho.float()) self.init_sigma = sigma.float().clone() self.init_rho = rho.float().clone() self.num_params = 2 def __call__(self, x1, x2): sigma = self.sigma.unsqueeze(0).unsqueeze(0) rho = self.rho.unsqueeze(0).unsqueeze(0) diff = torch.abs(x1 - x2) out = (sigma.pow(2) * \ (1 + 5**0.5*diff/rho + (5*diff.pow(2))/(3*rho.pow(2))) * \ torch.exp(-5**0.5*diff/rho)).sum(-1) return out def grad(self, x1, x2): sigma = self.sigma.unsqueeze(0).unsqueeze(0) rho = self.rho.unsqueeze(0).unsqueeze(0) diff = x1 - x2 out = -sigma.pow(2) * \ ((5*diff)/(3*rho.pow(2)) + \ (5*5**0.5*torch.sign(diff)*diff.pow(2))/(3*rho.pow(3))) * \ torch.exp(-5**0.5 * torch.abs(diff)/rho) return out def reset_params(self): self.sigma.data = self.init_sigma.clone() self.rho.data = self.init_rho.clone() def randomise_params(self): self.sigma.data = 1 + 4*torch.rand(self.sigma.size()) self.rho.data = 1 + 4*torch.rand(self.rho.size()) def get_params(self): params = [self.sigma.clone(), self.rho.clone()] return params def set_params(self, p): self.sigma.data = p[0] self.rho.data = p[1] class AdditiveSquaredExponentialKernel(Kernel): def __init__(self, sigma, rho): super(AdditiveSquaredExponentialKernel, self).__init__() self.sigma = nn.Parameter(sigma.float()) self.rho = nn.Parameter(rho.float()) self.init_sigma = sigma.float().clone() self.init_rho = rho.float().clone() self.num_params = 2 def __call__(self, x1, x2): sigma = self.sigma.unsqueeze(0).unsqueeze(0) rho = self.rho.unsqueeze(0).unsqueeze(0) out = (sigma.pow(2) * \ torch.exp(-(x1 - x2).pow(2) / (2 * rho.pow(2)))).sum(-1) return out def grad(self, x1, x2): sigma = self.sigma.unsqueeze(0).unsqueeze(0) rho = self.rho.unsqueeze(0).unsqueeze(0) diff = x1 - x2 out = -sigma.pow(2) * (diff / rho.pow(2)) * \ torch.exp(-diff.pow(2) / (2 * rho.pow(2))) return out def reset_params(self): self.sigma.data = self.init_sigma.clone() self.rho.data = self.init_rho.clone() def randomise_params(self): self.sigma.data = 1 + 4*torch.rand(self.sigma.size()) self.rho.data = 1 + 4*torch.rand(self.rho.size()) def get_params(self): params = [self.sigma.clone(), self.rho.clone()] return params def set_params(self, p): self.sigma.data = p[0] self.rho.data = p[1] class AdditivePeriodicKernel(Kernel): def __init__(self, sigma, rho, period): super(AdditivePeriodicKernel, self).__init__() self.sigma = nn.Parameter(sigma.float()) self.rho = nn.Parameter(rho.float()) self.period = nn.Parameter(period.float()) self.init_sigma = sigma.float().clone() self.init_rho = rho.float().clone() self.init_period = period.float().clone() self.num_params = 3 def __call__(self, x1, x2): sigma = self.sigma.unsqueeze(0).unsqueeze(0) rho = self.rho.unsqueeze(0).unsqueeze(0) period = self.period.unsqueeze(0).unsqueeze(0) out = (sigma.pow(2) * \ torch.exp(-2*torch.sin(pi*torch.abs(x1-x2)/period).pow(2)/ \ rho.pow(2))).sum(-1) return out def grad(self, x1, x2): sigma = self.sigma.unsqueeze(0).unsqueeze(0) rho = self.rho.unsqueeze(0).unsqueeze(0) period = self.period.unsqueeze(0).unsqueeze(0) diff = x1 - x2 inner = pi*torch.abs(diff)/period out = -((2*sigma.pow(2)*pi*torch.sign(diff))/(period*rho.pow(2))) * \ torch.sin(2*inner) * \ torch.exp(-2*torch.sin(inner).pow(2)/rho.pow(2)) return out def reset_params(self): self.sigma.data = self.init_sigma.clone() self.rho.data = self.init_rho.clone() self.period.data = self.init_period.clone() def randomise_params(self): self.sigma.data = 1 + 4*torch.rand(self.sigma.size()) self.rho.data = 1 + 4*torch.rand(self.rho.size()) self.period.data = 0.9 + 0.2*torch.rand(self.period.size()) def get_params(self): params = [self.sigma.clone(), self.rho.clone(), self.period.clone()] return params def set_params(self, p): self.sigma.data = p[0] self.rho.data = p[1] self.period.data = p[2] class MultiplicativeLinearKernel(Kernel): def __init__(self, sigma, rho, c): super(MultiplicativeLinearKernel, self).__init__() self.sigma = nn.Parameter(sigma.float()) self.rho = nn.Parameter(rho.float()) self.c = nn.Parameter(c.float()) self.init_sigma = sigma.float().clone() self.init_rho = rho.float().clone() self.init_c = c.float().clone() self.num_params = 3 def __call__(self, x1, x2): sigma = self.sigma.unsqueeze(0).unsqueeze(0) rho = self.rho.unsqueeze(0).unsqueeze(0) c = self.c.unsqueeze(0).unsqueeze(0) z = ((x1 - c) * (x2 - c)) out = (sigma.pow(2) + rho.pow(2)*z).prod(-1) return out def grad(self, x1, x2): raise NotImplementedError() def reset_params(self): self.sigma.data = self.init_sigma.clone() self.rho.data = self.init_rho.clone() self.c.data = self.init_c.clone() def randomise_params(self): self.sigma.data = torch.rand(self.sigma.size()) - 0.5 self.rho.data = torch.rand(self.rho.size()) - 0.5 self.c.data = torch.rand(self.c.size()) - 0.5 def get_params(self): params = [self.sigma.clone(), self.rho.clone(), self.c.clone()] return params def set_params(self, p): self.sigma.data = p[0] self.rho.data = p[1] self.c.data = p[2] class MultiplicativeMatern32Kernel(Kernel): def __init__(self, sigma, rho): super(MultiplicativeMatern32Kernel, self).__init__() self.sigma = nn.Parameter(sigma.float()) self.rho = nn.Parameter(rho.float()) self.init_sigma = sigma.float().clone() self.init_rho = rho.float().clone() self.num_params = 2 def __call__(self, x1, x2): sigma = self.sigma.unsqueeze(0).unsqueeze(0) rho = self.rho.unsqueeze(0).unsqueeze(0) diff = torch.abs(x1 - x2) out = (sigma.pow(2) * (1 + 3**0.5*diff/rho) * \ torch.exp(-3**0.5*diff*rho)).prod(-1) return out def grad(self, x1, x2): raise NotImplementedError() def reset_params(self): self.sigma.data = self.init_sigma.clone() self.rho.data = self.init_rho.clone() def randomise_params(self): self.sigma.data = 1 + 4*torch.rand(self.sigma.size()) self.rho.data = 1 + 4*torch.rand(self.rho.size()) def get_params(self): params = [self.sigma.clone(), self.rho.clone()] return params def set_params(self, p): self.sigma.data = p[0] self.rho.data = p[1] class MultiplicativeMatern52Kernel(Kernel): def __init__(self, sigma, rho): super(MultiplicativeMatern52Kernel, self).__init__() self.sigma = nn.Parameter(sigma.float()) self.rho = nn.Parameter(rho.float()) self.init_sigma = sigma.float().clone() self.init_rho = rho.float().clone() self.num_params = 2 def __call__(self, x1, x2): sigma = self.sigma.unsqueeze(0).unsqueeze(0) rho = self.rho.unsqueeze(0).unsqueeze(0) diff = torch.abs(x1 - x2) out = (sigma.pow(2) * \ (1 + 5**0.5*diff/rho + (5*diff.pow(2))/(3*rho.pow(2))) * \ torch.exp(-5**0.5*diff/rho)).prod(-1) return out def grad(self, x1, x2): raise NotImplementedError() def reset_params(self): self.sigma.data = self.init_sigma.clone() self.rho.data = self.init_rho.clone() def randomise_params(self): self.sigma.data = 1 + 4*torch.rand(self.sigma.size()) self.rho.data = 1 + 4*torch.rand(self.rho.size()) def get_params(self): params = [self.sigma.clone(), self.rho.clone()] return params def set_params(self, p): self.sigma.data = p[0] self.rho.data = p[1] class MultiplicativeSquaredExponentialKernel(Kernel): def __init__(self, sigma, rho): super(MultiplicativeSquaredExponentialKernel, self).__init__() self.sigma = nn.Parameter(sigma.float()) self.rho = nn.Parameter(rho.float()) self.init_sigma = sigma.float().clone() self.init_rho = rho.float().clone() self.num_params = 2 def __call__(self, x1, x2): sigma = self.sigma.unsqueeze(0).unsqueeze(0) rho = self.rho.unsqueeze(0).unsqueeze(0) out = (sigma.pow(2) * \ torch.exp(-(x1 - x2).pow(2) / (2 * rho.pow(2)))).prod(-1) return out def grad(self, x1, x2): raise NotImplementedError() def reset_params(self): self.sigma.data = self.init_sigma.clone() self.rho.data = self.init_rho.clone() def randomise_params(self): self.sigma.data = 1 + 4*torch.rand(self.sigma.size()) self.rho.data = 1 + 4*torch.rand(self.rho.size()) def get_params(self): params = [self.sigma.clone(), self.rho.clone()] return params def set_params(self, p): self.sigma.data = p[0] self.rho.data = p[1] class MultiplicativePeriodicKernel(Kernel): def __init__(self, sigma, rho, period): super(MultiplicativePeriodicKernel, self).__init__() self.sigma = nn.Parameter(sigma.float()) self.rho = nn.Parameter(rho.float()) self.period = nn.Parameter(period.float()) self.init_sigma = sigma.float().clone() self.init_rho = rho.float().clone() self.init_period = period.float().clone() self.num_params = 3 def __call__(self, x1, x2): sigma = self.sigma.unsqueeze(0).unsqueeze(0) rho = self.rho.unsqueeze(0).unsqueeze(0) period = self.period.unsqueeze(0).unsqueeze(0) out = (sigma.pow(2) * \ torch.exp(-2*torch.sin(pi*torch.abs(x1-x2)/period).pow(2)/ \ rho.pow(2))).prod(-1) return out def grad(self, x1, x2): raise NotImplementedError() def reset_params(self): self.sigma.data = self.init_sigma.clone() self.rho.data = self.init_rho.clone() self.period.data = self.init_period.clone() def randomise_params(self): self.sigma.data = 1 + 4*torch.rand(self.sigma.size()) self.rho.data = 1 + 4*torch.rand(self.rho.size()) self.period.data = 0.9 + 0.2*torch.rand(self.period.size()) def get_params(self): params = [self.sigma.clone(), self.rho.clone(), self.period.clone()] return params def set_params(self, p): self.sigma.data = p[0] self.rho.data = p[1] self.period.data = p[2]

34.271067

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0.053558

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0.895945

0.885922

0.850268

0.82938

0.820735

0

0.036702

0.293185

24,401

712

84

34.271067

0.721111

0

0.833916

0

1

0.213287

false

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0.005245

0.003497

0.333916

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0

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1

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null

0

1

0

8

560849b3fd6adaf67cd0777960481f5c272572c4

6,898

py

Python

cvnets/models/classification/config/mobilevit.py

apple/ml-cvnets

84d992f413e52c0468f86d23196efd9dad885e6f

[ "AML" ]

209

2021-10-30T08:32:10.000Z

2022-03-31T16:18:03.000Z

cvnets/models/classification/config/mobilevit.py

apple/ml-cvnets

84d992f413e52c0468f86d23196efd9dad885e6f

[ "AML" ]

12

2021-12-04T10:47:11.000Z

2022-03-31T15:39:40.000Z

cvnets/models/classification/config/mobilevit.py

apple/ml-cvnets

84d992f413e52c0468f86d23196efd9dad885e6f

[ "AML" ]

50

2021-11-01T08:15:02.000Z

2022-03-29T08:17:34.000Z

# # For licensing see accompanying LICENSE file. # Copyright (C) 2022 Apple Inc. All Rights Reserved. # from typing import Dict from utils import logger def get_configuration(opts) -> Dict: mode = getattr(opts, "model.classification.mit.mode", "small") if mode is None: logger.error("Please specify mode") head_dim = getattr(opts, "model.classification.mit.head_dim", None) num_heads = getattr(opts, "model.classification.mit.number_heads", 4) if head_dim is not None: if num_heads is not None: logger.error( "--model.classification.mit.head-dim and --model.classification.mit.number-heads " "are mutually exclusive." ) elif num_heads is not None: if head_dim is not None: logger.error( "--model.classification.mit.head-dim and --model.classification.mit.number-heads " "are mutually exclusive." ) mode = mode.lower() if mode == "xx_small": mv2_exp_mult = 2 config = { "layer1": { "out_channels": 16, "expand_ratio": mv2_exp_mult, "num_blocks": 1, "stride": 1, "block_type": "mv2", }, "layer2": { "out_channels": 24, "expand_ratio": mv2_exp_mult, "num_blocks": 3, "stride": 2, "block_type": "mv2", }, "layer3": { # 28x28 "out_channels": 48, "transformer_channels": 64, "ffn_dim": 128, "transformer_blocks": 2, "patch_h": 2, # 8, "patch_w": 2, # 8, "stride": 2, "mv_expand_ratio": mv2_exp_mult, "head_dim": head_dim, "num_heads": num_heads, "block_type": "mobilevit", }, "layer4": { # 14x14 "out_channels": 64, "transformer_channels": 80, "ffn_dim": 160, "transformer_blocks": 4, "patch_h": 2, # 4, "patch_w": 2, # 4, "stride": 2, "mv_expand_ratio": mv2_exp_mult, "head_dim": head_dim, "num_heads": num_heads, "block_type": "mobilevit", }, "layer5": { # 7x7 "out_channels": 80, "transformer_channels": 96, "ffn_dim": 192, "transformer_blocks": 3, "patch_h": 2, "patch_w": 2, "stride": 2, "mv_expand_ratio": mv2_exp_mult, "head_dim": head_dim, "num_heads": num_heads, "block_type": "mobilevit", }, "last_layer_exp_factor": 4, } elif mode == "x_small": mv2_exp_mult = 4 config = { "layer1": { "out_channels": 32, "expand_ratio": mv2_exp_mult, "num_blocks": 1, "stride": 1, "block_type": "mv2", }, "layer2": { "out_channels": 48, "expand_ratio": mv2_exp_mult, "num_blocks": 3, "stride": 2, "block_type": "mv2", }, "layer3": { # 28x28 "out_channels": 64, "transformer_channels": 96, "ffn_dim": 192, "transformer_blocks": 2, "patch_h": 2, "patch_w": 2, "stride": 2, "mv_expand_ratio": mv2_exp_mult, "head_dim": head_dim, "num_heads": num_heads, "block_type": "mobilevit", }, "layer4": { # 14x14 "out_channels": 80, "transformer_channels": 120, "ffn_dim": 240, "transformer_blocks": 4, "patch_h": 2, "patch_w": 2, "stride": 2, "mv_expand_ratio": mv2_exp_mult, "head_dim": head_dim, "num_heads": num_heads, "block_type": "mobilevit", }, "layer5": { # 7x7 "out_channels": 96, "transformer_channels": 144, "ffn_dim": 288, "transformer_blocks": 3, "patch_h": 2, "patch_w": 2, "stride": 2, "mv_expand_ratio": mv2_exp_mult, "head_dim": head_dim, "num_heads": num_heads, "block_type": "mobilevit", }, "last_layer_exp_factor": 4, } elif mode == "small": mv2_exp_mult = 4 config = { "layer1": { "out_channels": 32, "expand_ratio": mv2_exp_mult, "num_blocks": 1, "stride": 1, "block_type": "mv2", }, "layer2": { "out_channels": 64, "expand_ratio": mv2_exp_mult, "num_blocks": 3, "stride": 2, "block_type": "mv2", }, "layer3": { # 28x28 "out_channels": 96, "transformer_channels": 144, "ffn_dim": 288, "transformer_blocks": 2, "patch_h": 2, "patch_w": 2, "stride": 2, "mv_expand_ratio": mv2_exp_mult, "head_dim": head_dim, "num_heads": num_heads, "block_type": "mobilevit", }, "layer4": { # 14x14 "out_channels": 128, "transformer_channels": 192, "ffn_dim": 384, "transformer_blocks": 4, "patch_h": 2, "patch_w": 2, "stride": 2, "mv_expand_ratio": mv2_exp_mult, "head_dim": head_dim, "num_heads": num_heads, "block_type": "mobilevit", }, "layer5": { # 7x7 "out_channels": 160, "transformer_channels": 240, "ffn_dim": 480, "transformer_blocks": 3, "patch_h": 2, "patch_w": 2, "stride": 2, "mv_expand_ratio": mv2_exp_mult, "head_dim": head_dim, "num_heads": num_heads, "block_type": "mobilevit", }, "last_layer_exp_factor": 4, } else: raise NotImplementedError return config

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py

Python

test/features/eff_sats/fixtures_eff_sat.py

NikkaZ/dbtvault_spark

383723cd2a35a0bc7b82fd4e77fb1eda0f68cb07

[ "Apache-2.0" ]

null

test/features/eff_sats/fixtures_eff_sat.py

NikkaZ/dbtvault_spark

383723cd2a35a0bc7b82fd4e77fb1eda0f68cb07

[ "Apache-2.0" ]

null

test/features/eff_sats/fixtures_eff_sat.py

NikkaZ/dbtvault_spark

383723cd2a35a0bc7b82fd4e77fb1eda0f68cb07

[ "Apache-2.0" ]

null

from behave import fixture @fixture def eff_satellite(context): """ Define the structures and metadata to load effectivity satellites """ context.hashed_columns = { "STG_CUSTOMER": { "CUSTOMER_ORDER_PK": ["CUSTOMER_ID", "ORDER_ID"], "CUSTOMER_PK": "CUSTOMER_ID", "ORDER_PK": "ORDER_ID" } } context.vault_structure_columns = { "EFF_SAT": { "src_pk": "CUSTOMER_ORDER_PK", "src_dfk": ["ORDER_PK"], "src_sfk": "CUSTOMER_PK", "src_start_date": "START_DATE", "src_end_date": "END_DATE", "src_eff": "EFFECTIVE_FROM", "src_ldts": "LOAD_DATE", "src_source": "SOURCE" } } context.seed_config = { "RAW_STAGE": { "+column_types": { "CUSTOMER_ID": "NUMBER(38, 0)", "ORDER_ID": "VARCHAR", "START_DATE": "DATE", "END_DATE": "DATE", "LOAD_DATE": "DATE", "SOURCE": "VARCHAR" } }, "EFF_SAT": { "+column_types": { "CUSTOMER_ORDER_PK": "BINARY(16)", "CUSTOMER_PK": "BINARY(16)", "ORDER_PK": "BINARY(16)", "START_DATE": "DATE", "END_DATE": "DATE", "EFFECTIVE_FROM": "DATE", "LOAD_DATE": "DATE", "SOURCE": "VARCHAR" } } } @fixture def eff_satellite_testing_auto_end_dating(context): """ Define the structures and metadata to load effectivity satellites """ context.hashed_columns = { "STG_CUSTOMER_ORDER": { "CUSTOMER_ORDER_PK": ["CUSTOMER_ID", "ORDER_ID"], "CUSTOMER_PK": "CUSTOMER_ID", "ORDER_PK": "ORDER_ID" }, "STG_ORDER_CUSTOMER": { "ORDER_CUSTOMER_PK": ["CUSTOMER_ID", "ORDER_ID"], "CUSTOMER_PK": "CUSTOMER_ID", "ORDER_PK": "ORDER_ID" } } context.vault_structure_columns = { "LINK_CUSTOMER_ORDER": { "source_model": "STG_CUSTOMER_ORDER", "src_pk": "CUSTOMER_ORDER_PK", "src_fk": ["CUSTOMER_PK", "ORDER_PK"], "src_ldts": "LOAD_DATETIME", "src_source": "SOURCE" }, "LINK_ORDER_CUSTOMER": { "source_model": "STG_ORDER_CUSTOMER", "src_pk": "ORDER_CUSTOMER_PK", "src_fk": ["CUSTOMER_PK", "ORDER_PK"], "src_ldts": "LOAD_DATETIME", "src_source": "SOURCE" }, "EFF_SAT_CUSTOMER_ORDER": { "source_model": "STG_CUSTOMER_ORDER", "src_pk": "CUSTOMER_ORDER_PK", "src_dfk": ["CUSTOMER_PK"], "src_sfk": "ORDER_PK", "src_start_date": "START_DATE", "src_end_date": "END_DATE", "src_eff": "EFFECTIVE_FROM", "src_ldts": "LOAD_DATETIME", "src_source": "SOURCE" }, "EFF_SAT_ORDER_CUSTOMER": { "source_model": "STG_ORDER_CUSTOMER", "src_pk": "ORDER_CUSTOMER_PK", "src_dfk": ["ORDER_PK"], "src_sfk": "CUSTOMER_PK", "src_start_date": "START_DATE", "src_end_date": "END_DATE", "src_eff": "EFFECTIVE_FROM", "src_ldts": "LOAD_DATETIME", "src_source": "SOURCE" } } context.seed_config = { "RAW_STAGE_CUSTOMER_ORDER": { "+column_types": { "CUSTOMER_ID": "VARCHAR", "ORDER_ID": "VARCHAR", "START_DATE": "DATETIME", "END_DATE": "DATETIME", "EFFECTIVE_FROM": "DATETIME", "LOAD_DATETIME": "DATETIME", "SOURCE": "VARCHAR" } }, "RAW_STAGE_ORDER_CUSTOMER": { "+column_types": { "CUSTOMER_ID": "VARCHAR", "ORDER_ID": "VARCHAR", "START_DATE": "DATETIME", "END_DATE": "DATETIME", "EFFECTIVE_FROM": "DATETIME", "LOAD_DATETIME": "DATETIME", "SOURCE": "VARCHAR" } }, "LINK_CUSTOMER_ORDER": { "+column_types": { "CUSTOMER_ORDER_PK": "BINARY(16)", "CUSTOMER_PK": "BINARY(16)", "ORDER_PK": "BINARY(16)", "LOAD_DATETIME": "DATETIME", "SOURCE": "VARCHAR" } }, "LINK_ORDER_CUSTOMER": { "+column_types": { "ORDER_CUSTOMER_PK": "BINARY(16)", "CUSTOMER_PK": "BINARY(16)", "ORDER_PK": "BINARY(16)", "LOAD_DATETIME": "DATETIME", "SOURCE": "VARCHAR" } }, "EFF_SAT_CUSTOMER_ORDER": { "+column_types": { "CUSTOMER_ORDER_PK": "BINARY(16)", "CUSTOMER_PK": "BINARY(16)", "ORDER_PK": "BINARY(16)", "START_DATE": "DATETIME", "END_DATE": "DATETIME", "EFFECTIVE_FROM": "DATETIME", "LOAD_DATETIME": "DATETIME", "SOURCE": "VARCHAR" } }, "EFF_SAT_ORDER_CUSTOMER": { "+column_types": { "ORDER_CUSTOMER_PK": "BINARY(16)", "CUSTOMER_PK": "BINARY(16)", "ORDER_PK": "BINARY(16)", "START_DATE": "DATETIME", "END_DATE": "DATETIME", "EFFECTIVE_FROM": "DATETIME", "LOAD_DATETIME": "DATETIME", "SOURCE": "VARCHAR" } } } @fixture def eff_satellite_multipart(context): """ Define the structures and metadata to load effectivity satellites with multipart keys """ context.hashed_columns = { "STG_CUSTOMER": { "CUSTOMER_ORDER_PK": ["CUSTOMER_ID", "ORDER_ID", "NATION_ID", "PLATFORM_ID", "ORGANISATION_ID"], "CUSTOMER_PK": "CUSTOMER_ID", "NATION_PK": "NATION_ID", "ORDER_PK": "ORDER_ID", "PLATFORM_PK": "PLATFORM_ID", "ORGANISATION_PK": "ORGANISATION_ID" } } context.vault_structure_columns = { "EFF_SAT": { "src_pk": "CUSTOMER_ORDER_PK", "src_dfk": ["ORDER_PK", "PLATFORM_PK", "ORGANISATION_PK"], "src_sfk": ["CUSTOMER_PK", "NATION_PK"], "src_start_date": "START_DATE", "src_end_date": "END_DATE", "src_eff": "EFFECTIVE_FROM", "src_ldts": "LOAD_DATE", "src_source": "SOURCE" } } context.seed_config = { "RAW_STAGE": { "+column_types": { "CUSTOMER_ID": "NUMBER(38, 0)", "NATION_ID": "VARCHAR", "ORDER_ID": "VARCHAR", "PLATFORM_ID": "VARCHAR", "ORGANISATION_ID": "VARCHAR", "START_DATE": "DATE", "END_DATE": "DATE", "LOAD_DATE": "DATE", "SOURCE": "VARCHAR" } }, "EFF_SAT": { "+column_types": { "CUSTOMER_ORDER_PK": "BINARY(16)", "ORDER_PK": "BINARY(16)", "PLATFORM_PK": "BINARY(16)", "ORGANISATION_PK": "BINARY(16)", "CUSTOMER_PK": "BINARY(16)", "NATION_PK": "BINARY(16)", "START_DATE": "DATE", "END_DATE": "DATE", "EFFECTIVE_FROM": "DATE", "LOAD_DATE": "DATE", "SOURCE": "VARCHAR" } } } @fixture def eff_satellite_bigquery(context): """ Define the structures and metadata to load effectivity satellites """ context.hashed_columns = { "STG_CUSTOMER": { "CUSTOMER_ORDER_PK": ["CUSTOMER_ID", "ORDER_ID"], "CUSTOMER_PK": "CUSTOMER_ID", "ORDER_PK": "ORDER_ID" } } context.vault_structure_columns = { "EFF_SAT": { "src_pk": "CUSTOMER_ORDER_PK", "src_dfk": ["ORDER_PK"], "src_sfk": "CUSTOMER_PK", "src_start_date": "START_DATE", "src_end_date": "END_DATE", "src_eff": "EFFECTIVE_FROM", "src_ldts": "LOAD_DATE", "src_source": "SOURCE" } } context.seed_config = { "RAW_STAGE": { "+column_types": { "CUSTOMER_ID": "NUMERIC", "ORDER_ID": "STRING", "START_DATE": "DATE", "END_DATE": "DATE", "LOAD_DATE": "DATE", "SOURCE": "STRING" } }, "EFF_SAT": { "+column_types": { "CUSTOMER_ORDER_PK": "STRING", "CUSTOMER_PK": "STRING", "ORDER_PK": "STRING", "START_DATE": "DATE", "END_DATE": "DATE", "EFFECTIVE_FROM": "DATE", "LOAD_DATE": "DATE", "SOURCE": "STRING" } } } @fixture def eff_satellite_testing_auto_end_dating_bigquery(context): """ Define the structures and metadata to load effectivity satellites """ context.hashed_columns = { "STG_CUSTOMER_ORDER": { "CUSTOMER_ORDER_PK": ["CUSTOMER_ID", "ORDER_ID"], "CUSTOMER_PK": "CUSTOMER_ID", "ORDER_PK": "ORDER_ID" }, "STG_ORDER_CUSTOMER": { "ORDER_CUSTOMER_PK": ["CUSTOMER_ID", "ORDER_ID"], "CUSTOMER_PK": "CUSTOMER_ID", "ORDER_PK": "ORDER_ID" } } context.vault_structure_columns = { "LINK_CUSTOMER_ORDER": { "source_model": "STG_CUSTOMER_ORDER", "src_pk": "CUSTOMER_ORDER_PK", "src_fk": ["CUSTOMER_PK", "ORDER_PK"], "src_ldts": "LOAD_DATETIME", "src_source": "SOURCE" }, "LINK_ORDER_CUSTOMER": { "source_model": "STG_ORDER_CUSTOMER", "src_pk": "ORDER_CUSTOMER_PK", "src_fk": ["CUSTOMER_PK", "ORDER_PK"], "src_ldts": "LOAD_DATETIME", "src_source": "SOURCE" }, "EFF_SAT_CUSTOMER_ORDER": { "source_model": "STG_CUSTOMER_ORDER", "src_pk": "CUSTOMER_ORDER_PK", "src_dfk": ["CUSTOMER_PK"], "src_sfk": "ORDER_PK", "src_start_date": "START_DATE", "src_end_date": "END_DATE", "src_eff": "EFFECTIVE_FROM", "src_ldts": "LOAD_DATETIME", "src_source": "SOURCE" }, "EFF_SAT_ORDER_CUSTOMER": { "source_model": "STG_ORDER_CUSTOMER", "src_pk": "ORDER_CUSTOMER_PK", "src_dfk": ["ORDER_PK"], "src_sfk": "CUSTOMER_PK", "src_start_date": "START_DATE", "src_end_date": "END_DATE", "src_eff": "EFFECTIVE_FROM", "src_ldts": "LOAD_DATETIME", "src_source": "SOURCE" } } context.seed_config = { "RAW_STAGE_CUSTOMER_ORDER": { "+column_types": { "CUSTOMER_ID": "STRING", "ORDER_ID": "STRING", "START_DATE": "DATETIME", "END_DATE": "DATETIME", "EFFECTIVE_FROM": "DATETIME", "LOAD_DATETIME": "DATETIME", "SOURCE": "STRING" } }, "RAW_STAGE_ORDER_CUSTOMER": { "+column_types": { "CUSTOMER_ID": "STRING", "ORDER_ID": "STRING", "START_DATE": "DATETIME", "END_DATE": "DATETIME", "EFFECTIVE_FROM": "DATETIME", "LOAD_DATETIME": "DATETIME", "SOURCE": "STRING" } }, "LINK_CUSTOMER_ORDER": { "+column_types": { "CUSTOMER_ORDER_PK": "STRING", "CUSTOMER_PK": "STRING", "ORDER_PK": "STRING", "LOAD_DATETIME": "DATETIME", "SOURCE": "STRING" } }, "LINK_ORDER_CUSTOMER": { "+column_types": { "ORDER_CUSTOMER_PK": "STRING", "CUSTOMER_PK": "STRING", "ORDER_PK": "STRING", "LOAD_DATETIME": "DATETIME", "SOURCE": "STRING" } }, "EFF_SAT_CUSTOMER_ORDER": { "+column_types": { "CUSTOMER_ORDER_PK": "STRING", "CUSTOMER_PK": "STRING", "ORDER_PK": "STRING", "START_DATE": "DATETIME", "END_DATE": "DATETIME", "EFFECTIVE_FROM": "DATETIME", "LOAD_DATETIME": "DATETIME", "SOURCE": "STRING" } }, "EFF_SAT_ORDER_CUSTOMER": { "+column_types": { "ORDER_CUSTOMER_PK": "STRING", "CUSTOMER_PK": "STRING", "ORDER_PK": "STRING", "START_DATE": "DATETIME", "END_DATE": "DATETIME", "EFFECTIVE_FROM": "DATETIME", "LOAD_DATETIME": "DATETIME", "SOURCE": "STRING" } } } @fixture def eff_satellite_multipart_bigquery(context): """ Define the structures and metadata to load effectivity satellites with multipart keys """ context.hashed_columns = { "STG_CUSTOMER": { "CUSTOMER_ORDER_PK": ["CUSTOMER_ID", "ORDER_ID", "NATION_ID", "PLATFORM_ID", "ORGANISATION_ID"], "CUSTOMER_PK": "CUSTOMER_ID", "NATION_PK": "NATION_ID", "ORDER_PK": "ORDER_ID", "PLATFORM_PK": "PLATFORM_ID", "ORGANISATION_PK": "ORGANISATION_ID" } } context.vault_structure_columns = { "EFF_SAT": { "src_pk": "CUSTOMER_ORDER_PK", "src_dfk": ["ORDER_PK", "PLATFORM_PK", "ORGANISATION_PK"], "src_sfk": ["CUSTOMER_PK", "NATION_PK"], "src_start_date": "START_DATE", "src_end_date": "END_DATE", "src_eff": "EFFECTIVE_FROM", "src_ldts": "LOAD_DATE", "src_source": "SOURCE" } } context.seed_config = { "RAW_STAGE": { "+column_types": { "CUSTOMER_ID": "NUMERIC", "NATION_ID": "STRING", "ORDER_ID": "STRING", "PLATFORM_ID": "STRING", "ORGANISATION_ID": "STRING", "START_DATE": "DATE", "END_DATE": "DATE", "LOAD_DATE": "DATE", "SOURCE": "STRING" } }, "EFF_SAT": { "+column_types": { "CUSTOMER_ORDER_PK": "STRING", "ORDER_PK": "STRING", "PLATFORM_PK": "STRING", "ORGANISATION_PK": "STRING", "CUSTOMER_PK": "STRING", "NATION_PK": "STRING", "START_DATE": "DATE", "END_DATE": "DATE", "EFFECTIVE_FROM": "DATE", "LOAD_DATE": "DATE", "SOURCE": "STRING" } } } @fixture def eff_satellite_sqlserver(context): """ Define the structures and metadata to load effectivity satellites """ context.hashed_columns = { "STG_CUSTOMER": { "CUSTOMER_ORDER_PK": ["CUSTOMER_ID", "ORDER_ID"], "CUSTOMER_PK": "CUSTOMER_ID", "ORDER_PK": "ORDER_ID" } } context.vault_structure_columns = { "EFF_SAT": { "src_pk": "CUSTOMER_ORDER_PK", "src_dfk": ["ORDER_PK"], "src_sfk": "CUSTOMER_PK", "src_start_date": "START_DATE", "src_end_date": "END_DATE", "src_eff": "EFFECTIVE_FROM", "src_ldts": "LOAD_DATE", "src_source": "SOURCE" } } context.seed_config = { "RAW_STAGE": { "+column_types": { "CUSTOMER_ID": "DECIMAL(38, 0)", "ORDER_ID": "VARCHAR(50)", "START_DATE": "DATE", "END_DATE": "DATE", "EFFECTIVE_FROM": "DATE", "LOAD_DATE": "DATE", "SOURCE": "VARCHAR(50)" } }, "EFF_SAT": { "+column_types": { "CUSTOMER_ORDER_PK": "BINARY(16)", "CUSTOMER_PK": "BINARY(16)", "ORDER_PK": "BINARY(16)", "START_DATE": "DATE", "END_DATE": "DATE", "EFFECTIVE_FROM": "DATE", "LOAD_DATE": "DATE", "SOURCE": "VARCHAR(50)" } } } @fixture def eff_satellite_testing_auto_end_dating_sqlserver(context): """ Define the structures and metadata to load effectivity satellites """ context.hashed_columns = { "STG_CUSTOMER_ORDER": { "CUSTOMER_ORDER_PK": ["CUSTOMER_ID", "ORDER_ID"], "CUSTOMER_PK": "CUSTOMER_ID", "ORDER_PK": "ORDER_ID" }, "STG_ORDER_CUSTOMER": { "ORDER_CUSTOMER_PK": ["CUSTOMER_ID", "ORDER_ID"], "CUSTOMER_PK": "CUSTOMER_ID", "ORDER_PK": "ORDER_ID" } } context.vault_structure_columns = { "LINK_CUSTOMER_ORDER": { "source_model": "STG_CUSTOMER_ORDER", "src_pk": "CUSTOMER_ORDER_PK", "src_fk": ["CUSTOMER_PK", "ORDER_PK"], "src_ldts": "LOAD_DATETIME", "src_source": "SOURCE" }, "LINK_ORDER_CUSTOMER": { "source_model": "STG_ORDER_CUSTOMER", "src_pk": "ORDER_CUSTOMER_PK", "src_fk": ["CUSTOMER_PK", "ORDER_PK"], "src_ldts": "LOAD_DATETIME", "src_source": "SOURCE" }, "EFF_SAT_CUSTOMER_ORDER": { "source_model": "STG_CUSTOMER_ORDER", "src_pk": "CUSTOMER_ORDER_PK", "src_dfk": ["CUSTOMER_PK"], "src_sfk": "ORDER_PK", "src_start_date": "START_DATE", "src_end_date": "END_DATE", "src_eff": "EFFECTIVE_FROM", "src_ldts": "LOAD_DATETIME", "src_source": "SOURCE" }, "EFF_SAT_ORDER_CUSTOMER": { "source_model": "STG_ORDER_CUSTOMER", "src_pk": "ORDER_CUSTOMER_PK", "src_dfk": ["ORDER_PK"], "src_sfk": "CUSTOMER_PK", "src_start_date": "START_DATE", "src_end_date": "END_DATE", "src_eff": "EFFECTIVE_FROM", "src_ldts": "LOAD_DATETIME", "src_source": "SOURCE" } } context.seed_config = { "RAW_STAGE_CUSTOMER_ORDER": { "+column_types": { "CUSTOMER_ID": "VARCHAR(50)", "ORDER_ID": "VARCHAR(50)", "START_DATE": "DATETIME", "END_DATE": "DATETIME", "EFFECTIVE_FROM": "DATETIME", "LOAD_DATETIME": "DATETIME", "SOURCE": "VARCHAR(50)" } }, "RAW_STAGE_ORDER_CUSTOMER": { "+column_types": { "CUSTOMER_ID": "VARCHAR(50)", "ORDER_ID": "VARCHAR(50)", "START_DATE": "DATETIME", "END_DATE": "DATETIME", "EFFECTIVE_FROM": "DATETIME", "LOAD_DATETIME": "DATETIME", "SOURCE": "VARCHAR(50)" } }, "LINK_CUSTOMER_ORDER": { "+column_types": { "CUSTOMER_ORDER_PK": "BINARY(16)", "CUSTOMER_PK": "BINARY(16)", "ORDER_PK": "BINARY(16)", "LOAD_DATETIME": "DATETIME", "SOURCE": "VARCHAR(50)" } }, "LINK_ORDER_CUSTOMER": { "+column_types": { "ORDER_CUSTOMER_PK": "BINARY(16)", "CUSTOMER_PK": "BINARY(16)", "ORDER_PK": "BINARY(16)", "LOAD_DATETIME": "DATETIME", "SOURCE": "VARCHAR(50)" } }, "EFF_SAT_CUSTOMER_ORDER": { "+column_types": { "CUSTOMER_ORDER_PK": "BINARY(16)", "CUSTOMER_PK": "BINARY(16)", "ORDER_PK": "BINARY(16)", "START_DATE": "DATETIME", "END_DATE": "DATETIME", "EFFECTIVE_FROM": "DATETIME", "LOAD_DATETIME": "DATETIME", "SOURCE": "VARCHAR(50)" } }, "EFF_SAT_ORDER_CUSTOMER": { "+column_types": { "ORDER_CUSTOMER_PK": "BINARY(16)", "CUSTOMER_PK": "BINARY(16)", "ORDER_PK": "BINARY(16)", "START_DATE": "DATETIME", "END_DATE": "DATETIME", "EFFECTIVE_FROM": "DATETIME", "LOAD_DATETIME": "DATETIME", "SOURCE": "VARCHAR(50)" } } } @fixture def eff_satellite_multipart_sqlserver(context): """ Define the structures and metadata to load effectivity satellites with multipart keys """ context.hashed_columns = { "STG_CUSTOMER": { "CUSTOMER_ORDER_PK": ["CUSTOMER_ID", "ORDER_ID", "NATION_ID", "PLATFORM_ID", "ORGANISATION_ID"], "CUSTOMER_PK": "CUSTOMER_ID", "NATION_PK": "NATION_ID", "ORDER_PK": "ORDER_ID", "PLATFORM_PK": "PLATFORM_ID", "ORGANISATION_PK": "ORGANISATION_ID" } } context.vault_structure_columns = { "EFF_SAT": { "src_pk": "CUSTOMER_ORDER_PK", "src_dfk": ["ORDER_PK", "PLATFORM_PK", "ORGANISATION_PK"], "src_sfk": ["CUSTOMER_PK", "NATION_PK"], "src_start_date": "START_DATE", "src_end_date": "END_DATE", "src_eff": "EFFECTIVE_FROM", "src_ldts": "LOAD_DATE", "src_source": "SOURCE" } } context.seed_config = { "RAW_STAGE": { "+column_types": { "CUSTOMER_ID": "DECIMAL(38, 0)", "NATION_ID": "VARCHAR(50)", "ORDER_ID": "VARCHAR(50)", "PLATFORM_ID": "VARCHAR(50)", "ORGANISATION_ID": "VARCHAR(50)", "START_DATE": "DATE", "END_DATE": "DATE", "EFFECTIVE_FROM": "DATE", "LOAD_DATE": "DATE", "SOURCE": "VARCHAR(50)" } }, "EFF_SAT": { "+column_types": { "CUSTOMER_ORDER_PK": "BINARY(16)", "ORDER_PK": "BINARY(16)", "PLATFORM_PK": "BINARY(16)", "ORGANISATION_PK": "BINARY(16)", "CUSTOMER_PK": "BINARY(16)", "NATION_PK": "BINARY(16)", "START_DATE": "DATE", "END_DATE": "DATE", "EFFECTIVE_FROM": "DATE", "LOAD_DATE": "DATE", "SOURCE": "VARCHAR(50)" } } }

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0.465299

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23,342

4.900049

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0.041996

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0.982702

0.969703

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0.959904

0.948805

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109

32.151515

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null

0

1

0

1

0

null

0

7

56274fdec4e7752fdcdeea24bdae8aeb5957601a

69

py

Python

alesisvsysex/tests/__init__.py

wimcoelus/alesisvsysex

2e6c75acd6082ca592eb405c69dc8e43d14aa2c5

[ "MIT" ]

11

2018-08-09T22:37:36.000Z

2021-08-29T14:09:23.000Z

alesisvsysex/tests/__init__.py

wimcoelus/alesisvsysex

2e6c75acd6082ca592eb405c69dc8e43d14aa2c5

[ "MIT" ]

12

2019-04-01T00:36:55.000Z

2021-11-13T20:11:32.000Z

alesisvsysex/tests/__init__.py

wimcoelus/alesisvsysex

2e6c75acd6082ca592eb405c69dc8e43d14aa2c5

[ "MIT" ]

4

2019-12-12T07:54:59.000Z

2022-03-20T19:16:08.000Z

import alesisvsysex.tests.protocol import alesisvsysex.tests.device

17.25

34

0.869565

8

69

7.5

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0

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0

true

0

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0

null

0

1

0

1

0

1

0

7

562c74e26f5e3176301ded38bf117cc47d88864c

1,290

py

Python

examples/compile-examples.py

JarateKing/polymorph-lib

c34b7b326ec54f73beefee95ac8522824b6fbb0c

[ "MIT" ]

10

2020-04-25T21:21:10.000Z

2022-01-16T15:13:44.000Z

examples/compile-examples.py

JarateKing/polymorph-lib

c34b7b326ec54f73beefee95ac8522824b6fbb0c

[ "MIT" ]

null

examples/compile-examples.py

JarateKing/polymorph-lib

c34b7b326ec54f73beefee95ac8522824b6fbb0c

[ "MIT" ]

1

2021-04-17T04:51:46.000Z

import os import random import time # default seed, wait in between for different seed os.system('g++ -g -O2 -std=gnu++17 -static simple.cpp -o output/random1.exe') time.sleep(1) os.system('g++ -g -O2 -std=gnu++17 -static simple.cpp -o output/random2.exe') time.sleep(1) os.system('g++ -g -O2 -std=gnu++17 -static simple.cpp -o output/random3.exe') # fixed seed os.system('g++ -g -O2 -std=gnu++17 -D __POLY_RANDOM_SEED__=1234567890ull -static simple.cpp -o output/fixed1.exe') os.system('g++ -g -O2 -std=gnu++17 -D __POLY_RANDOM_SEED__=1234567890ull -static simple.cpp -o output/fixed2.exe') os.system('g++ -g -O2 -std=gnu++17 -D __POLY_RANDOM_SEED__=1234567890ull -static simple.cpp -o output/fixed3.exe') # external seed os.system('g++ -g -O2 -std=gnu++17 -D __POLY_RANDOM_SEED__=' + str(random.randrange(18446744073709551615)) + 'ull -static simple.cpp -o output/seeded1.exe') os.system('g++ -g -O2 -std=gnu++17 -D __POLY_RANDOM_SEED__=' + str(random.randrange(18446744073709551615)) + 'ull -static simple.cpp -o output/seeded2.exe') os.system('g++ -g -O2 -std=gnu++17 -D __POLY_RANDOM_SEED__=' + str(random.randrange(18446744073709551615)) + 'ull -static simple.cpp -o output/seeded3.exe') # different types os.system('g++ -g -O2 -std=gnu++17 -static types.cpp -o output/types.exe')

56.086957

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0.718605

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1,290

4.087156

0.201835

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0.103101

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23

157

56.086957

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1

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0

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null

0

1

0

1

0

null

0

1

0

8

5654210e84123e093c823d5cfe108ddeb00f20d5

35

py

Python

test.py

PANG-hans/CS305Proj

418b6f205ebb6101ec7445e359ad5df45cfc9d44

[ "MIT" ]

null

test.py

PANG-hans/CS305Proj

418b6f205ebb6101ec7445e359ad5df45cfc9d44

[ "MIT" ]

null

test.py

PANG-hans/CS305Proj

418b6f205ebb6101ec7445e359ad5df45cfc9d44

[ "MIT" ]

null

a:bytes = b'123456' print(a[2:4])

11.666667

20

0.6

8

35

2.625

0.875

0

0.266667

0.142857

35

2

21

17.5

0.433333

0

0.171429

0

1

0

true

0

0.5

1

0

null

0

1

0

1

0

1

0

null

0

1

0

1

0

7

569b0377d1cc8ce17afbe78e760acb78816a4441

1,863

py

Python

allocation/tests/test_adaptive_randomization.py

py-study-design/allocation

901a3324875f5aeb3a8646ccb935daa8c77534b1

[ "MIT" ]

1

2019-07-08T13:14:43.000Z

allocation/tests/test_adaptive_randomization.py

py-study-design/allocation

901a3324875f5aeb3a8646ccb935daa8c77534b1

[ "MIT" ]

2

2019-03-07T05:37:42.000Z

2019-03-18T20:27:39.000Z

allocation/tests/test_adaptive_randomization.py

py-study-design/allocation

901a3324875f5aeb3a8646ccb935daa8c77534b1

[ "MIT" ]

2

2019-03-07T01:30:27.000Z

2019-09-29T15:03:53.000Z

""" Test Cases for Adaptive Randomization module """ import pytest from ..adaptive_randomization import double_biased_coin_urn, double_biased_coin_minimize def test_double_biased_coin_minimize(): """ Test Cases for double_biased_coin_minimize """ result = double_biased_coin_minimize(5, 6, 7, 8) assert result in ["Control", "Treatment"] # Test that you cannot have more events than subjects. with pytest.raises(ValueError): double_biased_coin_minimize(6, 5, 7, 8) with pytest.raises(ValueError): double_biased_coin_minimize(6, 7, 9, 8) with pytest.raises(ValueError): double_biased_coin_minimize(6, 5, 7, 5) # Test that you can have the same number of events as subjects result = double_biased_coin_minimize(6, 6, 7, 8) assert result in ["Control", "Treatment"] result = double_biased_coin_minimize(6, 8, 7, 7) assert result in ["Control", "Treatment"] result = double_biased_coin_minimize(6, 6, 7, 8) assert result in ["Control", "Treatment"] def test_double_biased_coin_urn(): """ Test Cases for double_biased_coin_urn """ result = double_biased_coin_urn(5, 6, 7, 8) assert result in ["Control", "Treatment"] # Test that you cannot have more events than subjects. with pytest.raises(ValueError): double_biased_coin_urn(6, 5, 7, 8) with pytest.raises(ValueError): double_biased_coin_urn(6, 7, 9, 8) with pytest.raises(ValueError): double_biased_coin_urn(6, 5, 7, 5) # Test that you can have the same number of events as subjects result = double_biased_coin_urn(6, 6, 7, 8) assert result in ["Control", "Treatment"] result = double_biased_coin_urn(6, 8, 7, 7) assert result in ["Control", "Treatment"] result = double_biased_coin_urn(6, 6, 7, 8) assert result in ["Control", "Treatment"]

33.872727

88

0.696189

272

1,863

4.536765

0.154412

0.194489

0.259319

0.153971

0.880875

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0.753647

0

0.037634

0.201288

1,863

54

89

34.5

0.791667

0.19109

0

0.5625

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0

0.25

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0.0625

false

0

0.0625

0

0.125

0

null

0

1

0

1

0

null

0

7

3b417a6fba11449ad47e5ec137b6191a263350d2

67

py

Python

openbadges/verifier/actions/utils.py

iankpconcentricsky/badgecheck

4e3164ed363892dd2d5471ca814a4b554ff9e7cb

[ "Apache-2.0" ]

28

2017-08-28T18:01:17.000Z

2021-09-25T06:42:06.000Z

openbadges/verifier/actions/utils.py

iankpconcentricsky/badgecheck

4e3164ed363892dd2d5471ca814a4b554ff9e7cb

[ "Apache-2.0" ]

127

2017-03-13T18:24:21.000Z

2017-08-24T19:29:16.000Z

openbadges/verifier/actions/utils.py

iankpconcentricsky/badgecheck

4e3164ed363892dd2d5471ca814a4b554ff9e7cb

[ "Apache-2.0" ]

11

2017-10-08T16:23:25.000Z

2022-01-13T12:22:29.000Z

import uuid def generate_task_key(): return uuid.uuid4().hex

11.166667

27

0.716418

10

67

4.6

0.9

0

0.018182

0.179104

67

5

28

13.4

0.818182

0

1

0

1

0.333333

true

0

0.333333

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

8

3b56280530442bf77f6aa778c40f6279ed5aa055

45,862

py

Python

apteco_api/api/telemetry_api.py

Apteco/apteco-api

7440c98ab10ea6d8a5997187f6fc739ce1c75d2b

[ "Apache-2.0" ]

2

2020-05-21T14:24:16.000Z

2020-12-03T19:56:34.000Z

apteco_api/api/telemetry_api.py

Apteco/apteco-api

7440c98ab10ea6d8a5997187f6fc739ce1c75d2b

[ "Apache-2.0" ]

null

apteco_api/api/telemetry_api.py

Apteco/apteco-api

7440c98ab10ea6d8a5997187f6fc739ce1c75d2b

[ "Apache-2.0" ]

null

# coding: utf-8 """ Apteco API An API to allow access to Apteco Marketing Suite resources # noqa: E501 The version of the OpenAPI document: v2 Contact: support@apteco.com Generated by: https://openapi-generator.tech """ from __future__ import absolute_import import re # noqa: F401 # python 2 and python 3 compatibility library import six from apteco_api.api_client import ApiClient from apteco_api.exceptions import ( ApiTypeError, ApiValueError ) class TelemetryApi(object): """NOTE: This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the class manually. """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def telemetry_create_telemetry_session(self, data_view_name, state_id, session_id, **kwargs): # noqa: E501 """Creates a new telemetry session from the given details # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.telemetry_create_telemetry_session(data_view_name, state_id, session_id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str data_view_name: The name of the DataView to act on (required) :param str state_id: The id of the telemetry state the session belongs to (required) :param str session_id: The id of the telemetry session (required) :param CreateTelemetrySessionDetails create_telemetry_session_details: The details of the telemetry session to create :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: TelemetrySession If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.telemetry_create_telemetry_session_with_http_info(data_view_name, state_id, session_id, **kwargs) # noqa: E501 def telemetry_create_telemetry_session_with_http_info(self, data_view_name, state_id, session_id, **kwargs): # noqa: E501 """Creates a new telemetry session from the given details # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.telemetry_create_telemetry_session_with_http_info(data_view_name, state_id, session_id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str data_view_name: The name of the DataView to act on (required) :param str state_id: The id of the telemetry state the session belongs to (required) :param str session_id: The id of the telemetry session (required) :param CreateTelemetrySessionDetails create_telemetry_session_details: The details of the telemetry session to create :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(TelemetrySession, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = ['data_view_name', 'state_id', 'session_id', 'create_telemetry_session_details'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method telemetry_create_telemetry_session" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'data_view_name' is set if ('data_view_name' not in local_var_params or local_var_params['data_view_name'] is None): raise ApiValueError("Missing the required parameter `data_view_name` when calling `telemetry_create_telemetry_session`") # noqa: E501 # verify the required parameter 'state_id' is set if ('state_id' not in local_var_params or local_var_params['state_id'] is None): raise ApiValueError("Missing the required parameter `state_id` when calling `telemetry_create_telemetry_session`") # noqa: E501 # verify the required parameter 'session_id' is set if ('session_id' not in local_var_params or local_var_params['session_id'] is None): raise ApiValueError("Missing the required parameter `session_id` when calling `telemetry_create_telemetry_session`") # noqa: E501 collection_formats = {} path_params = {} if 'data_view_name' in local_var_params: path_params['dataViewName'] = local_var_params['data_view_name'] # noqa: E501 if 'state_id' in local_var_params: path_params['stateId'] = local_var_params['state_id'] # noqa: E501 if 'session_id' in local_var_params: path_params['sessionId'] = local_var_params['session_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'create_telemetry_session_details' in local_var_params: body_params = local_var_params['create_telemetry_session_details'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json', 'text/json', 'application/xml', 'text/xml']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json-patch+json', 'application/json', 'text/json', 'application/*+json', 'application/xml', 'text/xml', 'application/*+xml']) # noqa: E501 # Authentication setting auth_settings = ['faststats_auth'] # noqa: E501 return self.api_client.call_api( '/{dataViewName}/Telemetry/States/{stateId}/Sessions/{sessionId}', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='TelemetrySession', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def telemetry_create_telemetry_state(self, data_view_name, **kwargs): # noqa: E501 """Creates a new telemetry state from the given details # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.telemetry_create_telemetry_state(data_view_name, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str data_view_name: The name of the DataView to act on (required) :param CreateTelemetryStateDetails create_telemetry_state_details: The details of the telemetry state to create :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: TelemetryState If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.telemetry_create_telemetry_state_with_http_info(data_view_name, **kwargs) # noqa: E501 def telemetry_create_telemetry_state_with_http_info(self, data_view_name, **kwargs): # noqa: E501 """Creates a new telemetry state from the given details # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.telemetry_create_telemetry_state_with_http_info(data_view_name, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str data_view_name: The name of the DataView to act on (required) :param CreateTelemetryStateDetails create_telemetry_state_details: The details of the telemetry state to create :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(TelemetryState, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = ['data_view_name', 'create_telemetry_state_details'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method telemetry_create_telemetry_state" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'data_view_name' is set if ('data_view_name' not in local_var_params or local_var_params['data_view_name'] is None): raise ApiValueError("Missing the required parameter `data_view_name` when calling `telemetry_create_telemetry_state`") # noqa: E501 collection_formats = {} path_params = {} if 'data_view_name' in local_var_params: path_params['dataViewName'] = local_var_params['data_view_name'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'create_telemetry_state_details' in local_var_params: body_params = local_var_params['create_telemetry_state_details'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json', 'text/json', 'application/xml', 'text/xml']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json-patch+json', 'application/json', 'text/json', 'application/*+json', 'application/xml', 'text/xml', 'application/*+xml']) # noqa: E501 # Authentication setting auth_settings = ['faststats_auth'] # noqa: E501 return self.api_client.call_api( '/{dataViewName}/Telemetry/States', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='TelemetryState', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def telemetry_get_telemetry_session(self, data_view_name, state_id, session_id, **kwargs): # noqa: E501 """Returns the details of a particular telemetry session # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.telemetry_get_telemetry_session(data_view_name, state_id, session_id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str data_view_name: The name of the DataView to act on (required) :param str state_id: The id of the telemetry state the session belongs to (required) :param str session_id: The id of the telemetry session (required) :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: TelemetrySession If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.telemetry_get_telemetry_session_with_http_info(data_view_name, state_id, session_id, **kwargs) # noqa: E501 def telemetry_get_telemetry_session_with_http_info(self, data_view_name, state_id, session_id, **kwargs): # noqa: E501 """Returns the details of a particular telemetry session # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.telemetry_get_telemetry_session_with_http_info(data_view_name, state_id, session_id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str data_view_name: The name of the DataView to act on (required) :param str state_id: The id of the telemetry state the session belongs to (required) :param str session_id: The id of the telemetry session (required) :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(TelemetrySession, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = ['data_view_name', 'state_id', 'session_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method telemetry_get_telemetry_session" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'data_view_name' is set if ('data_view_name' not in local_var_params or local_var_params['data_view_name'] is None): raise ApiValueError("Missing the required parameter `data_view_name` when calling `telemetry_get_telemetry_session`") # noqa: E501 # verify the required parameter 'state_id' is set if ('state_id' not in local_var_params or local_var_params['state_id'] is None): raise ApiValueError("Missing the required parameter `state_id` when calling `telemetry_get_telemetry_session`") # noqa: E501 # verify the required parameter 'session_id' is set if ('session_id' not in local_var_params or local_var_params['session_id'] is None): raise ApiValueError("Missing the required parameter `session_id` when calling `telemetry_get_telemetry_session`") # noqa: E501 collection_formats = {} path_params = {} if 'data_view_name' in local_var_params: path_params['dataViewName'] = local_var_params['data_view_name'] # noqa: E501 if 'state_id' in local_var_params: path_params['stateId'] = local_var_params['state_id'] # noqa: E501 if 'session_id' in local_var_params: path_params['sessionId'] = local_var_params['session_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json', 'text/json', 'application/xml', 'text/xml']) # noqa: E501 # Authentication setting auth_settings = ['faststats_auth'] # noqa: E501 return self.api_client.call_api( '/{dataViewName}/Telemetry/States/{stateId}/Sessions/{sessionId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='TelemetrySession', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def telemetry_get_telemetry_state(self, data_view_name, state_id, **kwargs): # noqa: E501 """Returns the details of a particular telemetry state # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.telemetry_get_telemetry_state(data_view_name, state_id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str data_view_name: The name of the DataView to act on (required) :param str state_id: The id of the telemetry state to return (required) :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: TelemetryState If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.telemetry_get_telemetry_state_with_http_info(data_view_name, state_id, **kwargs) # noqa: E501 def telemetry_get_telemetry_state_with_http_info(self, data_view_name, state_id, **kwargs): # noqa: E501 """Returns the details of a particular telemetry state # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.telemetry_get_telemetry_state_with_http_info(data_view_name, state_id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str data_view_name: The name of the DataView to act on (required) :param str state_id: The id of the telemetry state to return (required) :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(TelemetryState, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = ['data_view_name', 'state_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method telemetry_get_telemetry_state" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'data_view_name' is set if ('data_view_name' not in local_var_params or local_var_params['data_view_name'] is None): raise ApiValueError("Missing the required parameter `data_view_name` when calling `telemetry_get_telemetry_state`") # noqa: E501 # verify the required parameter 'state_id' is set if ('state_id' not in local_var_params or local_var_params['state_id'] is None): raise ApiValueError("Missing the required parameter `state_id` when calling `telemetry_get_telemetry_state`") # noqa: E501 collection_formats = {} path_params = {} if 'data_view_name' in local_var_params: path_params['dataViewName'] = local_var_params['data_view_name'] # noqa: E501 if 'state_id' in local_var_params: path_params['stateId'] = local_var_params['state_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json', 'text/json', 'application/xml', 'text/xml']) # noqa: E501 # Authentication setting auth_settings = ['faststats_auth'] # noqa: E501 return self.api_client.call_api( '/{dataViewName}/Telemetry/States/{stateId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='TelemetryState', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def telemetry_get_telemetry_state_for_user(self, data_view_name, username, **kwargs): # noqa: E501 """Returns the details of a given user's telemetry state # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.telemetry_get_telemetry_state_for_user(data_view_name, username, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str data_view_name: The name of the DataView to act on (required) :param str username: The username of the telemetry state to return (required) :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: TelemetryState If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.telemetry_get_telemetry_state_for_user_with_http_info(data_view_name, username, **kwargs) # noqa: E501 def telemetry_get_telemetry_state_for_user_with_http_info(self, data_view_name, username, **kwargs): # noqa: E501 """Returns the details of a given user's telemetry state # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.telemetry_get_telemetry_state_for_user_with_http_info(data_view_name, username, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str data_view_name: The name of the DataView to act on (required) :param str username: The username of the telemetry state to return (required) :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(TelemetryState, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = ['data_view_name', 'username'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method telemetry_get_telemetry_state_for_user" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'data_view_name' is set if ('data_view_name' not in local_var_params or local_var_params['data_view_name'] is None): raise ApiValueError("Missing the required parameter `data_view_name` when calling `telemetry_get_telemetry_state_for_user`") # noqa: E501 # verify the required parameter 'username' is set if ('username' not in local_var_params or local_var_params['username'] is None): raise ApiValueError("Missing the required parameter `username` when calling `telemetry_get_telemetry_state_for_user`") # noqa: E501 collection_formats = {} path_params = {} if 'data_view_name' in local_var_params: path_params['dataViewName'] = local_var_params['data_view_name'] # noqa: E501 if 'username' in local_var_params: path_params['username'] = local_var_params['username'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json', 'text/json', 'application/xml', 'text/xml']) # noqa: E501 # Authentication setting auth_settings = ['faststats_auth'] # noqa: E501 return self.api_client.call_api( '/{dataViewName}/Telemetry/States/ForUser/{username}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='TelemetryState', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def telemetry_update_telemetry_session(self, data_view_name, state_id, session_id, **kwargs): # noqa: E501 """Update a particular telemetry session from the given details # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.telemetry_update_telemetry_session(data_view_name, state_id, session_id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str data_view_name: The name of the DataView to act on (required) :param str state_id: The id of the telemetry state the session belongs to (required) :param str session_id: The id of the telemetry session (required) :param UpdateTelemetrySessionDetails update_telemetry_session_details: The details of the telemetry session to update :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: TelemetrySession If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.telemetry_update_telemetry_session_with_http_info(data_view_name, state_id, session_id, **kwargs) # noqa: E501 def telemetry_update_telemetry_session_with_http_info(self, data_view_name, state_id, session_id, **kwargs): # noqa: E501 """Update a particular telemetry session from the given details # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.telemetry_update_telemetry_session_with_http_info(data_view_name, state_id, session_id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str data_view_name: The name of the DataView to act on (required) :param str state_id: The id of the telemetry state the session belongs to (required) :param str session_id: The id of the telemetry session (required) :param UpdateTelemetrySessionDetails update_telemetry_session_details: The details of the telemetry session to update :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(TelemetrySession, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = ['data_view_name', 'state_id', 'session_id', 'update_telemetry_session_details'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method telemetry_update_telemetry_session" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'data_view_name' is set if ('data_view_name' not in local_var_params or local_var_params['data_view_name'] is None): raise ApiValueError("Missing the required parameter `data_view_name` when calling `telemetry_update_telemetry_session`") # noqa: E501 # verify the required parameter 'state_id' is set if ('state_id' not in local_var_params or local_var_params['state_id'] is None): raise ApiValueError("Missing the required parameter `state_id` when calling `telemetry_update_telemetry_session`") # noqa: E501 # verify the required parameter 'session_id' is set if ('session_id' not in local_var_params or local_var_params['session_id'] is None): raise ApiValueError("Missing the required parameter `session_id` when calling `telemetry_update_telemetry_session`") # noqa: E501 collection_formats = {} path_params = {} if 'data_view_name' in local_var_params: path_params['dataViewName'] = local_var_params['data_view_name'] # noqa: E501 if 'state_id' in local_var_params: path_params['stateId'] = local_var_params['state_id'] # noqa: E501 if 'session_id' in local_var_params: path_params['sessionId'] = local_var_params['session_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'update_telemetry_session_details' in local_var_params: body_params = local_var_params['update_telemetry_session_details'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json', 'text/json', 'application/xml', 'text/xml']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json-patch+json', 'application/json', 'text/json', 'application/*+json', 'application/xml', 'text/xml', 'application/*+xml']) # noqa: E501 # Authentication setting auth_settings = ['faststats_auth'] # noqa: E501 return self.api_client.call_api( '/{dataViewName}/Telemetry/States/{stateId}/Sessions/{sessionId}/Update', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='TelemetrySession', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def telemetry_update_telemetry_state(self, data_view_name, state_id, **kwargs): # noqa: E501 """Updates a particular telemetry state from the given details # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.telemetry_update_telemetry_state(data_view_name, state_id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str data_view_name: The name of the DataView to act on (required) :param str state_id: The id of the telemetry state to update (required) :param UpdateTelemetryStateDetails update_telemetry_state_details: The details of the telemetry state to update :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: TelemetryState If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.telemetry_update_telemetry_state_with_http_info(data_view_name, state_id, **kwargs) # noqa: E501 def telemetry_update_telemetry_state_with_http_info(self, data_view_name, state_id, **kwargs): # noqa: E501 """Updates a particular telemetry state from the given details # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.telemetry_update_telemetry_state_with_http_info(data_view_name, state_id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str data_view_name: The name of the DataView to act on (required) :param str state_id: The id of the telemetry state to update (required) :param UpdateTelemetryStateDetails update_telemetry_state_details: The details of the telemetry state to update :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(TelemetryState, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = ['data_view_name', 'state_id', 'update_telemetry_state_details'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method telemetry_update_telemetry_state" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'data_view_name' is set if ('data_view_name' not in local_var_params or local_var_params['data_view_name'] is None): raise ApiValueError("Missing the required parameter `data_view_name` when calling `telemetry_update_telemetry_state`") # noqa: E501 # verify the required parameter 'state_id' is set if ('state_id' not in local_var_params or local_var_params['state_id'] is None): raise ApiValueError("Missing the required parameter `state_id` when calling `telemetry_update_telemetry_state`") # noqa: E501 collection_formats = {} path_params = {} if 'data_view_name' in local_var_params: path_params['dataViewName'] = local_var_params['data_view_name'] # noqa: E501 if 'state_id' in local_var_params: path_params['stateId'] = local_var_params['state_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'update_telemetry_state_details' in local_var_params: body_params = local_var_params['update_telemetry_state_details'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json', 'text/json', 'application/xml', 'text/xml']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json-patch+json', 'application/json', 'text/json', 'application/*+json', 'application/xml', 'text/xml', 'application/*+xml']) # noqa: E501 # Authentication setting auth_settings = ['faststats_auth'] # noqa: E501 return self.api_client.call_api( '/{dataViewName}/Telemetry/States/{stateId}/Update', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='TelemetryState', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats)

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3b779bc99f94678f9f463617041ea329e7206a88

75,105

py

Python

rlpyt/envs/mazeworld/mazeworld/envs/mazeworld_env.py

williamd4112/curiosity_baselines

45939f3f24c53cfff5153ef012486a6a058660be

[ "MIT" ]

null

rlpyt/envs/mazeworld/mazeworld/envs/mazeworld_env.py

williamd4112/curiosity_baselines

45939f3f24c53cfff5153ef012486a6a058660be

[ "MIT" ]

3

2021-09-08T03:31:49.000Z

2022-03-12T00:59:45.000Z

rlpyt/envs/mazeworld/mazeworld/envs/mazeworld_env.py

williamd4112/curiosity_baselines

45939f3f24c53cfff5153ef012486a6a058660be

[ "MIT" ]

null

from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from gym import spaces from pycolab.examples import (maze, fiveroom, fiveroom_whitenoise, fiveroom_flipped, fiveroom_all, fiveroom_long, fiveroom_longunpadded, fiveroom_longwide, fiveroom_longext, fiveroom_noobj, fiveroom_oneobj, fiveroom_onewhite, fiveroom_randomfixed, fiveroom_bouncing, fiveroom_brownian, fiveroom_moveable, fiveroom_moveable_stoch, fiveroom_extint, fiveroom_moveable_brownian, fiveroomlarge, fiveroomlarge_extint, fiveroomlarge_weather, fiveroomlarge_enemy, fiveroomlarge_whitenoise, fiveroomlargetext, fiveroomlargetext_whitenoise, fiveroomlarge_randomfixed, fiveroomlargetext_randomfixed, fiveroomlarge_moveable, fiveroomlarge_moveable_ext, fiveroomlargetext_moveable, fiveroomlarge_moveable_stoch, fiveroomlarge_moveable_stoch_ext, fiveroomlargetext_moveable_stoch, fiveroomlarge_moveable_brownian, fiveroomlargetext_moveable_brownian, fiveroomlarge_brownian, fiveroomlargetext_brownian, fiveroomlarge_all, fiveroomlarge_allext, fiveroomlarge_all_stoch, fiveroomlarge_all_stochext, fiveroomlargetext_all, eightroom, eightroom_ext, eightroom_weather, eightroomlarge_ext, eightroomlarge_weather, eightroomhard_ext, eightroomhard_weather, piano_long ) from pycolab import cropping from . import pycolab_env ############################################################################################################################# ######################################################## FIVE ROOM ########################################################## ############################################################################################################################# class FiveRoom(pycolab_env.PyColabEnv): """Deepmind World Discovery Models experiment 1. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['a', 'b'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoom, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=223, color_palette=1, reward_switch=[], reward_config=reward_config, dimensions=(19,19)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroom.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomWhitenoise(pycolab_env.PyColabEnv): """A 5 room map with whitenoise background, and a fixed object. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['a'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomWhitenoise, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=224, color_palette=1, reward_switch=[], reward_config=reward_config, dimensions=(19,19)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroom_whitenoise.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomFlipped(pycolab_env.PyColabEnv): """Deepmind World Discovery Models experiment 1, with the rooms flipped. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['a', 'b'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomFlipped, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=223, color_palette=1, reward_switch=[], reward_config=reward_config, dimensions=(19,19)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroom_flipped.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomAll(pycolab_env.PyColabEnv): """Map with all four objects (white noise, brownian, Fixed, moveable) """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['a', 'b', 'c', 'd'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomAll, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=221, color_palette=1, reward_switch=[], reward_config=reward_config, dimensions=(19,19)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroom_all.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomLong(pycolab_env.PyColabEnv): """A 5 room variant with a long corridor leading to another room. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['a'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomLong, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=236, color_palette=1, reward_switch=[], reward_config=reward_config, dimensions=(19,31)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroom_long.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomLongunpadded(pycolab_env.PyColabEnv): """5 room map with a long corridor, with the sides not padded with walls. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['a'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomLongunpadded, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=236, color_palette=1, reward_switch=[], reward_config=reward_config, dimensions=(19,31)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroom_longunpadded.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomLongwide(pycolab_env.PyColabEnv): """5 room maze with a wider long corridor. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['a'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomLongwide, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=208, color_palette=1, reward_switch=[], reward_config=reward_config, dimensions=(19,31)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroom_longwide.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomLongExt(pycolab_env.PyColabEnv): """5 room maze with a long corridor and an extrinsic reward in the far room. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config={'a':1.0}): self.level = level self.objects = ['a'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomLongExt, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=208, color_palette=3, reward_switch=[], reward_config=reward_config, dimensions=(19,31)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroom_longext.make_game(self.level, reward_config) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomNoobj(pycolab_env.PyColabEnv): """5 room map with no objects. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['a'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomNoobj, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=224, color_palette=1, reward_switch=[], reward_config=reward_config, dimensions=(19,24)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroom_noobj.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomOneobj(pycolab_env.PyColabEnv): """5 room map with a single fixed object. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['a'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomOneobj, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=224, color_palette=1, reward_switch=[], reward_config=reward_config, dimensions=(19,19)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroom_oneobj.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomOnewhite(pycolab_env.PyColabEnv): """5 room map with a white noise teleporter. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['b'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomOnewhite, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=224, color_palette=1, reward_switch=[], reward_config=reward_config, dimensions=(19,19)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroom_onewhite.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomRandomfixed(pycolab_env.PyColabEnv): """5 room map with a randomly relocating fixed object. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['a', 'b'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomRandomfixed, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=223, color_palette=1, reward_switch=[], reward_config=reward_config, dimensions=(19,19)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroom_randomfixed.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomBouncing(pycolab_env.PyColabEnv): """Deepmind World Discovery Models experiment 3. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['a', 'b', 'c'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomBouncing, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=222, color_palette=1, reward_switch=[], reward_config=reward_config, dimensions=(19,19)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroom_bouncing.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomBrownian(pycolab_env.PyColabEnv): """Deepmind World Discovery Models experiment 4. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['a', 'b'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomBrownian, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=223, color_palette=1, reward_switch=[], reward_config=reward_config, dimensions=(19,19)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroom_brownian.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class Maze(pycolab_env.PyColabEnv): """Deepmind World Discovery Models experiment 5. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['a', 'b', 'c', 'd', 'e'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(Maze, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), act_null_value=4, visitable_states=150., color_palette=0, reward_switch=[], reward_config=reward_config, dimensions=(13,21)) def make_game(self, reward_config): self._croppers = self.make_croppers() return maze.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomMoveable(pycolab_env.PyColabEnv): """A 5 room environment with a controllable object. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['e', 'b'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomMoveable, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), act_null_value=4, visitable_states=223, color_palette=1, reward_switch=[], reward_config=reward_config, dimensions=(19,19)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroom_moveable.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomMoveableBrownian(pycolab_env.PyColabEnv): """A 5 room environment with a controllable object and a brownian object. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['e', 'b'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomMoveableBrownian, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), act_null_value=4, visitable_states=223, color_palette=1, reward_switch=[], reward_config=reward_config, dimensions=(19,19)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroom_moveable_brownian.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomMoveableStoch(pycolab_env.PyColabEnv): """A 5 room environment with a stochastic controllable object. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['e', 'b'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomMoveableStoch, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), act_null_value=4, visitable_states=223, color_palette=1, reward_switch=[], reward_config=reward_config, dimensions=(19,19)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroom_moveable_stoch.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomExtInt(pycolab_env.PyColabEnv): """5 room map with an intrinsic attractor and an extrinsic attractor. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config={'a':0.0, 'b':1.0}): self.level = level self.objects = ['a', 'b'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomExtInt, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), act_null_value=4, visitable_states=223, color_palette=1, reward_switch=[], reward_config=reward_config, dimensions=(19,19)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroom_extint.make_game(self.level, reward_config) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class PianoLong(pycolab_env.PyColabEnv): """Piano map with no objects. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['a'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(PianoLong, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), act_null_value=4, visitable_states=1516, color_palette=1, reward_switch=[], reward_config=reward_config, dimensions=(18,113)) def make_game(self, reward_config): self._croppers = self.make_croppers() return piano_long.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] ############################################################################################################################# ###################################################### 5 ROOM LARGE ######################################################### ############################################################################################################################# class FiveRoomXL(pycolab_env.PyColabEnv): """Large version of the 5 room environment """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['a', 'b'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomXL, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=915, color_palette=3, reward_switch=[], reward_config=reward_config, dimensions=(42,42)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroomlarge.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomXLExtInt(pycolab_env.PyColabEnv): """5 room map with an intrinsic attractor and an extrinsic attractor. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config={'a':0.0, 'b':1.0}): self.level = level self.objects = ['a', 'b'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomXLExtInt, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), act_null_value=4, visitable_states=223, color_palette=1, reward_switch=[], reward_config=reward_config, dimensions=(42,42)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroomlarge_extint.make_game(self.level, reward_config) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomXLEnemy1(pycolab_env.PyColabEnv): """Large version of the 5 room environment with a bouncing enemy. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config={'a':1.0,'b':-2.0}): self.level = level self.objects = ['a', 'b'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomXLEnemy1, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=915, color_palette=3, reward_switch=[], reward_config=reward_config, dimensions=(42,42)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroomlarge_enemy.make_game(self.level, reward_config) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomXLEnemy2(pycolab_env.PyColabEnv): """Large version of the 5 room environment with a bouncing enemy. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config={'a':1.0,'b':-0.5}): self.level = level self.objects = ['a', 'b'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomXLEnemy2, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=915, color_palette=3, reward_switch=[], reward_config=reward_config, dimensions=(42,42)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroomlarge_enemy.make_game(self.level, reward_config) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomXLWeather(pycolab_env.PyColabEnv): """Large version of the 5 room environment with a changing central background color. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0.0, reward_config={'a':1.0,'b':0.0,'c':0.0,'d':0.0}): self.level = level self.objects = ['a', 'b', 'c', 'd'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomXLWeather, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=913, color_palette=3, reward_switch=['a', 'b', 'c', 'd'], reward_config=reward_config, switch_perturbations=[(-80., -80., 70.),(-65., 40., -65.),(-40., -50., 0.),(40., -65., -65.)], dimensions=(42,42)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroomlarge_weather.make_game(self.level, reward_config) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomXLText(pycolab_env.PyColabEnv): """Large version of the 5 room environment, with textures. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['a', 'b'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomXLText, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=891, color_palette=3, reward_switch=[], reward_config=reward_config, dimensions=(42,42)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroomlargetext.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomXLWhitenoise(pycolab_env.PyColabEnv): """Large version of the 5 room environment with a whitenoise background room. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['a'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomXLWhitenoise, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=916, color_palette=3, reward_switch=[], reward_config=reward_config, dimensions=(42,42)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroomlarge_whitenoise.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomXLTextWhitenoise(pycolab_env.PyColabEnv): """Large version of the 5 room environment with textures and a whitenoise background room. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['a'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomXLTextWhitenoise, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=892, color_palette=3, reward_switch=[], reward_config=reward_config, dimensions=(42,42)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroomlargetext_whitenoise.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomXLRandomfixed(pycolab_env.PyColabEnv): """Large version of the 5 room environment with a fixed object that moves """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['a', 'b'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomXLRandomfixed, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=915, color_palette=3, reward_switch=[], reward_config=reward_config, dimensions=(42,42)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroomlarge_randomfixed.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomXLTextRandomfixed(pycolab_env.PyColabEnv): """Large version of the 5 room environment with textures and a randomly initialized fixed. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['a', 'b'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomXLTextRandomfixed, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=891, color_palette=3, reward_switch=[], reward_config=reward_config, dimensions=(42,42)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroomlargetext_randomfixed.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomXLMoveable(pycolab_env.PyColabEnv): """Large version of the 5 room environment with a controllable object. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['e', 'b'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomXLMoveable, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=915, color_palette=3, reward_switch=[], reward_config=reward_config, dimensions=(42,42)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroomlarge_moveable.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomXLMoveableExt(pycolab_env.PyColabEnv): """Large version of the 5 room environment with a controllable object and extrinsic target. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config={'e':1.0}): self.level = level self.objects = ['e'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomXLMoveableExt, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=915, color_palette=3, reward_switch=[], reward_config=reward_config, dimensions=(42,42)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroomlarge_moveable_ext.make_game(self.level, reward_config) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomXLTextMoveable(pycolab_env.PyColabEnv): """Large version of the 5 room environment with a controllable object and textures. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['e', 'b'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomXLTextMoveable, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=891, color_palette=3, reward_switch=[], reward_config=reward_config, dimensions=(42,42)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroomlargetext_moveable.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomXLMoveableStoch(pycolab_env.PyColabEnv): """Large version of the 5 room environment with a stochastic controllable. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['e', 'b'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomXLMoveableStoch, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=915, color_palette=3, reward_switch=[], reward_config=reward_config, dimensions=(42,42)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroomlarge_moveable_stoch.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomXLMoveableStochExt(pycolab_env.PyColabEnv): """Large version of the 5 room environment with a stochastic controllable and an extrinsic reward target. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config={'e':1.0}): self.level = level self.objects = ['e'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomXLMoveableStochExt, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=915, color_palette=3, reward_switch=[], reward_config=reward_config, dimensions=(42,42)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroomlarge_moveable_stoch_ext.make_game(self.level, reward_config) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomXLTextMoveableStoch(pycolab_env.PyColabEnv): """Large version of the 5 room environment with a stochastic controllable and textured background. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['e', 'b'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomXLTextMoveableStoch, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=891, color_palette=3, reward_switch=[], reward_config=reward_config, dimensions=(42,42)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroomlargetext_moveable_stoch.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomXLMoveableBrownian(pycolab_env.PyColabEnv): """Large version of the 5 room environment with a controllable object and a brownian object. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['e', 'b'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomXLMoveableBrownian, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=915, color_palette=3, reward_switch=[], reward_config=reward_config, dimensions=(42,42)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroomlarge_moveable_brownian.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomXLTextMoveableBrownian(pycolab_env.PyColabEnv): """Large version of the 5 room environment with textured walls and a controllable object/brownian object. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['e', 'b'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomXLTextMoveableBrownian, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=891, color_palette=3, reward_switch=[], reward_config=reward_config, dimensions=(42,42)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroomlargetext_moveable_brownian.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomXLBrownian(pycolab_env.PyColabEnv): """Large version of the 5 room environment with a brownian object. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['a', 'b'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomXLBrownian, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=915, color_palette=3, reward_switch=[], reward_config=reward_config, dimensions=(42,42)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroomlarge_brownian.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomXLTextBrownian(pycolab_env.PyColabEnv): """Large version of the 5 room environment with wall textures and a brownian object. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['a', 'b'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomXLTextBrownian, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=891, color_palette=3, reward_switch=[], reward_config=reward_config, dimensions=(42,42)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroomlargetext_brownian.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomXLAll(pycolab_env.PyColabEnv): """Large version of the 5 room environment with a brownian, a fixed, a controllable, and a teleporter object. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['a', 'b', 'c', 'd'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomXLAll, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=913, color_palette=3, reward_switch=[], reward_config=reward_config, dimensions=(42,42)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroomlarge_all.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomXLAllExt(pycolab_env.PyColabEnv): """Large version of the 5 room environment with a brownian, a fixed, a controllable, and a teleporter object. An extrinsic reward target is in the top room. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config={'a':1.0}): self.level = level self.objects = ['a', 'b', 'c', 'd'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomXLAllExt, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=913, color_palette=3, reward_switch=[], reward_config=reward_config, dimensions=(42,42)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroomlarge_allext.make_game(self.level, reward_config) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomXLAllStoch(pycolab_env.PyColabEnv): """Large version of the 5 room environment with a brownian, a teleporter, a fixed object, and a stochastic controllable. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['a', 'b', 'c', 'd'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomXLAllStoch, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=913, color_palette=3, reward_switch=[], reward_config=reward_config, dimensions=(42,42)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroomlarge_all_stoch.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomXLAllStochExt(pycolab_env.PyColabEnv): """Large version of the 5 room environment with a brownian, a fixed, a stochastic controllable, and a teleporter object. An extrinsic reward target is in the top room. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config={'a':1.0}): self.level = level self.objects = ['a', 'b', 'c', 'd'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomXLAllStochExt, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=913, color_palette=3, reward_switch=[], reward_config=reward_config, dimensions=(42,42)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroomlarge_all_stochext.make_game(self.level, reward_config) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class FiveRoomXLTextAll(pycolab_env.PyColabEnv): """Large version of the 5 room environment with a brownian, a teleporter, a controllable, and a fixed object. Extra wall textures included. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['a', 'b', 'c', 'd'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(FiveRoomXLTextAll, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), visitable_states=889, color_palette=3, reward_switch=[], reward_config=reward_config, dimensions=(42,42)) def make_game(self, reward_config): self._croppers = self.make_croppers() return fiveroomlargetext_all.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] ############################################################################################################################# ####################################################### EIGHT ROOM ########################################################## ############################################################################################################################# class EightRoom(pycolab_env.PyColabEnv): """An eight room environment with 8 fixed object, each with the same color. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(EightRoom, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), act_null_value=4, visitable_states=633, color_palette=2, reward_switch=[], reward_config=reward_config, dimensions=(42,42)) def make_game(self, reward_config): self._croppers = self.make_croppers() return eightroom.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class EightRoomExt(pycolab_env.PyColabEnv): """An eight room environment with 8 fixed object, each with the same color. One object gives an extrinsic reward. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config={'d':1.0}): self.level = level self.objects = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(EightRoomExt, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), act_null_value=4, visitable_states=633, color_palette=2, reward_switch=[], reward_config=reward_config, dimensions=(42,42)) def make_game(self, reward_config): self._croppers = self.make_croppers() return eightroom_ext.make_game(self.level, reward_config) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class EightRoomRgb(pycolab_env.PyColabEnv): """An eight room environment with 8 fixed objects. Each object is a different color, using a color palette where each color averages to 60 across channels. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(EightRoomRgb, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), act_null_value=4, visitable_states=633, color_palette=3, reward_switch=[], reward_config=reward_config, dimensions=(42,42)) def make_game(self, reward_config): self._croppers = self.make_croppers() return eightroom.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class EightRoomDiff(pycolab_env.PyColabEnv): """An eight room environment with 8 fixed objects, each with a different color. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config=dict()): self.level = level self.objects = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(EightRoomDiff, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), act_null_value=4, visitable_states=633, color_palette=0, reward_switch=[], reward_config=reward_config, dimensions=(42,42)) def make_game(self, reward_config): self._croppers = self.make_croppers() return eightroom.make_game(self.level) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class EightRoomWeather(pycolab_env.PyColabEnv): """An eight room environment with 8 fixed objects, and a central room that changes color and switches the source of extrinsic reward. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config={'a':1.0,'b':0.0,'c':0.0,'d':0.0,'e':0.0,'f':0.0,'g':0.0,'h':0.0}): self.level = level self.objects = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(EightRoomWeather, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), act_null_value=4, visitable_states=633, color_palette=3, reward_switch=['a','b','c','d','e','f','g','h'], reward_config=reward_config, switch_perturbations=[(-45.,-25.,-20.),(-20.,-20.,-40.),(-70.,20.,-40.),(-60.,-60.,30.),(-90.,0.,0.),(0.,-90.,0.),(-20.,-20.,-40.),(-20.,-80.,10.)], dimensions=(42,42)) def make_game(self, reward_config): self._croppers = self.make_croppers() return eightroom_weather.make_game(self.level, reward_config) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class EightRoomXLExt(pycolab_env.PyColabEnv): """A large eight room environment with 8 fixed objects, and a central room that changes color and switches the source of extrinsic reward. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config={'a':0.0,'b':0.0,'c':0.0,'d':1.0,'e':0.0,'f':0.0,'g':0.0,'h':0.0}): self.level = level self.objects = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(EightRoomXLExt, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), act_null_value=4, visitable_states=952, color_palette=3, reward_switch=[], reward_config=reward_config, switch_perturbations=[], dimensions=(42,42)) def make_game(self, reward_config): self._croppers = self.make_croppers() return eightroomlarge_ext.make_game(self.level, reward_config) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class EightRoomXLWeather(pycolab_env.PyColabEnv): """A large eight room environment with 8 fixed objects, and a central room that changes color and switches the source of extrinsic reward. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config={'a':1.0,'b':0.0,'c':0.0,'d':0.0,'e':0.0,'f':0.0,'g':0.0,'h':0.0}): self.level = level self.objects = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(EightRoomXLWeather, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), act_null_value=4, visitable_states=952, color_palette=3, reward_switch=['a','b','c','d','e','f','g','h'], reward_config=reward_config, switch_perturbations=[(-45.,-25.,-20.),(-20.,-20.,-40.),(-70.,20.,-40.),(-60.,-60.,30.),(-90.,0.,0.),(0.,-90.,0.),(-20.,-20.,-40.),(-20.,-80.,10.)], dimensions=(42,42)) def make_game(self, reward_config): self._croppers = self.make_croppers() return eightroomlarge_weather.make_game(self.level, reward_config) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class EightRoomHardExt(pycolab_env.PyColabEnv): """A larger eight room environment with 8 fixed objects, with one object that has extrinsic reward. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config={'a':0.0,'b':0.0,'c':0.0,'d':1.0,'e':0.0,'f':0.0,'g':0.0,'h':0.0}): self.level = level self.objects = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(EightRoomHardExt, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), act_null_value=4, visitable_states=5504, color_palette=3, reward_switch=[], reward_config=reward_config, switch_perturbations=[], dimensions=(85,85)) def make_game(self, reward_config): self._croppers = self.make_croppers() return eightroomhard_ext.make_game(self.level, reward_config) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return [] class EightRoomHardWeather(pycolab_env.PyColabEnv): """A larger eight room environment with 8 fixed objects, and a central room that changes color and switches the source of extrinsic reward. """ def __init__(self, level=0, max_iterations=500, obs_type='mask', default_reward=0., reward_config={'a':1.0,'b':0.0,'c':0.0,'d':0.0,'e':0.0,'f':0.0,'g':0.0,'h':0.0}): self.level = level self.objects = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'] self.state_layer_chars = ['#'] + self.objects self.obs_type = obs_type super(EightRoomHardWeather, self).__init__( max_iterations=max_iterations, obs_type=obs_type, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), act_null_value=4, visitable_states=5504, color_palette=3, reward_switch=['a','b','c','d','e','f','g','h'], reward_config=reward_config, switch_perturbations=[(-45.,-25.,-20.),(-20.,-20.,-40.),(-70.,20.,-40.),(-60.,-60.,30.),(-90.,0.,0.),(0.,-90.,0.),(-20.,-20.,-40.),(-20.,-80.,10.)], dimensions=(85,85)) def make_game(self, reward_config): self._croppers = self.make_croppers() return eightroomhard_weather.make_game(self.level, reward_config) def make_croppers(self): if self.obs_type in {'rgb', 'mask'}: return [cropping.ScrollingCropper(rows=5, cols=5, to_track=['P'], scroll_margins=(None, None), pad_char=' ')] elif self.obs_type == 'rgb_full': return []

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0.902883

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0.308714

75,105

2,029

161

37.015771

0.745064

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3b7dbe2232e7e17167a5fa43e1c968562210c417

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py

Python

tests/test_matrix.py

ionelmc/python-matrix

e1a63879a6c94c37c3883386f1d86eb7c2179a5b

[ "BSD-2-Clause" ]

2

2015-03-09T10:22:46.000Z

2019-06-03T12:44:44.000Z

tests/test_matrix.py

ionelmc/python-matrix

e1a63879a6c94c37c3883386f1d86eb7c2179a5b

[ "BSD-2-Clause" ]

null

tests/test_matrix.py

ionelmc/python-matrix

e1a63879a6c94c37c3883386f1d86eb7c2179a5b

[ "BSD-2-Clause" ]

3

2015-03-09T07:45:22.000Z

2021-01-25T11:37:03.000Z

import os import sys from pprint import pformat from matrix import Entry from matrix import from_file from matrix import from_string from matrix import parse_config from matrix.cli import main def here(path): return os.path.join(os.path.dirname(__file__), path) def strip_stupid_unicode_prefix(gunk): if sys.version_info[:2] == (2, 7): return gunk.replace("u'", "'") else: return gunk def test_cli(monkeypatch, tmpdir): monkeypatch.setattr(sys, 'argv', ['matrix-render', '-c', 'tests/sample.cfg', '-s', 'config', 'tests/template.txt', '-d', str(tmpdir)]) main() output = tmpdir.join('template.txt').read() print(output) assert set(strip_stupid_unicode_prefix(output).splitlines()) == set(( "-13: [('bar', '3'), ('foo', '1')]", "-14: [('bar', '4'), ('foo', '1')]", "-23: [('bar', '3'), ('foo', '2')]", "-24: [('bar', '4'), ('foo', '2')]", )) def test_parse_1(): assert str(Entry("alias: val1 val2 val3")) == "Entry('val1 val2 val3', alias='alias')" def test_parse_2(): assert ( str(Entry("alias: val1 val2 val3 !exclude1[eglob1] &include1[iglob1] !exclude2[eglob2] &include2[iglob2]")) == "Entry('val1 val2 val3', exclude(exclude1[eglob1]), include(include1[iglob1]), exclude(exclude2[eglob2]), include(include2[iglob2]), alias='alias')" ) def test_parse_3(): assert ( str(Entry("alias: val1 val2 val3 !exclude1[eglob1!&] &include1[iglob1!&]")) == "Entry('val1 val2 val3', exclude(exclude1[eglob1!&]), include(include1[iglob1!&]), alias='alias')" ) def test_parse_4(): assert ( str(Entry("alias: val1 val2 val3 !exclude1[] &include1[]")) == "Entry('val1 val2 val3', alias='alias')" ) def test_parse_file_1(): assert strip_stupid_unicode_prefix(pformat(dict(parse_config(open(here('config_1.ini')))))) == """{'coverage_flags': [Entry('true', alias='cover'), Entry('false', alias='nocover')], 'dependencies': [Entry('python-signalfd', alias='python-signalfd'), Entry('python-signalfd gevent', exclude(python_versions[3.*]), alias='python-signalfd_gevent'), Entry('python-signalfd eventlet', exclude(python_versions[3.*]), alias='python-signalfd_eventlet'), Entry('eventlet', exclude(python_versions[3.*]), alias='eventlet'), Entry('gevent', exclude(python_versions[3.*]), alias='gevent'), Entry('', alias='')], 'environment_variables': [Entry('PATCH_THREAD=yes', include(dependencies[*event*]), alias='patchthread'), Entry('', alias='')], 'python_versions': [Entry('2.7', alias='2.7'), Entry('2.6', alias='2.6'), Entry('3.2', alias='3.2'), Entry('3.3', alias='3.3'), Entry('3.4', alias='3.4'), Entry('pypy', alias='pypy')]}""" def test_parse_file_2(): assert strip_stupid_unicode_prefix(pformat(dict(parse_config(open(here('config_2.ini')))), width=100)) == """{'coverage_flags': [Entry('true', alias=''), Entry('false', alias='nocover')], 'depencencies': [Entry('Django==1.3.7', exclude(python_versions[3.*]), alias='1.3'), Entry('Django==1.4.13', exclude(python_versions[3.*]), alias='1.4'), Entry('Django==1.5.8', alias='1.5'), Entry('Django==1.6.5', alias='1.6'), Entry('https://www.djangoproject.com/download/1.7.b4/tarball/', exclude(python_versions[2.6]), alias='1.7')], 'environment_variables': [Entry('', alias='')], 'python_versions': [Entry('2.6', alias='2.6'), Entry('2.7', alias='2.7'), Entry('3.3', alias='3.3'), Entry('3.4', alias='3.4'), Entry('pypy', alias='pypy')]}""" def test_parse_file_3(): assert strip_stupid_unicode_prefix(pformat(dict(parse_config(open(here('config_3.ini')))), width=100)) == """{'coverage_flags': [Entry('true', alias=''), Entry('false', alias='nocover')], 'depencencies': [Entry('Django==1.3.7', exclude(python_versions[3.*]), alias='Django==1.3.7'), Entry('Django==1.4.13', exclude(python_versions[3.*]), alias='Django==1.4.13'), Entry('Django==1.5.8', alias='Django==1.5.8'), Entry('Django==1.6.5', alias='Django==1.6.5'), Entry('https://www.djangoproject.com/download/1.7.b4/tarball/', exclude(python_versions[2.6]), alias='Django==1.7')], 'environment_variables': [Entry('', alias='')], 'python_versions': [Entry('2.6', alias='2.6'), Entry('2.7', alias='2.7'), Entry('3.3', alias='3.3'), Entry('3.4', alias='3.4'), Entry('pypy', alias='pypy')]}""" def test_make_matrix_1(): assert from_file(here('config_1.ini')) == { '2.6-cover': {'coverage_flags': 'true', 'dependencies': '', 'environment_variables': '', 'python_versions': '2.6'}, '2.6-eventlet-cover': {'coverage_flags': 'true', 'dependencies': 'eventlet', 'environment_variables': '', 'python_versions': '2.6'}, '2.6-eventlet-cover-patchthread': {'coverage_flags': 'true', 'dependencies': 'eventlet', 'environment_variables': 'PATCH_THREAD=yes', 'python_versions': '2.6'}, '2.6-eventlet-nocover': {'coverage_flags': 'false', 'dependencies': 'eventlet', 'environment_variables': '', 'python_versions': '2.6'}, '2.6-eventlet-nocover-patchthread': {'coverage_flags': 'false', 'dependencies': 'eventlet', 'environment_variables': 'PATCH_THREAD=yes', 'python_versions': '2.6'}, '2.6-gevent-cover': {'coverage_flags': 'true', 'dependencies': 'gevent', 'environment_variables': '', 'python_versions': '2.6'}, '2.6-gevent-cover-patchthread': {'coverage_flags': 'true', 'dependencies': 'gevent', 'environment_variables': 'PATCH_THREAD=yes', 'python_versions': '2.6'}, '2.6-gevent-nocover': {'coverage_flags': 'false', 'dependencies': 'gevent', 'environment_variables': '', 'python_versions': '2.6'}, '2.6-gevent-nocover-patchthread': {'coverage_flags': 'false', 'dependencies': 'gevent', 'environment_variables': 'PATCH_THREAD=yes', 'python_versions': '2.6'}, '2.6-nocover': {'coverage_flags': 'false', 'dependencies': '', 'environment_variables': '', 'python_versions': '2.6'}, '2.6-python-signalfd-cover': {'coverage_flags': 'true', 'dependencies': 'python-signalfd', 'environment_variables': '', 'python_versions': '2.6'}, '2.6-python-signalfd-nocover': {'coverage_flags': 'false', 'dependencies': 'python-signalfd', 'environment_variables': '', 'python_versions': '2.6'}, '2.6-python-signalfd_eventlet-cover': {'coverage_flags': 'true', 'dependencies': 'python-signalfd eventlet', 'environment_variables': '', 'python_versions': '2.6'}, '2.6-python-signalfd_eventlet-cover-patchthread': {'coverage_flags': 'true', 'dependencies': 'python-signalfd eventlet', 'environment_variables': 'PATCH_THREAD=yes', 'python_versions': '2.6'}, '2.6-python-signalfd_eventlet-nocover': {'coverage_flags': 'false', 'dependencies': 'python-signalfd eventlet', 'environment_variables': '', 'python_versions': '2.6'}, '2.6-python-signalfd_eventlet-nocover-patchthread': {'coverage_flags': 'false', 'dependencies': 'python-signalfd eventlet', 'environment_variables': 'PATCH_THREAD=yes', 'python_versions': '2.6'}, '2.6-python-signalfd_gevent-cover': {'coverage_flags': 'true', 'dependencies': 'python-signalfd gevent', 'environment_variables': '', 'python_versions': '2.6'}, '2.6-python-signalfd_gevent-cover-patchthread': {'coverage_flags': 'true', 'dependencies': 'python-signalfd gevent', 'environment_variables': 'PATCH_THREAD=yes', 'python_versions': '2.6'}, '2.6-python-signalfd_gevent-nocover': {'coverage_flags': 'false', 'dependencies': 'python-signalfd gevent', 'environment_variables': '', 'python_versions': '2.6'}, '2.6-python-signalfd_gevent-nocover-patchthread': {'coverage_flags': 'false', 'dependencies': 'python-signalfd gevent', 'environment_variables': 'PATCH_THREAD=yes', 'python_versions': '2.6'}, '2.7-cover': {'coverage_flags': 'true', 'dependencies': '', 'environment_variables': '', 'python_versions': '2.7'}, '2.7-eventlet-cover': {'coverage_flags': 'true', 'dependencies': 'eventlet', 'environment_variables': '', 'python_versions': '2.7'}, '2.7-eventlet-cover-patchthread': {'coverage_flags': 'true', 'dependencies': 'eventlet', 'environment_variables': 'PATCH_THREAD=yes', 'python_versions': '2.7'}, '2.7-eventlet-nocover': {'coverage_flags': 'false', 'dependencies': 'eventlet', 'environment_variables': '', 'python_versions': '2.7'}, '2.7-eventlet-nocover-patchthread': {'coverage_flags': 'false', 'dependencies': 'eventlet', 'environment_variables': 'PATCH_THREAD=yes', 'python_versions': '2.7'}, '2.7-gevent-cover': {'coverage_flags': 'true', 'dependencies': 'gevent', 'environment_variables': '', 'python_versions': '2.7'}, '2.7-gevent-cover-patchthread': {'coverage_flags': 'true', 'dependencies': 'gevent', 'environment_variables': 'PATCH_THREAD=yes', 'python_versions': '2.7'}, '2.7-gevent-nocover': {'coverage_flags': 'false', 'dependencies': 'gevent', 'environment_variables': '', 'python_versions': '2.7'}, '2.7-gevent-nocover-patchthread': {'coverage_flags': 'false', 'dependencies': 'gevent', 'environment_variables': 'PATCH_THREAD=yes', 'python_versions': '2.7'}, '2.7-nocover': {'coverage_flags': 'false', 'dependencies': '', 'environment_variables': '', 'python_versions': '2.7'}, '2.7-python-signalfd-cover': {'coverage_flags': 'true', 'dependencies': 'python-signalfd', 'environment_variables': '', 'python_versions': '2.7'}, '2.7-python-signalfd-nocover': {'coverage_flags': 'false', 'dependencies': 'python-signalfd', 'environment_variables': '', 'python_versions': '2.7'}, '2.7-python-signalfd_eventlet-cover': {'coverage_flags': 'true', 'dependencies': 'python-signalfd eventlet', 'environment_variables': '', 'python_versions': '2.7'}, '2.7-python-signalfd_eventlet-cover-patchthread': {'coverage_flags': 'true', 'dependencies': 'python-signalfd eventlet', 'environment_variables': 'PATCH_THREAD=yes', 'python_versions': '2.7'}, '2.7-python-signalfd_eventlet-nocover': {'coverage_flags': 'false', 'dependencies': 'python-signalfd eventlet', 'environment_variables': '', 'python_versions': '2.7'}, '2.7-python-signalfd_eventlet-nocover-patchthread': {'coverage_flags': 'false', 'dependencies': 'python-signalfd eventlet', 'environment_variables': 'PATCH_THREAD=yes', 'python_versions': '2.7'}, '2.7-python-signalfd_gevent-cover': {'coverage_flags': 'true', 'dependencies': 'python-signalfd gevent', 'environment_variables': '', 'python_versions': '2.7'}, '2.7-python-signalfd_gevent-cover-patchthread': {'coverage_flags': 'true', 'dependencies': 'python-signalfd gevent', 'environment_variables': 'PATCH_THREAD=yes', 'python_versions': '2.7'}, '2.7-python-signalfd_gevent-nocover': {'coverage_flags': 'false', 'dependencies': 'python-signalfd gevent', 'environment_variables': '', 'python_versions': '2.7'}, '2.7-python-signalfd_gevent-nocover-patchthread': {'coverage_flags': 'false', 'dependencies': 'python-signalfd gevent', 'environment_variables': 'PATCH_THREAD=yes', 'python_versions': '2.7'}, '3.2-cover': {'coverage_flags': 'true', 'dependencies': '', 'environment_variables': '', 'python_versions': '3.2'}, '3.2-nocover': {'coverage_flags': 'false', 'dependencies': '', 'environment_variables': '', 'python_versions': '3.2'}, '3.2-python-signalfd-cover': {'coverage_flags': 'true', 'dependencies': 'python-signalfd', 'environment_variables': '', 'python_versions': '3.2'}, '3.2-python-signalfd-nocover': {'coverage_flags': 'false', 'dependencies': 'python-signalfd', 'environment_variables': '', 'python_versions': '3.2'}, '3.3-cover': {'coverage_flags': 'true', 'dependencies': '', 'environment_variables': '', 'python_versions': '3.3'}, '3.3-nocover': {'coverage_flags': 'false', 'dependencies': '', 'environment_variables': '', 'python_versions': '3.3'}, '3.3-python-signalfd-cover': {'coverage_flags': 'true', 'dependencies': 'python-signalfd', 'environment_variables': '', 'python_versions': '3.3'}, '3.3-python-signalfd-nocover': {'coverage_flags': 'false', 'dependencies': 'python-signalfd', 'environment_variables': '', 'python_versions': '3.3'}, '3.4-cover': {'coverage_flags': 'true', 'dependencies': '', 'environment_variables': '', 'python_versions': '3.4'}, '3.4-nocover': {'coverage_flags': 'false', 'dependencies': '', 'environment_variables': '', 'python_versions': '3.4'}, '3.4-python-signalfd-cover': {'coverage_flags': 'true', 'dependencies': 'python-signalfd', 'environment_variables': '', 'python_versions': '3.4'}, '3.4-python-signalfd-nocover': {'coverage_flags': 'false', 'dependencies': 'python-signalfd', 'environment_variables': '', 'python_versions': '3.4'}, 'pypy-cover': {'coverage_flags': 'true', 'dependencies': '', 'environment_variables': '', 'python_versions': 'pypy'}, 'pypy-eventlet-cover': {'coverage_flags': 'true', 'dependencies': 'eventlet', 'environment_variables': '', 'python_versions': 'pypy'}, 'pypy-eventlet-cover-patchthread': {'coverage_flags': 'true', 'dependencies': 'eventlet', 'environment_variables': 'PATCH_THREAD=yes', 'python_versions': 'pypy'}, 'pypy-eventlet-nocover': {'coverage_flags': 'false', 'dependencies': 'eventlet', 'environment_variables': '', 'python_versions': 'pypy'}, 'pypy-eventlet-nocover-patchthread': {'coverage_flags': 'false', 'dependencies': 'eventlet', 'environment_variables': 'PATCH_THREAD=yes', 'python_versions': 'pypy'}, 'pypy-gevent-cover': {'coverage_flags': 'true', 'dependencies': 'gevent', 'environment_variables': '', 'python_versions': 'pypy'}, 'pypy-gevent-cover-patchthread': {'coverage_flags': 'true', 'dependencies': 'gevent', 'environment_variables': 'PATCH_THREAD=yes', 'python_versions': 'pypy'}, 'pypy-gevent-nocover': {'coverage_flags': 'false', 'dependencies': 'gevent', 'environment_variables': '', 'python_versions': 'pypy'}, 'pypy-gevent-nocover-patchthread': {'coverage_flags': 'false', 'dependencies': 'gevent', 'environment_variables': 'PATCH_THREAD=yes', 'python_versions': 'pypy'}, 'pypy-nocover': {'coverage_flags': 'false', 'dependencies': '', 'environment_variables': '', 'python_versions': 'pypy'}, 'pypy-python-signalfd-cover': {'coverage_flags': 'true', 'dependencies': 'python-signalfd', 'environment_variables': '', 'python_versions': 'pypy'}, 'pypy-python-signalfd-nocover': {'coverage_flags': 'false', 'dependencies': 'python-signalfd', 'environment_variables': '', 'python_versions': 'pypy'}, 'pypy-python-signalfd_eventlet-cover': {'coverage_flags': 'true', 'dependencies': 'python-signalfd eventlet', 'environment_variables': '', 'python_versions': 'pypy'}, 'pypy-python-signalfd_eventlet-cover-patchthread': {'coverage_flags': 'true', 'dependencies': 'python-signalfd eventlet', 'environment_variables': 'PATCH_THREAD=yes', 'python_versions': 'pypy'}, 'pypy-python-signalfd_eventlet-nocover': {'coverage_flags': 'false', 'dependencies': 'python-signalfd eventlet', 'environment_variables': '', 'python_versions': 'pypy'}, 'pypy-python-signalfd_eventlet-nocover-patchthread': {'coverage_flags': 'false', 'dependencies': 'python-signalfd eventlet', 'environment_variables': 'PATCH_THREAD=yes', 'python_versions': 'pypy'}, 'pypy-python-signalfd_gevent-cover': {'coverage_flags': 'true', 'dependencies': 'python-signalfd gevent', 'environment_variables': '', 'python_versions': 'pypy'}, 'pypy-python-signalfd_gevent-cover-patchthread': {'coverage_flags': 'true', 'dependencies': 'python-signalfd gevent', 'environment_variables': 'PATCH_THREAD=yes', 'python_versions': 'pypy'}, 'pypy-python-signalfd_gevent-nocover': {'coverage_flags': 'false', 'dependencies': 'python-signalfd gevent', 'environment_variables': '', 'python_versions': 'pypy'}, 'pypy-python-signalfd_gevent-nocover-patchthread': {'coverage_flags': 'false', 'dependencies': 'python-signalfd gevent', 'environment_variables': 'PATCH_THREAD=yes', 'python_versions': 'pypy'} } def test_make_matrix_2(): assert from_file(here('config_2.ini')) == { '2.6-1.3': {'coverage_flags': 'true', 'depencencies': 'Django==1.3.7', 'environment_variables': '', 'python_versions': '2.6'}, '2.6-1.3-nocover': {'coverage_flags': 'false', 'depencencies': 'Django==1.3.7', 'environment_variables': '', 'python_versions': '2.6'}, '2.6-1.4': {'coverage_flags': 'true', 'depencencies': 'Django==1.4.13', 'environment_variables': '', 'python_versions': '2.6'}, '2.6-1.4-nocover': {'coverage_flags': 'false', 'depencencies': 'Django==1.4.13', 'environment_variables': '', 'python_versions': '2.6'}, '2.6-1.5': {'coverage_flags': 'true', 'depencencies': 'Django==1.5.8', 'environment_variables': '', 'python_versions': '2.6'}, '2.6-1.5-nocover': {'coverage_flags': 'false', 'depencencies': 'Django==1.5.8', 'environment_variables': '', 'python_versions': '2.6'}, '2.6-1.6': {'coverage_flags': 'true', 'depencencies': 'Django==1.6.5', 'environment_variables': '', 'python_versions': '2.6'}, '2.6-1.6-nocover': {'coverage_flags': 'false', 'depencencies': 'Django==1.6.5', 'environment_variables': '', 'python_versions': '2.6'}, '2.7-1.3': {'coverage_flags': 'true', 'depencencies': 'Django==1.3.7', 'environment_variables': '', 'python_versions': '2.7'}, '2.7-1.3-nocover': {'coverage_flags': 'false', 'depencencies': 'Django==1.3.7', 'environment_variables': '', 'python_versions': '2.7'}, '2.7-1.4': {'coverage_flags': 'true', 'depencencies': 'Django==1.4.13', 'environment_variables': '', 'python_versions': '2.7'}, '2.7-1.4-nocover': {'coverage_flags': 'false', 'depencencies': 'Django==1.4.13', 'environment_variables': '', 'python_versions': '2.7'}, '2.7-1.5': {'coverage_flags': 'true', 'depencencies': 'Django==1.5.8', 'environment_variables': '', 'python_versions': '2.7'}, '2.7-1.5-nocover': {'coverage_flags': 'false', 'depencencies': 'Django==1.5.8', 'environment_variables': '', 'python_versions': '2.7'}, '2.7-1.6': {'coverage_flags': 'true', 'depencencies': 'Django==1.6.5', 'environment_variables': '', 'python_versions': '2.7'}, '2.7-1.6-nocover': {'coverage_flags': 'false', 'depencencies': 'Django==1.6.5', 'environment_variables': '', 'python_versions': '2.7'}, '2.7-1.7': {'coverage_flags': 'true', 'depencencies': 'https://www.djangoproject.com/download/1.7.b4/tarball/', 'environment_variables': '', 'python_versions': '2.7'}, '2.7-1.7-nocover': {'coverage_flags': 'false', 'depencencies': 'https://www.djangoproject.com/download/1.7.b4/tarball/', 'environment_variables': '', 'python_versions': '2.7'}, '3.3-1.5': {'coverage_flags': 'true', 'depencencies': 'Django==1.5.8', 'environment_variables': '', 'python_versions': '3.3'}, '3.3-1.5-nocover': {'coverage_flags': 'false', 'depencencies': 'Django==1.5.8', 'environment_variables': '', 'python_versions': '3.3'}, '3.3-1.6': {'coverage_flags': 'true', 'depencencies': 'Django==1.6.5', 'environment_variables': '', 'python_versions': '3.3'}, '3.3-1.6-nocover': {'coverage_flags': 'false', 'depencencies': 'Django==1.6.5', 'environment_variables': '', 'python_versions': '3.3'}, '3.3-1.7': {'coverage_flags': 'true', 'depencencies': 'https://www.djangoproject.com/download/1.7.b4/tarball/', 'environment_variables': '', 'python_versions': '3.3'}, '3.3-1.7-nocover': {'coverage_flags': 'false', 'depencencies': 'https://www.djangoproject.com/download/1.7.b4/tarball/', 'environment_variables': '', 'python_versions': '3.3'}, '3.4-1.5': {'coverage_flags': 'true', 'depencencies': 'Django==1.5.8', 'environment_variables': '', 'python_versions': '3.4'}, '3.4-1.5-nocover': {'coverage_flags': 'false', 'depencencies': 'Django==1.5.8', 'environment_variables': '', 'python_versions': '3.4'}, '3.4-1.6': {'coverage_flags': 'true', 'depencencies': 'Django==1.6.5', 'environment_variables': '', 'python_versions': '3.4'}, '3.4-1.6-nocover': {'coverage_flags': 'false', 'depencencies': 'Django==1.6.5', 'environment_variables': '', 'python_versions': '3.4'}, '3.4-1.7': {'coverage_flags': 'true', 'depencencies': 'https://www.djangoproject.com/download/1.7.b4/tarball/', 'environment_variables': '', 'python_versions': '3.4'}, '3.4-1.7-nocover': {'coverage_flags': 'false', 'depencencies': 'https://www.djangoproject.com/download/1.7.b4/tarball/', 'environment_variables': '', 'python_versions': '3.4'}, 'pypy-1.3': {'coverage_flags': 'true', 'depencencies': 'Django==1.3.7', 'environment_variables': '', 'python_versions': 'pypy'}, 'pypy-1.3-nocover': {'coverage_flags': 'false', 'depencencies': 'Django==1.3.7', 'environment_variables': '', 'python_versions': 'pypy'}, 'pypy-1.4': {'coverage_flags': 'true', 'depencencies': 'Django==1.4.13', 'environment_variables': '', 'python_versions': 'pypy'}, 'pypy-1.4-nocover': {'coverage_flags': 'false', 'depencencies': 'Django==1.4.13', 'environment_variables': '', 'python_versions': 'pypy'}, 'pypy-1.5': {'coverage_flags': 'true', 'depencencies': 'Django==1.5.8', 'environment_variables': '', 'python_versions': 'pypy'}, 'pypy-1.5-nocover': {'coverage_flags': 'false', 'depencencies': 'Django==1.5.8', 'environment_variables': '', 'python_versions': 'pypy'}, 'pypy-1.6': {'coverage_flags': 'true', 'depencencies': 'Django==1.6.5', 'environment_variables': '', 'python_versions': 'pypy'}, 'pypy-1.6-nocover': {'coverage_flags': 'false', 'depencencies': 'Django==1.6.5', 'environment_variables': '', 'python_versions': 'pypy'}, 'pypy-1.7': {'coverage_flags': 'true', 'depencencies': 'https://www.djangoproject.com/download/1.7.b4/tarball/', 'environment_variables': '', 'python_versions': 'pypy'}, 'pypy-1.7-nocover': {'coverage_flags': 'false', 'depencencies': 'https://www.djangoproject.com/download/1.7.b4/tarball/', 'environment_variables': '', 'python_versions': 'pypy'} } def test_make_matrix_3(): assert from_file(here('config_3.ini')) == { '2.6-Django==1.3.7': {'coverage_flags': 'true', 'depencencies': 'Django==1.3.7', 'environment_variables': '', 'python_versions': '2.6'}, '2.6-Django==1.3.7-nocover': {'coverage_flags': 'false', 'depencencies': 'Django==1.3.7', 'environment_variables': '', 'python_versions': '2.6'}, '2.6-Django==1.4.13': {'coverage_flags': 'true', 'depencencies': 'Django==1.4.13', 'environment_variables': '', 'python_versions': '2.6'}, '2.6-Django==1.4.13-nocover': {'coverage_flags': 'false', 'depencencies': 'Django==1.4.13', 'environment_variables': '', 'python_versions': '2.6'}, '2.6-Django==1.5.8': {'coverage_flags': 'true', 'depencencies': 'Django==1.5.8', 'environment_variables': '', 'python_versions': '2.6'}, '2.6-Django==1.5.8-nocover': {'coverage_flags': 'false', 'depencencies': 'Django==1.5.8', 'environment_variables': '', 'python_versions': '2.6'}, '2.6-Django==1.6.5': {'coverage_flags': 'true', 'depencencies': 'Django==1.6.5', 'environment_variables': '', 'python_versions': '2.6'}, '2.6-Django==1.6.5-nocover': {'coverage_flags': 'false', 'depencencies': 'Django==1.6.5', 'environment_variables': '', 'python_versions': '2.6'}, '2.7-Django==1.3.7': {'coverage_flags': 'true', 'depencencies': 'Django==1.3.7', 'environment_variables': '', 'python_versions': '2.7'}, '2.7-Django==1.3.7-nocover': {'coverage_flags': 'false', 'depencencies': 'Django==1.3.7', 'environment_variables': '', 'python_versions': '2.7'}, '2.7-Django==1.4.13': {'coverage_flags': 'true', 'depencencies': 'Django==1.4.13', 'environment_variables': '', 'python_versions': '2.7'}, '2.7-Django==1.4.13-nocover': {'coverage_flags': 'false', 'depencencies': 'Django==1.4.13', 'environment_variables': '', 'python_versions': '2.7'}, '2.7-Django==1.5.8': {'coverage_flags': 'true', 'depencencies': 'Django==1.5.8', 'environment_variables': '', 'python_versions': '2.7'}, '2.7-Django==1.5.8-nocover': {'coverage_flags': 'false', 'depencencies': 'Django==1.5.8', 'environment_variables': '', 'python_versions': '2.7'}, '2.7-Django==1.6.5': {'coverage_flags': 'true', 'depencencies': 'Django==1.6.5', 'environment_variables': '', 'python_versions': '2.7'}, '2.7-Django==1.6.5-nocover': {'coverage_flags': 'false', 'depencencies': 'Django==1.6.5', 'environment_variables': '', 'python_versions': '2.7'}, '2.7-Django==1.7': {'coverage_flags': 'true', 'depencencies': 'https://www.djangoproject.com/download/1.7.b4/tarball/', 'environment_variables': '', 'python_versions': '2.7'}, '2.7-Django==1.7-nocover': {'coverage_flags': 'false', 'depencencies': 'https://www.djangoproject.com/download/1.7.b4/tarball/', 'environment_variables': '', 'python_versions': '2.7'}, '3.3-Django==1.5.8': {'coverage_flags': 'true', 'depencencies': 'Django==1.5.8', 'environment_variables': '', 'python_versions': '3.3'}, '3.3-Django==1.5.8-nocover': {'coverage_flags': 'false', 'depencencies': 'Django==1.5.8', 'environment_variables': '', 'python_versions': '3.3'}, '3.3-Django==1.6.5': {'coverage_flags': 'true', 'depencencies': 'Django==1.6.5', 'environment_variables': '', 'python_versions': '3.3'}, '3.3-Django==1.6.5-nocover': {'coverage_flags': 'false', 'depencencies': 'Django==1.6.5', 'environment_variables': '', 'python_versions': '3.3'}, '3.3-Django==1.7': {'coverage_flags': 'true', 'depencencies': 'https://www.djangoproject.com/download/1.7.b4/tarball/', 'environment_variables': '', 'python_versions': '3.3'}, '3.3-Django==1.7-nocover': {'coverage_flags': 'false', 'depencencies': 'https://www.djangoproject.com/download/1.7.b4/tarball/', 'environment_variables': '', 'python_versions': '3.3'}, '3.4-Django==1.5.8': {'coverage_flags': 'true', 'depencencies': 'Django==1.5.8', 'environment_variables': '', 'python_versions': '3.4'}, '3.4-Django==1.5.8-nocover': {'coverage_flags': 'false', 'depencencies': 'Django==1.5.8', 'environment_variables': '', 'python_versions': '3.4'}, '3.4-Django==1.6.5': {'coverage_flags': 'true', 'depencencies': 'Django==1.6.5', 'environment_variables': '', 'python_versions': '3.4'}, '3.4-Django==1.6.5-nocover': {'coverage_flags': 'false', 'depencencies': 'Django==1.6.5', 'environment_variables': '', 'python_versions': '3.4'}, '3.4-Django==1.7': {'coverage_flags': 'true', 'depencencies': 'https://www.djangoproject.com/download/1.7.b4/tarball/', 'environment_variables': '', 'python_versions': '3.4'}, '3.4-Django==1.7-nocover': {'coverage_flags': 'false', 'depencencies': 'https://www.djangoproject.com/download/1.7.b4/tarball/', 'environment_variables': '', 'python_versions': '3.4'}, 'pypy-Django==1.3.7': {'coverage_flags': 'true', 'depencencies': 'Django==1.3.7', 'environment_variables': '', 'python_versions': 'pypy'}, 'pypy-Django==1.3.7-nocover': {'coverage_flags': 'false', 'depencencies': 'Django==1.3.7', 'environment_variables': '', 'python_versions': 'pypy'}, 'pypy-Django==1.4.13': {'coverage_flags': 'true', 'depencencies': 'Django==1.4.13', 'environment_variables': '', 'python_versions': 'pypy'}, 'pypy-Django==1.4.13-nocover': {'coverage_flags': 'false', 'depencencies': 'Django==1.4.13', 'environment_variables': '', 'python_versions': 'pypy'}, 'pypy-Django==1.5.8': {'coverage_flags': 'true', 'depencencies': 'Django==1.5.8', 'environment_variables': '', 'python_versions': 'pypy'}, 'pypy-Django==1.5.8-nocover': {'coverage_flags': 'false', 'depencencies': 'Django==1.5.8', 'environment_variables': '', 'python_versions': 'pypy'}, 'pypy-Django==1.6.5': {'coverage_flags': 'true', 'depencencies': 'Django==1.6.5', 'environment_variables': '', 'python_versions': 'pypy'}, 'pypy-Django==1.6.5-nocover': {'coverage_flags': 'false', 'depencencies': 'Django==1.6.5', 'environment_variables': '', 'python_versions': 'pypy'}, 'pypy-Django==1.7': {'coverage_flags': 'true', 'depencencies': 'https://www.djangoproject.com/download/1.7.b4/tarball/', 'environment_variables': '', 'python_versions': 'pypy'}, 'pypy-Django==1.7-nocover': {'coverage_flags': 'false', 'depencencies': 'https://www.djangoproject.com/download/1.7.b4/tarball/', 'environment_variables': '', 'python_versions': 'pypy'} } def test_make_matrix_from_string_1(): assert from_string(""" [matrix] python_versions = 2.6 2.7 3.3 3.4 pypy dependencies = : trollius MySQL-python !python_versions[3.*] win: trollius !python_versions[3.*] : asyncio &python_versions[3.3] : &python_versions[3.4] coverage_flags = : true nocover: false environment_variables = debug: ASPECTLIB_DEBUG=yes - """) == { '2.6': {'coverage_flags': 'true', 'dependencies': 'trollius MySQL-python', 'environment_variables': '', 'python_versions': '2.6'}, '2.6-debug': {'coverage_flags': 'true', 'dependencies': 'trollius MySQL-python', 'environment_variables': 'ASPECTLIB_DEBUG=yes', 'python_versions': '2.6'}, '2.6-nocover': {'coverage_flags': 'false', 'dependencies': 'trollius MySQL-python', 'environment_variables': '', 'python_versions': '2.6'}, '2.6-nocover-debug': {'coverage_flags': 'false', 'dependencies': 'trollius MySQL-python', 'environment_variables': 'ASPECTLIB_DEBUG=yes', 'python_versions': '2.6'}, '2.6-win': {'coverage_flags': 'true', 'dependencies': 'trollius', 'environment_variables': '', 'python_versions': '2.6'}, '2.6-win-debug': {'coverage_flags': 'true', 'dependencies': 'trollius', 'environment_variables': 'ASPECTLIB_DEBUG=yes', 'python_versions': '2.6'}, '2.6-win-nocover': {'coverage_flags': 'false', 'dependencies': 'trollius', 'environment_variables': '', 'python_versions': '2.6'}, '2.6-win-nocover-debug': {'coverage_flags': 'false', 'dependencies': 'trollius', 'environment_variables': 'ASPECTLIB_DEBUG=yes', 'python_versions': '2.6'}, '2.7': {'coverage_flags': 'true', 'dependencies': 'trollius MySQL-python', 'environment_variables': '', 'python_versions': '2.7'}, '2.7-debug': {'coverage_flags': 'true', 'dependencies': 'trollius MySQL-python', 'environment_variables': 'ASPECTLIB_DEBUG=yes', 'python_versions': '2.7'}, '2.7-nocover': {'coverage_flags': 'false', 'dependencies': 'trollius MySQL-python', 'environment_variables': '', 'python_versions': '2.7'}, '2.7-nocover-debug': {'coverage_flags': 'false', 'dependencies': 'trollius MySQL-python', 'environment_variables': 'ASPECTLIB_DEBUG=yes', 'python_versions': '2.7'}, '2.7-win': {'coverage_flags': 'true', 'dependencies': 'trollius', 'environment_variables': '', 'python_versions': '2.7'}, '2.7-win-debug': {'coverage_flags': 'true', 'dependencies': 'trollius', 'environment_variables': 'ASPECTLIB_DEBUG=yes', 'python_versions': '2.7'}, '2.7-win-nocover': {'coverage_flags': 'false', 'dependencies': 'trollius', 'environment_variables': '', 'python_versions': '2.7'}, '2.7-win-nocover-debug': {'coverage_flags': 'false', 'dependencies': 'trollius', 'environment_variables': 'ASPECTLIB_DEBUG=yes', 'python_versions': '2.7'}, '3.3': {'coverage_flags': 'true', 'dependencies': 'asyncio', 'environment_variables': '', 'python_versions': '3.3'}, '3.3-debug': {'coverage_flags': 'true', 'dependencies': 'asyncio', 'environment_variables': 'ASPECTLIB_DEBUG=yes', 'python_versions': '3.3'}, '3.3-nocover': {'coverage_flags': 'false', 'dependencies': 'asyncio', 'environment_variables': '', 'python_versions': '3.3'}, '3.3-nocover-debug': {'coverage_flags': 'false', 'dependencies': 'asyncio', 'environment_variables': 'ASPECTLIB_DEBUG=yes', 'python_versions': '3.3'}, '3.4': {'coverage_flags': 'true', 'dependencies': ' ', 'environment_variables': '', 'python_versions': '3.4'}, '3.4-debug': {'coverage_flags': 'true', 'dependencies': ' ', 'environment_variables': 'ASPECTLIB_DEBUG=yes', 'python_versions': '3.4'}, '3.4-nocover': {'coverage_flags': 'false', 'dependencies': ' ', 'environment_variables': '', 'python_versions': '3.4'}, '3.4-nocover-debug': {'coverage_flags': 'false', 'dependencies': ' ', 'environment_variables': 'ASPECTLIB_DEBUG=yes', 'python_versions': '3.4'}, 'pypy': {'coverage_flags': 'true', 'dependencies': 'trollius MySQL-python', 'environment_variables': '', 'python_versions': 'pypy'}, 'pypy-debug': {'coverage_flags': 'true', 'dependencies': 'trollius MySQL-python', 'environment_variables': 'ASPECTLIB_DEBUG=yes', 'python_versions': 'pypy'}, 'pypy-nocover': {'coverage_flags': 'false', 'dependencies': 'trollius MySQL-python', 'environment_variables': '', 'python_versions': 'pypy'}, 'pypy-nocover-debug': {'coverage_flags': 'false', 'dependencies': 'trollius MySQL-python', 'environment_variables': 'ASPECTLIB_DEBUG=yes', 'python_versions': 'pypy'}, 'pypy-win': {'coverage_flags': 'true', 'dependencies': 'trollius', 'environment_variables': '', 'python_versions': 'pypy'}, 'pypy-win-debug': {'coverage_flags': 'true', 'dependencies': 'trollius', 'environment_variables': 'ASPECTLIB_DEBUG=yes', 'python_versions': 'pypy'}, 'pypy-win-nocover': {'coverage_flags': 'false', 'dependencies': 'trollius', 'environment_variables': '', 'python_versions': 'pypy'}, 'pypy-win-nocover-debug': {'coverage_flags': 'false', 'dependencies': 'trollius', 'environment_variables': 'ASPECTLIB_DEBUG=yes', 'python_versions': 'pypy'} } def test_make_matrix_from_string_2(): assert from_string(""" [matrix] python_versions = # some 2.6 # junk 2.7 3.3 3.4 pypy dependencies = coverage_flags = : true nocover: false environment_variables = """) == { '2.6': {'coverage_flags': 'true', 'dependencies': '', 'environment_variables': '', 'python_versions': '2.6'}, '2.6-nocover': {'coverage_flags': 'false', 'dependencies': '', 'environment_variables': '', 'python_versions': '2.6'}, '2.7': {'coverage_flags': 'true', 'dependencies': '', 'environment_variables': '', 'python_versions': '2.7'}, '2.7-nocover': {'coverage_flags': 'false', 'dependencies': '', 'environment_variables': '', 'python_versions': '2.7'}, '3.3': {'coverage_flags': 'true', 'dependencies': '', 'environment_variables': '', 'python_versions': '3.3'}, '3.3-nocover': {'coverage_flags': 'false', 'dependencies': '', 'environment_variables': '', 'python_versions': '3.3'}, '3.4': {'coverage_flags': 'true', 'dependencies': '', 'environment_variables': '', 'python_versions': '3.4'}, '3.4-nocover': {'coverage_flags': 'false', 'dependencies': '', 'environment_variables': '', 'python_versions': '3.4'}, 'pypy': {'coverage_flags': 'true', 'dependencies': '', 'environment_variables': '', 'python_versions': 'pypy'}, 'pypy-nocover': {'coverage_flags': 'false', 'dependencies': '', 'environment_variables': '', 'python_versions': 'pypy'} }

57.522703

191

0.419686

4,186

54,474

5.281653

0.029384

0.134877

0.181103

0.236827

0.95088

0.943417

0.929395

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0.911167

0.902709

0

0.040672

0.430389

54,474

946

192

57.58351

0.671855

0

0.71899

0

0.009879

0.458072

0.137607

0

0.01427

1

0.016465

false

0

0.008782

0.001098

0.02854

0.002195

0

null

0

1

0

1

0

null

0

9

3b899ed220548317779a27e821e2b211b1d1047e

789

py

Python

CODE FILES/serial_scraper.py

magnusmel/Troubleshooting-Python-Application-Development

3a5a29b457244d46619c4744553bce411706a30d

[ "MIT" ]

1

2021-04-19T05:27:10.000Z

CODE FILES/serial_scraper.py

magnusmel/Troubleshooting-Python-Application-Development

3a5a29b457244d46619c4744553bce411706a30d

[ "MIT" ]

null

CODE FILES/serial_scraper.py

magnusmel/Troubleshooting-Python-Application-Development

3a5a29b457244d46619c4744553bce411706a30d

[ "MIT" ]

4

2019-06-28T11:03:24.000Z

2021-04-18T12:31:25.000Z

import requests import time results = [] urls = [ 'http://www.bbc.co.uk/news/uk-44190742', 'http://www.bbc.co.uk/news/uk-44173553', 'http://www.bbc.co.uk/news/uk-england-44194146', 'http://www.bbc.co.uk/news/health-44195456', 'http://www.bbc.co.uk/news/uk-44190742', 'http://www.bbc.co.uk/news/uk-44173553', 'http://www.bbc.co.uk/news/uk-england-44194146', 'http://www.bbc.co.uk/news/health-44195456', 'http://www.bbc.co.uk/news/uk-44190742', 'http://www.bbc.co.uk/news/uk-44173553', 'http://www.bbc.co.uk/news/uk-england-44194146', 'http://www.bbc.co.uk/news/health-44195456' ] start = time.time() for u in urls: r = requests.get(u) results.append(r.status_code) print(results) print('Done in {:.3}s.'.format(time.time()-start))

29.222222

52

0.64512

132

789

3.848485

0.234848

0.165354

0.23622

0.283465

0.726378

0

0.140173

0.12294

789

27

53

29.222222

0.593931

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0.478261

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0.626582

0

1

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false

0

0.086957

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0.086957

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null

0

1

0

1

0

1

0

null

0

8

8e70e0b0dd98fbacbe06cbf8905513a920b61a8c

95

py

Python

facextr/__init__.py

J77M/facextr

d13539f4816f0dfde300bd7612b18a9c06dcb3b1

[ "MIT" ]

null

facextr/__init__.py

J77M/facextr

d13539f4816f0dfde300bd7612b18a9c06dcb3b1

[ "MIT" ]

null

facextr/__init__.py

J77M/facextr

d13539f4816f0dfde300bd7612b18a9c06dcb3b1

[ "MIT" ]

null

from .face_extract import * from .garbage_recognition import * from .face_extract_dirs import *

31.666667

34

0.821053

13

95

5.692308

0.538462

0.216216

0.405405

0

0.115789

95

3

35

31.666667

0.880952

0

1

0

true

0

1

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1

0

1

0

null

1

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1

0

null

0

1

0

1

0

1

0

7

8e7d0347df975259a2f052900708d9241cdb42a9

707

py

Python

src/arch/x86/isa/insts/simd512/floating_point/data_conversion/vcvtsd2ss.py

jyhuang91/gem5-avx

f988da46080f8db49beb39e20af437219f3aa4cb

[ "BSD-3-Clause" ]

2

2021-01-15T17:32:18.000Z

2021-12-21T02:53:58.000Z

src/arch/x86/isa/insts/simd512/floating_point/data_conversion/vcvtsd2ss.py

jyhuang91/gem5-avx

f988da46080f8db49beb39e20af437219f3aa4cb

[ "BSD-3-Clause" ]

3

2021-03-26T20:33:59.000Z

2022-01-24T22:54:03.000Z

src/arch/x86/isa/insts/simd512/floating_point/data_conversion/vcvtsd2ss.py

jyhuang91/gem5-avx

f988da46080f8db49beb39e20af437219f3aa4cb

[ "BSD-3-Clause" ]

3

2021-03-27T16:36:19.000Z

2022-03-28T18:32:57.000Z

microcode = ''' def macroop VCVTSD2SS_XMM_XMM { cvtf2f xmm0, xmm0m, destSize=4, srcSize=8, ext=Scalar movfph2h xmm0, xmm0v, dataSize=4 movfp xmm1, xmm1v, dataSize=8 vclear dest=xmm2, destVL=16 }; def macroop VCVTSD2SS_XMM_M { ldfp ufp1, seg, sib, disp, dataSize=8 cvtf2f xmm0, ufp1, destSize=4, srcSize=8, ext=Scalar movfph2h xmm0, xmm0v, dataSize=4 movfp xmm1, xmm1v, dataSize=8 vclear dest=xmm2, destVL=16 }; def macroop VCVTSD2SS_XMM_P { rdip t7 ldfp ufp1, seg, riprel, disp, dataSize=8 cvtf2f xmm0, ufp1, destSize=4, srcSize=8, ext=Scalar movfph2h xmm0, xmm0v, dataSize=4 movfp xmm1, xmm1v, dataSize=8 vclear dest=xmm2, destVL=16 }; '''

26.185185

57

0.683168

104

707

4.586538

0.336538

0.09434

0.119497

0.138365

0.815514

0

0.095153

0.212164

707

27

58

26.185185

0.761221

0

0.608696

0

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0

1

0

false

0

null

0

1

0

1

0

null

0

7

8e8d03c41085dbd8dfaa21e60ea9d3d684d9fd97

9,036

py

Python

test/test_checksum.py

jdavidberger/myhdl_lib

28a7916a1ef331c45c5b19bdd12fbbae5f76f279

[ "MIT" ]

14

2015-08-25T00:46:31.000Z

2022-02-18T12:42:33.000Z

test/test_checksum.py

jdavidberger/myhdl_lib

28a7916a1ef331c45c5b19bdd12fbbae5f76f279

[ "MIT" ]

null

test/test_checksum.py

jdavidberger/myhdl_lib

28a7916a1ef331c45c5b19bdd12fbbae5f76f279

[ "MIT" ]

5

2015-07-02T15:34:58.000Z

2022-02-25T16:45:59.000Z

import unittest from myhdl import * from myhdl_lib.stream import checksum import myhdl_lib.simulation as sim import random from math import log, ceil class TestChecksum(unittest.TestCase): @classmethod def setUpClass(cls): cls.simulators = ["myhdl", "icarus"] def testRand(self): ''' CHECKSUM: Random ''' MAX_NUM_BYTES = 200 BYTES_PER_WORD = [2,4,6,8] getDut = sim.DUTer() def testbench(BYTES_PER_WORD): W16_PER_WORD = BYTES_PER_WORD//2 SUM_WIDTH = 16 + int(ceil(log(MAX_NUM_BYTES/2,2))) + 1 clk = sim.Clock(val=0, period=10, units="ns") rst = sim.ResetSync(clk=clk, val=0, active=1) rx_vld = Signal(bool(0)) rx_sop = Signal(bool(0)) rx_eop = Signal(bool(0)) rx_dat = Signal(intbv(0)[BYTES_PER_WORD*8:]) rx_mty = Signal(intbv(0, min=0, max=BYTES_PER_WORD)) sumN = Signal(intbv(0)[SUM_WIDTH:]) sum16 = Signal(intbv(0)[16:]) sum16n = Signal(intbv(0)[16:]) argl = {"rst":rst, "clk":clk, "rx_vld":rx_vld, "rx_sop":rx_sop, "rx_eop":rx_eop, "rx_dat":rx_dat, "rx_mty":rx_mty, "chksum":sum16n, "sum16":sum16, "sumN":sumN, "MAX_BYTES":MAX_NUM_BYTES} dut = getDut(checksum, **argl) clkgen = clk.gen() @instance def _stim(): rx_vld.next = 0 rx_sop.next = 0 rx_eop.next = 0 rx_dat.next = 0 rx_mty.next = 0 yield rst.pulse(10) yield clk.posedge S = intbv(0)[SUM_WIDTH:] S16 = S[16:] + S[:16] S16n = ~S16 & 0xFFFF assert S16n==sum16n, "checksum sum16n: expected {}, detected {}".format(hex(S16n), hex(sum16n)) assert S16==sum16, "checksum sum16: expected {}, detected {}".format(hex(S16), hex(sum16)) assert S==sumN, "checksum sumN: expected {}, detected {}".format(hex(S), hex(sumN)) for _ in range(10): bytes = random.randint(1,MAX_NUM_BYTES) words = int(ceil(float(bytes)/BYTES_PER_WORD)) empty = (BYTES_PER_WORD - bytes%BYTES_PER_WORD)%BYTES_PER_WORD S = intbv(0)[SUM_WIDTH:] S16 = S[16:] + S[:16] S16n = ~S16 & 0xFFFF for i in range(words): rx_vld.next = 1 rx_sop.next = (i==0) rx_eop.next = (i==words-1) rx_dat.next = random.randint(0,(2**(BYTES_PER_WORD*8))-1) rx_mty.next = empty if i==words-1 else 0 d = rx_dat.next if rx_eop.next: d[:] = d & ((intbv(0)[BYTES_PER_WORD*8:].max-1)<<(8*rx_mty.next)) # Sum the word for i in range(W16_PER_WORD): S[:] = S[:] + d[16*(i+1):16*i] # Resuce sum to 16 bit S16 = S[:] while S16>S16[16:]: S16[:] = S16[16:] + S16[:16] S16[:] = S16[16:] # Invert S16n = ~S16 & 0xFFFF yield clk.posedge yield delay(1) assert S16n==sum16n, "checksum sum16n: expected {}, detected {}".format(hex(S16n), hex(sum16n)) assert S16==sum16, "checksum sum16: expected {}, detected {}".format(hex(S16), hex(sum16)) assert S==sumN, "checksum sumN: expected {}, detected {}".format(hex(S), hex(sumN)) # Gap rx_vld.next = 0 for _ in range(random.randint(0,1)): yield clk.posedge yield clk.posedge raise StopSimulation return instances() for s in self.simulators: getDut.selectSimulator(s) for BPW in BYTES_PER_WORD: tb = testbench(BPW) Simulation(tb).run() del tb def testOverflow(self): ''' CHECKSUM: Overflow ''' MAX_NUM_BYTES = 201 BYTES_PER_WORD = [2,4,6,8] getDut = sim.DUTer() def testbench(BYTES_PER_WORD): W16_PER_WORD = BYTES_PER_WORD//2 SUM_WIDTH = 16 + int(ceil(log(MAX_NUM_BYTES/2,2))) + 1 INIT_WIDTH = 16 SUM_WIDTH += 1 clk = sim.Clock(val=0, period=10, units="ns") rst = sim.ResetSync(clk=clk, val=0, active=1) rx_vld = Signal(bool(0)) rx_sop = Signal(bool(0)) rx_eop = Signal(bool(0)) rx_dat = Signal(intbv(0)[BYTES_PER_WORD*8:]) rx_mty = Signal(intbv(0, min=0, max=BYTES_PER_WORD)) init_sum = Signal(intbv(0)[INIT_WIDTH:]) sumN = Signal(intbv(0)[SUM_WIDTH:]) sum16 = Signal(intbv(0)[16:]) sum16n = Signal(intbv(0)[16:]) argl = {"rst":rst, "clk":clk, "rx_vld":rx_vld, "rx_sop":rx_sop, "rx_eop":rx_eop, "rx_dat":rx_dat, "rx_mty":rx_mty, "init_sum":init_sum, "chksum":sum16n, "sum16":sum16, "sumN":sumN, "MAX_BYTES":MAX_NUM_BYTES} dut = getDut(checksum, **argl) clkgen = clk.gen() @instance def _stim(): rx_vld.next = 0 rx_sop.next = 0 rx_eop.next = 0 rx_dat.next = 0 rx_mty.next = 0 yield rst.pulse(10) yield clk.posedge S = intbv(0)[SUM_WIDTH:] S16 = S[16:] + S[:16] S16n = ~S16 & 0xFFFF assert S16n==sum16n, "checksum sum16n: expected {}, detected {}".format(hex(S16n), hex(sum16n)) assert S16==sum16, "checksum sum16: expected {}, detected {}".format(hex(S16), hex(sum16)) assert S==sumN, "checksum sumN: expected {}, detected {}".format(hex(S), hex(sumN)) for k in range(10): bytes = MAX_NUM_BYTES words = int(ceil(float(bytes)/BYTES_PER_WORD)) empty = (BYTES_PER_WORD - bytes%BYTES_PER_WORD)%BYTES_PER_WORD init_sum.next = init_sum.max-1-k S = intbv(init_sum.next)[SUM_WIDTH:] S16 = S[16:] + S[:16] S16n = ~S16 & 0xFFFF for i in range(words): rx_vld.next = 1 rx_sop.next = (i==0) rx_eop.next = (i==words-1) rx_dat.next = rx_dat.max-1-k rx_mty.next = empty if i==words-1 else 0 d = rx_dat.next if rx_eop.next: d = rx_dat.next & ((intbv(0)[BYTES_PER_WORD*8:].max-1)<<(8*rx_mty.next)) # Sum the word for i in range(W16_PER_WORD): S[:] = S[:] + d[16*(i+1):16*i] # Resuce sum to 16 bit S16 = S[:] while S16>S16[16:]: S16[:] = S16[16:] + S16[:16] S16[:] = S16[16:] # Invert S16n = ~S16 & 0xFFFF yield clk.posedge yield delay(1) assert S16n==sum16n, "checksum sum16n: expected {}, detected {}".format(hex(S16n), hex(sum16n)) assert S16==sum16, "checksum sum16: expected {}, detected {}".format(hex(S16), hex(sum16)) assert S==sumN, "checksum sumN: expected {}, detected {}".format(hex(S), hex(sumN)) init_sum.next = 0 # Gap rx_vld.next = 0 for _ in range(random.randint(0,1)): yield clk.posedge yield clk.posedge raise StopSimulation return instances() for s in self.simulators: getDut.selectSimulator(s) for BPW in BYTES_PER_WORD: tb = testbench(BPW) Simulation(tb).run() del tb if __name__ == "__main__": #import sys;sys.argv = ['', 'Test.testName'] unittest.main()

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153,110

py

Python

cisco-ios-xr/ydk/models/cisco_ios_xr/_meta/_Cisco_IOS_XR_pbr_vservice_mgr_oper.py

tkamata-test/ydk-py

b637e7853a8edbbd31fbc05afa3aa4110b31c5f9

[ "ECL-2.0", "Apache-2.0" ]

null

cisco-ios-xr/ydk/models/cisco_ios_xr/_meta/_Cisco_IOS_XR_pbr_vservice_mgr_oper.py

tkamata-test/ydk-py

b637e7853a8edbbd31fbc05afa3aa4110b31c5f9

[ "ECL-2.0", "Apache-2.0" ]

null

cisco-ios-xr/ydk/models/cisco_ios_xr/_meta/_Cisco_IOS_XR_pbr_vservice_mgr_oper.py

tkamata-test/ydk-py

b637e7853a8edbbd31fbc05afa3aa4110b31c5f9

[ "ECL-2.0", "Apache-2.0" ]

null

import re import collections from enum import Enum from ydk._core._dm_meta_info import _MetaInfoClassMember, _MetaInfoClass, _MetaInfoEnum from ydk.types import Empty, YList, YLeafList, DELETE, Decimal64, FixedBitsDict from ydk._core._dm_meta_info import ATTRIBUTE, REFERENCE_CLASS, REFERENCE_LIST, REFERENCE_LEAFLIST, REFERENCE_IDENTITY_CLASS, REFERENCE_ENUM_CLASS, REFERENCE_BITS, REFERENCE_UNION from ydk.errors import YPYError, YPYModelError from ydk.providers._importer import _yang_ns _meta_table = { 'VsNshStatsEnum' : _MetaInfoEnum('VsNshStatsEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_pbr_vservice_mgr_oper', { 'vs-nsh-stats-spi-si':'vs_nsh_stats_spi_si', 'vs-nsh-stats-ter-min-ate':'vs_nsh_stats_ter_min_ate', 'vs-nsh-stats-sf':'vs_nsh_stats_sf', 'vs-nsh-stats-sff':'vs_nsh_stats_sff', 'vs-nsh-stats-sff-local':'vs_nsh_stats_sff_local', 'vs-nsh-stats-sfp':'vs_nsh_stats_sfp', 'vs-nsh-stats-sfp-detail':'vs_nsh_stats_sfp_detail', 'vs-nsh-stats-max':'vs_nsh_stats_max', }, 'Cisco-IOS-XR-pbr-vservice-mgr-oper', _yang_ns._namespaces['Cisco-IOS-XR-pbr-vservice-mgr-oper']), 'GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data.Sfp.SpiSi' : { 'meta_info' : _MetaInfoClass('GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data.Sfp.SpiSi', False, [ _MetaInfoClassMember('processed-bytes', ATTRIBUTE, 'int' , None, None, [('0', '18446744073709551615')], [], ''' Total bytes processed ''', 'processed_bytes', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), _MetaInfoClassMember('processed-pkts', ATTRIBUTE, 'int' , None, None, [('0', '18446744073709551615')], [], ''' Number of packets processed ''', 'processed_pkts', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), ], 'Cisco-IOS-XR-pbr-vservice-mgr-oper', 'spi-si', _yang_ns._namespaces['Cisco-IOS-XR-pbr-vservice-mgr-oper'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_pbr_vservice_mgr_oper' ), }, 'GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data.Sfp.Term' : { 'meta_info' : _MetaInfoClass('GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data.Sfp.Term', False, [ _MetaInfoClassMember('terminated-bytes', ATTRIBUTE, 'int' , None, None, [('0', '18446744073709551615')], [], ''' Total bytes terminated ''', 'terminated_bytes', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), _MetaInfoClassMember('terminated-pkts', ATTRIBUTE, 'int' , None, None, [('0', '18446744073709551615')], [], ''' Number of terminated packets ''', 'terminated_pkts', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), ], 'Cisco-IOS-XR-pbr-vservice-mgr-oper', 'term', _yang_ns._namespaces['Cisco-IOS-XR-pbr-vservice-mgr-oper'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_pbr_vservice_mgr_oper' ), }, 'GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data.Sfp' : { 'meta_info' : _MetaInfoClass('GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data.Sfp', False, [ _MetaInfoClassMember('spi-si', REFERENCE_CLASS, 'SpiSi' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_pbr_vservice_mgr_oper', 'GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data.Sfp.SpiSi', [], [], ''' Service index counters ''', 'spi_si', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), _MetaInfoClassMember('term', REFERENCE_CLASS, 'Term' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_pbr_vservice_mgr_oper', 'GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data.Sfp.Term', [], [], ''' Terminate counters ''', 'term', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), ], 'Cisco-IOS-XR-pbr-vservice-mgr-oper', 'sfp', _yang_ns._namespaces['Cisco-IOS-XR-pbr-vservice-mgr-oper'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_pbr_vservice_mgr_oper' ), }, 'GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data.SpiSi' : { 'meta_info' : _MetaInfoClass('GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data.SpiSi', False, [ _MetaInfoClassMember('processed-bytes', ATTRIBUTE, 'int' , None, None, [('0', '18446744073709551615')], [], ''' Total bytes processed ''', 'processed_bytes', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), _MetaInfoClassMember('processed-pkts', ATTRIBUTE, 'int' , None, None, [('0', '18446744073709551615')], [], ''' Number of packets processed ''', 'processed_pkts', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), ], 'Cisco-IOS-XR-pbr-vservice-mgr-oper', 'spi-si', _yang_ns._namespaces['Cisco-IOS-XR-pbr-vservice-mgr-oper'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_pbr_vservice_mgr_oper' ), }, 'GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data.Term' : { 'meta_info' : _MetaInfoClass('GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data.Term', False, [ _MetaInfoClassMember('terminated-bytes', ATTRIBUTE, 'int' , None, None, [('0', '18446744073709551615')], [], ''' Total bytes terminated ''', 'terminated_bytes', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), _MetaInfoClassMember('terminated-pkts', ATTRIBUTE, 'int' , None, None, [('0', '18446744073709551615')], [], ''' Number of terminated packets ''', 'terminated_pkts', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), ], 'Cisco-IOS-XR-pbr-vservice-mgr-oper', 'term', _yang_ns._namespaces['Cisco-IOS-XR-pbr-vservice-mgr-oper'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_pbr_vservice_mgr_oper' ), }, 'GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data.Sf' : { 'meta_info' : _MetaInfoClass('GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data.Sf', False, [ _MetaInfoClassMember('processed-bytes', ATTRIBUTE, 'int' , None, None, [('0', '18446744073709551615')], [], ''' Total bytes processed ''', 'processed_bytes', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), _MetaInfoClassMember('processed-pkts', ATTRIBUTE, 'int' , None, None, [('0', '18446744073709551615')], [], ''' Number of packets processed ''', 'processed_pkts', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), ], 'Cisco-IOS-XR-pbr-vservice-mgr-oper', 'sf', _yang_ns._namespaces['Cisco-IOS-XR-pbr-vservice-mgr-oper'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_pbr_vservice_mgr_oper' ), }, 'GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data.Sff' : { 'meta_info' : _MetaInfoClass('GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data.Sff', False, [ _MetaInfoClassMember('processed-bytes', ATTRIBUTE, 'int' , None, None, [('0', '18446744073709551615')], [], ''' Total bytes processed ''', 'processed_bytes', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), _MetaInfoClassMember('processed-pkts', ATTRIBUTE, 'int' , None, None, [('0', '18446744073709551615')], [], ''' Number of packets processed ''', 'processed_pkts', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), ], 'Cisco-IOS-XR-pbr-vservice-mgr-oper', 'sff', _yang_ns._namespaces['Cisco-IOS-XR-pbr-vservice-mgr-oper'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_pbr_vservice_mgr_oper' ), }, 'GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data.SffLocal' : { 'meta_info' : _MetaInfoClass('GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data.SffLocal', False, [ _MetaInfoClassMember('lookup-err-bytes', ATTRIBUTE, 'int' , None, None, [('0', '18446744073709551615')], [], ''' Total bytes with unknown spi-si ''', 'lookup_err_bytes', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), _MetaInfoClassMember('lookup-err-pkts', ATTRIBUTE, 'int' , None, None, [('0', '18446744073709551615')], [], ''' Number of packets with unknown spi-si ''', 'lookup_err_pkts', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), _MetaInfoClassMember('malformed-err-bytes', ATTRIBUTE, 'int' , None, None, [('0', '18446744073709551615')], [], ''' Total bytes with invalid NSH header ''', 'malformed_err_bytes', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), _MetaInfoClassMember('malformed-err-pkts', ATTRIBUTE, 'int' , None, None, [('0', '18446744073709551615')], [], ''' Number of packets with invalid NSH header ''', 'malformed_err_pkts', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), ], 'Cisco-IOS-XR-pbr-vservice-mgr-oper', 'sff-local', _yang_ns._namespaces['Cisco-IOS-XR-pbr-vservice-mgr-oper'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_pbr_vservice_mgr_oper' ), }, 'GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data' : { 'meta_info' : _MetaInfoClass('GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data', False, [ _MetaInfoClassMember('sf', REFERENCE_CLASS, 'Sf' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_pbr_vservice_mgr_oper', 'GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data.Sf', [], [], ''' Service function stats ''', 'sf', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), _MetaInfoClassMember('sff', REFERENCE_CLASS, 'Sff' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_pbr_vservice_mgr_oper', 'GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data.Sff', [], [], ''' Service function forwarder stats ''', 'sff', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), _MetaInfoClassMember('sff-local', REFERENCE_CLASS, 'SffLocal' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_pbr_vservice_mgr_oper', 'GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data.SffLocal', [], [], ''' Local service function forwarder stats ''', 'sff_local', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), _MetaInfoClassMember('sfp', REFERENCE_CLASS, 'Sfp' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_pbr_vservice_mgr_oper', 'GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data.Sfp', [], [], ''' SFP stats ''', 'sfp', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), _MetaInfoClassMember('spi-si', REFERENCE_CLASS, 'SpiSi' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_pbr_vservice_mgr_oper', 'GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data.SpiSi', [], [], ''' SPI SI stats ''', 'spi_si', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), _MetaInfoClassMember('term', REFERENCE_CLASS, 'Term' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_pbr_vservice_mgr_oper', 'GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data.Term', [], [], ''' Terminate stats ''', 'term', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), _MetaInfoClassMember('type', REFERENCE_ENUM_CLASS, 'VsNshStatsEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_pbr_vservice_mgr_oper', 'VsNshStatsEnum', [], [], ''' type ''', 'type', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), ], 'Cisco-IOS-XR-pbr-vservice-mgr-oper', 'data', _yang_ns._namespaces['Cisco-IOS-XR-pbr-vservice-mgr-oper'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_pbr_vservice_mgr_oper' ), }, 'GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.SiArr.Data.SpiSi' : { 'meta_info' : _MetaInfoClass('GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.SiArr.Data.SpiSi', False, [ _MetaInfoClassMember('processed-bytes', ATTRIBUTE, 'int' , None, None, [('0', '18446744073709551615')], [], ''' Total bytes processed ''', 'processed_bytes', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), _MetaInfoClassMember('processed-pkts', ATTRIBUTE, 'int' , None, None, [('0', '18446744073709551615')], [], ''' Number of packets processed ''', 'processed_pkts', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), ], 'Cisco-IOS-XR-pbr-vservice-mgr-oper', 'spi-si', _yang_ns._namespaces['Cisco-IOS-XR-pbr-vservice-mgr-oper'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_pbr_vservice_mgr_oper' ), }, 'GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.SiArr.Data.Term' : { 'meta_info' : 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'term', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), _MetaInfoClassMember('type', REFERENCE_ENUM_CLASS, 'VsNshStatsEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_pbr_vservice_mgr_oper', 'VsNshStatsEnum', [], [], ''' type ''', 'type', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), ], 'Cisco-IOS-XR-pbr-vservice-mgr-oper', 'data', _yang_ns._namespaces['Cisco-IOS-XR-pbr-vservice-mgr-oper'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_pbr_vservice_mgr_oper' ), }, 'GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local.Error.SiArr' : { 'meta_info' : _MetaInfoClass('GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local.Error.SiArr', False, [ _MetaInfoClassMember('data', REFERENCE_CLASS, 'Data' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_pbr_vservice_mgr_oper', 'GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local.Error.SiArr.Data', [], [], ''' Stats counter for this index ''', 'data', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), _MetaInfoClassMember('si', ATTRIBUTE, 'int' , None, None, [('0', '255')], [], ''' Service index ''', 'si', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), ], 'Cisco-IOS-XR-pbr-vservice-mgr-oper', 'si-arr', _yang_ns._namespaces['Cisco-IOS-XR-pbr-vservice-mgr-oper'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_pbr_vservice_mgr_oper' ), }, 'GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local.Error' : { 'meta_info' : _MetaInfoClass('GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local.Error', False, [ _MetaInfoClassMember('data', REFERENCE_CLASS, 'Data' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_pbr_vservice_mgr_oper', 'GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local.Error.Data', [], [], ''' Statistics data ''', 'data', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), _MetaInfoClassMember('si-arr', REFERENCE_LIST, 'SiArr' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_pbr_vservice_mgr_oper', 'GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local.Error.SiArr', [], [], ''' SI array in case of detail stats ''', 'si_arr', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), ], 'Cisco-IOS-XR-pbr-vservice-mgr-oper', 'error', _yang_ns._namespaces['Cisco-IOS-XR-pbr-vservice-mgr-oper'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_pbr_vservice_mgr_oper' ), }, 'GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local' : { 'meta_info' : _MetaInfoClass('GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local', False, [ _MetaInfoClassMember('error', REFERENCE_CLASS, 'Error' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_pbr_vservice_mgr_oper', 'GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local.Error', [], [], ''' Error Statistics for local service function forwarder ''', 'error', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), ], 'Cisco-IOS-XR-pbr-vservice-mgr-oper', 'local', _yang_ns._namespaces['Cisco-IOS-XR-pbr-vservice-mgr-oper'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_pbr_vservice_mgr_oper' ), }, 'GlobalServiceFunctionChaining.ServiceFunctionForwarder' : { 'meta_info' : _MetaInfoClass('GlobalServiceFunctionChaining.ServiceFunctionForwarder', False, [ _MetaInfoClassMember('local', REFERENCE_CLASS, 'Local' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_pbr_vservice_mgr_oper', 'GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local', [], [], ''' Local Service Function Forwarder operational data ''', 'local', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), _MetaInfoClassMember('sff-names', REFERENCE_CLASS, 'SffNames' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_pbr_vservice_mgr_oper', 'GlobalServiceFunctionChaining.ServiceFunctionForwarder.SffNames', [], [], ''' List of Service Function Forwarder Names ''', 'sff_names', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), ], 'Cisco-IOS-XR-pbr-vservice-mgr-oper', 'service-function-forwarder', _yang_ns._namespaces['Cisco-IOS-XR-pbr-vservice-mgr-oper'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_pbr_vservice_mgr_oper' ), }, 'GlobalServiceFunctionChaining' : { 'meta_info' : _MetaInfoClass('GlobalServiceFunctionChaining', False, [ _MetaInfoClassMember('service-function', REFERENCE_CLASS, 'ServiceFunction' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_pbr_vservice_mgr_oper', 'GlobalServiceFunctionChaining.ServiceFunction', [], [], ''' Service Function operational data ''', 'service_function', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), _MetaInfoClassMember('service-function-forwarder', REFERENCE_CLASS, 'ServiceFunctionForwarder' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_pbr_vservice_mgr_oper', 'GlobalServiceFunctionChaining.ServiceFunctionForwarder', [], [], ''' Service Function Forwarder operational data ''', 'service_function_forwarder', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), _MetaInfoClassMember('service-function-path', REFERENCE_CLASS, 'ServiceFunctionPath' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_pbr_vservice_mgr_oper', 'GlobalServiceFunctionChaining.ServiceFunctionPath', [], [], ''' Service Function Path operational data ''', 'service_function_path', 'Cisco-IOS-XR-pbr-vservice-mgr-oper', False), ], 'Cisco-IOS-XR-pbr-vservice-mgr-oper', 'global-service-function-chaining', _yang_ns._namespaces['Cisco-IOS-XR-pbr-vservice-mgr-oper'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_pbr_vservice_mgr_oper' ), }, } _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data.Sfp.SpiSi']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data.Sfp']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data.Sfp.Term']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data.Sfp']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data.Sfp']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data.SpiSi']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data.Term']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data.Sf']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data.Sff']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data.SffLocal']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.SiArr.Data.SpiSi']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.SiArr.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.SiArr.Data.Term']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.SiArr.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.SiArr.Data']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.SiArr']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.Data']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail.SiArr']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Summarized.Data.Sfp.SpiSi']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Summarized.Data.Sfp']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Summarized.Data.Sfp.Term']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Summarized.Data.Sfp']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Summarized.Data.Sfp']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Summarized.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Summarized.Data.SpiSi']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Summarized.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Summarized.Data.Term']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Summarized.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Summarized.Data.Sf']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Summarized.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Summarized.Data.Sff']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Summarized.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Summarized.Data.SffLocal']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Summarized.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Summarized.SiArr.Data.SpiSi']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Summarized.SiArr.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Summarized.SiArr.Data.Term']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Summarized.SiArr.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Summarized.SiArr.Data']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Summarized.SiArr']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Summarized.Data']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Summarized']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Summarized.SiArr']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Summarized']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Detail']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats.Summarized']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.ServiceIndexes.ServiceIndex.Data.Sfp.SpiSi']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.ServiceIndexes.ServiceIndex.Data.Sfp']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.ServiceIndexes.ServiceIndex.Data.Sfp.Term']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.ServiceIndexes.ServiceIndex.Data.Sfp']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.ServiceIndexes.ServiceIndex.Data.Sfp']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.ServiceIndexes.ServiceIndex.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.ServiceIndexes.ServiceIndex.Data.SpiSi']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.ServiceIndexes.ServiceIndex.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.ServiceIndexes.ServiceIndex.Data.Term']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.ServiceIndexes.ServiceIndex.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.ServiceIndexes.ServiceIndex.Data.Sf']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.ServiceIndexes.ServiceIndex.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.ServiceIndexes.ServiceIndex.Data.Sff']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.ServiceIndexes.ServiceIndex.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.ServiceIndexes.ServiceIndex.Data.SffLocal']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.ServiceIndexes.ServiceIndex.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.ServiceIndexes.ServiceIndex.SiArr.Data.SpiSi']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.ServiceIndexes.ServiceIndex.SiArr.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.ServiceIndexes.ServiceIndex.SiArr.Data.Term']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.ServiceIndexes.ServiceIndex.SiArr.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.ServiceIndexes.ServiceIndex.SiArr.Data']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.ServiceIndexes.ServiceIndex.SiArr']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.ServiceIndexes.ServiceIndex.Data']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.ServiceIndexes.ServiceIndex']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.ServiceIndexes.ServiceIndex.SiArr']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.ServiceIndexes.ServiceIndex']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.ServiceIndexes.ServiceIndex']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.ServiceIndexes']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.Stats']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId.ServiceIndexes']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds.PathId']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath.PathIds']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunction.SfNames.SfName.Data.Sfp.SpiSi']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunction.SfNames.SfName.Data.Sfp']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunction.SfNames.SfName.Data.Sfp.Term']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunction.SfNames.SfName.Data.Sfp']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunction.SfNames.SfName.Data.Sfp']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunction.SfNames.SfName.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunction.SfNames.SfName.Data.SpiSi']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunction.SfNames.SfName.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunction.SfNames.SfName.Data.Term']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunction.SfNames.SfName.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunction.SfNames.SfName.Data.Sf']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunction.SfNames.SfName.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunction.SfNames.SfName.Data.Sff']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunction.SfNames.SfName.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunction.SfNames.SfName.Data.SffLocal']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunction.SfNames.SfName.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunction.SfNames.SfName.SiArr.Data.SpiSi']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunction.SfNames.SfName.SiArr.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunction.SfNames.SfName.SiArr.Data.Term']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunction.SfNames.SfName.SiArr.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunction.SfNames.SfName.SiArr.Data']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunction.SfNames.SfName.SiArr']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunction.SfNames.SfName.Data']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunction.SfNames.SfName']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunction.SfNames.SfName.SiArr']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunction.SfNames.SfName']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunction.SfNames.SfName']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunction.SfNames']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunction.SfNames']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunction']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.SffNames.SffName.Data.Sfp.SpiSi']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.SffNames.SffName.Data.Sfp']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.SffNames.SffName.Data.Sfp.Term']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.SffNames.SffName.Data.Sfp']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.SffNames.SffName.Data.Sfp']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.SffNames.SffName.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.SffNames.SffName.Data.SpiSi']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.SffNames.SffName.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.SffNames.SffName.Data.Term']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.SffNames.SffName.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.SffNames.SffName.Data.Sf']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.SffNames.SffName.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.SffNames.SffName.Data.Sff']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.SffNames.SffName.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.SffNames.SffName.Data.SffLocal']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.SffNames.SffName.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.SffNames.SffName.SiArr.Data.SpiSi']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.SffNames.SffName.SiArr.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.SffNames.SffName.SiArr.Data.Term']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.SffNames.SffName.SiArr.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.SffNames.SffName.SiArr.Data']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.SffNames.SffName.SiArr']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.SffNames.SffName.Data']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.SffNames.SffName']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.SffNames.SffName.SiArr']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.SffNames.SffName']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.SffNames.SffName']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.SffNames']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local.Error.Data.Sfp.SpiSi']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local.Error.Data.Sfp']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local.Error.Data.Sfp.Term']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local.Error.Data.Sfp']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local.Error.Data.Sfp']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local.Error.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local.Error.Data.SpiSi']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local.Error.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local.Error.Data.Term']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local.Error.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local.Error.Data.Sf']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local.Error.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local.Error.Data.Sff']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local.Error.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local.Error.Data.SffLocal']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local.Error.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local.Error.SiArr.Data.SpiSi']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local.Error.SiArr.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local.Error.SiArr.Data.Term']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local.Error.SiArr.Data']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local.Error.SiArr.Data']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local.Error.SiArr']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local.Error.Data']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local.Error']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local.Error.SiArr']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local.Error']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local.Error']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.SffNames']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder.Local']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionPath']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunction']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining']['meta_info'] _meta_table['GlobalServiceFunctionChaining.ServiceFunctionForwarder']['meta_info'].parent =_meta_table['GlobalServiceFunctionChaining']['meta_info']

59.161515

277

0.600464

13,535

153,110

6.552124

0.010196

0.075054

0.093817

0.092351

0.978587

0.974054

0.969915

0.960737

0.938094

0.905551

0

0.023005

0.271785

153,110

2,587

278

59.184383

0.77238

0

0.635043

0

0.553191

0.467993

0

1

0

false

0

0.003419

0

0.003419

0

null

0

1

0

1

null

0

7

d95f195bd1d5a3785f17fa49559a4d1d6e2cddcf

69

py

Python

bookstore/tests/test_archive.py

willingc/bookstore

6f3c4133777a9084de17b7b10676474ba2d46cb3

[ "BSD-3-Clause" ]

1

2018-10-29T08:13:59.000Z

bookstore/tests/test_archive.py

willingc/bookstore

6f3c4133777a9084de17b7b10676474ba2d46cb3

[ "BSD-3-Clause" ]

null

bookstore/tests/test_archive.py

willingc/bookstore

6f3c4133777a9084de17b7b10676474ba2d46cb3

[ "BSD-3-Clause" ]

null

"""Tests for archive""" import pytest def test_archive(): pass

9.857143

23

0.666667

9

69

5

0.888889

0

0.202899

69

6

24

11.5

0.818182

0.246377

0

1

0.333333

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

7

d96ca4e43286c125745406ce5d8f5f13fd0d7e8c

189

py

Python

ntripstreams/__init__.py

PointOneNav/ntripstreams

c26605710a53a1ebe1a16310565b5605e77228c1

[ "MIT" ]

2

2021-06-12T06:10:32.000Z

2022-03-08T22:48:31.000Z

ntripstreams/__init__.py

PointOneNav/ntripstreams

c26605710a53a1ebe1a16310565b5605e77228c1

[ "MIT" ]

null

ntripstreams/__init__.py

PointOneNav/ntripstreams

c26605710a53a1ebe1a16310565b5605e77228c1

[ "MIT" ]

5

2021-06-30T14:00:50.000Z

2021-09-25T14:03:50.000Z

__all__ = ["NtripStream", "Rtcm3", "crc24q", "crcNmea"] from ntripstreams.crc import crc24q, crcNmea from ntripstreams.ntripstreams import NtripStream from ntripstreams.rtcm3 import Rtcm3

31.5

55

0.798942

21

189

7

0.428571

0.326531

0.231293

0.394558

0

0.04142

0.10582

189

5

56

37.8

0.828402

0

0.153439

0

1

0

false

0

0.75

0

0.75

0

1

0

null

1

0

1

0

null

0

1

0

1

0

7

7996290f74a6648ac7920c171a79e5a39cbcb27b

295

py

Python

test_calculator.py

Assaviv/demo_calculator

c4c076b827638d0b9cae1e55e86c86aa5e08d818

[ "BSD-2-Clause" ]

null

test_calculator.py

Assaviv/demo_calculator

c4c076b827638d0b9cae1e55e86c86aa5e08d818

[ "BSD-2-Clause" ]

null

test_calculator.py

Assaviv/demo_calculator

c4c076b827638d0b9cae1e55e86c86aa5e08d818

[ "BSD-2-Clause" ]

null

import main def calculate(formula) -> str: return str(main.calculate(formula)) def test_addition(): assert calculate("1+1") == "2.0" assert calculate("10+20") == "30.0" def test_spaces(): assert calculate("1 + 1 ") == "2.0" assert calculate(" 10 + 20 ") == "30.0"

18.4375

48

0.586441

41

295

4.170732

0.414634

0.350877

0.187135

0.19883

0.479532

0

0.09607

0.223729

295

15

49

19.666667

0.650655

0

0.444444

0

0.142373

0

0.444444

1

0.333333

false

0

0.111111

0.555556

0

null

1

0

null

0

1

0

1

0

1

0

7

79ba52e1cad45b7a55198c79c20ef9288d025c62

28

py

Python

test.py

lyricqweqwe/linux

5e79173dd0f141ed22b40c69b4bcb8e5f561041a

[ "Apache-2.0" ]

null

test.py

lyricqweqwe/linux

5e79173dd0f141ed22b40c69b4bcb8e5f561041a

[ "Apache-2.0" ]

null

test.py

lyricqweqwe/linux

5e79173dd0f141ed22b40c69b4bcb8e5f561041a

[ "Apache-2.0" ]

null

print('11111') print (22222)

14

0.714286

4

28

5

0.75

0

0.384615

0.071429

28

2

15

14

0.384615

0

0.172414

0

1

0

true

0

1

0

null

0

1

0

1

0

1

0

null

0

1

0

1

0

7

8de64c090c9f072e2f40c6410c78893ba109ea4b

10,500

py

Python

lab_safety_web/device/views.py

Spico197/lab-safety-iot

1d792b236b7782cc9f6468f5a44bb90deb5d8af5

[ "MIT" ]

null

lab_safety_web/device/views.py

Spico197/lab-safety-iot

1d792b236b7782cc9f6468f5a44bb90deb5d8af5

[ "MIT" ]

null

lab_safety_web/device/views.py

Spico197/lab-safety-iot

1d792b236b7782cc9f6468f5a44bb90deb5d8af5

[ "MIT" ]

null

import datetime from django.shortcuts import render, redirect from django.core.paginator import Paginator from user.models import * from device.models import * def device(request): phone_number = request.session.get('phone_number', '') if phone_number == '': return redirect('/') user = UserModel.objects.get(phone_number=phone_number) if user.is_admin: if user.device_id: device = DeviceModel.objects.get(device_id=user.device_id) status = device.status devices = DeviceModel.objects.all() else: device = '管理员设备' status = True devices = DeviceModel.objects.all() else: devices = DeviceModel.objects.filter(device_id=user.device_id) device = DeviceModel.objects.get(device_id=user.device_id) status = device.status # print(devices) status = 'offline' if status is False else 'online' limit = 11 print(devices) paginator = Paginator(devices, limit) page = request.GET.get('page', 1) loaded = paginator.page(page) # print([dat.data_time for dat in devices[0:6]]) context_data = { 'name': user.name, 'device_id': device.device_id, 'device_status': status, 'devices': loaded, } return render(request, 'device_base.html', context=context_data) def log(request): phone_number = request.session.get('phone_number', '') if phone_number == '': return redirect('/') user = UserModel.objects.get(phone_number=phone_number) if user.is_admin: if user.device_id: device = DeviceModel.objects.get(device_id=user.device_id) status = device.status logs = DeviceLogModel.objects.order_by('-data_time') else: device = '管理员设备' status = True logs = DeviceLogModel.objects.order_by('-data_time') else: logs = DeviceModel.objects.filter(device_id=user.device_id).order_by('-data_time') device = DeviceModel.objects.get(device_id=user.device_id) status = device.status status = 'offline' if status is False else 'online' limit = 10 paginator = Paginator(logs, limit) page = request.GET.get('page', 1) loaded = paginator.page(page) # print([dat.data_time for dat in devices[0:6]]) context_data = { 'name': user.name, 'device_id': device.device_id, 'device_status': status, 'logs': loaded, } return render(request, 'device_log.html', context=context_data) def new(request): phone_number = request.session.get('phone_number', '') if phone_number == '': return redirect('/') user = UserModel.objects.get(phone_number=phone_number) if user.is_admin: if user.device_id: device = DeviceModel.objects.get(device_id=user.device_id) status = device.status else: device = '管理员设备' status = True else: device = DeviceModel.objects.get(device_id=user.device_id) status = device.status status = 'offline' if status is False else 'online' context_data = { 'name': user.name, 'device_id': user.device_id, 'device_status': status, 'return_instruction': '', } if request.method == "GET": return render(request, 'device_new.html', context=context_data) if not user.is_admin: context_data['return_instruction'] = "只有管理员才有权限新增设备！" return render(request, 'device_base.html', context=context_data) if request.method == "POST" and request.POST.get('device_id', '') == '': context_data['return_instruction'] = "请注意，设备号不能为空" return render(request, 'device_base.html', context=context_data) dev_new = DeviceModel() dev_new.device_id = request.POST.get('device_id', '') dev_new.comment = request.POST.get('comment', '') dev_new.status = False dev_new.date_joined = datetime.datetime.now() dev_new.save() context_data['return_instruction'] = "保存成功" return render(request, 'device_new.html', context=context_data) def edit(request): phone_number = request.session.get('phone_number', '') if phone_number == '': return redirect('/') user = UserModel.objects.get(phone_number=phone_number) if user.is_admin: if user.device_id: device = DeviceModel.objects.get(device_id=user.device_id) status = device.status else: device = '管理员设备' status = True else: device = DeviceModel.objects.get(device_id=user.device_id) status = device.status status = 'offline' if status is False else 'online' context_data = { 'name': user.name, 'device_id': user.device_id, 'device_status': status, 'return_instruction': '', 'edit_device_id': request.GET.get('device_id', ''), 'edit_comment': device.comment, } if not user.is_admin: context_data['return_instruction'] = "只有管理员才有权限编辑设备！" return render(request, 'device_base.html', context=context_data) if request.POST: status = request.POST.get('status', '上线') dev = DeviceModel.objects.get(device_id=request.POST.get('edit_device_id', '')) dev.comment = request.POST.get('comment', '') dev.status = False if status == "下线" else True dev.save() context_data['return_instruction'] = "编辑成功" return render(request, 'device_edit.html', context=context_data) def delete(request): phone_number = request.session.get('phone_number', '') if phone_number == '': return redirect('/') user = UserModel.objects.get(phone_number=phone_number) if user.is_admin: if user.device_id: device = DeviceModel.objects.get(device_id=user.device_id) status = device.status else: device = '管理员设备' status = True else: device = DeviceModel.objects.get(device_id=user.device_id) status = device.status status = 'offline' if status is False else 'online' context_data = { 'name': user.name, 'device_id': user.device_id, 'device_status': status, 'return_instruction': '', } if not user.is_admin: context_data['return_instruction'] = "只有管理员才有权限删除设备！" return render(request, 'device_base.html', context=context_data) if request.GET.get('device_id', '') == '': context_data['return_instruction'] = "请注意，设备号不能为空" return render(request, 'device_base.html', context=context_data) DeviceModel.objects.filter(device_id=request.GET.get('device_id', '')).delete() context_data['return_instruction'] = "删除成功" # print('删除成功') return redirect('/device/device/') def search(request): phone_number = request.session.get('phone_number', '') if phone_number == '': return redirect('/') user = UserModel.objects.get(phone_number=phone_number) if user.is_admin: if user.device_id: device = DeviceModel.objects.get(device_id=user.device_id) status = device.status devices = DeviceModel.objects.filter(device_id=request.GET.get('device_id', '')) else: device = '管理员设备' status = True devices = DeviceModel.objects.filter(device_id=request.GET.get('device_id', '')) else: if request.GET.get('device_id', '') != user.device_id: devices = [] else: devices = DeviceModel.objects.filter(device_id=user.device_id) device = DeviceModel.objects.get(device_id=user.device_id) status = device.status # print(devices) status = 'offline' if status is False else 'online' limit = 11 # print(devices) paginator = Paginator(devices, limit) page = request.GET.get('page', 1) loaded = paginator.page(page) # print([dat.data_time for dat in devices[0:6]]) context_data = { 'name': user.name, 'device_id': device.device_id, 'device_status': status, 'devices': loaded, 'return_instruction': '', } if not devices: context_data['return_instruction'] = '您没有权限搜索设备或该设备不存在' return render(request, 'device_base.html', context=context_data) def log_search(request): req_device_id = request.GET.get('device_id', '') req_action = request.GET.get('action', '') req_phone_number = request.GET.get('phone_number', '') if req_phone_number == '': return redirect('/') filter_terms = {} if req_device_id: filter_terms['device_id'] = req_device_id if req_action: filter_terms['action'] = req_action if req_phone_number: filter_terms['phone_number'] = req_phone_number phone_number = request.session.get('phone_number', '') user = UserModel.objects.get(phone_number=phone_number) if user.is_admin: if user.device_id: device = DeviceModel.objects.get(device_id=user.device_id) status = device.status logs = DeviceLogModel.objects.filter(**filter_terms).order_by('-data_time') else: device = '管理员设备' status = True logs = DeviceLogModel.objects.filter(**filter_terms).order_by('-data_time') else: logs = DeviceModel.objects.filter(**filter_terms).order_by('-data_time') device = DeviceModel.objects.get(device_id=user.device_id) status = device.status status = 'offline' if status is False else 'online' if req_device_id != user.device_id: logs = [] context_data = { 'name': user.name, 'device_id': device.device_id, 'device_status': status, 'logs': logs, 'return_instruction': '您无权查看其它设备的日志', } return render(request, 'device_log.html', context=context_data) status = 'offline' if status is False else 'online' limit = 10 paginator = Paginator(logs, limit) page = request.GET.get('page', 1) loaded = paginator.page(page) # print([dat.data_time for dat in devices[0:6]]) context_data = { 'name': user.name, 'device_id': device.device_id, 'device_status': status, 'logs': loaded, 'return_instruction': '', } return render(request, 'device_log.html', context=context_data)

32.110092

92

0.623905

1,233

10,500

5.117599

0.072182

0.101426

0.055151

0.062758

0.854517

0.822029

0.806181

0.790808

0.774643

0.703487

0

0.002548

0.252381

10,500

326

93

32.208589

0.801274

0.023429

0

0.736434

0

0.129722

0

1

0.027132

false

0

0.01938

0

0.127907

0.003876

0

null

0

1

0

1

0

null

0

7

5c37c9c88b76d4c8c3d3bf1ffbc739419a7c9586

212

py

Python

net153/blog/tests/conftest.py

smith153/Home

1ae7cac7e114c5bc5485b0472cd6d10ccce050c1

[ "BSD-2-Clause" ]

null

net153/blog/tests/conftest.py

smith153/Home

1ae7cac7e114c5bc5485b0472cd6d10ccce050c1

[ "BSD-2-Clause" ]

null

net153/blog/tests/conftest.py

smith153/Home

1ae7cac7e114c5bc5485b0472cd6d10ccce050c1

[ "BSD-2-Clause" ]

null

import pytest from .factories import PostFactory from net153.users.tests.factories import UserFactory @pytest.fixture def post(): return PostFactory() @pytest.fixture def user(): return UserFactory()

15.142857

52

0.764151

25

212

6.48

0.56

0.185185

0.197531

0

0.016667

0.150943

212

13

53

16.307692

0.883333

0

0.222222

0

1

0.222222

true

0

0.333333

0.222222

0.777778

0

1

0

null

0

1

0

null

0

1

0

1

0

7

5c412d09db47d7d8cc8adfb89c3aea56723c5a45

6,611

py

Python

default_args.py

locuslab/DC3

35437af7f22390e4ed032d9eef90cc525764d26f

[ "Apache-2.0" ]

42

2021-04-28T17:52:54.000Z

2022-03-21T10:54:47.000Z

default_args.py

locuslab/DC3

35437af7f22390e4ed032d9eef90cc525764d26f

[ "Apache-2.0" ]

1

2021-08-18T05:42:19.000Z

2021-08-23T03:48:18.000Z

default_args.py

locuslab/DC3

35437af7f22390e4ed032d9eef90cc525764d26f

[ "Apache-2.0" ]

5

2021-07-15T03:32:11.000Z

2021-12-12T20:58:23.000Z

def baseline_opt_default_args(prob_type): defaults = {} defaults['simpleVar'] = 100 defaults['simpleIneq'] = 50 defaults['simpleEq'] = 50 defaults['simpleEx'] = 10000 defaults['nonconvexVar'] = 100 defaults['nonconvexIneq'] = 50 defaults['nonconvexEq'] = 50 defaults['nonconvexEx'] = 10000 if prob_type == 'simple': defaults['corrEps'] = 1e-4 elif prob_type == 'nonconvex': defaults['corrEps'] = 1e-4 elif 'acopf' in prob_type: defaults['corrEps'] = 1e-4 return defaults def baseline_nn_default_args(prob_type): defaults = {} defaults['simpleVar'] = 100 defaults['simpleIneq'] = 50 defaults['simpleEq'] = 50 defaults['simpleEx'] = 10000 defaults['nonconvexVar'] = 100 defaults['nonconvexIneq'] = 50 defaults['nonconvexEq'] = 50 defaults['nonconvexEx'] = 10000 defaults['saveAllStats'] = True defaults['resultsSaveFreq'] = 50 if prob_type == 'simple': defaults['epochs'] = 1000 defaults['batchSize'] = 200 defaults['lr'] = 1e-4 defaults['hiddenSize'] = 200 defaults['softWeight'] = 100 defaults['softWeightEqFrac'] = 0.5 defaults['useTestCorr'] = True defaults['corrTestMaxSteps'] = 10 defaults['corrEps'] = 1e-4 defaults['corrLr'] = 1e-7 defaults['corrMomentum'] = 0.5 elif prob_type == 'nonconvex': defaults['epochs'] = 1000 defaults['batchSize'] = 200 defaults['lr'] = 1e-4 defaults['hiddenSize'] = 200 defaults['softWeight'] = 100 defaults['softWeightEqFrac'] = 0.5 defaults['useTestCorr'] = True defaults['corrTestMaxSteps'] = 10 defaults['corrEps'] = 1e-4 defaults['corrLr'] = 1e-7 defaults['corrMomentum'] = 0.5 elif 'acopf' in prob_type: defaults['epochs'] = 1000 defaults['batchSize'] = 200 defaults['lr'] = 1e-3 defaults['hiddenSize'] = 200 defaults['softWeight'] = 100 defaults['softWeightEqFrac'] = 0.5 defaults['useTestCorr'] = True defaults['corrTestMaxSteps'] = 5 defaults['corrEps'] = 1e-4 defaults['corrLr'] = 1e-5 defaults['corrMomentum'] = 0.5 else: raise NotImplementedError return defaults def baseline_eq_nn_default_args(prob_type): defaults = {} defaults['simpleVar'] = 100 defaults['simpleIneq'] = 50 defaults['simpleEq'] = 50 defaults['simpleEx'] = 10000 defaults['nonconvexVar'] = 100 defaults['nonconvexIneq'] = 50 defaults['nonconvexEq'] = 50 defaults['nonconvexEx'] = 10000 defaults['saveAllStats'] = True defaults['resultsSaveFreq'] = 50 if prob_type == 'simple': defaults['epochs'] = 1000 defaults['batchSize'] = 200 defaults['lr'] = 1e-4 defaults['hiddenSize'] = 200 defaults['softWeightEqFrac'] = 0.5 defaults['useTestCorr'] = True defaults['corrMode'] = 'partial' defaults['corrTestMaxSteps'] = 10 defaults['corrEps'] = 1e-4 defaults['corrLr'] = 1e-7 defaults['corrMomentum'] = 0.5 elif prob_type == 'nonconvex': defaults['epochs'] = 1000 defaults['batchSize'] = 200 defaults['lr'] = 1e-4 defaults['hiddenSize'] = 200 defaults['softWeightEqFrac'] = 0.5 defaults['useTestCorr'] = True defaults['corrMode'] = 'partial' defaults['corrTestMaxSteps'] = 10 defaults['corrEps'] = 1e-4 defaults['corrLr'] = 1e-7 defaults['corrMomentum'] = 0.5 elif 'acopf' in prob_type: defaults['epochs'] = 1000 defaults['batchSize'] = 200 defaults['lr'] = 1e-3 defaults['hiddenSize'] = 200 defaults['softWeightEqFrac'] = 0.5 defaults['useTestCorr'] = True defaults['corrMode'] = 'full' defaults['corrTestMaxSteps'] = 5 defaults['corrEps'] = 1e-4 defaults['corrLr'] = 1e-5 defaults['corrMomentum'] = 0.5 else: raise NotImplementedError return defaults def method_default_args(prob_type): defaults = {} defaults['simpleVar'] = 100 defaults['simpleIneq'] = 50 defaults['simpleEq'] = 50 defaults['simpleEx'] = 10000 defaults['nonconvexVar'] = 100 defaults['nonconvexIneq'] = 50 defaults['nonconvexEq'] = 50 defaults['nonconvexEx'] = 10000 defaults['saveAllStats'] = True defaults['resultsSaveFreq'] = 50 if prob_type == 'simple': defaults['epochs'] = 1000 defaults['batchSize'] = 200 defaults['lr'] = 1e-4 defaults['hiddenSize'] = 200 defaults['softWeight'] = 10 # use 100 if useCompl=False defaults['softWeightEqFrac'] = 0.5 defaults['useCompl'] = True defaults['useTrainCorr'] = True defaults['useTestCorr'] = True defaults['corrMode'] = 'partial' # use 'full' if useCompl=False defaults['corrTrainSteps'] = 10 defaults['corrTestMaxSteps'] = 10 defaults['corrEps'] = 1e-4 defaults['corrLr'] = 1e-7 defaults['corrMomentum'] = 0.5 elif prob_type == 'nonconvex': defaults['epochs'] = 1000 defaults['batchSize'] = 200 defaults['lr'] = 1e-4 defaults['hiddenSize'] = 200 defaults['softWeight'] = 10 # use 100 if useCompl=False defaults['softWeightEqFrac'] = 0.5 defaults['useCompl'] = True defaults['useTrainCorr'] = True defaults['useTestCorr'] = True defaults['corrMode'] = 'partial' # use 'full' if useCompl=False defaults['corrTrainSteps'] = 10 defaults['corrTestMaxSteps'] = 10 defaults['corrEps'] = 1e-4 defaults['corrLr'] = 1e-7 defaults['corrMomentum'] = 0.5 elif 'acopf' in prob_type: defaults['epochs'] = 1000 defaults['batchSize'] = 200 defaults['lr'] = 1e-3 defaults['hiddenSize'] = 200 defaults['softWeight'] = 10 # use 100 if useCompl=False defaults['softWeightEqFrac'] = 0.5 defaults['useCompl'] = True defaults['useTrainCorr'] = True defaults['useTestCorr'] = True defaults['corrMode'] = 'partial' # use 'full' if useCompl=False defaults['corrTrainSteps'] = 5 defaults['corrTestMaxSteps'] = 5 defaults['corrEps'] = 1e-4 defaults['corrLr'] = 1e-4 # use 1e-5 if useCompl=False defaults['corrMomentum'] = 0.5 else: raise NotImplementedError return defaults

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null

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7

30a9267695dbcdbc9472d2ba89fd942d6d70568a

45

py

Python

tests/test_package.py

pengutronix/pling

6ac6bb20e0adba9155c2bd84d9a614b71006e5e0

[ "MIT" ]

1

2020-12-17T12:53:56.000Z

tests/test_package.py

pengutronix/pling

6ac6bb20e0adba9155c2bd84d9a614b71006e5e0

[ "MIT" ]

null

tests/test_package.py

pengutronix/pling

6ac6bb20e0adba9155c2bd84d9a614b71006e5e0

[ "MIT" ]

null

def test_package(): import pling # NOQA

15

24

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7

eb6ac49740931d6e341be38a17688e11d61970d4

1,431

py

Python

scheduler/migrations/0006_added_args_kwargs.py

Intelladon-LLC/django-rq-scheduler

8ec71598eb701ffe47964bcf25cf3fcace2b7be3

[ "MIT" ]

null

scheduler/migrations/0006_added_args_kwargs.py

Intelladon-LLC/django-rq-scheduler

8ec71598eb701ffe47964bcf25cf3fcace2b7be3

[ "MIT" ]

null

scheduler/migrations/0006_added_args_kwargs.py

Intelladon-LLC/django-rq-scheduler

8ec71598eb701ffe47964bcf25cf3fcace2b7be3

[ "MIT" ]

2

2019-03-20T11:59:20.000Z

2021-03-11T07:30:33.000Z

# Generated by Django 2.0.6 on 2018-07-12 11:05 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('scheduler', '0005_added_result_ttl'), ] operations = [ migrations.AddField( model_name='cronjob', name='callable_args', field=models.CharField(blank=True, max_length=2048, verbose_name='args'), ), migrations.AddField( model_name='cronjob', name='callable_kwargs', field=models.CharField(blank=True, max_length=2048, verbose_name='kwargs'), ), migrations.AddField( model_name='repeatablejob', name='callable_args', field=models.CharField(blank=True, max_length=2048, verbose_name='args'), ), migrations.AddField( model_name='repeatablejob', name='callable_kwargs', field=models.CharField(blank=True, max_length=2048, verbose_name='kwargs'), ), migrations.AddField( model_name='scheduledjob', name='callable_args', field=models.CharField(blank=True, max_length=2048, verbose_name='args'), ), migrations.AddField( model_name='scheduledjob', name='callable_kwargs', field=models.CharField(blank=True, max_length=2048, verbose_name='kwargs'), ), ]

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9

ebc0fca409da701dab128b53aec799a114be78d1

52,274

py

Python

video_level_code/xp_video_level_models.py

mpekalski/Y8M

24b61107a0f482fdb36ab8b15b768cea24e5808a

[ "Apache-2.0" ]

32

2017-06-16T06:12:40.000Z

2021-09-19T17:22:02.000Z

video_level_code/xp_video_level_models.py

Kimilovesy/Y8M

24b61107a0f482fdb36ab8b15b768cea24e5808a

[ "Apache-2.0" ]

1

2018-05-21T07:52:04.000Z

video_level_code/xp_video_level_models.py

Kimilovesy/Y8M

24b61107a0f482fdb36ab8b15b768cea24e5808a

[ "Apache-2.0" ]

13

2017-06-11T16:45:48.000Z

2019-12-13T15:04:45.000Z

# 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 agreed to in writing, software # distributed under the License is distributed on an "AS-IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Contains model definitions.""" import math import numpy as np import models import tensorflow as tf import utils from tensorflow import flags import tensorflow.contrib.slim as slim FLAGS = flags.FLAGS flags.DEFINE_integer( "MoNN_num_experts", 4, "The number of mixtures (excluding the dummy 'expert') used for MoNNs.") #%% helper functions def weight_variable(shape): """Create a weight variable with appropriate initialization.""" initial = tf.truncated_normal(shape, stddev=1.0/np.sqrt(2*shape[0])) return tf.Variable(initial) def bias_variable(shape): """Create a bias variable with appropriate initialization.""" initial = tf.constant(0.1/shape[0], shape=shape) return tf.Variable(initial) def variable_summaries(var): """Attach a lot of summaries to a Tensor (for TensorBoard visualization).""" with tf.name_scope('summaries'): mean = tf.reduce_mean(var) tf.summary.scalar('mean', mean) with tf.name_scope('stddev'): stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean))) tf.summary.scalar('stddev', stddev) tf.summary.scalar('max', tf.reduce_max(var)) tf.summary.scalar('min', tf.reduce_min(var)) tf.summary.histogram('histogram', var) def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu): """Reusable code for making a simple neural net layer. It does a matrix multiply, bias add, and then uses relu to nonlinearize. It also sets up name scoping so that the resultant graph is easy to read, and adds a number of summary ops. """ # Adding a name scope ensures logical grouping of the layers in the graph. with tf.name_scope(layer_name): # This Variable will hold the state of the weights for the layer with tf.name_scope('weights'): weights = weight_variable([input_dim, output_dim]) variable_summaries(weights) regularizer = tf.nn.l2_loss(weights) with tf.name_scope('biases'): biases = bias_variable([output_dim]) variable_summaries(biases) with tf.name_scope('Wx_plus_b'): preactivate = tf.matmul(input_tensor, weights) + biases tf.summary.histogram('pre_activations', preactivate) activations = act(preactivate, name='activation') tf.summary.histogram('activations', activations) return activations, regularizer # # First part contains models we have used, # later there are some models we have tried/experimented with # # MoNN3L # MoNN2Lw # MoNN3Lw # MoNN4Ln # class MoNN3L(models.BaseModel): """A softmax over a mixture of logistic models (with L2 regularization).""" def create_model(self, model_input, vocab_size, num_mixtures=None, l2_penalty=1e-6, **unused_params): """Creates a Mixture of (Logistic) Experts model. The model consists of a per-class softmax distribution over a configurable number of logistic classifiers. One of the classifiers in the mixture is not trained, and always predicts 0. Args: model_input: 'batch_size' x 'num_features' matrix of input features. vocab_size: The number of classes in the dataset. num_mixtures: The number of mixtures (excluding a dummy 'expert' that always predicts the non-existence of an entity). l2_penalty: How much to penalize the squared magnitudes of parameter values. Returns: A dictionary with a tensor containing the probability predictions of the model in the 'predictions' key. The dimensions of the tensor are batch_size x num_classes. """ num_mixtures = num_mixtures or FLAGS.MoNN_num_experts gate_activations = slim.fully_connected( model_input, vocab_size * (num_mixtures + 1), activation_fn=None, biases_initializer=None, weights_regularizer=slim.l2_regularizer(l2_penalty), scope="gates") a1Units = 4096 A1 = slim.fully_connected( model_input, a1Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_HA1') a2Units = 4096 A2 = slim.fully_connected( A1, a2Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_HA2') a2Units = 4096 A3 = slim.fully_connected( A2, a2Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_HA3') expert_activations = slim.fully_connected( A3, vocab_size * num_mixtures, activation_fn=None, weights_regularizer=slim.l2_regularizer(l2_penalty), scope="experts") gating_distribution = tf.nn.softmax(tf.reshape( gate_activations, [-1, num_mixtures + 1])) # (Batch * #Labels) x (num_mixtures + 1) expert_distribution = tf.nn.sigmoid(tf.reshape( expert_activations, [-1, num_mixtures])) # (Batch * #Labels) x num_mixtures final_probabilities_by_class_and_batch = tf.reduce_sum( gating_distribution[:, :num_mixtures] * expert_distribution, 1) final_probabilities = tf.reshape(final_probabilities_by_class_and_batch, [-1, vocab_size]) return {"predictions": final_probabilities} # a wide model hoping to memorize rare labels better class MoNN2Lw(models.BaseModel): """A softmax over a mixture of logistic models (with L2 regularization).""" def create_model(self, model_input, vocab_size, num_mixtures=None, l2_penalty=1e-8, **unused_params): """Creates a Mixture of (Logistic) Experts model. The model consists of a per-class softmax distribution over a configurable number of logistic classifiers. One of the classifiers in the mixture is not trained, and always predicts 0. Args: model_input: 'batch_size' x 'num_features' matrix of input features. vocab_size: The number of classes in the dataset. num_mixtures: The number of mixtures (excluding a dummy 'expert' that always predicts the non-existence of an entity). l2_penalty: How much to penalize the squared magnitudes of parameter values. Returns: A dictionary with a tensor containing the probability predictions of the model in the 'predictions' key. The dimensions of the tensor are batch_size x num_classes. """ num_mixtures = num_mixtures or FLAGS.MoNN_num_experts gate_activations = slim.fully_connected( model_input, vocab_size * (num_mixtures + 1), activation_fn=None, biases_initializer=None, weights_regularizer=slim.l2_regularizer(l2_penalty), scope="MoN2w_gates") h1Units = 2305 * 6 A1 = slim.fully_connected( model_input, h1Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='MoN2w_H1') h2Units = 2305 * 3 A2 = slim.fully_connected( A1, h2Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='MoN2w_H2') # expert_activations = slim.fully_connected( A2, vocab_size * num_mixtures, activation_fn=None, weights_regularizer=slim.l2_regularizer(l2_penalty), scope="MoN2_experts") gating_distribution = tf.nn.softmax(tf.reshape( gate_activations, [-1, num_mixtures + 1])) # (Batch * #Labels) x (num_mixtures + 1) expert_distribution = tf.nn.sigmoid(tf.reshape( expert_activations, [-1, num_mixtures])) # (Batch * #Labels) x num_mixtures final_probabilities_by_class_and_batch = tf.reduce_sum( gating_distribution[:, :num_mixtures] * expert_distribution, 1) final_probabilities = tf.reshape(final_probabilities_by_class_and_batch, [-1, vocab_size]) return {"predictions": final_probabilities} class MoNN3Lw(models.BaseModel): """A softmax over a mixture of logistic models (with L2 regularization).""" def create_model(self, model_input, vocab_size, num_mixtures=None, l2_penalty=1e-8, **unused_params): num_mixtures = num_mixtures or FLAGS.MoNN_num_experts gate_activations = slim.fully_connected( model_input, vocab_size * (num_mixtures + 1), activation_fn=None, biases_initializer=None, weights_regularizer=slim.l2_regularizer(l2_penalty), scope="gates") a1Units = 2305*8 A1 = slim.fully_connected( model_input, a1Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_HA1') a2Units = 2305 A2 = slim.fully_connected( A1, a2Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_HA2') a2Units = 2305*4 A3 = slim.fully_connected( A2, a2Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_HA3') expert_activations = slim.fully_connected( A3, vocab_size * num_mixtures, activation_fn=None, weights_regularizer=slim.l2_regularizer(l2_penalty), scope="experts") gating_distribution = tf.nn.softmax(tf.reshape( gate_activations, [-1, num_mixtures + 1])) # (Batch * #Labels) x (num_mixtures + 1) expert_distribution = tf.nn.sigmoid(tf.reshape( expert_activations, [-1, num_mixtures])) # (Batch * #Labels) x num_mixtures final_probabilities_by_class_and_batch = tf.reduce_sum( gating_distribution[:, :num_mixtures] * expert_distribution, 1) final_probabilities = tf.reshape(final_probabilities_by_class_and_batch, [-1, vocab_size]) return {"predictions": final_probabilities} class MoNN4Ln(models.BaseModel): """A softmax over a mixture of logistic models (with L2 regularization).""" def create_model(self, model_input, vocab_size, num_mixtures=None, l2_penalty=1e-6, **unused_params): """Creates a Mixture of (Logistic) Experts model. The model consists of a per-class softmax distribution over a configurable number of logistic classifiers. One of the classifiers in the mixture is not trained, and always predicts 0. Args: model_input: 'batch_size' x 'num_features' matrix of input features. vocab_size: The number of classes in the dataset. num_mixtures: The number of mixtures (excluding a dummy 'expert' that always predicts the non-existence of an entity). l2_penalty: How much to penalize the squared magnitudes of parameter values. Returns: A dictionary with a tensor containing the probability predictions of the model in the 'predictions' key. The dimensions of the tensor are batch_size x num_classes. """ num_mixtures = num_mixtures or FLAGS.MoNN_num_experts gate_activations = slim.fully_connected( model_input, vocab_size * (num_mixtures + 1), activation_fn=None, biases_initializer=None, weights_regularizer=slim.l2_regularizer(l2_penalty), scope="gates") a1Units = 2048 A1 = slim.fully_connected( model_input, a1Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_HA1') a2Units = 2048 A2 = slim.fully_connected( A1, a2Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_HA2') a2Units = 2048 A3 = slim.fully_connected( A2, a2Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_HA3') a2Units = 2048 A4 = slim.fully_connected( A3, a2Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_HA4') expert_activations = slim.fully_connected( A4, vocab_size * num_mixtures, activation_fn=None, weights_regularizer=slim.l2_regularizer(l2_penalty), scope="experts") gating_distribution = tf.nn.softmax(tf.reshape( gate_activations, [-1, num_mixtures + 1])) # (Batch * #Labels) x (num_mixtures + 1) expert_distribution = tf.nn.sigmoid(tf.reshape( expert_activations, [-1, num_mixtures])) # (Batch * #Labels) x num_mixtures final_probabilities_by_class_and_batch = tf.reduce_sum( gating_distribution[:, :num_mixtures] * expert_distribution, 1) final_probabilities = tf.reshape(final_probabilities_by_class_and_batch, [-1, vocab_size]) return {"predictions": final_probabilities} # # Abandoned Experiments # #%% class MyNNModel0(models.BaseModel): """Logistic model with L2 regularization.""" def create_model(self, model_input, vocab_size, l2_penalty=1e-4, **unused_params): """Creates a logistic model. Args: model_input: 'batch' x 'num_features' matrix of input features. vocab_size: The number of classes in the dataset. Returns: A dictionary with a tensor containing the probability predictions of the model in the 'predictions' key. The dimensions of the tensor are batch_size x num_classes.""" with tf.name_scope('MyNNModel0'): h1Units = 2400 a1 = slim.fully_connected( model_input, h1Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC1') output = slim.fully_connected( a1, vocab_size, activation_fn=tf.nn.sigmoid, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC2') return {"predictions": output} #%% class MyNNModel1(models.BaseModel): """A simple NN models (with L2 regularization).""" def create_model(self, model_input, vocab_size, l2_penalty=1e-4, is_train=True, **unused_params): """Creates a logistic model. Args: model_input: 'batch' x 'num_features' matrix of input features. vocab_size: The number of classes in the dataset. Returns: A dictionary with a tensor containing the probability predictions of the model in the 'predictions' key. The dimensions of the tensor are batch_size x num_classes.""" with tf.name_scope('MyNNModel1'): h1Units = 1152 h2Units = 2248 h3Units = 3096 keep_prob = 0.90 A1 = slim.fully_connected( model_input, h1Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_H1') A2 = slim.fully_connected( A1, h2Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_H2') A3 = slim.fully_connected( A2, h3Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_H3') #A4 = tf.nn.dropout(A3, keep_prob) output = slim.fully_connected( A3, vocab_size, activation_fn=tf.nn.sigmoid, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_P') return {"predictions": output} #%% class MyNNModel2(models.BaseModel): """A simple NN models (with L2 regularization).""" def create_model(self, model_input, vocab_size, num_mixtures=None, l2_penalty=1e-4, **unused_params): """Creates a simple one-hidden-layer Neural Network model. Args: model_input: 'batch_size' x 'num_features' matrix of input features. vocab_size: The number of classes in the dataset. num_mixtures: The number of mixtures (excluding a dummy 'expert' that always predicts the non-existence of an entity). l2_penalty: How much to penalize the squared magnitudes of parameter values. Returns: A dictionary with a tensor containing the probability predictions of the model in the 'predictions' key. The dimensions of the tensor are batch_size x num_classes. """ #A1 = slim.fully_connected( # model_input, 800, activation_fn=tf.nn.sigmoid, # weights_regularizer=slim.l2_regularizer(l2_penalty), # scope='hidden1') # output = slim.fully_connected( # A1, vocab_size, activation_fn=tf.nn.sigmoid, # weights_regularizer=slim.l2_regularizer(l2_penalty)) h1Units = 3600 A1, reg1 = nn_layer(model_input, 1024+128, h1Units, 'Hidden1', act=tf.nn.relu) h2Units = 3600 A2, reg2 = nn_layer(A1, h1Units, h2Units, 'Hidden2', act=tf.nn.relu) output, reg3 = nn_layer(A2, h2Units, vocab_size, 'Pred', act=tf.nn.sigmoid) return {"predictions": output, "regularization_loss":l2_penalty*(reg1+reg2+reg3)} #%% def nn_layer2( input_tensor, input_dim, output_dim, var_scope, act=tf.nn.relu): with tf.variable_scope(var_scope): weights = weight_variable([input_dim, output_dim]) regularizer = tf.nn.l2_loss(weights) biases = bias_variable([output_dim]) preactivate = tf.matmul(input_tensor, weights) + biases activations = act(preactivate, name='activation') return activations, regularizer class MyNNModel3(models.BaseModel): """A simple NN models (with L2 regularization).""" def create_model(self, model_input, vocab_size, num_mixtures=None, l2_penalty=1e-4, **unused_params): """Creates a simple one-hidden-layer Neural Network model. Args: model_input: 'batch_size' x 'num_features' matrix of input features. vocab_size: The number of classes in the dataset. num_mixtures: The number of mixtures (excluding a dummy 'expert' that always predicts the non-existence of an entity). l2_penalty: How much to penalize the squared magnitudes of parameter values. Returns: A dictionary with a tensor containing the probability predictions of the model in the 'predictions' key. The dimensions of the tensor are batch_size x num_classes. """ #A1 = slim.fully_connected( # model_input, 800, activation_fn=tf.nn.sigmoid, # weights_regularizer=slim.l2_regularizer(l2_penalty), # scope='hidden1') # output = slim.fully_connected( # A1, vocab_size, activation_fn=tf.nn.sigmoid, # weights_regularizer=slim.l2_regularizer(l2_penalty)) with tf.variable_scope('MyNNModel3'): h1Units = 3600 A1,reg1 = nn_layer2(model_input, 1024+128, h1Units, 'Hidden1', act=tf.nn.relu) h2Units = 2400 A2, reg2 = nn_layer2(A1, h1Units, h2Units, 'Hidden2', act=tf.nn.relu) h3Units = 2400 A3, reg3 = nn_layer2(A2, h2Units, h3Units, 'Hidden3', act=tf.nn.relu) output, reg4 = nn_layer(A3, h3Units, vocab_size, 'ProdictionLayer', act=tf.nn.sigmoid) return {"predictions": output, "regularization_loss":l2_penalty*(reg1+reg2+reg3+reg4)} #%% class MoNN2L(models.BaseModel): """A softmax over a mixture of logistic models (with L2 regularization).""" def create_model(self, model_input, vocab_size, num_mixtures=None, l2_penalty=1e-6, **unused_params): """Creates a Mixture of (Logistic) Experts model. The model consists of a per-class softmax distribution over a configurable number of logistic classifiers. One of the classifiers in the mixture is not trained, and always predicts 0. Args: model_input: 'batch_size' x 'num_features' matrix of input features. vocab_size: The number of classes in the dataset. num_mixtures: The number of mixtures (excluding a dummy 'expert' that always predicts the non-existence of an entity). l2_penalty: How much to penalize the squared magnitudes of parameter values. Returns: A dictionary with a tensor containing the probability predictions of the model in the 'predictions' key. The dimensions of the tensor are batch_size x num_classes. """ num_mixtures = num_mixtures or FLAGS.MoNN_num_experts gate_activations = slim.fully_connected( model_input, vocab_size * (num_mixtures + 1), activation_fn=None, biases_initializer=None, weights_regularizer=slim.l2_regularizer(l2_penalty), scope="gates") h1Units = 4096 A1 = slim.fully_connected( model_input, h1Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_H1') h2Units = 4096 A2 = slim.fully_connected( A1, h2Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_H2') # expert_activations = slim.fully_connected( A2, vocab_size * num_mixtures, activation_fn=None, weights_regularizer=slim.l2_regularizer(l2_penalty), scope="experts") gating_distribution = tf.nn.softmax(tf.reshape( gate_activations, [-1, num_mixtures + 1])) # (Batch * #Labels) x (num_mixtures + 1) expert_distribution = tf.nn.sigmoid(tf.reshape( expert_activations, [-1, num_mixtures])) # (Batch * #Labels) x num_mixtures final_probabilities_by_class_and_batch = tf.reduce_sum( gating_distribution[:, :num_mixtures] * expert_distribution, 1) final_probabilities = tf.reshape(final_probabilities_by_class_and_batch, [-1, vocab_size]) return {"predictions": final_probabilities} class MoNN2L_L1(models.BaseModel): """A softmax over a mixture of logistic models (with L2 regularization).""" def create_model(self, model_input, vocab_size, num_mixtures=None, l2_penalty=1e-6, **unused_params): num_mixtures = num_mixtures or FLAGS.MoNN_num_experts gate_activations = slim.fully_connected( model_input, vocab_size * (num_mixtures + 1), activation_fn=None, biases_initializer=None, weights_regularizer=slim.l2_regularizer(l2_penalty), scope="gates") h1Units = 4096 A1 = slim.fully_connected( model_input, h1Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_H1') h2Units = 4096 A2 = slim.fully_connected( A1, h2Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l1_l2_regularizer(l2_penalty), scope='FC_H2') # expert_activations = slim.fully_connected( A2, vocab_size * num_mixtures, activation_fn=None, weights_regularizer=slim.l2_regularizer(l2_penalty), scope="experts") gating_distribution = tf.nn.softmax(tf.reshape( gate_activations, [-1, num_mixtures + 1])) # (Batch * #Labels) x (num_mixtures + 1) expert_distribution = tf.nn.sigmoid(tf.reshape( expert_activations, [-1, num_mixtures])) # (Batch * #Labels) x num_mixtures final_probabilities_by_class_and_batch = tf.reduce_sum( gating_distribution[:, :num_mixtures] * expert_distribution, 1) final_probabilities = tf.reshape(final_probabilities_by_class_and_batch, [-1, vocab_size]) return {"predictions": final_probabilities} from tensorflow import logging class MoNN2Drop(models.BaseModel): """A softmax over a mixture of logistic models (with L2 regularization).""" def create_model(self, model_input, vocab_size, layers_keep_probs, num_mixtures=None, l2_penalty=1e-6, **unused_params): num_mixtures = num_mixtures or FLAGS.MoNN_num_experts logging.info("MoNN2Drop " + str(layers_keep_probs)) drop_out = tf.nn.dropout(model_input, layers_keep_probs[0],name="var_dropout") gate_activations = slim.fully_connected( model_input, vocab_size * (num_mixtures + 1), activation_fn=None, biases_initializer=None, weights_regularizer=slim.l2_regularizer(l2_penalty), scope="gates") h1Units = 4096 A1 = slim.fully_connected( model_input, h1Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_H1') h2Units = 4096 A1a = tf.nn.dropout(A1, layers_keep_probs[1]) A2 = slim.fully_connected( A1a, h2Units, activation_fn=tf.nn.crelu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_H2') A2a = tf.nn.dropout(A2, layers_keep_probs[2]) expert_activations = slim.fully_connected( A2a, vocab_size * num_mixtures, activation_fn=None, weights_regularizer=slim.l2_regularizer(l2_penalty), scope="experts") gating_distribution = tf.nn.softmax(tf.reshape( gate_activations, [-1, num_mixtures + 1])) # (Batch * #Labels) x (num_mixtures + 1) expert_distribution = tf.nn.sigmoid(tf.reshape( expert_activations, [-1, num_mixtures])) # (Batch * #Labels) x num_mixtures final_probabilities_by_class_and_batch = tf.reduce_sum( gating_distribution[:, :num_mixtures] * expert_distribution, 1) final_probabilities = tf.reshape(final_probabilities_by_class_and_batch, [-1, vocab_size]) return {"predictions": final_probabilities} class MoNN2DropBNorm(models.BaseModel): """A softmax over a mixture of logistic models (with L2 regularization).""" def create_model(self, model_input, vocab_size, layers_keep_probs, num_mixtures=None, l2_penalty=1e-6, is_training=True, **unused_params): num_mixtures = num_mixtures or FLAGS.MoNN_num_experts logging.info("MoNN2Drop " + str(layers_keep_probs)) drop_out = tf.nn.dropout(model_input, layers_keep_probs[0],name="input/dropout") gate_activations = slim.fully_connected( model_input, vocab_size * (num_mixtures + 1), activation_fn=None, biases_initializer=None, weights_regularizer=slim.l2_regularizer(l2_penalty), scope="gates") model_input_norm = slim.batch_norm( model_input, center=True, scale=True, is_training=is_training, scope='input/batch_norm') h1Units = 4096 A1 = slim.fully_connected( model_input_norm, h1Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_H1') h2Units = 4096 A1a = tf.nn.dropout(A1, layers_keep_probs[1], name='layer1/dropout') A1b = slim.batch_norm( A1a, center=True, scale=True, is_training=is_training, scope='layer1/batch_norm') A2 = slim.fully_connected( A1b, h2Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_H2') A2a = tf.nn.dropout(A2, layers_keep_probs[2], name='layer2/dropout') expert_activations = slim.fully_connected( A2a, vocab_size * num_mixtures, activation_fn=None, weights_regularizer=slim.l2_regularizer(l2_penalty), scope="experts") gating_distribution = tf.nn.softmax(tf.reshape( gate_activations, [-1, num_mixtures + 1])) # (Batch * #Labels) x (num_mixtures + 1) expert_distribution = tf.nn.sigmoid(tf.reshape( expert_activations, [-1, num_mixtures])) # (Batch * #Labels) x num_mixtures final_probabilities_by_class_and_batch = tf.reduce_sum( gating_distribution[:, :num_mixtures] * expert_distribution, 1) final_probabilities = tf.reshape(final_probabilities_by_class_and_batch, [-1, vocab_size]) return {"predictions": final_probabilities} class MoNN2DropBNorm1Crelu(models.BaseModel): """A softmax over a mixture of logistic models (with L2 regularization).""" def create_model(self, model_input, vocab_size, layers_keep_probs, num_mixtures=None, l2_penalty=1e-6, is_training=True, **unused_params): num_mixtures = num_mixtures or FLAGS.MoNN_num_experts logging.info("MoNN2Drop " + str(layers_keep_probs)) drop_out = tf.nn.dropout(model_input, layers_keep_probs[0],name="input/dropout") gate_activations = slim.fully_connected( model_input, vocab_size * (num_mixtures + 1), activation_fn=None, biases_initializer=None, weights_regularizer=slim.l2_regularizer(l2_penalty), scope="gates") model_input_norm = slim.batch_norm( model_input, center=True, scale=True, is_training=is_training, scope='input/batch_norm') h1Units = 4096 A1 = slim.fully_connected( model_input, h1Units, activation_fn=tf.nn.crelu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_H1') h2Units = 4096 A1a = tf.nn.dropout(A1, layers_keep_probs[1], name='layer1/dropout') A1b = slim.batch_norm( A1a, center=True, scale=True, is_training=is_training, scope='layer1/batch_norm') A2 = slim.fully_connected( A1b, h2Units, activation_fn=tf.nn.crelu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_H2') A2a = tf.nn.dropout(A2, layers_keep_probs[2], name='layer2/dropout') expert_activations = slim.fully_connected( A2a, vocab_size * num_mixtures, activation_fn=None, weights_regularizer=slim.l2_regularizer(l2_penalty), scope="experts") gating_distribution = tf.nn.softmax(tf.reshape( gate_activations, [-1, num_mixtures + 1])) # (Batch * #Labels) x (num_mixtures + 1) expert_distribution = tf.nn.sigmoid(tf.reshape( expert_activations, [-1, num_mixtures])) # (Batch * #Labels) x num_mixtures final_probabilities_by_class_and_batch = tf.reduce_sum( gating_distribution[:, :num_mixtures] * expert_distribution, 1) final_probabilities = tf.reshape(final_probabilities_by_class_and_batch, [-1, vocab_size]) return {"predictions": final_probabilities} class MoNN4L(models.BaseModel): """A softmax over a mixture of logistic models (with L2 regularization).""" def create_model(self, model_input, vocab_size, num_mixtures=None, l2_penalty=1e-6, **unused_params): """Creates a Mixture of (Logistic) Experts model. The model consists of a per-class softmax distribution over a configurable number of logistic classifiers. One of the classifiers in the mixture is not trained, and always predicts 0. Args: model_input: 'batch_size' x 'num_features' matrix of input features. vocab_size: The number of classes in the dataset. num_mixtures: The number of mixtures (excluding a dummy 'expert' that always predicts the non-existence of an entity). l2_penalty: How much to penalize the squared magnitudes of parameter values. Returns: A dictionary with a tensor containing the probability predictions of the model in the 'predictions' key. The dimensions of the tensor are batch_size x num_classes. """ num_mixtures = num_mixtures or FLAGS.MoNN_num_experts gate_activations = slim.fully_connected( model_input, vocab_size * (num_mixtures + 1), activation_fn=None, biases_initializer=None, weights_regularizer=slim.l2_regularizer(l2_penalty), scope="gates") a1Units = 4096 A1 = slim.fully_connected( model_input, a1Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_HA1') a2Units = 4096 A2 = slim.fully_connected( A1, a2Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_HA2') a2Units = 4096 A3 = slim.fully_connected( A2, a2Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_HA3') a2Units = 4096 A4 = slim.fully_connected( A3, a2Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_HA4') expert_activations = slim.fully_connected( A4, vocab_size * num_mixtures, activation_fn=None, weights_regularizer=slim.l2_regularizer(l2_penalty), scope="experts") gating_distribution = tf.nn.softmax(tf.reshape( gate_activations, [-1, num_mixtures + 1])) # (Batch * #Labels) x (num_mixtures + 1) expert_distribution = tf.nn.sigmoid(tf.reshape( expert_activations, [-1, num_mixtures])) # (Batch * #Labels) x num_mixtures final_probabilities_by_class_and_batch = tf.reduce_sum( gating_distribution[:, :num_mixtures] * expert_distribution, 1) final_probabilities = tf.reshape(final_probabilities_by_class_and_batch, [-1, vocab_size]) return {"predictions": final_probabilities} class MoNN4LDropG2L(models.BaseModel): """A softmax over a mixture of logistic models (with L2 regularization).""" def create_model(self, model_input, vocab_size, layers_keep_probs, num_mixtures=None, l2_penalty=1e-6, is_training=True, **unused_params): """Creates a Mixture of (Logistic) Experts model. The model consists of a per-class softmax distribution over a configurable number of logistic classifiers. One of the classifiers in the mixture is not trained, and always predicts 0. Args: model_input: 'batch_size' x 'num_features' matrix of input features. vocab_size: The number of classes in the dataset. num_mixtures: The number of mixtures (excluding a dummy 'expert' that always predicts the non-existence of an entity). l2_penalty: How much to penalize the squared magnitudes of parameter values. Returns: A dictionary with a tensor containing the probability predictions of the model in the 'predictions' key. The dimensions of the tensor are batch_size x num_classes. """ num_mixtures = num_mixtures or FLAGS.MoNN_num_experts logging.info("MoNN4LDrop " + str(layers_keep_probs)) drop_model_input = tf.nn.dropout(model_input, layers_keep_probs[0]) # # Added one more layer to gate # X1 = slim.fully_connected( drop_model_input, vocab_size , activation_fn=None, biases_initializer=None, weights_regularizer=slim.l2_regularizer(l2_penalty), scope="gates_l1") gate_activations = slim.fully_connected( X1, vocab_size * (num_mixtures + 1), activation_fn=None, biases_initializer=None, weights_regularizer=slim.l2_regularizer(l2_penalty), scope="gates_l2") a1Units = 4096 A1 = slim.fully_connected( drop_model_input, a1Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_HA1') a2Units = 4096 A1d = tf.nn.dropout(A1, layers_keep_probs[1]) A2 = slim.fully_connected( A1d, a2Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_HA2') a2Units = 4096 A2d = tf.nn.dropout(A1, layers_keep_probs[2]) A3 = slim.fully_connected( A2d, a2Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_HA3') a2Units = 4096 A3d = tf.nn.dropout(A3, layers_keep_probs[3]) A4 = slim.fully_connected( A3d, a2Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_HA4') expert_activations = slim.fully_connected( A4, vocab_size * num_mixtures, activation_fn=None, weights_regularizer=slim.l2_regularizer(l2_penalty), scope="experts") gating_distribution = tf.nn.softmax(tf.reshape( gate_activations, [-1, num_mixtures + 1])) # (Batch * #Labels) x (num_mixtures + 1) expert_distribution = tf.nn.sigmoid(tf.reshape( expert_activations, [-1, num_mixtures])) # (Batch * #Labels) x num_mixtures final_probabilities_by_class_and_batch = tf.reduce_sum( gating_distribution[:, :num_mixtures] * expert_distribution, 1) final_probabilities = tf.reshape(final_probabilities_by_class_and_batch, [-1, vocab_size]) return {"predictions": final_probabilities} class MoNN4LDropG3L(models.BaseModel): """A softmax over a mixture of logistic models (with L2 regularization).""" def create_model(self, model_input, vocab_size, layers_keep_probs, num_mixtures=None, l2_penalty=1e-6, is_training=True, **unused_params): """Creates a Mixture of (Logistic) Experts model. The model consists of a per-class softmax distribution over a configurable number of logistic classifiers. One of the classifiers in the mixture is not trained, and always predicts 0. Args: model_input: 'batch_size' x 'num_features' matrix of input features. vocab_size: The number of classes in the dataset. num_mixtures: The number of mixtures (excluding a dummy 'expert' that always predicts the non-existence of an entity). l2_penalty: How much to penalize the squared magnitudes of parameter values. Returns: A dictionary with a tensor containing the probability predictions of the model in the 'predictions' key. The dimensions of the tensor are batch_size x num_classes. """ num_mixtures = num_mixtures or FLAGS.MoNN_num_experts logging.info("MoNN4LDrop " + str(layers_keep_probs)) drop_model_input = tf.nn.dropout(model_input, layers_keep_probs[0]) # # Added one more layer to gate # X1 = slim.fully_connected( drop_model_input, vocab_size , activation_fn=None, biases_initializer=None, weights_regularizer=slim.l2_regularizer(l2_penalty), scope="gates_l1") X2 = slim.fully_connected( X1, vocab_size, activation_fn=None, biases_initializer=None, weights_regularizer=slim.l2_regularizer(l2_penalty), scope="gates_l2") gate_activations = slim.fully_connected( X2, vocab_size * (num_mixtures + 1), activation_fn=None, biases_initializer=None, weights_regularizer=slim.l2_regularizer(l2_penalty), scope="gates_activation") a1Units = 4096 A1 = slim.fully_connected( drop_model_input, a1Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_HA1') a2Units = 4096 A1d = tf.nn.dropout(A1, layers_keep_probs[1]) A2 = slim.fully_connected( A1d, a2Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_HA2') a2Units = 4096 A2d = tf.nn.dropout(A2, layers_keep_probs[2]) A3 = slim.fully_connected( A2d, a2Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_HA3') a2Units = 4096 A3d = tf.nn.dropout(A3, layers_keep_probs[3]) A4 = slim.fully_connected( A3d, a2Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_HA4') expert_activations = slim.fully_connected( A4, vocab_size * num_mixtures, activation_fn=None, weights_regularizer=slim.l2_regularizer(l2_penalty), scope="experts") gating_distribution = tf.nn.softmax(tf.reshape( gate_activations, [-1, num_mixtures + 1])) # (Batch * #Labels) x (num_mixtures + 1) expert_distribution = tf.nn.sigmoid(tf.reshape( expert_activations, [-1, num_mixtures])) # (Batch * #Labels) x num_mixtures final_probabilities_by_class_and_batch = tf.reduce_sum( gating_distribution[:, :num_mixtures] * expert_distribution, 1) final_probabilities = tf.reshape(final_probabilities_by_class_and_batch, [-1, vocab_size]) return {"predictions": final_probabilities} from tensorflow import logging class MoNN2a128r1024G1L(models.BaseModel): """A softmax over a mixture of logistic models (with L2 regularization).""" def create_model(self, model_input, vocab_size, layers_keep_probs, num_mixtures=None, l2_penalty=1e-6, is_training=True, **unused_params): num_mixtures = num_mixtures or FLAGS.MoNN_num_experts logging.info("MoNN2Drop " + str(layers_keep_probs)) drop_out = tf.nn.dropout(model_input, layers_keep_probs[0],name="var_dropout") logging.info(model_input.shape) inputA = model_input[:,0:128] inputB = model_input[:,128:1152] inputAd = tf.nn.dropout(inputA, layers_keep_probs[0]) inputBd = tf.nn.dropout(inputB, layers_keep_probs[0]) inputAdn = slim.batch_norm( inputAd, center=True, scale=True, is_training=is_training, scope='inputAd/batch_norm') inputBdn = slim.batch_norm( inputBd, center=True, scale=True, is_training=is_training, scope='inputBd/batch_norm') X1 = slim.fully_connected( tf.concat([inputAdn,inputBdn],1), vocab_size , activation_fn=None, biases_initializer=None, weights_regularizer=slim.l2_regularizer(l2_penalty), scope="gates_l1") gate_activations = slim.fully_connected( X1, vocab_size * (num_mixtures + 1), activation_fn=None, biases_initializer=None, weights_regularizer=slim.l2_regularizer(l2_penalty), scope="gates") a_h1Units = 512 A1 = slim.fully_connected( inputAdn, a_h1Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_H1_audio') a_h2Units = 512 A1n = slim.batch_norm( A1, center=True, scale=True, is_training=is_training, scope='A1/batch_norm') A1a = tf.nn.dropout(A1n, layers_keep_probs[1]) logging.info("A1a") logging.info(A1a.shape) A2 = slim.fully_connected( A1a, a_h2Units, activation_fn=tf.nn.crelu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_H2_audio') A2n = slim.batch_norm( A2, center=True, scale=True, is_training=is_training, scope='A2/batch_norm') logging.info("A2") logging.info(A2.shape) b_h1Units = 2048 B1 = slim.fully_connected( inputBdn, b_h1Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_H1_rgb') B1n = slim.batch_norm( B1, center=True, scale=True, is_training=is_training, scope='B1/batch_norm') b_h2Units = 2048 B1a = tf.nn.dropout(B1n, layers_keep_probs[1]) logging.info("B1a") logging.info(B1a.shape) B2 = slim.fully_connected( B1a, b_h2Units, activation_fn=tf.nn.crelu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_H2_rgb') B2n = slim.batch_norm( B2, center=True, scale=True, is_training=is_training, scope='B2/batch_norm') A2na = tf.nn.dropout(A2n, layers_keep_probs[2]) B2na = tf.nn.dropout(B2n, layers_keep_probs[2]) logging.info(A2.shape) logging.info(B2.shape) C3 = tf.concat([inputAdn, inputBdn, A2na, B2na], 1) h3Units = 4096 C3a = slim.fully_connected( C3, h3Units, activation_fn=tf.nn.crelu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_H3_concat') C3ad = tf.nn.dropout(C3a, layers_keep_probs[3]) h4Units = 4096 C4a = slim.fully_connected( C3a, h4Units, activation_fn=tf.nn.crelu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_H4_concat') expert_activations = slim.fully_connected( C4a, vocab_size * num_mixtures, activation_fn=None, weights_regularizer=slim.l2_regularizer(l2_penalty), scope="experts") gating_distribution = tf.nn.softmax(tf.reshape( gate_activations, [-1, num_mixtures + 1])) # (Batch * #Labels) x (num_mixtures + 1) expert_distribution = tf.nn.sigmoid(tf.reshape( expert_activations, [-1, num_mixtures])) # (Batch * #Labels) x num_mixtures final_probabilities_by_class_and_batch = tf.reduce_sum( gating_distribution[:, :num_mixtures] * expert_distribution, 1) final_probabilities = tf.reshape(final_probabilities_by_class_and_batch, [-1, vocab_size]) return {"predictions": final_probabilities} class MoNN4Lw(models.BaseModel): """A softmax over a mixture of logistic models (with L2 regularization).""" def create_model(self, model_input, vocab_size, num_mixtures=None, l2_penalty=1e-8, **unused_params): num_mixtures = num_mixtures or FLAGS.MoNN_num_experts gate_activations = slim.fully_connected( model_input, vocab_size * (num_mixtures + 1), activation_fn=None, biases_initializer=None, weights_regularizer=slim.l2_regularizer(l2_penalty), scope="gates") a1Units = 2305*8 A1 = slim.fully_connected( model_input, a1Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_HA1') a2Units = 2305 A2 = slim.fully_connected( A1, a2Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_HA2') a2Units = 2305*4 A3 = slim.fully_connected( A2, a2Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_HA3') a2Units = 2305*2 A4 = slim.fully_connected( A3, a2Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_HA4') expert_activations = slim.fully_connected( A4, vocab_size * num_mixtures, activation_fn=None, weights_regularizer=slim.l2_regularizer(l2_penalty), scope="experts") gating_distribution = tf.nn.softmax(tf.reshape( gate_activations, [-1, num_mixtures + 1])) # (Batch * #Labels) x (num_mixtures + 1) expert_distribution = tf.nn.sigmoid(tf.reshape( expert_activations, [-1, num_mixtures])) # (Batch * #Labels) x num_mixtures final_probabilities_by_class_and_batch = tf.reduce_sum( gating_distribution[:, :num_mixtures] * expert_distribution, 1) final_probabilities = tf.reshape(final_probabilities_by_class_and_batch, [-1, vocab_size]) return {"predictions": final_probabilities} class MoNN1Lvw(models.BaseModel): """A softmax over a mixture of logistic models (with L2 regularization).""" def create_model(self, model_input, vocab_size, num_mixtures=None, l2_penalty=1e-8, **unused_params): num_mixtures = num_mixtures or FLAGS.MoNN_num_experts gate_activations = slim.fully_connected( model_input, vocab_size * (num_mixtures + 1), activation_fn=None, biases_initializer=None, weights_regularizer=slim.l2_regularizer(l2_penalty), scope="gates") a1Units = 2305*64 A1 = slim.fully_connected( model_input, a1Units, activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty), scope='FC_HA1') expert_activations = slim.fully_connected( A1, vocab_size * num_mixtures, activation_fn=None, weights_regularizer=slim.l2_regularizer(l2_penalty), scope="experts") gating_distribution = tf.nn.softmax(tf.reshape( gate_activations, [-1, num_mixtures + 1])) # (Batch * #Labels) x (num_mixtures + 1) expert_distribution = tf.nn.sigmoid(tf.reshape( expert_activations, [-1, num_mixtures])) # (Batch * #Labels) x num_mixtures final_probabilities_by_class_and_batch = tf.reduce_sum( gating_distribution[:, :num_mixtures] * expert_distribution, 1) final_probabilities = tf.reshape(final_probabilities_by_class_and_batch, [-1, vocab_size]) return {"predictions": final_probabilities}

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7

cceb249c95aa1246ae216643b6521289c5c849e1

170

py

Python

hf/__init__.py

zhaotudou/HeteroFusionRCNN

0eb589c660a50c9adad14758e204a2f152ec22cd

[ "MIT" ]

1

2021-08-11T12:18:25.000Z

hf/__init__.py

zhaotudou/HeteroFusionRCNN

0eb589c660a50c9adad14758e204a2f152ec22cd

[ "MIT" ]

null

hf/__init__.py

zhaotudou/HeteroFusionRCNN

0eb589c660a50c9adad14758e204a2f152ec22cd

[ "MIT" ]

1

2021-08-11T12:18:30.000Z

import os def root_dir(): return os.path.dirname(os.path.realpath(__file__)) def top_dir(): hf_root_dir = root_dir() return os.path.split(hf_root_dir)[0]

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1

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7

6936759f459811de9876676e4d501a50507228ea

13,237

py

Python

feature_generation/parse_transcripts.py

matejMartinc/ADReSSchallenge

679f24de02307aaac19506adf44f82330a80b82e

[ "MIT" ]

2

2021-06-29T17:56:28.000Z

2022-03-25T10:55:53.000Z

feature_generation/parse_transcripts.py

matejMartinc/ADReSSchallenge

679f24de02307aaac19506adf44f82330a80b82e

[ "MIT" ]

null

feature_generation/parse_transcripts.py

matejMartinc/ADReSSchallenge

679f24de02307aaac19506adf44f82330a80b82e

[ "MIT" ]

2

2020-11-17T13:59:29.000Z

2020-12-05T15:58:00.000Z

import os from collections import defaultdict import re import pandas as pd import argparse import stanfordnlp def generate_ud_features(sentence): nlp = stanfordnlp.Pipeline(use_gpu=False) doc = nlp(str(sentence)) seq = "" for sent in doc.sentences: for idx, dep_edge in enumerate(sent.dependencies): if int(dep_edge[0].index) == 0: dist = str(0) else: dist = str(abs(int(dep_edge[0].index) - (idx + 1))) #print(dist) seq = seq + " " + dist + '_' + str(dep_edge[1]) print(sentence) print(seq) return seq def build_csv(input_path, output_path): d = defaultdict(dict) with open(input_path, 'r', encoding="utf8") as f: for line in f: id, speaker, group, text = line.split('\t') text = re.sub(r" ?\[[^)]+\]", "", text) text = " ".join(text.split()).strip() if speaker.startswith('!MORPH'): text = " ".join([word.split('|')[0] if '|' in word else word for word in text.split()]) d[id][speaker] = text d[id]['target'] = group data = [] for id, values in d.items(): line = [id] line.append(values['target']) line.append(values['!PAR']) if '!INV' in values: line.append(values['!INV']) else: line.append('') line.append(values['!MORPH_PAR']) if '!MORPH_INV' in values: line.append(values['!MORPH_INV']) else: line.append('') line.append(values['!GRA_PAR']) if '!GRA_INV' in values: line.append(values['!GRA_INV']) else: line.append('') data.append(line) df = pd.DataFrame(data, columns=['id', 'target', '!PAR', '!INV', '!MORPH_PAR', '!MORPH_INV', '!GRA_PAR', '!GRA_INV']) df.to_csv(output_path, encoding="utf8", sep="\t", index=False) df = pd.read_csv(output_path, encoding="utf8", sep="\t") df['!UD_PAR'] = df['!PAR'].map(lambda x: generate_ud_features(x)) df['!UD_INV'] = df['!INV'].map(lambda x: generate_ud_features(x)) df.to_csv(output_path, encoding="utf8", sep="\t", index=False) def build_test_csv(input_path, output_path): d = defaultdict(dict) with open(input_path, 'r', encoding="utf8") as f: for line in f: id, speaker, text = line.split('\t') text = re.sub(r" ?\[[^)]+\]", "", text) text = " ".join(text.split()).strip() if speaker.startswith('!MORPH'): text = " ".join([word.split('|')[0] if '|' in word else word for word in text.split()]) d[id][speaker] = text data = [] for id, values in d.items(): line = [id] line.append(values['!PAR']) if '!INV' in values: line.append(values['!INV']) else: line.append('') line.append(values['!MORPH_PAR']) if '!MORPH_INV' in values: line.append(values['!MORPH_INV']) else: line.append('') line.append(values['!GRA_PAR']) if '!GRA_INV' in values: line.append(values['!GRA_INV']) else: line.append('') data.append(line) df = pd.DataFrame(data, columns=['id', '!PAR', '!INV', '!MORPH_PAR', '!MORPH_INV', '!GRA_PAR', '!GRA_INV']) df.to_csv(output_path, encoding="utf8", sep="\t", index=False) df = pd.read_csv(output_path, encoding="utf8", sep="\t") df['!UD_PAR'] = df['!PAR'].map(lambda x: generate_ud_features(x)) df['!UD_INV'] = df['!INV'].map(lambda x: generate_ud_features(x)) df.to_csv(output_path, encoding="utf8", sep="\t", index=False) def parse_transcripts(input_folder, output_path): output = open(output_path, 'w', encoding='utf8') pattern = r'.*?' groups = ['cc', 'cd'] for group in groups: path = os.path.join(input_folder, group) filenames = os.listdir(path) for filename in filenames: file_path = os.path.join(path, filename) representations = defaultdict(list) with open(file_path, 'r', encoding='utf8') as f: inv_bool = True last_rep = "" for line in f: line = re.sub(pattern, '', line) if line.startswith('@End') or line.startswith('@Begin') or line.startswith('@Languages') or line.startswith('@Participants') or line.startswith('@Media') or line.startswith('@Comment'): pass elif line.startswith('@UTF8') or line.startswith('%com'): pass elif line.startswith('@ID'): if 'Participant' in line: meta = line.split('Participant')[0].split('PAR')[-1] elif line.startswith('@PID'): num = line.split('\t')[-1] elif line.startswith('*PAR'): last_rep = 'PAR' inv_bool = False representations[last_rep].append(line.split('\t')[-1].strip()) elif line.startswith('*INV'): last_rep = 'INV' inv_bool = True representations[last_rep].append(line.split('\t')[-1].strip()) elif line.startswith('%mor'): line = line.split('\t')[-1].strip() if inv_bool: last_rep = 'INV_MORPH' representations[last_rep].append(line) else: last_rep = 'PAR_MORPH' representations[last_rep].append(line) elif line.startswith('%gra'): line = line.split('\t')[-1].strip() if inv_bool: last_rep = 'INV_GRA' representations[last_rep].append(line) else: last_rep = 'PAR_GRA' representations[last_rep].append(line) else: line = line.strip() if inv_bool: representations[last_rep][-1] = representations[last_rep][-1] + ' ' + line else: representations[last_rep][-1] = representations[last_rep][-1] + ' ' + line id = filename.split('.')[0] if group == 'cd': c = '!CD' if group == 'cc': c = '!CC' meta = '!PARMETA' + meta.strip() + num.strip() if representations['PAR']: instance = "\t".join([id.strip(), '!PAR', c.strip(), " ".join(representations['PAR'])]) output.write(instance + '\n') if representations['INV']: instance = "\t".join([id.strip(), '!INV', c.strip(), " ".join(representations['INV'])]) output.write(instance + '\n') if representations['PAR_MORPH']: instance = "\t".join([id.strip(), '!MORPH_PAR', c.strip(), " ".join(representations['PAR_MORPH'])]) output.write(instance + '\n') if representations['INV_MORPH']: instance = "\t".join([id.strip(), '!MORPH_INV', c.strip(), " ".join(representations['INV_MORPH'])]) output.write(instance + '\n') if representations['PAR_GRA']: instance = "\t".join([id.strip(), '!GRA_PAR', c.strip(), " ".join(representations['PAR_GRA'])]) output.write(instance + '\n') if representations['INV_GRA']: instance = "\t".join([id.strip(), '!GRA_INV', c.strip()," ".join(representations['INV_GRA'])]) output.write(instance + '\n') output.close() def parse_test_transcripts(input_folder, output_path): output = open(output_path, 'w', encoding='utf8') pattern = r'.*?' path = input_folder filenames = os.listdir(path) for filename in filenames: file_path = os.path.join(path, filename) representations = defaultdict(list) with open(file_path, 'r', encoding='utf8') as f: inv_bool = True last_rep = "" for line in f: line = re.sub(pattern, '', line) if line.startswith('@End') or line.startswith('@Begin') or line.startswith('@Languages') or line.startswith('@Participants') or line.startswith('@Media') or line.startswith('@Comment'): pass elif line.startswith('@UTF8') or line.startswith('%com'): pass elif line.startswith('@ID'): if 'Participant' in line: meta = line.split('Participant')[0].split('PAR')[-1] elif line.startswith('@PID'): num = line.split('\t')[-1] elif line.startswith('*PAR'): last_rep = 'PAR' inv_bool = False representations[last_rep].append(line.split('\t')[-1].strip()) elif line.startswith('*INV'): last_rep = 'INV' inv_bool = True representations[last_rep].append(line.split('\t')[-1].strip()) elif line.startswith('%mor'): line = line.split('\t')[-1].strip() if inv_bool: last_rep = 'INV_MORPH' representations[last_rep].append(line) else: last_rep = 'PAR_MORPH' representations[last_rep].append(line) elif line.startswith('%gra'): line = line.split('\t')[-1].strip() if inv_bool: last_rep = 'INV_GRA' representations[last_rep].append(line) else: last_rep = 'PAR_GRA' representations[last_rep].append(line) else: line = line.strip() if inv_bool: representations[last_rep][-1] = representations[last_rep][-1] + ' ' + line else: representations[last_rep][-1] = representations[last_rep][-1] + ' ' + line id = filename.split('.')[0] meta = '!PARMETA' + meta.strip() + num.strip() if representations['PAR']: instance = "\t".join([id.strip(), '!PAR', " ".join(representations['PAR'])]) output.write(instance + '\n') if representations['INV']: instance = "\t".join([id.strip(), '!INV', " ".join(representations['INV'])]) output.write(instance + '\n') if representations['PAR_MORPH']: instance = "\t".join([id.strip(), '!MORPH_PAR', " ".join(representations['PAR_MORPH'])]) output.write(instance + '\n') if representations['INV_MORPH']: instance = "\t".join([id.strip(), '!MORPH_INV', " ".join(representations['INV_MORPH'])]) output.write(instance + '\n') if representations['PAR_GRA']: instance = "\t".join([id.strip(), '!GRA_PAR', " ".join(representations['PAR_GRA'])]) output.write(instance + '\n') if representations['INV_GRA']: instance = "\t".join([id.strip(), '!GRA_INV'," ".join(representations['INV_GRA'])]) output.write(instance + '\n') output.close() if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--input_path', type=str, default='data/ADReSS-IS2020-data/train/transcription', help='path to transcription folder') parser.add_argument('--output_path', type=str, default='tsv_features', help='Path to folder that contain final tsv feature files used for classification') parser.add_argument('--test', action='store_true', help='Parse test set with no target class') args = parser.parse_args() if not os.path.exists(args.output_path): os.makedirs(args.output_path) stanfordnlp.download('en') # This downloads the English models for the neural pipeline if args.test: parse_test_transcripts(args.input_path, os.path.join(args.output_path, 'text_features_test.txt')) build_test_csv(os.path.join(args.output_path,'text_features_test.txt'), os.path.join(args.output_path, 'text_features_test.tsv')) os.remove(os.path.join(args.output_path, 'text_features_test.txt')) else: parse_transcripts(args.input_path, os.path.join(args.output_path, 'text_features_train.txt')) build_csv(os.path.join(args.output_path,'text_features_train.txt'), os.path.join(args.output_path, 'text_features_train.tsv')) os.remove(os.path.join(args.output_path, 'text_features_train.txt'))

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8

6950b2157041b9e51950a89e70d3d59b2972e11f

208

py

Python

tests/test_files/test_not_unique.py

micheller/flake8-fine-pytest

8f16722fe97e67740f0af72b6867988ec31dfaf9

[ "MIT" ]

4

2021-01-06T02:53:06.000Z

2022-02-24T14:11:23.000Z

tests/test_files/test_not_unique.py

micheller/flake8-fine-pytest

8f16722fe97e67740f0af72b6867988ec31dfaf9

[ "MIT" ]

7

2020-05-12T06:49:25.000Z

2022-03-05T05:03:25.000Z

tests/test_files/test_not_unique.py

micheller/flake8-fine-pytest

8f16722fe97e67740f0af72b6867988ec31dfaf9

[ "MIT" ]

6

2020-06-30T14:10:33.000Z

2020-12-21T10:19:01.000Z

import pytest def test_not_uniq(): pass def test_not_uniq(): pass @pytest.mark.skip def test_not_uniq_with_decorator(): pass @pytest.mark.skip def test_not_uniq_with_decorator(): pass

10.4

35

0.721154

32

208

4.3125

0.34375

0.202899

0.289855

0.405797

0.913043

0.681159

0

0.192308

208

19

36

10.947368

0.821429

0

0.909091

0

1

0.363636

true

0.363636

0.090909

0

0.454545

0

null

1

0

1

0

1

0

null

0

1

0

9

c608a7ea0b861adacf84d9c2a66fcc99e2e89261

378

py

Python

listas.py

soyjherom/fundamentos_programacion

f6cba2872550fd11fa92f7b1e676a96d36293900

[ "CC0-1.0" ]

null

listas.py

soyjherom/fundamentos_programacion

f6cba2872550fd11fa92f7b1e676a96d36293900

[ "CC0-1.0" ]

null

listas.py

soyjherom/fundamentos_programacion

f6cba2872550fd11fa92f7b1e676a96d36293900

[ "CC0-1.0" ]

1

2021-08-21T02:41:59.000Z

mensaje = "Registre el nombre del estudiante " estudiantes = [] estudiante = input(mensaje) estudiantes.append(estudiante) estudiante = input(mensaje) estudiantes.append(estudiante) estudiante = input(mensaje) estudiantes.append(estudiante) estudiante = input(mensaje) estudiantes.append(estudiante) estudiante = input(mensaje) estudiantes.append(estudiante) print(estudiantes)

27

46

0.814815

39

378

7.897436

0.25641

0.243506

0.357143

0.535714

0.795455

0

0.084656

378

13

47

29.076923

0.890173

0

0.769231

0

0.089947

0

1

0

false

0

0.076923

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null

1

0

1

0

1

0

null

0

9

c6173bad7bc06d773eac7bc1f7c690a359df1df4

28,719

py

Python

tests/__init__.py

airbus/decomon

f3668fbd8edd0def4e23aa0634eebfec58349c35

[ "MIT" ]

11

2021-11-03T12:09:50.000Z

2022-02-20T21:42:13.000Z

tests/__init__.py

airbus/decomon

f3668fbd8edd0def4e23aa0634eebfec58349c35

[ "MIT" ]

1

2022-02-18T13:40:46.000Z

tests/__init__.py

airbus/decomon

f3668fbd8edd0def4e23aa0634eebfec58349c35

[ "MIT" ]

null

from __future__ import absolute_import import numpy as np from tensorflow.keras import Input from numpy.testing import assert_almost_equal import tensorflow.python.keras.backend as K def get_standart_values_1d_box(n, dc_decomp=True, grad_bounds=False, nb=100): """ :param n: A set of functions with their monotonic decomposition for testing the activations :return: """ """ A set of functions with their monotonic decomposition for testing the activations """ w_u_ = np.ones(nb) b_u_ = np.zeros(nb) w_l_ = np.ones(nb) b_l_ = np.zeros(nb) if n == 0: # identity y_ = np.linspace(-2, -1, nb) x_ = np.linspace(-2, -1, nb) h_ = np.linspace(-2, -1, nb) g_ = np.zeros_like(x_) if n == 1: y_ = np.linspace(1, 2, nb) x_ = np.linspace(1, 2, nb) h_ = np.linspace(1, 2, nb) g_ = np.zeros_like(x_) if n == 2: y_ = np.linspace(-1, 1, nb) x_ = np.linspace(-1, 1, nb) h_ = np.linspace(-1, 1, nb) g_ = np.zeros_like(x_) if n == 3: # identity y_ = np.linspace(-2, -1, nb) x_ = np.linspace(-2, -1, nb) h_ = 2 * np.linspace(-2, -1, nb) g_ = -np.linspace(-2, -1, nb) if n == 4: y_ = np.linspace(1, 2, nb) x_ = np.linspace(1, 2, nb) h_ = 2 * np.linspace(1, 2, nb) g_ = -np.linspace(1, 2, nb) if n == 5: y_ = np.linspace(-1, 1, nb) x_ = np.linspace(-1, 1, nb) h_ = 2 * np.linspace(-1, 1, nb) g_ = -np.linspace(-1, 1, nb) if n == 6: assert nb == 100, "expected nb=100 samples" # cosine function x_ = np.linspace(-np.pi, np.pi, 100) y_ = np.cos(x_) h_ = np.concatenate([y_[:50], np.ones((50,))]) - 0.5 g_ = np.concatenate([np.ones((50,)), y_[50:]]) - 0.5 w_u_ = np.zeros_like(x_) w_l_ = np.zeros_like(x_) b_u_ = np.ones_like(x_) b_l_ = -np.ones_like(x_) if n == 7: # h and g >0 h_ = np.linspace(0.5, 2, nb) g_ = np.linspace(1, 2, nb)[::-1] x_ = h_ + g_ y_ = h_ + g_ if n == 8: # h <0 and g <0 # h_max+g_max <=0 h_ = np.linspace(-2, -1, nb) g_ = np.linspace(-2, -1, nb)[::-1] y_ = h_ + g_ x_ = h_ + g_ if n == 9: # h >0 and g <0 # h_min+g_min >=0 h_ = np.linspace(4, 5, nb) g_ = np.linspace(-2, -1, nb)[::-1] y_ = h_ + g_ x_ = h_ + g_ x_min_ = x_.min() + np.zeros_like(x_) x_max_ = x_.max() + np.zeros_like(x_) x_0_ = np.concatenate([x_min_[:, None], x_max_[:, None]], 1) u_c_ = np.max(y_) * np.ones((nb,)) l_c_ = np.min(y_) * np.ones((nb,)) if dc_decomp: return [ x_[:, None], y_[:, None], x_0_[:, :, None], u_c_[:, None], w_u_[:, None, None], b_u_[:, None], l_c_[:, None], w_l_[:, None, None], b_l_[:, None], h_[:, None], g_[:, None], ] return [ x_[:, None], y_[:, None], x_0_[:, :, None], u_c_[:, None], w_u_[:, None, None], b_u_[:, None], l_c_[:, None], w_l_[:, None, None], b_l_[:, None], ] def get_tensor_decomposition_1d_box(dc_decomp=True): if dc_decomp: return [ Input((1,), dtype=K.floatx()), Input((1,)), Input((2, 1)), Input((1,)), Input((1, 1)), Input((1,)), Input((1,)), Input((1, 1)), Input((1,)), Input((1,)), Input((1,)), ] return [ Input((1,)), Input((1,)), Input((2, 1)), Input((1,)), Input((1, 1)), Input((1,)), Input((1,)), Input((1, 1)), Input((1,)), ] def get_tensor_decomposition_multid_box(odd=1, dc_decomp=True): if odd: n = 3 else: n = 2 if dc_decomp: # x, y, z, u, w_u, b_u, l, w_l, b_l, h, g return [ Input((n,)), Input((n,)), Input((2, n)), Input((n,)), Input((n, n)), Input((n,)), Input((n,)), Input((n, n)), Input((n,)), Input((n,)), Input((n,)), ] return [ Input((n,)), Input((n,)), Input((2, n)), Input((n,)), Input((n, n)), Input((n,)), Input((n,)), Input((n, n)), Input((n,)), ] def get_standard_values_multid_box(odd=1, dc_decomp=True): if dc_decomp: ( x_0, y_0, z_0, u_c_0, w_u_0, b_u_0, l_c_0, w_l_0, b_l_0, h_0, g_0, ) = get_standart_values_1d_box(0, dc_decomp) ( x_1, y_1, z_1, u_c_1, w_u_1, b_u_1, l_c_1, w_l_1, b_l_1, h_1, g_1, ) = get_standart_values_1d_box(1, dc_decomp) else: ( x_0, y_0, z_0, u_c_0, w_u_0, b_u_0, l_c_0, w_l_0, b_l_0, ) = get_standart_values_1d_box(0, dc_decomp) ( x_1, y_1, z_1, u_c_1, w_u_1, b_u_1, l_c_1, w_l_1, b_l_1, ) = get_standart_values_1d_box(1, dc_decomp) if not odd: # output (x_0+x_1, x_0+2*x_0) x_ = np.concatenate([x_0, x_1], -1) z_min_ = np.concatenate([z_0[:, 0], z_1[:, 0]], -1) z_max_ = np.concatenate([z_0[:, 1], z_1[:, 1]], -1) z_ = np.concatenate([z_min_[:, None], z_max_[:, None]], 1) y_ = np.concatenate([y_0 + y_1, y_0 + 2 * y_1], -1) b_u_ = np.concatenate([b_u_0 + b_u_1, b_u_0 + 2 * b_u_1], -1) u_c_ = np.concatenate([u_c_0 + u_c_1, u_c_0 + 2 * u_c_1], -1) b_l_ = np.concatenate([b_l_0 + b_l_1, b_l_0 + 2 * b_l_1], -1) l_c_ = np.concatenate([l_c_0 + l_c_1, l_c_0 + 2 * l_c_1], -1) if dc_decomp: h_ = np.concatenate([h_0 + h_1, h_0 + 2 * h_1], -1) g_ = np.concatenate([g_0 + g_1, g_0 + 2 * g_1], -1) w_u_ = np.zeros((len(x_), 2, 2)) w_u_[:, 0, 0] = w_u_0[:, 0, 0] w_u_[:, 1, 0] = w_u_1[:, 0, 0] w_u_[:, 0, 1] = w_u_0[:, 0, 0] w_u_[:, 1, 1] = 2 * w_u_1[:, 0, 0] w_l_ = np.zeros((len(x_), 2, 2)) w_l_[:, 0, 0] = w_l_0[:, 0, 0] w_l_[:, 1, 0] = w_l_1[:, 0, 0] w_l_[:, 0, 1] = w_l_0[:, 0, 0] w_l_[:, 1, 1] = 2 * w_l_1[:, 0, 0] else: ( x_2, y_2, z_2, u_c_2, w_u_2, b_u_2, l_c_2, w_l_2, b_l_2, h_2, g_2, ) = get_standart_values_1d_box(2) # output (x_0+x_1, x_0+2*x_0, x_2) x_ = np.concatenate([x_0, x_1, x_2], -1) z_min_ = np.concatenate([z_0[:, 0], z_1[:, 0], z_2[:, 0]], -1) z_max_ = np.concatenate([z_0[:, 1], z_1[:, 1], z_2[:, 1]], -1) z_ = np.concatenate([z_min_[:, None], z_max_[:, None]], 1) y_ = np.concatenate([y_0 + y_1, y_0 + 2 * y_1, y_2], -1) b_u_ = np.concatenate([b_u_0 + b_u_1, b_u_0 + 2 * b_u_1, b_u_2], -1) b_l_ = np.concatenate([b_l_0 + b_l_1, b_l_0 + 2 * b_l_1, b_l_2], -1) u_c_ = np.concatenate([u_c_0 + u_c_1, u_c_0 + 2 * u_c_1, u_c_2], -1) l_c_ = np.concatenate([l_c_0 + l_c_1, l_c_0 + 2 * l_c_1, l_c_2], -1) if dc_decomp: h_ = np.concatenate([h_0 + h_1, h_0 + 2 * h_1, h_2], -1) g_ = np.concatenate([g_0 + g_1, g_0 + 2 * g_1, g_2], -1) w_u_ = np.zeros((len(x_), 3, 3)) w_u_[:, 0, 0] = w_u_0[:, 0, 0] w_u_[:, 1, 0] = w_u_1[:, 0, 0] w_u_[:, 0, 1] = w_u_0[:, 0, 0] w_u_[:, 1, 1] = 2 * w_u_1[:, 0, 0] w_u_[:, 2, 2] = w_u_2[:, 0, 0] w_l_ = np.zeros((len(x_), 3, 3)) w_l_[:, 0, 0] = w_l_0[:, 0, 0] w_l_[:, 1, 0] = w_l_1[:, 0, 0] w_l_[:, 0, 1] = w_l_0[:, 0, 0] w_l_[:, 1, 1] = 2 * w_l_1[:, 0, 0] w_l_[:, 2, 2] = w_l_2[:, 0, 0] if dc_decomp: return [x_, y_, z_, u_c_, w_u_, b_u_, l_c_, w_l_, b_l_, h_, g_] return [x_, y_, z_, u_c_, w_u_, b_u_, l_c_, w_l_, b_l_] def build_image_from_1D_box(odd=0, m=0, dc_decomp=True): if odd: n = 7 else: n = 6 if dc_decomp: ( x_, y_0, z_, u_c_0, w_u_0, b_u_0, l_c_0, w_l_0, b_l_0, h_0, g_0, ) = get_standart_values_1d_box(m, dc_decomp=dc_decomp) else: ( x_, y_0, z_, u_c_0, w_u_0, b_u_0, l_c_0, w_l_0, b_l_0, ) = get_standart_values_1d_box(m, dc_decomp=dc_decomp) y_ = np.concatenate([(i + 1) * y_0 for i in range(n * n)], -1).reshape((-1, n, n)) b_u_ = np.concatenate([(i + 1) * b_u_0 for i in range(n * n)], -1).reshape((-1, n, n)) b_l_ = np.concatenate([(i + 1) * b_l_0 for i in range(n * n)], -1).reshape((-1, n, n)) if dc_decomp: h_ = np.concatenate([(i + 1) * h_0 for i in range(n * n)], -1).reshape((-1, n, n)) g_ = np.concatenate([(i + 1) * g_0 for i in range(n * n)], -1).reshape((-1, n, n)) u_c_ = np.concatenate([(i + 1) * u_c_0 for i in range(n * n)], -1).reshape((-1, n, n)) l_c_ = np.concatenate([(i + 1) * l_c_0 for i in range(n * n)], -1).reshape((-1, n, n)) w_u_ = np.zeros((len(x_), 1, n * n)) w_l_ = np.zeros((len(x_), 1, n * n)) for i in range(n * n): w_u_[:, 0, i] = (i + 1) * w_u_0[:, 0, 0] w_l_[:, 0, i] = (i + 1) * w_l_0[:, 0, 0] w_u_ = w_u_.reshape((-1, 1, n, n)) w_l_ = w_l_.reshape((-1, 1, n, n)) if dc_decomp: return [x_, y_, z_, u_c_, w_u_, b_u_, l_c_, w_l_, b_l_, h_, g_] return [x_, y_, z_, u_c_, w_u_, b_u_, l_c_, w_l_, b_l_] def build_image_from_2D_box(odd=0, m0=0, m1=1, dc_decomp=True): if dc_decomp: ( x_0, y_0, z_0, u_c_0, w_u_0, b_u_0, l_c_0, w_l_0, b_l_0, h_0, g_0, ) = build_image_from_1D_box(odd, m0, dc_decomp) ( x_1, y_1, z_1, u_c_1, w_u_1, b_u_1, l_c_1, w_l_1, b_l_1, h_1, g_1, ) = build_image_from_1D_box(odd, m1, dc_decomp) else: ( x_0, y_0, z_0, u_c_0, w_u_0, b_u_0, l_c_0, w_l_0, b_l_0, ) = build_image_from_1D_box(odd, m0, dc_decomp) ( x_1, y_1, z_1, u_c_1, w_u_1, b_u_1, l_c_1, w_l_1, b_l_1, ) = build_image_from_1D_box(odd, m1, dc_decomp) x_ = np.concatenate([x_0, x_1], -1) z_min_ = np.concatenate([z_0[:, 0], z_1[:, 0]], -1) z_max_ = np.concatenate([z_0[:, 1], z_1[:, 1]], -1) z_ = np.concatenate([z_min_[:, None], z_max_[:, None]], 1) y_ = y_0 + y_1 b_u_ = b_u_0 + b_u_1 b_l_ = b_l_0 + b_l_1 u_c_ = u_c_0 + u_c_1 l_c_ = l_c_0 + l_c_1 w_u_ = np.concatenate([w_u_0, w_u_1], 1) w_l_ = np.concatenate([w_l_0, w_l_1], 1) if dc_decomp: h_ = h_0 + h_1 g_ = g_0 + g_1 assert_output_properties_box( x_, h_ + g_, h_, g_, z_[:, 0], z_[:, 1], u_c_, w_u_, b_u_, l_c_, w_l_, b_l_, "image from 2D", ) return [x_, y_, z_, u_c_, w_u_, b_u_, l_c_, w_l_, b_l_, h_, g_] return [x_, y_, z_, u_c_, w_u_, b_u_, l_c_, w_l_, b_l_] def get_standard_values_images_box(data_format="channels_last", odd=0, m0=0, m1=1, dc_decomp=True): output = build_image_from_2D_box(odd, m0, m1, dc_decomp) if dc_decomp: x_0, y_0, z_0, u_c_0, w_u_0, b_u_0, l_c_0, w_l_0, b_l_0, h_0, g_0 = output else: x_0, y_0, z_0, u_c_0, w_u_0, b_u_0, l_c_0, w_l_0, b_l_0 = output x_ = x_0 z_ = z_0 z_min_ = z_0[:, 0] z_max_ = z_0[:, 1] if data_format == "channels_last": y_0 = y_0[:, :, :, None] b_u_0 = b_u_0[:, :, :, None] b_l_0 = b_l_0[:, :, :, None] u_c_0 = u_c_0[:, :, :, None] l_c_0 = l_c_0[:, :, :, None] w_u_0 = w_u_0[:, :, :, :, None] w_l_0 = w_l_0[:, :, :, :, None] y_ = np.concatenate([y_0, y_0], -1) b_u_ = np.concatenate([b_u_0, b_u_0], -1) b_l_ = np.concatenate([b_l_0, b_l_0], -1) u_c_ = np.concatenate([u_c_0, u_c_0], -1) l_c_ = np.concatenate([l_c_0, l_c_0], -1) w_u_ = np.concatenate([w_u_0, w_u_0], -1) w_l_ = np.concatenate([w_l_0, w_l_0], -1) if dc_decomp: h_0 = h_0[:, :, :, None] g_0 = g_0[:, :, :, None] h_ = np.concatenate([h_0, h_0], -1) g_ = np.concatenate([g_0, g_0], -1) assert_output_properties_box( x_, y_, h_, g_, z_min_, z_max_, u_c_, w_u_, b_u_, l_c_, w_l_, b_l_, "images {},{},{},{}".format(data_format, odd, m0, m1), ) else: output = get_standard_values_images_box(data_format="channels_last", odd=odd, m0=m0, m1=m1, dc_decomp=dc_decomp) x_, y_, z_, u_c_, w_u_, b_u_, l_c_, w_l_, b_l_ = output[:9] if dc_decomp: h_, g_ = output[-2:] h_ = np.transpose(h_, (0, 3, 1, 2)) g_ = np.transpose(g_, (0, 3, 1, 2)) y_ = np.transpose(y_, (0, 3, 1, 2)) u_c_ = np.transpose(u_c_, (0, 3, 1, 2)) l_c_ = np.transpose(l_c_, (0, 3, 1, 2)) b_u_ = np.transpose(b_u_, (0, 3, 1, 2)) b_l_ = np.transpose(b_l_, (0, 3, 1, 2)) w_u_ = np.transpose(w_u_, (0, 1, 4, 2, 3)) w_l_ = np.transpose(w_l_, (0, 1, 4, 2, 3)) if dc_decomp: return [x_, y_, z_, u_c_, w_u_, b_u_, l_c_, w_l_, b_l_, h_, g_] else: return [x_, y_, z_, u_c_, w_u_, b_u_, l_c_, w_l_, b_l_] def get_tensor_decomposition_images_box(data_format, odd, dc_decomp=True): if odd: n = 7 else: n = 6 if data_format == "channels_last": # x, y, z, u, w_u, b_u, l, w_l, b_l output = [ Input((2,)), Input((n, n, 2)), Input((2, 2)), Input((n, n, 2)), Input((2, n, n, 2)), Input((n, n, 2)), Input((n, n, 2)), Input((2, n, n, 2)), Input((n, n, 2)), ] if dc_decomp: output += [Input((n, n, 2)), Input((n, n, 2))] else: output = [ Input((2,)), Input((2, n, n)), Input((2, 2)), Input((2, n, n)), Input((2, 2, n, n)), Input((2, n, n)), Input((2, n, n)), Input((2, 2, n, n)), Input((2, n, n)), ] if dc_decomp: output += [Input((n, n, 2)), Input((n, n, 2))] return output def assert_output_properties_box(x_, y_, h_, g_, x_min_, x_max_, u_c_, w_u_, b_u_, l_c_, w_l_, b_l_, name, decimal=5): if y_ is None: y_ = h_ + g_ if h_ is not None: assert_almost_equal( h_ + g_, y_, decimal=decimal, err_msg="decomposition error for function {}".format(name), ) assert np.min(x_min_ <= x_max_), "x_min >x_max for function {}".format(name) assert_almost_equal( np.clip(x_min_ - x_, 0, np.inf), 0.0, decimal=decimal, err_msg="x_min >x_ for function {}".format(name), ) assert_almost_equal( np.clip(x_ - x_max_, 0, np.inf), 0.0, decimal=decimal, err_msg="x_max < x_ for function {}".format(name), ) if w_u_ is not None: x_expand = x_ + np.zeros_like(x_) n_expand = len(w_u_.shape) - len(x_expand.shape) for i in range(n_expand): x_expand = np.expand_dims(x_expand, -1) lower_ = np.sum(w_l_ * x_expand, 1) + b_l_ upper_ = np.sum(w_u_ * x_expand, 1) + b_u_ # check that the functions h_ and g_ remains monotonic if h_ is not None: assert_almost_equal( np.clip(h_[:-1] - h_[1:], 0, np.inf), np.zeros_like(h_[1:]), decimal=decimal, err_msg="h is not increasing for function {}".format(name), ) assert_almost_equal( np.clip(g_[1:] - g_[:-1], 0, np.inf), np.zeros_like(g_[1:]), decimal=decimal, err_msg="g is not increasing for function {}".format(name), ) # if w_u_ is not None: assert_almost_equal( np.clip(lower_ - y_, 0.0, np.inf), np.zeros_like(y_), decimal=decimal, err_msg="lower_ >y", ) assert_almost_equal( np.clip(y_ - upper_, 0.0, 1e6), np.zeros_like(y_), decimal=decimal, err_msg="upper <y", ) if l_c_ is not None: assert_almost_equal( np.clip(l_c_ - y_, 0.0, np.inf), np.zeros_like(y_), decimal=decimal, err_msg="l_c >y", ) assert_almost_equal( np.clip(y_ - u_c_, 0.0, 1e6), np.zeros_like(y_), decimal=decimal, err_msg="u_c <y", ) # computer lower bounds on the domain if w_u_ is not None and l_c_ is not None: x_expand_min = x_min_ + np.zeros_like(x_) x_expand_max = x_max_ + np.zeros_like(x_) n_expand = len(w_u_.shape) - len(x_expand_min.shape) for i in range(n_expand): x_expand_min = np.expand_dims(x_expand_min, -1) x_expand_max = np.expand_dims(x_expand_max, -1) lower_ = np.sum(np.maximum(0, w_l_) * x_expand_min, 1) + np.sum(np.minimum(0, w_l_) * x_expand_max, 1) + b_l_ upper_ = np.sum(np.maximum(0, w_u_) * x_expand_max, 1) + np.sum(np.minimum(0, w_u_) * x_expand_min, 1) + b_u_ """ assert_almost_equal( np.clip(lower_.min(0) - l_c_.max(0), 0.0, np.inf), np.zeros_like(y_.min(0)), decimal=decimal, err_msg="lower_ >l_c", ) assert_almost_equal( np.clip(u_c_.min(0) - upper_.max(0), 0.0, 1e6), np.zeros_like(y_.min(0)), decimal=decimal, err_msg="upper <u_c", ) """ def assert_output_properties_box_linear(x_, y_, x_min_, x_max_, u_c_, w_u_, b_u_, l_c_, w_l_, b_l_, name, decimal=5): # flatten everything # flatten everyting n = len(x_) if y_ is not None: n = len(y_) y_ = y_.reshape((n, -1)) if l_c_ is not None: u_c_ = u_c_.reshape((n, -1)) l_c_ = l_c_.reshape((n, -1)) if w_u_ is not None: w_u_ = w_u_.reshape((n, w_u_.shape[1], -1)) w_l_ = w_l_.reshape((n, w_l_.shape[1], -1)) b_u_ = b_u_.reshape((n, -1)) b_l_ = b_l_.reshape((n, -1)) # assert_almost_equal(h_ + g_, y_, decimal=decimal, err_msg='decomposition error for function {}'.format(name)) assert np.min(x_min_ <= x_max_), "x_min >x_max for function {}".format(name) assert_almost_equal( np.clip(x_min_ - x_, 0, np.inf), 0.0, decimal=decimal, err_msg="x_min >x_ for function {}".format(name) ) assert_almost_equal( np.clip(x_ - x_max_, 0, np.inf), 0.0, decimal=decimal, err_msg="x_max < x_ for function {}".format(name) ) if w_u_ is not None: x_expand = x_ + np.zeros_like(x_) n_expand = len(w_u_.shape) - len(x_expand.shape) for i in range(n_expand): x_expand = np.expand_dims(x_expand, -1) lower_ = np.sum(w_l_ * x_expand, 1) + b_l_ upper_ = np.sum(w_u_ * x_expand, 1) + b_u_ # check that the functions h_ and g_ remains monotonic # assert_almost_equal(np.clip(h_[:-1] - h_[1:], 0, np.inf), np.zeros_like(h_[1:]), decimal=decimal, # err_msg='h is not increasing for function {}'.format(name)) # assert_almost_equal(np.clip(g_[1:] - g_[:-1], 0, np.inf), np.zeros_like(g_[1:]), decimal=decimal, # err_msg='g is not increasing for function {}'.format(name)) if y_ is not None: if l_c_ is not None: assert_almost_equal(np.clip(l_c_ - y_, 0.0, np.inf), np.zeros_like(y_), decimal=decimal, err_msg="l_c >y") assert_almost_equal(np.clip(y_ - u_c_, 0.0, 1e6), np.zeros_like(y_), decimal=decimal, err_msg="u_c <y") if w_u_ is not None: assert_almost_equal( np.clip(lower_ - y_, 0.0, np.inf), np.zeros_like(y_), decimal=decimal, err_msg="lower_ >y" ) assert_almost_equal(np.clip(y_ - upper_, 0.0, 1e6), np.zeros_like(y_), decimal=decimal, err_msg="upper <y") # computer lower bounds on the domain if w_u_ is not None: x_expand_min = x_min_ + np.zeros_like(x_) x_expand_max = x_max_ + np.zeros_like(x_) n_expand = len(w_u_.shape) - len(x_expand_min.shape) for i in range(n_expand): x_expand_min = np.expand_dims(x_expand_min, -1) x_expand_max = np.expand_dims(x_expand_max, -1) lower_ = np.sum(np.maximum(0, w_l_) * x_expand_min, 1) + np.sum(np.minimum(0, w_l_) * x_expand_max, 1) + b_l_ upper_ = np.sum(np.maximum(0, w_u_) * x_expand_max, 1) + np.sum(np.minimum(0, w_u_) * x_expand_min, 1) + b_u_ # import pdb; pdb.set_trace() if y_ is not None: assert_almost_equal(np.clip(lower_ - y_, 0.0, np.inf), np.zeros_like(y_), decimal=decimal, err_msg="l_c >y") assert_almost_equal(np.clip(y_ - upper_, 0.0, 1e6), np.zeros_like(y_), decimal=decimal, err_msg="u_c <y") # multi decomposition for convert def get_standard_values_multid_box_convert(odd=1, dc_decomp=True): if dc_decomp: ( x_0, y_0, z_0, u_c_0, w_u_0, b_u_0, l_c_0, w_l_0, b_l_0, h_0, g_0, ) = get_standart_values_1d_box(0, dc_decomp) ( x_1, y_1, z_1, u_c_1, w_u_1, b_u_1, l_c_1, w_l_1, b_l_1, h_1, g_1, ) = get_standart_values_1d_box(3, dc_decomp) else: ( x_0, y_0, z_0, u_c_0, w_u_0, b_u_0, l_c_0, w_l_0, b_l_0, ) = get_standart_values_1d_box(0, dc_decomp) ( x_1, y_1, z_1, u_c_1, w_u_1, b_u_1, l_c_1, w_l_1, b_l_1, ) = get_standart_values_1d_box(1, dc_decomp) if not odd: # output (x_0+x_1, x_0+2*x_0) (x_0, x_1) x_ = np.concatenate([x_0, x_1], -1) z_min_ = np.concatenate([z_0[:, 0], z_1[:, 0]], -1) z_max_ = np.concatenate([z_0[:, 1], z_1[:, 1]], -1) z_ = np.concatenate([z_min_[:, None], z_max_[:, None]], 1) y_ = np.concatenate([y_0, y_1], -1) b_u_ = np.concatenate([b_u_0, b_u_1], -1) u_c_ = np.concatenate([u_c_0, u_c_1], -1) b_l_ = np.concatenate([b_l_0, b_l_1], -1) l_c_ = np.concatenate([l_c_0, l_c_1], -1) if dc_decomp: h_ = np.concatenate([h_0, h_1], -1) g_ = np.concatenate([g_0, g_1], -1) w_u_ = np.zeros((len(x_), 2, 2)) w_u_[:, 0, 0] = w_u_0[:, 0, 0] w_u_[:, 1, 1] = w_u_1[:, 0, 0] w_l_ = np.zeros((len(x_), 2, 2)) w_l_[:, 0, 0] = w_l_0[:, 0, 0] w_l_[:, 1, 1] = w_l_1[:, 0, 0] else: if dc_decomp: ( x_2, y_2, z_2, u_c_2, w_u_2, b_u_2, l_c_2, w_l_2, b_l_2, h_2, g_2, ) = get_standart_values_1d_box(2, dc_decomp) else: (x_2, y_2, z_2, u_c_2, w_u_2, b_u_2, l_c_2, w_l_2, b_l_2) = get_standart_values_1d_box(2, dc_decomp) x_ = np.concatenate([x_0, x_1, x_2], -1) z_min_ = np.concatenate([z_0[:, 0], z_1[:, 0], z_2[:, 0]], -1) z_max_ = np.concatenate([z_0[:, 1], z_1[:, 1], z_2[:, 1]], -1) z_ = np.concatenate([z_min_[:, None], z_max_[:, None]], 1) y_ = np.concatenate([y_0, y_1, y_2], -1) b_u_ = np.concatenate([b_u_0, b_u_1, b_u_2], -1) b_l_ = np.concatenate([b_l_0, b_l_1, b_l_2], -1) u_c_ = np.concatenate([u_c_0, u_c_1, u_c_2], -1) l_c_ = np.concatenate([l_c_0, l_c_1, l_c_2], -1) if dc_decomp: h_ = np.concatenate([h_0, h_1, h_2], -1) g_ = np.concatenate([g_0, g_1, g_2], -1) w_u_ = np.zeros((len(x_), 3, 3)) w_u_[:, 0, 0] = w_u_0[:, 0, 0] w_u_[:, 1, 1] = w_u_1[:, 0, 0] w_u_[:, 2, 2] = w_u_2[:, 0, 0] w_l_ = np.zeros((len(x_), 3, 3)) w_l_[:, 0, 0] = w_l_0[:, 0, 0] w_l_[:, 1, 1] = w_l_1[:, 0, 0] w_l_[:, 2, 2] = w_l_2[:, 0, 0] if dc_decomp: return [x_, y_, z_, u_c_, w_u_, b_u_, l_c_, w_l_, b_l_, h_, g_] return [x_, y_, z_, u_c_, w_u_, b_u_, l_c_, w_l_, b_l_] def assert_output_properties_box_nodc(x_, y_, x_min_, x_max_, u_c_, w_u_, b_u_, l_c_, w_l_, b_l_, name, decimal=5): assert np.min(x_min_ <= x_max_), "x_min >x_max for function {}".format(name) assert_almost_equal( np.clip(x_min_ - x_, 0, np.inf), 0.0, decimal=decimal, err_msg="x_min >x_ for function {}".format(name), ) assert_almost_equal( np.clip(x_ - x_max_, 0, np.inf), 0.0, decimal=decimal, err_msg="x_max < x_ for function {}".format(name), ) x_expand = x_ + np.zeros_like(x_) n_expand = len(w_u_.shape) - len(x_expand.shape) for i in range(n_expand): x_expand = np.expand_dims(x_expand, -1) lower_ = np.sum(w_l_ * x_expand, 1) + b_l_ upper_ = np.sum(w_u_ * x_expand, 1) + b_u_ # check that the functions h_ and g_ remains monotonic assert_almost_equal( np.clip(l_c_ - y_, 0.0, np.inf), np.zeros_like(y_), decimal=decimal, err_msg="l_c >y", ) assert_almost_equal( np.clip(y_ - u_c_, 0.0, 1e6), np.zeros_like(y_), decimal=decimal, err_msg="u_c <y", ) # assert_almost_equal( np.clip(lower_ - y_, 0.0, np.inf), np.zeros_like(y_), decimal=decimal, err_msg="lower_ >y", ) assert_almost_equal( np.clip(y_ - upper_, 0.0, 1e6), np.zeros_like(y_), decimal=decimal, err_msg="upper <y", ) # computer lower bounds on the domain x_expand_min = x_min_ + np.zeros_like(x_) x_expand_max = x_max_ + np.zeros_like(x_) n_expand = len(w_u_.shape) - len(x_expand_min.shape) for i in range(n_expand): x_expand_min = np.expand_dims(x_expand_min, -1) x_expand_max = np.expand_dims(x_expand_max, -1) lower_ = np.sum(np.maximum(0, w_l_) * x_expand_min, 1) + np.sum(np.minimum(0, w_l_) * x_expand_max, 1) + b_l_ upper_ = np.sum(np.maximum(0, w_u_) * x_expand_max, 1) + np.sum(np.minimum(0, w_u_) * x_expand_min, 1) + b_u_ assert_almost_equal( np.clip(lower_.min(0) - l_c_.max(0), 0.0, np.inf), np.zeros_like(y_.min(0)), decimal=decimal, err_msg="lower_ >l_c", ) assert_almost_equal( np.clip(u_c_.min(0) - upper_.max(0), 0.0, 1e6), np.zeros_like(y_.min(0)), decimal=decimal, err_msg="upper <u_c", )

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py

Python

self_attention_cv/pos_embeddings/__init__.py

youtang1993/self-attention-cv

ae215541d3e33d39f26947924253a63585683226

[ "MIT" ]

1

2021-03-02T05:26:57.000Z

self_attention_cv/pos_embeddings/__init__.py

youtang1993/self-attention-cv

ae215541d3e33d39f26947924253a63585683226

[ "MIT" ]

null

self_attention_cv/pos_embeddings/__init__.py

youtang1993/self-attention-cv

ae215541d3e33d39f26947924253a63585683226

[ "MIT" ]

null

from .abs_pos_emb1D import AbsPosEmb1D from .relative_embeddings_1D import rel_pos_emb_1d, RelPosEmb1D from .relative_embeddings_2D import RelPosEmb2D from .relative_pos_enc_qkv import Relative2DPosEncQKV

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py

Python

pywemo/ouimeaux_device/outdoor_plug.py

sullivanmj/pywemo

2ef78e015476f5e983c1f39eefa3f274a4685bdd

[ "MIT" ]

102

2016-01-07T17:13:02.000Z

2021-03-11T09:04:20.000Z

pywemo/ouimeaux_device/outdoor_plug.py

sullivanmj/pywemo

2ef78e015476f5e983c1f39eefa3f274a4685bdd

[ "MIT" ]

188

2015-12-31T08:50:41.000Z

2021-03-10T16:51:26.000Z

pywemo/ouimeaux_device/outdoor_plug.py

sullivanmj/pywemo

2ef78e015476f5e983c1f39eefa3f274a4685bdd

[ "MIT" ]

61

2016-02-16T02:28:17.000Z

2021-03-07T23:05:38.000Z

"""Representation of a WeMo OutdoorPlug device.""" from .switch import Switch class OutdoorPlug(Switch): """Representation of a WeMo Motion device."""

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py

Python

version084/driving_benchmarks/corl2017/__init__.py

PDillis/driving-benchmarks

1614d19581d43eac9966e4001611cc8341bf5319

[ "MIT" ]

51

2018-11-28T15:30:05.000Z

2022-03-04T12:46:58.000Z

version084/driving_benchmarks/corl2017/__init__.py

Jeffgan99/driving-benchmarks

1614d19581d43eac9966e4001611cc8341bf5319

[ "MIT" ]

4

2019-05-08T10:58:11.000Z

2021-09-22T13:40:05.000Z

version084/driving_benchmarks/corl2017/__init__.py

Jeffgan99/driving-benchmarks

1614d19581d43eac9966e4001611cc8341bf5319

[ "MIT" ]

20

2019-02-10T09:41:22.000Z

2022-02-23T14:38:58.000Z

from .corl_2017 import CoRL2017

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d6df09d6a542900ab8dc7a138fe023f89bed3241

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py

Python

discordbot/migrations/0006_auto_20201022_1804.py

rafaelurben/django-discordbot

e0297b5d59446ae3185cbfd8ff410274174941a0

[ "MIT" ]

10

2020-08-04T11:38:02.000Z

2022-03-15T20:54:37.000Z

discordbot/migrations/0006_auto_20201022_1804.py

rafaelurben/django-discordbot

e0297b5d59446ae3185cbfd8ff410274174941a0

[ "MIT" ]

77

2020-08-04T10:17:05.000Z

2022-03-06T16:11:34.000Z

discordbot/migrations/0006_auto_20201022_1804.py

rafaelurben/django-discordbot

e0297b5d59446ae3185cbfd8ff410274174941a0

[ "MIT" ]

6

2020-10-30T18:24:12.000Z

2021-11-13T20:41:15.000Z

# Generated by Django 3.1.2 on 2020-10-22 16:04 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('discordbot', '0005_amongusgame_text_message_id'), ] operations = [ migrations.AlterField( model_name='amongusgame', name='p_black_exists', field=models.BooleanField(default=False, verbose_name='Black exists'), ), migrations.AlterField( model_name='amongusgame', name='p_blue_exists', field=models.BooleanField(default=False, verbose_name='Blue exists'), ), migrations.AlterField( model_name='amongusgame', name='p_brown_exists', field=models.BooleanField(default=False, verbose_name='Brown exists'), ), migrations.AlterField( model_name='amongusgame', name='p_cyan_exists', field=models.BooleanField(default=False, verbose_name='Cyan exists'), ), migrations.AlterField( model_name='amongusgame', name='p_green_exists', field=models.BooleanField(default=False, verbose_name='Green exists'), ), migrations.AlterField( model_name='amongusgame', name='p_lime_exists', field=models.BooleanField(default=False, verbose_name='Lime exists'), ), migrations.AlterField( model_name='amongusgame', name='p_orange_exists', field=models.BooleanField(default=False, verbose_name='Orange exists'), ), migrations.AlterField( model_name='amongusgame', name='p_pink_exists', field=models.BooleanField(default=False, verbose_name='Pink exists'), ), migrations.AlterField( model_name='amongusgame', name='p_purple_exists', field=models.BooleanField(default=False, verbose_name='Purple exists'), ), migrations.AlterField( model_name='amongusgame', name='p_red_exists', field=models.BooleanField(default=False, verbose_name='Red exists'), ), migrations.AlterField( model_name='amongusgame', name='p_white_exists', field=models.BooleanField(default=False, verbose_name='White exists'), ), migrations.AlterField( model_name='amongusgame', name='p_yellow_exists', field=models.BooleanField(default=False, verbose_name='Yellow exists'), ), ]

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ba465caa6cc01188b62dd1ba11b67d6bc5da6abf

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py

Python

events/element_events.py

mtasa-typescript/mtasa-wiki-dump

edea1746850fb6c99d6155d1d7891e2cceb33a5c

[ "MIT" ]

null

events/element_events.py

mtasa-typescript/mtasa-wiki-dump

edea1746850fb6c99d6155d1d7891e2cceb33a5c

[ "MIT" ]

1

2021-02-24T21:50:18.000Z

events/element_events.py

mtasa-typescript/mtasa-wiki-dump

edea1746850fb6c99d6155d1d7891e2cceb33a5c

[ "MIT" ]

null

# Autogenerated file. ANY CHANGES WILL BE OVERWRITTEN from to_python.core.types import FunctionType, \ FunctionArgument, \ FunctionArgumentValues, \ FunctionReturnTypes, \ FunctionSignature, \ FunctionDoc, \ EventData, \ CompoundEventData DUMP_PARTIAL = [ CompoundEventData( server=[ ], client=[ EventData( name='onClientElementColShapeHit', docs=FunctionDoc( description='This event is triggered when an element (like a player or vehicle) enters a collision shape.' , arguments={ "theShape": """the colshape that the element entered. """, "matchingDimension": """true if the element is in the same dimension as the colshape, false otherwise. """ }, result='' , ), arguments=FunctionArgumentValues( arguments=[ [ FunctionArgument( name='theShape', argument_type=FunctionType( names=['colshape'], is_optional=False, ), default_value=None, ) ], [ FunctionArgument( name='matchingDimension', argument_type=FunctionType( names=['bool'], is_optional=False, ), default_value=None, ) ] ], variable_length=False, ), ) ], ), CompoundEventData( server=[ ], client=[ EventData( name='onClientElementColShapeLeave', docs=FunctionDoc( description='This event is triggered when an element (like a player or vehicle) leaves a collision shape.' , arguments={ "theShape": """the colshape that the element left. """, "matchingDimension": """true if the element is in the same dimension as the colshape, false otherwise. """ }, result='' , ), arguments=FunctionArgumentValues( arguments=[ [ FunctionArgument( name='theShape', argument_type=FunctionType( names=['colshape'], is_optional=False, ), default_value=None, ) ], [ FunctionArgument( name='matchingDimension', argument_type=FunctionType( names=['bool'], is_optional=False, ), default_value=None, ) ] ], variable_length=False, ), ) ], ), CompoundEventData( server=[ ], client=[ EventData( name='onClientElementDataChange', docs=FunctionDoc( description='This event is triggered after an elements element data|data entry is changed. Such changes can be made on the client or the server using setElementData.' , arguments={ "theKey": """: The name of the element data entry that has changed. """, "oldValue": """: The old value of this entry before it changed. See element data for a list of possible datatypes. """, "newValue": """: the new value of this entry after it changed. This will be equivalent to getElementData(source, theKey). """ }, result='' , ), arguments=FunctionArgumentValues( arguments=[ [ FunctionArgument( name='theKey', argument_type=FunctionType( names=['string'], is_optional=False, ), default_value=None, ) ], [ FunctionArgument( name='oldValue', argument_type=FunctionType( names=['var'], is_optional=False, ), default_value=None, ) ], [ FunctionArgument( name='newValue', argument_type=FunctionType( names=['var'], is_optional=False, ), default_value=None, ) ] ], variable_length=False, ), ) ], ), CompoundEventData( server=[ ], client=[ EventData( name='onClientElementDestroy', docs=FunctionDoc( description='This event is triggered when an element gets destroyed by destroyElement or when the creator resource is stopping. It is also triggered when a children element of this element is destroyed. It is not triggered on a player when they quit.' , arguments={ }, result='' , ), arguments=FunctionArgumentValues( arguments=[ ], variable_length=False, ), ) ], ), CompoundEventData( server=[ ], client=[ EventData( name='onClientElementDimensionChange', docs=FunctionDoc( description='' , arguments={ "oldDimension": """: An int representing the dimension the element was in before. """, "newDimension": """: An int representing the dimension the element is in now. """ }, result='' , ), arguments=FunctionArgumentValues( arguments=[ [ FunctionArgument( name='oldDimension', argument_type=FunctionType( names=['int'], is_optional=False, ), default_value=None, ) ], [ FunctionArgument( name='newDimension', argument_type=FunctionType( names=['int'], is_optional=False, ), default_value=None, ) ] ], variable_length=False, ), ) ], ), CompoundEventData( server=[ ], client=[ EventData( name='onClientElementInteriorChange', docs=FunctionDoc( description='' , arguments={ "oldInterior": """: An int representing the interior the element was in before. """, "newInterior": """: An int representing the interior the element is in now. """ }, result='' , ), arguments=FunctionArgumentValues( arguments=[ [ FunctionArgument( name='oldInterior', argument_type=FunctionType( names=['int'], is_optional=False, ), default_value=None, ) ], [ FunctionArgument( name='newInterior', argument_type=FunctionType( names=['int'], is_optional=False, ), default_value=None, ) ] ], variable_length=False, ), ) ], ), CompoundEventData( server=[ ], client=[ EventData( name='onClientElementModelChange', docs=FunctionDoc( description='' , arguments={ "oldModel": """an int representing the model of the element before the change occurred. """, "newModel": """an int representing the new model of the element. """ }, result='' , ), arguments=FunctionArgumentValues( arguments=[ [ FunctionArgument( name='oldModel', argument_type=FunctionType( names=['int'], is_optional=False, ), default_value=None, ) ], [ FunctionArgument( name='newModel', argument_type=FunctionType( names=['int'], is_optional=False, ), default_value=None, ) ] ], variable_length=False, ), ) ], ), CompoundEventData( server=[ ], client=[ EventData( name='onClientElementStreamIn', docs=FunctionDoc( description='This event is triggered whenever a physical element is streamed in. This is triggered for all elements that are streamable, such as players, peds, vehicles, objects and markers. When this event is triggered, that element is guaranteed to be physically created as a GTA object.' , arguments={ }, result='' , ), arguments=FunctionArgumentValues( arguments=[ ], variable_length=False, ), ) ], ), CompoundEventData( server=[ ], client=[ EventData( name='onClientElementStreamOut', docs=FunctionDoc( description='This event is triggered whenever a physical element is streamed out. This is triggered for all elements that are streamable, such as players, peds, vehicles, objects and markers when the local player is leaving the element. When this event is triggered, that element is no longer physical and is now virtualized by MTA.' , arguments={ }, result='' , ), arguments=FunctionArgumentValues( arguments=[ ], variable_length=False, ), ) ], ), CompoundEventData( server=[ EventData( name='onElementClicked', docs=FunctionDoc( description='This event is triggered when an element is clicked on by the client. These events can only trigger when the client has its cursor enabled. It triggers for all three mousebuttons in both their up and down states.' , arguments={ "mouseButton": """: a string representing the mouse button that was clicked. This might be left, middle or right. """, "buttonState": """: a string representing what state the button clicked is in. This might be up or down. """, "playerWhoClicked": """: the player that clicked on the element. """, "clickPosX": """: the X position in the world the player clicked at. """, "clickPosY": """: the Y position in the world the player clicked at. """, "clickPosZ": """: the Z position in the world the player clicked at. """ }, result='' , ), arguments=FunctionArgumentValues( arguments=[ [ FunctionArgument( name='mouseButton', argument_type=FunctionType( names=['string'], is_optional=False, ), default_value=None, ) ], [ FunctionArgument( name='buttonState', argument_type=FunctionType( names=['string'], is_optional=False, ), default_value=None, ) ], [ FunctionArgument( name='playerWhoClicked', argument_type=FunctionType( names=['player'], is_optional=False, ), default_value=None, ) ], [ FunctionArgument( name='clickPosX', argument_type=FunctionType( names=['float'], is_optional=False, ), default_value=None, ) ], [ FunctionArgument( name='clickPosY', argument_type=FunctionType( names=['float'], is_optional=False, ), default_value=None, ) ], [ FunctionArgument( name='clickPosZ', argument_type=FunctionType( names=['float'], is_optional=False, ), default_value=None, ) ] ], variable_length=False, ), ) ], client=[ ], ), CompoundEventData( server=[ EventData( name='onElementColShapeHit', docs=FunctionDoc( description='This event is triggered when an player or vehicle element collides with a colshape.' , arguments={ "theColShape": """: the colshape that this element collided with. """, "matchingDimension": """: a boolean representing if the element and the colshape are in the same dimension. """ }, result='' , ), arguments=FunctionArgumentValues( arguments=[ [ FunctionArgument( name='theColShape', argument_type=FunctionType( names=['colshape'], is_optional=False, ), default_value=None, ) ], [ FunctionArgument( name='matchingDimension', argument_type=FunctionType( names=['bool'], is_optional=False, ), default_value=None, ) ] ], variable_length=False, ), ) ], client=[ ], ), CompoundEventData( server=[ EventData( name='onElementColShapeLeave', docs=FunctionDoc( description='This event is triggered when an player or vehicle element leaves the area of a colshape.' , arguments={ "theColShape": """: the colshape that this element left the area of. """, "matchingDimension": """: a boolean representing if the element and the colshape are in the same dimension. """ }, result='' , ), arguments=FunctionArgumentValues( arguments=[ [ FunctionArgument( name='theColShape', argument_type=FunctionType( names=['colshape'], is_optional=False, ), default_value=None, ) ], [ FunctionArgument( name='matchingDimension', argument_type=FunctionType( names=['bool'], is_optional=False, ), default_value=None, ) ] ], variable_length=False, ), ) ], client=[ ], ), CompoundEventData( server=[ EventData( name='onElementDataChange', docs=FunctionDoc( description='This event is triggered after an elements element data|data entry is changed. Such changes can be made on the client or the server using setElementData.' , arguments={ "theKey": """: The name of the element data entry that has changed. """, "oldValue": """: The old value of this entry before it changed. See element data for a list of possible datatypes. """, "newValue": """: the new value of this entry after it changed. This will be equivalent to getElementData(source, theKey). """, "source": """: The event system#Event source|source of this event is the element whose element data changed. """, "client": """: The event system#Event client|client global variable is set to the client that called setElementData, or nil if it was called on the server. """, "sourceResource": """: The resource which changed the element data. (Only works in versions above 1.3.4-5937) """ }, result='' , ), arguments=FunctionArgumentValues( arguments=[ [ FunctionArgument( name='theKey', argument_type=FunctionType( names=['string'], is_optional=False, ), default_value=None, ) ], [ FunctionArgument( name='oldValue', argument_type=FunctionType( names=['var'], is_optional=False, ), default_value=None, ) ], [ FunctionArgument( name='newValue', argument_type=FunctionType( names=['var'], is_optional=False, ), default_value=None, ) ] ], variable_length=False, ), ) ], client=[ ], ), CompoundEventData( server=[ EventData( name='onElementDestroy', docs=FunctionDoc( description='This event is triggered when an element gets destroyed by destroyElement or when the creator resource is stopping. It is also triggered when a parent element of this element is destroyed.' , arguments={ }, result='' , ), arguments=FunctionArgumentValues( arguments=[ ], variable_length=False, ), ) ], client=[ ], ), CompoundEventData( server=[ EventData( name='onElementDimensionChange', docs=FunctionDoc( description='' , arguments={ "oldDimension": """: An int representing the dimension the element was in before. """, "newDimension": """: An int representing the dimension the element is in now. """ }, result='' , ), arguments=FunctionArgumentValues( arguments=[ [ FunctionArgument( name='oldDimension', argument_type=FunctionType( names=['int'], is_optional=False, ), default_value=None, ) ], [ FunctionArgument( name='newDimension', argument_type=FunctionType( names=['int'], is_optional=False, ), default_value=None, ) ] ], variable_length=False, ), ) ], client=[ ], ), CompoundEventData( server=[ EventData( name='onElementInteriorChange', docs=FunctionDoc( description='' , arguments={ "oldInterior": """: an int representing the interior the element was in before. """, "newInterior": """: an int representing the interior the element is in now. """ }, result='' , ), arguments=FunctionArgumentValues( arguments=[ [ FunctionArgument( name='oldInterior', argument_type=FunctionType( names=['int'], is_optional=False, ), default_value=None, ) ], [ FunctionArgument( name='newInterior', argument_type=FunctionType( names=['int'], is_optional=False, ), default_value=None, ) ] ], variable_length=False, ), ) ], client=[ ], ), CompoundEventData( server=[ EventData( name='onElementModelChange', docs=FunctionDoc( description='This event is triggered when the model of an element is changed using setElementModel.' , arguments={ "oldModel": """an int representing the model of the element before the change occurred. """, "newModel": """an int representing the new model of the element. """ }, result='' , ), arguments=FunctionArgumentValues( arguments=[ [ FunctionArgument( name='oldModel', argument_type=FunctionType( names=['int'], is_optional=False, ), default_value=None, ) ], [ FunctionArgument( name='newModel', argument_type=FunctionType( names=['int'], is_optional=False, ), default_value=None, ) ] ], variable_length=False, ), ) ], client=[ ], ), CompoundEventData( server=[ EventData( name='onElementStartSync', docs=FunctionDoc( description='This event is triggered when an element becomes synced by a player.' , arguments={ "newSyncer": """: a player element representing the player who is now syncing the element. """ }, result='' , ), arguments=FunctionArgumentValues( arguments=[ [ FunctionArgument( name='newSyncer', argument_type=FunctionType( names=['player'], is_optional=False, ), default_value=None, ) ] ], variable_length=False, ), ) ], client=[ ], ), CompoundEventData( server=[ EventData( name='onElementStopSync', docs=FunctionDoc( description='This event is triggered when an element is no longer synced by a player.' , arguments={ "oldSyncer": """: a player element representing the last player who was syncing the element. """ }, result='' , ), arguments=FunctionArgumentValues( arguments=[ [ FunctionArgument( name='oldSyncer', argument_type=FunctionType( names=['player'], is_optional=False, ), default_value=None, ) ] ], variable_length=False, ), ) ], client=[ ], ) ]

38.730238

351

0.351659

1,667

30,868

6.437912

0.141572

0.063362

0.076034

0.091875

0.832836

0.819046

0.807119

0.773202

0.747484

0

0.000541

0.580601

30,868

796

352

38.778894

0.828441

0.001652

0

0.749674

1

0.016949

0.19643

0.011228

0

1

0

false

0

0.001304

0

0.001304

0

null

0

1

0

1

0

1

0

null

0

9

ba500b9f93f1f75c4b5871d32b4dd0cf28627abb

710

py

Python

Connect-Four-Game/server/test.py

MoamenSoroor/Connect-Four-Game

3d79adbde8c3987377313d03159aa0ff4d4983aa

[ "MIT" ]

null

Connect-Four-Game/server/test.py

MoamenSoroor/Connect-Four-Game

3d79adbde8c3987377313d03159aa0ff4d4983aa

[ "MIT" ]

null

Connect-Four-Game/server/test.py

MoamenSoroor/Connect-Four-Game

3d79adbde8c3987377313d03159aa0ff4d4983aa

[ "MIT" ]

null

mat = [[x for x in range(4)], [x for x in range(4,8)], [x for x in range(8,12)], [x for x in range(12,16)]] print(mat) d = 4 for k in range(0,d): print("d: ",end=" ") row , col = range(0,k + 1), range(k,-1,-1) for i,j in zip(row,col): print(mat[i][j],end =" ") print("") for k in range(0,d): print("d: ",end=" ") row , col = range(d-k-1 , d + 1 ), range(0,k + 1) for i,j in zip(row,col): print(mat[i][j],end =" ") print("") for k in range(0,d): print("d: ",end=" ") row , col = range(0,k + 1), range(d-k-1 , d + 1 ) for i,j in zip(row,col): print(mat[i][j],end =" ") print("")

21.515152

54

0.435211

132

710

2.340909

0.151515

0.158576

0.064725

0.090615

0.902913

0.825243

0.711974

0

0.055085

0.335211

710

33

55

21.515152

0.599576

0

0.625

0

0.022124

0

1

0

false

0

0.416667

0

null

0

1

0

null

0

1

0

7

baabcee16847dad5f5c885a6b1b3bc696f21b07c

4,059

py

Python

z2/part2/interactive/jm/random_normal_1/777411348.py

kozakusek/ipp-2020-testy

09aa008fa53d159672cc7cbf969a6b237e15a7b8

[ "MIT" ]

1

2020-04-16T12:13:47.000Z

z2/part2/interactive/jm/random_normal_1/777411348.py

kozakusek/ipp-2020-testy

09aa008fa53d159672cc7cbf969a6b237e15a7b8

[ "MIT" ]

18

2020-03-06T17:50:15.000Z

2020-05-19T14:58:30.000Z

z2/part2/interactive/jm/random_normal_1/777411348.py

kozakusek/ipp-2020-testy

09aa008fa53d159672cc7cbf969a6b237e15a7b8

[ "MIT" ]

18

2020-03-06T17:45:13.000Z

2020-06-09T19:18:31.000Z

from part1 import ( gamma_board, gamma_busy_fields, gamma_delete, gamma_free_fields, gamma_golden_move, gamma_golden_possible, gamma_move, gamma_new, ) """ scenario: test_random_actions uuid: 777411348 """ """ random actions, total chaos """ board = gamma_new(4, 6, 5, 2) assert board is not None assert gamma_move(board, 1, 3, 0) == 1 assert gamma_move(board, 1, 0, 3) == 1 assert gamma_move(board, 2, 3, 2) == 1 assert gamma_move(board, 3, 4, 1) == 0 assert gamma_move(board, 4, 3, 3) == 1 assert gamma_busy_fields(board, 4) == 1 assert gamma_golden_possible(board, 4) == 1 assert gamma_move(board, 5, 2, 5) == 1 assert gamma_move(board, 5, 3, 3) == 0 assert gamma_move(board, 1, 2, 0) == 1 assert gamma_move(board, 2, 3, 2) == 0 assert gamma_move(board, 3, 0, 1) == 1 assert gamma_move(board, 4, 2, 0) == 0 assert gamma_move(board, 4, 0, 0) == 1 assert gamma_move(board, 1, 3, 2) == 0 assert gamma_golden_possible(board, 1) == 1 assert gamma_move(board, 3, 4, 3) == 0 assert gamma_move(board, 4, 2, 0) == 0 assert gamma_move(board, 4, 1, 5) == 0 board593198159 = gamma_board(board) assert board593198159 is not None assert board593198159 == ("..5.\n" "....\n" "1..4\n" "...2\n" "3...\n" "4.11\n") del board593198159 board593198159 = None assert gamma_move(board, 5, 5, 3) == 0 assert gamma_move(board, 5, 2, 2) == 1 assert gamma_move(board, 1, 4, 0) == 0 assert gamma_move(board, 1, 3, 1) == 1 assert gamma_move(board, 2, 0, 2) == 1 assert gamma_move(board, 2, 3, 5) == 0 assert gamma_move(board, 3, 4, 0) == 0 assert gamma_move(board, 4, 0, 3) == 0 assert gamma_golden_possible(board, 4) == 1 assert gamma_move(board, 5, 4, 0) == 0 assert gamma_move(board, 5, 0, 0) == 0 assert gamma_move(board, 1, 4, 0) == 0 assert gamma_move(board, 1, 2, 0) == 0 assert gamma_move(board, 2, 0, 1) == 0 assert gamma_move(board, 3, 5, 3) == 0 assert gamma_free_fields(board, 3) == 13 assert gamma_move(board, 4, 0, 1) == 0 assert gamma_move(board, 4, 0, 3) == 0 assert gamma_golden_possible(board, 4) == 1 assert gamma_move(board, 5, 4, 2) == 0 assert gamma_move(board, 1, 1, 0) == 1 assert gamma_move(board, 1, 2, 3) == 0 assert gamma_free_fields(board, 1) == 4 assert gamma_move(board, 2, 3, 1) == 0 assert gamma_move(board, 2, 1, 0) == 0 assert gamma_move(board, 3, 4, 3) == 0 assert gamma_move(board, 3, 1, 2) == 1 assert gamma_move(board, 4, 1, 2) == 0 assert gamma_move(board, 5, 1, 1) == 0 assert gamma_move(board, 5, 2, 1) == 1 board255548095 = gamma_board(board) assert board255548095 is not None assert board255548095 == ("..5.\n" "....\n" "1..4\n" "2352\n" "3.51\n" "4111\n") del board255548095 board255548095 = None assert gamma_move(board, 1, 5, 1) == 0 assert gamma_move(board, 2, 3, 2) == 0 assert gamma_move(board, 2, 3, 3) == 0 assert gamma_move(board, 3, 4, 2) == 0 assert gamma_move(board, 3, 0, 5) == 0 assert gamma_move(board, 4, 4, 3) == 0 assert gamma_move(board, 5, 3, 2) == 0 assert gamma_move(board, 5, 3, 2) == 0 assert gamma_move(board, 1, 4, 0) == 0 assert gamma_move(board, 1, 0, 0) == 0 assert gamma_move(board, 2, 0, 5) == 0 assert gamma_move(board, 2, 3, 3) == 0 assert gamma_golden_possible(board, 2) == 1 assert gamma_move(board, 3, 4, 1) == 0 assert gamma_move(board, 3, 1, 3) == 1 assert gamma_move(board, 4, 5, 0) == 0 assert gamma_move(board, 4, 2, 2) == 0 assert gamma_move(board, 5, 0, 4) == 0 assert gamma_move(board, 1, 5, 1) == 0 assert gamma_move(board, 2, 4, 0) == 0 assert gamma_move(board, 3, 4, 0) == 0 assert gamma_move(board, 3, 0, 3) == 0 assert gamma_free_fields(board, 3) == 3 assert gamma_move(board, 4, 4, 1) == 0 assert gamma_move(board, 4, 3, 0) == 0 assert gamma_free_fields(board, 4) == 2 assert gamma_move(board, 5, 4, 1) == 0 assert gamma_move(board, 5, 2, 1) == 0 assert gamma_move(board, 1, 5, 1) == 0 assert gamma_golden_possible(board, 1) == 1 assert gamma_move(board, 2, 4, 0) == 0 assert gamma_free_fields(board, 2) == 0 assert gamma_move(board, 3, 4, 1) == 0 gamma_delete(board)

30.75

44

0.651392

748

4,059

3.385027

0.054813

0.35624

0.414692

0.552923

0.800158

0.786335

0.703002

0.528041

0.445498

0.415087

0

0.130408

0.18576

4,059

131

45

30.984733

0.635703

0

0.295652

0

0.018131

0

0.756522

1

0

false

0

0.008696

0

0.008696

0

null

1

0

null

0

1

0

7

245293fbcfc13f33936b1553865a396e8f15bae3

133

py

Python

examples/dict.update/ex3.py

mcorne/python-by-example

15339c0909c84b51075587a6a66391100971c033

[ "MIT" ]

null

examples/dict.update/ex3.py

mcorne/python-by-example

15339c0909c84b51075587a6a66391100971c033

[ "MIT" ]

null

examples/dict.update/ex3.py

mcorne/python-by-example

15339c0909c84b51075587a6a66391100971c033

[ "MIT" ]

null

# d.update((['two', 'II'], ['four', 4])) d = {'one': 1, 'two': 2, 'three': 3} print(d.update((['two', 'II'], ['four', 4]))) print(d)

26.6

45

0.451128

22

133

2.727273

0.545455

0.233333

0.333333

0.4

0.566667

0

0.043478

0.135338

133

4

46

33.25

0.478261

0.285714

0

0.215054

0

1

0

false

0

0.666667

1

0

null

1

0

1

0

1

0

null

0

1

0

7

030889bb18b3d10a9678dde5f9838c3287e73d92

84

py

Python

URI/1 - INICIANTE/Python/1019 - ConversaoTempo.py

william-james-pj/LogicaProgramacao

629f746e34da2e829dc7ea2e489ac36bb1b1fb13

[ "MIT" ]

1

2020-04-14T16:48:16.000Z

URI/1 - INICIANTE/Python/1019 - ConversaoTempo.py

william-james-pj/LogicaProgramacao

629f746e34da2e829dc7ea2e489ac36bb1b1fb13

[ "MIT" ]

null

URI/1 - INICIANTE/Python/1019 - ConversaoTempo.py

william-james-pj/LogicaProgramacao

629f746e34da2e829dc7ea2e489ac36bb1b1fb13

[ "MIT" ]

null

s = int(input()) print('{}:{}:{}'.format(s//3600, (s - (3600*(s//3600)))//60,s%60))

28

66

0.47619

14

84

2.857143

0.5

0.375

0.3

0.5

0

0.207792

0.083333

84

3

66

28

0.311688

0

0.094118

0

1

0

false

0

0.5

1

0

null

1

0

1

0

1

0

1

0

null

0

1

0

8

301450ee5c0a83ecae49eb50791998c00aeeca93

6,231

py

Python

envs/environments.py

hpi-sam/rl-4-self-repair

8a33b133346bf49a9398a024d8cdb59357a61653

[ "MIT" ]

null

envs/environments.py

hpi-sam/rl-4-self-repair

8a33b133346bf49a9398a024d8cdb59357a61653

[ "MIT" ]

19

2020-05-26T10:25:39.000Z

2020-07-04T21:43:38.000Z

envs/environments.py

hpi-sam/bandits-4-self-repair

6d30b37162c37b322d9ad27bc043bf567a3631cc

[ "MIT" ]

1

2022-01-06T08:47:09.000Z

import sys def check_environment(data_generation: str, take_component_id: bool, transformation: str, distinguishable: bool): component_selector = 'Optimal_Affected_Component_Uid' if take_component_id else 'Optimal_Affected_Component' if (data_generation, component_selector, transformation, distinguishable) in ENVIRONMENTS: i = '_id' if (take_component_id and (data_generation in ['LinearShifted', 'ARol', 'GARCH'])) else '' t = '_' + transformation if (data_generation in ['ARol', 'GARCH']) or distinguishable else '' d = '_dist' if distinguishable else '' filename = 'data/prepared_data/' + data_generation + i + t + d + '.csv' return (data_generation, component_selector, transformation, distinguishable), filename else: print('This environment does not exist. Please have a look on the "environments.py" file.') sys.exit(0) ENVIRONMENTS = [('Linear', 'Optimal_Affected_Component', 'raw', False), ('Linear', 'Optimal_Affected_Component_Uid', 'raw', False), ('Linear', 'Optimal_Affected_Component', 'sqt', False), ('Linear', 'Optimal_Affected_Component_Uid', 'sqt', False), ('Linear', 'Optimal_Affected_Component', 'cube', False), ('Linear', 'Optimal_Affected_Component_Uid', 'cube', False), ('Linear', 'Optimal_Affected_Component', 'log10', False), ('Linear', 'Optimal_Affected_Component_Uid', 'log10', False), ('Linear', 'Optimal_Affected_Component', 'ln', False), ('Linear', 'Optimal_Affected_Component_Uid', 'ln', False), ('Linear', 'Optimal_Affected_Component', 'log2', False), ('Linear', 'Optimal_Affected_Component_Uid', 'log2', False), ('Saturating', 'Optimal_Affected_Component', 'raw', False), ('Saturating', 'Optimal_Affected_Component_Uid', 'raw', False), ('Saturating', 'Optimal_Affected_Component', 'sqt', False), ('Saturating', 'Optimal_Affected_Component_Uid', 'sqt', False), ('Saturating', 'Optimal_Affected_Component', 'cube', False), ('Saturating', 'Optimal_Affected_Component_Uid', 'cube', False), ('Saturating', 'Optimal_Affected_Component', 'log10', False), ('Saturating', 'Optimal_Affected_Component_Uid', 'log10', False), ('Saturating', 'Optimal_Affected_Component', 'ln', False), ('Saturating', 'Optimal_Affected_Component_Uid', 'ln', False), ('Saturating', 'Optimal_Affected_Component', 'log2', False), ('Saturating', 'Optimal_Affected_Component_Uid', 'log2', False), ('Combined', 'Optimal_Affected_Component', 'raw', False), ('Combined', 'Optimal_Affected_Component_Uid', 'raw', False), ('Combined', 'Optimal_Affected_Component', 'sqt', False), ('Combined', 'Optimal_Affected_Component_Uid', 'sqt', False), ('Combined', 'Optimal_Affected_Component', 'cube', False), ('Combined', 'Optimal_Affected_Component_Uid', 'cube', False), ('Combined', 'Optimal_Affected_Component', 'log10', False), ('Combined', 'Optimal_Affected_Component_Uid', 'log10', False), ('Combined', 'Optimal_Affected_Component', 'ln', False), ('Combined', 'Optimal_Affected_Component_Uid', 'ln', False), ('Combined', 'Optimal_Affected_Component', 'log2', False), ('Combined', 'Optimal_Affected_Component_Uid', 'log2', False), ('Discontinuous', 'Optimal_Affected_Component', 'raw', False), ('Discontinuous', 'Optimal_Affected_Component_Uid', 'raw', False), ('Discontinuous', 'Optimal_Affected_Component', 'sqt', False), ('Discontinuous', 'Optimal_Affected_Component_Uid', 'sqt', False), ('Discontinuous', 'Optimal_Affected_Component', 'cube', False), ('Discontinuous', 'Optimal_Affected_Component_Uid', 'cube', False), ('Discontinuous', 'Optimal_Affected_Component', 'log10', False), ('Discontinuous', 'Optimal_Affected_Component_Uid', 'log10', False), ('Discontinuous', 'Optimal_Affected_Component', 'ln', False), ('Discontinuous', 'Optimal_Affected_Component_Uid', 'ln', False), ('Discontinuous', 'Optimal_Affected_Component', 'log2', False), ('Discontinuous', 'Optimal_Affected_Component_Uid', 'log2', False), #-------------------- Linear Shifted Data --------------------# ('LinearShifted', 'Optimal_Affected_Component_Uid', 'raw', False), ('LinearShifted', 'Optimal_Affected_Component_Uid', 'sqt', False), ('LinearShifted', 'Optimal_Affected_Component_Uid', 'cube', False), ('LinearShifted', 'Optimal_Affected_Component_Uid', 'log10', False), ('LinearShifted', 'Optimal_Affected_Component_Uid', 'ln', False), ('LinearShifted', 'Optimal_Affected_Component_Uid', 'log2', False), #-------------------- Non-stationary Data --------------------# ('ARol', 'Optimal_Affected_Component_Uid', 'raw', False), ('ARol', 'Optimal_Affected_Component_Uid', 'sqt', False), ('ARol', 'Optimal_Affected_Component_Uid', 'cube', False), ('ARol', 'Optimal_Affected_Component_Uid', 'log10', False), ('ARol', 'Optimal_Affected_Component_Uid', 'ln', False), ('ARol', 'Optimal_Affected_Component_Uid', 'log2', False), ('GARCH', 'Optimal_Affected_Component_Uid', 'raw', False), ('GARCH', 'Optimal_Affected_Component_Uid', 'sqt', False), ('GARCH', 'Optimal_Affected_Component_Uid', 'cube', False), ('GARCH', 'Optimal_Affected_Component_Uid', 'log10', False), ('GARCH', 'Optimal_Affected_Component_Uid', 'ln', False), ('GARCH', 'Optimal_Affected_Component_Uid', 'log2', False) ]

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1

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null

0

8

3029302cf83f4af88c41c6729f4e46a399ae4581

1,146

py

Python

seq2seq_construction/__init__.py

HKUNLP/UnifiedSKG

49a2ff950bb312b980c22ad72b11520db72ab6a3

[ "Apache-2.0" ]

191

2021-12-14T11:33:09.000Z

2022-03-31T09:20:41.000Z

seq2seq_construction/__init__.py

HKUNLP/UnifiedSKG

49a2ff950bb312b980c22ad72b11520db72ab6a3

[ "Apache-2.0" ]

7

2022-01-20T05:41:51.000Z

2022-03-20T06:43:22.000Z

seq2seq_construction/__init__.py

HKUNLP/UnifiedSKG

49a2ff950bb312b980c22ad72b11520db72ab6a3

[ "Apache-2.0" ]

22

2021-12-14T12:59:42.000Z

2022-03-29T03:45:51.000Z

# # class TrainDataset(Dataset): # # def __init__(self, args, raw_datasets): # # This tab processor is for table truncation and linearize. # self.raw_datasets = raw_datasets # # def __getitem__(self, index) -> T_co: # raw_data = self.raw_datasets[index] # # return raw_data.update({"struct_in": struct_in, "text_in": text_in, "seq_out": seq_out}) # # # class DevDataset(Dataset): # # def __init__(self, args, raw_datasets): # # This tab processor is for table truncation and linearize. # self.raw_datasets = raw_datasets # # def __getitem__(self, index): # raw_data = self.raw_datasets[index] # # return raw_data.update({"struct_in": struct_in, "text_in": text_in, "seq_out": seq_out}) # # # class TestDataset(Dataset): # # def __init__(self, args, raw_datasets): # # This tab processor is for table truncation and linearize. # self.raw_datasets = raw_datasets # # def __getitem__(self, index): # raw_data = self.raw_datasets[index] # # return raw_data.update({"struct_in": struct_in, "text_in": text_in, "seq_out": seq_out})

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8

305ad4050f818049a1511d47ae9eef221e847fe9

12,906

py

Python

tests/unit/test_build_helper.py

IceWreck/packit-service

ab8a3ae7b7f078f4a5bf3465516c1abc894fe3dc

[ "MIT" ]

null

tests/unit/test_build_helper.py

IceWreck/packit-service

ab8a3ae7b7f078f4a5bf3465516c1abc894fe3dc

[ "MIT" ]

null

tests/unit/test_build_helper.py

IceWreck/packit-service

ab8a3ae7b7f078f4a5bf3465516c1abc894fe3dc

[ "MIT" ]

null

import pytest from flexmock import flexmock from packit.config import PackageConfig, JobConfig, JobType, JobConfigTriggerType from packit.config.aliases import ALIASES from packit.config.job_config import JobMetadataConfig from packit_service.service.events import TheJobTriggerType from packit_service.worker.build.copr_build import CoprBuildJobHelper from packit_service.worker.build.koji_build import KojiBuildJobHelper STABLE_VERSIONS = ALIASES["fedora-stable"] STABLE_CHROOTS = {f"{version}-x86_64" for version in STABLE_VERSIONS} ONE_CHROOT_SET = {list(STABLE_CHROOTS)[0]} STABLE_KOJI_TARGETS = {f"f{version[-2:]}" for version in STABLE_VERSIONS} ONE_KOJI_TARGET_SET = {list(STABLE_KOJI_TARGETS)[0]} @pytest.mark.parametrize( "jobs,trigger,job_config_trigger_type,build_chroots,test_chroots", [ pytest.param( [ JobConfig( type=JobType.copr_build, trigger=JobConfigTriggerType.pull_request, metadata=JobMetadataConfig(targets=STABLE_VERSIONS), ) ], TheJobTriggerType.pull_request, JobConfigTriggerType.pull_request, STABLE_CHROOTS, set(), id="build_with_targets", ), pytest.param( [ JobConfig( type=JobType.copr_build, trigger=JobConfigTriggerType.pull_request, metadata=JobMetadataConfig(targets=STABLE_VERSIONS), ) ], TheJobTriggerType.pr_comment, JobConfigTriggerType.pull_request, STABLE_CHROOTS, set(), id="build_with_targets&pr_comment", ), pytest.param( [ JobConfig( type=JobType.copr_build, trigger=JobConfigTriggerType.release, metadata=JobMetadataConfig(targets=STABLE_VERSIONS), ) ], TheJobTriggerType.release, JobConfigTriggerType.release, STABLE_CHROOTS, set(), id="build_with_targets&release", ), pytest.param( [ JobConfig( type=JobType.copr_build, trigger=JobConfigTriggerType.commit, metadata=JobMetadataConfig(targets=STABLE_VERSIONS), ) ], TheJobTriggerType.push, JobConfigTriggerType.commit, STABLE_CHROOTS, set(), id="build_with_targets&push", ), pytest.param( [ JobConfig( type=JobType.copr_build, trigger=JobConfigTriggerType.pull_request, metadata=JobMetadataConfig(targets=STABLE_VERSIONS), ), JobConfig( type=JobType.copr_build, trigger=JobConfigTriggerType.commit, metadata=JobMetadataConfig(targets=["different", "os", "target"]), ), ], TheJobTriggerType.pull_request, JobConfigTriggerType.pull_request, STABLE_CHROOTS, set(), id="build_with_targets&pull_request_with_pr_and_push_defined", ), pytest.param( [ JobConfig( type=JobType.copr_build, trigger=JobConfigTriggerType.pull_request, metadata=JobMetadataConfig(targets=STABLE_VERSIONS), ), JobConfig( type=JobType.copr_build, trigger=JobConfigTriggerType.commit, metadata=JobMetadataConfig(targets=["different", "os", "target"]), ), ], TheJobTriggerType.pr_comment, JobConfigTriggerType.pull_request, STABLE_CHROOTS, set(), id="build_with_targets&pr_comment_with_pr_and_push_defined", ), pytest.param( [ JobConfig( type=JobType.copr_build, trigger=JobConfigTriggerType.pull_request, metadata=JobMetadataConfig(targets=["different", "os", "target"]), ), JobConfig( type=JobType.copr_build, trigger=JobConfigTriggerType.commit, metadata=JobMetadataConfig(targets=STABLE_VERSIONS), ), ], TheJobTriggerType.push, JobConfigTriggerType.commit, STABLE_CHROOTS, set(), id="build_with_targets&push_with_pr_and_push_defined", ), pytest.param( [ JobConfig( type=JobType.copr_build, trigger=JobConfigTriggerType.pull_request, ) ], TheJobTriggerType.pull_request, JobConfigTriggerType.pull_request, STABLE_CHROOTS, set(), id="build_without_targets", ), pytest.param( [ JobConfig( type=JobType.tests, trigger=JobConfigTriggerType.pull_request, ) ], TheJobTriggerType.pull_request, JobConfigTriggerType.pull_request, STABLE_CHROOTS, STABLE_CHROOTS, id="test_without_targets", ), pytest.param( [ JobConfig( type=JobType.tests, trigger=JobConfigTriggerType.pull_request, metadata=JobMetadataConfig(targets=STABLE_VERSIONS), ) ], TheJobTriggerType.pull_request, JobConfigTriggerType.pull_request, STABLE_CHROOTS, STABLE_CHROOTS, id="test_with_targets", ), pytest.param( [ JobConfig( type=JobType.copr_build, trigger=JobConfigTriggerType.pull_request, ), JobConfig( type=JobType.tests, trigger=JobConfigTriggerType.pull_request, ), ], TheJobTriggerType.pull_request, JobConfigTriggerType.pull_request, STABLE_CHROOTS, STABLE_CHROOTS, id="build_without_target&test_without_targets", ), pytest.param( [ JobConfig( type=JobType.copr_build, trigger=JobConfigTriggerType.pull_request, metadata=JobMetadataConfig(targets=STABLE_VERSIONS), ), JobConfig( type=JobType.tests, trigger=JobConfigTriggerType.pull_request, ), ], TheJobTriggerType.pull_request, JobConfigTriggerType.pull_request, STABLE_CHROOTS, STABLE_CHROOTS, id="build_with_target&test_without_targets", ), pytest.param( [ JobConfig( type=JobType.copr_build, trigger=JobConfigTriggerType.pull_request, ), JobConfig( type=JobType.tests, trigger=JobConfigTriggerType.pull_request, metadata=JobMetadataConfig(targets=STABLE_VERSIONS), ), ], TheJobTriggerType.pull_request, JobConfigTriggerType.pull_request, STABLE_CHROOTS, STABLE_CHROOTS, id="build_without_target&test_with_targets", ), pytest.param( [ JobConfig( type=JobType.copr_build, trigger=JobConfigTriggerType.pull_request, ), JobConfig( type=JobType.tests, trigger=JobConfigTriggerType.pull_request, metadata=JobMetadataConfig(targets=list(ONE_CHROOT_SET)), ), ], TheJobTriggerType.pull_request, JobConfigTriggerType.pull_request, ONE_CHROOT_SET, ONE_CHROOT_SET, id="build_without_target&test_with_one_str_target", ), pytest.param( [ JobConfig( type=JobType.build, trigger=JobConfigTriggerType.pull_request, ), JobConfig( type=JobType.tests, trigger=JobConfigTriggerType.pull_request, metadata=JobMetadataConfig(targets=list(ONE_CHROOT_SET)), ), ], TheJobTriggerType.pull_request, JobConfigTriggerType.pull_request, ONE_CHROOT_SET, ONE_CHROOT_SET, id="build_with_mixed_build_alias", ), pytest.param( [ JobConfig( type=JobType.production_build, trigger=JobConfigTriggerType.pull_request, metadata=JobMetadataConfig(targets=STABLE_VERSIONS), ) ], TheJobTriggerType.pull_request, JobConfigTriggerType.pull_request, STABLE_CHROOTS, set(), id="koji_build_with_targets_for_pr", ), ], ) def test_targets(jobs, trigger, job_config_trigger_type, build_chroots, test_chroots): copr_build_handler = CoprBuildJobHelper( service_config=flexmock(), package_config=PackageConfig(jobs=jobs), job_config=jobs[0], # BuildHelper looks at all jobs in the end project=flexmock(), metadata=flexmock(trigger=trigger, pr_id=None), db_trigger=flexmock(job_config_trigger_type=job_config_trigger_type), ) assert copr_build_handler.package_config.jobs assert [j.type for j in copr_build_handler.package_config.jobs] assert copr_build_handler.build_targets == build_chroots assert copr_build_handler.tests_targets == test_chroots @pytest.mark.parametrize( "jobs,trigger,job_config_trigger_type,build_targets,koji_targets", [ pytest.param( [ JobConfig( type=JobType.production_build, trigger=JobConfigTriggerType.pull_request, metadata=JobMetadataConfig(targets=STABLE_VERSIONS), ) ], TheJobTriggerType.pull_request, JobConfigTriggerType.pull_request, set(STABLE_VERSIONS), STABLE_KOJI_TARGETS, id="koji_build_with_targets_for_pr", ), pytest.param( [ JobConfig( type=JobType.production_build, trigger=JobConfigTriggerType.commit, metadata=JobMetadataConfig( targets=STABLE_VERSIONS, branch="build-branch" ), ) ], TheJobTriggerType.push, JobConfigTriggerType.commit, set(STABLE_VERSIONS), STABLE_KOJI_TARGETS, id="koji_build_with_targets_for_commit", ), pytest.param( [ JobConfig( type=JobType.production_build, trigger=JobConfigTriggerType.release, metadata=JobMetadataConfig( targets=STABLE_VERSIONS, branch="build-branch" ), ) ], TheJobTriggerType.release, JobConfigTriggerType.release, set(STABLE_VERSIONS), STABLE_KOJI_TARGETS, id="koji_build_with_targets_for_release", ), ], ) def test_targets_for_koji_build( jobs, trigger, job_config_trigger_type, build_targets, koji_targets ): pr_id = 41 if trigger == TheJobTriggerType.pull_request else None koji_build_handler = KojiBuildJobHelper( service_config=flexmock(), package_config=PackageConfig(jobs=jobs), job_config=jobs[0], project=flexmock(), metadata=flexmock(trigger=trigger, pr_id=pr_id), db_trigger=flexmock(job_config_trigger_type=job_config_trigger_type), ) assert koji_build_handler.package_config.jobs assert [j.type for j in koji_build_handler.package_config.jobs] assert koji_build_handler.configured_build_targets == build_targets assert koji_build_handler.build_targets == koji_targets

35.358904

86

0.544166

996

12,906

6.74498

0.086345

0.078595

0.156892

0.113129

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0.82078

0.798898

0.784311

0.717327

0

0.001385

0.384395

12,906

364

87

35.456044

0.844179

0.003099

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0.757062

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0.054571

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0.00565

false

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0.022599

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0.028249

0

null

0

1

0

1

0

null

0

7

0644af6721b18cac036b53f4844564ec3a798a96

220

py

Python

Codewars/8kyu/convert-a-string-to-a-number/Python/test.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

7

2017-09-20T16:40:39.000Z

2021-08-31T18:15:08.000Z

Codewars/8kyu/convert-a-string-to-a-number/Python/test.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

null

Codewars/8kyu/convert-a-string-to-a-number/Python/test.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

null

# Python - 3.6.0 test.assert_equals(string_to_number('1234'), 1234) test.assert_equals(string_to_number('605'), 605) test.assert_equals(string_to_number('1405'), 1405) test.assert_equals(string_to_number('1234'), 1234)

31.428571

50

0.777273

36

220

4.416667

0.361111

0.251572

0.402516

0.553459

0.855346

0.477987

0

0.15942

0.059091

220

6

51

36.666667

0.608696

0.063636

0

0.5

0

0.073529

0

1

0

true

0

null

1

0

1

0

null

0

1

0

1

0

8

067d0638bf8bdbe8010c7fd360175835490c6b45

28,903

py

Python

tests/api/symbol/r_api_test.py

NetApp/santricity-webapi-pythonsdk

1d3df4a00561192f4cdcdd1890f4d27547ed2de2

[ "BSD-3-Clause-Clear" ]

5

2016-08-23T17:52:22.000Z

2019-05-16T08:45:30.000Z

tests/api/symbol/r_api_test.py

NetApp/santricity-webapi-pythonsdk

1d3df4a00561192f4cdcdd1890f4d27547ed2de2

[ "BSD-3-Clause-Clear" ]

2

2016-11-10T05:30:21.000Z

2019-04-05T15:03:37.000Z

tests/api/symbol/r_api_test.py

NetApp/santricity-webapi-pythonsdk

1d3df4a00561192f4cdcdd1890f4d27547ed2de2

[ "BSD-3-Clause-Clear" ]

7

2016-08-25T16:11:44.000Z

2021-02-22T05:31:25.000Z

#!/usr/bin/env python # coding: utf-8 """ r_api_test.py The Clear BSD License Copyright (c) – 2016, NetApp, Inc. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted (subject to the limitations in the disclaimer below) provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of NetApp, Inc. nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """ import unittest from netapp.santricity.rest import ApiException from netapp.santricity.api.symbol.r_api import RApi class RApiTest(unittest.TestCase): def test_symbol_read_mgmt_client_records(self): api = RApi() r_api = None try: r_api = api.symbol_read_mgmt_client_records(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_read_unreadable_sector_database(self): api = RApi() r_api = None try: r_api = api.symbol_read_unreadable_sector_database(system_id="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_recover_async_mirror_group_member(self): api = RApi() r_api = None try: r_api = api.symbol_recover_async_mirror_group_member(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_recover_async_mirror_group_member_delete_recovery_point(self): api = RApi() r_api = None try: r_api = api.symbol_recover_async_mirror_group_member_delete_recovery_point(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_recover_from_miswire(self): api = RApi() r_api = None try: r_api = api.symbol_recover_from_miswire(system_id="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_recover_volume(self): api = RApi() r_api = None try: r_api = api.symbol_recover_volume(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_recreate_snapshot(self): api = RApi() r_api = None try: r_api = api.symbol_recreate_snapshot(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_recreate_snapshot_collection(self): api = RApi() r_api = None try: r_api = api.symbol_recreate_snapshot_collection(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_refresh_iscsi_dhcp_parameters(self): api = RApi() r_api = None try: r_api = api.symbol_refresh_iscsi_dhcp_parameters(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_refresh_isns_server_location(self): api = RApi() r_api = None try: r_api = api.symbol_refresh_isns_server_location(system_id="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_reinitialize_thin_volume(self): api = RApi() r_api = None try: r_api = api.symbol_reinitialize_thin_volume(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_release_support_data(self): api = RApi() r_api = None try: r_api = api.symbol_release_support_data(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_remove_async_mirror_group_member(self): api = RApi() r_api = None try: r_api = api.symbol_remove_async_mirror_group_member(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_remove_bundle_keys(self): api = RApi() r_api = None try: r_api = api.symbol_remove_bundle_keys(system_id="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_remove_drives_from_flash_cache(self): api = RApi() r_api = None try: r_api = api.symbol_remove_drives_from_flash_cache(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_remove_mirror(self): api = RApi() r_api = None try: r_api = api.symbol_remove_mirror(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_remove_pit_consistency_group_member(self): api = RApi() r_api = None try: r_api = api.symbol_remove_pit_consistency_group_member(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_remove_snmp_community(self): api = RApi() r_api = None try: r_api = api.symbol_remove_snmp_community(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_remove_snmp_trap_destination(self): api = RApi() r_api = None try: r_api = api.symbol_remove_snmp_trap_destination(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_remove_volume_copy(self): api = RApi() r_api = None try: r_api = api.symbol_remove_volume_copy(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_rename_cluster(self): api = RApi() r_api = None try: r_api = api.symbol_rename_cluster(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_rename_host(self): api = RApi() r_api = None try: r_api = api.symbol_rename_host(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_rename_host_port(self): api = RApi() r_api = None try: r_api = api.symbol_rename_host_port(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_replace_drive(self): api = RApi() r_api = None try: r_api = api.symbol_replace_drive(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_reprovision_drive(self): api = RApi() r_api = None try: r_api = api.symbol_reprovision_drive(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_request_removal(self): api = RApi() r_api = None try: r_api = api.symbol_request_removal(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_request_to_remove(self): api = RApi() r_api = None try: r_api = api.symbol_request_to_remove(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_reserve_support_data(self): api = RApi() r_api = None try: r_api = api.symbol_reserve_support_data(system_id="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_reset_async_mirror_sync_statistics(self): api = RApi() r_api = None try: r_api = api.symbol_reset_async_mirror_sync_statistics(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_reset_controller(self): api = RApi() r_api = None try: r_api = api.symbol_reset_controller(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_reset_cumulative_statistics(self): api = RApi() r_api = None try: r_api = api.symbol_reset_cumulative_statistics(system_id="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_reset_discrete_time_series(self): api = RApi() r_api = None try: r_api = api.symbol_reset_discrete_time_series(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_reset_histogram(self): api = RApi() r_api = None try: r_api = api.symbol_reset_histogram(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_reset_mel(self): api = RApi() r_api = None try: r_api = api.symbol_reset_mel(system_id="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_reset_sa_configuration(self): api = RApi() r_api = None try: r_api = api.symbol_reset_sa_configuration(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_restart_pit_consistency_group_view(self): api = RApi() r_api = None try: r_api = api.symbol_restart_pit_consistency_group_view(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_restart_pit_view(self): api = RApi() r_api = None try: r_api = api.symbol_restart_pit_view(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_resume_async_mirror_group(self): api = RApi() r_api = None try: r_api = api.symbol_resume_async_mirror_group(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_resume_consistency_group(self): api = RApi() r_api = None try: r_api = api.symbol_resume_consistency_group(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_resume_flash_cache(self): api = RApi() r_api = None try: r_api = api.symbol_resume_flash_cache(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_resume_mirror(self): api = RApi() r_api = None try: r_api = api.symbol_resume_mirror(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_resume_mirror_list(self): api = RApi() r_api = None try: r_api = api.symbol_resume_mirror_list(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_resume_pit_rollback(self): api = RApi() r_api = None try: r_api = api.symbol_resume_pit_rollback(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_resume_snapshot_rollback(self): api = RApi() r_api = None try: r_api = api.symbol_resume_snapshot_rollback(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_revive_pit_group(self): api = RApi() r_api = None try: r_api = api.symbol_revive_pit_group(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_revive_pit_view(self): api = RApi() r_api = None try: r_api = api.symbol_revive_pit_view(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None) def test_symbol_role_change(self): api = RApi() r_api = None try: r_api = api.symbol_role_change(system_id="test", body="test", ) # For the DELETE calls, there's no reponse returned and we want to set that as a valid sdk call. if r_api is None: r_api = 1 except (ApiException, IOError) as exp: # The API call went through but got a HTTP errorcode, which means the SDK works r_api = 1 self.assertNotEqual(r_api, None)

41.767341

844

0.593814

4,097

28,903

4.0227

0.063217

0.068928

0.045628

0.034221

0.894363

0.874462

0.86718

0.864875

0

0.005224

0.344359

28,903

691

845

41.827786

0.864433

0.339688

0

0.793249

0

0.019029

0

0.099156

1

0.099156

false

0

0.006329

0

0.107595

0

null

0

1

0

1

0

null

0

7

06b1a4dd35f71d1c8b2960fccfc7d9ec895936c6

597,628

py

Python

debug/endpoints_old.py

gabelev/MISMI

b3d34632e491d9c96824f9a58cabc093755d8b83

[ "MIT" ]

null

debug/endpoints_old.py

gabelev/MISMI

b3d34632e491d9c96824f9a58cabc093755d8b83

[ "MIT" ]

null

debug/endpoints_old.py

gabelev/MISMI

b3d34632e491d9c96824f9a58cabc093755d8b83

[ "MIT" ]

null

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'handling+pressure':['http://www.amazon.com/s/rh=%2Ck%3Ahandling+pressure&page=1','http://www.amazon.com/s/rh=%2Ck%3Ahandling+pressure&page=2','http://www.amazon.com/s/rh=%2Ck%3Ahandling+pressure&page=3','http://www.amazon.com/s/rh=%2Ck%3Ahandling+pressure&page=4','http://www.amazon.com/s/rh=%2Ck%3Ahandling+pressure&page=5','http://www.amazon.com/s/rh=%2Ck%3Ahandling+pressure&page=6','http://www.amazon.com/s/rh=%2Ck%3Ahandling+pressure&page=7','http://www.amazon.com/s/rh=%2Ck%3Ahandling+pressure&page=8','http://www.amazon.com/s/rh=%2Ck%3Ahandling+pressure&page=9','http://www.amazon.com/s/rh=%2Ck%3Ahandling+pressure&page=10','http://www.amazon.com/s/rh=%2Ck%3Ahandling+pressure&page=11','http://www.amazon.com/s/rh=%2Ck%3Ahandling+pressure&page=12','http://www.amazon.com/s/rh=%2Ck%3Ahandling+pressure&page=13','http://www.amazon.com/s/rh=%2Ck%3Ahandling+pressure&page=14','http://www.amazon.com/s/rh=%2Ck%3Ahandling+pressure&page=15','http://www.amazon.com/s/rh=%2Ck%3Ahandling+pressure&page=16','http://www.amazon.com/s/rh=%2Ck%3Ahandling+pressure&page=17','http://www.amazon.com/s/rh=%2Ck%3Ahandling+pressure&page=18','http://www.amazon.com/s/rh=%2Ck%3Ahandling+pressure&page=19','http://www.amazon.com/s/rh=%2Ck%3Ahandling+pressure&page=20'], 'handling+stress':['http://www.amazon.com/s/rh=%2Ck%3Ahandling+stress&page=1','http://www.amazon.com/s/rh=%2Ck%3Ahandling+stress&page=2','http://www.amazon.com/s/rh=%2Ck%3Ahandling+stress&page=3','http://www.amazon.com/s/rh=%2Ck%3Ahandling+stress&page=4','http://www.amazon.com/s/rh=%2Ck%3Ahandling+stress&page=5','http://www.amazon.com/s/rh=%2Ck%3Ahandling+stress&page=6','http://www.amazon.com/s/rh=%2Ck%3Ahandling+stress&page=7','http://www.amazon.com/s/rh=%2Ck%3Ahandling+stress&page=8','http://www.amazon.com/s/rh=%2Ck%3Ahandling+stress&page=9','http://www.amazon.com/s/rh=%2Ck%3Ahandling+stress&page=10','http://www.amazon.com/s/rh=%2Ck%3Ahandling+stress&page=11','http://www.amazon.com/s/rh=%2Ck%3Ahandling+stress&page=12','http://www.amazon.com/s/rh=%2Ck%3Ahandling+stress&page=13','http://www.amazon.com/s/rh=%2Ck%3Ahandling+stress&page=14','http://www.amazon.com/s/rh=%2Ck%3Ahandling+stress&page=15','http://www.amazon.com/s/rh=%2Ck%3Ahandling+stress&page=16','http://www.amazon.com/s/rh=%2Ck%3Ahandling+stress&page=17','http://www.amazon.com/s/rh=%2Ck%3Ahandling+stress&page=18','http://www.amazon.com/s/rh=%2Ck%3Ahandling+stress&page=19','http://www.amazon.com/s/rh=%2Ck%3Ahandling+stress&page=20'], 'face+pressure':['http://www.amazon.com/s/rh=%2Ck%3Aface+pressure&page=1','http://www.amazon.com/s/rh=%2Ck%3Aface+pressure&page=2','http://www.amazon.com/s/rh=%2Ck%3Aface+pressure&page=3','http://www.amazon.com/s/rh=%2Ck%3Aface+pressure&page=4','http://www.amazon.com/s/rh=%2Ck%3Aface+pressure&page=5','http://www.amazon.com/s/rh=%2Ck%3Aface+pressure&page=6','http://www.amazon.com/s/rh=%2Ck%3Aface+pressure&page=7','http://www.amazon.com/s/rh=%2Ck%3Aface+pressure&page=8','http://www.amazon.com/s/rh=%2Ck%3Aface+pressure&page=9','http://www.amazon.com/s/rh=%2Ck%3Aface+pressure&page=10','http://www.amazon.com/s/rh=%2Ck%3Aface+pressure&page=11','http://www.amazon.com/s/rh=%2Ck%3Aface+pressure&page=12','http://www.amazon.com/s/rh=%2Ck%3Aface+pressure&page=13','http://www.amazon.com/s/rh=%2Ck%3Aface+pressure&page=14','http://www.amazon.com/s/rh=%2Ck%3Aface+pressure&page=15','http://www.amazon.com/s/rh=%2Ck%3Aface+pressure&page=16','http://www.amazon.com/s/rh=%2Ck%3Aface+pressure&page=17','http://www.amazon.com/s/rh=%2Ck%3Aface+pressure&page=18','http://www.amazon.com/s/rh=%2Ck%3Aface+pressure&page=19','http://www.amazon.com/s/rh=%2Ck%3Aface+pressure&page=20'], 'health+and+human+potential':['http://www.amazon.com/s/rh=%2Ck%3Ahealth+and+human+potential&page=1','http://www.amazon.com/s/rh=%2Ck%3Ahealth+and+human+potential&page=2','http://www.amazon.com/s/rh=%2Ck%3Ahealth+and+human+potential&page=3','http://www.amazon.com/s/rh=%2Ck%3Ahealth+and+human+potential&page=4','http://www.amazon.com/s/rh=%2Ck%3Ahealth+and+human+potential&page=5','http://www.amazon.com/s/rh=%2Ck%3Ahealth+and+human+potential&page=6','http://www.amazon.com/s/rh=%2Ck%3Ahealth+and+human+potential&page=7','http://www.amazon.com/s/rh=%2Ck%3Ahealth+and+human+potential&page=8','http://www.amazon.com/s/rh=%2Ck%3Ahealth+and+human+potential&page=9','http://www.amazon.com/s/rh=%2Ck%3Ahealth+and+human+potential&page=10','http://www.amazon.com/s/rh=%2Ck%3Ahealth+and+human+potential&page=11','http://www.amazon.com/s/rh=%2Ck%3Ahealth+and+human+potential&page=12','http://www.amazon.com/s/rh=%2Ck%3Ahealth+and+human+potential&page=13','http://www.amazon.com/s/rh=%2Ck%3Ahealth+and+human+potential&page=14','http://www.amazon.com/s/rh=%2Ck%3Ahealth+and+human+potential&page=15','http://www.amazon.com/s/rh=%2Ck%3Ahealth+and+human+potential&page=16','http://www.amazon.com/s/rh=%2Ck%3Ahealth+and+human+potential&page=17','http://www.amazon.com/s/rh=%2Ck%3Ahealth+and+human+potential&page=18','http://www.amazon.com/s/rh=%2Ck%3Ahealth+and+human+potential&page=19','http://www.amazon.com/s/rh=%2Ck%3Ahealth+and+human+potential&page=20'], 'IHHP':['http://www.amazon.com/s/rh=%2Ck%3AIHHP&page=1','http://www.amazon.com/s/rh=%2Ck%3AIHHP&page=2','http://www.amazon.com/s/rh=%2Ck%3AIHHP&page=3','http://www.amazon.com/s/rh=%2Ck%3AIHHP&page=4','http://www.amazon.com/s/rh=%2Ck%3AIHHP&page=5','http://www.amazon.com/s/rh=%2Ck%3AIHHP&page=6','http://www.amazon.com/s/rh=%2Ck%3AIHHP&page=7','http://www.amazon.com/s/rh=%2Ck%3AIHHP&page=8','http://www.amazon.com/s/rh=%2Ck%3AIHHP&page=9','http://www.amazon.com/s/rh=%2Ck%3AIHHP&page=10','http://www.amazon.com/s/rh=%2Ck%3AIHHP&page=11','http://www.amazon.com/s/rh=%2Ck%3AIHHP&page=12','http://www.amazon.com/s/rh=%2Ck%3AIHHP&page=13','http://www.amazon.com/s/rh=%2Ck%3AIHHP&page=14','http://www.amazon.com/s/rh=%2Ck%3AIHHP&page=15','http://www.amazon.com/s/rh=%2Ck%3AIHHP&page=16','http://www.amazon.com/s/rh=%2Ck%3AIHHP&page=17','http://www.amazon.com/s/rh=%2Ck%3AIHHP&page=18','http://www.amazon.com/s/rh=%2Ck%3AIHHP&page=19','http://www.amazon.com/s/rh=%2Ck%3AIHHP&page=20'] }

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null

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12

233361baa094e87b49281518ae0c449ec4c17cfd

47

py

Python

localpdb/plugins/__init__.py

labstructbioinf/localpdb

870feca6256487c27a64e8bd5121f47d9618469c

[ "MIT" ]

28

2020-05-25T17:28:28.000Z

2022-03-29T21:18:13.000Z

localpdb/plugins/__init__.py

labstructbioinf/localpdb

870feca6256487c27a64e8bd5121f47d9618469c

[ "MIT" ]

10

2021-04-30T10:15:46.000Z

2021-11-18T15:03:01.000Z

localpdb/plugins/__init__.py

labstructbioinf/localpdb

870feca6256487c27a64e8bd5121f47d9618469c

[ "MIT" ]

1

2022-02-25T08:24:49.000Z

from .PluginVersioneer import PluginVersioneer

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0.893617

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7

88fd6b650bce345158dcbc7062d16afc4ababea6

67

py

Python

perses/utils/__init__.py

schallerdavid/perses

58bd6e626e027879e136f56e175683893e016f8c

[ "MIT" ]

99

2016-01-19T18:10:37.000Z

2022-03-26T02:43:08.000Z

perses/utils/__init__.py

schallerdavid/perses

58bd6e626e027879e136f56e175683893e016f8c

[ "MIT" ]

878

2015-09-18T19:25:30.000Z

2022-03-31T02:33:04.000Z

perses/utils/__init__.py

schallerdavid/perses

58bd6e626e027879e136f56e175683893e016f8c

[ "MIT" ]

30

2015-09-21T15:26:35.000Z

2022-01-10T20:07:24.000Z

from perses.utils.data import * from perses.utils.openeye import *

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7

cc82030a525a6c63ceffdd899fe12660dd09c66c

15,361

py

Python

pymc3/sampling_jax.py

roblem/pymc3

460b6c8876c3fe9046e25984cec49d018ebc6d3a

[ "Apache-2.0" ]

null

pymc3/sampling_jax.py

roblem/pymc3

460b6c8876c3fe9046e25984cec49d018ebc6d3a

[ "Apache-2.0" ]

null

pymc3/sampling_jax.py

roblem/pymc3

460b6c8876c3fe9046e25984cec49d018ebc6d3a

[ "Apache-2.0" ]

null

# pylint: skip-file import os import re import warnings import pdb as pdb xla_flags = os.getenv("XLA_FLAGS", "").lstrip("--") xla_flags = re.sub(r"xla_force_host_platform_device_count=.+\s", "", xla_flags).split() os.environ["XLA_FLAGS"] = " ".join(["--xla_force_host_platform_device_count={}".format(100)]) import arviz as az import jax import numpy as np import pandas as pd import theano.graph.fg from theano.link.jax.jax_dispatch import jax_funcify import pymc3 as pm from pymc3 import modelcontext warnings.warn("This module is experimental.") # Disable C compilation by default # theano.config.cxx = "" # This will make the JAX Linker the default # theano.config.mode = "JAX" def sample_tfp_nuts( draws=1000, tune=1000, chains=4, target_accept=0.8, random_seed=10, model=None, num_tuning_epoch=2, num_compute_step_size=500, ): import jax from tensorflow_probability.substrates import jax as tfp model = modelcontext(model) seed = jax.random.PRNGKey(random_seed) fgraph = theano.graph.fg.FunctionGraph(model.free_RVs, [model.logpt]) fns = jax_funcify(fgraph) logp_fn_jax = fns[0] rv_names = [rv.name for rv in model.free_RVs] init_state = [model.test_point[rv_name] for rv_name in rv_names] init_state_batched = jax.tree_map(lambda x: np.repeat(x[None, ...], chains, axis=0), init_state) @jax.pmap def _sample(init_state, seed): def gen_kernel(step_size): hmc = tfp.mcmc.NoUTurnSampler(target_log_prob_fn=logp_fn_jax, step_size=step_size) return tfp.mcmc.DualAveragingStepSizeAdaptation( hmc, tune // num_tuning_epoch, target_accept_prob=target_accept ) def trace_fn(_, pkr): return pkr.new_step_size def get_tuned_stepsize(samples, step_size): return step_size[-1] * jax.numpy.std(samples[-num_compute_step_size:]) step_size = jax.tree_map(jax.numpy.ones_like, init_state) for i in range(num_tuning_epoch - 1): tuning_hmc = gen_kernel(step_size) init_samples, tuning_result, kernel_results = tfp.mcmc.sample_chain( num_results=tune // num_tuning_epoch, current_state=init_state, kernel=tuning_hmc, trace_fn=trace_fn, return_final_kernel_results=True, seed=seed, ) step_size = jax.tree_multimap(get_tuned_stepsize, list(init_samples), tuning_result) init_state = [x[-1] for x in init_samples] # Run inference sample_kernel = gen_kernel(step_size) mcmc_samples, leapfrog_num = tfp.mcmc.sample_chain( num_results=draws, num_burnin_steps=tune // num_tuning_epoch, current_state=init_state, kernel=sample_kernel, trace_fn=lambda _, pkr: pkr.inner_results.leapfrogs_taken, seed=seed, ) return mcmc_samples, leapfrog_num print("Compiling and sampling...") tic2 = pd.Timestamp.now() map_seed = jax.random.split(seed, chains) mcmc_samples, leapfrog_num = _sample(init_state_batched, map_seed) # map_seed = jax.random.split(seed, chains) # mcmc_samples = _sample(init_state_batched, map_seed) # tic4 = pd.Timestamp.now() # print("Sampling time = ", tic4 - tic3) posterior = {k: v for k, v in zip(rv_names, mcmc_samples)} az_trace = az.from_dict(posterior=posterior) tic3 = pd.Timestamp.now() print("Compilation + sampling time = ", tic3 - tic2) return az_trace # , leapfrog_num, tic3 - tic2 def sample_numpyro_nuts( draws=1000, tune=1000, chains=4, target_accept=0.8, random_seed=10, model=None, progress_bar=True, ): from numpyro.infer import MCMC, NUTS from pymc3 import modelcontext model = modelcontext(model) seed = jax.random.PRNGKey(random_seed) fgraph = theano.graph.fg.FunctionGraph(model.free_RVs, [model.logpt]) fns = jax_funcify(fgraph) logp_fn_jax = fns[0] rv_names = [rv.name for rv in model.free_RVs] init_state = [model.test_point[rv_name] for rv_name in rv_names] init_state_batched = jax.tree_map(lambda x: np.repeat(x[None, ...], chains, axis=0), init_state) @jax.jit def _sample(current_state, seed): step_size = jax.tree_map(jax.numpy.ones_like, init_state) nuts_kernel = NUTS( potential_fn=lambda x: -logp_fn_jax(*x), # model=model, target_accept_prob=target_accept, adapt_step_size=True, adapt_mass_matrix=True, dense_mass=False, ) pmap_numpyro = MCMC( nuts_kernel, num_warmup=tune, num_samples=draws, num_chains=chains, postprocess_fn=None, chain_method="parallel", progress_bar=progress_bar, ) pmap_numpyro.run(seed, init_params=current_state, extra_fields=("num_steps",)) samples = pmap_numpyro.get_samples(group_by_chain=True) leapfrogs_taken = pmap_numpyro.get_extra_fields(group_by_chain=True)["num_steps"] return samples, leapfrogs_taken print("Compiling and sampling...") tic2 = pd.Timestamp.now() map_seed = jax.random.split(seed, chains) mcmc_samples, leapfrogs_taken = _sample(init_state_batched, map_seed) # map_seed = jax.random.split(seed, chains) # mcmc_samples = _sample(init_state_batched, map_seed) # tic4 = pd.Timestamp.now() # print("Sampling time = ", tic4 - tic3) posterior = {k: v for k, v in zip(rv_names, mcmc_samples)} az_trace = az.from_dict(posterior=posterior) tic3 = pd.Timestamp.now() print("Compilation + sampling time = ", tic3 - tic2) return az_trace # , leapfrogs_taken, tic3 - tic2 # # Better batch versions # def sample_tfp_nuts_jit_vmap( draws=1000, tune=1000, chains=4, target_accept=0.8, random_seed=10, model=None, num_tuning_epoch=2, num_compute_step_size=500, ): import jax from tensorflow_probability.substrates import jax as tfp model = modelcontext(model) seed = jax.random.PRNGKey(random_seed) fgraph = theano.graph.fg.FunctionGraph(model.free_RVs, [model.logpt]) fns = jax_funcify(fgraph) logp_fn_jax = fns[0] rv_names = [rv.name for rv in model.free_RVs] init_state = [model.test_point[rv_name] for rv_name in rv_names] init_state_batched = jax.tree_map(lambda x: np.repeat(x[None, ...], chains, axis=0), init_state) def _sample(init_state, seed): def gen_kernel(step_size): hmc = tfp.mcmc.NoUTurnSampler(target_log_prob_fn=logp_fn_jax, step_size=step_size) return tfp.mcmc.DualAveragingStepSizeAdaptation( hmc, jax.numpy.array(tune // num_tuning_epoch, dtype=jax.numpy.int32), target_accept_prob=target_accept ) def trace_fn(_, pkr): return pkr.new_step_size def get_tuned_stepsize(samples, step_size): return step_size[-1] * jax.numpy.std(samples[-num_compute_step_size:]) step_size = jax.tree_map(jax.numpy.ones_like, init_state) for i in range(num_tuning_epoch - 1): tuning_hmc = gen_kernel(step_size) init_samples, tuning_result, kernel_results = tfp.mcmc.sample_chain( num_results=jax.numpy.array(tune // num_tuning_epoch, dtype=jax.numpy.int32), current_state=init_state, kernel=tuning_hmc, trace_fn=trace_fn, return_final_kernel_results=True, seed=seed, ) step_size = jax.tree_multimap(get_tuned_stepsize, list(init_samples), tuning_result) init_state = [x[-1] for x in init_samples] # Run inference sample_kernel = gen_kernel(step_size) mcmc_samples, leapfrog_num = tfp.mcmc.sample_chain( num_results=draws, num_burnin_steps=tune // num_tuning_epoch, current_state=init_state, kernel=sample_kernel, trace_fn=lambda _, pkr: pkr.inner_results.leapfrogs_taken, seed=seed, ) return mcmc_samples, leapfrog_num _sample_jit_vmap = jax.jit(jax.vmap(_sample)) print("Compiling and sampling...") tic2 = pd.Timestamp.now() map_seed = jax.random.split(seed, chains) mcmc_samples, leapfrog_num = _sample_jit_vmap(init_state_batched, map_seed) # map_seed = jax.random.split(seed, chains) # mcmc_samples = _sample(init_state_batched, map_seed) # tic4 = pd.Timestamp.now() # print("Sampling time = ", tic4 - tic3) posterior = {k: v for k, v in zip(rv_names, mcmc_samples)} az_trace = az.from_dict(posterior=posterior) tic3 = pd.Timestamp.now() print("Compilation + sampling time = ", tic3 - tic2) return az_trace # , leapfrog_num, tic3 - tic2 def sample_numpyro_nuts_vmap( draws=1000, tune=1000, chains=4, target_accept=0.8, random_seed=10, model=None, progress_bar=True, chain_method="parallel" ): from numpyro.infer import MCMC, NUTS from pymc3 import modelcontext model = modelcontext(model) seed = jax.random.PRNGKey(random_seed) fgraph = theano.graph.fg.FunctionGraph(model.free_RVs, [model.logpt]) fns = jax_funcify(fgraph) logp_fn_jax = fns[0] rv_names = [rv.name for rv in model.free_RVs] init_state = [model.test_point[rv_name] for rv_name in rv_names] init_state_batched = jax.tree_map(lambda x: np.repeat(x[None, ...], chains, axis=0), init_state) @jax.jit def _sample(current_state, seed): step_size = jax.tree_map(jax.numpy.ones_like, init_state) nuts_kernel = NUTS( potential_fn=lambda x: -logp_fn_jax(*x), # model=model, target_accept_prob=target_accept, adapt_step_size=True, adapt_mass_matrix=True, dense_mass=False, ) pmap_numpyro = MCMC( nuts_kernel, num_warmup=tune, num_samples=draws, num_chains=chains, postprocess_fn=None, chain_method=chain_method, progress_bar=progress_bar, ) pmap_numpyro.run(seed, init_params=current_state, extra_fields=("num_steps",)) samples = pmap_numpyro.get_samples(group_by_chain=True) leapfrogs_taken = pmap_numpyro.get_extra_fields(group_by_chain=True)["num_steps"] return samples, leapfrogs_taken print("Compiling and sampling...") tic2 = pd.Timestamp.now() map_seed = jax.random.split(seed, chains) mcmc_samples, leapfrogs_taken = _sample(init_state_batched, map_seed) # map_seed = jax.random.split(seed, chains) # mcmc_samples = _sample(init_state_batched, map_seed) # tic4 = pd.Timestamp.now() # print("Sampling time = ", tic4 - tic3) posterior = {k: v for k, v in zip(rv_names, mcmc_samples)} az_trace = az.from_dict(posterior=posterior) tic3 = pd.Timestamp.now() print("Compilation + sampling time = ", tic3 - tic2) return az_trace # , leapfrogs_taken, tic3 - tic2 ## ## This is for mhrw in tf ## def sample_tfp_mhrw( draws=1000, tune=5000, burnin=1000, thin=0, chains=4, random_seed=10, model=None, num_tuning_epoch=5, step_size = .1, ): import jax from tensorflow_probability.substrates import jax as tfp model = modelcontext(model) seed = jax.random.PRNGKey(random_seed) fgraph = theano.graph.fg.FunctionGraph(model.free_RVs, [model.logpt]) fns = jax_funcify(fgraph) logp_fn_jax = fns[0] rv_names = [rv.name for rv in model.free_RVs] init_state = [model.test_point[rv_name] for rv_name in rv_names] init_state_batched = jax.tree_map(lambda x: np.repeat(x[None, ...], chains, axis=0), init_state) @jax.vmap def _sample(init_state, seed, step_size): def trace_is_accepted(states, previous_kernel_results): return previous_kernel_results.is_accepted def gen_kernel(step_size): kernel_ = tfp.mcmc.RandomWalkMetropolis( target_log_prob_fn=logp_fn_jax, new_state_fn=tfp.mcmc.random_walk_normal_fn(scale=step_size)) return kernel_ accept_rate = 0. for i in range(num_tuning_epoch - 1): print(jax.numpy.mean(accept_rate)) print(jax.numpy.std(step_size)) #print(f"Tuning step {i+1:2.0f} of {num_tuning_epoch:2.0f}. Accept rate: {jax.numpy.mean(accept_rate):1.4f}") tuning_mhrw = gen_kernel(step_size) samples, stats = tfp.mcmc.sample_chain(num_results=burnin//num_tuning_epoch, current_state=init_state, kernel = tuning_mhrw, num_burnin_steps=burnin, num_steps_between_results=thin, trace_fn = trace_is_accepted, seed=seed ) #pdb.set_trace() accept_rate = jax.numpy.ravel(stats).mean() step_size = vtune(step_size, accept_rate) # Run inference sample_kernel = gen_kernel(step_size) mcmc_samples, mcmc_stats = tfp.mcmc.sample_chain( num_results=draws, num_burnin_steps=tune // num_tuning_epoch, current_state=init_state, kernel=sample_kernel, trace_fn=trace_is_accepted, seed=seed, ) return mcmc_samples, mcmc_stats print("Compiling and sampling...") tic2 = pd.Timestamp.now() #pdb.set_trace() map_seed = jax.random.split(seed, chains) map_stepsize = jax.tree_map(jax.numpy.ones, chains) mcmc_samples, accept_rate = _sample(init_state_batched, map_seed, map_stepsize) # map_seed = jax.random.split(seed, chains) # mcmc_samples = _sample(init_state_batched, map_seed) # tic4 = pd.Timestamp.now() # print("Sampling time = ", tic4 - tic3) posterior = {k: v for k, v in zip(rv_names, mcmc_samples)} az_trace = az.from_dict(posterior=posterior) tic3 = pd.Timestamp.now() print("Compilation + sampling time = ", tic3 - tic2) return az_trace, accept_rate def vtune(scale, acc_rate): """ This is a vectorized version of the pymc3 tune function Tunes the scaling parameter for the proposal distribution according to the acceptance rate over the last tune_interval: Rate Variance adaptation ---- ------------------- <0.001 x 0.1 <0.05 x 0.5 <0.2 x 0.9 >0.5 x 1.1 >0.75 x 2 >0.95 x 10 """ scale_ = (acc_rate < 0.001)*scale*0.1 +\ ((acc_rate >= 0.001) & (acc_rate < 0.05))*scale*.5 +\ ((acc_rate >= 0.05) & (acc_rate < 0.24))*scale * 0.9 +\ (acc_rate > 0.95)*scale * 10.0 +\ ((acc_rate <= 0.95) & (acc_rate > 0.75))*scale * 2.0 +\ ((acc_rate <= 0.75) & (acc_rate > 0.5))*scale*1.1 +\ ((acc_rate>=.24) & (acc_rate<=.5))*scale return scale_

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Python

yardstick/tests/unit/network_services/vnf_generic/vnf/test_vpp_helpers.py

upfront710/yardstick

2c3898f2ca061962cedbfc7435f78b59aa39b097

[ "Apache-2.0" ]

28

2017-02-07T07:46:42.000Z

2021-06-30T08:11:06.000Z

yardstick/tests/unit/network_services/vnf_generic/vnf/test_vpp_helpers.py

upfront710/yardstick

2c3898f2ca061962cedbfc7435f78b59aa39b097

[ "Apache-2.0" ]

6

2018-01-18T08:00:54.000Z

2019-04-11T04:51:41.000Z

yardstick/tests/unit/network_services/vnf_generic/vnf/test_vpp_helpers.py

upfront710/yardstick

2c3898f2ca061962cedbfc7435f78b59aa39b097

[ "Apache-2.0" ]

46

2016-12-13T10:05:47.000Z

2021-02-18T07:33:06.000Z

# Copyright (c) 2019 Viosoft Corporation # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import ipaddress import unittest import mock from yardstick.common import exceptions from yardstick.network_services.helpers import cpu from yardstick.network_services.vnf_generic.vnf import vpp_helpers from yardstick.network_services.vnf_generic.vnf.base import VnfdHelper from yardstick.network_services.vnf_generic.vnf.vpp_helpers import \ VppSetupEnvHelper, VppConfigGenerator, VatTerminal class TestVppConfigGenerator(unittest.TestCase): def test_add_config_item(self): test_item = {} vpp_config_generator = VppConfigGenerator() vpp_config_generator.add_config_item(test_item, '/tmp/vpe.log', ['unix', 'log']) self.assertEqual({'unix': {'log': '/tmp/vpe.log'}}, test_item) def test_add_config_item_str(self): test_item = {'unix': ''} vpp_config_generator = VppConfigGenerator() vpp_config_generator.add_config_item(test_item, '/tmp/vpe.log', ['unix', 'log']) self.assertEqual({'unix': {'log': '/tmp/vpe.log'}}, test_item) def test_add_unix_log(self): vpp_config_generator = VppConfigGenerator() vpp_config_generator.add_unix_log() self.assertEqual('unix\n{\n log /tmp/vpe.log\n}\n', vpp_config_generator.dump_config()) def test_add_unix_cli_listen(self): vpp_config_generator = VppConfigGenerator() vpp_config_generator.add_unix_cli_listen() self.assertEqual('unix\n{\n cli-listen /run/vpp/cli.sock\n}\n', vpp_config_generator.dump_config()) def test_add_unix_nodaemon(self): vpp_config_generator = VppConfigGenerator() vpp_config_generator.add_unix_nodaemon() self.assertEqual('unix\n{\n nodaemon \n}\n', vpp_config_generator.dump_config()) def test_add_unix_coredump(self): vpp_config_generator = VppConfigGenerator() vpp_config_generator.add_unix_coredump() self.assertEqual('unix\n{\n full-coredump \n}\n', vpp_config_generator.dump_config()) def test_add_dpdk_dev(self): vpp_config_generator = VppConfigGenerator() vpp_config_generator.add_dpdk_dev('0000:00:00.0') self.assertEqual('dpdk\n{\n dev 0000:00:00.0 \n}\n', vpp_config_generator.dump_config()) def test_add_dpdk_cryptodev(self): vpp_config_generator = VppConfigGenerator() vpp_config_generator.add_dpdk_cryptodev(2, '0000:00:00.0') self.assertEqual( 'dpdk\n{\n dev 0000:00:01.0 \n dev 0000:00:01.1 \n uio-driver igb_uio\n}\n', vpp_config_generator.dump_config()) def test_add_dpdk_sw_cryptodev(self): vpp_config_generator = VppConfigGenerator() vpp_config_generator.add_dpdk_sw_cryptodev('aesni_gcm', 0, 2) self.assertEqual( 'dpdk\n{\n vdev cryptodev_aesni_gcm_pmd,socket_id=0 \n}\n', vpp_config_generator.dump_config()) def test_add_dpdk_dev_default_rxq(self): vpp_config_generator = VppConfigGenerator() vpp_config_generator.add_dpdk_dev_default_rxq(1) self.assertEqual( 'dpdk\n{\n dev default\n {\n num-rx-queues 1\n }\n}\n', vpp_config_generator.dump_config()) def test_add_dpdk_dev_default_rxd(self): vpp_config_generator = VppConfigGenerator() vpp_config_generator.add_dpdk_dev_default_rxd(2048) self.assertEqual( 'dpdk\n{\n dev default\n {\n num-rx-desc 2048\n }\n}\n', vpp_config_generator.dump_config()) def test_add_dpdk_dev_default_txd(self): vpp_config_generator = VppConfigGenerator() vpp_config_generator.add_dpdk_dev_default_txd(2048) self.assertEqual( 'dpdk\n{\n dev default\n {\n num-tx-desc 2048\n }\n}\n', vpp_config_generator.dump_config()) def test_add_dpdk_log_level(self): vpp_config_generator = VppConfigGenerator() vpp_config_generator.add_dpdk_log_level('debug') self.assertEqual('dpdk\n{\n log-level debug\n}\n', vpp_config_generator.dump_config()) def test_add_dpdk_socketmem(self): vpp_config_generator = VppConfigGenerator() vpp_config_generator.add_dpdk_socketmem('1024,1024') self.assertEqual('dpdk\n{\n socket-mem 1024,1024\n}\n', vpp_config_generator.dump_config()) def test_add_dpdk_num_mbufs(self): vpp_config_generator = VppConfigGenerator() vpp_config_generator.add_dpdk_num_mbufs(32768) self.assertEqual('dpdk\n{\n num-mbufs 32768\n}\n', vpp_config_generator.dump_config()) def test_add_dpdk_uio_driver(self): vpp_config_generator = VppConfigGenerator() vpp_config_generator.add_dpdk_uio_driver('igb_uio') self.assertEqual('dpdk\n{\n uio-driver igb_uio\n}\n', vpp_config_generator.dump_config()) def test_add_cpu_main_core(self): vpp_config_generator = VppConfigGenerator() vpp_config_generator.add_cpu_main_core('1,2') self.assertEqual('cpu\n{\n main-core 1,2\n}\n', vpp_config_generator.dump_config()) def test_add_cpu_corelist_workers(self): vpp_config_generator = VppConfigGenerator() vpp_config_generator.add_cpu_corelist_workers('1,2') self.assertEqual('cpu\n{\n corelist-workers 1,2\n}\n', vpp_config_generator.dump_config()) def test_add_heapsize(self): vpp_config_generator = VppConfigGenerator() vpp_config_generator.add_heapsize('4G') self.assertEqual('heapsize 4G\n', vpp_config_generator.dump_config()) def test_add_ip6_hash_buckets(self): vpp_config_generator = VppConfigGenerator() vpp_config_generator.add_ip6_hash_buckets(2000000) self.assertEqual('ip6\n{\n hash-buckets 2000000\n}\n', vpp_config_generator.dump_config()) def test_add_ip6_heap_size(self): vpp_config_generator = VppConfigGenerator() vpp_config_generator.add_ip6_heap_size('4G') self.assertEqual('ip6\n{\n heap-size 4G\n}\n', vpp_config_generator.dump_config()) def test_add_ip_heap_size(self): vpp_config_generator = VppConfigGenerator() vpp_config_generator.add_ip_heap_size('4G') self.assertEqual('ip\n{\n heap-size 4G\n}\n', vpp_config_generator.dump_config()) def test_add_statseg_size(self): vpp_config_generator = VppConfigGenerator() vpp_config_generator.add_statseg_size('4G') self.assertEqual('statseg\n{\n size 4G\n}\n', vpp_config_generator.dump_config()) def test_add_plugin(self): vpp_config_generator = VppConfigGenerator() vpp_config_generator.add_plugin('enable', ['dpdk_plugin.so']) self.assertEqual( 'plugins\n{\n plugin [\'dpdk_plugin.so\']\n {\n enable \n }\n}\n', vpp_config_generator.dump_config()) def test_add_dpdk_no_multi_seg(self): vpp_config_generator = VppConfigGenerator() vpp_config_generator.add_dpdk_no_multi_seg() self.assertEqual('dpdk\n{\n no-multi-seg \n}\n', vpp_config_generator.dump_config()) def test_add_dpdk_no_tx_checksum_offload(self): vpp_config_generator = VppConfigGenerator() vpp_config_generator.add_dpdk_no_tx_checksum_offload() self.assertEqual('dpdk\n{\n no-tx-checksum-offload \n}\n', vpp_config_generator.dump_config()) def test_dump_config(self): vpp_config_generator = VppConfigGenerator() vpp_config_generator.add_unix_log() self.assertEqual('unix\n{\n log /tmp/vpe.log\n}\n', vpp_config_generator.dump_config()) def test_pci_dev_check(self): self.assertTrue(VppConfigGenerator.pci_dev_check('0000:00:00.0')) def test_pci_dev_check_error(self): with self.assertRaises(ValueError) as raised: VppConfigGenerator.pci_dev_check('0000:00:0.0') self.assertIn( 'PCI address 0000:00:0.0 is not in valid format xxxx:xx:xx.x', str(raised.exception)) class TestVppSetupEnvHelper(unittest.TestCase): VNFD_0 = { "benchmark": { "kpi": [ "packets_in", "packets_fwd", "packets_dropped" ] }, "connection-point": [ { "name": "xe0", "type": "VPORT" }, { "name": "xe1", "type": "VPORT" } ], "description": "VPP IPsec", "id": "VipsecApproxVnf", "mgmt-interface": { "ip": "10.10.10.101", "password": "r00t", "user": "root", "vdu-id": "ipsecvnf-baremetal" }, "name": "IpsecVnf", "short-name": "IpsecVnf", "vdu": [ { "description": "VPP Ipsec", "external-interface": [ { "name": "xe0", "virtual-interface": { "driver": "igb_uio", "dst_ip": "192.168.100.1", "dst_mac": "90:e2:ba:7c:30:e8", "ifname": "xe0", "local_ip": "192.168.100.2", "local_mac": "90:e2:ba:7c:41:a8", "netmask": "255.255.255.0", "network": {}, "node_name": "vnf__0", "peer_ifname": "xe0", "peer_intf": { "dpdk_port_num": 0, "driver": "igb_uio", "dst_ip": "192.168.100.2", "dst_mac": "90:e2:ba:7c:41:a8", "ifname": "xe0", "local_ip": "192.168.100.1", "local_mac": "90:e2:ba:7c:30:e8", "netmask": "255.255.255.0", "network": {}, "node_name": "tg__0", "peer_ifname": "xe0", "peer_name": "vnf__0", "vld_id": "uplink_0", "vpci": "0000:81:00.0" }, "peer_name": "tg__0", "vld_id": "uplink_0", "vpci": "0000:ff:06.0" }, "vnfd-connection-point-ref": "xe0" }, { "name": "xe1", "virtual-interface": { "driver": "igb_uio", "dst_ip": "1.1.1.2", "dst_mac": "0a:b1:ec:fd:a2:66", "ifname": "xe1", "local_ip": "1.1.1.1", "local_mac": "4e:90:85:d3:c5:13", "netmask": "255.255.255.0", "network": {}, "node_name": "vnf__0", "peer_ifname": "xe1", "peer_intf": { "driver": "igb_uio", "dst_ip": "1.1.1.1", "dst_mac": "4e:90:85:d3:c5:13", "ifname": "xe1", "local_ip": "1.1.1.2", "local_mac": "0a:b1:ec:fd:a2:66", "netmask": "255.255.255.0", "network": {}, "node_name": "vnf__1", "peer_ifname": "xe1", "peer_name": "vnf__0", "vld_id": "ciphertext", "vpci": "0000:00:07.0" }, "peer_name": "vnf__1", "vld_id": "ciphertext", "vpci": "0000:ff:07.0" }, "vnfd-connection-point-ref": "xe1" } ], "id": "ipsecvnf-baremetal", "name": "ipsecvnf-baremetal", "routing_table": [] } ] } VNFD_1 = { "benchmark": { "kpi": [ "packets_in", "packets_fwd", "packets_dropped" ] }, "connection-point": [ { "name": "xe0", "type": "VPORT" }, { "name": "xe1", "type": "VPORT" } ], "description": "VPP IPsec", "id": "VipsecApproxVnf", "mgmt-interface": { "ip": "10.10.10.101", "password": "r00t", "user": "root", "vdu-id": "ipsecvnf-baremetal" }, "name": "IpsecVnf", "short-name": "IpsecVnf", "vdu": [ { "description": "VPP Ipsec", "external-interface": [ { "name": "xe0", "virtual-interface": { "driver": "igb_uio", "dst_ip": "192.168.100.1", "dst_mac": "90:e2:ba:7c:30:e8", "ifname": "xe0", "local_ip": "192.168.100.2", "local_mac": "90:e2:ba:7c:41:a8", "netmask": "255.255.255.0", "network": {}, "node_name": "vnf__0", "peer_ifname": "xe0", "peer_intf": { "dpdk_port_num": 0, "driver": "igb_uio", "dst_ip": "192.168.100.2", "dst_mac": "90:e2:ba:7c:41:a8", "ifname": "xe0", "local_ip": "192.168.100.1", "local_mac": "90:e2:ba:7c:30:e8", "netmask": "255.255.255.0", "network": {}, "node_name": "tg__0", "peer_ifname": "xe0", "peer_name": "vnf__0", "vld_id": "uplink_0", "vpci": "0000:81:00.0" }, "peer_name": "tg__0", "vld_id": "uplink_0", "vpci": "0000:ff:06.0" }, "vnfd-connection-point-ref": "xe0" }, { "name": "xe1", "virtual-interface": { "driver": "igb_uio", "dst_ip": "1.1.1.2", "dst_mac": "0a:b1:ec:fd:a2:66", "ifname": "xe1", "local_ip": "1.1.1.1", "local_mac": "4e:90:85:d3:c5:13", "netmask": "255.255.255.0", "network": {}, "node_name": "vnf__0", "peer_ifname": "xe1", "peer_intf": { "driver": "igb_uio", "dst_ip": "1.1.1.1", "dst_mac": "4e:90:85:d3:c5:13", "ifname": "xe1", "local_ip": "1.1.1.2", "local_mac": "0a:b1:ec:fd:a2:66", "netmask": "255.255.255.0", "network": {}, "node_name": "vnf__1", "peer_ifname": "xe1", "peer_name": "vnf__0", "vld_id": "ciphertext", "vpci": "0000:00:07.0" }, "peer_name": "vnf__1", "vld_id": "ciphertext", "vpci": "0000:ff:07.0" }, "vnfd-connection-point-ref": "xe1" } ], "id": "ipsecvnf-baremetal", "name": "ipsecvnf-baremetal", "routing_table": [] } ] } VNFD_2 = { "benchmark": { "kpi": [ "packets_in", "packets_fwd", "packets_dropped" ] }, "connection-point": [ { "name": "xe0", "type": "VPORT" }, { "name": "xe1", "type": "VPORT" } ], "description": "VPP IPsec", "id": "VipsecApproxVnf", "mgmt-interface": { "ip": "10.10.10.101", "password": "r00t", "user": "root", "vdu-id": "ipsecvnf-baremetal" }, "name": "IpsecVnf", "short-name": "IpsecVnf", "vdu": [ { "description": "VPP Ipsec", "external-interface": [ { "name": "xe0", "virtual-interface": { "driver": "igb_uio", "dst_ip": "192.168.100.1", "dst_mac": "90:e2:ba:7c:30:e8", "ifname": "xe0", "local_ip": "192.168.100.2", "local_mac": "90:e2:ba:7c:41:a8", "netmask": "255.255.255.0", "network": {}, "node_name": "vnf__0", "peer_ifname": "xe0", "peer_intf": { "dpdk_port_num": 0, "driver": "igb_uio", "dst_ip": "192.168.100.2", "dst_mac": "90:e2:ba:7c:41:a8", "ifname": "xe0", "local_ip": "192.168.100.1", "local_mac": "90:e2:ba:7c:30:e8", "netmask": "255.255.255.0", "network": {}, "node_name": "tg__0", "peer_ifname": "xe0", "peer_name": "vnf__0", "vld_id": "uplink_0", "vpci": "0000:81:00.0" }, "peer_name": "tg__0", "vld_id": "uplink_0", "vpci": "0000:ff:06.0" }, "vnfd-connection-point-ref": "xe0" }, { "name": "xe1", "virtual-interface": { "driver": "igb_uio", "dst_ip": "1.1.1.2", "dst_mac": "0a:b1:ec:fd:a2:66", "ifname": "xe1", "local_ip": "1.1.1.1", "local_mac": "4e:90:85:d3:c5:13", "netmask": "255.255.255.0", "network": {}, "node_name": "vnf__0", "peer_ifname": "xe1", "peer_intf": { "driver": "igb_uio", "dst_ip": "1.1.1.1", "dst_mac": "4e:90:85:d3:c5:13", "ifname": "xe1", "local_ip": "1.1.1.2", "local_mac": "0a:b1:ec:fd:a2:66", "netmask": "255.255.255.0", "network": {}, "node_name": "vnf__1", "peer_ifname": "xe1", "peer_name": "vnf__0", "vld_id": "ciphertext", "vpci": "0000:00:07.0" }, "peer_name": "vnf__1", "vld_id": "ciphertext", "vpci": "0000:ff:07.0" }, "vnfd-connection-point-ref": "xe1" } ], "id": "ipsecvnf-baremetal", "name": "ipsecvnf-baremetal", "routing_table": [] } ] } VNFD = { 'vnfd:vnfd-catalog': { 'vnfd': [ VNFD_0, ], }, } VPP_INTERFACES_DUMP = [ { "sw_if_index": 0, "sup_sw_if_index": 0, "l2_address_length": 0, "l2_address": [0, 0, 0, 0, 0, 0, 0, 0], "interface_name": "local0", "admin_up_down": 0, "link_up_down": 0, "link_duplex": 0, "link_speed": 0, "mtu": 0, "sub_id": 0, "sub_dot1ad": 0, "sub_number_of_tags": 0, "sub_outer_vlan_id": 0, "sub_inner_vlan_id": 0, "sub_exact_match": 0, "sub_default": 0, "sub_outer_vlan_id_any": 0, "sub_inner_vlan_id_any": 0, "vtr_op": 0, "vtr_push_dot1q": 0, "vtr_tag1": 0, "vtr_tag2": 0 }, { "sw_if_index": 1, "sup_sw_if_index": 1, "l2_address_length": 6, "l2_address": [144, 226, 186, 124, 65, 168, 0, 0], "interface_name": "TenGigabitEthernetff/6/0", "admin_up_down": 0, "link_up_down": 0, "link_duplex": 2, "link_speed": 32, "mtu": 9202, "sub_id": 0, "sub_dot1ad": 0, "sub_number_of_tags": 0, "sub_outer_vlan_id": 0, "sub_inner_vlan_id": 0, "sub_exact_match": 0, "sub_default": 0, "sub_outer_vlan_id_any": 0, "sub_inner_vlan_id_any": 0, "vtr_op": 0, "vtr_push_dot1q": 0, "vtr_tag1": 0, "vtr_tag2": 0 }, { "sw_if_index": 2, "sup_sw_if_index": 2, "l2_address_length": 6, "l2_address": [78, 144, 133, 211, 197, 19, 0, 0], "interface_name": "VirtualFunctionEthernetff/7/0", "admin_up_down": 0, "link_up_down": 0, "link_duplex": 2, "link_speed": 32, "mtu": 9206, "sub_id": 0, "sub_dot1ad": 0, "sub_number_of_tags": 0, "sub_outer_vlan_id": 0, "sub_inner_vlan_id": 0, "sub_exact_match": 0, "sub_default": 0, "sub_outer_vlan_id_any": 0, "sub_inner_vlan_id_any": 0, "vtr_op": 0, "vtr_push_dot1q": 0, "vtr_tag1": 0, "vtr_tag2": 0 } ] VPP_INTERFACES_DUMP_MAC_ERR = [ { "sw_if_index": 0, "sup_sw_if_index": 0, "l2_address_length": 0, "l2_address": [0, 0, 0, 0, 0, 0, 0, 0], "interface_name": "local0", "admin_up_down": 0, "link_up_down": 0, "link_duplex": 0, "link_speed": 0, "mtu": 0, "sub_id": 0, "sub_dot1ad": 0, "sub_number_of_tags": 0, "sub_outer_vlan_id": 0, "sub_inner_vlan_id": 0, "sub_exact_match": 0, "sub_default": 0, "sub_outer_vlan_id_any": 0, "sub_inner_vlan_id_any": 0, "vtr_op": 0, "vtr_push_dot1q": 0, "vtr_tag1": 0, "vtr_tag2": 0 }, { "sw_if_index": 1, "sup_sw_if_index": 1, "l2_address_length": 6, "l2_address": [144, 226, 186, 124, 65, 169, 0, 0], "interface_name": "TenGigabitEthernetff/6/0", "admin_up_down": 0, "link_up_down": 0, "link_duplex": 2, "link_speed": 32, "mtu": 9202, "sub_id": 0, "sub_dot1ad": 0, "sub_number_of_tags": 0, "sub_outer_vlan_id": 0, "sub_inner_vlan_id": 0, "sub_exact_match": 0, "sub_default": 0, "sub_outer_vlan_id_any": 0, "sub_inner_vlan_id_any": 0, "vtr_op": 0, "vtr_push_dot1q": 0, "vtr_tag1": 0, "vtr_tag2": 0 }, { "sw_if_index": 2, "sup_sw_if_index": 2, "l2_address_length": 6, "l2_address": [78, 144, 133, 211, 197, 20, 0, 0], "interface_name": "VirtualFunctionEthernetff/7/0", "admin_up_down": 0, "link_up_down": 0, "link_duplex": 2, "link_speed": 32, "mtu": 9206, "sub_id": 0, "sub_dot1ad": 0, "sub_number_of_tags": 0, "sub_outer_vlan_id": 0, "sub_inner_vlan_id": 0, "sub_exact_match": 0, "sub_default": 0, "sub_outer_vlan_id_any": 0, "sub_inner_vlan_id_any": 0, "vtr_op": 0, "vtr_push_dot1q": 0, "vtr_tag1": 0, "vtr_tag2": 0 } ] CPU_LAYOUT = {'cpuinfo': [[0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 1, 1, 0]]} CPU_SMT = {'cpuinfo': [[0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 1, 1, 0], [2, 1, 0, 0, 0, 2, 2, 1], [3, 1, 0, 0, 0, 3, 3, 1], [4, 2, 0, 0, 0, 4, 4, 2], [5, 2, 0, 0, 0, 5, 5, 2], [6, 3, 0, 0, 0, 6, 6, 3], [7, 3, 0, 0, 0, 7, 7, 3], [8, 4, 0, 0, 0, 8, 8, 4], [9, 5, 0, 1, 0, 0, 0, 0], [10, 6, 0, 1, 0, 1, 1, 0], [11, 6, 0, 1, 0, 2, 2, 1], [12, 7, 0, 1, 0, 3, 3, 1], [13, 7, 0, 1, 0, 4, 4, 2], [14, 8, 0, 1, 0, 5, 5, 2], [15, 8, 0, 1, 0, 6, 6, 3], [16, 9, 0, 1, 0, 7, 7, 3], [17, 9, 0, 1, 0, 8, 8, 4]]} def test_kill_vnf(self): vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() ssh_helper.execute.return_value = 0, 0, 0 scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) vpp_setup_env_helper.kill_vnf() def test_kill_vnf_error(self): vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() ssh_helper.execute.return_value = 1, 0, 0 scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) with self.assertRaises(RuntimeError) as raised: vpp_setup_env_helper.kill_vnf() self.assertIn('Failed to stop service vpp', str(raised.exception)) def test_tear_down(self): vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) vpp_setup_env_helper.tear_down() def test_start_vpp_service(self): vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() ssh_helper.execute.return_value = 0, 0, 0 scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) vpp_setup_env_helper.start_vpp_service() def test_start_vpp_service_error(self): vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() ssh_helper.execute.return_value = 1, 0, 0 scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) with self.assertRaises(RuntimeError) as raised: vpp_setup_env_helper.start_vpp_service() self.assertIn('Failed to start service vpp', str(raised.exception)) def test__update_vnfd_helper(self): vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) vpp_setup_env_helper._update_vnfd_helper( {'vpp-data': {'vpp-key': 'vpp-value'}}) self.assertEqual({'vpp-key': 'vpp-value'}, vpp_setup_env_helper.vnfd_helper.get('vpp-data', {})) def test__update_vnfd_helper_with_key(self): vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) vpp_setup_env_helper._update_vnfd_helper({'driver': 'qat'}, 'xe0') self.assertEqual('qat', vpp_setup_env_helper.get_value_by_interface_key( 'xe0', 'driver')) def test__update_vnfd_helper_dict_without_key(self): vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) vpp_setup_env_helper._update_vnfd_helper( {'mgmt-interface': {'name': 'net'}}) self.assertEqual({'ip': '10.10.10.101', 'name': 'net', 'password': 'r00t', 'user': 'root', 'vdu-id': 'ipsecvnf-baremetal'}, vpp_setup_env_helper.vnfd_helper.get('mgmt-interface', {})) def test_get_value_by_interface_key(self): vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) vpp_setup_env_helper._update_vnfd_helper( {'vpp-data': {'vpp-key': 'vpp-value'}}, 'xe0') self.assertEqual({'vpp-key': 'vpp-value'}, vpp_setup_env_helper.get_value_by_interface_key( 'xe0', 'vpp-data')) def test_get_value_by_interface_key_error(self): vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) vpp_setup_env_helper._update_vnfd_helper( {'vpp-data': {'vpp-key': 'vpp-value'}}, 'xe0') self.assertIsNone(vpp_setup_env_helper.get_value_by_interface_key( 'xe2', 'vpp-err')) def test_crypto_device_init(self): vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) vpp_setup_env_helper.dpdk_bind_helper.load_dpdk_driver = mock.Mock() vpp_setup_env_helper.dpdk_bind_helper.bind = mock.Mock() vpp_setup_env_helper.kill_vnf = mock.Mock() vpp_setup_env_helper.pci_driver_unbind = mock.Mock() with mock.patch.object(vpp_setup_env_helper, 'get_pci_dev_driver') as \ mock_get_pci_dev_driver, \ mock.patch.object(vpp_setup_env_helper, 'set_sriov_numvfs') as \ mock_set_sriov_numvfs: mock_get_pci_dev_driver.return_value = 'igb_uio' self.assertIsNone( vpp_setup_env_helper.crypto_device_init('0000:ff:06.0', 32)) mock_set_sriov_numvfs.assert_called() def test_get_sriov_numvfs(self): vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() ssh_helper.execute.return_value = 0, '32', '' scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) self.assertEqual(32, vpp_setup_env_helper.get_sriov_numvfs('0000:ff:06.0')) def test_get_sriov_numvfs_error(self): vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() ssh_helper.execute.return_value = 0, 'err', '' scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) self.assertEqual(0, vpp_setup_env_helper.get_sriov_numvfs('0000:ff:06.0')) def test_set_sriov_numvfs(self): vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) vpp_setup_env_helper.set_sriov_numvfs('0000:ff:06.0') self.assertEqual(ssh_helper.execute.call_count, 1) def test_pci_driver_unbind(self): vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) vpp_setup_env_helper.pci_driver_unbind('0000:ff:06.0') self.assertEqual(ssh_helper.execute.call_count, 1) def test_get_pci_dev_driver(self): vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() ssh_helper.execute.return_value = \ 0, 'Slot: ff:07.0\n' \ 'Class: Ethernet controller\n' \ 'Vendor: Intel Corporation\n' \ 'Device: 82599 Ethernet Controller Virtual Function\n' \ 'SVendor: Intel Corporation\n' \ 'SDevice: 82599 Ethernet Controller Virtual Function\n' \ 'Rev: 01\n' \ 'Driver: igb_uio\n' \ 'Module: ixgbevf', '' scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) self.assertEqual('igb_uio', vpp_setup_env_helper.get_pci_dev_driver( '0000:ff:06.0')) def test_get_pci_dev_driver_error(self): vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() ssh_helper.execute.return_value = 1, 'err', '' scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) with self.assertRaises(RuntimeError) as raised: vpp_setup_env_helper.get_pci_dev_driver( '0000:ff:06.0') self.assertIn("'lspci -vmmks 0000:ff:06.0' failed", str(raised.exception)) def test_get_pci_dev_driver_output_error(self): vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() ssh_helper.execute.return_value = \ 0, 'Slot: ff:07.0\n' \ '\n\t' \ 'Vendor: Intel Corporation\n' \ 'Device: 82599 Ethernet Controller Virtual Function\n' \ 'SVendor: Intel Corporation\n' \ 'SDevice: 82599 Ethernet Controller Virtual Function\n' \ 'Rev: 01\n' \ 'Driver_err: igb_uio\n' \ 'Module: ixgbevf', '' scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) self.assertIsNone( vpp_setup_env_helper.get_pci_dev_driver('0000:ff:06.0')) def test_vpp_create_ipsec_tunnels(self): vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() ssh_helper.execute.return_value = 0, '', '' scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) self.assertIsNone( vpp_setup_env_helper.vpp_create_ipsec_tunnels('10.10.10.2', '10.10.10.1', 'xe0', 1, 1, mock.Mock(), 'crypto_key', mock.Mock(), 'integ_key', '20.20.20.0')) self.assertGreaterEqual(ssh_helper.execute.call_count, 2) def test_vpp_create_ipsec_1000_tunnels(self): vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() ssh_helper.execute.return_value = 0, '', '' scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) self.assertIsNone( vpp_setup_env_helper.vpp_create_ipsec_tunnels('10.10.10.2', '10.10.10.1', 'xe0', 1000, 128000, mock.Mock(), 'crypto_key', mock.Mock(), 'integ_key', '20.20.20.0')) self.assertGreaterEqual(ssh_helper.execute.call_count, 2) def test_apply_config(self): vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() ssh_helper.execute.return_value = 0, '', '' scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) self.assertIsNone(vpp_setup_env_helper.apply_config(mock.Mock())) self.assertGreaterEqual(ssh_helper.execute.call_count, 2) def test_apply_config_error(self): vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() ssh_helper.execute.return_value = 1, '', '' scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) with self.assertRaises(RuntimeError) as raised: vpp_setup_env_helper.apply_config(mock.Mock()) self.assertIn('Writing config file failed', str(raised.exception)) def test_vpp_route_add(self): vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) with mock.patch.object(vpp_helpers.VatTerminal, 'vat_terminal_exec_cmd_from_template') as \ mock_vat_terminal_exec_cmd_from_template: mock_vat_terminal_exec_cmd_from_template.return_value = '' self.assertIsNone( vpp_setup_env_helper.vpp_route_add('xe0', '10.10.10.1', 24)) def test_vpp_route_add_without_index(self): vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) with mock.patch.object(vpp_helpers.VatTerminal, 'vat_terminal_exec_cmd_from_template') as \ mock_vat_terminal_exec_cmd_from_template: mock_vat_terminal_exec_cmd_from_template.return_value = '' self.assertIsNone( vpp_setup_env_helper.vpp_route_add('xe0', '10.10.10.1', 24, interface='xe0', use_sw_index=False)) def test_add_arp_on_dut(self): vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) with mock.patch.object(vpp_helpers.VatTerminal, 'vat_terminal_exec_cmd_from_template') as \ mock_vat_terminal_exec_cmd_from_template: mock_vat_terminal_exec_cmd_from_template.return_value = '' self.assertEqual('', vpp_setup_env_helper.add_arp_on_dut('xe0', '10.10.10.1', '00:00:00:00:00:00')) def test_set_ip(self): vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) with mock.patch.object(vpp_helpers.VatTerminal, 'vat_terminal_exec_cmd_from_template') as \ mock_vat_terminal_exec_cmd_from_template: mock_vat_terminal_exec_cmd_from_template.return_value = '' self.assertEqual('', vpp_setup_env_helper.set_ip('xe0', '10.10.10.1', 24)) def test_set_interface_state(self): vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) with mock.patch.object(vpp_helpers.VatTerminal, 'vat_terminal_exec_cmd_from_template') as \ mock_vat_terminal_exec_cmd_from_template: mock_vat_terminal_exec_cmd_from_template.return_value = '' self.assertEqual('', vpp_setup_env_helper.set_interface_state('xe0', 'up')) def test_set_interface_state_error(self): vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) with mock.patch.object(vpp_helpers.VatTerminal, 'vat_terminal_exec_cmd_from_template') as \ mock_vat_terminal_exec_cmd_from_template: mock_vat_terminal_exec_cmd_from_template.return_value = '' with self.assertRaises(ValueError) as raised: vpp_setup_env_helper.set_interface_state('xe0', 'error') self.assertIn('Unexpected interface state: error', str(raised.exception)) def test_set_interface_down_state(self): vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) with mock.patch.object(vpp_helpers.VatTerminal, 'vat_terminal_exec_cmd_from_template') as \ mock_vat_terminal_exec_cmd_from_template: mock_vat_terminal_exec_cmd_from_template.return_value = '' self.assertEqual('', vpp_setup_env_helper.set_interface_state('xe0', 'down')) def test_vpp_set_interface_mtu(self): vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) with mock.patch.object(vpp_helpers.VatTerminal, 'vat_terminal_exec_cmd_from_template') as \ mock_vat_terminal_exec_cmd_from_template: mock_vat_terminal_exec_cmd_from_template.return_value = '' self.assertIsNone( vpp_setup_env_helper.vpp_set_interface_mtu('xe0', 9200)) def test_vpp_interfaces_ready_wait(self): json_output = [self.VPP_INTERFACES_DUMP] vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) with mock.patch.object(vpp_helpers.VatTerminal, 'vat_terminal_exec_cmd_from_template') as \ mock_vat_terminal_exec_cmd_from_template: mock_vat_terminal_exec_cmd_from_template.return_value = json_output self.assertIsNone(vpp_setup_env_helper.vpp_interfaces_ready_wait()) def test_vpp_interfaces_ready_wait_timeout(self): json_output = [[ { "sw_if_index": 0, "sup_sw_if_index": 0, "l2_address_length": 0, "l2_address": [0, 0, 0, 0, 0, 0, 0, 0], "interface_name": "xe0", "admin_up_down": 1, "link_up_down": 0, "link_duplex": 0, "link_speed": 0, "mtu": 0, "sub_id": 0, "sub_dot1ad": 0, "sub_number_of_tags": 0, "sub_outer_vlan_id": 0, "sub_inner_vlan_id": 0, "sub_exact_match": 0, "sub_default": 0, "sub_outer_vlan_id_any": 0, "sub_inner_vlan_id_any": 0, "vtr_op": 0, "vtr_push_dot1q": 0, "vtr_tag1": 0, "vtr_tag2": 0 }]] vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) with mock.patch.object(vpp_helpers.VatTerminal, 'vat_terminal_exec_cmd_from_template') as \ mock_vat_terminal_exec_cmd_from_template: mock_vat_terminal_exec_cmd_from_template.return_value = json_output with self.assertRaises(RuntimeError) as raised: vpp_setup_env_helper.vpp_interfaces_ready_wait(5) self.assertIn('timeout, not up [\'xe0\']', str(raised.exception)) def test_vpp_get_interface_data(self): json_output = [self.VPP_INTERFACES_DUMP] vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) with mock.patch.object(vpp_helpers.VatTerminal, 'vat_terminal_exec_cmd_from_template') as \ mock_vat_terminal_exec_cmd_from_template: mock_vat_terminal_exec_cmd_from_template.return_value = json_output self.assertEqual(json_output[0], vpp_setup_env_helper.vpp_get_interface_data()) def test_vpp_get_interface_data_ifname(self): json_output = [self.VPP_INTERFACES_DUMP] vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) with mock.patch.object(vpp_helpers.VatTerminal, 'vat_terminal_exec_cmd_from_template') as \ mock_vat_terminal_exec_cmd_from_template: mock_vat_terminal_exec_cmd_from_template.return_value = json_output self.assertEqual(json_output[0][2], vpp_setup_env_helper.vpp_get_interface_data( 'VirtualFunctionEthernetff/7/0')) def test_vpp_get_interface_data_ifname_error(self): json_output = [self.VPP_INTERFACES_DUMP] vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) with mock.patch.object(vpp_helpers.VatTerminal, 'vat_terminal_exec_cmd_from_template') as \ mock_vat_terminal_exec_cmd_from_template: mock_vat_terminal_exec_cmd_from_template.return_value = json_output self.assertEqual({}, vpp_setup_env_helper.vpp_get_interface_data( 'error')) def test_vpp_get_interface_data_ifindex(self): json_output = [self.VPP_INTERFACES_DUMP] vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) with mock.patch.object(vpp_helpers.VatTerminal, 'vat_terminal_exec_cmd_from_template') as \ mock_vat_terminal_exec_cmd_from_template: mock_vat_terminal_exec_cmd_from_template.return_value = json_output self.assertEqual(json_output[0][1], vpp_setup_env_helper.vpp_get_interface_data(1)) def test_vpp_get_interface_data_error(self): json_output = [self.VPP_INTERFACES_DUMP] vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) with mock.patch.object(vpp_helpers.VatTerminal, 'vat_terminal_exec_cmd_from_template') as \ mock_vat_terminal_exec_cmd_from_template: mock_vat_terminal_exec_cmd_from_template.return_value = json_output with self.assertRaises(TypeError) as raised: vpp_setup_env_helper.vpp_get_interface_data(1.0) self.assertEqual('', str(raised.exception)) def test_update_vpp_interface_data(self): output = '{}\n{}'.format(self.VPP_INTERFACES_DUMP, 'dump_interface_table:6019: JSON output ' \ 'supported only for VPE API calls and dump_stats_table\n' \ '/opt/nsb_bin/vpp/templates/dump_interfaces.vat(2): \n' \ 'dump_interface_table error: Misc') vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() ssh_helper.execute.return_value = 0, output.replace("\'", "\""), '' ssh_helper.join_bin_path.return_value = '/opt/nsb_bin/vpp/templates' scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) self.assertIsNone(vpp_setup_env_helper.update_vpp_interface_data()) self.assertGreaterEqual(ssh_helper.execute.call_count, 1) self.assertEqual('TenGigabitEthernetff/6/0', vpp_setup_env_helper.get_value_by_interface_key( 'xe0', 'vpp_name')) self.assertEqual(1, vpp_setup_env_helper.get_value_by_interface_key( 'xe0', 'vpp_sw_index')) self.assertEqual('VirtualFunctionEthernetff/7/0', vpp_setup_env_helper.get_value_by_interface_key( 'xe1', 'vpp_name')) self.assertEqual(2, vpp_setup_env_helper.get_value_by_interface_key( 'xe1', 'vpp_sw_index')) def test_update_vpp_interface_data_error(self): output = '{}\n{}'.format(self.VPP_INTERFACES_DUMP_MAC_ERR, 'dump_interface_table:6019: JSON output ' \ 'supported only for VPE API calls and dump_stats_table\n' \ '/opt/nsb_bin/vpp/templates/dump_interfaces.vat(2): \n' \ 'dump_interface_table error: Misc') vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() ssh_helper.execute.return_value = 0, output.replace("\'", "\""), '' ssh_helper.join_bin_path.return_value = '/opt/nsb_bin/vpp/templates' scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) self.assertIsNone(vpp_setup_env_helper.update_vpp_interface_data()) self.assertGreaterEqual(ssh_helper.execute.call_count, 1) def test_iface_update_numa(self): vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() ssh_helper.execute.return_value = 0, '0', '' scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) self.assertIsNone(vpp_setup_env_helper.iface_update_numa()) self.assertGreaterEqual(ssh_helper.execute.call_count, 2) self.assertEqual(0, vpp_setup_env_helper.get_value_by_interface_key( 'xe0', 'numa_node')) self.assertEqual(0, vpp_setup_env_helper.get_value_by_interface_key( 'xe1', 'numa_node')) def test_iface_update_numa_error(self): vnfd_helper = VnfdHelper(self.VNFD_1) ssh_helper = mock.Mock() ssh_helper.execute.return_value = 0, '-1', '' scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) with mock.patch.object(cpu.CpuSysCores, 'get_cpu_layout') as \ mock_get_cpu_layout: mock_get_cpu_layout.return_value = self.CPU_LAYOUT sys_cores = cpu.CpuSysCores(ssh_helper) vpp_setup_env_helper._update_vnfd_helper( sys_cores.get_cpu_layout()) self.assertIsNone(vpp_setup_env_helper.iface_update_numa()) self.assertGreaterEqual(ssh_helper.execute.call_count, 2) self.assertEqual(0, vpp_setup_env_helper.get_value_by_interface_key( 'xe0', 'numa_node')) self.assertEqual(0, vpp_setup_env_helper.get_value_by_interface_key( 'xe1', 'numa_node')) def test_iface_update_without_numa(self): vnfd_helper = VnfdHelper(self.VNFD_2) ssh_helper = mock.Mock() ssh_helper.execute.return_value = 0, '-1', '' scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) with mock.patch.object(cpu.CpuSysCores, 'get_cpu_layout') as \ mock_get_cpu_layout: mock_get_cpu_layout.return_value = self.CPU_SMT sys_cores = cpu.CpuSysCores(ssh_helper) vpp_setup_env_helper._update_vnfd_helper( sys_cores.get_cpu_layout()) self.assertIsNone(vpp_setup_env_helper.iface_update_numa()) self.assertGreaterEqual(ssh_helper.execute.call_count, 2) self.assertIsNone(vpp_setup_env_helper.get_value_by_interface_key( 'xe0', 'numa_node')) self.assertIsNone(vpp_setup_env_helper.get_value_by_interface_key( 'xe1', 'numa_node')) def test_execute_script(self): vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) vpp_setup_env_helper.execute_script('dump_interfaces.vat', True, True) self.assertGreaterEqual(ssh_helper.put_file.call_count, 1) self.assertGreaterEqual(ssh_helper.execute.call_count, 1) def test_execute_script_error(self): vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() ssh_helper.execute.side_effect = Exception scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) with self.assertRaises(Exception) as raised: vpp_setup_env_helper.execute_script('dump_interfaces.vat', True, True) self.assertIn( 'VAT script execution failed: vpp_api_test json in dump_interfaces.vat script', str(raised.exception)) self.assertGreaterEqual(ssh_helper.put_file.call_count, 1) def test_execute_script_json_out(self): json_output = [ { "sw_if_index": 0, "sup_sw_if_index": 0 }, { "l2_address_length": 6, "l2_address": [144, 226, 186, 124, 65, 168, 0, 0] }, { "interface_name": "VirtualFunctionEthernetff/7/0", "admin_up_down": 0 } ] output = '{}\n{}'.format(json_output, 'dump_interface_table:6019: JSON output ' \ 'supported only for VPE API calls and dump_stats_table\n' \ '/opt/nsb_bin/vpp/templates/dump_interfaces.vat(2): \n' \ 'dump_interface_table error: Misc') vnfd_helper = VnfdHelper(self.VNFD_0) ssh_helper = mock.Mock() ssh_helper.execute.return_value = 0, output, '' ssh_helper.join_bin_path.return_value = '/opt/nsb_bin/vpp/templates' scenario_helper = mock.Mock() vpp_setup_env_helper = VppSetupEnvHelper(vnfd_helper, ssh_helper, scenario_helper) self.assertEqual(str(json_output), vpp_setup_env_helper.execute_script_json_out( 'dump_interfaces.vat')) def test_self_cleanup_vat_json_output(self): json_output = [ { "sw_if_index": 0, "sup_sw_if_index": 0 }, { "l2_address_length": 6, "l2_address": [144, 226, 186, 124, 65, 168, 0, 0] }, { "interface_name": "VirtualFunctionEthernetff/7/0", "admin_up_down": 0 } ] output = '{}\n{}'.format(json_output, 'dump_interface_table:6019: JSON output ' \ 'supported only for VPE API calls and dump_stats_table\n' \ '/opt/nsb_bin/vpp/templates/dump_interfaces.vat(2): \n' \ 'dump_interface_table error: Misc') self.assertEqual(str(json_output), VppSetupEnvHelper.cleanup_vat_json_output(output, '/opt/nsb_bin/vpp/templates/dump_interfaces.vat')) def test__convert_mac_to_number_list(self): self.assertEqual([144, 226, 186, 124, 65, 168], VppSetupEnvHelper._convert_mac_to_number_list( '90:e2:ba:7c:41:a8')) def test_get_vpp_interface_by_mac(self): mac_address = '90:e2:ba:7c:41:a8' self.assertEqual({'admin_up_down': 0, 'interface_name': 'TenGigabitEthernetff/6/0', 'l2_address': [144, 226, 186, 124, 65, 168, 0, 0], 'l2_address_length': 6, 'link_duplex': 2, 'link_speed': 32, 'link_up_down': 0, 'mtu': 9202, 'sub_default': 0, 'sub_dot1ad': 0, 'sub_exact_match': 0, 'sub_id': 0, 'sub_inner_vlan_id': 0, 'sub_inner_vlan_id_any': 0, 'sub_number_of_tags': 0, 'sub_outer_vlan_id': 0, 'sub_outer_vlan_id_any': 0, 'sup_sw_if_index': 1, 'sw_if_index': 1, 'vtr_op': 0, 'vtr_push_dot1q': 0, 'vtr_tag1': 0, 'vtr_tag2': 0}, VppSetupEnvHelper.get_vpp_interface_by_mac( self.VPP_INTERFACES_DUMP, mac_address)) def test_get_vpp_interface_by_mac_error(self): mac_address = '90:e2:ba:7c:41:a9' with self.assertRaises(ValueError) as raised: VppSetupEnvHelper.get_vpp_interface_by_mac( [{ "sw_if_index": 1, "sup_sw_if_index": 1, "l2_address_length": 7, "l2_address": [144, 226, 186, 124, 65, 169, 0, 0], "interface_name": "TenGigabitEthernetff/6/0", "admin_up_down": 0, "link_up_down": 0, "link_duplex": 2, "link_speed": 32, "mtu": 9202, "sub_id": 0, "sub_dot1ad": 0, "sub_number_of_tags": 0, "sub_outer_vlan_id": 0, "sub_inner_vlan_id": 0, "sub_exact_match": 0, "sub_default": 0, "sub_outer_vlan_id_any": 0, "sub_inner_vlan_id_any": 0, "vtr_op": 0, "vtr_push_dot1q": 0, "vtr_tag1": 0, "vtr_tag2": 0 }], mac_address) self.assertIn('l2_address_length value is not 6.', str(raised.exception)) def test_get_vpp_interface_by_mac_l2_error(self): mac_address = '90:e2:ba:7c:41:a7' with self.assertRaises(KeyError) as raised: VppSetupEnvHelper.get_vpp_interface_by_mac( [{ "sw_if_index": 1, "sup_sw_if_index": 1, "l2_address_length": 6, "l2_address_err": [144, 226, 186, 124, 65, 167, 0, 0], "interface_name": "TenGigabitEthernetff/6/0", "admin_up_down": 0, "link_up_down": 0, "link_duplex": 2, "link_speed": 32, "mtu": 9202, "sub_id": 0, "sub_dot1ad": 0, "sub_number_of_tags": 0, "sub_outer_vlan_id": 0, "sub_inner_vlan_id": 0, "sub_exact_match": 0, "sub_default": 0, "sub_outer_vlan_id_any": 0, "sub_inner_vlan_id_any": 0, "vtr_op": 0, "vtr_push_dot1q": 0, "vtr_tag1": 0, "vtr_tag2": 0 }], mac_address) self.assertIn( 'key l2_address not found in interface dict.Probably input list ' \ 'is not parsed from correct VAT json output.', str(raised.exception)) def test_get_vpp_interface_by_mac_l2_length_error(self): mac_address = '90:e2:ba:7c:41:a6' with self.assertRaises(KeyError) as raised: VppSetupEnvHelper.get_vpp_interface_by_mac( [{ "sw_if_index": 1, "sup_sw_if_index": 1, "l2_address_length_err": 6, "l2_address": [144, 226, 186, 124, 65, 166, 0, 0], "interface_name": "TenGigabitEthernetff/6/0", "admin_up_down": 0, "link_up_down": 0, "link_duplex": 2, "link_speed": 32, "mtu": 9202, "sub_id": 0, "sub_dot1ad": 0, "sub_number_of_tags": 0, "sub_outer_vlan_id": 0, "sub_inner_vlan_id": 0, "sub_exact_match": 0, "sub_default": 0, "sub_outer_vlan_id_any": 0, "sub_inner_vlan_id_any": 0, "vtr_op": 0, "vtr_push_dot1q": 0, "vtr_tag1": 0, "vtr_tag2": 0 }], mac_address) self.assertIn( 'key l2_address_length not found in interface dict. Probably ' \ 'input list is not parsed from correct VAT json output.', str(raised.exception)) def test_get_prefix_length(self): start_ip = '10.10.10.0' end_ip = '10.10.10.127' ips = [ipaddress.ip_address(ip) for ip in [str(ipaddress.ip_address(start_ip)), str(end_ip)]] lowest_ip, highest_ip = min(ips), max(ips) self.assertEqual(25, VppSetupEnvHelper.get_prefix_length(int(lowest_ip), int(highest_ip), lowest_ip.max_prefixlen)) def test_get_prefix_length_zero_prefix(self): start_ip = '10.0.0.0' end_ip = '10.0.0.0' ips = [ipaddress.ip_address(ip) for ip in [str(ipaddress.ip_address(start_ip)), str(end_ip)]] lowest_ip, highest_ip = min(ips), max(ips) self.assertEqual(0, VppSetupEnvHelper.get_prefix_length(int(lowest_ip), int(highest_ip), 0)) class TestVatTerminal(unittest.TestCase): def test___init___error(self): ssh_helper = mock.Mock() ssh_helper.interactive_terminal_open.side_effect = exceptions.SSHTimeout with self.assertRaises(RuntimeError) as raised: VatTerminal(ssh_helper, json_param=True) self.assertIn('Cannot open interactive terminal', str(raised.exception)) def test___init___exec_error(self): ssh_helper = mock.Mock() ssh_helper.interactive_terminal_exec_command.side_effect = exceptions.SSHTimeout VatTerminal(ssh_helper, json_param=True) def test_vat_terminal_exec_cmd(self): ssh_helper = mock.Mock() ssh_helper.interactive_terminal_exec_command.return_value = str( {'empty': 'value'}).replace("\'", "\"") vat_terminal = VatTerminal(ssh_helper, json_param=True) self.assertEqual({'empty': 'value'}, vat_terminal.vat_terminal_exec_cmd( "hw_interface_set_mtu sw_if_index 1 mtu 9200")) def test_vat_terminal_exec_cmd_array(self): ssh_helper = mock.Mock() ssh_helper.interactive_terminal_exec_command.return_value = str( [{'empty': 'value'}]).replace("\'", "\"") vat_terminal = VatTerminal(ssh_helper, json_param=True) self.assertEqual([{'empty': 'value'}], vat_terminal.vat_terminal_exec_cmd( "hw_interface_set_mtu sw_if_index 1 mtu 9200")) def test_vat_terminal_exec_cmd_without_output(self): ssh_helper = mock.Mock() ssh_helper.interactive_terminal_exec_command.return_value = str( {'empty': 'value'}).replace("\'", "\"") vat_terminal = VatTerminal(ssh_helper, json_param=False) self.assertIsNone(vat_terminal.vat_terminal_exec_cmd( "hw_interface_set_mtu sw_if_index 1 mtu 9200")) def test_vat_terminal_exec_cmd_error(self): ssh_helper = mock.Mock() ssh_helper.interactive_terminal_exec_command.return_value = str( {'empty': 'value'}).replace("\'", "\"") ssh_helper.interactive_terminal_exec_command.side_effect = exceptions.SSHTimeout vat_terminal = VatTerminal(ssh_helper, json_param=True) with self.assertRaises(RuntimeError) as raised: vat_terminal.vat_terminal_exec_cmd( "hw_interface_set_mtu sw_if_index 1 mtu 9200") self.assertIn( 'VPP is not running on node. VAT command hw_interface_set_mtu ' \ 'sw_if_index 1 mtu 9200 execution failed', str(raised.exception)) def test_vat_terminal_exec_cmd_output_error(self): ssh_helper = mock.Mock() ssh_helper.interactive_terminal_exec_command.return_value = str( 'empty: value').replace("\'", "\"") vat_terminal = VatTerminal(ssh_helper, json_param=True) with self.assertRaises(RuntimeError) as raised: vat_terminal.vat_terminal_exec_cmd( "hw_interface_set_mtu sw_if_index 1 mtu 9200") self.assertIn( 'VAT command hw_interface_set_mtu sw_if_index 1 mtu 9200: no JSON data.', str(raised.exception)) def test_vat_terminal_close(self): ssh_helper = mock.Mock() vat_terminal = VatTerminal(ssh_helper, json_param=False) self.assertIsNone(vat_terminal.vat_terminal_close()) def test_vat_terminal_close_error(self): ssh_helper = mock.Mock() ssh_helper.interactive_terminal_exec_command.side_effect = exceptions.SSHTimeout vat_terminal = VatTerminal(ssh_helper, json_param=False) with self.assertRaises(RuntimeError) as raised: vat_terminal.vat_terminal_close() self.assertIn('Failed to close VAT console', str(raised.exception)) def test_vat_terminal_close_vat_error(self): ssh_helper = mock.Mock() ssh_helper.interactive_terminal_close.side_effect = exceptions.SSHTimeout vat_terminal = VatTerminal(ssh_helper, json_param=False) with self.assertRaises(RuntimeError) as raised: vat_terminal.vat_terminal_close() self.assertIn('Cannot close interactive terminal', str(raised.exception)) def test_vat_terminal_exec_cmd_from_template(self): ssh_helper = mock.Mock() vat_terminal = VatTerminal(ssh_helper, json_param=False) with mock.patch.object(vat_terminal, 'vat_terminal_exec_cmd') as \ mock_vat_terminal_exec_cmd: mock_vat_terminal_exec_cmd.return_value = 'empty' self.assertEqual(['empty'], vat_terminal.vat_terminal_exec_cmd_from_template( "hw_interface_set_mtu.vat", sw_if_index=1, mtu=9200))

43.261021

92

0.508313

7,936

74,582

4.405368

0.05872

0.041189

0.035554

0.054947

0.903006

0.874432

0.843225

0.815966

0.799605

0.784045

0

0.045576

0.389853

74,582

1,723

93

43.286129

0.722696

0.007522

0

0.705577

0

0.003243

0.172227

0.027727

0

0.082361

1

0.060311

false

0.002594

0.005188

0

0.072633

0

null

0

1

0

1

0

null

0

7

aea248ff823554edb3b306812ae026692af6f335

186

py

Python

task_3.py

andriidem308/homework_10

7285a86a1f5ebabcfa19f53b2884ac477a82b6f8

[ "MIT" ]

null

task_3.py

andriidem308/homework_10

7285a86a1f5ebabcfa19f53b2884ac477a82b6f8

[ "MIT" ]

1

2021-04-12T13:18:51.000Z

task_3.py

andriidem308/homework_10

7285a86a1f5ebabcfa19f53b2884ac477a82b6f8

[ "MIT" ]

null

def three_highest(cars): return sorted(cars, key=lambda car: car.dimensions[2], reverse=True)[:3] def three_fastest(cars): return sorted(cars, key=lambda car: car.to100km)[:3]

26.571429

76

0.715054

29

186

4.517241

0.551724

0.122137

0.244275

0.305344

0.534351

0

0.037267

0.134409

186

6

77

31

0.776398

0

1

0.5

false

0

0.5

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

7

4e3d3a81a1a955afbec59f1ff803f5f69af7b74e

168

py

Python

lambda-code/decompress_gz/lambda_function.py

aws-samples/amazon-s3-object-lambda-decompression

ebe3851d5f0f867863e5efa73ff8e089c2581d9d

[ "MIT-0" ]

5

2021-04-06T14:37:49.000Z

2022-01-04T13:41:01.000Z

lambda-code/decompress_gz/lambda_function.py

aws-samples/amazon-s3-object-lambda-decompression

ebe3851d5f0f867863e5efa73ff8e089c2581d9d

[ "MIT-0" ]

null

lambda-code/decompress_gz/lambda_function.py

aws-samples/amazon-s3-object-lambda-decompression

ebe3851d5f0f867863e5efa73ff8e089c2581d9d

[ "MIT-0" ]

2

2021-04-19T13:51:14.000Z

2021-06-10T18:54:04.000Z

import gzip import common_functions def decompress(event, context): return common_functions.decompress(event, lambda data:gzip.GzipFile(fileobj=data, mode="rb"))

28

98

0.791667

22

168

5.954545

0.681818

0.229008

0

0.107143

168

5

99

33.6

0.873333

0

0.011905

0

1

0.25

false

0

0.5

0.25

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

7

9d6cfe627c7a7190494b8df9c7062738dbb076f8

23

py

Python

pythonAlgorithm/breadthsearch/Test.py

Sky-zzt/lintcodePractice

d6436b296c5865d85e55c8ad4fcdbb0165d4ebb1

[ "MIT" ]

1

2020-09-15T07:58:55.000Z

pythonAlgorithm/breadthsearch/Test.py

Sky-zzt/lintcodePractice

d6436b296c5865d85e55c8ad4fcdbb0165d4ebb1

[ "MIT" ]

null

pythonAlgorithm/breadthsearch/Test.py

Sky-zzt/lintcodePractice

d6436b296c5865d85e55c8ad4fcdbb0165d4ebb1

[ "MIT" ]

null

print(100000 * 100000)

11.5

22

0.73913

3

23

5.666667

0.666667

0

0.6

0.130435

23

1

23

0.25

0

1

0

true

0

1

0

null

0

1

0

1

0

1

0

null

0

1

0

1

0

7

9dad62f4bd5f01b29292c508ddb9d7c7ee4be1b6

459

py

Python

seleniumbase/translate/__init__.py

AndriiMykytiuk/SeleniumBase

d675f19ca8c8550607a0da1305704772eec34fa8

[ "MIT" ]

2,745

2016-07-20T09:13:15.000Z

2022-03-29T15:07:31.000Z

seleniumbase/translate/__init__.py

AndriiMykytiuk/SeleniumBase

d675f19ca8c8550607a0da1305704772eec34fa8

[ "MIT" ]

384

2016-07-17T20:45:26.000Z

2022-03-31T22:35:35.000Z

seleniumbase/translate/__init__.py

AndriiMykytiuk/SeleniumBase

d675f19ca8c8550607a0da1305704772eec34fa8

[ "MIT" ]

704

2016-07-17T20:47:04.000Z

2022-03-31T04:32:35.000Z

from seleniumbase.translate import chinese # noqa from seleniumbase.translate import dutch # noqa from seleniumbase.translate import french # noqa from seleniumbase.translate import italian # noqa from seleniumbase.translate import japanese # noqa from seleniumbase.translate import korean # noqa from seleniumbase.translate import portuguese # noqa from seleniumbase.translate import russian # noqa from seleniumbase.translate import spanish # noqa

45.9

53

0.823529

54

459

7

0.259259

0.380952

0.595238

0.738095

0.740741

0

0.137255

459

9

54

51

0.954545

0.095861

0

1

0

true

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

7

9dd406089c86ab43d2d7605bfa6f82a5c9d5d7a0

4,707

py

Python

sys/controller/ImageHelper.py

frankdede/CMPUT410W14T07

18b88ae8d12459eeb0073c48e0708fb2b175b9ba

[ "Apache-2.0" ]

1

2017-01-04T11:55:21.000Z

sys/controller/ImageHelper.py

frankdede/CMPUT410W14T07

18b88ae8d12459eeb0073c48e0708fb2b175b9ba

[ "Apache-2.0" ]

null

sys/controller/ImageHelper.py

frankdede/CMPUT410W14T07

18b88ae8d12459eeb0073c48e0708fb2b175b9ba

[ "Apache-2.0" ]

null

import json from mysql.connector.errors import Error from DatabaseAdapter import * import Utility import sys sys.path.append("sys/model") from image import * class ImageHelper: dbAdapter = None def __init__(self,dbAdapter): self.dbAdapter = dbAdapter '''get image by author id''' def getImageByAid(self,aid): cur = self.dbAdapter.getcursor() query = "SELECT * FROM image WHERE aid ='%s'"%(aid) try: cur.execute(query) except mysql.connector.Error as err: print("****************************************") print("SQLException from addNewCircle():") print("Error code:", err.errno) print("SQLSTATE value:", err.sqlstate) print("Error message:", err.msg) print("Query:",query) print("****************************************") return False re = [] if cur is not None: for row in cur: re.append(Image(row[0],row[2],row[1],row[3],row[4])) return re else: return None '''get image by post id''' def getImageByPid(self,pid): cur = self.dbAdapter.getcursor() query = "SELECT * FROM image WHERE pid ='%s'"%(pid) try: cur.execute(query) except mysql.connector.Error as err: print("****************************************") print("SQLException from addNewCircle():") print("Error code:", err.errno) print("SQLSTATE value:", err.sqlstate) print("Error message:", err.msg) print("Query:",query) print("****************************************") return False re = [] if cur is not None: for row in cur: re.append(Image(row[0],row[2],row[1],row[3],row[4])) return re else: return None '''insert image path to database by post id, author id''' def insertImage(self,path,aid,pid): cur = self.dbAdapter.getcursor() iid = Utility.getid() query = "INSERT INTO image VALUES('%s',NULL,'%s','%s','%s')"%(iid,path,aid,pid) try: cur.execute(query) except mysql.connector.Error as err: print("****************************************") print("SQLException from addNewCircle():") print("Error code:", err.errno) print("SQLSTATE value:", err.sqlstate) print("Error message:", err.msg) print("Query:",query) print("****************************************") return False return iid '''delete image by author id''' def deleteImageByAid(self,aid): cur = self.dbAdapter.getcursor() query = "DELETE FROM image WHERE aid ='%s'"%(aid) try: cur.execute(query) except mysql.connector.Error as err: print("****************************************") print("SQLException from addNewCircle():") print("Error code:", err.errno) print("SQLSTATE value:", err.sqlstate) print("Error message:", err.msg) print("Query:",query) print("****************************************") return False return cur.rowcount>0 '''delete image by post id''' def deleteImageByPid(self,pid): cur = self.dbAdapter.getcursor() query = "DELETE FROM image WHERE pid ='%s'"%(pid) try: cur.execute(query) except mysql.connector.Error as err: print("****************************************") print("SQLException from addNewCircle():") print("Error code:", err.errno) print("SQLSTATE value:", err.sqlstate) print("Error message:", err.msg) print("Query:",query) print("****************************************") return False return cur.rowcount>0 '''delete image by image id''' def deleteImageByImageId(self,iid): cur = self.dbAdapter.getcursor() query = "DELETE FROM image WHERE image_id ='%s'"%(iid) try: cur.execute(query) except mysql.connector.Error as err: print("****************************************") print("SQLException from addNewCircle():") print("Error code:", err.errno) print("SQLSTATE value:", err.sqlstate) print("Error message:", err.msg) print("Query:",query) print("****************************************") return False return cur.rowcount>0

35.659091

87

0.476737

463

4,707

4.835853

0.170626

0.053595

0.042876

0.066994

0.801251

0.761501

0.748995

0.662349

0

0.003977

0.305502

4,707

131

88

35.931298

0.680942

0

0.780702

0

0.263427

0.113626

0

1

0.061404

false

0

0.052632

0

0.254386

0.368421

0

null

0

1

0

1

0

null

0

7

9de8f25185d5a85a4ad9dc37fe0f70fbc48d907f

2,643

py

Python

tests/data_structures/commons/binary_search_tree_unit_test.py

vertexproject/pyalgs

11b9ea37afc9e9f9e38ffacc42b53f9cd96f5f83

[ "BSD-3-Clause" ]

12

2017-05-01T10:31:42.000Z

2021-06-23T14:03:28.000Z

tests/data_structures/commons/binary_search_tree_unit_test.py

vertexproject/pyalgs

11b9ea37afc9e9f9e38ffacc42b53f9cd96f5f83

[ "BSD-3-Clause" ]

2

2018-08-01T10:09:09.000Z

2020-07-16T11:41:46.000Z

tests/data_structures/commons/binary_search_tree_unit_test.py

vertexproject/pyalgs

11b9ea37afc9e9f9e38ffacc42b53f9cd96f5f83

[ "BSD-3-Clause" ]

6

2017-06-04T01:41:14.000Z

2021-01-19T05:05:44.000Z

import unittest from pyalgs.data_structures.commons.binary_search_tree import BinarySearchTree class BinarySearchTreeUnitTest(unittest.TestCase): def test_binarySearchTree(self): bst = BinarySearchTree.create() bst.put("one", 1) bst.put("two", 2) bst.put("three", 3) bst.put("six", 6) bst.put("ten", 10) bst.put("ten", 10) self.assertEqual(1, bst.get("one")) self.assertEqual(2, bst.get("two")) self.assertEqual(3, bst.get("three")) self.assertTrue(bst.contains_key("one")) self.assertTrue(bst.contains_key("two")) self.assertEqual(5, bst.size()) self.assertFalse(bst.is_empty()) bst.delete("one") self.assertFalse(bst.contains_key("one")) self.assertEqual(4, bst.size()) bst.delete("ten") self.assertFalse(bst.contains_key("ten")) self.assertEqual(3, bst.size()) bst.delete("three") self.assertFalse(bst.contains_key("three")) self.assertEqual(2, bst.size()) for i in range(100): bst.put(str(i), i) self.assertEqual(i, bst.get(str(i))) for key in bst.keys(): print(key) for i in range(100): bst.delete(str(i)) self.assertFalse(bst.contains_key(str(i))) class RedBlackTreeUnitTest(unittest.TestCase): def test_binarySearchTree(self): bst = BinarySearchTree.create_red_black_tree() bst.put("one", 1) bst.put("two", 2) bst.put("three", 3) bst.put("six", 6) bst.put("ten", 10) bst.put("ten", 10) self.assertEqual(1, bst.get("one")) self.assertEqual(2, bst.get("two")) self.assertEqual(3, bst.get("three")) self.assertTrue(bst.contains_key("one")) self.assertTrue(bst.contains_key("two")) self.assertEqual(5, bst.size()) self.assertFalse(bst.is_empty()) bst.delete("one") self.assertFalse(bst.contains_key("one")) self.assertEqual(4, bst.size()) bst.delete("ten") self.assertFalse(bst.contains_key("ten")) self.assertEqual(3, bst.size()) bst.delete("three") self.assertFalse(bst.contains_key("three")) self.assertEqual(2, bst.size()) for i in range(100): bst.put(str(i), i) self.assertEqual(i, bst.get(str(i))) for key in bst.keys(): print(key) for i in range(100): bst.delete(str(i)) self.assertFalse(bst.contains_key(str(i))) if __name__ == '__main__': unittest.main()

26.69697

78

0.573969

334

2,643

4.452096

0.167665

0.161399

0.112979

0.139879

0.885003

0.793544

0

0.021717

0.268256

2,643

99

79

26.69697

0.747156

0

0.885714

0

0.047655

0

0.428571

1

0.028571

false

0

0.028571

0

0.085714

0.028571

0

null

0

1

0

1

0

null

0

1

0

8

d17fbd29edb607a4cb5eb355af3b1d0f452de1af

45

py

Python

coder/blueprints/user/__init__.py

mikkokotila/coder-1

c0462fb63bd23d4367a31f86f9c7f29b1ece93bd

[ "MIT" ]

1

2019-03-11T12:44:33.000Z

coder/blueprints/user/__init__.py

mikkokotila/coder-1

c0462fb63bd23d4367a31f86f9c7f29b1ece93bd

[ "MIT" ]

1

2019-01-02T09:52:17.000Z

coder/blueprints/user/__init__.py

mikkokotila/coder-1

c0462fb63bd23d4367a31f86f9c7f29b1ece93bd

[ "MIT" ]

1

2019-01-04T12:44:50.000Z

from coder.blueprints.user.views import user

22.5

44

0.844444

7

45

5.428571

0.857143

0

0.088889

45

1

45

0.926829

0

1

0

true

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

7

d183a60bc196dd666c6fa4ea24626fb9b16f9587

128

py

Python

python/testData/completion/heavyStarPropagation/lib/_pkg1/_pkg1_1/_pkg1_1_1/_pkg1_1_1_0/_pkg1_1_1_0_0/_mod1_1_1_0_0_0.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/completion/heavyStarPropagation/lib/_pkg1/_pkg1_1/_pkg1_1_1/_pkg1_1_1_0/_pkg1_1_1_0_0/_mod1_1_1_0_0_0.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/completion/heavyStarPropagation/lib/_pkg1/_pkg1_1/_pkg1_1_1/_pkg1_1_1_0/_pkg1_1_1_0_0/_mod1_1_1_0_0_0.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

name1_1_1_0_0_0_0 = None name1_1_1_0_0_0_1 = None name1_1_1_0_0_0_2 = None name1_1_1_0_0_0_3 = None name1_1_1_0_0_0_4 = None

14.222222

24

0.820313

40

128

1.875

0.175

0.293333

0.24

0.533333

0.88

0.746667

0

0.318182

0.140625

128

9

25

14.222222

0.363636

0

1

0

false

0

1

null

1

0

1

0

1

0

1

0

null

0

10

d1df47d81193ae5cccf66e879b58e0130d7aa637

68,578

py

Python

benchmarks/SimResults/micro_pinned_train_combos/cmpC_perlbenchgamessbzip2calculix/power.py

TugberkArkose/MLScheduler

e493b6cbf7b9d29a2c9300d7dd6f0c2f102e4061

[ "Unlicense" ]

null

benchmarks/SimResults/micro_pinned_train_combos/cmpC_perlbenchgamessbzip2calculix/power.py

TugberkArkose/MLScheduler

e493b6cbf7b9d29a2c9300d7dd6f0c2f102e4061

[ "Unlicense" ]

null

benchmarks/SimResults/micro_pinned_train_combos/cmpC_perlbenchgamessbzip2calculix/power.py

TugberkArkose/MLScheduler

e493b6cbf7b9d29a2c9300d7dd6f0c2f102e4061

[ "Unlicense" ]

null

power = {'BUSES': {'Area': 1.33155, 'Bus/Area': 1.33155, 'Bus/Gate Leakage': 0.00662954, 'Bus/Peak Dynamic': 0.0, 'Bus/Runtime Dynamic': 0.0, 'Bus/Subthreshold Leakage': 0.0691322, 'Bus/Subthreshold Leakage with power gating': 0.0259246, 'Gate Leakage': 0.00662954, 'Peak Dynamic': 0.0, 'Runtime Dynamic': 0.0, 'Subthreshold Leakage': 0.0691322, 'Subthreshold Leakage with power gating': 0.0259246}, 'Core': [{'Area': 32.6082, 'Execution Unit/Area': 8.2042, 'Execution Unit/Complex ALUs/Area': 0.235435, 'Execution Unit/Complex ALUs/Gate Leakage': 0.0132646, 'Execution Unit/Complex ALUs/Peak Dynamic': 0.0, 'Execution Unit/Complex ALUs/Runtime Dynamic': 0.202689, 'Execution Unit/Complex ALUs/Subthreshold Leakage': 0.20111, 'Execution Unit/Complex ALUs/Subthreshold Leakage with power gating': 0.0754163, 'Execution Unit/Floating Point Units/Area': 4.6585, 'Execution Unit/Floating Point Units/Gate Leakage': 0.0656156, 'Execution Unit/Floating Point Units/Peak Dynamic': 0.0, 'Execution Unit/Floating Point Units/Runtime Dynamic': 0.304033, 'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829, 'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061, 'Execution Unit/Gate Leakage': 0.122718, 'Execution Unit/Instruction Scheduler/Area': 2.17927, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.328073, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.00115349, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.20978, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.383391, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.017004, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00962066, 'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00730101, 'Execution Unit/Instruction Scheduler/Instruction Window/Area': 1.00996, 'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00529112, 'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 2.07911, 'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.663895, 'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0800117, 'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0455351, 'Execution Unit/Instruction Scheduler/Peak Dynamic': 4.84781, 'Execution Unit/Instruction Scheduler/ROB/Area': 0.841232, 'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.000856399, 'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.55892, 'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.380763, 'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.0178624, 'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00897339, 'Execution Unit/Instruction Scheduler/Runtime Dynamic': 1.42805, 'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.114878, 'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.0641291, 'Execution Unit/Integer ALUs/Area': 0.47087, 'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291, 'Execution Unit/Integer ALUs/Peak Dynamic': 0.378967, 'Execution Unit/Integer ALUs/Runtime Dynamic': 0.101344, 'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222, 'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833, 'Execution Unit/Peak Dynamic': 5.62175, 'Execution Unit/Register Files/Area': 0.570804, 'Execution Unit/Register Files/Floating Point RF/Area': 0.208131, 'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788, 'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.0, 'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.0138982, 'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698, 'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968, 'Execution Unit/Register Files/Gate Leakage': 0.000622708, 'Execution Unit/Register Files/Integer RF/Area': 0.362673, 'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992, 'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.100502, 'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.102786, 'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175, 'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675, 'Execution Unit/Register Files/Peak Dynamic': 0.100502, 'Execution Unit/Register Files/Runtime Dynamic': 0.116684, 'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387, 'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643, 'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0442632, 'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00607074, 'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.242854, 'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 0.729337, 'Execution Unit/Results Broadcast Bus/Subthreshold Leakage': 0.0920413, 'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0345155, 'Execution Unit/Runtime Dynamic': 2.88214, 'Execution Unit/Subthreshold Leakage': 1.83518, 'Execution Unit/Subthreshold Leakage with power gating': 0.709678, 'Gate Leakage': 0.372997, 'Instruction Fetch Unit/Area': 5.86007, 'Instruction Fetch Unit/Branch Predictor/Area': 0.138516, 'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221, 'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362, 'Instruction Fetch Unit/Branch Predictor/Chooser/Peak Dynamic': 0.0168831, 'Instruction Fetch Unit/Branch Predictor/Chooser/Runtime Dynamic': 0.00266729, 'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719, 'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236, 'Instruction Fetch Unit/Branch Predictor/Gate Leakage': 0.000757657, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Area': 0.0435221, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Gate Leakage': 0.000278362, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.00266729, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage': 0.00759719, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.00236392, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 0.000937378, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045, 'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838, 'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732, 'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05, 'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602, 'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.00147653, 'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505, 'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733, 'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00917504, 'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703, 'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282, 'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954, 'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758, 'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867, 'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.0241192, 'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682, 'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357, 'Instruction Fetch Unit/Gate Leakage': 0.0590479, 'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323, 'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05, 'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827, 'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0988108, 'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885, 'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682, 'Instruction Fetch Unit/Instruction Cache/Area': 3.14635, 'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931, 'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 6.28521, 'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.331624, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386, 'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799, 'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493, 'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404, 'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.335606, 'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943, 'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104, 'Instruction Fetch Unit/Peak Dynamic': 8.81343, 'Instruction Fetch Unit/Runtime Dynamic': 0.799335, 'Instruction Fetch Unit/Subthreshold Leakage': 0.932587, 'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.408542, 'L2/Area': 4.53318, 'L2/Gate Leakage': 0.015464, 'L2/Peak Dynamic': 0.076365, 'L2/Runtime Dynamic': 0.0211489, 'L2/Subthreshold Leakage': 0.834142, 'L2/Subthreshold Leakage with power gating': 0.401066, 'Load Store Unit/Area': 8.80969, 'Load Store Unit/Data Cache/Area': 6.84535, 'Load Store Unit/Data Cache/Gate Leakage': 0.0279261, 'Load Store Unit/Data Cache/Peak Dynamic': 4.53307, 'Load Store Unit/Data Cache/Runtime Dynamic': 1.61158, 'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675, 'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085, 'Load Store Unit/Gate Leakage': 0.0351387, 'Load Store Unit/LoadQ/Area': 0.0836782, 'Load Store Unit/LoadQ/Gate Leakage': 0.00059896, 'Load Store Unit/LoadQ/Peak Dynamic': 0.106632, 'Load Store Unit/LoadQ/Runtime Dynamic': 0.106632, 'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961, 'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918, 'Load Store Unit/Peak Dynamic': 5.03866, 'Load Store Unit/Runtime Dynamic': 2.24408, 'Load Store Unit/StoreQ/Area': 0.322079, 'Load Store Unit/StoreQ/Gate Leakage': 0.00329971, 'Load Store Unit/StoreQ/Peak Dynamic': 0.262936, 'Load Store Unit/StoreQ/Runtime Dynamic': 0.525872, 'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621, 'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004, 'Load Store Unit/Subthreshold Leakage': 0.591622, 'Load Store Unit/Subthreshold Leakage with power gating': 0.283406, 'Memory Management Unit/Area': 0.434579, 'Memory Management Unit/Dtlb/Area': 0.0879726, 'Memory Management Unit/Dtlb/Gate Leakage': 0.00088729, 'Memory Management Unit/Dtlb/Peak Dynamic': 0.0933168, 'Memory Management Unit/Dtlb/Runtime Dynamic': 0.09446, 'Memory Management Unit/Dtlb/Subthreshold Leakage': 0.0155699, 'Memory Management Unit/Dtlb/Subthreshold Leakage with power gating': 0.00887485, 'Memory Management Unit/Gate Leakage': 0.00813591, 'Memory Management Unit/Itlb/Area': 0.301552, 'Memory Management Unit/Itlb/Gate Leakage': 0.00393464, 'Memory Management Unit/Itlb/Peak Dynamic': 0.390792, 'Memory Management Unit/Itlb/Runtime Dynamic': 0.0543757, 'Memory Management Unit/Itlb/Subthreshold Leakage': 0.0413758, 'Memory Management Unit/Itlb/Subthreshold Leakage with power gating': 0.0235842, 'Memory Management Unit/Peak Dynamic': 0.710617, 'Memory Management Unit/Runtime Dynamic': 0.148836, 'Memory Management Unit/Subthreshold Leakage': 0.0769113, 'Memory Management Unit/Subthreshold Leakage with power gating': 0.0399462, 'Peak Dynamic': 24.8225, 'Renaming Unit/Area': 0.369768, 'Renaming Unit/FP Front End RAT/Area': 0.168486, 'Renaming Unit/FP Front End RAT/Gate Leakage': 0.00489731, 'Renaming Unit/FP Front End RAT/Peak Dynamic': 3.33511, 'Renaming Unit/FP Front End RAT/Runtime Dynamic': 0.0, 'Renaming Unit/FP Front End RAT/Subthreshold Leakage': 0.0437281, 'Renaming Unit/FP Front End RAT/Subthreshold Leakage with power gating': 0.024925, 'Renaming Unit/Free List/Area': 0.0414755, 'Renaming Unit/Free List/Gate Leakage': 4.15911e-05, 'Renaming Unit/Free List/Peak Dynamic': 0.0401324, 'Renaming Unit/Free List/Runtime Dynamic': 0.0196045, 'Renaming Unit/Free List/Subthreshold Leakage': 0.000670426, 'Renaming Unit/Free List/Subthreshold Leakage with power gating': 0.000377987, 'Renaming Unit/Gate Leakage': 0.00863632, 'Renaming Unit/Int Front End RAT/Area': 0.114751, 'Renaming Unit/Int Front End RAT/Gate Leakage': 0.00038343, 'Renaming Unit/Int Front End RAT/Peak Dynamic': 0.86945, 'Renaming Unit/Int Front End RAT/Runtime Dynamic': 0.20417, 'Renaming Unit/Int Front End RAT/Subthreshold Leakage': 0.00611897, 'Renaming Unit/Int Front End RAT/Subthreshold Leakage with power gating': 0.00348781, 'Renaming Unit/Peak Dynamic': 4.56169, 'Renaming Unit/Runtime Dynamic': 0.223774, 'Renaming Unit/Subthreshold Leakage': 0.070483, 'Renaming Unit/Subthreshold Leakage with power gating': 0.0362779, 'Runtime Dynamic': 6.31931, 'Subthreshold Leakage': 6.21877, 'Subthreshold Leakage with power gating': 2.58311}, {'Area': 32.0201, 'Execution Unit/Area': 7.68434, 'Execution Unit/Complex ALUs/Area': 0.235435, 'Execution Unit/Complex ALUs/Gate Leakage': 0.0132646, 'Execution Unit/Complex ALUs/Peak Dynamic': 0.242356, 'Execution Unit/Complex ALUs/Runtime Dynamic': 0.393046, 'Execution Unit/Complex ALUs/Subthreshold Leakage': 0.20111, 'Execution Unit/Complex ALUs/Subthreshold Leakage with power gating': 0.0754163, 'Execution Unit/Floating Point Units/Area': 4.6585, 'Execution Unit/Floating Point Units/Gate Leakage': 0.0656156, 'Execution Unit/Floating Point Units/Peak Dynamic': 1.3081, 'Execution Unit/Floating Point Units/Runtime Dynamic': 0.304033, 'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829, 'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061, 'Execution Unit/Gate Leakage': 0.120359, 'Execution Unit/Instruction Scheduler/Area': 1.66526, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.275653, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.000977433, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.04181, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.337839, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.0143453, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00810519, 'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00568913, 'Execution Unit/Instruction Scheduler/Instruction Window/Area': 0.805223, 'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00414562, 'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 1.6763, 'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.544921, 'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0625755, 'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0355964, 'Execution Unit/Instruction Scheduler/Peak Dynamic': 3.82262, 'Execution Unit/Instruction Scheduler/ROB/Area': 0.584388, 'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.00056608, 'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.10451, 'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.275058, 'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.00906853, 'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00364446, 'Execution Unit/Instruction Scheduler/Runtime Dynamic': 1.15782, 'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.0859892, 'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.047346, 'Execution Unit/Integer ALUs/Area': 0.47087, 'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291, 'Execution Unit/Integer ALUs/Peak Dynamic': 0.185839, 'Execution Unit/Integer ALUs/Runtime Dynamic': 0.101344, 'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222, 'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833, 'Execution Unit/Peak Dynamic': 6.4919, 'Execution Unit/Register Files/Area': 0.570804, 'Execution Unit/Register Files/Floating Point RF/Area': 0.208131, 'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788, 'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.247129, 'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.0141705, 'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698, 'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968, 'Execution Unit/Register Files/Gate Leakage': 0.000622708, 'Execution Unit/Register Files/Integer RF/Area': 0.362673, 'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992, 'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.193233, 'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.104799, 'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175, 'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675, 'Execution Unit/Register Files/Peak Dynamic': 0.440361, 'Execution Unit/Register Files/Runtime Dynamic': 0.11897, 'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387, 'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643, 'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0390912, 'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00537402, 'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.46769, 'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 0.913563, 'Execution Unit/Results Broadcast Bus/Subthreshold Leakage': 0.081478, 'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0305543, 'Execution Unit/Runtime Dynamic': 2.98877, 'Execution Unit/Subthreshold Leakage': 1.79543, 'Execution Unit/Subthreshold Leakage with power gating': 0.688821, 'Gate Leakage': 0.368936, 'Instruction Fetch Unit/Area': 5.85939, 'Instruction Fetch Unit/Branch Predictor/Area': 0.138516, 'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221, 'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362, 'Instruction Fetch Unit/Branch Predictor/Chooser/Peak Dynamic': 0.0168831, 'Instruction Fetch Unit/Branch Predictor/Chooser/Runtime Dynamic': 0.000795173, 'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719, 'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236, 'Instruction Fetch Unit/Branch Predictor/Gate Leakage': 0.000757657, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Area': 0.0435221, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Gate Leakage': 0.000278362, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.000795173, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage': 0.00759719, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.000691243, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 0.000266852, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045, 'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838, 'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732, 'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05, 'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602, 'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.00150545, 'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505, 'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733, 'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00378704, 'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703, 'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282, 'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954, 'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758, 'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867, 'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.00767235, 'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682, 'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357, 'Instruction Fetch Unit/Gate Leakage': 0.0589979, 'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323, 'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05, 'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827, 'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.100746, 'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885, 'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682, 'Instruction Fetch Unit/Instruction Cache/Area': 3.14635, 'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931, 'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 6.40833, 'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.248861, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386, 'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799, 'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493, 'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404, 'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.34218, 'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943, 'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104, 'Instruction Fetch Unit/Peak Dynamic': 8.93785, 'Instruction Fetch Unit/Runtime Dynamic': 0.703246, 'Instruction Fetch Unit/Subthreshold Leakage': 0.932286, 'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.40843, 'L2/Area': 4.53318, 'L2/Gate Leakage': 0.015464, 'L2/Peak Dynamic': 0.0657589, 'L2/Runtime Dynamic': 0.00414676, 'L2/Subthreshold Leakage': 0.834142, 'L2/Subthreshold Leakage with power gating': 0.401066, 'Load Store Unit/Area': 8.80901, 'Load Store Unit/Data Cache/Area': 6.84535, 'Load Store Unit/Data Cache/Gate Leakage': 0.0279261, 'Load Store Unit/Data Cache/Peak Dynamic': 2.97935, 'Load Store Unit/Data Cache/Runtime Dynamic': 0.838906, 'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675, 'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085, 'Load Store Unit/Gate Leakage': 0.0350888, 'Load Store Unit/LoadQ/Area': 0.0836782, 'Load Store Unit/LoadQ/Gate Leakage': 0.00059896, 'Load Store Unit/LoadQ/Peak Dynamic': 0.0563652, 'Load Store Unit/LoadQ/Runtime Dynamic': 0.0563653, 'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961, 'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918, 'Load Store Unit/Peak Dynamic': 3.24552, 'Load Store Unit/Runtime Dynamic': 1.17325, 'Load Store Unit/StoreQ/Area': 0.322079, 'Load Store Unit/StoreQ/Gate Leakage': 0.00329971, 'Load Store 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'Execution Unit/Complex ALUs/Gate Leakage': 0.0132646, 'Execution Unit/Complex ALUs/Peak Dynamic': 0.108777, 'Execution Unit/Complex ALUs/Runtime Dynamic': 0.288127, 'Execution Unit/Complex ALUs/Subthreshold Leakage': 0.20111, 'Execution Unit/Complex ALUs/Subthreshold Leakage with power gating': 0.0754163, 'Execution Unit/Floating Point Units/Area': 4.6585, 'Execution Unit/Floating Point Units/Gate Leakage': 0.0656156, 'Execution Unit/Floating Point Units/Peak Dynamic': 0.460421, 'Execution Unit/Floating Point Units/Runtime Dynamic': 0.304033, 'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829, 'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061, 'Execution Unit/Gate Leakage': 0.120359, 'Execution Unit/Instruction Scheduler/Area': 1.66526, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.275653, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.000977433, 'Execution Unit/Instruction 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'Execution Unit/Instruction Scheduler/ROB/Area': 0.584388, 'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.00056608, 'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.10451, 'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.219775, 'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.00906853, 'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00364446, 'Execution Unit/Instruction Scheduler/Runtime Dynamic': 0.925111, 'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.0859892, 'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.047346, 'Execution Unit/Integer ALUs/Area': 0.47087, 'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291, 'Execution Unit/Integer ALUs/Peak Dynamic': 0.238141, 'Execution Unit/Integer ALUs/Runtime Dynamic': 0.101344, 'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222, 'Execution Unit/Integer ALUs/Subthreshold Leakage with power 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'Instruction Fetch Unit/Branch Predictor/Area': 0.138516, 'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221, 'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362, 'Instruction Fetch Unit/Branch Predictor/Chooser/Peak Dynamic': 0.0168831, 'Instruction Fetch Unit/Branch Predictor/Chooser/Runtime Dynamic': 0.00103398, 'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719, 'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236, 'Instruction Fetch Unit/Branch Predictor/Gate Leakage': 0.000757657, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Area': 0.0435221, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Gate Leakage': 0.000278362, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.00103398, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage': 0.00759719, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.000935842, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 0.000381555, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045, 'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838, 'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732, 'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05, 'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602, 'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.00120287, 'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505, 'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733, 'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00420668, 'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703, 'Instruction Fetch Unit/Branch Predictor/Subthreshold 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'Instruction Fetch Unit/Instruction Cache/Area': 3.14635, 'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931, 'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 5.12034, 'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.197675, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386, 'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799, 'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493, 'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404, 'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.273406, 'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943, 'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104, 'Instruction Fetch Unit/Peak Dynamic': 7.58735, 'Instruction Fetch Unit/Runtime Dynamic': 0.56444, 'Instruction Fetch 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'Execution Unit/Instruction Scheduler/Area': 1.66526, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.275653, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.000977433, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.04181, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.194462, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.0143453, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00810519, 'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00568913, 'Execution Unit/Instruction Scheduler/Instruction Window/Area': 0.805223, 'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00414562, 'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 1.6763, 'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.31366, 'Execution 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Predictor/Gate Leakage': 0.000278362, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.00249126, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage': 0.00759719, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.00223238, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 0.000898376, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045, 'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838, 'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732, 'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05, 'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602, 'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.000866545, 'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505, 'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733, 'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00808145, 'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703, 'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282, 'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954, 'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758, 'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867, 'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.0216529, 'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682, 'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357, 'Instruction Fetch Unit/Gate Leakage': 0.0589979, 'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323, 'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05, 'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827, 'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0579901, 'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885, 'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682, 'Instruction Fetch Unit/Instruction Cache/Area': 3.14635, 'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931, 'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 3.68867, 'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.22962, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386, 'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799, 'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493, 'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404, 'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.19696, 'Instruction Fetch Unit/Instruction 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Store Unit/LoadQ/Area': 0.0836782, 'Load Store Unit/LoadQ/Gate Leakage': 0.00059896, 'Load Store Unit/LoadQ/Peak Dynamic': 0.0595289, 'Load Store Unit/LoadQ/Runtime Dynamic': 0.0595291, 'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961, 'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918, 'Load Store Unit/Peak Dynamic': 3.35825, 'Load Store Unit/Runtime Dynamic': 1.23966, 'Load Store Unit/StoreQ/Area': 0.322079, 'Load Store Unit/StoreQ/Gate Leakage': 0.00329971, 'Load Store Unit/StoreQ/Peak Dynamic': 0.146788, 'Load Store Unit/StoreQ/Runtime Dynamic': 0.293577, 'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621, 'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004, 'Load Store Unit/Subthreshold Leakage': 0.591321, 'Load Store Unit/Subthreshold Leakage with power gating': 0.283293, 'Memory Management Unit/Area': 0.4339, 'Memory Management Unit/Dtlb/Area': 0.0879726, 'Memory Management Unit/Dtlb/Gate Leakage': 0.00088729, 'Memory Management Unit/Dtlb/Peak Dynamic': 0.0520956, 'Memory Management Unit/Dtlb/Runtime Dynamic': 0.052352, 'Memory Management Unit/Dtlb/Subthreshold Leakage': 0.0155699, 'Memory Management Unit/Dtlb/Subthreshold Leakage with power gating': 0.00887485, 'Memory Management Unit/Gate Leakage': 0.00808595, 'Memory Management Unit/Itlb/Area': 0.301552, 'Memory Management Unit/Itlb/Gate Leakage': 0.00393464, 'Memory Management Unit/Itlb/Peak Dynamic': 0.229348, 'Memory Management Unit/Itlb/Runtime Dynamic': 0.0382063, 'Memory Management Unit/Itlb/Subthreshold Leakage': 0.0413758, 'Memory Management Unit/Itlb/Subthreshold Leakage with power gating': 0.0235842, 'Memory Management Unit/Peak Dynamic': 0.474948, 'Memory Management Unit/Runtime Dynamic': 0.0905583, 'Memory Management Unit/Subthreshold Leakage': 0.0766103, 'Memory Management Unit/Subthreshold Leakage with power gating': 0.0398333, 'Peak Dynamic': 17.7967, 'Renaming Unit/Area': 0.303608, 'Renaming Unit/FP Front End RAT/Area': 0.131045, 'Renaming Unit/FP Front End RAT/Gate Leakage': 0.00351123, 'Renaming Unit/FP Front End RAT/Peak Dynamic': 2.51468, 'Renaming Unit/FP Front End RAT/Runtime Dynamic': 4.00713e-06, 'Renaming Unit/FP Front End RAT/Subthreshold Leakage': 0.0308571, 'Renaming Unit/FP Front End RAT/Subthreshold Leakage with power gating': 0.0175885, 'Renaming Unit/Free List/Area': 0.0340654, 'Renaming Unit/Free List/Gate Leakage': 2.5481e-05, 'Renaming Unit/Free List/Peak Dynamic': 0.0306032, 'Renaming Unit/Free List/Runtime Dynamic': 0.00877363, 'Renaming Unit/Free List/Subthreshold Leakage': 0.000370144, 'Renaming Unit/Free List/Subthreshold Leakage with power gating': 0.000201064, 'Renaming Unit/Gate Leakage': 0.00708398, 'Renaming Unit/Int Front End RAT/Area': 0.0941223, 'Renaming Unit/Int Front End RAT/Gate Leakage': 0.000283242, 'Renaming Unit/Int Front End RAT/Peak Dynamic': 0.731965, 'Renaming Unit/Int Front End RAT/Runtime Dynamic': 0.0992607, 'Renaming Unit/Int Front End RAT/Subthreshold Leakage': 0.00435488, 'Renaming Unit/Int Front End RAT/Subthreshold Leakage with power gating': 0.00248228, 'Renaming Unit/Peak Dynamic': 3.58947, 'Renaming Unit/Runtime Dynamic': 0.108038, 'Renaming Unit/Subthreshold Leakage': 0.0552466, 'Renaming Unit/Subthreshold Leakage with power gating': 0.0276461, 'Runtime Dynamic': 3.62776, 'Subthreshold Leakage': 6.16288, 'Subthreshold Leakage with power gating': 2.55328}], 'DRAM': {'Area': 0, 'Gate Leakage': 0, 'Peak Dynamic': 0.5823551314870238, 'Runtime Dynamic': 0.5823551314870238, 'Subthreshold Leakage': 4.252, 'Subthreshold Leakage with power gating': 4.252}, 'L3': [{'Area': 61.9075, 'Gate Leakage': 0.0484137, 'Peak Dynamic': 0.219259, 'Runtime Dynamic': 0.129716, 'Subthreshold Leakage': 6.80085, 'Subthreshold Leakage with power gating': 3.32364}], 'Processor': {'Area': 191.908, 'Gate Leakage': 1.53485, 'Peak Dynamic': 86.7121, 'Peak Power': 119.824, 'Runtime Dynamic': 20.8783, 'Subthreshold Leakage': 31.5774, 'Subthreshold Leakage with power gating': 13.9484, 'Total Cores/Area': 128.669, 'Total Cores/Gate Leakage': 1.4798, 'Total Cores/Peak Dynamic': 86.4929, 'Total Cores/Runtime Dynamic': 20.7486, 'Total Cores/Subthreshold Leakage': 24.7074, 'Total Cores/Subthreshold Leakage with power gating': 10.2429, 'Total L3s/Area': 61.9075, 'Total L3s/Gate Leakage': 0.0484137, 'Total L3s/Peak Dynamic': 0.219259, 'Total L3s/Runtime Dynamic': 0.129716, 'Total L3s/Subthreshold Leakage': 6.80085, 'Total L3s/Subthreshold Leakage with power gating': 3.32364, 'Total Leakage': 33.1122, 'Total NoCs/Area': 1.33155, 'Total NoCs/Gate Leakage': 0.00662954, 'Total NoCs/Peak Dynamic': 0.0, 'Total NoCs/Runtime Dynamic': 0.0, 'Total NoCs/Subthreshold Leakage': 0.0691322, 'Total NoCs/Subthreshold Leakage with power gating': 0.0259246}}

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7

06126228d6a6f1b6b11bb0f54e30cd54adb75f3e

8,147

py

Python

intro/part05-17_phone_book_v1/test/test_phone_book_v1.py

Hannah-Abi/python-pro-21

2ce32c4bf118054329d19afdf83c50561be1ada8

[ "MIT" ]

null

intro/part05-17_phone_book_v1/test/test_phone_book_v1.py

Hannah-Abi/python-pro-21

2ce32c4bf118054329d19afdf83c50561be1ada8

[ "MIT" ]

null

intro/part05-17_phone_book_v1/test/test_phone_book_v1.py

Hannah-Abi/python-pro-21

2ce32c4bf118054329d19afdf83c50561be1ada8

[ "MIT" ]

null

import unittest from unittest.mock import patch from tmc import points from tmc.utils import load, load_module, reload_module, get_stdout from functools import reduce import os import textwrap from random import randint exercise = 'src.phone_book_v1' def f(d): return '\n'.join(d) def s(d): return d.split('\n') @points('5.phone_book_v1') class PhoneBookV2Test(unittest.TestCase): @classmethod def setUpClass(cls): with patch('builtins.input', side_effect =["3"]): cls.module = load_module(exercise, 'en') def test_1_program_stops(self): with patch('builtins.input', side_effect = ["3", AssertionError("Input is asked too many times.")]): try: reload_module(self.module) output = get_stdout() except: self.assertTrue(False, f"Make sure, that the program stops with the input\n3") def test_2_not_added_is_not_found(self): test_input="""1 mary 3""" words = s(test_input) with patch('builtins.input', side_effect = s(test_input) + [ AssertionError("Input is asked too many times.")]): try: reload_module(self.module) output_all = get_stdout() except: self.assertTrue(False, f"Make sure, that the program stops with the input\n{f(words)}") exp = """no number quitting...""" expWordrs = exp.split('\n') mssage = """\nPlease note, that in this exercise, no code should be included inside if __name__ == "__main__": block """ #\n{mssage}") self.assertTrue(len(output_all)>0, f"Your program does not print out anything with the input\n{f(words)}\n{mssage}") output = [line.strip() for line in output_all.split("\n") if len(line) > 0] self.assertEqual(len(expWordrs), len(output), f"Instead of {len(expWordrs)} rows, your program prints out {len(output)} rows:\n{output_all}\nwith the input:\n{f(words)}\nexpected print out is\n{exp}") for i in range(len(expWordrs)): e = expWordrs[i] line = output[i] self.assertEqual(line, e, f"Your program is not working correctly with the input\n{f(words)}\nprint out on row {i+1} is incorrect, it should be\n{e}\nbut it is\n{line}\nThe whole print out is:\n{output_all}\nThe expected print out is\n{exp}") def test_3_added_is_found(self): test_input="""2 mary 040-234567 1 mary 3""" words = s(test_input) with patch('builtins.input', side_effect = s(test_input) + [ AssertionError("Input is asked too many times.")]): try: reload_module(self.module) output_all = get_stdout() except: self.assertTrue(False, f"Make sure, that the program stops with the input\n{f(words)}") exp = """ok! 040-234567 quitting...""" expWordrs = exp.split('\n') self.assertTrue(len(output_all)>0, f"Your program does not print out anything with the input\n{f(words)}") output = [line.strip() for line in output_all.split("\n") if len(line) > 0] self.assertEqual(len(expWordrs), len(output), f"Instead of {len(expWordrs)} rows, your program prints out {len(output)} rows:\n{output_all}\nwith the input:\n{f(words)}\nexpected print out is\n{exp}") for i in range(len(expWordrs)): e = expWordrs[i] line = output[i] self.assertEqual(line, e, f"Your program is not working correctly with the input\n{f(words)}\nprint out on row {i+1} is incorrect, it should be\n{e}\nbut it is\n{line}\nThe whole print out is:\n{output_all}\nThe expected print out is\n{exp}") def test_4_old_is_replaced(self): test_input="""2 mary 040-234567 2 mary 09-334455 1 mary 3""" words = s(test_input) with patch('builtins.input', side_effect = s(test_input) + [ AssertionError("Input is asked too many times.")]): try: reload_module(self.module) output_all = get_stdout() except: self.assertTrue(False, f"Make sure, that the program stops with the input\n{f(words)}") exp = """ok! ok! 09-334455 quitting...""" expWordrs = exp.split('\n') self.assertTrue(len(output_all)>0, f"Your program does not print out anything with the input\n{f(words)}") output = [line.strip() for line in output_all.split("\n") if len(line) > 0] self.assertEqual(len(expWordrs), len(output), f"Instead of {len(expWordrs)} rows, your program prints out {len(output)} rows:\n{output_all}\nwith the input:\n{f(words)}\nexpected print out is\n{exp}") for i in range(len(expWordrs)): e = expWordrs[i] line = output[i] self.assertEqual(line, e, f"Your program is not working correctly with the input\n{f(words)}\nprint out on row {i+1} is incorrect, it should be\n{e}\nbut it is\n{line}\nThe whole print out is:\n{output_all}\nThe expected print out is\n{exp}") def test_5_many_commands(self): test_input="""2 mike 040-234567 2 mary 09-334455 1 mary 1 mike 1 becky 2 mike 045-554433 1 mike 3""" words = s(test_input) with patch('builtins.input', side_effect = s(test_input) + [ AssertionError("Input is asked too many times.")]): try: reload_module(self.module) output_all = get_stdout() except: self.assertTrue(False, f"Make sure, that the program stops with the input\n{f(words)}") exp = """ok! ok! 09-334455 040-234567 no number ok! 045-554433 quitting...""" expWordrs = exp.split('\n') self.assertTrue(len(output_all)>0, f"Your program does not print out anything with the input\n{f(words)}") output = [line.strip() for line in output_all.split("\n") if len(line) > 0] self.assertEqual(len(expWordrs), len(output), f"Instead of {len(expWordrs)} rows, your program prints out {len(output)} rows:\n{output_all}\nwith the input:\n{f(words)}\nexpected print out is\n{exp}") for i in range(len(expWordrs)): e = expWordrs[i] line = output[i] self.assertEqual(line, e, f"Your program is not working correctly with the input\n{f(words)}\nprint out on row {i+1} is incorrect, it should be\n{e}\nbut it is\n{line}\nThe whole print out is:\n{output_all}\nThe expected print out is\n{exp}") def test_6_many_commands(self): test_input="""2 jack 040-1212334 2 wendy 09-334455 2 william 050-2255433 1 mary 1 wendy 1 william 2 jack 045-554433 1 jack 3""" words = s(test_input) with patch('builtins.input', side_effect = s(test_input) + [ AssertionError("Input is asked too many times.")]): try: reload_module(self.module) output_all = get_stdout() except: self.assertTrue(False, f"Make sure, that the program stops with the input\n{f(words)}") exp = """ok! ok! ok! no number 09-334455 050-2255433 ok! 045-554433 quitting...""" expWordrs = exp.split('\n') self.assertTrue(len(output_all)>0, f"Your program does not print out anything with the input\n{f(words)}") output = [line.strip() for line in output_all.split("\n") if len(line) > 0] self.assertEqual(len(expWordrs), len(output), f"Instead of {len(expWordrs)} rows, your program prints out {len(output)} rows:\n{output_all}\nwith the input:\n{f(words)}\nexpected print out is\n{exp}") for i in range(len(expWordrs)): e = expWordrs[i] line = output[i] self.assertEqual(line, e, f"Your program is not working correctly with the input\n{f(words)}\nprint out on row {i+1} is incorrect, it should be\n{e}\nbut it is\n{line}\nThe whole print out is:\n{output_all}\nThe expected print out is\n{exp}") if __name__ == '__main__': unittest.main()

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0

null

0

1

0

1

0

null

0

7

ae37c7196dba5f27379307f6dc2fe5c0229bbdcc

136

py

Python

cherrypy/PressProduction.py

maarons/pressui

38b1ac4f7f8fb8ad13d9cc1fcc18a932cfaeade4

[ "MIT" ]

null

cherrypy/PressProduction.py

maarons/pressui

38b1ac4f7f8fb8ad13d9cc1fcc18a932cfaeade4

[ "MIT" ]

null

cherrypy/PressProduction.py

maarons/pressui

38b1ac4f7f8fb8ad13d9cc1fcc18a932cfaeade4

[ "MIT" ]

null

__is_prod = False def set_production(is_prod): global __is_prod __is_prod = is_prod def is_production(): return __is_prod

15.111111

28

0.735294

21

136

4

0.380952

0.428571

0.190476

0.285714

0

0.205882

136

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0

1

0.333333

false

0

0.166667

0.5

0

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null

1

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1

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null

0

1

0

1

0

7

ae6b51fdf800d5bcdbf53f55d25d4d77ce0a0193

44,540

py

Python

ibm_cloud_networking_services/range_applications_v1.py

IBM/networking-services-python-sdk

a19e47db6a5971562a502982d69a5868997245f3

[ "Apache-2.0" ]

1

2020-12-22T03:51:33.000Z

ibm_cloud_networking_services/range_applications_v1.py

IBM/networking-services-python-sdk

a19e47db6a5971562a502982d69a5868997245f3

[ "Apache-2.0" ]

57

2020-06-24T06:58:01.000Z

2022-03-28T14:52:33.000Z

ibm_cloud_networking_services/range_applications_v1.py

IBM/networking-services-python-sdk

a19e47db6a5971562a502982d69a5868997245f3

[ "Apache-2.0" ]

10

2020-06-23T04:09:28.000Z

2022-03-26T18:20:35.000Z

# coding: utf-8 # (C) Copyright IBM Corp. 2020. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Range Applications """ from datetime import datetime from enum import Enum from typing import Dict, List import json from ibm_cloud_sdk_core import BaseService, DetailedResponse from ibm_cloud_sdk_core.authenticators.authenticator import Authenticator from ibm_cloud_sdk_core.get_authenticator import get_authenticator_from_environment from ibm_cloud_sdk_core.utils import convert_model, datetime_to_string, string_to_datetime from .common import get_sdk_headers ############################################################################## # Service ############################################################################## class RangeApplicationsV1(BaseService): """The Range Applications V1 service.""" DEFAULT_SERVICE_URL = 'https://api.cis.cloud.ibm.com' DEFAULT_SERVICE_NAME = 'range_applications' @classmethod def new_instance(cls, crn: str, zone_identifier: str, service_name: str = DEFAULT_SERVICE_NAME, ) -> 'RangeApplicationsV1': """ Return a new client for the Range Applications service using the specified parameters and external configuration. :param str crn: Full url-encoded cloud resource name (CRN) of resource instance. :param str zone_identifier: zone identifier. """ if crn is None: raise ValueError('crn must be provided') if zone_identifier is None: raise ValueError('zone_identifier must be provided') authenticator = get_authenticator_from_environment(service_name) service = cls( crn, zone_identifier, authenticator ) service.configure_service(service_name) return service def __init__(self, crn: str, zone_identifier: str, authenticator: Authenticator = None, ) -> None: """ Construct a new client for the Range Applications service. :param str crn: Full url-encoded cloud resource name (CRN) of resource instance. :param str zone_identifier: zone identifier. :param Authenticator authenticator: The authenticator specifies the authentication mechanism. Get up to date information from https://github.com/IBM/python-sdk-core/blob/master/README.md about initializing the authenticator of your choice. """ if crn is None: raise ValueError('crn must be provided') if zone_identifier is None: raise ValueError('zone_identifier must be provided') BaseService.__init__(self, service_url=self.DEFAULT_SERVICE_URL, authenticator=authenticator) self.crn = crn self.zone_identifier = zone_identifier ######################### # Range Applications ######################### def list_range_apps(self, *, page: int = None, per_page: int = None, order: str = None, direction: str = None, **kwargs ) -> DetailedResponse: """ List range applications. Get a list of currently existing Range Applications inside a zone. :param int page: (optional) Page number of paginated results. :param int per_page: (optional) Maximum number of Range applications per page. :param str order: (optional) Field by which to order the list of Range applications. :param str direction: (optional) Direction in which to order results [ascending/descending order]. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse with `dict` result representing a `RangeApplications` object """ headers = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='list_range_apps') headers.update(sdk_headers) params = { 'page': page, 'per_page': per_page, 'order': order, 'direction': direction } if 'headers' in kwargs: headers.update(kwargs.get('headers')) url = '/v1/{0}/zones/{1}/range/apps'.format( *self.encode_path_vars(self.crn, self.zone_identifier)) request = self.prepare_request(method='GET', url=url, headers=headers, params=params) response = self.send(request) return response def create_range_app(self, protocol: str, dns: 'RangeAppReqDns', *, origin_direct: List[str] = None, origin_dns: 'RangeAppReqOriginDns' = None, origin_port: int = None, ip_firewall: bool = None, proxy_protocol: str = None, edge_ips: 'RangeAppReqEdgeIps' = None, traffic_type: str = None, tls: str = None, **kwargs ) -> DetailedResponse: """ Create Range Application. Create a Range Applications inside a zone. :param str protocol: Defines the protocol and port for this application. :param RangeAppReqDns dns: Name and type of the DNS record for this application. :param List[str] origin_direct: (optional) IP address and port of the origin for this Range application. If configuring a load balancer, use 'origin_dns' and 'origin_port'. This can not be combined with 'origin_dns' and 'origin_port'. :param RangeAppReqOriginDns origin_dns: (optional) DNS record pointing to the origin for this Range application. This is used for configuring a load balancer. When specifying an individual IP address, use 'origin_direct'. This requires 'origin_port' and can not be combined with 'origin_direct'. :param int origin_port: (optional) Port at the origin that listens to traffic from this Range application. Requires 'origin_dns' and can not be combined with 'origin_direct'. :param bool ip_firewall: (optional) Enables the IP Firewall for this application. Only available for TCP applications. :param str proxy_protocol: (optional) Allows for the true client IP to be passed to the service. :param RangeAppReqEdgeIps edge_ips: (optional) Configures IP version for the hostname of this application. Default is {"type":"dynamic", "connectivity":"all"}. :param str traffic_type: (optional) Configure how traffic is handled at the edge. If set to "direct" traffic is passed through to the service. In the case of "http" or "https" HTTP/s features at the edge are applied ot this traffic. :param str tls: (optional) Configure if and how TLS connections are terminated at the edge. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse with `dict` result representing a `RangeApplicationResp` object """ if protocol is None: raise ValueError('protocol must be provided') if dns is None: raise ValueError('dns must be provided') dns = convert_model(dns) if origin_dns is not None: origin_dns = convert_model(origin_dns) if edge_ips is not None: edge_ips = convert_model(edge_ips) headers = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='create_range_app') headers.update(sdk_headers) data = { 'protocol': protocol, 'dns': dns, 'origin_direct': origin_direct, 'origin_dns': origin_dns, 'origin_port': origin_port, 'ip_firewall': ip_firewall, 'proxy_protocol': proxy_protocol, 'edge_ips': edge_ips, 'traffic_type': traffic_type, 'tls': tls } data = {k: v for (k, v) in data.items() if v is not None} data = json.dumps(data) headers['content-type'] = 'application/json' if 'headers' in kwargs: headers.update(kwargs.get('headers')) url = '/v1/{0}/zones/{1}/range/apps'.format( *self.encode_path_vars(self.crn, self.zone_identifier)) request = self.prepare_request(method='POST', url=url, headers=headers, data=data) response = self.send(request) return response def get_range_app(self, app_identifier: str, **kwargs ) -> DetailedResponse: """ Get range application a zone. Get the application configuration of a specific application inside a zone. :param str app_identifier: application identifier. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse with `dict` result representing a `RangeApplicationResp` object """ if app_identifier is None: raise ValueError('app_identifier must be provided') headers = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='get_range_app') headers.update(sdk_headers) if 'headers' in kwargs: headers.update(kwargs.get('headers')) url = '/v1/{0}/zones/{1}/range/apps/{2}'.format( *self.encode_path_vars(self.crn, self.zone_identifier, app_identifier)) request = self.prepare_request(method='GET', url=url, headers=headers) response = self.send(request) return response def update_range_app(self, app_identifier: str, protocol: str, dns: 'RangeAppReqDns', *, origin_direct: List[str] = None, origin_dns: 'RangeAppReqOriginDns' = None, origin_port: int = None, ip_firewall: bool = None, proxy_protocol: str = None, edge_ips: 'RangeAppReqEdgeIps' = None, traffic_type: str = None, tls: str = None, **kwargs ) -> DetailedResponse: """ Update range application. Update a Range Application inside a zone. :param str app_identifier: application identifier. :param str protocol: Defines the protocol and port for this application. :param RangeAppReqDns dns: Name and type of the DNS record for this application. :param List[str] origin_direct: (optional) IP address and port of the origin for this Range application. If configuring a load balancer, use 'origin_dns' and 'origin_port'. This can not be combined with 'origin_dns' and 'origin_port'. :param RangeAppReqOriginDns origin_dns: (optional) DNS record pointing to the origin for this Range application. This is used for configuring a load balancer. When specifying an individual IP address, use 'origin_direct'. This requires 'origin_port' and can not be combined with 'origin_direct'. :param int origin_port: (optional) Port at the origin that listens to traffic from this Range application. Requires 'origin_dns' and can not be combined with 'origin_direct'. :param bool ip_firewall: (optional) Enables the IP Firewall for this application. Only available for TCP applications. :param str proxy_protocol: (optional) Allows for the true client IP to be passed to the service. :param RangeAppReqEdgeIps edge_ips: (optional) Configures IP version for the hostname of this application. Default is {"type":"dynamic", "connectivity":"all"}. :param str traffic_type: (optional) Configure how traffic is handled at the edge. If set to "direct" traffic is passed through to the service. In the case of "http" or "https" HTTP/s features at the edge are applied ot this traffic. :param str tls: (optional) Configure if and how TLS connections are terminated at the edge. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse with `dict` result representing a `RangeApplicationResp` object """ if app_identifier is None: raise ValueError('app_identifier must be provided') if protocol is None: raise ValueError('protocol must be provided') if dns is None: raise ValueError('dns must be provided') dns = convert_model(dns) if origin_dns is not None: origin_dns = convert_model(origin_dns) if edge_ips is not None: edge_ips = convert_model(edge_ips) headers = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='update_range_app') headers.update(sdk_headers) data = { 'protocol': protocol, 'dns': dns, 'origin_direct': origin_direct, 'origin_dns': origin_dns, 'origin_port': origin_port, 'ip_firewall': ip_firewall, 'proxy_protocol': proxy_protocol, 'edge_ips': edge_ips, 'traffic_type': traffic_type, 'tls': tls } data = {k: v for (k, v) in data.items() if v is not None} data = json.dumps(data) headers['content-type'] = 'application/json' if 'headers' in kwargs: headers.update(kwargs.get('headers')) url = '/v1/{0}/zones/{1}/range/apps/{2}'.format( *self.encode_path_vars(self.crn, self.zone_identifier, app_identifier)) request = self.prepare_request(method='PUT', url=url, headers=headers, data=data) response = self.send(request) return response def delete_range_app(self, app_identifier: str, **kwargs ) -> DetailedResponse: """ Delete range application. Delete a specific application configuration. :param str app_identifier: application identifier. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse with `dict` result representing a `RangeApplicationResp` object """ if app_identifier is None: raise ValueError('app_identifier must be provided') headers = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='delete_range_app') headers.update(sdk_headers) if 'headers' in kwargs: headers.update(kwargs.get('headers')) url = '/v1/{0}/zones/{1}/range/apps/{2}'.format( *self.encode_path_vars(self.crn, self.zone_identifier, app_identifier)) request = self.prepare_request(method='DELETE', url=url, headers=headers) response = self.send(request) return response class ListRangeAppsEnums: """ Enums for list_range_apps parameters. """ class Order(str, Enum): """ Field by which to order the list of Range applications. """ PROTOCOL = 'protocol' APP_ID = 'app_id' CREATED_ON = 'created_on' MODIFIED_ON = 'modified_on' DNS = 'dns' class Direction(str, Enum): """ Direction in which to order results [ascending/descending order]. """ ASC = 'asc' DESC = 'desc' ############################################################################## # Models ############################################################################## class RangeAppReqDns(): """ Name and type of the DNS record for this application. :attr str type: (optional) DNS record type. :attr str name: (optional) DNS record name. """ def __init__(self, *, type: str = None, name: str = None) -> None: """ Initialize a RangeAppReqDns object. :param str type: (optional) DNS record type. :param str name: (optional) DNS record name. """ self.type = type self.name = name @classmethod def from_dict(cls, _dict: Dict) -> 'RangeAppReqDns': """Initialize a RangeAppReqDns object from a json dictionary.""" args = {} if 'type' in _dict: args['type'] = _dict.get('type') if 'name' in _dict: args['name'] = _dict.get('name') return cls(**args) @classmethod def _from_dict(cls, _dict): """Initialize a RangeAppReqDns object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'type') and self.type is not None: _dict['type'] = self.type if hasattr(self, 'name') and self.name is not None: _dict['name'] = self.name return _dict def _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this RangeAppReqDns object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'RangeAppReqDns') -> bool: """Return `true` when self and other are equal, false otherwise.""" if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other: 'RangeAppReqDns') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class RangeAppReqEdgeIps(): """ Configures IP version for the hostname of this application. Default is {"type":"dynamic", "connectivity":"all"}. :attr str type: (optional) The type of edge IP configuration. :attr str connectivity: (optional) Specifies the IP version (or all). """ def __init__(self, *, type: str = None, connectivity: str = None) -> None: """ Initialize a RangeAppReqEdgeIps object. :param str type: (optional) The type of edge IP configuration. :param str connectivity: (optional) Specifies the IP version (or all). """ self.type = type self.connectivity = connectivity @classmethod def from_dict(cls, _dict: Dict) -> 'RangeAppReqEdgeIps': """Initialize a RangeAppReqEdgeIps object from a json dictionary.""" args = {} if 'type' in _dict: args['type'] = _dict.get('type') if 'connectivity' in _dict: args['connectivity'] = _dict.get('connectivity') return cls(**args) @classmethod def _from_dict(cls, _dict): """Initialize a RangeAppReqEdgeIps object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'type') and self.type is not None: _dict['type'] = self.type if hasattr(self, 'connectivity') and self.connectivity is not None: _dict['connectivity'] = self.connectivity return _dict def _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this RangeAppReqEdgeIps object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'RangeAppReqEdgeIps') -> bool: """Return `true` when self and other are equal, false otherwise.""" if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other: 'RangeAppReqEdgeIps') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class TypeEnum(str, Enum): """ The type of edge IP configuration. """ DYNAMIC = 'dynamic' class ConnectivityEnum(str, Enum): """ Specifies the IP version (or all). """ IPV4 = 'ipv4' IPV6 = 'ipv6' ALL = 'all' class RangeAppReqOriginDns(): """ DNS record pointing to the origin for this Range application. This is used for configuring a load balancer. When specifying an individual IP address, use 'origin_direct'. This requires 'origin_port' and can not be combined with 'origin_direct'. :attr str name: Name of the origin. """ def __init__(self, name: str) -> None: """ Initialize a RangeAppReqOriginDns object. :param str name: Name of the origin. """ self.name = name @classmethod def from_dict(cls, _dict: Dict) -> 'RangeAppReqOriginDns': """Initialize a RangeAppReqOriginDns object from a json dictionary.""" args = {} if 'name' in _dict: args['name'] = _dict.get('name') else: raise ValueError('Required property \'name\' not present in RangeAppReqOriginDns JSON') return cls(**args) @classmethod def _from_dict(cls, _dict): """Initialize a RangeAppReqOriginDns object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'name') and self.name is not None: _dict['name'] = self.name return _dict def _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this RangeAppReqOriginDns object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'RangeAppReqOriginDns') -> bool: """Return `true` when self and other are equal, false otherwise.""" if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other: 'RangeAppReqOriginDns') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class RangeApplicationObjectDns(): """ The name and type of DNS record for the Range application. :attr str type: (optional) The type of DNS record associated with the application. :attr str name: (optional) The name of the DNS record associated with the application. """ def __init__(self, *, type: str = None, name: str = None) -> None: """ Initialize a RangeApplicationObjectDns object. :param str type: (optional) The type of DNS record associated with the application. :param str name: (optional) The name of the DNS record associated with the application. """ self.type = type self.name = name @classmethod def from_dict(cls, _dict: Dict) -> 'RangeApplicationObjectDns': """Initialize a RangeApplicationObjectDns object from a json dictionary.""" args = {} if 'type' in _dict: args['type'] = _dict.get('type') if 'name' in _dict: args['name'] = _dict.get('name') return cls(**args) @classmethod def _from_dict(cls, _dict): """Initialize a RangeApplicationObjectDns object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'type') and self.type is not None: _dict['type'] = self.type if hasattr(self, 'name') and self.name is not None: _dict['name'] = self.name return _dict def _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this RangeApplicationObjectDns object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'RangeApplicationObjectDns') -> bool: """Return `true` when self and other are equal, false otherwise.""" if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other: 'RangeApplicationObjectDns') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class TypeEnum(str, Enum): """ The type of DNS record associated with the application. """ CNAME = 'CNAME' class RangeApplicationObjectEdgeIps(): """ Configures IP version for the hostname of this application. :attr str type: (optional) The type of edge IP configuration. :attr str connectivity: (optional) Specifies the IP version (or all). """ def __init__(self, *, type: str = None, connectivity: str = None) -> None: """ Initialize a RangeApplicationObjectEdgeIps object. :param str type: (optional) The type of edge IP configuration. :param str connectivity: (optional) Specifies the IP version (or all). """ self.type = type self.connectivity = connectivity @classmethod def from_dict(cls, _dict: Dict) -> 'RangeApplicationObjectEdgeIps': """Initialize a RangeApplicationObjectEdgeIps object from a json dictionary.""" args = {} if 'type' in _dict: args['type'] = _dict.get('type') if 'connectivity' in _dict: args['connectivity'] = _dict.get('connectivity') return cls(**args) @classmethod def _from_dict(cls, _dict): """Initialize a RangeApplicationObjectEdgeIps object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'type') and self.type is not None: _dict['type'] = self.type if hasattr(self, 'connectivity') and self.connectivity is not None: _dict['connectivity'] = self.connectivity return _dict def _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this RangeApplicationObjectEdgeIps object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'RangeApplicationObjectEdgeIps') -> bool: """Return `true` when self and other are equal, false otherwise.""" if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other: 'RangeApplicationObjectEdgeIps') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class TypeEnum(str, Enum): """ The type of edge IP configuration. """ DYNAMIC = 'dynamic' class ConnectivityEnum(str, Enum): """ Specifies the IP version (or all). """ IPV4 = 'ipv4' IPV6 = 'ipv6' ALL = 'all' class RangeApplicationObject(): """ range application object. :attr str id: (optional) Application identifier. :attr str protocol: (optional) Port configuration. :attr RangeApplicationObjectDns dns: (optional) The name and type of DNS record for the Range application. :attr List[str] origin_direct: (optional) A list of destination addresses to the origin. :attr bool ip_firewall: (optional) Enables the IP Firewall for this application. :attr str proxy_protocol: (optional) Allows for the true client IP to be passed to the service. :attr RangeApplicationObjectEdgeIps edge_ips: (optional) Configures IP version for the hostname of this application. :attr str tls: (optional) Specifies the TLS termination at the edge. :attr str traffic_type: (optional) Configure how traffic is handled at the edge. If set to "direct" traffic is passed through to the service. In the case of "http" or "https" HTTP/s features at the edge are applied ot this traffic. :attr datetime created_on: (optional) When the Application was created. :attr datetime modified_on: (optional) When the Application was last modified. """ def __init__(self, *, id: str = None, protocol: str = None, dns: 'RangeApplicationObjectDns' = None, origin_direct: List[str] = None, ip_firewall: bool = None, proxy_protocol: str = None, edge_ips: 'RangeApplicationObjectEdgeIps' = None, tls: str = None, traffic_type: str = None, created_on: datetime = None, modified_on: datetime = None) -> None: """ Initialize a RangeApplicationObject object. :param str id: (optional) Application identifier. :param str protocol: (optional) Port configuration. :param RangeApplicationObjectDns dns: (optional) The name and type of DNS record for the Range application. :param List[str] origin_direct: (optional) A list of destination addresses to the origin. :param bool ip_firewall: (optional) Enables the IP Firewall for this application. :param str proxy_protocol: (optional) Allows for the true client IP to be passed to the service. :param RangeApplicationObjectEdgeIps edge_ips: (optional) Configures IP version for the hostname of this application. :param str tls: (optional) Specifies the TLS termination at the edge. :param str traffic_type: (optional) Configure how traffic is handled at the edge. If set to "direct" traffic is passed through to the service. In the case of "http" or "https" HTTP/s features at the edge are applied ot this traffic. :param datetime created_on: (optional) When the Application was created. :param datetime modified_on: (optional) When the Application was last modified. """ self.id = id self.protocol = protocol self.dns = dns self.origin_direct = origin_direct self.ip_firewall = ip_firewall self.proxy_protocol = proxy_protocol self.edge_ips = edge_ips self.tls = tls self.traffic_type = traffic_type self.created_on = created_on self.modified_on = modified_on @classmethod def from_dict(cls, _dict: Dict) -> 'RangeApplicationObject': """Initialize a RangeApplicationObject object from a json dictionary.""" args = {} if 'id' in _dict: args['id'] = _dict.get('id') if 'protocol' in _dict: args['protocol'] = _dict.get('protocol') if 'dns' in _dict: args['dns'] = RangeApplicationObjectDns.from_dict(_dict.get('dns')) if 'origin_direct' in _dict: args['origin_direct'] = _dict.get('origin_direct') if 'ip_firewall' in _dict: args['ip_firewall'] = _dict.get('ip_firewall') if 'proxy_protocol' in _dict: args['proxy_protocol'] = _dict.get('proxy_protocol') if 'edge_ips' in _dict: args['edge_ips'] = RangeApplicationObjectEdgeIps.from_dict(_dict.get('edge_ips')) if 'tls' in _dict: args['tls'] = _dict.get('tls') if 'traffic_type' in _dict: args['traffic_type'] = _dict.get('traffic_type') if 'created_on' in _dict: args['created_on'] = string_to_datetime(_dict.get('created_on')) if 'modified_on' in _dict: args['modified_on'] = string_to_datetime(_dict.get('modified_on')) return cls(**args) @classmethod def _from_dict(cls, _dict): """Initialize a RangeApplicationObject object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'id') and self.id is not None: _dict['id'] = self.id if hasattr(self, 'protocol') and self.protocol is not None: _dict['protocol'] = self.protocol if hasattr(self, 'dns') and self.dns is not None: _dict['dns'] = self.dns.to_dict() if hasattr(self, 'origin_direct') and self.origin_direct is not None: _dict['origin_direct'] = self.origin_direct if hasattr(self, 'ip_firewall') and self.ip_firewall is not None: _dict['ip_firewall'] = self.ip_firewall if hasattr(self, 'proxy_protocol') and self.proxy_protocol is not None: _dict['proxy_protocol'] = self.proxy_protocol if hasattr(self, 'edge_ips') and self.edge_ips is not None: _dict['edge_ips'] = self.edge_ips.to_dict() if hasattr(self, 'tls') and self.tls is not None: _dict['tls'] = self.tls if hasattr(self, 'traffic_type') and self.traffic_type is not None: _dict['traffic_type'] = self.traffic_type if hasattr(self, 'created_on') and self.created_on is not None: _dict['created_on'] = datetime_to_string(self.created_on) if hasattr(self, 'modified_on') and self.modified_on is not None: _dict['modified_on'] = datetime_to_string(self.modified_on) return _dict def _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this RangeApplicationObject object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'RangeApplicationObject') -> bool: """Return `true` when self and other are equal, false otherwise.""" if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other: 'RangeApplicationObject') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class ProxyProtocolEnum(str, Enum): """ Allows for the true client IP to be passed to the service. """ OFF = 'off' V1 = 'v1' V2 = 'v2' SIMPLE = 'simple' class TlsEnum(str, Enum): """ Specifies the TLS termination at the edge. """ OFF = 'off' FLEXIBLE = 'flexible' FULL = 'full' STRICT = 'strict' class TrafficTypeEnum(str, Enum): """ Configure how traffic is handled at the edge. If set to "direct" traffic is passed through to the service. In the case of "http" or "https" HTTP/s features at the edge are applied ot this traffic. """ DIRECT = 'direct' HTTP = 'http' HTTPS = 'https' class RangeApplicationResp(): """ range application response. :attr bool success: Was the get successful. :attr List[List[str]] errors: errors. :attr List[List[str]] messages: messages. :attr RangeApplicationObject result: range application object. """ def __init__(self, success: bool, errors: List[List[str]], messages: List[List[str]], result: 'RangeApplicationObject') -> None: """ Initialize a RangeApplicationResp object. :param bool success: Was the get successful. :param List[List[str]] errors: errors. :param List[List[str]] messages: messages. :param RangeApplicationObject result: range application object. """ self.success = success self.errors = errors self.messages = messages self.result = result @classmethod def from_dict(cls, _dict: Dict) -> 'RangeApplicationResp': """Initialize a RangeApplicationResp object from a json dictionary.""" args = {} if 'success' in _dict: args['success'] = _dict.get('success') else: raise ValueError('Required property \'success\' not present in RangeApplicationResp JSON') if 'errors' in _dict: args['errors'] = _dict.get('errors') else: raise ValueError('Required property \'errors\' not present in RangeApplicationResp JSON') if 'messages' in _dict: args['messages'] = _dict.get('messages') else: raise ValueError('Required property \'messages\' not present in RangeApplicationResp JSON') if 'result' in _dict: args['result'] = RangeApplicationObject.from_dict(_dict.get('result')) else: raise ValueError('Required property \'result\' not present in RangeApplicationResp JSON') return cls(**args) @classmethod def _from_dict(cls, _dict): """Initialize a RangeApplicationResp object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'success') and self.success is not None: _dict['success'] = self.success if hasattr(self, 'errors') and self.errors is not None: _dict['errors'] = self.errors if hasattr(self, 'messages') and self.messages is not None: _dict['messages'] = self.messages if hasattr(self, 'result') and self.result is not None: _dict['result'] = self.result.to_dict() return _dict def _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this RangeApplicationResp object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'RangeApplicationResp') -> bool: """Return `true` when self and other are equal, false otherwise.""" if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other: 'RangeApplicationResp') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class RangeApplications(): """ range application. :attr bool success: Was the get successful. :attr List[List[str]] errors: errors. :attr List[List[str]] messages: messages. :attr List[RangeApplicationObject] result: Container for Range application objects. """ def __init__(self, success: bool, errors: List[List[str]], messages: List[List[str]], result: List['RangeApplicationObject']) -> None: """ Initialize a RangeApplications object. :param bool success: Was the get successful. :param List[List[str]] errors: errors. :param List[List[str]] messages: messages. :param List[RangeApplicationObject] result: Container for Range application objects. """ self.success = success self.errors = errors self.messages = messages self.result = result @classmethod def from_dict(cls, _dict: Dict) -> 'RangeApplications': """Initialize a RangeApplications object from a json dictionary.""" args = {} if 'success' in _dict: args['success'] = _dict.get('success') else: raise ValueError('Required property \'success\' not present in RangeApplications JSON') if 'errors' in _dict: args['errors'] = _dict.get('errors') else: raise ValueError('Required property \'errors\' not present in RangeApplications JSON') if 'messages' in _dict: args['messages'] = _dict.get('messages') else: raise ValueError('Required property \'messages\' not present in RangeApplications JSON') if 'result' in _dict: args['result'] = [RangeApplicationObject.from_dict(x) for x in _dict.get('result')] else: raise ValueError('Required property \'result\' not present in RangeApplications JSON') return cls(**args) @classmethod def _from_dict(cls, _dict): """Initialize a RangeApplications object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'success') and self.success is not None: _dict['success'] = self.success if hasattr(self, 'errors') and self.errors is not None: _dict['errors'] = self.errors if hasattr(self, 'messages') and self.messages is not None: _dict['messages'] = self.messages if hasattr(self, 'result') and self.result is not None: _dict['result'] = [x.to_dict() for x in self.result] return _dict def _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this RangeApplications object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'RangeApplications') -> bool: """Return `true` when self and other are equal, false otherwise.""" if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other: 'RangeApplications') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other

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881cea5585d07f56df0a3e21434ecc693732ae0b

110,256

py

Python

GolemQ/utils/gfx.py

wangdecheng/QAStrategy

d970242ea61cff2f1a6f69545dc7f65e8efd1672

[ "MIT" ]

76

2020-10-14T03:33:47.000Z

2022-01-25T13:34:05.000Z

GolemQ/utils/gfx.py

wangdecheng/QAStrategy

d970242ea61cff2f1a6f69545dc7f65e8efd1672

[ "MIT" ]

4

2020-10-15T09:23:34.000Z

2021-07-15T04:25:00.000Z

GolemQ/utils/gfx.py

wangdecheng/QAStrategy

d970242ea61cff2f1a6f69545dc7f65e8efd1672

[ "MIT" ]

37

2020-10-14T03:35:55.000Z

2021-12-20T09:58:32.000Z

# # The MIT License (MIT) # # Copyright (c) 2018-2020 azai/Rgveda/GolemQuant # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. # """ 这里定义输出gfx图片 """ import matplotlib.pyplot as plt import mplfinance as mpf import matplotlib.dates as mdates import numpy as np import math from PIL import Image from GolemQ.utils.parameter import ( AKA, INDICATOR_FIELD as FLD, TREND_STATUS as ST, FEATURES as FTR, ) def ohlc_plot_protype(ohlc_data, features, code=None, codename=None, title=None): # 暗色主题 plt.style.use('Solarize_Light2') # 正常显示中文字体 plt.rcParams['font.sans-serif'] = ['Microsoft YaHei'] fig = plt.figure(figsize = (22,9)) plt.subplots_adjust(left=0.04, right=0.96) if (title is None): fig.suptitle(u'阿财的 {:s}（{:s}）机器学习买入点判断'.format(codename, code), fontsize=16) else: fig.suptitle(title, fontsize=16) ax1 = plt.subplot2grid((4,3),(0,0), rowspan=3, colspan=3) ax2 = plt.subplot2grid((4,3),(3,0), rowspan=1, colspan=3, sharex=ax1) #ax1 = fig.add_subplot(111) ax3 = ax1.twinx() # 绘制K线 ohlc_data = ohlc_data.reset_index([1], drop=False) mc_stock_cn = mpf.make_marketcolors(up='r',down='g') s_stock_cn = mpf.make_mpf_style(marketcolors=mc_stock_cn) mpf.plot(data=ohlc_data, ax=ax1, type='candle', style=s_stock_cn) # 设定x标轴时间 datetime_index = ohlc_data.index.get_level_values(level=0).to_series() DATETIME_LABEL = datetime_index.apply(lambda x: x.strftime("%Y-%m-%d %H:%M")[2:16]) ax1.plot(DATETIME_LABEL, features[FLD.BOLL_UB], lw=0.75, color='cyan', alpha=0.6) ax1.plot(DATETIME_LABEL, features[FLD.BOLL_LB], lw=0.75, color='fuchsia', alpha=0.6) ax1.plot(DATETIME_LABEL, features[FLD.BOLL], lw=0.75, color='purple', alpha=0.6) ax1.plot(DATETIME_LABEL, features[FLD.MA30], lw=0.75, color='green', alpha=0.6) ax1.fill_between(DATETIME_LABEL, features[FLD.BOLL_UB], features[FLD.BOLL_LB], color='lightskyblue', alpha=0.15) ax1.plot(DATETIME_LABEL, features[FLD.MA90], lw=1, color='crimson', alpha=0.5) ax1.plot(DATETIME_LABEL, features[FLD.MA120], lw=1, color='limegreen', alpha=0.5) ax1.plot(DATETIME_LABEL, features[FLD.ATR_UB], lw=1, color='crimson', alpha=0.5) ax1.plot(DATETIME_LABEL, features[FLD.LINEAREG_PRICE], lw=1, color='crimson', alpha=0.8, linestyle='--') ax1.plot(DATETIME_LABEL, features[FLD.HMA5], lw=1, color='olive', alpha=0.8, linestyle='--') ax1.plot(DATETIME_LABEL, features[FLD.HMA10], lw=1, color='green', alpha=0.8, linestyle='-.') ax1.plot(DATETIME_LABEL, features[FLD.ATR_LB], lw=1, color='crimson', alpha=0.5) ax1.set_xticks(range(0, len(DATETIME_LABEL), round(len(DATETIME_LABEL) / 12))) ax1.set_xticklabels(DATETIME_LABEL[::round(len(DATETIME_LABEL) / 12)], rotation=15) ax1.grid(False) ax2.plot(DATETIME_LABEL, features[FLD.DIF], color='orange', lw=1, label=FLD.DIF) ax2.plot(DATETIME_LABEL, features[FLD.DEA], color = 'purple', lw = 1, label = FLD.DEA) barlist = ax2.bar(DATETIME_LABEL, features[FLD.MACD], width=0.6, label=FLD.MACD) for i in range(len(DATETIME_LABEL.index)): if features[FLD.MACD][i] <= 0: barlist[i].set_color('g') else: barlist[i].set_color('r') ax2.set(ylabel='MACD(26,12,9)') ax2.set_xticks(range(0, len(DATETIME_LABEL), round(len(DATETIME_LABEL) / 12))) ax2.set_xticklabels(DATETIME_LABEL[::round(len(DATETIME_LABEL) / 12)], rotation=15) ax2.grid(False) ax3.set_xticks(range(0, len(DATETIME_LABEL), round(len(DATETIME_LABEL) / 12))) ax3.set_xticklabels(DATETIME_LABEL[::round(len(DATETIME_LABEL) / 12)], rotation=15) ax3.grid(False) #plt.show() return ax1, ax2, ax3, DATETIME_LABEL def ohlc_plot_protype_imf(ohlc_data, features, code, codename): # 暗色主题 plt.style.use('Solarize_Light2') # 正常显示中文字体 plt.rcParams['font.sans-serif'] = ['Microsoft YaHei'] fig = plt.figure(figsize = (22,9)) plt.subplots_adjust(left=0.04, right=0.96) if (title is None): fig.suptitle(u'阿财的 {:s}（{:s}）机器学习买入点判断'.format(codename, code), fontsize=16) else: fig.suptitle(title, fontsize=16) ax1 = plt.subplot2grid((4,3),(0,0), rowspan=3, colspan=3) ax2 = plt.subplot2grid((4,3),(3,0), rowspan=1, colspan=3, sharex=ax1) #ax1 = fig.add_subplot(111) ax3 = ax1.twinx() # 绘制K线 ohlc_data = ohlc_data.reset_index([1], drop=False) mc_stock_cn = mpf.make_marketcolors(up='r',down='g') s_stock_cn = mpf.make_mpf_style(marketcolors=mc_stock_cn) mpf.plot(data=ohlc_data, ax=ax1, type='candle', style=s_stock_cn) # 设定x标轴时间 datetime_index = ohlc_data.index.get_level_values(level=0).to_series() DATETIME_LABEL = datetime_index.apply(lambda x: x.strftime("%Y-%m-%d %H:%M")[2:16]) ax1.plot(DATETIME_LABEL, features[FLD.BOLL_UB], lw=0.75, color='cyan', alpha=0.6) ax1.plot(DATETIME_LABEL, features[FLD.BOLL_LB], lw=0.75, color='fuchsia', alpha=0.6) ax1.plot(DATETIME_LABEL, features[FLD.BOLL], lw=0.75, color='purple', alpha=0.6) ax1.plot(DATETIME_LABEL, features[FLD.MA30], lw=0.75, color='green', alpha=0.6) ax1.fill_between(DATETIME_LABEL, features[FLD.BOLL_UB], features[FLD.BOLL_LB], color='lightskyblue', alpha=0.15) ax1.plot(DATETIME_LABEL, features[FLD.MA90], lw=1, color='crimson', alpha=0.5) ax1.plot(DATETIME_LABEL, features[FLD.MA120], lw=1, color='limegreen', alpha=0.5) ax1.plot(DATETIME_LABEL, features[FLD.ATR_UB], lw=1, color='crimson', alpha=0.5) ax1.plot(DATETIME_LABEL, features[FLD.LINEAREG_PRICE], lw=1, color='crimson', alpha=0.8, linestyle='--') ax1.plot(DATETIME_LABEL, features[FLD.ATR_LB], lw=1, color='crimson', alpha=0.5) ax1.set_xticks(range(0, len(DATETIME_LABEL), round(len(DATETIME_LABEL) / 12))) ax1.set_xticklabels(DATETIME_LABEL[::round(len(DATETIME_LABEL) / 12)], rotation=15) ax1.grid(False) ax2.plot(DATETIME_LABEL, features[FLD.DIF], color='green', lw=1, label=FLD.DIF) ax2.plot(DATETIME_LABEL, features[FLD.DEA], color='purple', lw=1, label=FLD.DEA) ax3.plot(DATETIME_LABEL, np.where((features[FLD.MAINFEST_UPRISING_TIMING_LAG] < 0), (min(features[FTR.BEST_IMF3].min(), features[FTR.BEST_IMF4].min()) - 400 * 0.0168), np.nan), color='green', lw=1.5, label=FLD.RENKO_BOOST_L_TIMING_LAG, alpha=0.8) ax3.plot(DATETIME_LABEL, np.where((features[FLD.MAINFEST_UPRISING_TIMING_LAG] > 0), (min(features[FTR.BEST_IMF3].min(), features[FTR.BEST_IMF4].min()) - 400 * 0.0168), np.nan), color='red', lw=1.5, label=FLD.RENKO_BOOST_L_TIMING_LAG, alpha=0.8) ax3.plot(DATETIME_LABEL, np.where((features[FTR.BEST_IMF4] < features[FTR.BEST_IMF4].shift(1)), features[FTR.BEST_IMF4], np.nan), color='lime', lw=1, label=FLD.COMBINE_DENSITY, alpha=0.5) ax3.plot(DATETIME_LABEL, np.where((features[FTR.BEST_IMF4] > features[FTR.BEST_IMF4].shift(1)), features[FTR.BEST_IMF4], np.nan), color='salmon', lw=1, label=FLD.COMBINE_DENSITY, alpha=0.5) barlist = ax2.bar(DATETIME_LABEL, features[FLD.MACD], width=0.6, label=FLD.MACD) for i in range(len(DATETIME_LABEL.index)): if features[FLD.MACD][i] <= 0: barlist[i].set_color('g') else: barlist[i].set_color('r') ax2.set(ylabel='MACD(26,12,9)') ax2.set_xticks(range(0, len(DATETIME_LABEL), round(len(DATETIME_LABEL) / 12))) ax2.set_xticklabels(DATETIME_LABEL[::round(len(DATETIME_LABEL) / 12)], rotation=15) ax2.grid(False) ax3.grid(False) ax3.plot(DATETIME_LABEL, np.where((features[ST.BOOTSTRAP_GROUND_ZERO] > 0), (min(features[FTR.BEST_IMF3].min(), features[FTR.BEST_IMF4].min()) - 400 * 0.0382), np.nan), 'y^', alpha = 0.33) #plt.show() return ax1, ax2, ax3, DATETIME_LABEL def ohlc_plot_mapower(ohlc_data, features, code=None, codename=None, title=None): # 暗色主题 plt.style.use('Solarize_Light2') # 正常显示中文字体 plt.rcParams['font.sans-serif'] = ['Microsoft YaHei'] fig = plt.figure(figsize = (22,9)) plt.subplots_adjust(left=0.04, right=0.96) if (title is None): fig.suptitle(u'阿财的 {:s}（{:s}）MA多头趋势买入点判断'.format(codename, code), fontsize=16) else: fig.suptitle(title, fontsize=16) ax1 = plt.subplot2grid((4,3),(0,0), rowspan=3, colspan=3) ax2 = plt.subplot2grid((4,3),(3,0), rowspan=1, colspan=3) ax1.zorder = 2.5 ax2.zorder = 0 #ax1 = fig.add_subplot(111) ax3 = ax1.twinx() ax3.zorder = 2.5 ohlc_data = ohlc_data.reset_index([1], drop=False) ohlc_data = ohlc_data.rename(columns={AKA.OPEN: "Open", AKA.HIGH: "High", AKA.LOW: "Low", AKA.CLOSE: "Close"}) mc_stock_cn = mpf.make_marketcolors(up='r',down='g') s_stock_cn = mpf.make_mpf_style(marketcolors=mc_stock_cn) mpf.plot(data=ohlc_data, ax=ax1, type='candle', style=s_stock_cn) DATETIME_LABEL = ohlc_data.index.get_level_values(level=0).to_series().apply(lambda x: x.strftime("%Y-%m-%d %H:%M")[2:16]) ax1.plot(DATETIME_LABEL, features[FLD.BOLL_UB], lw=0.75, color='cyan', alpha=0.6) ax1.plot(DATETIME_LABEL, features[FLD.BOLL_LB], lw=0.75, color='fuchsia', alpha=0.6) ax1.plot(DATETIME_LABEL, features[FLD.BOLL], lw=0.75, color='purple', alpha=0.6) ax1.plot(DATETIME_LABEL, features[FLD.MA30], lw=0.75, color='green', alpha=0.6) ax1.fill_between(DATETIME_LABEL, features[FLD.BOLL_UB], features[FLD.BOLL_LB], color='lightskyblue', alpha=0.15) ax1.fill_between(DATETIME_LABEL, np.where((features[FLD.MAINFEST_UPRISING_TIMING_LAG] > 0), features[FLD.BOLL_UB], np.nan), np.where((features[FLD.MAINFEST_UPRISING_TIMING_LAG] > 0), features[FLD.BOLL_LB], np.nan), color='orangered', alpha=0.15) ax1.fill_between(DATETIME_LABEL, np.where((features[FLD.MAINFEST_DOWNRISK_TIMING_LAG] < 0), features[FLD.BOLL_UB], np.nan), np.where((features[FLD.MAINFEST_DOWNRISK_TIMING_LAG] < 0), features[FLD.BOLL_LB], np.nan), color='darkseagreen', alpha=0.15) ax3.plot(DATETIME_LABEL, np.where((features[FLD.MAINFEST_DOWNRISK_TIMING_LAG] < 0), features[FLD.CCI_NORM].min() - 0.0618, np.nan), 'g.', alpha = 0.75) ax3.plot(DATETIME_LABEL, np.where((features[FLD.MAINFEST_DOWNRISK_TIMING_LAG] > 0) & \ ((features[FLD.MAINFEST_DOWNRISK_TIMING_LAG] + features[FLD.ZEN_DASH_TIMING_LAG_MAJOR]) < 0) & \ (np.minimum(features[FLD.DEADPOOL_CANDIDATE_TIMING_LAG], features[FLD.DEADPOOL_REMIX_TIMING_LAG]) < 0), features[FLD.CCI_NORM].min() - 0.0618, np.nan), 'g.', alpha = 0.25) ax1.plot(DATETIME_LABEL, features[FLD.MA90], lw=1, color='crimson', alpha=0.5) ax1.plot(DATETIME_LABEL, features[FLD.MA120], lw=1, color='limegreen', alpha=0.5) ax1.plot(DATETIME_LABEL, features[FLD.ATR_UB], lw=1, color='crimson', alpha=0.5) ax1.plot(DATETIME_LABEL, features[FLD.LINEAREG_PRICE], lw=1, color='crimson', alpha=0.8, linestyle='--') ax1.plot(DATETIME_LABEL, features[FLD.HMA5], lw=1, color='olive', alpha=0.8, linestyle='--') ax1.plot(DATETIME_LABEL, features[FLD.HMA10], lw=1, color='green', alpha=0.8, linestyle='-.') ax1.plot(DATETIME_LABEL, features[FLD.ATR_LB], lw=1, color='crimson', alpha=0.5) ax1.set_xticks(range(0, len(DATETIME_LABEL), round(len(DATETIME_LABEL) / 12))) ax1.set_xticklabels(DATETIME_LABEL[::round(len(DATETIME_LABEL) / 12)], rotation=15) ax1.grid(False) ax2.plot(DATETIME_LABEL, features[FLD.DIF], color='orange', lw=1, label=FLD.DIF) ax2.plot(DATETIME_LABEL, features[FLD.DEA], color = 'purple', lw = 1, label = FLD.DEA) barlist = ax2.bar(DATETIME_LABEL, features[FLD.MACD], width = 0.6, label = FLD.MACD) for i in range(len(DATETIME_LABEL.index)): if features[FLD.MACD][i] <= 0: barlist[i].set_color('g') else: barlist[i].set_color('r') ax2.set(ylabel='MACD(26,12,9)') ax2.set_xticks(range(0, len(DATETIME_LABEL), round(len(DATETIME_LABEL) / 12))) ax2.set_xticklabels(DATETIME_LABEL[::round(len(DATETIME_LABEL) / 12)], rotation=15) ax2.grid(False) ax3.set_xticks(range(0, len(DATETIME_LABEL), round(len(DATETIME_LABEL) / 12))) ax3.set_xticklabels(DATETIME_LABEL[::round(len(DATETIME_LABEL) / 12)], rotation=15) ax3.grid(False) #plt.show() return ax1, ax2, ax3, DATETIME_LABEL def save_png24_to_png8(png_filename): im = Image.open(png_filename) # PIL complains if you don't load explicitly im.load() # Get the alpha band alpha = im.split()[-1] im = im.convert('RGB').convert('P', palette=Image.ADAPTIVE, colors=255) # Set all pixel values below 128 to 255, # and the rest to 0 mask = Image.eval(alpha, lambda a: 255 if a <= 128 else 0) # Paste the color of index 255 and use alpha as a mask im.paste(255, mask) # The transparency index is 255 im.save(png_filename, transparency=255) def mark_annotate_phase2(checkpoints, features, ax1, ax3, colorwarp='skyblue'): ''' 绘制标记_phase2 ''' if (ST.PREDICT_LONG in features.columns): markers = features.loc[checkpoints, [FLD.PEAK_LOW_TIMING_LAG, FLD.COMBINE_DENSITY, FLD.MAPOWER30_MAJOR, FLD.HMAPOWER120_MAJOR, FLD.MAPOWER30, FLD.HMAPOWER120, FLD.BOLL_LB, FLD.ATR_LB, FLD.MA90_CLEARANCE, FLD.MA120_CLEARANCE, FLD.BOLL_CHANNEL, FLD.MA_CHANNEL, FLD.MA120_CHANNEL, FTR.UPRISING_RAIL_TIMING_LAG, FLD.MAPOWER_BASELINE_TIMING_LAG, FLD.MAPOWER30_PEAK_LOW_BEFORE, FLD.MAPOWER30_PEAK_HIGH_BEFORE, FLD.MAPOWER30_PEAK_LOW_BEFORE_MAJOR, FLD.MAPOWER30_PEAK_HIGH_BEFORE_MAJOR, FLD.MAPOWER30_TIMING_LAG_MAJOR, FLD.DRAWDOWN_RATIO, FLD.DRAWDOWN_RATIO_MAJOR, FLD.ZEN_WAVELET_TIMING_LAG, FLD.RENKO_TREND_S_TIMING_LAG, FLD.RENKO_TREND_L_TIMING_LAG, FLD.DEADPOOL_CANDIDATE_TIMING_LAG, FLD.MAPOWER_HMAPOWER120_TIMING_LAG, ST.PREDICT_LONG, FLD.PREDICT_PROB_LONG, FLD.BOLL_JX_RSI, FLD.BOLL_JX_MAXFACTOR,]].copy() else: markers = features.loc[checkpoints, [FLD.PEAK_LOW_TIMING_LAG, FLD.COMBINE_DENSITY, FLD.MAPOWER30_MAJOR, FLD.HMAPOWER120_MAJOR, FLD.MAPOWER30, FLD.HMAPOWER120, FLD.BOLL_LB, FLD.ATR_LB, FLD.MA90_CLEARANCE, FLD.MA120_CLEARANCE, FLD.BOLL_CHANNEL, FLD.MA_CHANNEL, FLD.MA120_CHANNEL, FTR.UPRISING_RAIL_TIMING_LAG, FLD.MAPOWER_BASELINE_TIMING_LAG, FLD.MAPOWER30_PEAK_LOW_BEFORE, FLD.MAPOWER30_PEAK_HIGH_BEFORE, FLD.MAPOWER30_PEAK_LOW_BEFORE_MAJOR, FLD.MAPOWER30_PEAK_HIGH_BEFORE_MAJOR, FLD.MAPOWER30_TIMING_LAG_MAJOR, FLD.DRAWDOWN_RATIO, FLD.DRAWDOWN_RATIO_MAJOR, FLD.ZEN_WAVELET_TIMING_LAG, FLD.RENKO_TREND_S_TIMING_LAG, FLD.RENKO_TREND_L_TIMING_LAG, FLD.DEADPOOL_CANDIDATE_TIMING_LAG, FLD.MAPOWER_HMAPOWER120_TIMING_LAG,]].copy() #print(markers) for index, marker in markers.iterrows(): mkr_x = marker.name[0].strftime("%Y-%m-%d %H:%M")[2:16] if (ST.PREDICT_LONG in features.columns) and (marker.at[ST.PREDICT_LONG] < 0.1): mkr_text_template = '*{}*\nxgboost_pred:{:02d}\npred_prob:{:.2f}\nRSI:{:.3f}/MFT:{:.3f}\nDRAWDOWN:{:.3f}/{:.3f}' mkr_text = mkr_text_template.format(mkr_x, int(marker.at[ST.PREDICT_LONG]), marker.at[FLD.PREDICT_PROB_LONG], marker.at[FLD.BOLL_JX_RSI], marker.at[FLD.BOLL_JX_MAXFACTOR], marker.at[FLD.DRAWDOWN_RATIO], marker.at[FLD.DRAWDOWN_RATIO_MAJOR],) else: mkr_text_template = '''*{}* COMB:{:.3f}/BOL:{:.3f} MAPWR3:{:.2f}/HMA12:{:.2f} MACH:{:.2f}/MA12CH:{:.2f} MA9CLR:{:.2f}/{:.2f}/{:.2f} PEAK_LO:{:03d}/{:03d}/{:03d} PEAK_HI:{:03d}/{:03d}/{:03d} DRAWDOWN:{:.3f}/{:.3f} RENKO:{:03d}/{:03d}/ZEN:{:03d} MAPWR_BSL_HMAPWR12:{:02d}''' mkr_text = mkr_text_template.format(mkr_x, marker.at[FLD.COMBINE_DENSITY], marker.at[FLD.BOLL_CHANNEL], marker.at[FLD.MAPOWER30_MAJOR], marker.at[FLD.HMAPOWER120_MAJOR], marker.at[FLD.MA_CHANNEL], marker.at[FLD.MA120_CHANNEL], marker.at[FLD.MA90_CLEARANCE], marker.at[FLD.MA120_CLEARANCE], marker.at[FLD.MAPOWER30_MAJOR] + marker.at[FLD.HMAPOWER120_MAJOR] + marker.at[FLD.MAPOWER30], int(marker.at[FLD.MAPOWER30_PEAK_LOW_BEFORE]), int(marker.at[FLD.MAPOWER30_PEAK_LOW_BEFORE_MAJOR]), int(marker.at[FLD.DEADPOOL_CANDIDATE_TIMING_LAG]), int(marker.at[FLD.MAPOWER30_PEAK_HIGH_BEFORE]), int(marker.at[FLD.MAPOWER30_PEAK_HIGH_BEFORE_MAJOR]), int(marker.at[FLD.MAPOWER30_TIMING_LAG_MAJOR]), marker.at[FLD.DRAWDOWN_RATIO], marker.at[FLD.DRAWDOWN_RATIO_MAJOR], int(marker.at[FLD.RENKO_TREND_S_TIMING_LAG]), int(marker.at[FLD.RENKO_TREND_L_TIMING_LAG]), int(marker.at[FLD.ZEN_WAVELET_TIMING_LAG]), int(marker.at[FLD.MAPOWER_HMAPOWER120_TIMING_LAG])) if (marker.at[FTR.UPRISING_RAIL_TIMING_LAG] == 1) or \ (marker.at[FLD.MAPOWER_BASELINE_TIMING_LAG] == 1): mkr_y = (marker.at[FLD.MAPOWER30_MAJOR] + marker.at[FLD.HMAPOWER120_MAJOR]) / 2 ax3.annotate(mkr_text, xy=(mkr_x, mkr_y), xycoords='data', xytext=(20, -60), textcoords='offset points', fontsize=6, va="center", ha="center", zorder=4, alpha=0.66, bbox=dict(boxstyle="round4", fc="w", color=colorwarp), arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2", color=colorwarp)) else: mkr_y = (marker.at[FLD.BOLL_LB] + marker.at[FLD.ATR_LB]) / 2.09 ax1.annotate(mkr_text, xy=(mkr_x, mkr_y), xycoords='data', xytext=(20, -60), textcoords='offset points', fontsize=6, va="center", ha="center", zorder=4, alpha=0.66, bbox=dict(boxstyle="round4", fc="w", color=colorwarp), arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2", color=colorwarp)) def mark_annotate_div(checkpoints, features, ax1, colorwarp='olive', codex=0): ''' 绘制标记 ''' markers = features.loc[checkpoints, [FLD.ATR_LB, FLD.ATR_UB, FLD.HMA10, FLD.POLY9_REGTREE_DIVERGENCE, ST.BOOTSTRAP_GROUND_ZERO, FLD.DRAWDOWN_RATIO, FLD.DRAWDOWN_RATIO_MAJOR, FLD.BOLL_JX_RSI, FLD.BOLL_JX_MAXFACTOR, FLD.POLYNOMIAL9, ST.PREDICT_LONG, FLD.PREDICT_PROB_LONG, FLD.ZEN_BOOST_TIMING_LAG, FLD.ZEN_WAVELET_TIMING_LAG, FLD.RENKO_TREND_S_TIMING_LAG, FTR.POLYNOMIAL9_EMPIRICAL, FLD.POLY9_MA30_DIVERGENCE, FLD.POLY9_MA90_DIVERGENCE, FLD.POLYNOMIAL9_TIMING_GAP, FLD.POLYNOMIAL9_TIMING_LAG, FLD.MAPOWER30_TIMING_GAP, FLD.HMAPOWER120_TIMING_GAP, FLD.MAXFACTOR_TREND_TIMING_LAG, FLD.LRC_HMA10_TIMING_LAG, FLD.MA30_HMA5_TIMING_LAG, FLD.BOLL_RAISED_TIMING_LAG, FLD.ZEN_PEAK_TIMING_LAG, FLD.ZEN_DASH_TIMING_LAG, FLD.ZEN_PEAK_TIMING_LAG_MAJOR, FLD.ZEN_DASH_TIMING_LAG_MAJOR, FLD.MAPOWER30_PEAK_LOW_BEFORE, FLD.MA90_CLEARANCE_TIMING_LAG, FLD.MA90_TREND_TIMING_LAG, FLD.DEA_ZERO_TIMING_LAG, FLD.BOOTSTRAP_GROUND_ZERO_TIMING_LAG, FLD.MA120_CHANNEL, FLD.BOLL_CHANNEL, FLD.BOLL_JX_RANK, FLD.BOOTSTRAP_GROUND_ZERO_RANK, FLD.POLYNOMIAL9_CHECKOUT_TIMING_LAG, FLD.ATR_SuperTrend_TIMING_LAG, FLD.ATR_Stopline_TIMING_LAG, FLD.BOOTSTRAP_COMBO_TIMING_LAG, ST.BOOTSTRAP_ENDPOINTS, FLD.MAINFEST_UPRISING_COEFFICIENT, FTR.BOOTSTRAP_ENHANCED_TIMING_LAG, FLD.BOLL_JX_RANK_REMARK, ST.FULLSTACK_COEFFICIENT, ST.HALFSTACK_COEFFICIENT, FLD.ZEN_DASH_RETURNS, FLD.ZEN_PEAK_RETURNS, FLD.ZEN_PEAK_RETURNS_MAJOR,]].copy() #print(markers) codex_list = [int(math.pow(2, i)) for i in range(0, 31)] for index, marker in markers.iterrows(): mkr_x = marker.name[0].strftime("%Y-%m-%d %H:%M")[2:16] mkr_text_template = """*{}* BOLL:{:.3f}\MA12CH:{:.3f} MA90CLR:{:03d}/{:03d}/DEA:{:03d} POLY9:{:02d}\RT_DIV:{:.3f} DIVERGENCE:{:.3f}/{:.3f} RSI:{:.3f}/MFT:{:.3f} DRAWDOWN:{:.3f}/{:.3f} GAP:{:02d}\{:02d}\{:02d}\{:02d}\MFT:{:02d} LRC:{:02d}\MA30:{:02d}:BOLL:{:02d} ZEN:{:03d}\{:03d}\{:03d}\{:03d}\{:02d}\{:02d} ATR:{:03d}\BST:{:03d}\COEF:{:03d} RNK:{:02d}\{:02d}\ATR:{:03d}\CB:{:02d} FS:{}/RET:{:.02%} HS:{}/RET:{:.02%}""" mkr_text = mkr_text_template.format(mkr_x if (codex == 0) else '{} C:{:02x}'.format(mkr_x, int(math.log2(codex) + 1)), marker.at[FLD.BOLL_CHANNEL], marker.at[FLD.MA120_CHANNEL], int(marker.at[FLD.MA90_CLEARANCE_TIMING_LAG]), int(marker.at[FLD.MA90_TREND_TIMING_LAG]), int(marker.at[FLD.DEA_ZERO_TIMING_LAG]), int(marker.at[FLD.POLYNOMIAL9_TIMING_LAG]), max(min((marker.at[FLD.POLY9_REGTREE_DIVERGENCE]), 255), -255), marker.at[FLD.POLY9_MA30_DIVERGENCE], marker.at[FLD.POLY9_MA90_DIVERGENCE], marker.at[FLD.BOLL_JX_RSI], marker.at[FLD.BOLL_JX_MAXFACTOR], marker.at[FLD.DRAWDOWN_RATIO], marker.at[FLD.DRAWDOWN_RATIO_MAJOR], int(marker.at[FLD.POLYNOMIAL9_TIMING_GAP] if not np.isnan(marker.at[FLD.POLYNOMIAL9_TIMING_GAP]) else 0), int(marker.at[FLD.MAPOWER30_TIMING_GAP] if not np.isnan(marker.at[FLD.MAPOWER30_TIMING_GAP]) else 0), int(marker.at[FLD.HMAPOWER120_TIMING_GAP] if not np.isnan(marker.at[FLD.HMAPOWER120_TIMING_GAP]) else 0), int(marker.at[FLD.POLYNOMIAL9_CHECKOUT_TIMING_LAG]), int(marker.at[FLD.MAXFACTOR_TREND_TIMING_LAG]), int(marker.at[FLD.LRC_HMA10_TIMING_LAG]), int(marker.at[FLD.MA30_HMA5_TIMING_LAG]), int(marker.at[FLD.BOLL_RAISED_TIMING_LAG]), int(marker.at[FLD.ZEN_PEAK_TIMING_LAG]), int(marker.at[FLD.ZEN_DASH_TIMING_LAG]), int(marker.at[FLD.ZEN_PEAK_TIMING_LAG_MAJOR]), int(marker.at[FLD.ZEN_DASH_TIMING_LAG_MAJOR]), int(marker.at[FLD.ZEN_BOOST_TIMING_LAG]), int(marker.at[FLD.ZEN_WAVELET_TIMING_LAG]), int(marker.at[FLD.ATR_SuperTrend_TIMING_LAG]), int(marker.at[FTR.BOOTSTRAP_ENHANCED_TIMING_LAG]), int(marker.at[FLD.MAINFEST_UPRISING_COEFFICIENT]), int(marker.at[FLD.BOLL_JX_RANK]), int(marker.at[FLD.BOOTSTRAP_GROUND_ZERO_RANK]), int(marker.at[FLD.ATR_Stopline_TIMING_LAG]), int(marker.at[FLD.BOOTSTRAP_COMBO_TIMING_LAG]), list(filter(None, [int(math.log2(codex_list[i]) + 1) if (int(marker.at[ST.FULLSTACK_COEFFICIENT]) & codex_list[i]) != 0 else None for i in range(31)])), marker.at[FLD.ZEN_DASH_RETURNS], list(filter(None, [int(math.log2(codex_list[i]) + 1) if (int(marker.at[ST.HALFSTACK_COEFFICIENT]) & codex_list[i]) != 0 else None for i in range(31)])), marker.at[FLD.ZEN_PEAK_RETURNS] + marker.at[FLD.ZEN_PEAK_RETURNS_MAJOR],) mkr_y = marker.at[FLD.POLYNOMIAL9] if (marker.at[FLD.BOOTSTRAP_GROUND_ZERO_TIMING_LAG] < 0) or \ (marker.at[ST.BOOTSTRAP_ENDPOINTS] > 0) or \ (marker.at[ST.BOOTSTRAP_GROUND_ZERO] < 0) or \ ((marker.at[FLD.ZEN_DASH_TIMING_LAG] < 0) and \ (marker.at[FLD.ZEN_PEAK_TIMING_LAG] < 0)) or \ ((marker.at[FLD.ZEN_DASH_TIMING_LAG_MAJOR] < 0) and \ (marker.at[FLD.ZEN_DASH_TIMING_LAG] < 0)) or \ ((marker.at[FLD.ZEN_PEAK_TIMING_LAG_MAJOR] < 0) and \ (marker.at[FLD.ZEN_DASH_TIMING_LAG] < 0)): if (marker.at[FLD.POLYNOMIAL9] < marker.at[FLD.HMA10]): mkr_y = marker.at[FLD.ATR_UB] * 1.01 ax1.annotate(mkr_text, xy=(mkr_x, mkr_y), xycoords='data', xytext=(20, 50), textcoords='offset points', fontsize=6, va="center", ha="center", zorder=4, alpha=0.66, bbox=dict(boxstyle="round4", fc="w", color=colorwarp), arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2", color=colorwarp)) elif (marker.at[FLD.BOLL_JX_RANK] < 0): mkr_text_template = """*{}* ZEN:{:03d}\{:03d}\{:03d}\{:03d} ZEN:{:03d}\{:03d}\POLY_CHK:{:03d} RC:{} FS:{}/RET:{:.02%} HS:{}/RET:{:.02%} EJECTED RNK:{:02d}\{:02d}""" mkr_text = mkr_text_template.format(mkr_x if (codex == 0) else '{} C:{:02x}'.format(mkr_x, int(math.log2(codex) + 1)), int(marker.at[FLD.ZEN_DASH_TIMING_LAG]), int(marker.at[FLD.ZEN_PEAK_TIMING_LAG]), int(marker.at[FLD.ZEN_DASH_TIMING_LAG_MAJOR]), int(marker.at[FLD.ZEN_PEAK_TIMING_LAG_MAJOR]), int(marker.at[FLD.ZEN_BOOST_TIMING_LAG]), int(marker.at[FLD.ZEN_WAVELET_TIMING_LAG]), int(marker.at[FLD.POLYNOMIAL9_CHECKOUT_TIMING_LAG]), list(filter(None, [int(math.log2(codex_list[i]) + 1) if (int(marker.at[FLD.BOLL_JX_RANK_REMARK]) & codex_list[i]) != 0 else None for i in range(31)])), list(filter(None, [int(math.log2(codex_list[i]) + 1) if (int(marker.at[ST.FULLSTACK_COEFFICIENT]) & codex_list[i]) != 0 else None for i in range(31)])), marker.at[FLD.ZEN_DASH_RETURNS], list(filter(None, [int(math.log2(codex_list[i]) + 1) if (int(marker.at[ST.HALFSTACK_COEFFICIENT]) & codex_list[i]) != 0 else None for i in range(31)])), marker.at[FLD.ZEN_PEAK_RETURNS] + marker.at[FLD.ZEN_PEAK_RETURNS_MAJOR], int(marker.at[FLD.BOLL_JX_RANK]), int(marker.at[FLD.BOOTSTRAP_GROUND_ZERO_RANK]),) ax1.annotate(mkr_text, xy=(mkr_x, mkr_y), xycoords='data', xytext=(20, -50), textcoords='offset points', fontsize=6, va="center", ha="center", zorder=4, alpha=0.66, bbox=dict(boxstyle="round4", fc="w", color=colorwarp), arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2", color=colorwarp)) else: if (marker.at[FLD.POLYNOMIAL9] > marker.at[FLD.HMA10]): mkr_y = marker.at[FLD.ATR_LB] * 0.99 ax1.annotate(mkr_text, xy=(mkr_x, mkr_y), xycoords='data', xytext=(20, -50), textcoords='offset points', fontsize=6, va="center", ha="center", zorder=4, alpha=0.66, bbox=dict(boxstyle="round4", fc="w", color=colorwarp), arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2", color=colorwarp)) def mark_annotate_roof(checkpoints, features, ax1, colorwarp='olive', codex=0): ''' 绘制标记 ''' markers = features.loc[checkpoints, [FLD.ZEN_PEAK_TIMING_LAG, FLD.ZEN_DASH_TIMING_LAG, FLD.ZEN_PEAK_TIMING_LAG_MAJOR, FLD.ZEN_DASH_TIMING_LAG_MAJOR, FLD.MAPOWER30_PEAK_HIGH_BEFORE, FLD.MAPOWER30_PEAK_HIGH_BEFORE_MAJOR, ST.BOOTSTRAP_ENDPOINTS, FLD.MAINFEST_UPRISING_COEFFICIENT, FTR.BOOTSTRAP_ENHANCED_TIMING_LAG, FLD.BOLL_JX_RANK_REMARK, ST.FULLSTACK_COEFFICIENT, ST.HALFSTACK_COEFFICIENT, FLD.ZEN_DASH_RETURNS, FLD.ZEN_PEAK_RETURNS, FLD.ZEN_PEAK_RETURNS_MAJOR,]].copy() #print(markers) codex_list = [int(math.pow(2, i)) for i in range(0, 31)] for index, marker in markers.iterrows(): mkr_x = marker.name[0].strftime("%Y-%m-%d %H:%M")[2:16] mkr_text_template = """*{}* BOLL:{:.3f}\MA12CH:{:.3f} MA90CLR:{:03d}/{:03d}/DEA:{:03d} POLY9:{:02d}\RT_DIV:{:.3f} DIVERGENCE:{:.3f}/{:.3f} RSI:{:.3f}/MFT:{:.3f} DRAWDOWN:{:.3f}/{:.3f} GAP:{:02d}\{:02d}\{:02d}\{:02d}\MFT:{:02d} LRC:{:02d}\MA30:{:02d}:BOLL:{:02d} ZEN:{:03d}\{:03d}\{:03d}\{:03d}\{:02d}\{:02d} ATR:{:03d}\BST:{:03d}\COEF:{:03d} RNK:{:02d}\{:02d}\ATR:{:03d}\CB:{:02d} FS:{}/RET:{:.02%} HS:{}/RET:{:.02%}""" mkr_text = mkr_text_template.format(mkr_x if (codex == 0) else '{} C:{:02x}'.format(mkr_x, int(math.log2(codex) + 1)), marker.at[FLD.BOLL_CHANNEL], marker.at[FLD.MA120_CHANNEL], int(marker.at[FLD.MA90_CLEARANCE_TIMING_LAG]), int(marker.at[FLD.MA90_TREND_TIMING_LAG]), int(marker.at[FLD.DEA_ZERO_TIMING_LAG]), int(marker.at[FLD.POLYNOMIAL9_TIMING_LAG]), max(min((marker.at[FLD.POLY9_REGTREE_DIVERGENCE]), 255), -255), marker.at[FLD.POLY9_MA30_DIVERGENCE], marker.at[FLD.POLY9_MA90_DIVERGENCE], marker.at[FLD.BOLL_JX_RSI], marker.at[FLD.BOLL_JX_MAXFACTOR], marker.at[FLD.DRAWDOWN_RATIO], marker.at[FLD.DRAWDOWN_RATIO_MAJOR], int(marker.at[FLD.POLYNOMIAL9_TIMING_GAP] if not np.isnan(marker.at[FLD.POLYNOMIAL9_TIMING_GAP]) else 0), int(marker.at[FLD.MAPOWER30_TIMING_GAP] if not np.isnan(marker.at[FLD.MAPOWER30_TIMING_GAP]) else 0), int(marker.at[FLD.HMAPOWER120_TIMING_GAP] if not np.isnan(marker.at[FLD.HMAPOWER120_TIMING_GAP]) else 0), int(marker.at[FLD.POLYNOMIAL9_CHECKOUT_TIMING_LAG]), int(marker.at[FLD.MAXFACTOR_TREND_TIMING_LAG]), int(marker.at[FLD.LRC_HMA10_TIMING_LAG]), int(marker.at[FLD.MA30_HMA5_TIMING_LAG]), int(marker.at[FLD.BOLL_RAISED_TIMING_LAG]), int(marker.at[FLD.ZEN_PEAK_TIMING_LAG]), int(marker.at[FLD.ZEN_DASH_TIMING_LAG]), int(marker.at[FLD.ZEN_PEAK_TIMING_LAG_MAJOR]), int(marker.at[FLD.ZEN_DASH_TIMING_LAG_MAJOR]), int(marker.at[FLD.ZEN_BOOST_TIMING_LAG]), int(marker.at[FLD.ZEN_WAVELET_TIMING_LAG]), int(marker.at[FLD.ATR_SuperTrend_TIMING_LAG]), int(marker.at[FTR.BOOTSTRAP_ENHANCED_TIMING_LAG]), int(marker.at[FLD.MAINFEST_UPRISING_COEFFICIENT]), int(marker.at[FLD.BOLL_JX_RANK]), int(marker.at[FLD.BOOTSTRAP_GROUND_ZERO_RANK]), int(marker.at[FLD.ATR_Stopline_TIMING_LAG]), int(marker.at[FLD.BOOTSTRAP_COMBO_TIMING_LAG]), list(filter(None, [int(math.log2(codex_list[i]) + 1) if (int(marker.at[ST.FULLSTACK_COEFFICIENT]) & codex_list[i]) != 0 else None for i in range(31)])), marker.at[FLD.ZEN_DASH_RETURNS], list(filter(None, [int(math.log2(codex_list[i]) + 1) if (int(marker.at[ST.HALFSTACK_COEFFICIENT]) & codex_list[i]) != 0 else None for i in range(31)])), marker.at[FLD.ZEN_PEAK_RETURNS] + marker.at[FLD.ZEN_PEAK_RETURNS_MAJOR],) mkr_y = marker.at[FLD.POLYNOMIAL9] if (marker.at[FLD.BOOTSTRAP_GROUND_ZERO_TIMING_LAG] < 0) or \ (marker.at[ST.BOOTSTRAP_ENDPOINTS] > 0) or \ (marker.at[ST.BOOTSTRAP_GROUND_ZERO] < 0) or \ ((marker.at[FLD.ZEN_DASH_TIMING_LAG] < 0) and \ (marker.at[FLD.ZEN_PEAK_TIMING_LAG] < 0)) or \ ((marker.at[FLD.ZEN_DASH_TIMING_LAG_MAJOR] < 0) and \ (marker.at[FLD.ZEN_DASH_TIMING_LAG] < 0)) or \ ((marker.at[FLD.ZEN_PEAK_TIMING_LAG_MAJOR] < 0) and \ (marker.at[FLD.ZEN_DASH_TIMING_LAG] < 0)): if (marker.at[FLD.POLYNOMIAL9] < marker.at[FLD.HMA10]): mkr_y = marker.at[FLD.ATR_UB] * 1.01 ax1.annotate(mkr_text, xy=(mkr_x, mkr_y), xycoords='data', xytext=(20, 50), textcoords='offset points', fontsize=6, va="center", ha="center", zorder=4, alpha=0.66, bbox=dict(boxstyle="round4", fc="w", color=colorwarp), arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2", color=colorwarp)) elif (marker.at[FLD.BOLL_JX_RANK] < 0): mkr_text_template = """*{}* ZEN:{:03d}\{:03d}\{:03d}\{:03d} ZEN:{:03d}\{:03d}\POLY_CHK:{:03d} RC:{} FS:{}/RET:{:.02%} HS:{}/RET:{:.02%} EJECTED RNK:{:02d}\{:02d}""" mkr_text = mkr_text_template.format(mkr_x if (codex == 0) else '{} C:{:02x}'.format(mkr_x, int(math.log2(codex) + 1)), int(marker.at[FLD.ZEN_DASH_TIMING_LAG]), int(marker.at[FLD.ZEN_PEAK_TIMING_LAG]), int(marker.at[FLD.ZEN_DASH_TIMING_LAG_MAJOR]), int(marker.at[FLD.ZEN_PEAK_TIMING_LAG_MAJOR]), int(marker.at[FLD.ZEN_BOOST_TIMING_LAG]), int(marker.at[FLD.ZEN_WAVELET_TIMING_LAG]), int(marker.at[FLD.POLYNOMIAL9_CHECKOUT_TIMING_LAG]), list(filter(None, [int(math.log2(codex_list[i]) + 1) if (int(marker.at[FLD.BOLL_JX_RANK_REMARK]) & codex_list[i]) != 0 else None for i in range(31)])), list(filter(None, [int(math.log2(codex_list[i]) + 1) if (int(marker.at[ST.FULLSTACK_COEFFICIENT]) & codex_list[i]) != 0 else None for i in range(31)])), marker.at[FLD.ZEN_DASH_RETURNS], list(filter(None, [int(math.log2(codex_list[i]) + 1) if (int(marker.at[ST.HALFSTACK_COEFFICIENT]) & codex_list[i]) != 0 else None for i in range(31)])), marker.at[FLD.ZEN_PEAK_RETURNS] + marker.at[FLD.ZEN_PEAK_RETURNS_MAJOR], int(marker.at[FLD.BOLL_JX_RANK]), int(marker.at[FLD.BOOTSTRAP_GROUND_ZERO_RANK]),) ax1.annotate(mkr_text, xy=(mkr_x, mkr_y), xycoords='data', xytext=(20, -50), textcoords='offset points', fontsize=6, va="center", ha="center", zorder=4, alpha=0.66, bbox=dict(boxstyle="round4", fc="w", color=colorwarp), arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2", color=colorwarp)) else: if (marker.at[FLD.POLYNOMIAL9] > marker.at[FLD.HMA10]): mkr_y = marker.at[FLD.ATR_LB] * 0.99 ax1.annotate(mkr_text, xy=(mkr_x, mkr_y), xycoords='data', xytext=(20, -50), textcoords='offset points', fontsize=6, va="center", ha="center", zorder=4, alpha=0.66, bbox=dict(boxstyle="round4", fc="w", color=colorwarp), arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2", color=colorwarp)) def mark_annotate(checkpoints, features, ax3, colorwarp='olive'): ''' 绘制标记 ''' if (ST.PREDICT_LONG in features.columns): markers = features.loc[checkpoints, [FLD.PEAK_LOW_TIMING_LAG, FLD.COMBINE_DENSITY, FLD.MAPOWER30_MAJOR, FLD.HMAPOWER120_MAJOR, FLD.MAPOWER30, FLD.HMAPOWER120, FLD.BOLL_LB, FLD.ATR_LB, FLD.MA90_CLEARANCE, FLD.MA120_CLEARANCE, FLD.BOLL_CHANNEL, FLD.MA_CHANNEL, FLD.MA120_CHANNEL, FTR.UPRISING_RAIL_TIMING_LAG, FLD.MAPOWER30_PEAK_LOW_BEFORE, FLD.MAPOWER30_PEAK_HIGH_BEFORE, FLD.MAPOWER30_PEAK_LOW_BEFORE_MAJOR, FLD.MAPOWER30_PEAK_HIGH_BEFORE_MAJOR, FLD.MAPOWER30_TIMING_LAG_MAJOR, FLD.DRAWDOWN_RATIO, FLD.DRAWDOWN_RATIO_MAJOR, FLD.DEADPOOL_CANDIDATE_TIMING_LAG, FLD.ZEN_WAVELET_TIMING_LAG, FLD.RENKO_TREND_S_TIMING_LAG, FLD.RENKO_TREND_L_TIMING_LAG, FLD.HMAPOWER30_MA_TIMING_LAG, FLD.MAPOWER_HMAPOWER120_TIMING_LAG, ST.PREDICT_LONG, FLD.PREDICT_PROB_LONG, FLD.BOLL_JX_RSI, FLD.BOLL_JX_MAXFACTOR,]].copy() else: markers = features.loc[checkpoints, [FLD.PEAK_LOW_TIMING_LAG, FLD.COMBINE_DENSITY, FLD.MAPOWER30_MAJOR, FLD.HMAPOWER120_MAJOR, FLD.MAPOWER30, FLD.HMAPOWER120, FLD.BOLL_LB, FLD.ATR_LB, FLD.MA90_CLEARANCE, FLD.MA120_CLEARANCE, FLD.BOLL_CHANNEL, FLD.MA_CHANNEL, FLD.MA120_CHANNEL, FTR.UPRISING_RAIL_TIMING_LAG, FLD.MAPOWER30_PEAK_LOW_BEFORE, FLD.MAPOWER30_PEAK_HIGH_BEFORE, FLD.MAPOWER30_PEAK_LOW_BEFORE_MAJOR, FLD.MAPOWER30_PEAK_HIGH_BEFORE_MAJOR, FLD.MAPOWER30_TIMING_LAG_MAJOR, FLD.DRAWDOWN_RATIO, FLD.DRAWDOWN_RATIO_MAJOR, FLD.DEADPOOL_CANDIDATE_TIMING_LAG, FLD.ZEN_WAVELET_TIMING_LAG, FLD.RENKO_TREND_S_TIMING_LAG, FLD.RENKO_TREND_L_TIMING_LAG, FLD.HMAPOWER30_MA_TIMING_LAG, FLD.MAPOWER_HMAPOWER120_TIMING_LAG,]].copy() #print(markers) for index, marker in markers.iterrows(): mkr_x = marker.name[0].strftime("%Y-%m-%d %H:%M")[2:16] if (ST.PREDICT_LONG in features.columns) and (marker.at[ST.PREDICT_LONG] < 0.1): mkr_text_template = '''*{}* xgboost_pred:{:02d} pred_prob:{:.2f} RSI:{:.3f}/MFT:{:.3f} DRAWDOWN:{:.3f}/{:.3f}''' mkr_text = mkr_text_template.format(mkr_x, int(marker.at[ST.PREDICT_LONG]), marker.at[FLD.PREDICT_PROB_LONG], marker.at[FLD.BOLL_JX_RSI], marker.at[FLD.BOLL_JX_MAXFACTOR], marker.at[FLD.DRAWDOWN_RATIO], marker.at[FLD.DRAWDOWN_RATIO_MAJOR],) else: mkr_text_template = '''*{}* COMB:{:.3f}/BOL:{:.3f} MAPWR3:{:.2f}/HMA12:{:.2f} MACH:{:.2f}/MA12CH:{:.2f} MA9CLR:{:.2f}/{:.2f}/{:.2f} PEAK_LO:{:03d}/{:03d}/{:03d} PEAK_HI:{:03d}/{:03d}/{:03d} DRAWDOWN:{:.3f}/{:.3f} RENKO:{:03d}/{:03d}/ZEN:{:03d} MAPWR_BSL_HMAPWR12:{:02d}''' mkr_text = mkr_text_template.format(mkr_x, marker.at[FLD.COMBINE_DENSITY], marker.at[FLD.BOLL_CHANNEL], marker.at[FLD.MAPOWER30], marker.at[FLD.HMAPOWER120], marker.at[FLD.MA_CHANNEL], marker.at[FLD.MA120_CHANNEL], marker.at[FLD.MA90_CLEARANCE], marker.at[FLD.MA120_CLEARANCE], marker.at[FLD.MAPOWER30_MAJOR] + marker.at[FLD.HMAPOWER120_MAJOR] + marker.at[FLD.MAPOWER30], int(marker.at[FLD.MAPOWER30_PEAK_LOW_BEFORE]), int(marker.at[FLD.MAPOWER30_PEAK_LOW_BEFORE_MAJOR]), int(marker.at[FLD.DEADPOOL_CANDIDATE_TIMING_LAG]), int(marker.at[FLD.MAPOWER30_PEAK_HIGH_BEFORE]), int(marker.at[FLD.MAPOWER30_PEAK_HIGH_BEFORE_MAJOR]), int(marker.at[FLD.MAPOWER30_TIMING_LAG_MAJOR]), marker.at[FLD.DRAWDOWN_RATIO], marker.at[FLD.DRAWDOWN_RATIO_MAJOR], int(marker.at[FLD.RENKO_TREND_S_TIMING_LAG]), int(marker.at[FLD.RENKO_TREND_L_TIMING_LAG]), int(marker.at[FLD.ZEN_WAVELET_TIMING_LAG]), int(marker.at[FLD.MAPOWER_HMAPOWER120_TIMING_LAG]),) mkr_y = min(features[FLD.CCI_NORM].min(), 0) + 0.0168 ax3.annotate(mkr_text, xy=(mkr_x, mkr_y), xycoords='data', xytext=(20, -50), textcoords='offset points', fontsize=6, va="center", ha="center", zorder=4, alpha=0.66, bbox=dict(boxstyle="round4", fc="w", color=colorwarp), arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2", color=colorwarp)) def mark_annotate_punch(checkpoints, features, ax3, colorwarp='olive'): ''' 绘制标记 ''' if (ST.PREDICT_LONG in features.columns): markers = features.loc[checkpoints, [FLD.PEAK_LOW_TIMING_LAG, FLD.COMBINE_DENSITY, FLD.MAPOWER30_MAJOR, FLD.HMAPOWER120_MAJOR, FLD.MAPOWER30, FLD.HMAPOWER120, FLD.BOLL_LB, FLD.ATR_LB, FLD.MA90_CLEARANCE, FLD.MA120_CLEARANCE, FLD.BOLL_CHANNEL, FLD.MA_CHANNEL, FLD.MA120_CHANNEL, FTR.UPRISING_RAIL_TIMING_LAG, FLD.MAPOWER30_PEAK_LOW_BEFORE, FLD.MAPOWER30_PEAK_HIGH_BEFORE, FLD.MAPOWER30_PEAK_LOW_BEFORE_MAJOR, FLD.MAPOWER30_PEAK_HIGH_BEFORE_MAJOR, FLD.MAPOWER30_TIMING_LAG_MAJOR, FLD.DRAWDOWN_RATIO, FLD.DRAWDOWN_RATIO_MAJOR, FLD.DEADPOOL_CANDIDATE_TIMING_LAG, FLD.ZEN_WAVELET_TIMING_LAG, FLD.RENKO_TREND_S_TIMING_LAG, FLD.RENKO_TREND_L_TIMING_LAG, FLD.HMAPOWER30_MA_TIMING_LAG, FLD.MAPOWER_HMAPOWER120_TIMING_LAG, FLD.BOLL_JX_RSI, FLD.BOLL_JX_MAXFACTOR, FLD.BOLL_JX_MAPOWER30, FLD.BOLL_JX_HMAPOWER120, FLD.DEADPOOL_REMIX_TIMING_LAG, FLD.MAINFEST_UPRISING_COEFFICIENT, FLD.DRAWDOWN_CHANNEL, FLD.DRAWDOWN_CHANNEL_MAJOR, FLD.HMA10_CLEARANCE, FLD.HMA10_CLEARANCE_ZSCORE, ST.PREDICT_LONG, FLD.PREDICT_PROB_LONG,]].copy() else: markers = features.loc[checkpoints, [FLD.PEAK_LOW_TIMING_LAG, FLD.COMBINE_DENSITY, FLD.MAPOWER30_MAJOR, FLD.HMAPOWER120_MAJOR, FLD.MAPOWER30, FLD.HMAPOWER120, FLD.BOLL_LB, FLD.ATR_LB, FLD.MA90_CLEARANCE, FLD.MA120_CLEARANCE, FLD.BOLL_CHANNEL, FLD.MA_CHANNEL, FLD.MA120_CHANNEL, FTR.UPRISING_RAIL_TIMING_LAG, FLD.MAPOWER30_PEAK_LOW_BEFORE, FLD.MAPOWER30_PEAK_HIGH_BEFORE, FLD.MAPOWER30_PEAK_LOW_BEFORE_MAJOR, FLD.MAPOWER30_PEAK_HIGH_BEFORE_MAJOR, FLD.MAPOWER30_TIMING_LAG_MAJOR, FLD.DRAWDOWN_RATIO, FLD.DRAWDOWN_RATIO_MAJOR, FLD.DEADPOOL_CANDIDATE_TIMING_LAG, FLD.ZEN_WAVELET_TIMING_LAG, FLD.RENKO_TREND_S_TIMING_LAG, FLD.RENKO_TREND_L_TIMING_LAG, FLD.HMAPOWER30_MA_TIMING_LAG, FLD.MAPOWER_HMAPOWER120_TIMING_LAG, FLD.BOLL_JX_RSI, FLD.BOLL_JX_MAXFACTOR, FLD.BOLL_JX_MAPOWER30, FLD.BOLL_JX_HMAPOWER120, FLD.DEADPOOL_REMIX_TIMING_LAG, FLD.MAINFEST_UPRISING_COEFFICIENT, FLD.DRAWDOWN_CHANNEL, FLD.DRAWDOWN_CHANNEL_MAJOR, FLD.HMA10_CLEARANCE, FLD.HMA10_CLEARANCE_ZSCORE,]].copy() #print(markers) for index, marker in markers.iterrows(): mkr_x = marker.name[0].strftime("%Y-%m-%d %H:%M")[2:16] if (ST.PREDICT_LONG in features.columns) and (marker.at[ST.PREDICT_LONG] < 0.1): mkr_text_template = '*{}*\nxgboost_pred:{:02d}\npred_prob:{:.2f}\nRSI:{:.3f}/MFT:{:.3f}\nDRAWDOWN:{:.3f}/{:.3f}' mkr_text = mkr_text_template.format(mkr_x, int(marker.at[ST.PREDICT_LONG]), marker.at[FLD.PREDICT_PROB_LONG], marker.at[FLD.BOLL_JX_RSI], marker.at[FLD.BOLL_JX_MAXFACTOR], marker.at[FLD.DRAWDOWN_RATIO], marker.at[FLD.DRAWDOWN_RATIO_MAJOR],) else: mkr_text_template = '*{}*\nCOMB:{:.3f}/BOL:{:.3f}\nMACH:{:.2f}/MA12CH:{:.2f}\nDRAWDOWN:{:.3f}/{:.3f}\nRENKO:{:03d}/{:03d}/ZEN:{:03d}\nMAPWR_BSL_HMAPWR12:{:02d}\nRSI:{:.3f}/MFT:{:.3f}\nBJMPWR:{:.3f}/HMA12:{:.3f}\nDead:{:03d}/COEF:{:03d}\nDRDN_CHN:{:.3f}/{:.3f}\nHMA_LRC:{:.3f}/{:.3f}' mkr_text = mkr_text_template.format(mkr_x, marker.at[FLD.COMBINE_DENSITY], marker.at[FLD.BOLL_CHANNEL], marker.at[FLD.MA_CHANNEL], marker.at[FLD.MA120_CHANNEL], marker.at[FLD.DRAWDOWN_RATIO], marker.at[FLD.DRAWDOWN_RATIO_MAJOR], int(marker.at[FLD.RENKO_TREND_S_TIMING_LAG]), int(marker.at[FLD.RENKO_TREND_L_TIMING_LAG]), int(marker.at[FLD.ZEN_WAVELET_TIMING_LAG]), int(marker.at[FLD.MAPOWER_HMAPOWER120_TIMING_LAG]), marker.at[FLD.BOLL_JX_RSI], marker.at[FLD.BOLL_JX_MAXFACTOR], marker.at[FLD.BOLL_JX_MAPOWER30], marker.at[FLD.BOLL_JX_HMAPOWER120], int(marker.at[FLD.DEADPOOL_REMIX_TIMING_LAG]), int(marker.at[FLD.MAINFEST_UPRISING_COEFFICIENT]), marker.at[FLD.DRAWDOWN_CHANNEL], marker.at[FLD.DRAWDOWN_CHANNEL_MAJOR], marker.at[FLD.HMA10_CLEARANCE], marker.at[FLD.HMA10_CLEARANCE_ZSCORE],) mkr_y = min(features[FLD.CCI_NORM].min(), 0) - 0.0168 ax3.annotate(mkr_text, xy=(mkr_x, mkr_y), xycoords='data', xytext=(20, -50), textcoords='offset points', fontsize=6, va="center", ha="center", zorder=4, alpha=0.66, bbox=dict(boxstyle="round4", fc="w", color=colorwarp), arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2", color=colorwarp)) def mark_annotate_mainfest(checkpoints, features, ax3, colorwarp='olive'): ''' 绘制标记 ''' if (ST.PREDICT_LONG in features.columns): markers = features.loc[checkpoints, [FLD.PEAK_LOW_TIMING_LAG, FLD.COMBINE_DENSITY, FLD.MAPOWER30_MAJOR, FLD.HMAPOWER120_MAJOR, FLD.MAPOWER30, FLD.HMAPOWER120, FLD.BOLL_LB, FLD.ATR_LB, FLD.MA90_CLEARANCE, FLD.MA120_CLEARANCE, FLD.BOLL_CHANNEL, FLD.MA_CHANNEL, FLD.MA120_CHANNEL, FTR.UPRISING_RAIL_TIMING_LAG, FLD.MAPOWER30_PEAK_LOW_BEFORE, FLD.MAPOWER30_PEAK_HIGH_BEFORE, FLD.MAPOWER30_PEAK_LOW_BEFORE_MAJOR, FLD.MAPOWER30_PEAK_HIGH_BEFORE_MAJOR, FLD.MAPOWER30_TIMING_LAG_MAJOR, FLD.DRAWDOWN_RATIO, FLD.DRAWDOWN_RATIO_MAJOR, FLD.DEADPOOL_CANDIDATE_TIMING_LAG, FLD.ZEN_WAVELET_TIMING_LAG, FLD.RENKO_TREND_S_TIMING_LAG, FLD.RENKO_TREND_L_TIMING_LAG, FLD.HMAPOWER30_MA_TIMING_LAG, FLD.MAPOWER_HMAPOWER120_TIMING_LAG, FLD.BOLL_JX_RSI, FLD.BOLL_JX_MAXFACTOR, FLD.BOLL_JX_MAPOWER30, FLD.BOLL_JX_HMAPOWER120, FLD.DEADPOOL_REMIX_TIMING_LAG, FLD.MAINFEST_UPRISING_COEFFICIENT, FLD.DRAWDOWN_CHANNEL, FLD.DRAWDOWN_CHANNEL_MAJOR, FLD.HMA10_CLEARANCE, FLD.HMA10_CLEARANCE_ZSCORE, FLD.MA90_CLEARANCE_TIMING_LAG, FLD.MA90_TREND_TIMING_LAG, FLD.MAPOWER_CROSS_TIMING_LAG, FLD.POLYNOMIAL9_DELTA, FLD.BOLL_DIFF, FLD.MA30_DIFF, FLD.PREDICT_GROWTH, ST.PREDICT_LONG, FLD.PREDICT_PROB_LONG, FLD.POLY9_MA30_DIVERGENCE, FLD.POLY9_MA90_DIVERGENCE,]].copy() else: markers = features.loc[checkpoints, [FLD.PEAK_LOW_TIMING_LAG, FLD.COMBINE_DENSITY, FLD.MAPOWER30_MAJOR, FLD.HMAPOWER120_MAJOR, FLD.MAPOWER30, FLD.HMAPOWER120, FLD.BOLL_LB, FLD.ATR_LB, FLD.MA90_CLEARANCE, FLD.MA120_CLEARANCE, FLD.BOLL_CHANNEL, FLD.MA_CHANNEL, FLD.MA120_CHANNEL, FTR.UPRISING_RAIL_TIMING_LAG, FLD.MAPOWER30_PEAK_LOW_BEFORE, FLD.MAPOWER30_PEAK_HIGH_BEFORE, FLD.MAPOWER30_PEAK_LOW_BEFORE_MAJOR, FLD.MAPOWER30_PEAK_HIGH_BEFORE_MAJOR, FLD.MAPOWER30_TIMING_LAG_MAJOR, FLD.DRAWDOWN_RATIO, FLD.DRAWDOWN_RATIO_MAJOR, FLD.DEADPOOL_CANDIDATE_TIMING_LAG, FLD.ZEN_WAVELET_TIMING_LAG, FLD.RENKO_TREND_S_TIMING_LAG, FLD.RENKO_TREND_L_TIMING_LAG, FLD.HMAPOWER30_MA_TIMING_LAG, FLD.MAPOWER_HMAPOWER120_TIMING_LAG, FLD.BOLL_JX_RSI, FLD.BOLL_JX_MAXFACTOR, FLD.BOLL_JX_MAPOWER30, FLD.BOLL_JX_HMAPOWER120, FLD.DEADPOOL_REMIX_TIMING_LAG, FLD.MAINFEST_UPRISING_COEFFICIENT, FLD.DRAWDOWN_CHANNEL, FLD.DRAWDOWN_CHANNEL_MAJOR, FLD.HMA10_CLEARANCE, FLD.HMA10_CLEARANCE_ZSCORE, FLD.MA90_CLEARANCE_TIMING_LAG, FLD.MA90_TREND_TIMING_LAG, FLD.MAPOWER_CROSS_TIMING_LAG, FLD.POLYNOMIAL9_DELTA, FLD.BOLL_DIFF, FLD.MA30_DIFF, FLD.PREDICT_GROWTH, FLD.POLY9_MA30_DIVERGENCE, FLD.POLY9_MA90_DIVERGENCE]].copy() #print(markers) for index, marker in markers.iterrows(): mkr_x = marker.name[0].strftime("%Y-%m-%d %H:%M")[2:16] if (ST.PREDICT_LONG in features.columns) and (marker.at[ST.PREDICT_LONG] < 0.1): mkr_text_template = '*{}*\nxgboost_pred:{:02d}\npred_prob:{:.2f}\nRSI:{:.3f}/MFT:{:.3f}\nDRAWDOWN:{:.3f}/{:.3f}\nDIVERGENCE:{:.3f}/{:.3f}' mkr_text = mkr_text_template.format(mkr_x, int(marker.at[ST.PREDICT_LONG]), marker.at[FLD.PREDICT_PROB_LONG], marker.at[FLD.BOLL_JX_RSI], marker.at[FLD.BOLL_JX_MAXFACTOR], marker.at[FLD.DRAWDOWN_RATIO], marker.at[FLD.DRAWDOWN_RATIO_MAJOR], marker.at[FLD.POLY9_MA30_DIVERGENCE], marker.at[FLD.POLY9_MA90_DIVERGENCE],) else: mkr_text_template = '*{}*\nCOMB:{:.3f}/BOL:{:.3f}\nMACH:{:.2f}/MA12CH:{:.2f}\nMA9CLR:{:.2f}/{:.2f}/{:.2f}\nPEAK_LO:{:03d}/{:03d}/{:03d}\nPEAK_HI:{:03d}/{:03d}/{:03d}\nDRAWDOWN:{:.3f}/{:.3f}\nRENKO:{:03d}/{:03d}/ZEN:{:03d}\nMAPWR_BSL_HMAPWR12:{:02d}\nRSI:{:.3f}/MFT:{:.3f}\nBJMPWR:{:.3f}/HMA12:{:.3f}\nDead:{:03d}/COEF:{:03d}/MAT:{:03d}\nMA90CLR:{:03d}/TRD:{:03d}\nDRDN_CHN:{:.3f}/{:.3f}\nHMA_LRC:{:.3f}/{:.3f}\nDIFF_POLY9:{:.3f}/GROW:{:.3f}\nMA20:{:.3f}/30:{:.3f}' mkr_text = mkr_text_template.format(mkr_x, marker.at[FLD.COMBINE_DENSITY], marker.at[FLD.BOLL_CHANNEL], marker.at[FLD.MA_CHANNEL], marker.at[FLD.MA120_CHANNEL], marker.at[FLD.MA90_CLEARANCE], marker.at[FLD.MA120_CLEARANCE], marker.at[FLD.MAPOWER30_MAJOR] + marker.at[FLD.HMAPOWER120_MAJOR] + marker.at[FLD.MAPOWER30], int(marker.at[FLD.MAPOWER30_PEAK_LOW_BEFORE]), int(marker.at[FLD.MAPOWER30_PEAK_LOW_BEFORE_MAJOR]), int(marker.at[FLD.DEADPOOL_CANDIDATE_TIMING_LAG]), int(marker.at[FLD.MAPOWER30_PEAK_HIGH_BEFORE]), int(marker.at[FLD.MAPOWER30_PEAK_HIGH_BEFORE_MAJOR]), int(marker.at[FLD.MAPOWER30_TIMING_LAG_MAJOR]), marker.at[FLD.DRAWDOWN_RATIO], marker.at[FLD.DRAWDOWN_RATIO_MAJOR], int(marker.at[FLD.RENKO_TREND_S_TIMING_LAG]), int(marker.at[FLD.RENKO_TREND_L_TIMING_LAG]), int(marker.at[FLD.ZEN_WAVELET_TIMING_LAG]), int(marker.at[FLD.MAPOWER_HMAPOWER120_TIMING_LAG]), marker.at[FLD.BOLL_JX_RSI], marker.at[FLD.BOLL_JX_MAXFACTOR], marker.at[FLD.BOLL_JX_MAPOWER30], marker.at[FLD.BOLL_JX_HMAPOWER120], int(marker.at[FLD.DEADPOOL_REMIX_TIMING_LAG]), int(marker.at[FLD.MAINFEST_UPRISING_COEFFICIENT]), int(marker.at[FLD.MAPOWER_CROSS_TIMING_LAG]), int(marker.at[FLD.MA90_CLEARANCE_TIMING_LAG]), int(marker.at[FLD.MA90_TREND_TIMING_LAG]), marker.at[FLD.DRAWDOWN_CHANNEL], marker.at[FLD.DRAWDOWN_CHANNEL_MAJOR], marker.at[FLD.HMA10_CLEARANCE], marker.at[FLD.HMA10_CLEARANCE_ZSCORE], marker.at[FLD.POLYNOMIAL9_DELTA], marker.at[FLD.PREDICT_GROWTH], marker.at[FLD.BOLL_DIFF], marker.at[FLD.MA30_DIFF],) mkr_y = min(features[FLD.CCI_NORM].min(), 0) - 0.0382 ax3.annotate(mkr_text, xy=(mkr_x, mkr_y), xycoords='data', xytext=(20, -50), textcoords='offset points', fontsize=6, va="top", ha="center", zorder=4, alpha=0.66, bbox=dict(boxstyle="round4", fc="w", color=colorwarp), arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2", color=colorwarp)) def mark_annotate_subplot(checkpoints, features, ax2, colorwarp='violet', marker=None): ''' 绘制标记 ''' if (ST.PREDICT_LONG in features.columns): markers = features.loc[checkpoints, [FLD.PEAK_LOW_TIMING_LAG, FLD.COMBINE_DENSITY, FLD.MAPOWER30_MAJOR, FLD.HMAPOWER120_MAJOR, FLD.MAPOWER30, FLD.HMAPOWER120, FLD.BOLL_LB, FLD.ATR_LB, FLD.DIF, FLD.DEA, FLD.MA90_CLEARANCE, FLD.MA120_CLEARANCE, FLD.BOLL_CHANNEL, FLD.MA_CHANNEL, FLD.MA120_CHANNEL, FTR.UPRISING_RAIL_TIMING_LAG, FLD.MAPOWER30_PEAK_LOW_BEFORE, FLD.MAPOWER30_PEAK_HIGH_BEFORE, FLD.MAPOWER30_PEAK_LOW_BEFORE_MAJOR, FLD.MAPOWER30_PEAK_HIGH_BEFORE_MAJOR, FLD.MAPOWER30_TIMING_LAG_MAJOR, FLD.DRAWDOWN_RATIO, FLD.DRAWDOWN_RATIO_MAJOR, FLD.DEADPOOL_CANDIDATE_TIMING_LAG, FLD.ZEN_WAVELET_TIMING_LAG, FLD.RENKO_TREND_S_TIMING_LAG, FLD.RENKO_TREND_L_TIMING_LAG, FLD.MAPOWER_HMAPOWER120_TIMING_LAG, FLD.BOLL_SX_DRAWDOWN, ST.PREDICT_LONG, FLD.PREDICT_PROB_LONG, FLD.BOLL_JX_RSI, FLD.BOLL_JX_MAXFACTOR,]].copy() else: markers = features.loc[checkpoints, [FLD.PEAK_LOW_TIMING_LAG, FLD.COMBINE_DENSITY, FLD.MAPOWER30_MAJOR, FLD.HMAPOWER120_MAJOR, FLD.MAPOWER30, FLD.HMAPOWER120, FLD.BOLL_LB, FLD.ATR_LB, FLD.DIF, FLD.DEA, FLD.MA90_CLEARANCE, FLD.MA120_CLEARANCE, FLD.BOLL_CHANNEL, FLD.MA_CHANNEL, FLD.MA120_CHANNEL, FTR.UPRISING_RAIL_TIMING_LAG, FLD.MAPOWER30_PEAK_LOW_BEFORE, FLD.MAPOWER30_PEAK_HIGH_BEFORE, FLD.MAPOWER30_PEAK_LOW_BEFORE_MAJOR, FLD.MAPOWER30_PEAK_HIGH_BEFORE_MAJOR, FLD.MAPOWER30_TIMING_LAG_MAJOR, FLD.DRAWDOWN_RATIO, FLD.DRAWDOWN_RATIO_MAJOR, FLD.DEADPOOL_CANDIDATE_TIMING_LAG, FLD.ZEN_WAVELET_TIMING_LAG, FLD.RENKO_TREND_S_TIMING_LAG, FLD.RENKO_TREND_L_TIMING_LAG, FLD.MAPOWER_HMAPOWER120_TIMING_LAG, FLD.BOLL_SX_DRAWDOWN,]].copy() #print(markers) for index, marker in markers.iterrows(): mkr_x = marker.name[0].strftime("%Y-%m-%d %H:%M")[2:16] if (ST.PREDICT_LONG in features.columns) and (marker.at[ST.PREDICT_LONG] < 0.1): mkr_text_template = '*{}*\nxgboost_pred:{:02d}\npred_prob:{:.2f}\nRSI:{:.3f}/MFT:{:.3f}\nDRAWDOWN:{:.3f}/{:.3f}' mkr_text = mkr_text_template.format(mkr_x, int(marker.at[ST.PREDICT_LONG]), marker.at[FLD.PREDICT_PROB_LONG], marker.at[FLD.BOLL_JX_RSI], marker.at[FLD.BOLL_JX_MAXFACTOR], marker.at[FLD.DRAWDOWN_RATIO], marker.at[FLD.DRAWDOWN_RATIO_MAJOR],) else: mkr_text_template = '*{}*\nCOMB:{:.3f}/BOL:{:.3f}\nMAPWR3:{:.2f}/HMA12:{:.2f}\nMACH:{:.2f}/MA12CH:{:.2f}\nMA9CLR:{:.2f}/{:.2f}/{:.2f}\nPEAK_LO:{:03d}/{:03d}/{:03d}\nPEAK_HI:{:03d}/{:03d}/{:03d}\nDRAWDOWN:{:.3f}/{:.3f}/{:.3f}\nRENKO:{:03d}/{:03d}/ZEN:{:03d}\nMAPWR_BSL_HMAPWR12:{:02d}' mkr_text = mkr_text_template.format(mkr_x, marker.at[FLD.COMBINE_DENSITY], marker.at[FLD.BOLL_CHANNEL], marker.at[FLD.MAPOWER30], marker.at[FLD.HMAPOWER120], marker.at[FLD.MA_CHANNEL], marker.at[FLD.MA120_CHANNEL], marker.at[FLD.MA90_CLEARANCE], marker.at[FLD.MA120_CLEARANCE], marker.at[FLD.MAPOWER30_MAJOR] + marker.at[FLD.HMAPOWER120_MAJOR] + marker.at[FLD.MAPOWER30], int(marker.at[FLD.MAPOWER30_PEAK_LOW_BEFORE]), int(marker.at[FLD.MAPOWER30_PEAK_LOW_BEFORE_MAJOR]), int(marker.at[FLD.DEADPOOL_CANDIDATE_TIMING_LAG]), int(marker.at[FLD.MAPOWER30_PEAK_HIGH_BEFORE]), int(marker.at[FLD.MAPOWER30_PEAK_HIGH_BEFORE_MAJOR]), int(marker.at[FLD.MAPOWER30_TIMING_LAG_MAJOR]), marker.at[FLD.DRAWDOWN_RATIO], marker.at[FLD.DRAWDOWN_RATIO_MAJOR], marker.at[FLD.BOLL_SX_DRAWDOWN], int(marker.at[FLD.RENKO_TREND_S_TIMING_LAG]), int(marker.at[FLD.RENKO_TREND_L_TIMING_LAG]), int(marker.at[FLD.ZEN_WAVELET_TIMING_LAG]), int(marker.at[FLD.MAPOWER_HMAPOWER120_TIMING_LAG]),) mkr_y = min(marker.at[FLD.DEA], marker.at[FLD.DIF]) * 1.10 ax2.annotate(mkr_text, xy=(mkr_x, mkr_y), xycoords='data', xytext=(20, -60), textcoords='offset points', fontsize=6, va="top", ha="center", zorder=4, alpha=0.66, bbox=dict(boxstyle="round4", fc="w", color=colorwarp), arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2", color=colorwarp)) def back_plot(): pass #if (FLD.COMBINE_DENSITY in features.columns): # ax3.plot(DATETIME_LABEL, # features[FLD.COMBINE_DENSITY], # lw=0.75, color ='lightskyblue', alpha=0.33) # ax3.plot(DATETIME_LABEL, # np.where(features[FLD.ATR_SuperTrend_TIMING_LAG] > 0, # features[FLD.COMBINE_DENSITY], np.nan), # lw=0.75, color ='crimson', alpha=0.33) #if (FLD.CCI in features.columns): # ax3.plot(DATETIME_LABEL, # features[FLD.RSI] / 100, # lw=0.75, color ='green', alpha=0.33) # ax3.plot(DATETIME_LABEL, # features[FLD.CCI_NORM], # lw=0.75, color ='coral', alpha=0.25) #if (FLD.HMAPOWER30 in features.columns): # ax3.plot(DATETIME_LABEL, # np.where((features[FLD.HMAPOWER30_TIMING_LAG] > -1), # features[FLD.HMAPOWER30], np.nan), # lw=1.25, color ='pink', alpha=0.8) # ax3.plot(DATETIME_LABEL, # np.where((features[FLD.HMAPOWER30_TIMING_LAG] < 1), # features[FLD.HMAPOWER30], np.nan), # lw=1.25, color ='skyblue', alpha=0.8) #if (FLD.MAPOWER30 in features.columns): # ax3.plot(DATETIME_LABEL, # np.where((features[FLD.MAPOWER30_TIMING_LAG] < 1), # features[FLD.MAPOWER30], np.nan), # lw=0.75, color ='lime', alpha=0.8) # ax3.plot(DATETIME_LABEL, # np.where((features[FLD.MAPOWER30_TIMING_LAG] > -1), # features[FLD.MAPOWER30], np.nan), # lw=0.75, color ='gray', alpha=0.5) #if (FLD.MAPOWER120 in features.columns): # ax3.plot(DATETIME_LABEL, # np.where(features[FLD.MAPOWER120_TIMING_LAG] < 1, # features[FLD.MAPOWER120], np.nan), # lw=0.75, color ='olive', alpha=0.8) # ax3.plot(DATETIME_LABEL, # np.where(features[FLD.MAPOWER120_TIMING_LAG] > -1, # features[FLD.MAPOWER120], np.nan), # lw=0.75, color ='orange', alpha=0.8) #if (FLD.RENKO_BOOST_L_TIMING_LAG in features.columns): # ax3.plot(DATETIME_LABEL, # np.where((features[FLD.RENKO_BOOST_L_TIMING_LAG] > 0), # features[FLD.CCI_NORM].min() - 0.0168, np.nan), # lw=1, color ='red', alpha=0.8) # ax3.plot(DATETIME_LABEL, # np.where((features[FLD.RENKO_TREND_L_TIMING_LAG] > 0), # features[FLD.CCI_NORM].min() - 0.0168, np.nan), # lw=1, color ='coral', alpha=0.5) #if (FLD.HMAPOWER120 in features.columns): # ax3.plot(DATETIME_LABEL, # np.where((features[FLD.HMAPOWER30_MA_TIMING_LAG] > 0), # features[FLD.CCI_NORM].min() - 0.00512, np.nan), # lw=1.75, color ='orange', alpha=0.8) # ax3.plot(DATETIME_LABEL, # features[FLD.HMAPOWER120], # lw=0.75, color ='gray', alpha=0.22) # ax3.plot(DATETIME_LABEL, # np.where(features[FLD.HMAPOWER120_TIMING_LAG] <= 1, # features[FLD.HMAPOWER120], np.nan,), # lw=0.75, color ='darkgray', alpha=0.33) # ax3.plot(DATETIME_LABEL, # np.where(features[FLD.HMAPOWER120_TIMING_LAG] >= -1, # features[FLD.HMAPOWER120], np.nan,), # lw=1.25, color ='magenta', alpha=0.66) #if (FLD.ZEN_BOOST_TIMING_LAG in features.columns) and \ # (FLD.MAPOWER120 in features.columns): # ax1.plot(DATETIME_LABEL, # np.where((features[FLD.ZEN_BOOST_TIMING_LAG] == 1), # (features[FLD.BOLL_LB] + features[FLD.ATR_LB]) / 2.08, np.nan), # 'r.', alpha = 0.33) # ax1.plot(DATETIME_LABEL, # np.where(((features[FLD.HMAPOWER120_TIMING_LAG] == 1) | \ # (features[FLD.ZEN_BOOST_TIMING_LAG] == 1)) & \ # (features[ST.CLUSTER_GROUP_TOWARDS] > 0), # (features[FLD.BOLL_LB] + features[FLD.ATR_LB]) / 2.11, np.nan), # 'k+', alpha = 0.25) # ax1.plot(DATETIME_LABEL, # np.where((features[FLD.ZEN_BOOST_TIMING_LAG] == 1) & \ # (features[ST.CLUSTER_GROUP_TOWARDS] > 0) & \ # (features[FLD.PEAK_LOW_TIMING_LAG] <= 5) & \ # (features[FLD.DIF].shift(2) < features[FLD.DIF]) & \ # (features[FLD.MACD].shift(2) < features[FLD.MACD]) & \ # (features[AKA.CLOSE] > features[FLD.PEAK_ANCHOR_PREV_LOW_REF6]) & \ # (((features[FLD.HMAPOWER30_MA_TIMING_GAP] > 0) & \ # ((features[FLD.HMAPOWER30_MA_TIMING_GAP] > 36) | \ # (features[FLD.HMAPOWER30_MA_TIMING_GAP] > features[FLD.DEA_ZERO_TIMING_LAG]) | \ # (features[FLD.HMAPOWER30_MA_TIMING_GAP] > features[FLD.DIF_ZERO_TIMING_LAG]))) | \ # ((features[FLD.BOLL_JX_RSI] < 43.3) & \ # (features[FLD.BOLL_JX_MAXFACTOR] < -200)) | \ # ((features[FLD.BOLL_JX_RSI] < 46.6) & \ # ((features[FLD.BOLL_JX_MAXFACTOR] < -150) | \ # ((features[FLD.BOLL_JX_MAXFACTOR] < 0) & \ # (features[FLD.DEA_ZERO_TIMING_LAG] < 0) & \ # (features[FLD.DIF_ZERO_TIMING_LAG] < 0) & \ # (features[FLD.PEAK_LOW_TIMING_LAG] == 1) & \ # (features[FLD.MACD_ZERO_TIMING_LAG] == 1) & \ # (features[FLD.BOLL_JX_MAPOWER120] < 0.1954))) & \ # (features[FLD.BOLL_JX_HMAPOWER120] < 0.1236)) | \ # ((features[FLD.BOLL_JX_RSI] < 43.3) & \ # (features[FLD.BOLL_JX_MAXFACTOR] < -50) & \ # (features[FLD.BOLL_JX_MAPOWER120] < 0.168) & \ # (features[FLD.BOLL_JX_HMAPOWER120] < 0.1236)) | \ # ((features[FLD.BOLL_JX_RSI] < 45) & \ # (features[FLD.BOLL_JX_MAXFACTOR] < -200) & \ # (features[FLD.BOLL_JX_HMAPOWER120] < 0.0927))), # (features[FLD.BOLL_LB] + features[FLD.ATR_LB]) / 2.095, np.nan), # 'mP', alpha = 0.66) # ax1.plot(DATETIME_LABEL, # np.where((features[FLD.ZEN_BOOST_TIMING_LAG] == 1) & \ # (features[ST.CLUSTER_GROUP_TOWARDS] > 0) & \ # (features[FLD.PEAK_LOW_TIMING_LAG] <= 5) & \ # (features[FLD.DIF].shift(2) > features[FLD.DIF]) & \ # (features[FLD.MACD].shift(2) > features[FLD.MACD]) & \ # (features[AKA.CLOSE] > features[FLD.PEAK_ANCHOR_PREV_LOW_REF6]) & \ # (((features[FLD.HMAPOWER30_MA_TIMING_GAP] > 0) & \ # ((features[FLD.HMAPOWER30_MA_TIMING_GAP] > 36) | \ # (features[FLD.HMAPOWER30_MA_TIMING_GAP] > features[FLD.DEA_ZERO_TIMING_LAG]) | \ # (features[FLD.HMAPOWER30_MA_TIMING_GAP] > features[FLD.DIF_ZERO_TIMING_LAG]))) | \ # ((features[FLD.BOLL_JX_RSI] < 43.3) & \ # (features[FLD.BOLL_JX_MAXFACTOR] < -200)) | \ # ((features[FLD.BOLL_JX_RSI] < 46.6) & \ # ((features[FLD.BOLL_JX_MAXFACTOR] < -150) | \ # ((features[FLD.BOLL_JX_MAXFACTOR] < 0) & \ # (features[FLD.DEA_ZERO_TIMING_LAG] < 0) & \ # (features[FLD.DIF_ZERO_TIMING_LAG] < 0) & \ # (features[FLD.PEAK_LOW_TIMING_LAG] == 1) & \ # (features[FLD.MACD_ZERO_TIMING_LAG] == 1) & \ # (features[FLD.BOLL_JX_MAPOWER120] < 0.1954))) & \ # (features[FLD.BOLL_JX_HMAPOWER120] < 0.1236)) | \ # ((features[FLD.BOLL_JX_RSI] < 43.3) & \ # (features[FLD.BOLL_JX_MAXFACTOR] < -50) & \ # (features[FLD.BOLL_JX_MAPOWER120] < 0.168) & \ # (features[FLD.BOLL_JX_HMAPOWER120] < 0.1236)) | \ # ((features[FLD.BOLL_JX_RSI] < 45) & \ # (features[FLD.BOLL_JX_MAXFACTOR] < -200) & \ # (features[FLD.BOLL_JX_HMAPOWER120] < 0.0927))), # (features[FLD.BOLL_LB] + features[FLD.ATR_LB]) / 2.11, np.nan), # 'kx', alpha = 0.66) # ax1.plot(DATETIME_LABEL, # np.where(((features[FLD.HMAPOWER120_TIMING_LAG] == 1) | \ # (features[FLD.ZEN_BOOST_TIMING_LAG] == 1)) & \ # (features[FLD.PEAK_LOW_TIMING_LAG] <= 5) & \ # (features[FLD.DIF].shift(1) < features[FLD.DIF]) & \ # (features[FLD.MACD_DELTA] > 0) & \ # (features[AKA.CLOSE] > features[FLD.PEAK_ANCHOR_PREV_LOW_REF6]) & \ # (((features[FLD.HMAPOWER30_MA_TIMING_GAP] > 0) & \ # ((features[FLD.HMAPOWER30_MA_TIMING_GAP] > 36) | \ # (features[FLD.HMAPOWER30_MA_TIMING_GAP] > features[FLD.DEA_ZERO_TIMING_LAG]) | \ # (features[FLD.HMAPOWER30_MA_TIMING_GAP] > features[FLD.DIF_ZERO_TIMING_LAG]))) | \ # ((features[FLD.BOLL_JX_RSI] < 43.3) & \ # (features[FLD.BOLL_JX_MAXFACTOR] < -200)) | \ # ((features[FLD.BOLL_JX_RSI] < 46.6) & \ # ((features[FLD.BOLL_JX_MAXFACTOR] < -150) | \ # ((features[FLD.BOLL_JX_MAXFACTOR] < 0) & \ # (features[FLD.DEA_ZERO_TIMING_LAG] < 0) & \ # (features[FLD.DIF_ZERO_TIMING_LAG] < 0) & \ # (features[FLD.PEAK_LOW_TIMING_LAG] == 1) & \ # (features[FLD.MACD_ZERO_TIMING_LAG] == 1) & \ # (features[FLD.BOLL_JX_MAPOWER120] < 0.1954))) & \ # (features[FLD.BOLL_JX_HMAPOWER120] < 0.1236)) | \ # ((features[FLD.BOLL_JX_RSI] < 43.3) & \ # (features[FLD.BOLL_JX_MAXFACTOR] < -50) & \ # (features[FLD.BOLL_JX_MAPOWER120] < 0.168) & \ # (features[FLD.BOLL_JX_HMAPOWER120] < 0.1236)) | \ # ((features[FLD.BOLL_JX_RSI] < 45) & \ # (features[FLD.BOLL_JX_MAXFACTOR] < -200) & \ # (features[FLD.BOLL_JX_HMAPOWER120] < 0.0927))), # (features[FLD.BOLL_LB] + features[FLD.ATR_LB]) / 2.095, np.nan), # 'cP', alpha = 0.66) # ax1.plot(DATETIME_LABEL, # np.where(((features[FLD.HMAPOWER120_TIMING_LAG] == 1) | \ # (features[FLD.ZEN_BOOST_TIMING_LAG] == 1)) & \ # (features[FLD.PEAK_LOW_TIMING_LAG] <= 5) & \ # ((features[FLD.DIF].shift(1) > features[FLD.DIF]) | \ # (features[FLD.MACD_DELTA] < 0)) & \ # (features[AKA.CLOSE] > features[FLD.PEAK_ANCHOR_PREV_LOW_REF6]) & \ # (((features[FLD.HMAPOWER30_MA_TIMING_GAP] > 0) & \ # ((features[FLD.HMAPOWER30_MA_TIMING_GAP] > 36) | \ # (features[FLD.HMAPOWER30_MA_TIMING_GAP] > features[FLD.DEA_ZERO_TIMING_LAG]) | \ # (features[FLD.HMAPOWER30_MA_TIMING_GAP] > features[FLD.DIF_ZERO_TIMING_LAG]))) | \ # ((features[FLD.BOLL_JX_RSI] < 43.3) & \ # (features[FLD.BOLL_JX_MAXFACTOR] < -200)) | \ # ((features[FLD.BOLL_JX_RSI] < 46.6) & \ # ((features[FLD.BOLL_JX_MAXFACTOR] < -150) | \ # ((features[FLD.BOLL_JX_MAXFACTOR] < 0) & \ # (features[FLD.DEA_ZERO_TIMING_LAG] < 0) & \ # (features[FLD.DIF_ZERO_TIMING_LAG] < 0) & \ # (features[FLD.PEAK_LOW_TIMING_LAG] == 1) & \ # (features[FLD.MACD_ZERO_TIMING_LAG] == 1) & \ # (features[FLD.BOLL_JX_MAPOWER120] < 0.1954))) & \ # (features[FLD.BOLL_JX_HMAPOWER120] < 0.1236)) | \ # ((features[FLD.BOLL_JX_RSI] < 43.3) & \ # (features[FLD.BOLL_JX_MAXFACTOR] < -50) & \ # (features[FLD.BOLL_JX_MAPOWER120] < 0.168) & \ # (features[FLD.BOLL_JX_HMAPOWER120] < 0.1236)) | \ # ((features[FLD.BOLL_JX_RSI] < 45) & \ # (features[FLD.BOLL_JX_MAXFACTOR] < -200) & \ # (features[FLD.BOLL_JX_HMAPOWER120] < 0.0927))), # (features[FLD.BOLL_LB] + features[FLD.ATR_LB]) / 2.095, np.nan), # 'kP', alpha = 0.66) # ax1.plot(DATETIME_LABEL, # np.where((features[FLD.ZEN_BOOST_TIMING_LAG] == 1) & \ # (features[FLD.HMAPOWER120] < features[FLD.COMBINE_DENSITY]) & \ # ((features[FLD.HMAPOWER120_TIMING_LAG] == 1) | \ # (features[FLD.HMAPOWER120_TIMING_LAG] == 2) | \ # (features[FLD.HMAPOWER120_TIMING_LAG] == 3)) & \ # ((features[FLD.PEAK_LOW_TIMING_LAG] == 1) | \ # ((features[FLD.DEA_ZERO_TIMING_LAG] > 0) & \ # (features[FLD.PEAK_LOW_TIMING_LAG] <= 1) & \ # (features[FLD.PEAK_ANCHOR_PREV_LOW_REF6] < features[AKA.CLOSE]))) & \ # ((features[FLD.BOLL_JX_RSI] > 66.6) | \ # (features[FLD.BOLL_JX_RSI] < 43.3)), # (features[FLD.BOLL_LB] + features[FLD.ATR_LB]) / 2.08, np.nan), # 'ro', alpha = 0.33) # ax1.plot(DATETIME_LABEL, # np.where((features[FLD.ZEN_BOOST_TIMING_LAG] == 1) & \ # (features[FLD.ZEN_WAVELET_TIMING_LAG] > features[FLD.ZEN_BOOST_TIMING_LAG]) & \ # (features[FLD.HMAPOWER30_MA_TIMING_GAP] > 0) & \ # (features[FLD.PEAK_ANCHOR_PREV_LOW_REF6] < features[AKA.CLOSE]) & \ # (features[FLD.BOLL_JX_RSI] > 66.6) & \ # (features[FLD.BOLL_JX_MAXFACTOR] > 200) & \ # (features[FLD.MAPOWER120] > 0.918), # (features[FLD.BOLL_LB] + features[FLD.ATR_LB]) / 2.08, np.nan), # 'ro', alpha = 0.33) # ax1.plot(DATETIME_LABEL, # np.where((features[FLD.ZEN_BOOST_TIMING_LAG] == 1) & \ # (features[FLD.MAPOWER30] < features[FLD.COMBINE_DENSITY]) & \ # ((features[FLD.MAPOWER30_TIMING_LAG] == 1) | \ # (features[FLD.MAPOWER30_TIMING_LAG] == 2)) & \ # ((features[FLD.PEAK_LOW_TIMING_LAG] == 1) | \ # ((features[FLD.DEA_ZERO_TIMING_LAG] > 0) & \ # (features[FLD.PEAK_LOW_TIMING_LAG] <= 1))) & \ # (((features[FLD.BOLL_JX_RSI] < 43.3) & \ # (features[FLD.BOLL_JX_MAXFACTOR] < -200)) | \ # ((features[FLD.BOLL_JX_RSI] < 46.6) & \ # ((features[FLD.BOLL_JX_MAXFACTOR] < -150) | \ # ((features[FLD.BOLL_JX_MAXFACTOR] < 0) & \ # (features[FLD.DEA_ZERO_TIMING_LAG] < 0) & \ # (features[FLD.DIF_ZERO_TIMING_LAG] < 0) & \ # (features[FLD.PEAK_LOW_TIMING_LAG] == 1) & \ # (features[FLD.MACD_ZERO_TIMING_LAG] == 1) & \ # (features[FLD.BOLL_JX_MAPOWER120] < 0.1954))) & \ # (features[FLD.BOLL_JX_HMAPOWER120] < 0.1236)) | \ # ((features[FLD.BOLL_JX_RSI] < 43.3) & \ # (features[FLD.BOLL_JX_MAXFACTOR] < -50) & \ # (features[FLD.BOLL_JX_MAPOWER120] < 0.168) & \ # (features[FLD.BOLL_JX_HMAPOWER120] < 0.1236)) | \ # ((features[FLD.BOLL_JX_RSI] < 45) & \ # (features[FLD.BOLL_JX_MAXFACTOR] < -200) & \ # (features[FLD.BOLL_JX_HMAPOWER120] < 0.0927))), # (features[FLD.BOLL_LB] + features[FLD.ATR_LB]) / 2.08, np.nan), # 'go', alpha = 0.33) # ax1.plot(DATETIME_LABEL, # np.where((features[FLD.ZEN_BOOST_TIMING_LAG] == 1) & \ # (features[FLD.HMAPOWER120] < features[FLD.COMBINE_DENSITY]) & \ # (features[FLD.HMAPOWER120_TIMING_LAG] > 0) & \ # ((features[FLD.PEAK_LOW_TIMING_LAG] == 1) | \ # ((features[FLD.DEA_ZERO_TIMING_LAG] > 0) & \ # (features[FLD.PEAK_LOW_TIMING_LAG] <= 1))) & \ # (((features[FLD.BOLL_JX_RSI] < 43.3) & \ # (features[FLD.BOLL_JX_MAXFACTOR] < -200)) | \ # ((features[FLD.BOLL_JX_RSI] < 46.6) & \ # ((features[FLD.BOLL_JX_MAXFACTOR] < -150) | \ # ((features[FLD.BOLL_JX_MAXFACTOR] < 0) & \ # (features[FLD.DEA_ZERO_TIMING_LAG] < 0) & \ # (features[FLD.DIF_ZERO_TIMING_LAG] < 0) & \ # (features[FLD.PEAK_LOW_TIMING_LAG] == 1) & \ # (features[FLD.MACD_ZERO_TIMING_LAG] == 1) & \ # (features[FLD.BOLL_JX_MAPOWER120] < 0.1954))) & \ # (features[FLD.BOLL_JX_HMAPOWER120] < 0.1236)) | \ # ((features[FLD.BOLL_JX_RSI] < 43.3) & \ # (features[FLD.BOLL_JX_MAXFACTOR] < -50) & \ # (features[FLD.BOLL_JX_MAPOWER120] < 0.168) & \ # (features[FLD.BOLL_JX_HMAPOWER120] < 0.1236)) | \ # ((features[FLD.BOLL_JX_RSI] < 45) & \ # (features[FLD.BOLL_JX_MAXFACTOR] < -200) & \ # (features[FLD.BOLL_JX_HMAPOWER120] < 0.0927))), # (features[FLD.BOLL_LB] + features[FLD.ATR_LB]) / 2.08, np.nan), # 'mo', alpha = 0.33) # ax1.plot(DATETIME_LABEL, # np.where((features[FLD.ZEN_BOOST_TIMING_LAG] == 1) & \ # (features[FLD.HMAPOWER30] < features[FLD.COMBINE_DENSITY]) & \ # (features[FLD.PEAK_ANCHOR_PREV_LOW_REF6] < features[AKA.CLOSE]) & \ # (features[FLD.BOLL_CROSS_JX_BEFORE] < features[FLD.BOLL_CROSS_SX_BEFORE]) & \ # (features[FLD.DEA_ZERO_TIMING_LAG] > 0) & \ # (features[FLD.PEAK_LOW_TIMING_LAG] <= 1) & \ # (((features[FLD.BOLL_JX_RSI] < 43.3) & \ # (features[FLD.BOLL_JX_MAXFACTOR] < -200)) | \ # ((features[FLD.BOLL_JX_RSI] < 46.6) & \ # ((features[FLD.BOLL_JX_MAXFACTOR] < -150) | \ # ((features[FLD.BOLL_JX_MAXFACTOR] < 0) & \ # (features[FLD.DEA_ZERO_TIMING_LAG] < 0) & \ # (features[FLD.DIF_ZERO_TIMING_LAG] < 0) & \ # (features[FLD.PEAK_LOW_TIMING_LAG] == 1) & \ # (features[FLD.MACD_ZERO_TIMING_LAG] == 1) & \ # (features[FLD.BOLL_JX_MAPOWER120] < 0.1954))) & \ # (features[FLD.BOLL_JX_HMAPOWER120] < 0.1236)) | \ # ((features[FLD.BOLL_JX_RSI] < 43.3) & \ # (features[FLD.BOLL_JX_MAXFACTOR] < -50) & \ # (features[FLD.BOLL_JX_MAPOWER120] < 0.168) & \ # (features[FLD.BOLL_JX_HMAPOWER120] < 0.1236)) | \ # ((features[FLD.BOLL_JX_RSI] < 45) & \ # (features[FLD.BOLL_JX_MAXFACTOR] < -200) & \ # (features[FLD.BOLL_JX_HMAPOWER120] < 0.0927))), # (features[FLD.BOLL_LB] + features[FLD.ATR_LB]) / 2.08, np.nan), # 'co', alpha = 0.33) # checkpoint = ((features[FLD.HMAPOWER120_TIMING_LAG] == 1) | \ # (features[FLD.ZEN_BOOST_TIMING_LAG] == 1)) & \ # (features[FLD.PEAK_LOW_TIMING_LAG] <= 5) & \ # (features[AKA.CLOSE] > features[FLD.PEAK_ANCHOR_PREV_LOW_REF6]) & \ # (((features[FLD.BOLL_JX_RSI] < 43.3) & \ # (features[FLD.BOLL_JX_MAXFACTOR] < -200)) | \ # ((features[FLD.BOLL_JX_RSI] < 46.6) & \ # ((features[FLD.BOLL_JX_MAXFACTOR] < -150) | \ # ((features[FLD.BOLL_JX_MAXFACTOR] < 0) & \ # (features[FLD.DEA_ZERO_TIMING_LAG] < 0) & \ # (features[FLD.DIF_ZERO_TIMING_LAG] < 0) & \ # (features[FLD.PEAK_LOW_TIMING_LAG] == 1) & \ # (features[FLD.MACD_ZERO_TIMING_LAG] == 1) & \ # (features[FLD.BOLL_JX_MAPOWER120] < 0.1954))) & \ # (features[FLD.BOLL_JX_HMAPOWER120] < 0.1236)) | \ # ((features[FLD.BOLL_JX_RSI] < 43.3) & \ # (features[FLD.BOLL_JX_MAXFACTOR] < -50) & \ # (features[FLD.BOLL_JX_MAPOWER120] < 0.168) & \ # (features[FLD.BOLL_JX_HMAPOWER120] < 0.1236)) | \ # ((features[FLD.BOLL_JX_RSI] < 45) & \ # (features[FLD.BOLL_JX_MAXFACTOR] < -200) & \ # (features[FLD.BOLL_JX_HMAPOWER120] < 0.0927))) # print(features.loc[checkpoint, # [FLD.PEAK_LOW_TIMING_LAG, # FLD.HMAPOWER120_TIMING_LAG, # FLD.MACD_ZERO_TIMING_LAG, # FLD.DIF_ZERO_TIMING_LAG, # FLD.DEA_ZERO_TIMING_LAG, # FLD.ZEN_BOOST_TIMING_LAG, # AKA.CLOSE, # FLD.PEAK_ANCHOR_PREV_LOW_REF6, # FLD.MA90_CLEARANCE_TIMING_LAG, # FLD.MA90_TREND_TIMING_LAG, # FTR.ATR_BAND_MA90_TIMING_LAG, # FLD.HMAPOWER120_DUAL_TIMING_LAG, # FLD.BOLL_JX_MAXFACTOR, # FLD.BOLL_JX_RSI, # FLD.BOLL_JX_MAPOWER120, # FLD.BOLL_JX_HMAPOWER120,]]) # ax3.plot(DATETIME_LABEL, # np.where((features[FLD.ZEN_BOOST_TIMING_LAG] == -1), # np.maximum(features[FLD.MAPOWER30], # features[FLD.MAPOWER120]), np.nan), # 'g.', alpha = 0.33) # ax3.plot(DATETIME_LABEL, # np.where((features[FLD.RENKO_TREND_L_TIMING_LAG] > 0) & \ # ((features[FLD.RENKO_TREND_L_TIMING_LAG] > # features[FLD.DEA_ZERO_TIMING_LAG]) | \ # (features[FLD.REGTREE_TIMING_LAG] > # features[FLD.DEA_ZERO_TIMING_LAG]) | \ # ((features[FLD.MA90_CLEARANCE_TIMING_LAG] > # features[FLD.DEA_ZERO_TIMING_LAG]) & \ # (features[FLD.MA90] > features[FLD.MA120]) & \ # ((features[FLD.BOLL] + features[FLD.MA90]) > # (features[FLD.MA120] + features[FLD.MA30])) & \ # (features[FLD.MA90_CLEARANCE_TIMING_LAG] / # features[FLD.MA90_CROSS_JX_BEFORE] > 0.618)) | \ # ((features[FLD.MA120_CLEARANCE_TIMING_LAG] > # features[FLD.DEA_ZERO_TIMING_LAG]) & \ # (features[FLD.MA90] > features[FLD.MA120]) & \ # ((features[FLD.BOLL] + features[FLD.MA90]) > # (features[FLD.MA120] + features[FLD.MA30])) & \ # (features[FLD.MA120_CLEARANCE_TIMING_LAG] / # features[FLD.MA90_CROSS_JX_BEFORE] > 0.618))), # (features[FLD.CCI] / 600 + 0.5).min() - 0.00382, # np.nan), # lw = 1.75, color = 'crimson', alpha = 0.5)

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8865315c91d8577f01399e99ba92415b644b53b1

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py

Python

src/test/test_elements.py

yenkuanlee/libwally-core

9adc3c2fbbb64125afa8a63a791fc9c7dfb77dd9

[ "MIT" ]

212

2017-09-01T21:08:04.000Z

2022-03-19T07:27:46.000Z

src/test/test_elements.py

yenkuanlee/libwally-core

9adc3c2fbbb64125afa8a63a791fc9c7dfb77dd9

[ "MIT" ]

236

2017-07-25T10:34:43.000Z

2022-03-31T11:12:30.000Z

src/test/test_elements.py

yenkuanlee/libwally-core

9adc3c2fbbb64125afa8a63a791fc9c7dfb77dd9

[ "MIT" ]

131

2017-06-27T04:06:27.000Z

2022-03-24T08:01:46.000Z

import unittest from util import * # the private part of our blinding key UNBLIND_OUR_SK, UNBLIND_OUR_SK_LEN = make_cbuffer('e8ba74f899e6b06da05fb255511c7adcea41f186326ef4fc45290fa8043f7af5') # the sender's pubkey used to blind the output UNBLIND_SENDER_PK, UNBLIND_SENDER_PK_LEN = make_cbuffer('0378d8b53305ed6482db0c8f5eb8b0ca3d5c314d7773c584faa8cf587ee8137244') # amount rangeproof UNBLIND_RANGEPROOF, UNBLIND_RANGEPROOF_LEN = make_cbuffer('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' + '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' + '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' + 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+ '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') # scriptPubKey of the output UNBLIND_SCRIPT, UNBLIND_SCRIPT_LEN = make_cbuffer('76a9145976d83033bde4f12713ae3706b25e92fb608b9188ac') # asset commitment UNBLIND_ASSET_COMMITMENT, UNBLIND_ASSET_COMMITMENT_LEN = make_cbuffer('0b9d043d60286407330e12001e539559f6227c9999abf251b7497bba53ac20cd70') # value commitment UNBLIND_VALUE_COMMITMENT, UNBLIND_VALUE_COMMITMENT_LEN = make_cbuffer('09b67565b370abf41d81fe0ed6378e7228e9ae01d1b72b69582f83db1fca522148') UNBLINDED_ASSET = make_cbuffer('25b251070e29ca19043cf33ccd7324e2ddab03ecc4ae0b5e77c4fc0e5cf6c95a')[0] UNBLINDED_ABF = make_cbuffer('3f2f58e80fbe77e8aad8268f1baebc3548c777ba8271f99ce73210ad993d907d')[0] UNBLINDED_VBF = make_cbuffer('51f109e34d0282b6efac36d118131060f7f79b867b67a95bebd087eda2ccd796')[0] # TODO: expand these tests a little bit... class ElementsTests(unittest.TestCase): def test_asset_unblind(self): asset_out, _ = make_cbuffer('00' * 32) abf_out, _ = make_cbuffer('00' * 32) vbf_out, _ = make_cbuffer('00' * 32) args = ( UNBLIND_SENDER_PK, UNBLIND_SENDER_PK_LEN, UNBLIND_OUR_SK, UNBLIND_OUR_SK_LEN, UNBLIND_RANGEPROOF, UNBLIND_RANGEPROOF_LEN, UNBLIND_VALUE_COMMITMENT, UNBLIND_VALUE_COMMITMENT_LEN, UNBLIND_SCRIPT, UNBLIND_SCRIPT_LEN, UNBLIND_ASSET_COMMITMENT, UNBLIND_ASSET_COMMITMENT_LEN, asset_out, 32, abf_out, 32, vbf_out, 32 ) ret, value_out = wally_asset_unblind(*args) self.assertEqual((ret, value_out, asset_out, abf_out, vbf_out), (WALLY_OK, 80000000, UNBLINDED_ASSET, UNBLINDED_ABF, UNBLINDED_VBF)) def test_asset_unblind_with_nonce(self): out_nonce, _ = make_cbuffer('00'*32) wally_ecdh(UNBLIND_SENDER_PK, UNBLIND_SENDER_PK_LEN, UNBLIND_OUR_SK, UNBLIND_OUR_SK_LEN, out_nonce, len(out_nonce)) wally_sha256(out_nonce, 32, out_nonce, 32); asset_out, _ = make_cbuffer('00' * 32) abf_out, _ = make_cbuffer('00' * 32) vbf_out, _ = make_cbuffer('00' * 32) args = ( out_nonce, len(out_nonce), UNBLIND_RANGEPROOF, UNBLIND_RANGEPROOF_LEN, UNBLIND_VALUE_COMMITMENT, UNBLIND_VALUE_COMMITMENT_LEN, UNBLIND_SCRIPT, UNBLIND_SCRIPT_LEN, UNBLIND_ASSET_COMMITMENT, UNBLIND_ASSET_COMMITMENT_LEN, asset_out, 32, abf_out, 32, vbf_out, 32 ) ret, value_out = wally_asset_unblind_with_nonce(*args) self.assertEqual((ret, value_out, asset_out, abf_out, vbf_out), (WALLY_OK, 80000000, UNBLINDED_ASSET, UNBLINDED_ABF, UNBLINDED_VBF)) if __name__ == '__main__': _, val = wally_is_elements_build() if val != 0: unittest.main()

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888e5c8dd96fc1deb0721a8e8beb2f0504352b8b

6,867

py

Python

src/abaqus/BoundaryCondition/ConnVelocityBCState.py

Haiiliin/PyAbaqus

f20db6ebea19b73059fe875a53be370253381078

[ "MIT" ]

7

2022-01-21T09:15:45.000Z

2022-02-15T09:31:58.000Z

src/abaqus/BoundaryCondition/ConnVelocityBCState.py

Haiiliin/PyAbaqus

f20db6ebea19b73059fe875a53be370253381078

[ "MIT" ]

null

src/abaqus/BoundaryCondition/ConnVelocityBCState.py

Haiiliin/PyAbaqus

f20db6ebea19b73059fe875a53be370253381078

[ "MIT" ]

null

from abaqusConstants import * from .BoundaryConditionState import BoundaryConditionState class ConnVelocityBCState(BoundaryConditionState): """The ConnVelocityBCState object stores the propagating data for a velocity boundary condition in a step. One instance of this object is created internally by the ConnVelocityBC object for each step. The instance is also deleted internally by the ConnVelocityBC object. The ConnVelocityBCState object has no constructor or methods. The ConnVelocityBCState object is derived from the BoundaryConditionState object. Attributes ---------- v1: float A Float specifying the velocity component in the connector's local 1-direction. v2: float A Float specifying the velocity component in the connector's local 2-direction. v3: float A Float specifying the velocity component in the connector's local 3-direction. vr1: float A Float specifying the rotational velocity component in the connector's local 4-direction. vr2: float A Float specifying the rotational velocity component in the connector's local 5-direction. vr3: float A Float specifying the rotational velocity component in the connector's local 6-direction. v1State: SymbolicConstant A SymbolicConstant specifying the propagation state of the velocity component in the connector's local 1-direction. Possible values are UNSET, SET, UNCHANGED, FREED, and MODIFIED. v2State: SymbolicConstant A SymbolicConstant specifying the propagation state of the velocity component in the connector's local 2-direction. Possible values are UNSET, SET, UNCHANGED, FREED, and MODIFIED. v3State: SymbolicConstant A SymbolicConstant specifying the propagation state of the velocity component in the connector's local 3-direction. Possible values are UNSET, SET, UNCHANGED, FREED, and MODIFIED. vr1State: SymbolicConstant A SymbolicConstant specifying the propagation state of the rotational velocity component in the connector's local 4–direction. Possible values are UNSET, SET, UNCHANGED, FREED, and MODIFIED. vr2State: SymbolicConstant A SymbolicConstant specifying the propagation state of the rotational velocity component in the connector's local 5–direction. Possible values are UNSET, SET, UNCHANGED, FREED, and MODIFIED. vr3State: SymbolicConstant A SymbolicConstant specifying the propagation state of the rotational velocity component in the connector's local 6–direction. Possible values are UNSET, SET, UNCHANGED, FREED, and MODIFIED. amplitudeState: SymbolicConstant A SymbolicConstant specifying the propagation state of the amplitude reference. Possible values are UNSET, SET, UNCHANGED, FREED, and MODIFIED. status: SymbolicConstant A SymbolicConstant specifying the propagation state of the :py:class:`~abaqus.BoundaryCondition.BoundaryConditionState.BoundaryConditionState` object. Possible values are: NOT_YET_ACTIVE CREATED PROPAGATED MODIFIED DEACTIVATED NO_LONGER_ACTIVE TYPE_NOT_APPLICABLE INSTANCE_NOT_APPLICABLE PROPAGATED_FROM_BASE_STATE MODIFIED_FROM_BASE_STATE DEACTIVATED_FROM_BASE_STATE BUILT_INTO_MODES amplitude: str A String specifying the name of the amplitude reference. The String is empty if the boundary condition has no amplitude reference. Notes ----- This object can be accessed by: .. code-block:: python import load mdb.models[name].steps[name].boundaryConditionStates[name] The corresponding analysis keywords are: - CONNECTOR MOTION """ # A Float specifying the velocity component in the connector's local 1-direction. v1: float = None # A Float specifying the velocity component in the connector's local 2-direction. v2: float = None # A Float specifying the velocity component in the connector's local 3-direction. v3: float = None # A Float specifying the rotational velocity component in the connector's local # 4-direction. vr1: float = None # A Float specifying the rotational velocity component in the connector's local # 5-direction. vr2: float = None # A Float specifying the rotational velocity component in the connector's local # 6-direction. vr3: float = None # A SymbolicConstant specifying the propagation state of the velocity component in the # connector's local 1-direction. Possible values are UNSET, SET, UNCHANGED, FREED, and # MODIFIED. v1State: SymbolicConstant = None # A SymbolicConstant specifying the propagation state of the velocity component in the # connector's local 2-direction. Possible values are UNSET, SET, UNCHANGED, FREED, and # MODIFIED. v2State: SymbolicConstant = None # A SymbolicConstant specifying the propagation state of the velocity component in the # connector's local 3-direction. Possible values are UNSET, SET, UNCHANGED, FREED, and # MODIFIED. v3State: SymbolicConstant = None # A SymbolicConstant specifying the propagation state of the rotational velocity component # in the connector's local 4–direction. Possible values are UNSET, SET, UNCHANGED, FREED, # and MODIFIED. vr1State: SymbolicConstant = None # A SymbolicConstant specifying the propagation state of the rotational velocity component # in the connector's local 5–direction. Possible values are UNSET, SET, UNCHANGED, FREED, # and MODIFIED. vr2State: SymbolicConstant = None # A SymbolicConstant specifying the propagation state of the rotational velocity component # in the connector's local 6–direction. Possible values are UNSET, SET, UNCHANGED, FREED, # and MODIFIED. vr3State: SymbolicConstant = None # A SymbolicConstant specifying the propagation state of the amplitude reference. Possible # values are UNSET, SET, UNCHANGED, FREED, and MODIFIED. amplitudeState: SymbolicConstant = None # A SymbolicConstant specifying the propagation state of the BoundaryConditionState object. Possible values are: # NOT_YET_ACTIVE # CREATED # PROPAGATED # MODIFIED # DEACTIVATED # NO_LONGER_ACTIVE # TYPE_NOT_APPLICABLE # INSTANCE_NOT_APPLICABLE # PROPAGATED_FROM_BASE_STATE # MODIFIED_FROM_BASE_STATE # DEACTIVATED_FROM_BASE_STATE # BUILT_INTO_MODES status: SymbolicConstant = None # A String specifying the name of the amplitude reference. The String is empty if the # boundary condition has no amplitude reference. amplitude: str = ''

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9

88aa939ceddb662055f93e0aafcc1f5420e0ff42

208

py

Python

keras/layers/wrappers.py

ikingye/keras

1a3ee8441933fc007be6b2beb47af67998d50737

[ "MIT" ]

5

2020-11-30T22:26:03.000Z

2020-12-01T22:34:25.000Z

keras/layers/wrappers.py

ikingye/keras

1a3ee8441933fc007be6b2beb47af67998d50737

[ "MIT" ]

10

2020-12-01T22:55:29.000Z

2020-12-11T18:31:46.000Z

keras/layers/wrappers.py

ikingye/keras

1a3ee8441933fc007be6b2beb47af67998d50737

[ "MIT" ]

15

2020-11-30T22:12:22.000Z

2020-12-09T01:32:48.000Z

"""Layers that augment the functionality of a base layer.""" from tensorflow.keras.layers import Wrapper from tensorflow.keras.layers import TimeDistributed from tensorflow.keras.layers import Bidirectional

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7

c9a74de06a35953c65ea7f93cc0c139c485e913b

17,779

py

Python

checker/tests/test_logic.py

ucam-cl-dtg/equality_checker

6a31d3dd360f821e36c4742e1d5139d7292f8319

[ "Apache-2.0" ]

null

checker/tests/test_logic.py

ucam-cl-dtg/equality_checker

6a31d3dd360f821e36c4742e1d5139d7292f8319

[ "Apache-2.0" ]

null

checker/tests/test_logic.py

ucam-cl-dtg/equality_checker

6a31d3dd360f821e36c4742e1d5139d7292f8319

[ "Apache-2.0" ]

null

import unittest from checker import logic as api from checker.utils import EqualityType from checker.parsing import logic_parser as parsing EQUALITY_TYPES = [t.value for t in EqualityType if t is not EqualityType.KNOWN] ##### # These tests check behavior for very simple cases, and for complicated cases # where exact matching is important. ##### class TestFundamentals(unittest.TestCase): def test_symbols_equal(self): print("\n\n\n" + " Test if Symbols can be Equal ".center(75, "#")) test_str = "P" target_str = "P" symbols = None response = api.check(test_str, target_str, symbols=symbols) self.assertTrue("error" not in response, 'Unexpected "error" in response!') self.assertTrue("equal" in response, 'Key "equal" not in response!') self.assertTrue(response["equal"] == "true", 'Expected "equal" to be "true", got "{}"!'.format(response["equal"])) self.assertTrue("equality_type" in response, 'Key "equality_type" not in response!') self.assertTrue(response["equality_type"] in EQUALITY_TYPES, 'Unexpected "equality_type": "{}"!'.format(response["equality_type"])) self.assertTrue(response["equality_type"] == "exact", 'For these expressions, expected "equality_type" to be "exact", got "{}"!'.format(response["equality_type"])) print(" PASS ".center(75, "#")) def test_and(self): print("\n\n\n" + " Test if AND (&) Works ".center(75, "#")) test_str = "P & Q" target_str = "Q & P" symbols = None response = api.check(test_str, target_str, symbols=symbols) self.assertTrue("error" not in response, 'Unexpected "error" in response!') self.assertTrue("equal" in response, 'Key "equal" not in response!') self.assertTrue(response["equal"] == "true", 'Expected "equal" to be "true", got "{}"!'.format(response["equal"])) self.assertTrue("equality_type" in response, 'Key "equality_type" not in response!') self.assertTrue(response["equality_type"] in EQUALITY_TYPES, 'Unexpected "equality_type": "{}"!'.format(response["equality_type"])) self.assertTrue(response["equality_type"] == "exact", 'For these expressions, expected "equality_type" to be "exact", got "{}"!'.format(response["equality_type"])) print(" PASS ".center(75, "#")) def test_or(self): print("\n\n\n" + " Test if OR (|) Works ".center(75, "#")) test_str = "P | Q" target_str = "Q | P" symbols = None response = api.check(test_str, target_str, symbols=symbols) self.assertTrue("error" not in response, 'Unexpected "error" in response!') self.assertTrue("equal" in response, 'Key "equal" not in response!') self.assertTrue(response["equal"] == "true", 'Expected "equal" to be "true", got "{}"!'.format(response["equal"])) self.assertTrue("equality_type" in response, 'Key "equality_type" not in response!') self.assertTrue(response["equality_type"] in EQUALITY_TYPES, 'Unexpected "equality_type": "{}"!'.format(response["equality_type"])) self.assertTrue(response["equality_type"] == "exact", 'For these expressions, expected "equality_type" to be "exact", got "{}"!'.format(response["equality_type"])) print(" PASS ".center(75, "#")) def test_not(self): print("\n\n\n" + " Test if NOT (~) Works ".center(75, "#")) test_str = "~P" target_str = "~P" symbols = None response = api.check(test_str, target_str, symbols=symbols) self.assertTrue("error" not in response, 'Unexpected "error" in response!') self.assertTrue("equal" in response, 'Key "equal" not in response!') self.assertTrue(response["equal"] == "true", 'Expected "equal" to be "true", got "{}"!'.format(response["equal"])) self.assertTrue("equality_type" in response, 'Key "equality_type" not in response!') self.assertTrue(response["equality_type"] in EQUALITY_TYPES, 'Unexpected "equality_type": "{}"!'.format(response["equality_type"])) self.assertTrue(response["equality_type"] == "exact", 'For these expressions, expected "equality_type" to be "exact", got "{}"!'.format(response["equality_type"])) print(" PASS ".center(75, "#")) def test_xor(self): print("\n\n\n" + " Test if XOR (^) Works ".center(75, "#")) test_str = "P ^ Q" target_str = "(P & ~Q) | (Q & ~P)" symbols = None response = api.check(test_str, target_str, symbols=symbols) self.assertTrue("error" not in response, 'Unexpected "error" in response!') self.assertTrue("equal" in response, 'Key "equal" not in response!') self.assertTrue(response["equal"] == "true", 'Expected "equal" to be "true", got "{}"!'.format(response["equal"])) self.assertTrue("equality_type" in response, 'Key "equality_type" not in response!') self.assertTrue(response["equality_type"] in EQUALITY_TYPES, 'Unexpected "equality_type": "{}"!'.format(response["equality_type"])) self.assertTrue(response["equality_type"] == "symbolic", 'For these expressions, expected "equality_type" to be "symbolic", got "{}"!'.format(response["equality_type"])) print(" PASS ".center(75, "#")) def test_equivalence(self): print("\n\n\n" + " Test if Equivalence (==) Works ".center(75, "#")) test_str = "P == Q" target_str = "Q == P" symbols = None response = api.check(test_str, target_str, symbols=symbols) self.assertTrue("error" not in response, 'Unexpected "error" in response!') self.assertTrue("equal" in response, 'Key "equal" not in response!') self.assertTrue(response["equal"] == "true", 'Expected "equal" to be "true", got "{}"!'.format(response["equal"])) self.assertTrue("equality_type" in response, 'Key "equality_type" not in response!') self.assertTrue(response["equality_type"] in EQUALITY_TYPES, 'Unexpected "equality_type": "{}"!'.format(response["equality_type"])) self.assertTrue(response["equality_type"] == "exact", 'For these expressions, expected "equality_type" to be "exact", got "{}"!'.format(response["equality_type"])) print(" PASS ".center(75, "#")) def test_implies(self): print("\n\n\n" + " Test if Implies (>>) Works ".center(75, "#")) test_str = "P >> Q" target_str = "P >> Q" symbols = None response = api.check(test_str, target_str, symbols=symbols) self.assertTrue("error" not in response, 'Unexpected "error" in response!') self.assertTrue("equal" in response, 'Key "equal" not in response!') self.assertTrue(response["equal"] == "true", 'Expected "equal" to be "true", got "{}"!'.format(response["equal"])) self.assertTrue("equality_type" in response, 'Key "equality_type" not in response!') self.assertTrue(response["equality_type"] in EQUALITY_TYPES, 'Unexpected "equality_type": "{}"!'.format(response["equality_type"])) self.assertTrue(response["equality_type"] == "exact", 'For these expressions, expected "equality_type" to be "exact", got "{}"!'.format(response["equality_type"])) print(" PASS ".center(75, "#")) def test_demorgan_and(self): print("\n\n\n" + " Test De Morgan's Law (AND) ".center(75, "#")) test_str = "~(A & B)" target_str = "~A | ~B" symbols = None response = api.check(test_str, target_str, symbols=symbols) self.assertTrue("error" not in response, 'Unexpected "error" in response!') self.assertTrue("equal" in response, 'Key "equal" not in response!') self.assertTrue(response["equal"] == "true", 'Expected "equal" to be "true", got "{}"!'.format(response["equal"])) self.assertTrue("equality_type" in response, 'Key "equality_type" not in response!') self.assertTrue(response["equality_type"] in EQUALITY_TYPES, 'Unexpected "equality_type": "{}"!'.format(response["equality_type"])) self.assertTrue(response["equality_type"] == "symbolic", 'For these expressions, expected "equality_type" to be "symbolic", got "{}"!'.format(response["equality_type"])) print(" PASS ".center(75, "#")) def test_demorgan_or(self): print("\n\n\n" + " Test De Morgan's Law (OR) ".center(75, "#")) test_str = "~(A | B)" target_str = "~A & ~B" symbols = None response = api.check(test_str, target_str, symbols=symbols) self.assertTrue("error" not in response, 'Unexpected "error" in response!') self.assertTrue("equal" in response, 'Key "equal" not in response!') self.assertTrue(response["equal"] == "true", 'Expected "equal" to be "true", got "{}"!'.format(response["equal"])) self.assertTrue("equality_type" in response, 'Key "equality_type" not in response!') self.assertTrue(response["equality_type"] in EQUALITY_TYPES, 'Unexpected "equality_type": "{}"!'.format(response["equality_type"])) self.assertTrue(response["equality_type"] == "symbolic", 'For these expressions, expected "equality_type" to be "symbolic", got "{}"!'.format(response["equality_type"])) print(" PASS ".center(75, "#")) def test_not_not(self): print("\n\n\n" + " Test NOT NOT Cancellation ".center(75, "#")) test_str = "~~A" target_str = "A" symbols = None response = api.check(test_str, target_str, symbols=symbols) self.assertTrue("error" not in response, 'Unexpected "error" in response!') self.assertTrue("equal" in response, 'Key "equal" not in response!') self.assertTrue(response["equal"] == "true", 'Expected "equal" to be "true", got "{}"!'.format(response["equal"])) self.assertTrue("equality_type" in response, 'Key "equality_type" not in response!') self.assertTrue(response["equality_type"] in EQUALITY_TYPES, 'Unexpected "equality_type": "{}"!'.format(response["equality_type"])) self.assertTrue(response["equality_type"] == "symbolic", 'For these expressions, expected "equality_type" to be "symbolic", got "{}"!'.format(response["equality_type"])) print(" PASS ".center(75, "#")) def test_no_simplification(self): print("\n\n\n" + " Test No Simplification ".center(75, "#")) test_str = "A and ~A" result = api.parse_expression(test_str) self.assertTrue(len(result.free_symbols) == 1, 'Expected variable in result!') print(" PASS ".center(75, "#")) def test_true_and_false(self): print("\n\n\n" + " Test True and False ".center(75, "#")) test_str = "not False" target_str = "True" symbols = None response = api.check(test_str, target_str, symbols=symbols) self.assertTrue("error" not in response, 'Unexpected "error" in response!') self.assertTrue("equal" in response, 'Key "equal" not in response!') self.assertTrue(response["equal"] == "true", 'Expected "equal" to be "true", got "{}"!'.format(response["equal"])) print(" PASS ".center(75, "#")) def test_boolean_keyword_precedence(self): print("\n\n\n" + " Test Boolean Keyword Precedence ".center(75, "#")) test_str = "A == B or C" target_str = "A == (B or C)" response = api.check(test_str, target_str) self.assertTrue("error" not in response, 'Unexpected "error" in response!') self.assertTrue("equal" in response, 'Key "equal" not in response!') self.assertTrue(response["equal"] == "true", 'Expected "equal" to be "true", got "{}"!'.format(response["equal"])) self.assertTrue(response["equality_type"] in EQUALITY_TYPES, 'Unexpected "equality_type": "{}"!'.format(response["equality_type"])) self.assertTrue(response["equality_type"] == "exact", 'For these expressions, expected "equality_type" to be "exact", got "{}"!'.format(response["equality_type"])) print(" PASS ".center(75, "#")) def test_boolean_and_bitwise(self): print("\n\n\n" + " Test Boolean and Bitwise ".center(75, "#")) test_str = "A and B or C xor D" target_str = "D ^ C | B & A" response = api.check(test_str, target_str) self.assertTrue("error" not in response, 'Unexpected "error" in response!') self.assertTrue("equal" in response, 'Key "equal" not in response!') self.assertTrue(response["equal"] == "true", 'Expected "equal" to be "true", got "{}"!'.format(response["equal"])) self.assertTrue(response["equality_type"] in EQUALITY_TYPES, 'Unexpected "equality_type": "{}"!'.format(response["equality_type"])) self.assertTrue(response["equality_type"] == "exact", 'For these expressions, expected "equality_type" to be "exact", got "{}"!'.format(response["equality_type"])) print(" PASS ".center(75, "#")) def test_one_is_true_boolean(self): print("\n\n\n" + " Test One is True Boolean ".center(75, "#")) test_str = "1" target_str = "True" response = api.check(test_str, target_str) self.assertTrue("error" not in response, 'Unexpected "error" in response!') self.assertTrue("equal" in response, 'Key "equal" not in response!') self.assertTrue(response["equal"] == "true", 'Expected "equal" to be "true", got "{}"!'.format(response["equal"])) self.assertTrue(response["equality_type"] in EQUALITY_TYPES, 'Unexpected "equality_type": "{}"!'.format(response["equality_type"])) self.assertTrue(response["equality_type"] == "exact", 'For these expressions, expected "equality_type" to be "exact", got "{}"!'.format(response["equality_type"])) print(" PASS ".center(75, "#")) def test_zero_is_false_boolean(self): print("\n\n\n" + " Test Zero is False Boolean ".center(75, "#")) test_str = "0" target_str = "False" response = api.check(test_str, target_str) self.assertTrue("error" not in response, 'Unexpected "error" in response!') self.assertTrue("equal" in response, 'Key "equal" not in response!') self.assertTrue(response["equal"] == "true", 'Expected "equal" to be "true", got "{}"!'.format(response["equal"])) self.assertTrue(response["equality_type"] in EQUALITY_TYPES, 'Unexpected "equality_type": "{}"!'.format(response["equality_type"])) self.assertTrue(response["equality_type"] == "exact", 'For these expressions, expected "equality_type" to be "exact", got "{}"!'.format(response["equality_type"])) print(" PASS ".center(75, "#")) ##### # These tests check the error behaviour when invalid values are passed. ##### class TestErrorBehaviour(unittest.TestCase): def test_blank_input(self): print("\n\n\n" + " Test if Blank Input Detected ".center(75, "#")) test_str = "" target_str = "A" response = api.check(test_str, target_str) # Implicitly we're testing no unhandled exceptions. self.assertTrue("error" in response, 'Expected "error" in response!') print(" PASS ".center(75, "#")) def test_unsafe_input(self): print("\n\n\n" + " Test if Unsafe Input Detected ".center(75, "#")) test_str = "().__class__.__blah__" target_str = "A" response = api.check(test_str, target_str) # Implicitly we're testing no unhandled exceptions. self.assertTrue("error" in response, 'Expected "error" in response!') print(" PASS ".center(75, "#")) def test_invalid_target(self): print("\n\n\n" + " Test if Invalid Target Detected ".center(75, "#")) test_str = "A & B" target_str = "A & B & " response = api.check(test_str, target_str) # Implicitly we're testing no unhandled exceptions. self.assertTrue("error" in response, 'Expected "error" in response!') self.assertTrue("code" in response, 'Expected "code" in response!') self.assertTrue(response["code"] == 400, 'Expected error "code" 400 in response, got "{}"!'.format(response["code"])) print(" PASS ".center(75, "#")) def test_invalid_test_str(self): print("\n\n\n" + " Test if Invalid Test Str Detected ".center(75, "#")) test_str = "A & B & " target_str = "A & B" response = api.check(test_str, target_str) # Implicitly we're testing no unhandled exceptions. self.assertTrue("error" in response, 'Expected "error" in response!') self.assertTrue("code" not in response, 'Expected "code" in response!') print(" PASS ".center(75, "#")) def test_missing_symbols(self): print("\n\n\n" + " Test if Missing Symbols Detected ".center(75, "#")) test_str = "A" target_str = "B" response = api.check(test_str, target_str, check_symbols=True) self.assertTrue("error" not in response, 'Unexpected "error" in response!') self.assertTrue("equal" in response, 'Key "equal" not in response!') self.assertTrue(response["equal"] == "false", 'Expected "equal" to be "false", got "{}"!'.format(response["equal"])) self.assertTrue("equality_type" in response, 'Key "equality_type" not in response!') self.assertTrue(response["equality_type"] in EQUALITY_TYPES, 'Unexpected "equality_type": "{}"!'.format(response["equality_type"])) self.assertTrue(response["equality_type"] == "symbolic", 'For these expressions, expected "equality_type" to be "exact", got "{}"!'.format(response["equality_type"])) self.assertTrue("incorrect_symbols" in response, 'Key "incorrect_symbols" not in response!') print(" PASS ".center(75, "#")) if __name__ == '__main__': unittest.main()

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8

c9c392711b545cbe910529a6d15bda6e0c7e11fb

205

py

Python

pyreactors/__init__.py

michelemontuschi/pyreactors

1b1f7edccb2ca7f9b1281385dbc9017d3791510d

[ "MIT" ]

null

pyreactors/__init__.py

michelemontuschi/pyreactors

1b1f7edccb2ca7f9b1281385dbc9017d3791510d

[ "MIT" ]

null

pyreactors/__init__.py

michelemontuschi/pyreactors

1b1f7edccb2ca7f9b1281385dbc9017d3791510d

[ "MIT" ]

null

from .distance import __init__ from .fuels import __init__ from .mueller import __init__ from .oscillations import __init__ from .spectra import * from .xs import __init__ from .resolution import __init__

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4ea7e5a1000b820f87dcc8a66910b7bca3257cbb

8,466

py

Python

config.py

vanurag/draw

6d24e6485c7688c94affed11183ea6f3df8418d3

[ "Apache-2.0" ]

null

config.py

vanurag/draw

6d24e6485c7688c94affed11183ea6f3df8418d3

[ "Apache-2.0" ]

null

config.py

vanurag/draw

6d24e6485c7688c94affed11183ea6f3df8418d3

[ "Apache-2.0" ]

null

train_config = {} # # MODEL PARAMETERS ## # # MNIST train_config['A'] = 28 # image width train_config['B'] = 28 # image height train_config['img_size'] = train_config['B'] * train_config['A'] # the canvas size train_config['draw_with_white'] = True # draw with white ink or black ink train_config['enc_rnn_mode'] = 'BASIC' # The low level implementation of lstm cell. choose between "BASIC", "BLOCK" and "GRU" train_config['enc_size'] = 256 # number of hidden units / output size in LSTM layer train_config['n_enc_layers'] = 1 # number of layers in encoder LSTM train_config['dec_rnn_mode'] = 'BASIC' # The low level implementation of lstm cell. choose between "BASIC", "BLOCK" and "GRU" train_config['dec_size'] = 256 train_config['n_dec_layers'] = 1 # number of layers in decoder LSTM train_config['n_hidden_units'] = 0 # number of hidden units on the linear layer train_config['z_size'] = 100 # QSampler output size train_config['T'] = 64 # MNIST generation sequence length train_config['read_n'] = 3 # read glimpse grid width/height train_config['write_n'] = 1 # write glimpse grid width/height train_config['write_radius'] = 3 train_config['read_attn'] = True train_config['read_size'] = 2 * train_config['read_n'] * train_config['read_n'] if train_config['read_attn'] else 2 * train_config['img_size'] train_config['write_attn'] = True train_config['write_size'] = train_config['write_n'] * train_config['write_n'] if train_config['write_attn'] else train_config['img_size'] train_config['batch_size'] = 100 # training minibatch size train_config['n_summary_per_batch'] = 10 train_config['n_epochs'] = 50 # number of times the entire dataset is processed during training train_config['save_checkpoints_every_epoch'] = 1 # save chpnt after atleast these many epochs # HOTSTART: Initialize canvas as cropped input image train_config['use_hot_start'] = False train_config['crop_fraction_increase_rate'] = 0.2 # rate at which cropped part is grown each epoch train_config['eps'] = 1e-8 # epsilon for numerical stability train_config['annealing_schedules'] = { # decay type: ['fixed', 'linear' or 'exponential'] "learning_rate": { "init": 1e-4, "min": 1e-7, "decay_type": "fixed", "factor": 0.1, "iters": 5000, "staircase": True }, # "write_decision_prior_log_odds": { # "init":2.71 ** -2, "min": 0.000000001, # "decay_type": "fixed", "factor": 0.1, "iters": 1000, # "staircase": False, "log": True # }, "write_decision_prior_log_odds": { "init":-2.0, "min":-9.0, "decay_type": "fixed", "factor":-0.25, "iters": 1000, "staircase": False }, "stop_writing_threshold": { "init": 25.0, "min": 15.0, "decay_type": "fixed", "factor": 0.2, "iters": 3000, "staircase": True }, } # # ETH # train_config['A'] = 32 # image width # train_config['B'] = 32 # image height # train_config['img_size'] = train_config['B'] * train_config['A'] # the canvas size # train_config['draw_with_white'] = False # draw with white ink or black ink # # train_config['enc_rnn_mode'] = 'BASIC' # The low level implementation of lstm cell. choose between "BASIC", "BLOCK" and "GRU" # train_config['enc_size'] = 400 # number of hidden units / output size in LSTM layer # train_config['n_enc_layers'] = 1 # number of layers in encoder LSTM # train_config['dec_rnn_mode'] = 'BASIC' # The low level implementation of lstm cell. choose between "BASIC", "BLOCK" and "GRU" # train_config['dec_size'] = 400 # train_config['n_dec_layers'] = 1 # number of layers in decoder LSTM # train_config['n_hidden_units'] = 64 # number of hidden units on the linear layer # train_config['z_size'] = 200 # QSampler output size # train_config['T'] = 100 # MNIST generation sequence length # # train_config['read_n'] = int(0.2 * max(train_config['A'], train_config['B'])) # read glimpse grid width/height # train_config['write_n'] = 1 # write glimpse grid width/height # train_config['write_radius'] = 4 # 0.125 * max(train_config['A'], train_config['B']) # train_config['read_attn'] = True # train_config['read_size'] = 2 * train_config['read_n'] * train_config['read_n'] if train_config['read_attn'] else 2 * train_config['img_size'] # train_config['write_attn'] = True # train_config['write_size'] = train_config['write_n'] * train_config['write_n'] if train_config['write_attn'] else train_config['img_size'] # # train_config['batch_size'] = 100 # training minibatch size # train_config['n_summary_per_batch'] = 10 # train_config['n_epochs'] = 20 # number of times the entire dataset is processed during training # train_config['save_checkpoints_every_epoch'] = 1 # save chpnt after atleast these many epochs # train_config['use_hot_start'] = False # Initialize canvas as cropped input image # train_config['crop_fraction_increase_rate'] = 0.25 # rate at which cropped part is grown # train_config['epochs_per_crop_fraction'] = 2 # TODO: number of epochs for each crop fraction # train_config['eps'] = 1e-8 # epsilon for numerical stability # train_config['annealing_schedules'] = { # decay type: ['fixed', 'linear' or 'exponential'] # "learning_rate": { # "init": 1e-4, "min": 1e-7, # "decay_type": "linear", "factor": 0.1, "iters": 5000, # "staircase": True # }, # "write_decision_prior_log_odds": { # "init": 10000.0, "min": 0.000000001, # "decay_type": "exponential", "factor": 0.1, "iters": 3000, # "staircase": False, "log": True # }, # "stop_writing_threshold": { # "init": 45.0, "min": 15.0, # "decay_type": "linear", "factor": 0.2, "iters": 3000, # "staircase": True # }, # } # # DEBUG # train_config['A'] = 4 # image width # train_config['B'] = 4 # image height # train_config['img_size'] = train_config['B'] * train_config['A'] # the canvas size # train_config['draw_with_white'] = False # draw with white ink or black ink # # train_config['enc_rnn_mode'] = 'BASIC' # The low level implementation of lstm cell. choose between "BASIC", "BLOCK" and "GRU" # train_config['enc_size'] = 5 # number of hidden units / output size in LSTM layer # train_config['n_enc_layers'] = 1 # number of layers in encoder LSTM # train_config['dec_rnn_mode'] = 'BASIC' # The low level implementation of lstm cell. choose between "BASIC", "BLOCK" and "GRU" # train_config['dec_size'] = 5 # train_config['n_dec_layers'] = 1 # number of layers in decoder LSTM # train_config['z_size'] = 5 # QSampler output size # train_config['T'] = 5 # MNIST generation sequence length # # train_config['read_n'] = 3 # read glimpse grid width/height # train_config['write_n'] = 1 # write glimpse grid width/height # train_config['write_radius'] = 2 # 0.125 * max(train_config['A'], train_config['B']) # train_config['read_attn'] = True # train_config['read_size'] = 2 * train_config['read_n'] * train_config['read_n'] if train_config['read_attn'] else 2 * train_config['img_size'] # train_config['write_attn'] = True # train_config['write_size'] = train_config['write_n'] * train_config['write_n'] if train_config['write_attn'] else train_config['img_size'] # # train_config['batch_size'] = 100 # training minibatch size # train_config['n_summary_per_batch'] = 2 # train_config['n_epochs'] = 2 # number of times the entire dataset is processed during training # train_config['save_checkpoints_every_epoch'] = 300 # save chpnt after atleast these many epochs # train_config['learning_rate'] = 1e-4 # learning rate for optimizer # train_config['learning_rate_type'] = 'fixed' # ['fixed', 'exponential', 'linear'] # train_config['learning_rate_decay_steps'] = 5000 # train_config['learning_rate_decay_rate'] = 0.1 # HOTSTART: Initialize canvas as cropped input image # train_config['use_hot_start'] = True # train_config['crop_fraction_increase_rate'] = 0.5 # rate at which cropped part is grown each epoch # train_config['eps'] = 1e-8 # epsilon for numerical stability # train_config['stop_writing_threshold'] = 10.0 # higher the threshold, longer the network is allowed to draw test_config = train_config.copy() test_config['model_dir'] = '/media/anurag/DATA-EXT/texture-synthesis/texture-datasets/ETH_Synthesizability/logs/DRAW_1556207845/' # TODO path to the model that you want to evaluate test_config['checkpoint_id'] = None # if None, the last checkpoint will be used # For layer-wise painting of a texture texture_config = test_config.copy() texture_config['batch_size'] = 1 texture_config['n_summary_per_batch'] = 1

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null

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7

4edb87e5d87a0dd4f6ebb916ca90b6d2ac83cb13

98

py

Python

jiminy/gym/envs/board_game/__init__.py

sibeshkar/jiminy

7754f86fb0f246e7d039ea0cbfd9950fcae4adfb

[ "MIT" ]

3

2020-03-16T13:50:40.000Z

2021-06-09T05:26:13.000Z

jiminy/gym/envs/board_game/__init__.py

sibeshkar/jiminy

7754f86fb0f246e7d039ea0cbfd9950fcae4adfb

[ "MIT" ]

null

jiminy/gym/envs/board_game/__init__.py

sibeshkar/jiminy

7754f86fb0f246e7d039ea0cbfd9950fcae4adfb

[ "MIT" ]

null

from jiminy.gym.envs.board_game.go import GoEnv from jiminy.gym.envs.board_game.hex import HexEnv

32.666667

49

0.836735

18

98

4.444444

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0.25

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1

0

1

0

8

14d24e842e755e56f428570e8199ba183dd062f0

2,714

py

Python

Other files (test scripts etc.)/res_img32.py

dg1223/InsectRecognition-UniversityOfGuelph_SemiosBio-Project

597ae45e04895984669b001129871ebed1a3178c

[ "MIT" ]

2

2015-10-18T04:30:22.000Z

2017-01-03T15:24:27.000Z

Other files (test scripts etc.)/res_img32.py

dg1223/InsectRecognition-UniversityOfGuelph_SemiosBio-Project

597ae45e04895984669b001129871ebed1a3178c

[ "MIT" ]

null

Other files (test scripts etc.)/res_img32.py

dg1223/InsectRecognition-UniversityOfGuelph_SemiosBio-Project

597ae45e04895984669b001129871ebed1a3178c

[ "MIT" ]

null

In [7]: total_matches Out[7]: array([[6, 6, 6, 5, 4, 1, 0, 0, 0, 0, 6, 6, 6, 5, 4, 1, 0, 0, 0, 0, 6, 6, 6, 5, 4, 1, 0, 0, 0, 0, 6, 6, 6, 5, 4, 1, 0, 0, 0, 0, 6, 6, 6, 5, 4, 1, 0, 0, 0, 0, 6, 6, 6, 5, 4, 1, 0, 0, 0, 0, 3, 3, 3, 3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]) In [8]: total_FPs Out[8]: array([[44, 35, 31, 27, 21, 19, 18, 15, 12, 0, 44, 35, 31, 27, 21, 19, 18, 15, 12, 0, 44, 35, 31, 27, 21, 19, 18, 15, 12, 0, 44, 35, 31, 27, 21, 19, 18, 15, 12, 0, 44, 35, 31, 27, 21, 19, 18, 15, 12, 0, 44, 35, 31, 27, 21, 19, 18, 15, 12, 0, 47, 38, 34, 29, 23, 20, 18, 15, 12, 0, 50, 41, 37, 32, 25, 20, 18, 15, 12, 0, 50, 41, 37, 32, 25, 20, 18, 15, 12, 0, 50, 41, 37, 32, 25, 20, 18, 15, 12, 0]]) In [9]: total_FNs Out[9]: array([[0, 0, 0, 1, 2, 5, 6, 6, 6, 6, 0, 0, 0, 1, 2, 5, 6, 6, 6, 6, 0, 0, 0, 1, 2, 5, 6, 6, 6, 6, 0, 0, 0, 1, 2, 5, 6, 6, 6, 6, 0, 0, 0, 1, 2, 5, 6, 6, 6, 6, 0, 0, 0, 1, 2, 5, 6, 6, 6, 6, 3, 3, 3, 3, 4, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6]]) In [10]: miss_rates Out[10]: array([[ 0. , 0. , 0. , 0.16666667, 0.33333333, 0.83333333, 1. , 1. , 1. , 1. , 0. , 0. , 0. , 0.16666667, 0.33333333, 0.83333333, 1. , 1. , 1. , 1. , 0. , 0. , 0. , 0.16666667, 0.33333333, 0.83333333, 1. , 1. , 1. , 1. , 0. , 0. , 0. , 0.16666667, 0.33333333, 0.83333333, 1. , 1. , 1. , 1. , 0. , 0. , 0. , 0.16666667, 0.33333333, 0.83333333, 1. , 1. , 1. , 1. , 0. , 0. , 0. , 0.16666667, 0.33333333, 0.83333333, 1. , 1. , 1. , 1. , 0.5 , 0.5 , 0.5 , 0.5 , 0.66666667, 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. ]])

56.541667

75

0.271555

451

2,714

1.625277

0.097561

0.223738

0.257844

0.240109

0.843111

0.832196

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0.5121

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null

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1

0

1

0

null

0

1

0

14

14f736c4dc5f110e6b22604fdbe2657bd8504821

102

py

Python

utils.py

sububer/maze-game

e3b359bcd3ce449046a2de7db4e5b036c26deee4

[ "MIT" ]

null

utils.py

sububer/maze-game

e3b359bcd3ce449046a2de7db4e5b036c26deee4

[ "MIT" ]

null

utils.py

sububer/maze-game

e3b359bcd3ce449046a2de7db4e5b036c26deee4

[ "MIT" ]

null

import config as cfg import pygame def get_maze_dimensions(): return cfg.MAZE_ROWS, cfg.MAZE_COLS

20.4

39

0.794118

17

102

4.529412

0.705882

0.181818

0

0.147059

102

5

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20.4

0.885057

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1

0

null

0

1

0

1

0

7

11c9a2568aeb09da0c3a5890f4a86b77766595b2

92

py

Python

kwat/information/get_icd.py

KwatME/ccal

d96dfa811482eee067f346386a2181ec514625f4

[ "MIT" ]

5

2017-05-05T17:50:28.000Z

2019-01-30T19:23:02.000Z

kwat/information/get_icd.py

KwatME/ccal

d96dfa811482eee067f346386a2181ec514625f4

[ "MIT" ]

5

2017-05-05T01:52:31.000Z

2019-04-20T21:06:05.000Z

kwat/information/get_icd.py

KwatME/ccal

d96dfa811482eee067f346386a2181ec514625f4

[ "MIT" ]

5

2017-07-17T18:55:54.000Z

2019-02-02T04:46:19.000Z

from .get_ic import get_ic def get_icd(ve1, ve2): return (-get_ic(ve1, ve2) + 1) / 2

13.142857

38

0.630435

18

92

3

0.611111

0.277778

0

0.084507

0.228261

92

6

39

15.333333

0.676056

0

1

0.333333

false

0

0.333333

1

0

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null

1

0

1

0

null

0

1

0

1

0

7

11def3b953bd987aef044420c13ff0b56f40f81e

1,314

py

Python

Networking1222_B/loadConfig.py

yifei970327/Implement-the-network

4e7146597943ba1fda9b039a55286e6300c7d45b

[ "MIT" ]

1

2018-05-03T15:44:21.000Z

Networking1222_B/loadConfig.py

yifei970327/Implement-the-network

4e7146597943ba1fda9b039a55286e6300c7d45b

[ "MIT" ]

null

Networking1222_B/loadConfig.py

yifei970327/Implement-the-network

4e7146597943ba1fda9b039a55286e6300c7d45b

[ "MIT" ]

null

import json # 在其他.py文件中： # import loadJson # a = loadJson.loadTestIP def loadTestIP(): f = open("config.json", encoding='utf-8') setting = json.load(f) return setting["test"]["ip"] def loadTestPort(): f = open("config.json", encoding='utf-8') setting = json.load(f) return setting["test"]["port"] def loadTest(): f = open("config.json", encoding='utf-8') setting = json.load(f) return setting["test"]["ip"],setting["test"]["port"] def loadLocalHostIP(): f = open("config.json", encoding='utf-8') setting = json.load(f) return setting["srcIP"] def loadDstIP(): f = open("config.json", encoding='utf-8') setting = json.load(f) return setting["dstIP"] def loadPort(): f = open("config.json", encoding='utf-8') setting = json.load(f) return setting["port"] def loadloss(): f = open("config.json", encoding='utf-8') setting = json.load(f) return setting["loss"] def loadDevice(): f = open("config.json", encoding='utf-8') setting = json.load(f) return setting["device"] def loadAll(): f = open("config.json", encoding='utf-8') setting = json.load(f) return setting["test"]["ip"],setting["test"]["port"],setting["port"],setting["loss"],setting["device"]

26.28

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0.596651

169

1,314

4.639053

0.189349

0.057398

0.126276

0.172194

0.709184

0

0.008671

0.210046

1,314

49

108

26.816327

0.746628

0.038052

0

0.486486

0

0.184971

0

1

0.243243

false

0

0.027027

0

0.513514

0

null

0

1

0

1

0

null

0

1

0

1

0

8

ee9cd1af2f1d9fdb3ff9f00172d3aa14e0849217

11,355

py

Python

Chennai_Floods_code/24hrs_all.py

tunir27/ICDCN-2019

98a976d7d4c0b50f67c1bb95a67f3125168a6107

[ "MIT" ]

null

Chennai_Floods_code/24hrs_all.py

tunir27/ICDCN-2019

98a976d7d4c0b50f67c1bb95a67f3125168a6107

[ "MIT" ]

null

Chennai_Floods_code/24hrs_all.py

tunir27/ICDCN-2019

98a976d7d4c0b50f67c1bb95a67f3125168a6107

[ "MIT" ]

null

import codecs import numpy as np import matplotlib.pyplot as plt import re import ast ##content=codecs.open('Cluster2 Tweets.txt',"r",encoding="utf-8") ##regex = r"\w*crocodile\w*" ##c1=c2=c3=c4=c5=c6=c7=c8=c9=c10=c11=c12=0 ##for i in content: ## matches = re.finditer(regex,i) ## ## for match in matches: ## if match: ## #print(i) ## if i.count('2015-12-01'): ## x = ast.literal_eval(i) ## temp=re.findall('2015-12-01 00|2015-12-01 01|2015-12-01 02|2015-12-01 03',x[3]) ## if temp: ## c1+=1 ## temp=re.findall('2015-12-01 04|2015-12-01 05|2015-12-01 06|2015-12-01 07',x[3]) ## if temp: ## c2+=1 ## temp=re.findall('2015-12-01 08|2015-12-01 09|2015-12-01 10|2015-12-01 11',x[3]) ## if temp: ## c3+=1 ## temp=re.findall('2015-12-01 12|2015-12-01 13|2015-12-01 14|2015-12-01 15',x[3]) ## if temp: ## c4+=1 ## temp=re.findall('2015-12-01 16|2015-12-01 17|2015-12-01 18|2015-12-01 19',x[3]) ## if temp: ## c5+=1 ## temp=re.findall('2015-12-01 20|2015-12-01 21|2015-12-01 22|2015-12-01 23',x[3]) ## if temp: ## c6+=1 ## if i.count('2015-12-02'): ## x = ast.literal_eval(i) ## temp=re.findall('2015-12-02 00|2015-12-02 01|2015-12-02 02|2015-12-02 03',x[3]) ## if temp: ## c7+=1 ## temp=re.findall('2015-12-02 04|2015-12-02 05|2015-12-02 06|2015-12-02 07',x[3]) ## if temp: ## c8+=1 ## temp=re.findall('2015-12-02 08|2015-12-02 09|2015-12-02 10|2015-12-02 11',x[3]) ## if temp: ## c9+=1 ## temp=re.findall('2015-12-02 12|2015-12-02 13|2015-12-02 14|2015-12-02 15',x[3]) ## if temp: ## c10+=1 ## temp=re.findall('2015-12-02 16|2015-12-02 17|2015-12-02 18|2015-12-02 19',x[3]) ## if temp: ## c11+=1 ## temp=re.findall('2015-12-02 20|2015-12-02 21|2015-12-02 22|2015-12-02 23',x[3]) ## if temp: ## c12+=1 ## ##print(c1) ##print(c2) ##print(c3) ##print(c4) ##print(c5) ##print(c6) ##print(c7) ##print(c8) ##print(c9) ##print(c10) ##print(c11) ##print(c12) ##print("==============================================================") ## ##fig, ax=plt.subplots() ##index=np.arange(13) ##bar_width=0.15 ##opacity=0.9 ## ##belief0=(c1,c2,c3,c4,c5,c6,c7,c8,c9,c10,c11,c12) ##y0=[1,2,3,4,5,6,7,8,9,10,11,12] ## ## ##content=codecs.open('Cluster2 Tweets.txt',"r",encoding="utf-8") ##regex = r"\w*chembarambakkam\w*" ##c1=c2=c3=c4=c5=c6=c7=c8=c9=c10=c11=c12=0 ##for i in content: ## matches = re.finditer(regex,i) ## ## for match in matches: ## if match: ## #print(i) ## if i.count('2015-12-01'): ## x = ast.literal_eval(i) ## temp=re.findall('2015-12-01 00|2015-12-01 01|2015-12-01 02|2015-12-01 03',x[3]) ## if temp: ## c1+=1 ## temp=re.findall('2015-12-01 04|2015-12-01 05|2015-12-01 06|2015-12-01 07',x[3]) ## if temp: ## c2+=1 ## temp=re.findall('2015-12-01 08|2015-12-01 09|2015-12-01 10|2015-12-01 11',x[3]) ## if temp: ## c3+=1 ## temp=re.findall('2015-12-01 12|2015-12-01 13|2015-12-01 14|2015-12-01 15',x[3]) ## if temp: ## c4+=1 ## temp=re.findall('2015-12-01 16|2015-12-01 17|2015-12-01 18|2015-12-01 19',x[3]) ## if temp: ## c5+=1 ## temp=re.findall('2015-12-01 20|2015-12-01 21|2015-12-01 22|2015-12-01 23',x[3]) ## if temp: ## c6+=1 ## if i.count('2015-12-02'): ## x = ast.literal_eval(i) ## temp=re.findall('2015-12-02 00|2015-12-02 01|2015-12-02 02|2015-12-02 03',x[3]) ## if temp: ## c7+=1 ## temp=re.findall('2015-12-02 04|2015-12-02 05|2015-12-02 06|2015-12-02 07',x[3]) ## if temp: ## c8+=1 ## temp=re.findall('2015-12-02 08|2015-12-02 09|2015-12-02 10|2015-12-02 11',x[3]) ## if temp: ## c9+=1 ## temp=re.findall('2015-12-02 12|2015-12-02 13|2015-12-02 14|2015-12-02 15',x[3]) ## if temp: ## c10+=1 ## temp=re.findall('2015-12-02 16|2015-12-02 17|2015-12-02 18|2015-12-02 19',x[3]) ## if temp: ## c11+=1 ## temp=re.findall('2015-12-02 20|2015-12-02 21|2015-12-02 22|2015-12-02 23',x[3]) ## if temp: ## c12+=1 ## ##print(c1) ##print(c2) ##print(c3) ##print(c4) ##print(c5) ##print(c6) ##print(c7) ##print(c8) ##print(c9) ##print(c10) ##print(c11) ##print(c12) ##print("==============================================================") ## ##fig, ax=plt.subplots() ##index=np.arange(13) ##bar_width=0.15 ##opacity=0.9 ## ##belief1=(c1,c2,c3,c4,c5,c6,c7,c8,c9,c10,c11,c12) ##y1=[1,2,3,4,5,6,7,8,9,10,11,12] ##plt.plot(y,belief) ##plt.xlabel('Time in hrs') ##plt.ylabel('No. of tweets') ##plt.xticks(index+(6.6*bar_width),('0-3','4-7','8-11','12-15','16-19','20-23','0-3','4-7','8-11','12-15','16-19','20-23')) ##plt.plot(y,belief,'bo',linestyle='-') content=codecs.open('Cluster3 Tweets.txt',"r",encoding="utf-8") regex = r"\w*little girl\w*" c1=c2=c3=c4=c5=c6=c7=c8=c9=c10=c11=c12=0 for i in content: matches = re.finditer(regex,i) for match in matches: if match: #print(i) if i.count('2015-12-04'): x = ast.literal_eval(i) temp=re.findall('2015-12-04 00|2015-12-04 01|2015-12-04 02|2015-12-04 03',x[3]) if temp: c1+=1 temp=re.findall('2015-12-04 04|2015-12-04 05|2015-12-04 06|2015-12-04 07',x[3]) if temp: c2+=1 temp=re.findall('2015-12-04 08|2015-12-04 09|2015-12-04 10|2015-12-04 11',x[3]) if temp: c3+=1 temp=re.findall('2015-12-04 12|2015-12-04 13|2015-12-04 14|2015-12-04 15',x[3]) if temp: c4+=1 temp=re.findall('2015-12-04 16|2015-12-04 17|2015-12-04 18|2015-12-04 19',x[3]) if temp: c5+=1 temp=re.findall('2015-12-04 20|2015-12-04 21|2015-12-04 22|2015-12-04 23',x[3]) if temp: c6+=1 if i.count('2015-12-05'): x = ast.literal_eval(i) temp=re.findall('2015-12-05 00|2015-12-05 01|2015-12-05 02|2015-12-05 03',x[3]) if temp: c7+=1 temp=re.findall('2015-12-05 04|2015-12-05 05|2015-12-05 06|2015-12-05 07',x[3]) if temp: c8+=1 temp=re.findall('2015-12-05 08|2015-12-05 09|2015-12-05 10|2015-12-05 11',x[3]) if temp: c9+=1 temp=re.findall('2015-12-05 12|2015-12-05 13|2015-12-05 14|2015-12-05 15',x[3]) if temp: c10+=1 temp=re.findall('2015-12-05 16|2015-12-05 17|2015-12-05 18|2015-12-05 19',x[3]) if temp: c11+=1 temp=re.findall('2015-12-05 20|2015-12-05 21|2015-12-05 22|2015-12-05 23',x[3]) if temp: c12+=1 print(c1) print(c2) print(c3) print(c4) print(c5) print(c6) print(c7) print(c8) print(c9) print(c10) print(c11) print(c12) print("==============================================================") fig, ax=plt.subplots() index=np.arange(13) bar_width=0.15 opacity=0.9 belief2=(c1,c2,c3,c4,c5,c6,c7,c8,c9,c10,c11,c12) y2=[1,2,3,4,5,6,7,8,9,10,11,12] ##plt.plot(y,belief) ##plt.xlabel('Time in hrs') ##plt.ylabel('No. of tweets') ##plt.xticks(index+(6.6*bar_width),('0-3','4-7','8-11','12-15','16-19','20-23','0-3','4-7','8-11','12-15','16-19','20-23')) ##plt.plot(y,belief,'bo',linestyle='-') content=codecs.open('Cluster5 Tweets.txt',"r",encoding="utf-8") regex = r"\w*passport\w*" c1=c2=c3=c4=c5=c6=c7=c8=c9=c10=c11=c12=0 for i in content: matches = re.finditer(regex,i) for match in matches: if match: #print(i) if i.count('2015-12-07'): x = ast.literal_eval(i) temp=re.findall('2015-12-07 00|2015-12-07 01|2015-12-07 02|2015-12-07 03',x[3]) if temp: c1+=1 temp=re.findall('2015-12-07 04|2015-12-07 05|2015-12-07 06|2015-12-07 07',x[3]) if temp: c2+=1 temp=re.findall('2015-12-07 08|2015-12-07 09|2015-12-07 10|2015-12-07 11',x[3]) if temp: c3+=1 temp=re.findall('2015-12-07 12|2015-12-07 13|2015-12-07 14|2015-12-07 15',x[3]) if temp: c4+=1 temp=re.findall('2015-12-07 16|2015-12-07 17|2015-12-07 18|2015-12-07 19',x[3]) if temp: c5+=1 temp=re.findall('2015-12-07 20|2015-12-07 21|2015-12-07 22|2015-12-07 23',x[3]) if temp: c6+=1 if i.count('2015-12-08'): x = ast.literal_eval(i) temp=re.findall('2015-12-08 00|2015-12-08 01|2015-12-08 02|2015-12-08 03',x[3]) if temp: c7+=1 temp=re.findall('2015-12-08 04|2015-12-08 05|2015-12-08 06|2015-12-08 07',x[3]) if temp: c8+=1 temp=re.findall('2015-12-08 08|2015-12-08 09|2015-12-08 10|2015-12-08 11',x[3]) if temp: c9+=1 temp=re.findall('2015-12-08 12|2015-12-08 13|2015-12-08 14|2015-12-08 15',x[3]) if temp: c10+=1 temp=re.findall('2015-12-08 16|2015-12-08 17|2015-12-08 18|2015-12-08 19',x[3]) if temp: c11+=1 temp=re.findall('2015-12-08 20|2015-12-08 21|2015-12-08 22|2015-12-08 23',x[3]) if temp: c12+=1 print(c1) print(c2) print(c3) print(c4) print(c5) print(c6) print(c7) print(c8) print(c9) print(c10) print(c11) print(c12) print("==============================================================") fig, ax=plt.subplots() index=np.arange(13) bar_width=0.15 opacity=0.9 belief3=(c1,c2,c3,c4,c5,c6,c7,c8,c9,c10,c11,c12) y3=[1,2,3,4,5,6,7,8,9,10,11,12] #plt.plot(y0,belief0,y1,belief1,y2,belief2,y3,belief3) plt.xlabel('Time in hrs') plt.ylabel('No. of tweets') plt.xticks(index+(6.6*bar_width),('0-3','4-7','8-11','12-15','16-19','20-23','0-3','4-7','8-11','12-15','16-19','20-23')) plt.plot(y2,belief2,'bo',linestyle='-',label='Little girl lost') plt.plot(y3,belief3,'yo',linestyle='-',label='Passport Damaged') plt.legend() plt.show()

36.047619

123

0.472655

1,924

11,355

2.781705

0.06341

0.224215

0.074738

0.152466

0.807362

0.805867

0.798393

0.787556

0

0.325369

0.307089

11,355

314

124

36.16242

0.354855

0.478644

0

0.666667

0

0.166667

0.29909

0.022115

0

1

0

false

0.013889

0.034722

0

0.034722

0.180556

0

null

1

0

1

0

1

0

1

0

null

0

9

eeb8d46402d8f912a47a501b919150266a3d016a

8,292

py

Python

errors/output.py

waveto/mr-ray-open

d1238752c40f5b2551dd2a3d5963b54bcc4f1fd4

[ "Apache-2.0" ]

2

2021-07-30T06:52:46.000Z

2021-07-30T14:37:50.000Z

errors/output.py

waveto/mr-ray-open

d1238752c40f5b2551dd2a3d5963b54bcc4f1fd4

[ "Apache-2.0" ]

null

errors/output.py

waveto/mr-ray-open

d1238752c40f5b2551dd2a3d5963b54bcc4f1fd4

[ "Apache-2.0" ]

null

''' Copyright 2011 Acknack 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 writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ''' ''' Collection of classes that write out error pages or error codes ''' import os import config from google.appengine.ext.webapp import template class AuthenticationDenied: def __init__(self, handler): ''' @param handler: the request handler for to return the error through ''' self.handler = handler def RenderPage(self, error_code): ''' Renders a page that informs the user the page could not be found or that validation failed @param error_code: the error code to add to the page ''' self.handler.response.clear() path = os.path.join(os.path.dirname(__file__), '../templates/denied.html') self.handler.response.out.write(template.render(path, {'robot_web': config.ROBOT_WEB, 'error_code': error_code})) self.handler.response.set_status(401) def ReturnResponse(self): ''' Returns the response code that indicates the user is not authorized to view this page ''' self.handler.response.clear() self.handler.response.set_status(401) class WaveRpcDownloadProblem: def __init__(self, handler): ''' @param handler: the request handler for to return the error through ''' self.handler = handler def RenderPage(self, error_code): ''' Renders a page that informs the user the page could not be found or that validation failed @param error_code: the error code to add to the page ''' self.handler.response.clear() path = os.path.join(os.path.dirname(__file__), '../templates/cantcontactwave.html') self.handler.response.out.write(template.render(path, {'robot_web': config.ROBOT_WEB, 'error_code': error_code})) self.handler.response.set_status(502) def ReturnResponse(self): ''' Returns the response code that indicates the user is not authorized to view this page ''' self.handler.response.clear() self.handler.response.set_status(502) class RayNotWaveParticipant: def __init__(self, handler): ''' @param handler: the request handler for to return the error through ''' self.handler = handler def RenderPage(self, error_code): ''' Renders a page that informs the user the page could not be found or that validation failed @param error_code: the error code to add to the page ''' self.handler.response.clear() path = os.path.join(os.path.dirname(__file__), '../templates/robotremoved.html') self.handler.response.out.write(template.render(path, {'robot_web': config.ROBOT_WEB, 'error_code': error_code})) self.handler.response.set_status(403) def ReturnResponse(self): ''' Returns the response code that indicates the user is not authorized to view this page ''' self.handler.response.clear() self.handler.response.set_status(403) class DeadlineExceeded: def __init__(self, handler): ''' @param handler: the request handler for to return the error through ''' self.handler = handler def RenderPage(self, error_code): ''' Renders a page that informs the user the page could not be found or that validation failed @param error_code: the error code to add to the page ''' self.handler.response.clear() path = os.path.join(os.path.dirname(__file__), '../templates/standarderror.html') self.handler.response.out.write(template.render(path, {'robot_web': config.ROBOT_WEB, 'error_code': error_code})) self.handler.response.set_status(503) def ReturnResponse(self): ''' Returns the response code that indicates the user is not authorized to view this page ''' self.handler.response.clear() self.handler.response.set_status(503) class UnknownProblem: def __init__(self, handler): ''' @param handler: the request handler for to return the error through ''' self.handler = handler def RenderPage(self, error_code): ''' Renders a page that informs the user the page could not be found or that validation failed @param error_code: the error code to add to the page ''' self.handler.response.clear() path = os.path.join(os.path.dirname(__file__), '../templates/standarderror.html') self.handler.response.out.write(template.render(path, {'robot_web': config.ROBOT_WEB, 'error_code': error_code})) self.handler.response.set_status(500) def ReturnResponse(self): ''' Returns the response code that indicates the user is not authorized to view this page ''' self.handler.response.clear() self.handler.response.set_status(500) class JsonDecodingProblem: def __init__(self, handler): ''' @param handler: the request handler for to return the error through ''' self.handler = handler def ReturnResponse(self): ''' Returns the response code that indicates the user is not authorized to view this page ''' self.handler.response.clear() self.handler.response.set_status(400) class InadequatePermission: def __init__(self, handler): ''' @param handler: the request handler for to return the error through ''' self.handler = handler def RenderPage(self, error_code): ''' Renders a page that informs the user the page could not be found or that validation failed @param error_code: the error code to add to the page ''' self.handler.response.clear() path = os.path.join(os.path.dirname(__file__), '../templates/inadequatepermission.html') self.handler.response.out.write(template.render(path, {'robot_web': config.ROBOT_WEB, 'error_code': error_code})) self.handler.response.set_status(401) def ReturnResponse(self): ''' Returns the response code that indicates the user is not authorized to view this page ''' self.handler.response.clear() self.handler.response.set_status(401) class UserDeleted: def __init__(self, handler): ''' @param handler: the request handler for to return the error through ''' self.handler = handler def RenderPage(self, error_code): ''' Renders a page that informs the user the page could not be found or that validation failed @param error_code: the error code to add to the page ''' self.handler.response.clear() path = os.path.join(os.path.dirname(__file__), '../templates/userdeleted.html') self.handler.response.out.write(template.render(path, {'robot_web': config.ROBOT_WEB, 'error_code': error_code})) self.handler.response.set_status(403) def ReturnResponse(self): ''' Returns the response code that indicates the user is not authorized to view this page ''' self.handler.response.clear() self.handler.response.set_status(403) class MalformedRequest: def __init__(self, handler): ''' @param handler: the request handler for to return the error through ''' self.handler = handler def ReturnResponse(self): ''' Returns the response code that indicates the user is not authorized to view this page ''' self.handler.response.clear() self.handler.response.set_status(400)

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null

0

1

0

8

eebae74c74e3677c6a43bd299eb5f6793f296e4f

1,056

py

Python

onlineassessmentsystem/users/decorators.py

nevilparmar11/SDP_Online_Assessment_System

012a1ec7dfca2973a5e03b0f970394cfa674b61e

[ "MIT" ]

2

2021-05-22T15:44:19.000Z

2021-05-22T17:59:58.000Z

onlineassessmentsystem/users/decorators.py

jwalit21/SDP_Online_Assessment_System

a778a0e0ae264fe74037a5f0b210d205ebc18d98

[ "MIT" ]

7

2021-01-18T06:06:38.000Z

2021-03-03T15:09:17.000Z

onlineassessmentsystem/users/decorators.py

jwalit21/SDP_Online_Assessment_System

a778a0e0ae264fe74037a5f0b210d205ebc18d98

[ "MIT" ]

2

2021-03-05T12:28:28.000Z

2021-05-24T16:10:07.000Z

from django.contrib.auth import REDIRECT_FIELD_NAME from django.contrib.auth.decorators import user_passes_test def student_required(function = None, redirect_field_name = REDIRECT_FIELD_NAME, login_url = '/users/login'): ''' Custom Decorator that is used to authorise the user as Student. ''' actual_decorator = user_passes_test( lambda user : user.is_active and user.isStudent, login_url = login_url, redirect_field_name = redirect_field_name ) if function: return actual_decorator(function) return actual_decorator def faculty_required(function = None, redirect_field_name = REDIRECT_FIELD_NAME, login_url = '/users/accessDenied'): ''' Custom Decorator that is used to authorise the user as Student. ''' actual_decorator = user_passes_test( lambda user : user.is_active and not user.isStudent, login_url = login_url, redirect_field_name = redirect_field_name ) if function: return actual_decorator(function) return actual_decorator

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8

eedcff04cf9371299dec4277c05a4f0b22460b0f

150

py

Python

tests/testformatter.py

cristoper/wav2vec

22a3fd9ce04e352c171a79cc5d59c69a56243d65

[ "WTFPL" ]

82

2017-08-27T21:26:48.000Z

2022-02-25T02:22:40.000Z

tests/testformatter.py

cristoper/wav2vec

22a3fd9ce04e352c171a79cc5d59c69a56243d65

[ "WTFPL" ]

2

2019-11-20T22:56:45.000Z

2021-12-17T02:15:40.000Z

tests/testformatter.py

cristoper/wav2vec

22a3fd9ce04e352c171a79cc5d59c69a56243d65

[ "WTFPL" ]

9

2017-10-05T10:40:29.000Z

2022-02-01T18:26:56.000Z

from wav2vec.formatter import Formatter import unittest # TODO: maybe write some unit tests for Formatter. For now the validation tests # should do.

25

79

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150

5.454545

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7

010766114cd1e29bd444f21e1c571cccd0413bb4

78

py

Python

tests/sat/Models/example7.satelite.variable.elimination.cnf.SAT.test.py

bernardocuteri/wasp

05c8f961776dbdbf7afbf905ee00fc262eba51ad

[ "Apache-2.0" ]

19

2015-12-03T08:53:45.000Z

2022-03-31T02:09:43.000Z

tests/sat/Models/example7.satelite.variable.elimination.cnf.SAT.test.py

bernardocuteri/wasp

05c8f961776dbdbf7afbf905ee00fc262eba51ad

[ "Apache-2.0" ]

80

2017-11-25T07:57:32.000Z

2018-06-10T19:03:30.000Z

tests/sat/Models/example7.satelite.variable.elimination.cnf.SAT.test.py

bernardocuteri/wasp

05c8f961776dbdbf7afbf905ee00fc262eba51ad

[ "Apache-2.0" ]

6

2015-01-15T07:51:48.000Z

2020-06-18T14:47:48.000Z

input = """ p cnf 3 4 -1 2 3 0 1 -2 0 1 -3 0 -1 -2 0 """ output = """ SAT """

7.090909

12

0.423077

20

78

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0.5

0.181818

0.242424

0.30303

0

0.288462

0.333333

78

10

13

7.8

0.346154

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1

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1

0

1

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null

0

7

0123d3b9de267277f35a527b16848f9fbd54315e

222

py

Python

gluoncv/model_zoo/cascade_rcnn/__init__.py

zzdang/cascade_rcnn_gluon

b4018001719ec56a688be26c2aab18be664e4bdd

[ "Apache-2.0" ]

41

2018-07-13T06:19:24.000Z

2021-11-11T14:29:16.000Z

gluoncv/model_zoo/cascade_rcnn/__init__.py

zzdang/cascade_rcnn_gluon

b4018001719ec56a688be26c2aab18be664e4bdd

[ "Apache-2.0" ]

9

2018-08-14T09:13:32.000Z

2019-11-09T08:32:01.000Z

gluoncv/model_zoo/cascade_rcnn/__init__.py

zzdang/cascade_rcnn_gluon

b4018001719ec56a688be26c2aab18be664e4bdd

[ "Apache-2.0" ]

8

2018-08-13T05:10:40.000Z

2020-12-02T04:53:24.000Z

# pylint: disable=wildcard-import """Faster-RCNN Object Detection.""" from __future__ import absolute_import from .cascade_rcnn import * from .cascade_rfcn import * from .resnet50_v2a import * from .vgg16_pruned import *

24.666667

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5.724138

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0.204819

0

0.025641

0.121622

222

8

39

27.75

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0

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7

6d7173da3676a491bf6dbda01e56124a1089bb29

199

py

Python

applications/STORAGE/matmul/src/fake_block.py

srgrr/TFM

f6c302547fdfc9b1df3423cc4e927cd6bb83b345

[ "MIT" ]

6

2019-01-02T10:39:32.000Z

2019-05-22T14:01:41.000Z

applications/STORAGE/matmul/src/fake_block.py

srgrr/TFM

f6c302547fdfc9b1df3423cc4e927cd6bb83b345

[ "MIT" ]

null

applications/STORAGE/matmul/src/fake_block.py

srgrr/TFM

f6c302547fdfc9b1df3423cc4e927cd6bb83b345

[ "MIT" ]

null

class Block(object): def __init__(self, block): self.block = block def get_block(self): return self.block def set_block(self, new_block): self.block = new_block

19.9

35

0.623116

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0.241379

0

0.281407

199

9

36

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false

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1

0

null

0

1

0

1

0

7

6df7a44baccb265c7cdfea6ec9560f0a3b610b86

5,301

py

Python

01-presentation-example/06_pandas.py

ryansmccoy/201911-spreadsheets-to-dataframes

22b4a3393626a8df24e8f5a188b3407d20a6430f

[ "MIT" ]

28

2019-11-09T17:35:29.000Z

2021-07-28T06:21:03.000Z

01-presentation-example/06_pandas.py

ryansmccoy/201911-spreadsheets-to-dataframes

22b4a3393626a8df24e8f5a188b3407d20a6430f

[ "MIT" ]

1

2019-11-05T21:28:19.000Z

2019-11-05T21:44:36.000Z

01-presentation-example/06_pandas.py

ryansmccoy/201911-spreadsheets-to-dataframes

22b4a3393626a8df24e8f5a188b3407d20a6430f

[ "MIT" ]

7

2020-02-13T19:18:01.000Z

2021-09-12T12:36:24.000Z

import pandas as pd pd.set_option('display.float_format', lambda x: f'{x:.5f}') pd.set_option('display.max_columns', 100) pd.set_option('display.max_rows', 100) pd.set_option('display.width', 600) filename = r'data\WMT_US.csv' df = pd.read_csv(filename) # check the data types of each columns print(df.dtypes) """ Ticker object Company Name object Year End object Total Sales int64 Total Expenses int64 dtype: object """ # convert the date column to python date object # which makes it easier to work with df['Year End'] = pd.to_datetime(df['Year End']) # print(df.dtypes) """ Ticker object Company Name object Year End datetime64[ns] Total Sales int64 Total Expenses int64 dtype: object """ # calculate total profit df['Total Profit'] = df['Total Sales'] - df['Total Expenses'] # print(df) """ Ticker Company Name Year End Total Sales Total Expenses Total Profit 0 WMT US WAL-MART STORES INC 2014-12-31 476293988352 460271988736 16021999616 1 WMT US WAL-MART STORES INC 2013-12-31 469162000384 452163000320 16999000064 2 WMT US WAL-MART STORES INC 2012-12-31 446950014976 431251014656 15699000320 3 WMT US WAL-MART STORES INC 2011-12-31 421849006080 405460005888 16389000192 4 WMT US WAL-MART STORES INC 2010-12-31 408214011904 393879012352 14334999552 5 WMT US WAL-MART STORES INC 2009-12-31 405606989824 392206989312 13400000512 6 WMT US WAL-MART STORES INC 2008-12-31 378798997504 366067997696 12730999808 7 WMT US WAL-MART STORES INC 2007-12-31 348650012672 337366012928 11283999744 8 WMT US WAL-MART STORES INC 2006-12-31 312426987520 301195987968 11230999552 9 WMT US WAL-MART STORES INC 2005-12-31 287989006336 277722006528 10266999808 10 WMT US WAL-MART STORES INC 2004-12-31 256329007104 247275006976 9054000128 11 WMT US WAL-MART STORES INC 2003-12-31 229615992832 221660993024 7954999808 """ df['Profit Margin'] = (df['Total Profit'] / df['Total Sales']) * 100 # print(df) """ Ticker Company Name Year End Total Sales Total Expenses Total Profit Profit Margin 0 WMT US WAL-MART STORES INC 2014-12-31 476293988352 460271988736 16021999616 3.36389 1 WMT US WAL-MART STORES INC 2013-12-31 469162000384 452163000320 16999000064 3.62327 2 WMT US WAL-MART STORES INC 2012-12-31 446950014976 431251014656 15699000320 3.51247 3 WMT US WAL-MART STORES INC 2011-12-31 421849006080 405460005888 16389000192 3.88504 4 WMT US WAL-MART STORES INC 2010-12-31 408214011904 393879012352 14334999552 3.51164 5 WMT US WAL-MART STORES INC 2009-12-31 405606989824 392206989312 13400000512 3.30369 6 WMT US WAL-MART STORES INC 2008-12-31 378798997504 366067997696 12730999808 3.36089 7 WMT US WAL-MART STORES INC 2007-12-31 348650012672 337366012928 11283999744 3.23648 8 WMT US WAL-MART STORES INC 2006-12-31 312426987520 301195987968 11230999552 3.59476 9 WMT US WAL-MART STORES INC 2005-12-31 287989006336 277722006528 10266999808 3.56507 10 WMT US WAL-MART STORES INC 2004-12-31 256329007104 247275006976 9054000128 3.53218 11 WMT US WAL-MART STORES INC 2003-12-31 229615992832 221660993024 7954999808 3.46448 """ # percent change needs to be ascending dates df.sort_values("Year End", inplace=True) df['Sales Growth YoY %'] = df['Total Sales'].pct_change() * 100 # print(df) """ Ticker Company Name Year End Total Sales Total Expenses Total Profit Profit Margin Sales Growth YoY % 11 WMT US WAL-MART STORES INC 2003-12-31 229615992832 221660993024 7954999808 3.46448 nan 10 WMT US WAL-MART STORES INC 2004-12-31 256329007104 247275006976 9054000128 3.53218 11.63378 9 WMT US WAL-MART STORES INC 2005-12-31 287989006336 277722006528 10266999808 3.56507 12.35131 8 WMT US WAL-MART STORES INC 2006-12-31 312426987520 301195987968 11230999552 3.59476 8.48573 7 WMT US WAL-MART STORES INC 2007-12-31 348650012672 337366012928 11283999744 3.23648 11.59408 6 WMT US WAL-MART STORES INC 2008-12-31 378798997504 366067997696 12730999808 3.36089 8.64735 5 WMT US WAL-MART STORES INC 2009-12-31 405606989824 392206989312 13400000512 3.30369 7.07710 4 WMT US WAL-MART STORES INC 2010-12-31 408214011904 393879012352 14334999552 3.51164 0.64275 3 WMT US WAL-MART STORES INC 2011-12-31 421849006080 405460005888 16389000192 3.88504 3.34016 2 WMT US WAL-MART STORES INC 2012-12-31 446950014976 431251014656 15699000320 3.51247 5.95024 1 WMT US WAL-MART STORES INC 2013-12-31 469162000384 452163000320 16999000064 3.62327 4.96968 0 WMT US WAL-MART STORES INC 2014-12-31 476293988352 460271988736 16021999616 3.36389 1.52015 """ new_filename = filename.replace(".csv", "_pandas.csv") df.to_csv(new_filename)

51.466019

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8

0972b7e2faa68ce932bccba651381ced81652cff

19,067

py

Python

graphgen/lfr_generators.py

luanvcmartins/graphgen

4a2166934f17ebaff94683ccf94ea245782b3552

[ "MIT" ]

4

2019-05-29T11:59:27.000Z

2021-08-12T12:20:31.000Z

graphgen/lfr_generators.py

luanvcmartins/graphgen

4a2166934f17ebaff94683ccf94ea245782b3552

[ "MIT" ]

3

2018-05-15T11:54:34.000Z

2021-06-17T19:37:44.000Z

graphgen/lfr_generators.py

luanvcmartins/graphgen

4a2166934f17ebaff94683ccf94ea245782b3552

[ "MIT" ]

2

2019-09-22T14:49:58.000Z

2021-03-01T00:48:40.000Z

from graphgen.weighted_undirected_graph_generator import GenerateWeightedUndirectedGraph from graphgen.unweighted_undirected_graph_generator import GenerateUnweightedUndirectedGraph from graphgen.weighted_directed_graph_generator import GenerateWeightedDirectedGraph from graphgen.unweighted_directed_graph_generator import GenerateUnweightedDirectedGraph import networkx as nx import numpy as np import inspect DEFAULT_FLOAT = np.float32 DEFAULT_INT = np.int64 def weighted_undirected_lfr_graph(num_nodes, average_k, max_degree, mut, muw, com_size_min, com_size_max, seed, beta=1.5, tau=2.0, tau2=1.0, overlapping_nodes=0, overlap_membership=0, fixed_range=True, excess=False, defect=False, randomf=False, avg_clustering=0.0, edge_dtype=None, weight_dtype=None): """ Nodes start at 0 and are contiguous Return Ex2 numpy array, tuple of community memberships for each node, and a numpy array of weights corresponding to each edge in edge list (i.e the ith weight belongs to the ith edge) :param num_nodes: Number of nodes in the network (starts id at 0) :param average_k: average degree of the nodes :param max_degree: largest degree of the nodes :param mut: mixing parameter, fraction of bridges :param muw: weight mixing parameter :param beta: minus exponent for weight distribution :param com_size_min: smallest community size :param com_size_max: largest community size :param seed: for rng :param tau: minus exponent for degree sequence :param tau2: minus exponent for community size distribution :param overlapping_nodes: number of overlapping nodes :param overlap_membership: number of memberships of overlapping nodes :param fixed_range: If True, uses com_size_min/max, else distribution determines the range :param excess: - :param defect: - :param randomf: - :param avg_clustering: the average clustering coefficient :param edge_dtype: dtype of edge array. Default: DEFAULT_INT :param weight_dtype: dtype of weights. Default: DEFAULT_FLOAT :return: (Ex2 numpy array, tuple community memberships for each node, E numpy array) * Order of resulting Numpy array is: Ex2 with major axis as [0]=tail, [1]=head * Row major format, so [edge#][0]=tail, [edge#][1]=head """ if edge_dtype is None: edge_dtype = DEFAULT_INT if weight_dtype is None: weight_dtype = DEFAULT_FLOAT edge_array, community_memberships, weights = GenerateWeightedUndirectedGraph( num_nodes, average_k, max_degree, mut, muw, com_size_min, com_size_max, seed, tau, tau2, overlapping_nodes, overlap_membership, fixed_range, excess, defect, randomf, beta, avg_clustering) if edge_array.dtype != edge_dtype: edge_array = edge_array.astype(edge_dtype) if weights.dtype != weight_dtype: weights = weights.astype(weight_dtype) return edge_array, community_memberships, weights def weighted_undirected_lfr_as_nx(*args, **kwargs): """ Nodes start at 0 and are contiguous Calls weighted_undirected_lfr_graph and converts to a networkx graph :return: networkx graph """ edge_array, community_memberships, weights = weighted_undirected_lfr_graph(*args, **kwargs) nx_graph = nx.Graph() # Add nodes and attributes to graph nodes_and_memberships = [] for node, node_memberships in enumerate(community_memberships): attributes = {'communities': node_memberships} for com_level, membership in enumerate(node_memberships): attributes['com_level'] = membership nodes_and_memberships.append((node, attributes)) nx_graph.add_nodes_from(nodes_and_memberships) # Add edges to graph nx_graph.add_edges_from(edge_array) nx.set_edge_attributes(nx_graph, 'weight', {tuple(edge): weights[i] for i, edge in enumerate(edge_array)}) return nx_graph def weighted_undirected_lfr_as_adj(*args, **kwargs): """ Calls weighted_undirected_lfr_graph and converts to a numpy matrix :param transpose: transpose the matrix representation :return: NxN float32 numpy array, and community membership adj matrix: axis1 (minor) is tail, axis2 (major) is head or (transpose): axis1 is head, axis2 is tail """ graph_pars = {key: value for key, value in kwargs.items() if key in inspect.getfullargspec(weighted_undirected_lfr_graph).args} converter_pars = {key: value for key, value in kwargs.items() if key in inspect.getfullargspec(convert_weighted_to_numpy_matrix).args} edge_array, community_membership, weights = weighted_undirected_lfr_graph(*args, **graph_pars) return (convert_weighted_to_numpy_matrix(edge_array, weights=weights, **converter_pars), community_membership) def weighted_directed_lfr_graph(num_nodes, average_k, max_degree, mut, muw, com_size_min, com_size_max, seed, beta=1.5, tau=2.0, tau2=1.0, overlapping_nodes=0, overlap_membership=0, fixed_range=True, excess=False, defect=False, randomf=False, edge_dtype=None, weight_dtype=None): """ Nodes start at 0 and are contiguous Return Ex2 numpy array, tuple of community memberships for each node, and a numpy array of weights corresponding to each edge in edge list (i.e the ith weight belongs to the ith edge) :param num_nodes: Number of nodes in the network (starts id at 0) :param average_k: average degree of the nodes :param max_degree: largest degree of the nodes :param mut: mixing parameter, fraction of bridges :param muw: weight mixing parameter :param beta: minus exponent for weight distribution :param com_size_min: smallest community size :param com_size_max: largest community size :param seed: for rng :param tau: minus exponent for degree sequence :param tau2: minus exponent for community size distribution :param overlapping_nodes: number of overlapping nodes :param overlap_membership: number of memberships of overlapping nodes :param fixed_range: If True, uses com_size_min/max, else distribution determines the range :param excess: - :param defect: - :param randomf: - :param edge_dtype: dtype of edge array. Default: DEFAULT_INT :param weight_dtype: dtype of weights. Default: DEFAULT_FLOAT :return: (Ex2 numpy array, tuple community memberships for each node, E numpy array) * Order of resulting Numpy array is: Ex2 with major axis as [0]=tail, [1]=head * Row major format, so [edge#][0]=tail, [edge#][1]=head """ if edge_dtype is None: edge_dtype = DEFAULT_INT if weight_dtype is None: weight_dtype = DEFAULT_FLOAT edge_array, community_memberships, weights = GenerateWeightedDirectedGraph( num_nodes, average_k, max_degree, mut, muw, com_size_min, com_size_max, seed, tau, tau2, overlapping_nodes, overlap_membership, fixed_range, excess, defect, randomf, beta) if edge_array.dtype != edge_dtype: edge_array = edge_array.astype(edge_dtype) if weights.dtype != weight_dtype: weights = weights.astype(weight_dtype) return edge_array, community_memberships, weights def weighted_directed_lfr_as_nx(*args, **kwargs): """ Nodes start at 0 and are contiguous Calls weighted_directed_lfr_graph and converts to a networkx graph :return: networkx graph """ edge_array, community_memberships, weights = weighted_directed_lfr_graph(*args, **kwargs) nx_graph = nx.DiGraph() # Add nodes and attributes to graph nodes_and_memberships = [] for node, node_memberships in enumerate(community_memberships): attributes = {'communities': node_memberships} for com_level, membership in enumerate(node_memberships): attributes['com_level'] = membership nodes_and_memberships.append((node, attributes)) nx_graph.add_nodes_from(nodes_and_memberships) # Add edges to graph nx_graph.add_edges_from(edge_array) nx.set_edge_attributes(nx_graph, 'weight', {tuple(edge): weights[i] for i, edge in enumerate(edge_array)}) return nx_graph def weighted_directed_lfr_as_adj(*args, **kwargs): """ Calls weighted_directed_lfr_graph and converts to a numpy matrix :param transpose: transpose the matrix representation :return: NxN float32 numpy array, and community membership adj matrix: axis1 (minor) is tail, axis2 (major) is head or (transpose): axis1 is head, axis2 is tail """ graph_pars = {key: value for key, value in kwargs.items() if key in inspect.getfullargspec(weighted_directed_lfr_graph).args} converter_pars = {key: value for key, value in kwargs.items() if key in inspect.getfullargspec(convert_weighted_to_numpy_matrix).args} edge_array, community_membership, weights = weighted_directed_lfr_graph(*args, **graph_pars) return (convert_weighted_to_numpy_matrix(edge_array, weights=weights, **converter_pars), community_membership) def unweighted_undirected_lfr_graph(num_nodes, average_k, max_degree, mu, com_size_min, com_size_max, seed, tau=2.0, tau2=1.0, overlapping_nodes=0, overlap_membership=0, fixed_range=True, excess=False, defect=False, randomf=False, avg_clustering=0.0, edge_dtype=None): """ Nodes start at 0 and are contiguous Return Ex2 numpy array and tuple of community memberships for each node :param num_nodes: Number of nodes in the network (starts id at 0) :param average_k: average degree of the nodes :param max_degree: largest degree of the nodes :param mu: mixing parameter, fraction of bridges :param com_size_min: smallest community size :param com_size_max: largest community size :param seed: for rng :param tau: minus exponent for degree sequence :param tau2: minus exponent for community size distribution :param overlapping_nodes: number of overlapping nodes :param overlap_membership: number of memberships of overlapping nodes :param fixed_range: If True, uses com_size_min/max, else distribution determines the range :param excess: - :param defect: - :param randomf: - :param avg_clustering: the average clustering coefficient :param edge_dtype: return type of edge list. Default: DEFAULT_INT :return: (Ex2 numpy array, tuple community memberships for each node) """ if edge_dtype is None: edge_dtype = DEFAULT_INT edge_array, community_memberships = GenerateUnweightedUndirectedGraph( num_nodes, average_k, max_degree, mu, com_size_min, com_size_max, seed, tau, tau2, overlapping_nodes, overlap_membership, fixed_range, excess, defect, randomf, avg_clustering) if edge_array.dtype != edge_dtype: edge_array = edge_array.astype(edge_dtype) return edge_array, community_memberships def unweighted_undirected_lfr_as_nx(*args, **kwargs): """ Nodes start at 0 and are contiguous Calls unweighted_undirected_lfr_graph and converts to a networkx graph :return: networkx graph """ edge_array, community_memberships = unweighted_undirected_lfr_graph(*args, **kwargs) nx_graph = nx.Graph() # Add nodes and attributes to graph nodes_and_memberships = [] for node, node_memberships in enumerate(community_memberships): attributes = {'communities': node_memberships} for com_level, membership in enumerate(node_memberships): attributes['com_level'] = membership nodes_and_memberships.append((node, attributes)) nx_graph.add_nodes_from(nodes_and_memberships) # Add edges to graph nx_graph.add_edges_from(edge_array) return nx_graph def unweighted_undirected_lfr_as_adj(*args, **kwargs): """ Nodes start at 0 and are contiguous Calls unweighted_undirected_lfr_graph and converts to an adjacency matrix :param transpose: transpose the matrix representation :return: Return a adj matrix: axis1 (minor) is tail, axis2 (major) is head and return community membership or (transpose): axis1 is head, axis2 is tail """ graph_pars = {key: value for key, value in kwargs.items() if key in inspect.getfullargspec(unweighted_undirected_lfr_graph).args} converter_pars = {key: value for key, value in kwargs.items() if key in inspect.getfullargspec(convert_unweighted_to_numpy_matrix).args} edge_array, community_memberships = unweighted_undirected_lfr_graph(*args, **graph_pars) return (convert_unweighted_to_numpy_matrix(edge_array, **converter_pars), community_memberships) def unweighted_directed_lfr_graph(num_nodes, average_k, max_degree, mu, com_size_min, com_size_max, seed, tau=2.0, tau2=1.0, overlapping_nodes=0, overlap_membership=0, fixed_range=True, excess=False, defect=False, randomf=False, edge_dtype=None): """ Nodes start at 0 and are contiguous Return Ex2 numpy array and tuple of community memberships for each node :param num_nodes: Number of nodes in the network (starts id at 0) :param average_k: average degree of the nodes :param max_degree: largest degree of the nodes :param mu: mixing parameter, fraction of bridges :param com_size_min: smallest community size :param com_size_max: largest community size :param seed: for rng :param tau: minus exponent for degree sequence :param tau2: minus exponent for community size distribution :param overlapping_nodes: number of overlapping nodes :param overlap_membership: number of memberships of overlapping nodes :param fixed_range: If True, uses com_size_min/max, else distribution determines the range :param excess: - :param defect: - :param randomf: - :param edge_dtype: dtype of edge array. Default: DEFAULT_INT :return: (Ex2 numpy array, tuple community memberships for each node) * Order of resulting Numpy array is: Ex2 with major axis as [0]=tail, [1]=head * Row major format, so [edge#][0]=tail, [edge#][1]=head """ if edge_dtype is None: edge_dtype = DEFAULT_INT edge_array, community_memberships = GenerateUnweightedDirectedGraph( num_nodes, average_k, max_degree, mu, com_size_min, com_size_max, seed, tau, tau2, overlapping_nodes, overlap_membership, fixed_range, excess, defect, randomf) if edge_array.dtype != edge_dtype: edge_array = edge_array.astype(edge_dtype) return edge_array, community_memberships def unweighted_directed_lfr_as_nx(*args, **kwargs): """ Nodes start at 0 and are contiguous Calls unweighted_directed_lfr_graph and converts to a networkx graph :return: networkx graph """ edge_array, community_memberships = unweighted_directed_lfr_graph(*args, **kwargs) nx_graph = nx.DiGraph() # Add nodes and attributes to graph nodes_and_memberships = [] for node, node_memberships in enumerate(community_memberships): attributes = {'communities': node_memberships} for com_level, membership in enumerate(node_memberships): attributes['com_level'] = membership nodes_and_memberships.append((node, attributes)) nx_graph.add_nodes_from(nodes_and_memberships) # Add edges to graph nx_graph.add_edges_from(edge_array) return nx_graph def unweighted_directed_lfr_as_adj(*args, **kwargs): """ Nodes start at 0 and are contiguous Calls unweighted_directed_lfr_graph and converts to an adjacency matrix :param transpose: transpose the matrix representation :return: Return a adj matrix: axis1 (minor) is tail, axis2 (major) is head and return community membership or (transpose): axis1 is head, axis2 is tail """ graph_pars = {key: value for key, value in kwargs.items() if key in inspect.getfullargspec(unweighted_directed_lfr_graph).args} converter_pars = {key: value for key, value in kwargs.items() if key in inspect.getfullargspec(convert_unweighted_to_numpy_matrix).args} edge_array, community_memberships = unweighted_directed_lfr_graph(*args, **graph_pars) return (convert_unweighted_to_numpy_matrix(edge_array, **converter_pars), community_memberships) def convert_weighted_to_numpy_matrix(edge_array, num_nodes, weights, transpose=False, weight_dtype=None): """ :param edge_array: Ex2 numpy array :param num_nodes: N :param weights: E np.float32 array :param transpose: transposes output matrix to reverse representation order default: False :param weight_dtype: dtype of return matrix :return: dtype=np.float32 NxN matrix """ if weight_dtype is None: weight_dtype = DEFAULT_FLOAT matrix = np.zeros((num_nodes, num_nodes), dtype=weight_dtype) for i, edge in enumerate(edge_array): matrix[edge[0], edge[1]] = weights[i] if transpose: return matrix.transpose().copy() return matrix def convert_unweighted_to_numpy_matrix(edge_array, num_nodes, transpose=False, edge_dtype=None): """ :param edge_array: Ex2 numpy array :param num_nodes: N :param transpose: transposes output matrix to reverse representation order default: False :param edge_dtype: dtype of return matrix :return: dtype=np.uint64 NxN matrix """ if edge_dtype is None: edge_dtype = DEFAULT_INT matrix = np.zeros((num_nodes, num_nodes), dtype=edge_dtype) for edge in edge_array: matrix[edge[0], edge[1]] = 1 if transpose: return matrix.transpose().copy() return matrix if __name__ == '__main__': """ """ pass

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0

null

0

8

09b6a4ecd5c3083cbb95fa186153142f2674a4d7

5,073

py

Python

tests/test_allow_mock_accuracy.py

KeynesYouDigIt/pyxform

6ca3484ea56bee2a286ed46f24fa2ef1f88611fe

[ "BSD-2-Clause" ]

67

2015-02-02T17:49:25.000Z

2022-02-18T06:31:52.000Z

tests/test_allow_mock_accuracy.py

yanokwa/pyxform

d2d453ca5c70c27934300bc2a819abad19d93082

[ "BSD-2-Clause" ]

477

2015-01-14T15:48:44.000Z

2022-03-29T16:37:22.000Z

tests/test_allow_mock_accuracy.py

yanokwa/pyxform

d2d453ca5c70c27934300bc2a819abad19d93082

[ "BSD-2-Clause" ]

86

2015-01-26T13:16:26.000Z

2022-01-20T21:40:54.000Z

42.275

99

0.384782

333

5,073

5.786787

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null

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8

1124aa2b68a488a53a34fa6c0e2647cf8994e9dd

4,466

py

Python

graphene_django/tests/test_types_permissions.py

mrmilu/graphene-django

dfdfeffa939ff5490b4bf3607de4220b7bc20729

[ "MIT" ]

null

graphene_django/tests/test_types_permissions.py

mrmilu/graphene-django

dfdfeffa939ff5490b4bf3607de4220b7bc20729

[ "MIT" ]

null

graphene_django/tests/test_types_permissions.py

mrmilu/graphene-django

dfdfeffa939ff5490b4bf3607de4220b7bc20729

[ "MIT" ]

null

# -*- coding: utf-8 -*- # General imports from mock import patch # App imports from graphene_django.tests.test_types import with_local_registry from .. import registry from ..types import DjangoObjectType from .models import Reporter as ReporterModel registry.reset_global_registry() class Reporter(DjangoObjectType): """Reporter description""" class Meta: model = ReporterModel @with_local_registry @patch("graphene_django.tests.models.Reporter.objects.get", return_value=Reporter(id=1)) def test_django_objecttype_with_permissions_denied(get): class Info(object): pass class IsAuthenticated(object): @staticmethod def has_permission(context): return False class Reporter(DjangoObjectType): class Meta: model = ReporterModel exclude_fields = ("first_name",) permission_fields = { "email": [IsAuthenticated()] } info = Info() info.context = Info() info.context.user = Info() reporter = Reporter.get_node(info, 1) get.assert_called_with(pk=1) assert reporter.id == 1 fields = list(Reporter._meta.fields.keys()) assert "email" not in fields assert "first_name" not in fields assert "id" in fields @with_local_registry @patch("graphene_django.tests.models.Reporter.objects.get", return_value=Reporter(id=1)) def test_django_objecttype_with_multiple_permissions_denied(get): class Info(object): pass class IsAuthenticated(object): @staticmethod def has_permission(context): return False class IsAllowed(object): @staticmethod def has_permission(context): # return context.user and context.user.is_authenticated return True class Reporter(DjangoObjectType): class Meta: model = ReporterModel exclude_fields = ("first_name",) permission_fields = { "email": [IsAuthenticated(), IsAllowed()] } info = Info() info.context = Info() info.context.user = Info() reporter = Reporter.get_node(info, 1) get.assert_called_with(pk=1) assert reporter.id == 1 fields = list(Reporter._meta.fields.keys()) assert "email" not in fields assert "first_name" not in fields assert "id" in fields @with_local_registry @patch("graphene_django.tests.models.Reporter.objects.get", return_value=Reporter(id=1)) def test_django_objecttype_with_permissions_allowed(get): class Info(object): pass class IsAuthenticated(object): @staticmethod def has_permission(context): # return context.user and context.user.is_authenticated return True class Reporter(DjangoObjectType): class Meta: model = ReporterModel exclude_fields = ("first_name",) permission_fields = { "email": [IsAuthenticated()] } info = Info() info.context = Info() info.context.user = Info() reporter = Reporter.get_node(info, 1) get.assert_called_with(pk=1) assert reporter.id == 1 fields = list(Reporter._meta.fields.keys()) assert "email" in fields assert "first_name" not in fields assert "id" in fields @with_local_registry @patch("graphene_django.tests.models.Reporter.objects.get", return_value=Reporter(id=1)) def test_django_objecttype_with_multiple_permissions_allowed(get): class Info(object): pass class IsAuthenticated(object): @staticmethod def has_permission(context): # return context.user and context.user.is_authenticated return True class IsAllowed(object): @staticmethod def has_permission(context): # return context.user and context.user.is_authenticated return True class Reporter(DjangoObjectType): class Meta: model = ReporterModel exclude_fields = ("first_name",) permission_fields = { "email": [IsAuthenticated(), IsAllowed()] } info = Info() info.context = Info() info.context.user = Info() reporter = Reporter.get_node(info, 1) get.assert_called_with(pk=1) assert reporter.id == 1 fields = list(Reporter._meta.fields.keys()) assert "email" in fields assert "first_name" not in fields assert "id" in fields

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null

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1

0

null

0

7

3a0ed34dc9e5d8c3ec8634aeef9fc53947760248

7,449

py

Python

pynes/tests/adc_test.py

BmanisKing/Mine

d33fc14c84af336ae16fefda2dcebc9f0e1e12ab

[ "BSD-3-Clause" ]

null

pynes/tests/adc_test.py

BmanisKing/Mine

d33fc14c84af336ae16fefda2dcebc9f0e1e12ab

[ "BSD-3-Clause" ]

null

pynes/tests/adc_test.py

BmanisKing/Mine

d33fc14c84af336ae16fefda2dcebc9f0e1e12ab

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- ''' ADC, Add with Carry Test This is an arithmetic instruction of the 6502. ''' import unittest from pynes.compiler import lexical, syntax, semantic class AdcTest(unittest.TestCase): def test_adc_imm(self): ''' Test the arithmetic operation ADC between decimal 16 and the content of the accumulator. ''' tokens = list(lexical('ADC #$10')) self.assertEquals(2, len(tokens)) self.assertEquals('T_INSTRUCTION', tokens[0]['type']) self.assertEquals('T_HEX_NUMBER', tokens[1]['type']) ast = syntax(tokens) self.assertEquals(1, len(ast)) self.assertEquals('S_IMMEDIATE', ast[0]['type']) code = semantic(ast) self.assertEquals(code, [0x69, 0x10]) def test_adc_imm_with_decimal(self): ''' Test the arithmetic operation ADC between decimal 10 and the content of the accumulator. ''' tokens = list(lexical('ADC #10')) self.assertEquals(2, len(tokens)) self.assertEquals('T_INSTRUCTION', tokens[0]['type']) self.assertEquals('T_DECIMAL_NUMBER', tokens[1]['type']) ast = syntax(tokens) self.assertEquals(1, len(ast)) self.assertEquals('S_IMMEDIATE', ast[0]['type']) code = semantic(ast) self.assertEquals(code, [0x69, 0x0A]) def test_adc_imm_with_binary(self): ''' Test the arithmetic operation ADC between binary %00000100 (Decimal 4) and the content of the accumulator. ''' tokens = list(lexical('ADC #%00000100')) self.assertEquals(2, len(tokens)) self.assertEquals('T_INSTRUCTION', tokens[0]['type']) self.assertEquals('T_BINARY_NUMBER', tokens[1]['type']) ast = syntax(tokens) self.assertEquals(1, len(ast)) self.assertEquals('S_IMMEDIATE', ast[0]['type']) code = semantic(ast) self.assertEquals(code, [0x69, 0x04]) def test_adc_zp(self): ''' Test the arithmetic operation ADC between the content of the accumulator and the content of the zero page address. ''' tokens = list(lexical('ADC $00')) self.assertEquals(2, len(tokens)) self.assertEquals('T_INSTRUCTION', tokens[0]['type']) self.assertEquals('T_ADDRESS', tokens[1]['type']) ast = syntax(tokens) self.assertEquals(1, len(ast)) self.assertEquals('S_ZEROPAGE', ast[0]['type']) code = semantic(ast) self.assertEquals(code, [0x65, 0x00]) def test_adc_zpx(self): ''' Test the arithmetic operation ADC between the content of the accumulator and the content of the zero page with address calculated from $10 adding content of X. ''' tokens = list(lexical('ADC $10,X')) self.assertEquals(4, len(tokens)) self.assertEquals('T_INSTRUCTION', tokens[0]['type']) self.assertEquals('T_ADDRESS', tokens[1]['type']) self.assertEquals('T_SEPARATOR', tokens[2]['type']) self.assertEquals('T_REGISTER', tokens[3]['type']) ast = syntax(tokens) self.assertEquals(1, len(ast)) self.assertEquals('S_ZEROPAGE_X', ast[0]['type']) code = semantic(ast) self.assertEquals(code, [0x75, 0x10]) def test_adc_abs(self): ''' Test the arithmetic operation ADC between the content of the accumulator and the content located at address $1234. ''' tokens = list(lexical('ADC $1234')) self.assertEquals(2, len(tokens)) self.assertEquals('T_INSTRUCTION', tokens[0]['type']) self.assertEquals('T_ADDRESS', tokens[1]['type']) self.assertEquals('$1234', tokens[1]['value']) ast = syntax(tokens) self.assertEquals(1, len(ast)) self.assertEquals('S_ABSOLUTE', ast[0]['type']) code = semantic(ast) self.assertEquals(code, [0x6d, 0x34, 0x12]) def test_adc_absx(self): ''' Test the arithmetic operation ADC between the content of the accumulator and the content located at address $1234 adding the content of X. ''' tokens = list(lexical('ADC $1234,X')) self.assertEquals(4, len(tokens)) self.assertEquals('T_INSTRUCTION', tokens[0]['type']) self.assertEquals('T_ADDRESS', tokens[1]['type']) self.assertEquals('$1234', tokens[1]['value']) self.assertEquals('T_SEPARATOR', tokens[2]['type']) self.assertEquals('T_REGISTER', tokens[3]['type']) ast = syntax(tokens) self.assertEquals(1, len(ast)) self.assertEquals('S_ABSOLUTE_X', ast[0]['type']) code = semantic(ast) self.assertEquals(code, [0x7d, 0x34, 0x12]) def test_adc_absy(self): ''' Test the arithmetic operation ADC between the content of the accumulator and the content located at address $1234 adding the content of Y. ''' tokens = list(lexical('ADC $1234,Y')) self.assertEquals(4, len(tokens)) self.assertEquals('T_INSTRUCTION', tokens[0]['type']) self.assertEquals('T_ADDRESS', tokens[1]['type']) self.assertEquals('T_SEPARATOR', tokens[2]['type']) self.assertEquals('T_REGISTER', tokens[3]['type']) ast = syntax(tokens) self.assertEquals(1, len(ast)) self.assertEquals('S_ABSOLUTE_Y', ast[0]['type']) code = semantic(ast) self.assertEquals(code, [0x79, 0x34, 0x12]) def test_adc_indx(self): ''' Test the arithmetic ADC operation between the content of the accumulator and the content located at the address obtained from the address calculated from the value stored in the address $20 adding the content of Y. ''' tokens = list(lexical('ADC ($20,X)')) self.assertEquals(6, len(tokens)) self.assertEquals('T_INSTRUCTION', tokens[0]['type']) self.assertEquals('T_OPEN', tokens[1]['type']) self.assertEquals('T_ADDRESS', tokens[2]['type']) self.assertEquals('$20', tokens[2]['value']) self.assertEquals('T_SEPARATOR', tokens[3]['type']) self.assertEquals('T_REGISTER', tokens[4]['type']) self.assertEquals('T_CLOSE', tokens[5]['type']) ast = syntax(tokens) self.assertEquals(1, len(ast)) self.assertEquals('S_INDIRECT_X', ast[0]['type']) code = semantic(ast) self.assertEquals(code, [0x61, 0x20]) def test_adc_indy(self): ''' Test arithmetic operation ADC between the content of the accumulator and the content located at the address obtained from the address calculated from the value stored in the address $20 adding the content of Y. ''' tokens = list(lexical('ADC ($20),Y')) self.assertEquals(6, len(tokens)) self.assertEquals('T_INSTRUCTION', tokens[0]['type']) self.assertEquals('T_OPEN', tokens[1]['type']) self.assertEquals('T_ADDRESS', tokens[2]['type']) self.assertEquals('T_CLOSE', tokens[3]['type']) self.assertEquals('T_SEPARATOR', tokens[4]['type']) self.assertEquals('T_REGISTER', tokens[5]['type']) ast = syntax(tokens) self.assertEquals(1, len(ast)) self.assertEquals('S_INDIRECT_Y', ast[0]['type']) code = semantic(ast) self.assertEquals(code, [0x71, 0x20])

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null

0

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8

3a0fd0fa2df83f4ca7fd3b983cca4856b99d1375

9,146

py

Python

tests/test_api_csv.py

SbastianGarzon/geoextent

b1731c3d69eeb0af18a9d5a0178cf47ae2492b13

[ "MIT" ]

1

2021-01-02T06:07:09.000Z

tests/test_api_csv.py

SbastianGarzon/geoextent

b1731c3d69eeb0af18a9d5a0178cf47ae2492b13

[ "MIT" ]

119

2019-08-16T08:37:53.000Z

2021-08-20T13:16:10.000Z

tests/test_api_csv.py

SbastianGarzon/geoextent

b1731c3d69eeb0af18a9d5a0178cf47ae2492b13

[ "MIT" ]

6

2019-08-12T08:11:55.000Z

2020-09-24T18:39:02.000Z

import pytest from help_functions_test import tolerance import geoextent.lib.extent as geoextent def test_csv_extract_bbox(): result = geoextent.fromFile('tests/testdata/csv/cities_NL_lat&long.csv', bbox=True) assert "bbox" in result assert "crs" in result assert "tbox" not in result assert result["bbox"] == pytest.approx([4.3175, 51.434444, 6.574722, 53.217222], abs=tolerance) assert result["crs"] == "4326" def test_csv_extract_tbox(): result = geoextent.fromFile('tests/testdata/csv/cities_NL.csv', bbox=False, tbox=True) assert "bbox" not in result assert "tbox" in result assert result["tbox"] == ['2017-08-01', '2019-09-30'] def test_csv_extract_bbox_and_tbox(): result = geoextent.fromFile('tests/testdata/csv/cities_NL.csv', bbox=True, tbox=True) assert "bbox" in result assert "tbox" in result assert "crs" in result assert result["bbox"] == pytest.approx([4.3175, 51.434444, 6.574722, 53.217222], abs=tolerance) assert result["tbox"] == ['2017-08-01', '2019-09-30'] assert result["crs"] == "4326" def test_empty_csv_file(): result = geoextent.fromFile('tests/testdata/csv/empty_csv.csv', bbox=True) assert result is None def test_csv_extract_bbox_and_tbox_semicolon_delimiter(): result = geoextent.fromFile('tests/testdata/csv/csv_semicolon_delimiter.csv', bbox=True, tbox=True) assert "bbox" in result assert "tbox" in result assert "crs" in result assert result["bbox"] == pytest.approx([4.3175, 51.434444, 6.574722, 53.217222], abs=tolerance) assert result["crs"] == "4326" assert result["tbox"] == ['2017-08-01', '2019-09-30'] def test_csv_extract_bbox_and_tbox_comma_delimiter(): result = geoextent.fromFile('tests/testdata/csv/csv_comma_delimiter.csv', bbox=True, tbox=True) assert "bbox" in result assert "tbox" in result assert "crs" in result assert result["bbox"] == pytest.approx([4.3175, 51.434444, 6.574722, 53.217222], abs=tolerance) assert result["crs"] == "4326" assert result["tbox"] == ['2017-08-01', '2019-09-30'] def test_csv_extract_bbox_and_tbox_with_time(): result = geoextent.fromFile('tests/testdata/csv/cities_NL_Time.csv', bbox=True, tbox=True) assert "bbox" in result assert "tbox" in result assert "crs" in result assert result["bbox"] == pytest.approx([4.3175, 51.434444, 6.574722, 53.217222], abs=tolerance) assert result["crs"] == "4326" assert result["tbox"] == ['2017-08-01', '2019-09-30'] def test_csv_extract_bbox_and_tbox_with_timestamp(): result = geoextent.fromFile('tests/testdata/csv/cities_NL_TimeStamp.csv', bbox=True, tbox=True) assert "bbox" in result assert "tbox" in result assert "crs" in result assert result["bbox"] == pytest.approx([4.3175, 51.434444, 6.574722, 53.217222], abs=tolerance) assert result["crs"] == "4326" assert result["tbox"] == ['2017-08-01', '2019-09-30'] def test_csv_extract_bbox_and_tbox_with_datetime(): result = geoextent.fromFile('tests/testdata/csv/cities_NL_Datetime.csv', bbox=True, tbox=True) assert "bbox" in result assert "crs" in result assert "tbox" in result assert result["bbox"] == pytest.approx([4.3175, 51.434444, 6.574722, 53.217222], abs=tolerance) assert result["crs"] == "4326" assert result["tbox"] == ['2017-08-01', '2019-09-30'] def test_csv_extract_bbox_and_tbox_with_lat_long(): result = geoextent.fromFile('tests/testdata/csv/cities_NL_LATITUDE.csv', bbox=True, tbox=True) assert "bbox" in result assert "crs" in result assert "tbox" in result assert result["bbox"] == pytest.approx([4.3175, 51.434444, 6.574722, 53.217222], abs=tolerance) assert result["crs"] == "4326" assert result["tbox"] == ['2017-08-01', '2019-09-30'] def test_csv_extract_bbox_and_tbox_with_lat_long_2(): result = geoextent.fromFile('tests/testdata/csv/cities_NL_LAT.csv', bbox=True, tbox=True) assert "bbox" in result assert "crs" in result assert "tbox" in result assert result["bbox"] == pytest.approx([4.3175, 51.434444, 6.574722, 53.217222], abs=tolerance) assert result["crs"] == "4326" assert result["tbox"] == ['2017-08-01', '2019-09-30'] def test_csv_extract_bbox_and_tbox_with_time_date(): result = geoextent.fromFile('tests/testdata/csv/cities_NL_TIME_DATE.csv', bbox=True, tbox=True) assert "bbox" in result assert "crs" in result assert "tbox" in result assert result["bbox"] == pytest.approx([4.3175, 51.434444, 6.574722, 53.217222], abs=tolerance) assert result["crs"] == "4326" assert result["tbox"] == ['2010-09-01', '2019-09-30'] def test_csv_extract_bbox_and_tbox_with_cols_diff_order(): result = geoextent.fromFile('tests/testdata/csv/cities_NL_case1.csv', bbox=True, tbox=True) assert "bbox" in result assert "crs" in result assert "tbox" in result assert result["bbox"] == pytest.approx([4.3175, 51.434444, 6.574722, 53.217222], abs=tolerance) assert result["crs"] == "4326" assert result["tbox"] == ['2017-08-01', '2019-09-30'] def test_csv_extract_bbox_and_tbox_with_cols_diff_order_caps(): result = geoextent.fromFile('tests/testdata/csv/cities_NL_case2.csv', bbox=True, tbox=True) assert "bbox" in result assert "crs" in result assert "tbox" in result assert result["bbox"] == pytest.approx([4.3175, 51.434444, 6.574722, 53.217222], abs=tolerance) assert result["crs"] == "4326" assert result["tbox"] == ['2017-08-01', '2019-09-30'] def test_csv_extract_bbox_and_tbox_with_cols_diff_order_and_alt_names1(): result = geoextent.fromFile('tests/testdata/csv/cities_NL_case3.csv', bbox=True, tbox=True) assert "bbox" in result assert "crs" in result assert "tbox" in result assert result["bbox"] == pytest.approx([4.3175, 51.434444, 6.574722, 53.217222], abs=tolerance) assert result["crs"] == "4326" assert result["tbox"] == ['2017-08-01', '2019-09-30'] def test_csv_extract_bbox_and_tbox_with_cols_diff_order_and_alt_names2(): result = geoextent.fromFile('tests/testdata/csv/cities_NL_case4.csv', bbox=True, tbox=True) assert "bbox" in result assert "crs" in result assert "tbox" in result assert result["bbox"] == pytest.approx([4.3175, 51.434444, 6.574722, 53.217222], abs=tolerance) assert result["crs"] == "4326" assert result["tbox"] == ['2017-08-01', '2019-09-30'] def test_csv_extract_bbox_and_tbox_with_cols_diff_order_and_alt_names3(): result = geoextent.fromFile('tests/testdata/csv/cities_NL_case5.csv', bbox=True, tbox=True) assert "bbox" in result assert "crs" in result assert "tbox" in result assert result["bbox"] == pytest.approx([4.3175, 51.434444, 6.574722, 53.217222], abs=tolerance) assert result["tbox"] == ['2017-08-01', '2019-09-30'] assert result["crs"] == "4326" def test_csv_extract_tbox_iso8601_time_format(): result = geoextent.fromFile('tests/testdata/csv/3DCMTcatalog_TakemuraEPS.csv', bbox=False, tbox=True) assert "bbox" not in result assert "crs" not in result assert "tbox" in result assert result["tbox"] == ['2017-04-08', '2020-02-06'] def test_csv_extract_tbox_dd_mm_yyyy_time_format(): result = geoextent.fromFile('tests/testdata/csv/3DCMTcatalog_TakemuraEPS_dd_mm_yyyy.csv', bbox=False, tbox=True) assert "bbox" not in result assert "crs" not in result assert "tbox" in result assert result["tbox"] == ['2017-04-19', '2018-01-31'] def test_csv_extract_tbox_month_abbr_dd_yyyy_time_formats(): result = geoextent.fromFile('tests/testdata/csv/3DCMTcatalog_TakemuraEPS_month_abbr_dd_yyyy_time_format.csv', bbox=False, tbox=True) assert "bbox" not in result assert "crs" not in result assert result["tbox"] == ['2017-04-09', '2017-07-20'] def test_csv_extract_tbox_two_diff_time_formats(): result = geoextent.fromFile('tests/testdata/csv/3DCMTcatalog_TakemuraEPS_mixed_time_formats.csv', bbox=False, tbox=True) assert "bbox" not in result assert "tbox" not in result assert "crs" not in result def test_csv_extract_tbox_random_sample(): result = geoextent.fromFile('tests/testdata/csv/3DCMTcatalog_TakemuraEPS.csv', bbox=False, tbox=True, num_sample=5) assert "bbox" not in result assert "crs" not in result assert "tbox" in result assert result["tbox"] == ['2017-04-08', '2020-02-06'] def test_csv_extract_tbox_random_sample_invalid(): result = geoextent.fromFile('tests/testdata/csv/3DCMTcatalog_TakemuraEPS.csv', bbox=False, tbox=True, num_sample=-1) assert "bbox" not in result assert "crs" not in result assert "tbox" not in result def test_csv_extract_tbox_random_sample_value_larger_than_data(): result = geoextent.fromFile('tests/testdata/csv/3DCMTcatalog_TakemuraEPS.csv', bbox=False, tbox=True, num_sample=1000000) assert "bbox" not in result assert "crs" not in result assert "tbox" in result assert result["tbox"] == ['2017-04-08', '2020-02-06']

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0

null

0

1

0

null

0

1

0

7

3a150722a48528ff39195732f5f661ffe7947faf

165

py

Python

python/ctranslate2/specs/__init__.py

funboarder13920/CTranslate2

6b14d9a948c5d30c08cba8c9d20c49e73de97523

[ "MIT" ]

259

2019-10-09T13:14:30.000Z

2022-03-28T02:54:28.000Z

python/ctranslate2/specs/__init__.py

funboarder13920/CTranslate2

6b14d9a948c5d30c08cba8c9d20c49e73de97523

[ "MIT" ]

197

2019-10-10T08:56:29.000Z

2022-03-31T12:07:04.000Z

python/ctranslate2/specs/__init__.py

funboarder13920/CTranslate2

6b14d9a948c5d30c08cba8c9d20c49e73de97523

[ "MIT" ]

69

2019-10-09T13:31:10.000Z

2022-03-09T11:15:08.000Z

from ctranslate2.specs.model_spec import LayerSpec from ctranslate2.specs.model_spec import ModelSpec from ctranslate2.specs.transformer_spec import TransformerSpec

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1

0

1

0

8

3a162ad43cf8e5617b16a4221ce0a985346c0c69

1,023

py

Python

test33.py

spatole12/ssw555tmashishningrohnitshivani2019Spring

737f2ac2548c232c1181b0da4dd327d3a12f3c3e

[ "MIT" ]

null

test33.py

spatole12/ssw555tmashishningrohnitshivani2019Spring

737f2ac2548c232c1181b0da4dd327d3a12f3c3e

[ "MIT" ]

null

test33.py

spatole12/ssw555tmashishningrohnitshivani2019Spring

737f2ac2548c232c1181b0da4dd327d3a12f3c3e

[ "MIT" ]

2

2019-03-09T22:28:21.000Z

2019-04-29T21:09:38.000Z

import unittest from userstories_an import * class testcase(unittest.TestCase): def test_33(self): result=userstory_an.us_33([['1994-06-12',4],['1994-04-03',6],17,'I1']) self.assertEqual(result,'I1') result=userstory_an.us_33([['1994-06-12',4],['1994-04-03',6],19,'I1']) self.assertEqual(result,0) result=userstory_an.us_33([['1994-06-12',4],['Invalid',6],17,'I1']) self.assertEqual(result,0) result=userstory_an.us_33([['Invalid',4],['1994-04-03',6],17,'I1']) self.assertEqual(result,0) result=userstory_an.us_33([['Invalid',4],['Invalid',6],17,'I1']) self.assertEqual(result,0) result=userstory_an.us_33(['NA',['1994-04-03',6],17,'I1']) self.assertEqual(result,0) result=userstory_an.us_33([['1994-06-12',4],'NA',17,'I1']) self.assertEqual(result,0) result=userstory_an.us_33(['NA','NA',17,'I1']) self.assertEqual(result,0) if __name__=='__main__': unittest.main()

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false

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0.090909

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0.181818

0

null

1

0

null

0

9

3a24387be635f9158853b112e2461be235641b47

109

py

Python

limited/lviewer/tests/__init__.py

b7w/limited-fm

9d0908251b94f5a3bde418a780ecb43db274d6d8

[ "BSD-3-Clause" ]

null

limited/lviewer/tests/__init__.py

b7w/limited-fm

9d0908251b94f5a3bde418a780ecb43db274d6d8

[ "BSD-3-Clause" ]

null

limited/lviewer/tests/__init__.py

b7w/limited-fm

9d0908251b94f5a3bde418a780ecb43db274d6d8

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- from limited.lviewer.tests.utils import * from limited.lviewer.tests.views import *

21.8

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null

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1

0

1

0

8

28bb265b6dafdbecafd956de8f8d2b279c50ef73

7,147

py

Python

rlkit/envs/ant_vel.py

michaelwan11/hfr

a92c2f46f746cc70c91e7f879c7e2b7c475ba73f

[ "MIT" ]

2

2021-12-29T07:37:15.000Z

2022-01-19T23:33:33.000Z

rlkit/envs/ant_vel.py

michaelwan11/hfr

a92c2f46f746cc70c91e7f879c7e2b7c475ba73f

[ "MIT" ]

null

rlkit/envs/ant_vel.py

michaelwan11/hfr

a92c2f46f746cc70c91e7f879c7e2b7c475ba73f

[ "MIT" ]

1

2021-12-29T07:37:18.000Z

import numpy as np from rlkit.envs.ant_multitask_base import MultitaskAntEnv from . import register_env # Copy task structure from https://github.com/jonasrothfuss/ProMP/blob/master/meta_policy_search/envs/mujoco_envs/ant_rand_goal.py @register_env("ant-vel") class AntVelEnv(MultitaskAntEnv): # Note that goal here refers to goal velocity def __init__( self, task={}, n_tasks=2, randomize_tasks=True, **kwargs ): np.random.seed(3) super(AntVelEnv, self).__init__(task, n_tasks, **kwargs) def step(self, action): xposbefore = np.copy(self.get_body_com("torso")) self.do_simulation(action, self.frame_skip) xposafter = self.get_body_com("torso") comvel = (xposafter[0] - xposbefore[0]) / self.dt forward_reward = -np.abs(comvel - self._goal) + 1.0 lb, ub = self.action_bounds scaling = (ub - lb) * 0.5 ctrl_cost = 0.5 * 1e-2 * np.sum(np.square(action / scaling)) contact_cost = ( 0.5 * 1e-3 * np.sum(np.square(np.clip(self.sim.data.cfrc_ext, -1, 1))) ) survive_reward = 0.05 reward = forward_reward - ctrl_cost - contact_cost + survive_reward state = self.state_vector() notdone = np.isfinite(state).all() and state[2] >= 0.2 and \ state[2] <= 1.0 done = not notdone ob = self._get_obs() return ( ob, reward, done, dict( reward_forward=forward_reward, reward_ctrl=-ctrl_cost, reward_contact=-contact_cost, reward_survive=survive_reward, comvel=comvel, state=state, ), ) def reward(self, info, goal): comvel = info["comvel"] forward_reward = -np.abs(comvel - goal) + 1.0 reward_ctrl = info["reward_ctrl"] reward_contact = info["reward_contact"] reward_survive = info["reward_survive"] state = info["state"] notdone = np.isfinite(state).all() and state[2] >= 0.2 and \ state[2] <= 1.0 reward = forward_reward + reward_ctrl + reward_contact + reward_survive done = not notdone return reward, done def sample_tasks(self, num_tasks): tasks = np.random.uniform(0.0, 3.0, (num_tasks, )) tasks = [{"goal": goal} for goal in tasks] return tasks def _get_obs(self): """ return np.concatenate( [ self.sim.data.qpos.flat, self.sim.data.qvel.flat, np.clip(self.sim.data.cfrc_ext, -1, 1).flat, self.get_body_xmat("torso").flat, self.get_body_com("torso"), ] ).reshape(-1) """ return np.concatenate([ self.sim.data.qpos.flat, self.sim.data.qvel.flat, np.clip(self.sim.data.cfrc_ext, -1, 1).flat, ]) @property def action_bounds(self): bounds = self.sim.model.actuator_ctrlrange.copy().astype(np.float32) return bounds.T def reset_model(self): self.comvel = np.array([0., 0., 0.]) qpos = self.init_qpos + self.np_random.uniform(size=self.model.nq, low=-.1, high=.1) qvel = self.init_qvel + self.np_random.randn(self.model.nv) * .1 self.set_state(qpos, qvel) return self._get_obs() @register_env("ant-vel-sparse") class AntVelSparseEnv(MultitaskAntEnv): # Note that goal here refers to goal velocity def __init__( self, task={}, n_tasks=2, randomize_tasks=True, **kwargs ): np.random.seed(3) self.goal_radius = 0.5 super(AntVelSparseEnv, self).__init__(task, n_tasks, **kwargs) def step(self, action): xposbefore = np.copy(self.get_body_com("torso")) self.do_simulation(action, self.frame_skip) xposafter = self.get_body_com("torso") comvel = (xposafter[0] - xposbefore[0]) / self.dt # forward_reward = -np.abs(comvel - self._goal) + 1.0 forward_reward = -np.abs(comvel - self._goal) lb, ub = self.action_bounds scaling = (ub - lb) * 0.5 ctrl_cost = 0.5 * 1e-2 * np.sum(np.square(action / scaling)) contact_cost = ( 0.5 * 1e-3 * np.sum(np.square(np.clip(self.sim.data.cfrc_ext, -1, 1))) ) survive_reward = 0.05 forward_reward = self.sparsify_rewards(forward_reward) reward = forward_reward - ctrl_cost - contact_cost + survive_reward state = self.state_vector() notdone = np.isfinite(state).all() and state[2] >= 0.2 and \ state[2] <= 1.0 done = not notdone ob = self._get_obs() return ( ob, reward, done, dict( reward_forward=forward_reward, reward_ctrl=-ctrl_cost, reward_contact=-contact_cost, reward_survive=survive_reward, comvel=comvel, state=state, ), ) def reward(self, info, goal): comvel = info["comvel"] # forward_reward = -np.abs(comvel - goal) + 1.0 forward_reward = -np.abs(comvel - goal) forward_reward = self.sparsify_rewards(forward_reward) reward_ctrl = info["reward_ctrl"] reward_contact = info["reward_contact"] reward_survive = info["reward_survive"] state = info["state"] notdone = np.isfinite(state).all() and state[2] >= 0.2 and \ state[2] <= 1.0 reward = forward_reward + reward_ctrl + reward_contact + reward_survive done = not notdone return reward, done def sample_tasks(self, num_tasks): tasks = np.random.uniform(-1.5, 1.5, (num_tasks, )) tasks = [{"goal": goal} for goal in tasks] return tasks def sparsify_rewards(self, r): """ if r < -self.goal_radius: r = -2 # r = -1 r = r + 2 """ if r > -self.goal_radius: r = r + 1 # r = r + 1 return r def _get_obs(self): """ return np.concatenate( [ self.sim.data.qpos.flat, self.sim.data.qvel.flat, np.clip(self.sim.data.cfrc_ext, -1, 1).flat, self.get_body_xmat("torso").flat, self.get_body_com("torso"), ] ).reshape(-1) """ return np.concatenate([ self.sim.data.qpos.flat, self.sim.data.qvel.flat, np.clip(self.sim.data.cfrc_ext, -1, 1).flat, ]) @property def action_bounds(self): bounds = self.sim.model.actuator_ctrlrange.copy().astype(np.float32) return bounds.T def reset_model(self): self.comvel = np.array([0., 0., 0.]) qpos = self.init_qpos + self.np_random.uniform(size=self.model.nq, low=-.1, high=.1) qvel = self.init_qvel + self.np_random.randn(self.model.nv) * .1 self.set_state(qpos, qvel) return self._get_obs()

35.034314

130

0.558836

904

7,147

4.235619

0.149336

0.054322

0.040219

0.021938

0.905458

0.895012

0.892922

0.856882

0

0.022713

0.316217

7,147

203

131

35.206897

0.760794

0.122709

0

0.842105

0

0.024657

0

1

0.098684

false

0

0.019737

0

0.217105

0

null

0

1

0

1

0

null

0

7

e92e03dd60fa050cbac29d11d9fe9b2d9bc93fab

130

py

Python

code/chapter-1/exercise1_7.py

Kevin-Oudai/python-solutions

d67f6b14723b000fec0011c3e8156b805eb288f7

[ "MIT" ]

null

code/chapter-1/exercise1_7.py

Kevin-Oudai/python-solutions

d67f6b14723b000fec0011c3e8156b805eb288f7

[ "MIT" ]

null

code/chapter-1/exercise1_7.py

Kevin-Oudai/python-solutions

d67f6b14723b000fec0011c3e8156b805eb288f7

[ "MIT" ]

null

print(4 * (1 - 1.0/3 + 1.0/5 - 1.0/7 + 1.0/9 - 1.0/11)) print(4 * (1 - 1.0/3 + 1.0/5 - 1.0/7 + 1.0/9 - 1.0/11 + 1.0/13 - 1.0/15))

43.333333

73

0.415385

42

130

1.285714

0.261905

0.444444

0.259259

0.296296

0.851852

0

0.44

0.230769

130

2

74

65

0.1

0

1

0

true

0

1

0

1

null

1

0

1

0

1

0

1

0

null

0

1

0

1

0

15

c940161422dbbc8d32bb7bd7ffa8f3d865ff73af

55,056

py

Python

Main.py

onurerkin/AmazonArbitrage

60236ac26a6a440dafc001e72acdff80dc2ea4a4

[ "MIT" ]

null

Main.py

onurerkin/AmazonArbitrage

60236ac26a6a440dafc001e72acdff80dc2ea4a4

[ "MIT" ]

null

Main.py

onurerkin/AmazonArbitrage

60236ac26a6a440dafc001e72acdff80dc2ea4a4

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Thu Dec 20 22:28:42 2018 @author: Erkin """ #%% import pandas as pd import numpy as np import matplotlib.pyplot as plt import matplotlib import seaborn as sns import warnings warnings.simplefilter(action='ignore', category=FutureWarning) def warn(*args, **kwargs): pass import warnings warnings.warn = warn # T VALUE FUNCTION #def t_ind(quotes, tgt_margin=0.2, n_days=30): # quotes=quotes[['date','lowest_newprice']] # quotes=quotes.reset_index(drop=True) # # t_matrix=pd.DataFrame(quotes.date).iloc[0:len(quotes)-n_days,] # t_matrix['T']=0 # t_matrix['decision']='hold' # for i in range(len(quotes)-n_days): # a=quotes.iloc[i:i+n_days,:] # a['first_price']=quotes.iloc[i,1] # a['variation']=(a.lowest_newprice-a.first_price)/a.first_price # t_value=len(a[(a.variation>tgt_margin)]) - len(a[(a.variation<-tgt_margin)]) # t_matrix.iloc[i,1]=t_value # if (t_value > 10): # t_matrix.iloc[i,2]='buy' # elif(t_value < -10): # t_matrix.iloc[i,2]='sell' # # plt.subplot(2, 1, 1) # dates = matplotlib.dates.date2num(t_matrix.date) # plt.plot_date(dates, t_matrix['T'],linestyle='solid', marker='None') # plt.title(' T vs time ') # plt.xlabel('Time') # plt.ylabel('T value') # # # plt.subplot(2, 1, 2) # dates = matplotlib.dates.date2num(quotes.iloc[0:len(quotes)-n_days,].date) # plt.plot_date(dates, quotes.iloc[0:len(quotes)-n_days,]['lowest_newprice'],linestyle='solid', marker='None') # plt.xlabel('Time') # plt.ylabel('price') # plt.show() # plt.show() # return t_matrix # # FUNCTION ENDS # importing necessary datasets. product_info=pd.read_csv('/Users/Erkin/Desktop/McGill/personal project/data/product_info.csv') product_info=product_info.drop('Unnamed: 0',axis=1) product_info_head=product_info.head(1000) product=pd.read_csv('/Users/Erkin/Desktop/McGill/personal project/data/product.csv') product=product.drop('Unnamed: 0',axis=1) product_head=product.head(1000) map_product=pd.read_csv('/Users/Erkin/Desktop/McGill/personal project/data/map_product.csv') map_product=map_product.drop('Unnamed: 0',axis=1) map_product_head=map_product.head(1000) product_answer=pd.read_csv('/Users/Erkin/Desktop/McGill/personal project/data/product_answer.csv') product_answer=product_answer.drop('Unnamed: 0',axis=1) product_answer_head=product_answer.head(1000) product_question=pd.read_csv('/Users/Erkin/Desktop/McGill/personal project/data/product_question.csv') product_question=product_question.drop('Unnamed: 0',axis=1) product_question_head=product_question.head(1000) product_review=pd.read_csv('/Users/Erkin/Desktop/McGill/personal project/data/product_review.csv') product_review=product_review.drop('Unnamed: 0',axis=1) product_review_head=product_review.head(1000) merged=pd.read_csv('/Users/Erkin/Desktop/McGill/personal project/data/merged.csv') merged=merged.drop('Unnamed: 0',axis=1) merged_head=merged.head(1000) #product names #product_names=product.iloc[:,1:3] #merged=pd.merge(product_names,product_info,how='right',on='asin') #merged_head=merged.head(1000) # ##lowest price na replacement #merged=merged.drop('Unnamed: 0',axis=1) #merged['lowest_newprice']=merged['lowest_newprice'].fillna(merged['list_price']) #merged_head=merged.head(1000) #merged.isna().sum() #removing values with less than 200 observations. #asd=merged.groupby(['asin']).count() #asd=asd[asd.date > 200] #asd.reset_index(level=0, inplace=True) #merged=merged[merged.asin.isin(asd.asin)] #merged=merged.reset_index(drop=True) #merged['date'] = pd.to_datetime(merged['date']).dt.date # #unique_asins=merged.asin.unique() #merged['T']=99 #for asin in unique_asins: # print(asin) # quotes=merged[merged.asin==asin] # iterable=quotes.iloc[0:len(quotes)-n_days,] # for i, row in iterable.iterrows(): # a=quotes.loc[i:i+n_days,:] # a['first_price']=quotes.loc[i,'lowest_newprice'] # a['variation']=(a.lowest_newprice-a.first_price)/a.first_price # t_value=len(a[(a.variation>tgt_margin)]) - len(a[(a.variation<-tgt_margin)]) # merged.loc[i,'T']=t_value #asins=merged.asin.unique().tolist() #product0=t_ind(quotes=merged[merged.asin==asins[0]]) #product1=t_ind(quotes=merged[merged.asin==asins[1]]) #product2=t_ind(quotes=merged[merged.asin==asins[2]]) #product3=t_ind(quotes=merged[merged.asin==asins[3]]) #product4=t_ind(quotes=merged[merged.asin==asins[4]]) #product5=t_ind(quotes=merged[merged.asin==asins[5]]) #product6=t_ind(quotes=merged[merged.asin==asins[6]]) # ## Create the time index #product6.set_index('date', inplace=True) #ts=product6.drop('decision',axis=1) # # ## Verify the time index #product6.head() #product6.info() #product6.index ## Run the AutoRegressive model #from statsmodels.tsa.ar_model import AR #ar1=AR(ts) #model1=ar1.fit() ## View the results #print('Lag: %s' % model1.k_ar) #print('Coefficients: %s' % model1.params) # ## Separate the data into training and test #split_size = round(len(ts)*0.3) #ts_train,ts_test = ts[0:len(ts)-split_size], ts[len(ts)-split_size:] # ## Run the model again on the training data #ar2=AR(ts_train) #model2=ar2.fit() # ## Predicting the outcome based on the test data #ts_test_pred_ar = model2.predict(start=len(ts_train),end=len(ts_train)+len(ts_test)-1,dynamic=False) #ts_test_pred_ar.index=ts_test.index # ## Calculating the mean squared error of the model #from sklearn.metrics import mean_squared_error #error = mean_squared_error(ts_test,ts_test_pred_ar) #print('Test MSE: %.3f' %error) # ## Plot the graph comparing the real value and predicted value #from matplotlib import pyplot #fig = plt.figure(dpi=100) #pyplot.plot(ts_test) #pyplot.plot(ts_test_pred_ar) #df_dateasin=merged[['date','asin']] # # # #reviews_sorted=product_review.sort_values('review_date') #reviews_sorted['number_of_reviews']=reviews_sorted.groupby(['asin','review_date']).cumcount()+1 #reviews_sorted['star_tot']=reviews_sorted.groupby('asin').star.cumsum() #reviews_sorted = reviews_sorted.drop_duplicates(['asin','review_date'], keep='last') #df_dateasin = df_dateasin.drop_duplicates(['asin','date'], keep='last') #df_dateasin.columns=['review_date','asin'] #reviews_sorted = pd.merge(df_dateasin,reviews_sorted,how='left') #reviews_sorted_head=reviews_sorted.head(1000) # # # # # #reviews_sorted['reviews_total']=reviews_sorted.groupby('asin').number_of_reviews.cumsum() #reviews_sorted['star_avg']=reviews_sorted.star_tot/reviews_sorted.number_of_reviews #reviews_sorted = reviews_sorted.drop_duplicates(['asin','review_date'], keep='last') #t1=reviews_sorted[['review_date','asin','number_of_reviews','star_avg']] #t1.columns=['date','asin','number_of_reviews','star_avg'] #merged2 = pd.merge(merged,t1,how='left') # #t2 = pd.merge(df_dateasin,t1,how='left') # #nul = merged2['number_of_reviews'].isnull() #nul.groupby((nul.diff() == 1).cumsum()).cumsum()*3 + merged2['number_of_reviews'].ffill() # # FEATURE ENGINEERING # aggregation of number of reviews and average star rating reviews_sorted=product_review.set_index('review_date').sort_index() reviews_sorted['number_of_reviews']=reviews_sorted.groupby('asin').cumcount()+1 reviews_sorted['star_tot']=reviews_sorted.groupby('asin').star.cumsum() reviews_sorted=reviews_sorted.reset_index() reviews_sorted['star_avg']=reviews_sorted.star_tot/reviews_sorted.number_of_reviews reviews_sorted = reviews_sorted.drop_duplicates(['asin','review_date'], keep='last') t1=reviews_sorted[['review_date','asin','number_of_reviews','star_avg']] t1.columns=['date','asin','number_of_reviews','star_avg'] merged2 = pd.merge(merged,t1,how='left') merged2['number_of_reviews']=merged2.groupby('asin').number_of_reviews.fillna(method='ffill') merged2['number_of_reviews']=merged2.groupby('asin').number_of_reviews.fillna(method='bfill') merged2['star_avg']=merged2.groupby('asin').star_avg.fillna(method='ffill') merged2['star_avg']=merged2.groupby('asin').star_avg.fillna(method='bfill') # hold and buy merged2_head=merged2.head(10000) df_pred = merged2[merged2['T'] < 40] df_pred_head=df_pred.head(10000) df_pred['decision']=0 #don't buy df_pred.loc[(df_pred['T']>5),'decision']=1 #buy df_pred_head=df_pred.head(10000) # ## price diff #price_diff=[] #df_pred['price_diff']=0 #for game in df_pred.asin.unique(): # price_diff.append(0) # for row in range(1,len(df_pred[df_pred.asin==game])): # price_diff.append((df_pred.iloc[row,4]-df_pred.iloc[row-1,4])/df_pred.iloc[row-1,4]) #df_pred['price_diff']=price_diff #df_pred_head=df_pred.head(10000) # # #removing products less than 150 datapoints asd=df_pred.groupby(['asin']).count() asd=asd[asd.date > 150] asd.reset_index(level=0, inplace=True) df_pred=df_pred[df_pred.asin.isin(asd.asin)] df_pred=df_pred.reset_index(drop=True) df_pred=df_pred.dropna(subset=['sales_rank']) #%% # BENCHMARK MODEL #random forest asins=df_pred.asin.unique().tolist() from sklearn.ensemble import RandomForestClassifier from sklearn.feature_selection import SelectFromModel products=[] accuracies=[] precisions=[] recalls=[] fscores=[] supports=[] d = {} for i in range(len(asins)): d["product" + str(i)] = df_pred[df_pred.asin==asins[i]] benchmark_model={} benchmark_ytest={} for key, value in d.items(): X=value[['lowest_newprice','total_new','total_used','sales_rank']] y=value.decision split_size = round(len(X)*0.3) X_train,X_test = X[0:len(X)-split_size], X[len(X)-split_size:] y_train, y_test = y[0:len(y)-split_size], y[len(y)-split_size:] y_test=y_test.reset_index(drop=True) # from sklearn.model_selection import train_test_split # X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 5) randomforest = RandomForestClassifier(random_state=0,n_estimators=100,max_depth=10) model = randomforest.fit(X_train, y_train) # sfm = SelectFromModel(model, threshold=0.03) # sfm.fit(X_train, y_train) # for feature_list_index in sfm.get_support(indices=True): # print(X_train.columns[feature_list_index]) y_test_pred=pd.DataFrame(model.predict(X_test)) test_pred=pd.concat([y_test,y_test_pred],axis=1) benchmark_ytest[str(key)]=test_pred from sklearn.metrics import accuracy_score benchmark_model[str(key)]=accuracy_score(y_test,y_test_pred) from sklearn.metrics import precision_recall_fscore_support as score precision, recall, fscore, support = score(y_test, y_test_pred) products.append(key) accuracies.append(accuracy_score(y_test,y_test_pred)) precisions.append(precision) recalls.append(recall) fscores.append(fscore) supports.append(support) products_df=pd.DataFrame({'products':products}) accuracies_df=pd.DataFrame({'accuracy':accuracies}) precisions_df=pd.DataFrame(precisions, columns=['precision_hold','precision_buy']) recalls_df=pd.DataFrame(recalls, columns=['recall_hold','recall_buy']) fscores_df=pd.DataFrame(fscores, columns=['fscore_hold','fscore_buy']) supports_df=pd.DataFrame(supports, columns=['support_hold','support_buy']) benchmark_scores=pd.concat([products_df,accuracies_df,precisions_df,recalls_df,fscores_df,supports_df],axis=1) benchmark_scores=benchmark_scores.dropna() benchmark_scores=benchmark_scores[benchmark_scores['support_buy']>10] benchmark_scores.precision_buy.mean() #precision is 52.7% benchmark_scores.recall_buy.mean() #recall is 38% benchmark_scores.accuracy.mean() #accuracy is 70% #%% #regression asins=df_pred.asin.unique().tolist() from sklearn.ensemble import RandomForestClassifier from sklearn.ensemble import RandomForestRegressor from sklearn.feature_selection import SelectFromModel from sklearn.metrics import accuracy_score from sklearn.metrics import precision_recall_fscore_support as score products=[] accuracies=[] precisions=[] recalls=[] fscores=[] supports=[] d = {} for i in range(len(asins)): d["product" + str(i)] = df_pred[df_pred.asin==asins[i]] benchmark_model={} benchmark_ytest={} for key, value in d.items(): X=value[['lowest_newprice','total_new','total_used','sales_rank']] y=value['T'] dec=value.decision split_size = round(len(X)*0.3) X_train,X_test = X[0:len(X)-split_size], X[len(X)-split_size:] y_train, y_test = y[0:len(y)-split_size], y[len(y)-split_size:] y_test=y_test.reset_index(drop=True) dec_train,dec_test=dec[0:len(dec)-split_size], dec[len(dec)-split_size:] # from sklearn.model_selection import train_test_split # X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 5) randomforest = RandomForestRegressor(random_state=0,n_estimators=100,max_depth=10) model = randomforest.fit(X_train, y_train) # sfm = SelectFromModel(model, threshold=0.03) # sfm.fit(X_train, y_train) # for feature_list_index in sfm.get_support(indices=True): # print(X_train.columns[feature_list_index]) y_test_pred=pd.DataFrame(model.predict(X_test)) y_test_pred['decision']=0 y_test_pred.loc[y_test_pred[0]>5,'decision']=1 y_test_pred=y_test_pred.drop([0],axis=1) test_pred=pd.concat([dec_test,y_test_pred],axis=1) benchmark_ytest[str(key)]=test_pred benchmark_model[str(key)]=accuracy_score(dec_test,y_test_pred) precision, recall, fscore, support = score(dec_test, y_test_pred) products.append(key) accuracies.append(accuracy_score(dec_test,y_test_pred)) precisions.append(precision) recalls.append(recall) fscores.append(fscore) supports.append(support) products_df=pd.DataFrame({'products':products}) accuracies_df=pd.DataFrame({'accuracy':accuracies}) precisions_df=pd.DataFrame(precisions, columns=['precision_hold','precision_buy']) recalls_df=pd.DataFrame(recalls, columns=['recall_hold','recall_buy']) fscores_df=pd.DataFrame(fscores, columns=['fscore_hold','fscore_buy']) supports_df=pd.DataFrame(supports, columns=['support_hold','support_buy']) benchmark_scores=pd.concat([products_df,accuracies_df,precisions_df,recalls_df,fscores_df,supports_df],axis=1) benchmark_scores=benchmark_scores.dropna() benchmark_scores=benchmark_scores[benchmark_scores['support_buy']>10] benchmark_scores.precision_buy.mean() #precision is 51% - 57 benchmark_scores.recall_buy.mean() #recall is 56% - 46 benchmark_scores.accuracy.mean() #accuracy is 78% - 71 #%% # all products (# just a random trial) # #X=df_pred[['total_new','total_used','sales_rank','price_diff']] #y=df_pred.decision # #from sklearn.model_selection import train_test_split #X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 5) #randomforest = RandomForestClassifier(random_state=0,n_estimators=100,max_depth=10) #model = randomforest.fit(X_train, y_train) # #sfm = SelectFromModel(model, threshold=0.03) #sfm.fit(X_train, y_train) #for feature_list_index in sfm.get_support(indices=True): # print(X_train.columns[feature_list_index]) #y_test_pred=pd.DataFrame(model.predict(X_test)) # # #from sklearn import metrics #from sklearn.metrics import classification_report # #print("MODEL B1: All Products \n") # #print ('The precision for this classifier is ' + str(metrics.precision_score(y_test, y_test_pred))) #print ('The recall for this classifier is ' + str(metrics.recall_score(y_test, y_test_pred))) #print ('The f1 for this classifier is ' + str(metrics.f1_score(y_test, y_test_pred))) #print ('The accuracy for this classifier is ' + str(metrics.accuracy_score(y_test, y_test_pred))) # #print ('\nHere is the classification report:') #print (classification_report(y_test, y_test_pred)) # #from sklearn.metrics import confusion_matrix #print(pd.DataFrame(confusion_matrix(y_test, y_test_pred, labels=[1, 0]), index=['true:1', 'true:0'], columns=['pred:1', 'pred:0'])) # # IMPROVED MODEL #asins=df_pred.asin.unique().tolist() # ##Accuracy for product 0 #product0=df_pred[df_pred.asin==asins[0]] #X=product0[['lowest_newprice','total_new','total_used','sales_rank','number_of_reviews','star_avg']] #y=product0.decision # # #from sklearn.ensemble import RandomForestClassifier #randomforest = RandomForestClassifier(random_state=0) #model = randomforest.fit(X, y) # #from sklearn.feature_selection import SelectFromModel #sfm = SelectFromModel(model, threshold=0.05) #sfm.fit(X, y) #for feature_list_index in sfm.get_support(indices=True): # print(X.columns[feature_list_index]) # #pd.DataFrame(list(zip(X.columns,model.feature_importances_)), columns = ['predictor','Gini coefficient']) # # #from sklearn.model_selection import train_test_split #X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 5) # #model = randomforest.fit(X_train, y_train) # # #y_test_pred=pd.DataFrame(model.predict(X_test)) # #from sklearn.metrics import accuracy_score #accuracy_score(y_test,y_test_pred) # # ##Accuracy for product 1 # #product2=df_pred[df_pred.asin==asins[2]] #X=product2[['lowest_newprice','total_new','total_used','sales_rank','number_of_reviews','star_avg']] #y=product2.decision # # #from sklearn.ensemble import RandomForestClassifier #randomforest = RandomForestClassifier(random_state=0) #model = randomforest.fit(X, y) # #from sklearn.feature_selection import SelectFromModel #sfm = SelectFromModel(model, threshold=0.05) #sfm.fit(X, y) #for feature_list_index in sfm.get_support(indices=True): # print(X.columns[feature_list_index]) # #pd.DataFrame(list(zip(X.columns,model.feature_importances_)), columns = ['predictor','Gini coefficient']) # # #from sklearn.model_selection import train_test_split #X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 5) # #model = randomforest.fit(X_train, y_train) # # #y_test_pred=pd.DataFrame(model.predict(X_test)) # #from sklearn.metrics import accuracy_score #accuracy_score(y_test,y_test_pred) # # ##Accuracy for product 7 # #product7=df_pred[df_pred.asin==asins[7]] #X=product7[['lowest_newprice','total_new','total_used','sales_rank','number_of_reviews','star_avg']] #y=product7.decision # # #from sklearn.ensemble import RandomForestClassifier #randomforest = RandomForestClassifier(random_state=0) #model = randomforest.fit(X, y) # #from sklearn.feature_selection import SelectFromModel #sfm = SelectFromModel(model, threshold=0.05) #sfm.fit(X, y) #for feature_list_index in sfm.get_support(indices=True): # print(X.columns[feature_list_index]) # #pd.DataFrame(list(zip(X.columns,model.feature_importances_)), columns = ['predictor','Gini coefficient']) # # #from sklearn.model_selection import train_test_split #X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 5) # #model = randomforest.fit(X_train, y_train) # # #y_test_pred=pd.DataFrame(model.predict(X_test)) # #from sklearn.metrics import accuracy_score #accuracy_score(y_test,y_test_pred) # # # # # ##Accuracy for product 9 # #product9=df_pred[df_pred.asin==asins[9]] #X=product9[['lowest_newprice','total_new','total_used','sales_rank','number_of_reviews','star_avg']] #y=product9.decision # # #from sklearn.ensemble import RandomForestClassifier #randomforest = RandomForestClassifier(random_state=0) #model = randomforest.fit(X, y) # #from sklearn.feature_selection import SelectFromModel #sfm = SelectFromModel(model, threshold=0.05) #sfm.fit(X, y) #for feature_list_index in sfm.get_support(indices=True): # print(X.columns[feature_list_index]) # #pd.DataFrame(list(zip(X.columns,model.feature_importances_)), columns = ['predictor','Gini coefficient']) # # #from sklearn.model_selection import train_test_split #X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 5) # #model = randomforest.fit(X_train, y_train) # # #y_test_pred=pd.DataFrame(model.predict(X_test)) # #from sklearn.metrics import accuracy_score #print(accuracy_score(y_test,y_test_pred)) #y_test_pred['actual']=y_test.reset_index(drop=True) # #%% # MATCHING QUESTION AND ANSWERS WITH PRODUCTS matching=product[['asin','forum_id']] product_question_asin = pd.merge(product_question,matching, on=['forum_id']) matching=product_question_asin[['asin','forum_id','question_id']] product_answer_asin=pd.merge(product_answer,matching, on=['question_id']) # FEATURE ENGINEERING FOR QUESTIONS AND ANSWERS # for questions questions_sorted=product_question_asin.set_index('question_date').sort_index() questions_sorted['sentiment_total']=questions_sorted.groupby('asin').sentiment.cumsum() questions_sorted['number_of_questions']=questions_sorted.groupby('asin').sentiment.cumcount()+1 questions_sorted['sentiment_avg']=questions_sorted.sentiment_total/questions_sorted.number_of_questions questions_sorted['polarity_total']=questions_sorted.groupby('asin').polarity.cumsum() questions_sorted['polarity_avg']=questions_sorted.polarity_total/questions_sorted.number_of_questions questions_sorted['subjectivity_total']=questions_sorted.groupby('asin').subjectivity.cumsum() questions_sorted['subjectivity_avg']=questions_sorted.subjectivity_total/questions_sorted.number_of_questions questions_sorted['len_question']=questions_sorted.question.apply(len) questions_sorted['len_question_total']=questions_sorted.groupby('asin').len_question.cumsum() questions_sorted['question_lenght_avg']=questions_sorted['len_question_total']/questions_sorted.number_of_questions questions_sorted=questions_sorted.reset_index() questions_sorted = questions_sorted.drop_duplicates(['asin','question_date'], keep='last') questions_useful=questions_sorted[['question_date','asin', 'sentiment_total', 'number_of_questions', 'sentiment_avg', 'polarity_total', 'polarity_avg', 'subjectivity_total', 'subjectivity_avg', 'len_question_total', 'question_lenght_avg']] questions_useful.columns=['date','asin', 'sentiment_total_question', 'number_of_questions', 'sentiment_avg_question', 'polarity_total_question', 'polarity_avg_question', 'subjectivity_total_question', 'subjectivity_avg_question', 'len_question_total', 'question_lenght_avg'] merged_ques = pd.merge(df_pred,questions_useful,how='left') merged_ques[['sentiment_total_question', 'number_of_questions', 'sentiment_avg_question', 'polarity_total_question', 'polarity_avg_question', 'subjectivity_total_question', 'subjectivity_avg_question', 'len_question_total', 'question_lenght_avg']]=merged_ques.groupby('asin')[['sentiment_total_question', 'number_of_questions', 'sentiment_avg_question', 'polarity_total_question', 'polarity_avg_question', 'subjectivity_total_question', 'subjectivity_avg_question', 'len_question_total', 'question_lenght_avg']].fillna(method='ffill') merged_ques[['sentiment_total_question', 'number_of_questions', 'sentiment_avg_question', 'polarity_total_question', 'polarity_avg_question', 'subjectivity_total_question', 'subjectivity_avg_question', 'len_question_total', 'question_lenght_avg']]=merged_ques.groupby('asin')[['sentiment_total_question', 'number_of_questions', 'sentiment_avg_question', 'polarity_total_question', 'polarity_avg_question', 'subjectivity_total_question', 'subjectivity_avg_question', 'len_question_total', 'question_lenght_avg']].fillna(method='bfill') merged_ques_head=merged_ques.head(10000) #for answers product_answer_sorted=product_answer_asin.set_index('answer_date').sort_index() product_answer_sorted['number_of_answers']=product_answer_sorted.groupby('asin').cumcount()+1 product_answer_sorted['sentiment_total']=product_answer_sorted.groupby('asin').sentiment.cumsum() product_answer_sorted['sentiment_avg']=product_answer_sorted.sentiment_total/product_answer_sorted.number_of_answers product_answer_sorted['polarity_total']=product_answer_sorted.groupby('asin').polarity.cumsum() product_answer_sorted['polarity_avg']=product_answer_sorted.polarity_total/product_answer_sorted.number_of_answers product_answer_sorted['subjectivity_total']=product_answer_sorted.groupby('asin').subjectivity.cumsum() product_answer_sorted['subjectivity_avg']=product_answer_sorted.subjectivity_total/product_answer_sorted.number_of_answers product_answer_sorted['len_answer']=product_answer_sorted.answer.apply(len) product_answer_sorted['len_answer_total']=product_answer_sorted.groupby('asin').len_answer.cumsum() product_answer_sorted['answer_lenght_avg']=product_answer_sorted['len_answer_total']/product_answer_sorted.number_of_answers product_answer_sorted=product_answer_sorted.reset_index() product_answer_useful=product_answer_sorted[['answer_date','asin', 'number_of_answers', 'sentiment_total', 'sentiment_avg', 'polarity_total', 'polarity_avg', 'subjectivity_total', 'subjectivity_avg', 'len_answer_total', 'answer_lenght_avg']] product_answer_useful.columns=['date','asin', 'number_of_answers', 'sentiment_total_answer', 'sentiment_avg_answer', 'polarity_total_answer', 'polarity_avg_answer', 'subjectivity_total_answer', 'subjectivity_avg_answer', 'len_answer_total', 'answer_lenght_avg'] merged_ans = pd.merge(merged_ques,product_answer_useful,how='left') merged_ans[ ['number_of_answers', 'sentiment_total_answer', 'sentiment_avg_answer', 'polarity_total_answer', 'polarity_avg_answer', 'subjectivity_total_answer', 'subjectivity_avg_answer', 'len_answer_total', 'answer_lenght_avg']]=merged_ans.groupby('asin')[['number_of_answers', 'sentiment_total_answer', 'sentiment_avg_answer', 'polarity_total_answer', 'polarity_avg_answer', 'subjectivity_total_answer', 'subjectivity_avg_answer', 'len_answer_total', 'answer_lenght_avg']].fillna(method='ffill') merged_ans[ ['number_of_answers', 'sentiment_total_answer', 'sentiment_avg_answer', 'polarity_total_answer', 'polarity_avg_answer', 'subjectivity_total_answer', 'subjectivity_avg_answer', 'len_answer_total', 'answer_lenght_avg']]=merged_ans.groupby('asin')[['number_of_answers', 'sentiment_total_answer', 'sentiment_avg_answer', 'polarity_total_answer', 'polarity_avg_answer', 'subjectivity_total_answer', 'subjectivity_avg_answer', 'len_answer_total', 'answer_lenght_avg']].fillna(method='bfill') merged_ans_head=merged_ans.head(20000) merged_ans.len_answer_total.isna().sum() merged_ans_dropedna=merged_ans.dropna() len(df_pred[df_pred.decision==0])/len(df_pred) #%% # #### IMPROVED MODEL #### #random forest classificiation ## keeping products with more than 150 data points asd=merged_ans_dropedna.groupby(['asin']).count() asd=asd[asd.date > 150] asd.reset_index(level=0, inplace=True) merged_ans_dropedna=merged_ans_dropedna[merged_ans_dropedna.asin.isin(asd.asin)] merged_ans_dropedna=merged_ans_dropedna.reset_index(drop=True) asins=merged_ans_dropedna.asin.unique().tolist() from sklearn.ensemble import RandomForestClassifier from sklearn.feature_selection import SelectFromModel from sklearn.metrics import accuracy_score from sklearn.metrics import precision_recall_fscore_support as score # products=[] accuracies=[] precisions=[] recalls=[] fscores=[] supports=[] d = {} for i in range(len(asins)): d["product" + str(i)] = merged_ans_dropedna[merged_ans_dropedna.asin==asins[i]] importance = pd.DataFrame() improved_model={} improved_ytest={} for key, value in d.items(): print(key) X=value[['lowest_newprice','total_new','total_used','sales_rank','number_of_reviews','star_avg']] # X=value.drop(['asin', 'name', 'date', 'list_price','lowest_usedprice','tradein_value','T','decision'],axis=1) y=value.decision ## feature selection randomforest = RandomForestClassifier(random_state=0) model = randomforest.fit(X, y) sfm = SelectFromModel(model, threshold=0.01) sfm.fit(X, y) for feature_list_index in sfm.get_support(indices=True): print(X.columns[feature_list_index]) feature_idx = sfm.get_support() feature_name = X.columns[feature_idx] print(pd.DataFrame(list(zip(X.columns,model.feature_importances_)), columns = ['predictor','Gini coefficient']).sort_values('Gini coefficient',ascending=False)) temp_importance=pd.DataFrame([list(model.feature_importances_)],columns=X.columns) key_index=[key] temp_importance.index = key_index importance=importance.append(temp_importance) X_important = pd.DataFrame(sfm.transform(X)) X_important.columns = feature_name # model split_size = round(len(X_important)*0.3) X_train,X_test = X[0:len(X)-split_size], X[len(X)-split_size:] # X_train,X_test = X_important[0:len(X_important)-split_size], X_important[len(X_important)-split_size:] y_train, y_test = y[0:len(y)-split_size], y[len(y)-split_size:] y_test=y_test.reset_index(drop=True) # from sklearn.model_selection import train_test_split # X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 5) randomforest = RandomForestClassifier(random_state=0,n_estimators=100,max_depth=10) model = randomforest.fit(X_train, y_train) # prediction y_test_pred=pd.DataFrame(model.predict(X_test)) test_pred=pd.concat([y_test,y_test_pred],axis=1) improved_ytest[str(key)]=test_pred improved_model[str(key)]=accuracy_score(y_test,y_test_pred) precision, recall, fscore, support = score(y_test, y_test_pred) products.append(key) accuracies.append(accuracy_score(y_test,y_test_pred)) precisions.append(precision) recalls.append(recall) fscores.append(fscore) supports.append(support) products_df=pd.DataFrame({'products':products}) accuracies_df=pd.DataFrame({'accuracy':accuracies}) precisions_df=pd.DataFrame(precisions, columns=['precision_hold','precision_buy']) recalls_df=pd.DataFrame(recalls, columns=['recall_hold','recall_buy']) fscores_df=pd.DataFrame(fscores, columns=['fscore_hold','fscore_buy']) supports_df=pd.DataFrame(supports, columns=['support_hold','support_buy']) improved_scores=pd.concat([products_df,accuracies_df,precisions_df,recalls_df,fscores_df,supports_df],axis=1) improved_scores=improved_scores.dropna() improved_scores=improved_scores[improved_scores['support_buy']!=0] print('precision:',improved_scores.precision_buy.mean()) #precision is 40% print('recall:',improved_scores.recall_buy.mean()) #recall is 43% print('accuracy:',improved_scores.accuracy.mean()) #accuracy is 75.3% # importance dataframe removing zeros importance=importance[importance.lowest_newprice!=0] print(importance.mean().sort_values(ascending=False)) #%% #regression asd=merged_ans_dropedna.groupby(['asin']).count() asd=asd[asd.date > 150] asd.reset_index(level=0, inplace=True) merged_ans_dropedna=merged_ans_dropedna[merged_ans_dropedna.asin.isin(asd.asin)] merged_ans_dropedna=merged_ans_dropedna.reset_index(drop=True) asins=merged_ans_dropedna.asin.unique().tolist() from sklearn.ensemble import RandomForestRegressor from sklearn.feature_selection import SelectFromModel from sklearn.metrics import accuracy_score from sklearn.metrics import precision_recall_fscore_support as score # products=[] accuracies=[] precisions=[] recalls=[] fscores=[] supports=[] d = {} for i in range(len(asins)): d["product" + str(i)] = merged_ans_dropedna[merged_ans_dropedna.asin==asins[i]] importance = pd.DataFrame() improved_model={} improved_ytest={} for key, value in d.items(): print(key) X=value[['lowest_newprice','total_new','total_used','sales_rank','number_of_reviews','star_avg']] # X=value.drop(['asin', 'name', 'date', 'list_price','lowest_usedprice','tradein_value','T','decision'],axis=1) y=value['T'] dec=value.decision ## feature selection randomforest = RandomForestRegressor(random_state=0) model = randomforest.fit(X, y) sfm = SelectFromModel(model, threshold=0.01) sfm.fit(X, y) for feature_list_index in sfm.get_support(indices=True): print(X.columns[feature_list_index]) feature_idx = sfm.get_support() feature_name = X.columns[feature_idx] print(pd.DataFrame(list(zip(X.columns,model.feature_importances_)), columns = ['predictor','Gini coefficient']).sort_values('Gini coefficient',ascending=False)) temp_importance=pd.DataFrame([list(model.feature_importances_)],columns=X.columns) key_index=[key] temp_importance.index = key_index importance=importance.append(temp_importance) X_important = pd.DataFrame(sfm.transform(X)) X_important.columns = feature_name # model split_size = round(len(X_important)*0.3) X_train,X_test = X[0:len(X)-split_size], X[len(X)-split_size:] # X_train,X_test = X_important[0:len(X_important)-split_size], X_important[len(X_important)-split_size:] y_train, y_test = y[0:len(y)-split_size], y[len(y)-split_size:] y_test=y_test.reset_index(drop=True) dec_train,dec_test=dec[0:len(dec)-split_size], dec[len(dec)-split_size:] # from sklearn.model_selection import train_test_split # X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 5) randomforest = RandomForestRegressor(random_state=0,n_estimators=100,max_depth=10) model = randomforest.fit(X_train, y_train) # prediction y_test_pred=pd.DataFrame(model.predict(X_test)) y_test_pred['decision']=0 y_test_pred.loc[y_test_pred[0]>5,'decision']=1 y_test_pred=y_test_pred.drop([0],axis=1) test_pred=pd.concat([dec_test,y_test_pred],axis=1) improved_ytest[str(key)]=test_pred improved_model[str(key)]=accuracy_score(dec_test,y_test_pred) precision, recall, fscore, support = score(dec_test, y_test_pred) products.append(key) accuracies.append(accuracy_score(dec_test,y_test_pred)) precisions.append(precision) recalls.append(recall) fscores.append(fscore) supports.append(support) products_df=pd.DataFrame({'products':products}) accuracies_df=pd.DataFrame({'accuracy':accuracies}) precisions_df=pd.DataFrame(precisions, columns=['precision_hold','precision_buy']) recalls_df=pd.DataFrame(recalls, columns=['recall_hold','recall_buy']) fscores_df=pd.DataFrame(fscores, columns=['fscore_hold','fscore_buy']) supports_df=pd.DataFrame(supports, columns=['support_hold','support_buy']) improved_scores=pd.concat([products_df,accuracies_df,precisions_df,recalls_df,fscores_df,supports_df],axis=1) improved_scores=improved_scores.dropna() improved_scores=improved_scores[improved_scores['support_buy']>10] print('RandomForest Regression Improved Precison',improved_scores.precision_buy.mean()) #precision is 55% print('RandomForest Regression Improved Recall',improved_scores.recall_buy.mean()) #recall is 47.5% print('RandomForest Regression Improved Accuracy',improved_scores.accuracy.mean()) #accuracy is 70% #baseline accuracy is 63% improved_scores.support_hold.sum()/(improved_scores.support_buy.sum()+improved_scores.support_hold.sum()) # importance dataframe removing zeros importance=importance[importance.lowest_newprice!=0] print(importance.mean().sort_values(ascending=False)) #%% ## KNN #regression asd=merged_ans_dropedna.groupby(['asin']).count() asd=asd[asd.date > 150] asd.reset_index(level=0, inplace=True) merged_ans_dropedna=merged_ans_dropedna[merged_ans_dropedna.asin.isin(asd.asin)] merged_ans_dropedna=merged_ans_dropedna.reset_index(drop=True) asins=merged_ans_dropedna.asin.unique().tolist() from sklearn.ensemble import RandomForestClassifier from sklearn.feature_selection import SelectFromModel from sklearn.metrics import accuracy_score from sklearn.metrics import precision_recall_fscore_support as score from sklearn.neighbors import KNeighborsRegressor from sklearn.model_selection import GridSearchCV from sklearn.preprocessing import StandardScaler products=[] accuracies=[] precisions=[] recalls=[] fscores=[] supports=[] d = {} for i in range(len(asins)): d["product" + str(i)] = merged_ans_dropedna[merged_ans_dropedna.asin==asins[i]] importance = pd.DataFrame() improved_model={} improved_ytest={} for key, value in d.items(): print(key) X=value[['lowest_newprice','total_new','total_used','sales_rank','number_of_reviews','star_avg']] # X=value.drop(['asin', 'name', 'date', 'list_price','lowest_usedprice','tradein_value','T','decision'],axis=1) y=value['T'] dec=value.decision scaler = StandardScaler() X = scaler.fit_transform(X) # ## feature selection # randomforest = RandomForestRegressor(random_state=0) # model = randomforest.fit(X, y) # # sfm = SelectFromModel(model, threshold=0.01) # sfm.fit(X, y) # for feature_list_index in sfm.get_support(indices=True): # print(X.columns[feature_list_index]) # # # feature_idx = sfm.get_support() # feature_name = X.columns[feature_idx] # print(pd.DataFrame(list(zip(X.columns,model.feature_importances_)), columns = ['predictor','Gini coefficient']).sort_values('Gini coefficient',ascending=False)) # temp_importance=pd.DataFrame([list(model.feature_importances_)],columns=X.columns) # key_index=[key] # temp_importance.index = key_index # importance=importance.append(temp_importance) # X_important = pd.DataFrame(sfm.transform(X)) # X_important.columns = feature_name # model split_size = round(len(X)*0.3) X_train,X_test = X[0:len(X)-split_size], X[len(X)-split_size:] # X_train,X_test = X_important[0:len(X_important)-split_size], X_important[len(X_important)-split_size:] y_train, y_test = y[0:len(y)-split_size], y[len(y)-split_size:] y_test=y_test.reset_index(drop=True) dec_train,dec_test=dec[0:len(dec)-split_size], dec[len(dec)-split_size:] # from sklearn.model_selection import train_test_split # X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 5) knn_reg=KNeighborsRegressor(n_neighbors=2) # params = {'n_neighbors':[5,6,7,8,9,10], # 'leaf_size':[1,2,3,5], # 'weights':['uniform', 'distance'], # 'algorithm':['auto', 'ball_tree','kd_tree','brute'], # 'n_jobs':[-1]} # model1 = GridSearchCV(model, param_grid=params, n_jobs=1) model=knn_reg.fit(X_train,y_train) # print("Best Hyper Parameters:\n",model1.best_params_) # randomforest = RandomForestRegressor(random_state=0,n_estimators=100,max_depth=10) # model = randomforest.fit(X_train, y_train) # prediction y_test_pred=pd.DataFrame(model.predict(X_test)) y_test_pred['decision']=0 y_test_pred.loc[y_test_pred[0]>5,'decision']=1 y_test_pred=y_test_pred.drop([0],axis=1) test_pred=pd.concat([dec_test,y_test_pred],axis=1) improved_ytest[str(key)]=test_pred improved_model[str(key)]=accuracy_score(dec_test,y_test_pred) precision, recall, fscore, support = score(dec_test, y_test_pred) products.append(key) accuracies.append(accuracy_score(dec_test,y_test_pred)) precisions.append(precision) recalls.append(recall) fscores.append(fscore) supports.append(support) products_df=pd.DataFrame({'products':products}) accuracies_df=pd.DataFrame({'accuracy':accuracies}) precisions_df=pd.DataFrame(precisions, columns=['precision_hold','precision_buy']) recalls_df=pd.DataFrame(recalls, columns=['recall_hold','recall_buy']) fscores_df=pd.DataFrame(fscores, columns=['fscore_hold','fscore_buy']) supports_df=pd.DataFrame(supports, columns=['support_hold','support_buy']) improved_scores_knn=pd.concat([products_df,accuracies_df,precisions_df,recalls_df,fscores_df,supports_df],axis=1) improved_scores_knn=improved_scores_knn.dropna() improved_scores_knn=improved_scores_knn[improved_scores_knn['support_buy']>10] print('KNN Regression Improved Precision',improved_scores_knn.precision_buy.mean()) #precision is 50% print('KNN Regression Improved Recall',improved_scores_knn.recall_buy.mean()) #recall is 34% print('KNN Regression Improved Accuracy',improved_scores_knn.accuracy.mean()) #accuracy is 67% #baseline accuracy is 63% improved_scores.support_hold.sum()/(improved_scores.support_buy.sum()+improved_scores.support_hold.sum()) # importance dataframe removing zeros #importance=importance[importance.lowest_newprice!=0] #print(importance.mean().sort_values(ascending=False)) #%% #### KNN classification asd=merged_ans_dropedna.groupby(['asin']).count() asd=asd[asd.date > 150] asd.reset_index(level=0, inplace=True) merged_ans_dropedna=merged_ans_dropedna[merged_ans_dropedna.asin.isin(asd.asin)] merged_ans_dropedna=merged_ans_dropedna.reset_index(drop=True) asins=merged_ans_dropedna.asin.unique().tolist() from sklearn.ensemble import RandomForestClassifier from sklearn.feature_selection import SelectFromModel from sklearn.neighbors import KNeighborsClassifier from sklearn.model_selection import GridSearchCV from sklearn.preprocessing import StandardScaler from sklearn.metrics import accuracy_score from sklearn.metrics import precision_recall_fscore_support as score # products=[] accuracies=[] precisions=[] recalls=[] fscores=[] supports=[] d = {} for i in range(len(asins)): d["product" + str(i)] = merged_ans_dropedna[merged_ans_dropedna.asin==asins[i]] importance = pd.DataFrame() improved_model={} improved_ytest={} for key, value in d.items(): print(key) X=value[['lowest_newprice','total_new','total_used','sales_rank','number_of_reviews','star_avg']] # X=value.drop(['asin', 'name', 'date', 'list_price','lowest_usedprice','tradein_value','T','decision'],axis=1) y=value.decision scaler = StandardScaler() X = scaler.fit_transform(X) # ## feature selection # randomforest = RandomForestClassifier(random_state=0) # model = randomforest.fit(X, y) # # sfm = SelectFromModel(model, threshold=0.01) # sfm.fit(X, y) # for feature_list_index in sfm.get_support(indices=True): # print(X.columns[feature_list_index]) # # # feature_idx = sfm.get_support() # feature_name = X.columns[feature_idx] # print(pd.DataFrame(list(zip(X.columns,model.feature_importances_)), columns = ['predictor','Gini coefficient']).sort_values('Gini coefficient',ascending=False)) # temp_importance=pd.DataFrame([list(model.feature_importances_)],columns=X.columns) # key_index=[key] # temp_importance.index = key_index # importance=importance.append(temp_importance) # X_important = pd.DataFrame(sfm.transform(X)) # X_important.columns = feature_name # model split_size = round(len(X)*0.3) X_train,X_test = X[0:len(X)-split_size], X[len(X)-split_size:] # X_train,X_test = X_important[0:len(X_important)-split_size], X_important[len(X_important)-split_size:] y_train, y_test = y[0:len(y)-split_size], y[len(y)-split_size:] y_test=y_test.reset_index(drop=True) # from sklearn.model_selection import train_test_split # X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 5) knn_class=KNeighborsClassifier(n_neighbors=3) model = knn_class.fit(X_train, y_train) # prediction y_test_pred=pd.DataFrame(model.predict(X_test)) test_pred=pd.concat([y_test,y_test_pred],axis=1) improved_ytest[str(key)]=test_pred improved_model[str(key)]=accuracy_score(y_test,y_test_pred) precision, recall, fscore, support = score(y_test, y_test_pred) products.append(key) accuracies.append(accuracy_score(y_test,y_test_pred)) precisions.append(precision) recalls.append(recall) fscores.append(fscore) supports.append(support) products_df=pd.DataFrame({'products':products}) accuracies_df=pd.DataFrame({'accuracy':accuracies}) precisions_df=pd.DataFrame(precisions, columns=['precision_hold','precision_buy']) recalls_df=pd.DataFrame(recalls, columns=['recall_hold','recall_buy']) fscores_df=pd.DataFrame(fscores, columns=['fscore_hold','fscore_buy']) supports_df=pd.DataFrame(supports, columns=['support_hold','support_buy']) improved_scores_knn_clas=pd.concat([products_df,accuracies_df,precisions_df,recalls_df,fscores_df,supports_df],axis=1) improved_scores_knn_clas=improved_scores_knn_clas.dropna() improved_scores_knn_clas=improved_scores_knn_clas[improved_scores_knn_clas['support_buy']>10] print('KNN Classification Improved Precision:',improved_scores_knn_clas.precision_buy.mean()) #precision is 42% print('KNN Classification Improved Recall:',improved_scores_knn_clas.recall_buy.mean()) #recall is 27% print('KNN Classification Improved Accuracy:',improved_scores_knn_clas.accuracy.mean()) #accuracy is 67% #%% # ANN Classification from sklearn.neural_network import MLPClassifier asd=merged_ans_dropedna.groupby(['asin']).count() asd=asd[asd.date > 150] asd.reset_index(level=0, inplace=True) merged_ans_dropedna=merged_ans_dropedna[merged_ans_dropedna.asin.isin(asd.asin)] merged_ans_dropedna=merged_ans_dropedna.reset_index(drop=True) asins=merged_ans_dropedna.asin.unique().tolist() from sklearn.ensemble import RandomForestClassifier from sklearn.feature_selection import SelectFromModel from sklearn.model_selection import GridSearchCV from sklearn.neural_network import MLPClassifier from sklearn.preprocessing import StandardScaler from sklearn.metrics import precision_score, make_scorer custom_scorer = make_scorer(precision_score, greater_is_better=True, pos_label=0) # products=[] accuracies=[] precisions=[] recalls=[] fscores=[] supports=[] d = {} for i in range(len(asins)): d["product" + str(i)] = merged_ans_dropedna[merged_ans_dropedna.asin==asins[i]] importance = pd.DataFrame() improved_model={} improved_ytest={} for key, value in d.items(): print(key) X=value[['lowest_newprice','total_new','total_used','sales_rank','number_of_reviews','star_avg']] # X=value.drop(['asin', 'name', 'date', 'list_price','lowest_usedprice','tradein_value','T','decision'],axis=1) y=value.decision scaler = StandardScaler() X = scaler.fit_transform(X) # ## feature selection # randomforest = RandomForestClassifier(random_state=0) # model = randomforest.fit(X, y) # # sfm = SelectFromModel(model, threshold=0.01) # sfm.fit(X, y) # for feature_list_index in sfm.get_support(indices=True): # print(X.columns[feature_list_index]) # # # feature_idx = sfm.get_support() # feature_name = X.columns[feature_idx] # print(pd.DataFrame(list(zip(X.columns,model.feature_importances_)), columns = ['predictor','Gini coefficient']).sort_values('Gini coefficient',ascending=False)) # temp_importance=pd.DataFrame([list(model.feature_importances_)],columns=X.columns) # key_index=[key] # temp_importance.index = key_index # importance=importance.append(temp_importance) # X_important = pd.DataFrame(sfm.transform(X)) # X_important.columns = feature_name # model split_size = round(len(X)*0.3) X_train,X_test = X[0:len(X)-split_size], X[len(X)-split_size:] # X_train,X_test = X_important[0:len(X_important)-split_size], X_important[len(X_important)-split_size:] y_train, y_test = y[0:len(y)-split_size], y[len(y)-split_size:] y_test=y_test.reset_index(drop=True) # from sklearn.model_selection import train_test_split # X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 5) # knn_class=KNeighborsClassifier(n_neighbors=6) # model = knn_class.fit(X_train, y_train) mlp = MLPClassifier(max_iter=500, hidden_layer_sizes=(10), activation='tanh', learning_rate='adaptive', alpha=0.05) # parameter_space = { # 'hidden_layer_sizes': [(5,5,5), (5,10,5), (5,5), (5,10), (5), (10)], # 'activation': ['tanh', 'relu'], # 'solver': ['sgd', 'adam'], # 'alpha': [0.0001, 0.05], # 'learning_rate': ['constant','adaptive'], # } # clf = GridSearchCV(estimator=mlp,scoring=custom_scorer,param_grid=parameter_space, n_jobs=-1) mlp.fit(X_train, y_train) # print("Best Hyper Parameters:\n",clf.best_params_) # prediction y_test_pred=pd.DataFrame(mlp.predict(X_test)) test_pred=pd.concat([y_test,y_test_pred],axis=1) improved_ytest[str(key)]=test_pred from sklearn.metrics import accuracy_score improved_model[str(key)]=accuracy_score(y_test,y_test_pred) from sklearn.metrics import precision_recall_fscore_support as score precision, recall, fscore, support = score(y_test, y_test_pred) products.append(key) accuracies.append(accuracy_score(y_test,y_test_pred)) precisions.append(precision) recalls.append(recall) fscores.append(fscore) supports.append(support) products_df=pd.DataFrame({'products':products}) accuracies_df=pd.DataFrame({'accuracy':accuracies}) precisions_df=pd.DataFrame(precisions, columns=['precision_hold','precision_buy']) recalls_df=pd.DataFrame(recalls, columns=['recall_hold','recall_buy']) fscores_df=pd.DataFrame(fscores, columns=['fscore_hold','fscore_buy']) supports_df=pd.DataFrame(supports, columns=['support_hold','support_buy']) improved_scores_ann_clas=pd.concat([products_df,accuracies_df,precisions_df,recalls_df,fscores_df,supports_df],axis=1) improved_scores_ann_clas=improved_scores_ann_clas.dropna() improved_scores_ann_clas=improved_scores_ann_clas[improved_scores_ann_clas['support_buy']>10] print('ANN Classification Improved Precision:',improved_scores_ann_clas.precision_buy.mean()) #precision is 46% print('ANN Classification Improved Recall:',improved_scores_ann_clas.recall_buy.mean()) #recall is 52% print('ANN Classification Improved Accuracy:',improved_scores_ann_clas.accuracy.mean()) #accuracy is 62% #%% #regression ANN asd=merged_ans_dropedna.groupby(['asin']).count() asd=asd[asd.date > 150] asd.reset_index(level=0, inplace=True) merged_ans_dropedna=merged_ans_dropedna[merged_ans_dropedna.asin.isin(asd.asin)] merged_ans_dropedna=merged_ans_dropedna.reset_index(drop=True) asins=merged_ans_dropedna.asin.unique().tolist() from sklearn.ensemble import RandomForestRegressor from sklearn.feature_selection import SelectFromModel from sklearn.metrics import accuracy_score from sklearn.metrics import precision_recall_fscore_support as score from sklearn.model_selection import GridSearchCV from sklearn.neural_network import MLPRegressor products=[] accuracies=[] precisions=[] recalls=[] fscores=[] supports=[] d = {} for i in range(len(asins)): d["product" + str(i)] = merged_ans_dropedna[merged_ans_dropedna.asin==asins[i]] importance = pd.DataFrame() improved_model={} improved_ytest={} for key, value in d.items(): print(key) X=value[['lowest_newprice','total_new','total_used','sales_rank','number_of_reviews','star_avg']] # X=value.drop(['asin', 'name', 'date', 'list_price','lowest_usedprice','tradein_value','T','decision'],axis=1) y=value['T'] dec=value.decision scaler = StandardScaler() X = scaler.fit_transform(X) # ## feature selection # randomforest = RandomForestRegressor(random_state=0) # model = randomforest.fit(X, y) # # sfm = SelectFromModel(model, threshold=0.01) # sfm.fit(X, y) # for feature_list_index in sfm.get_support(indices=True): # print(X.columns[feature_list_index]) # # # feature_idx = sfm.get_support() # feature_name = X.columns[feature_idx] # print(pd.DataFrame(list(zip(X.columns,model.feature_importances_)), columns = ['predictor','Gini coefficient']).sort_values('Gini coefficient',ascending=False)) # temp_importance=pd.DataFrame([list(model.feature_importances_)],columns=X.columns) # key_index=[key] # temp_importance.index = key_index # importance=importance.append(temp_importance) # X_important = pd.DataFrame(sfm.transform(X)) # X_important.columns = feature_name # model split_size = round(len(X)*0.3) X_train,X_test = X[0:len(X)-split_size], X[len(X)-split_size:] # X_train,X_test = X_important[0:len(X_important)-split_size], X_important[len(X_important)-split_size:] y_train, y_test = y[0:len(y)-split_size], y[len(y)-split_size:] y_test=y_test.reset_index(drop=True) dec_train,dec_test=dec[0:len(dec)-split_size], dec[len(dec)-split_size:] # from sklearn.model_selection import train_test_split # X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 5) mlp = MLPRegressor(max_iter=500, hidden_layer_sizes=(5,5,5), activation='tanh', learning_rate='adaptive', alpha=0.05) # params = {'n_neighbors':[5,6,7,8,9,10], # 'leaf_size':[1,2,3,5], # 'weights':['uniform', 'distance'], # 'algorithm':['auto', 'ball_tree','kd_tree','brute'], # 'n_jobs':[-1]} # model1 = GridSearchCV(model, param_grid=params, n_jobs=1) model=mlp.fit(X_train,y_train) # print("Best Hyper Parameters:\n",model1.best_params_) # randomforest = RandomForestRegressor(random_state=0,n_estimators=100,max_depth=10) # model = randomforest.fit(X_train, y_train) # prediction y_test_pred=pd.DataFrame(model.predict(X_test)) print(y_test_pred) y_test_pred['decision']=0 y_test_pred.loc[y_test_pred[0]>5,'decision']=1 y_test_pred=y_test_pred.drop([0],axis=1) test_pred=pd.concat([dec_test,y_test_pred],axis=1) improved_ytest[str(key)]=test_pred improved_model[str(key)]=accuracy_score(dec_test,y_test_pred) precision, recall, fscore, support = score(dec_test, y_test_pred) products.append(key) accuracies.append(accuracy_score(dec_test,y_test_pred)) precisions.append(precision) recalls.append(recall) fscores.append(fscore) supports.append(support) products_df=pd.DataFrame({'products':products}) accuracies_df=pd.DataFrame({'accuracy':accuracies}) precisions_df=pd.DataFrame(precisions, columns=['precision_hold','precision_buy']) recalls_df=pd.DataFrame(recalls, columns=['recall_hold','recall_buy']) fscores_df=pd.DataFrame(fscores, columns=['fscore_hold','fscore_buy']) supports_df=pd.DataFrame(supports, columns=['support_hold','support_buy']) improved_scores_ann_reg=pd.concat([products_df,accuracies_df,precisions_df,recalls_df,fscores_df,supports_df],axis=1) improved_scores_ann_reg=improved_scores_ann_reg.dropna() improved_scores_ann_reg=improved_scores_ann_reg[improved_scores_ann_reg['support_buy']>10] print('ANN Regression Improved Precision:',improved_scores_ann_reg.precision_buy.mean()) #precision is 25% print('ANN Regression Improved Recall:',improved_scores_ann_reg.recall_buy.mean()) #recall is %15 print('ANN Regression Improved Accuracy:',improved_scores_ann_reg.accuracy.mean()) #accuracy is %66 #baseline accuracy is 63% improved_scores_ann_reg.support_hold.sum()/(improved_scores_ann_reg.support_buy.sum()+improved_scores_ann_reg.support_hold.sum()) # importance dataframe removing zeros #importance=importance[importance.lowest_newprice!=0] #print(importance.mean().sort_values(ascending=False)) #%% ### Boosting regression #import xgboost as xgb import pandas as pd

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a30a591a91d8d470c89544a7909697a145c80cd3

498

py

Python

Shared/networkingSchemas/destiny/__init__.py

LukasSchmid97/elevatorbot

77c45d8945c735c8dce9bc75563086bce265dc18

[ "MIT" ]

null

Shared/networkingSchemas/destiny/__init__.py

LukasSchmid97/elevatorbot

77c45d8945c735c8dce9bc75563086bce265dc18

[ "MIT" ]

89

2021-08-12T15:23:05.000Z

2022-01-11T12:33:21.000Z

Shared/networkingSchemas/destiny/__init__.py

TheDescend/elevatorbot

0909ec9ba213480bdf7f790c3d115dd8c4f3ae17

[ "MIT" ]

1

2021-10-20T20:07:22.000Z

from Shared.networkingSchemas.destiny.account import * from Shared.networkingSchemas.destiny.activities import * from Shared.networkingSchemas.destiny.clan import * from Shared.networkingSchemas.destiny.items import * from Shared.networkingSchemas.destiny.lfgSystem import * from Shared.networkingSchemas.destiny.profile import * from Shared.networkingSchemas.destiny.roles import * from Shared.networkingSchemas.destiny.steamPlayers import * from Shared.networkingSchemas.destiny.weapons import *

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0.71831

0.751174

0

0.072289

498

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55.333333

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true

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